From cbd2f353724526516ad9f14d655371fdc5f53bc5 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Tue, 18 Aug 2026 14:44:02 -0700
Subject: [PATCH 01/38] Adding blur, improving slider performance, and adding
 threshold brush

---
 frontend/src/app/pages/AnnotatePage.tsx       |  37 +-
 .../annotate/AnnotationCanvas/index.tsx       | 436 ++++++++++++++++--
 .../annotate/DisplayControls/index.tsx        |  74 ++-
 .../annotate/HistogramControl/index.tsx       |  49 +-
 .../src/components/annotate/Toolbar/index.tsx | 100 +++-
 frontend/src/hooks/useKeybinds.ts             |   5 +-
 frontend/src/lib/blur.test.ts                 |  68 +++
 frontend/src/lib/blur.ts                      | 106 +++++
 frontend/src/lib/clipToClasses.ts             |   8 +-
 frontend/src/lib/displayTransform.test.ts     | 118 +++++
 frontend/src/lib/displayTransform.ts          | 118 +++++
 frontend/src/lib/livewire.test.ts             |  16 +
 frontend/src/lib/livewire.ts                  |  16 +-
 frontend/src/lib/magicwand.test.ts            |  63 ++-
 frontend/src/lib/magicwand.ts                 |  69 ++-
 frontend/src/lib/mergeSameClass.ts            |   6 +-
 frontend/src/lib/polybool.ts                  |  13 +
 frontend/src/lib/rasterize.test.ts            |  51 +-
 frontend/src/lib/rasterize.ts                 |  31 +-
 frontend/src/lib/sam/adjust.ts                |  52 ++-
 frontend/src/stores/toolStore.ts              |  25 +-
 21 files changed, 1367 insertions(+), 94 deletions(-)
 create mode 100644 frontend/src/lib/blur.test.ts
 create mode 100644 frontend/src/lib/blur.ts
 create mode 100644 frontend/src/lib/displayTransform.test.ts
 create mode 100644 frontend/src/lib/displayTransform.ts

diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index c9d1062..a1b1fd8 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -1,7 +1,7 @@
 /**
  * AnnotatePage — react-konva canvas workspace with sidebar tools.
  */
-import { useState, useEffect, useCallback } from 'react';
+import { useState, useEffect, useCallback, useMemo } from 'react';
 import { useNavigate } from 'react-router';
 import { DownloadSimple, FloppyDisk, ClockCounterClockwise, CircleDashed, ChartBar } from '@phosphor-icons/react';
 import { useDatasetStore } from '@/stores/datasetStore';
@@ -66,9 +66,26 @@ export default function AnnotatePage() {
   // Display-only false-color map + gamma.
   const [colormap, setColormap] = useState<ColormapName>('gray');
   const [gamma, setGamma] = useState(1);
-  // Display-only nonlinear preprocessors (adaptive CLAHE / Sharpen).
+  // Display-only nonlinear preprocessors (Gaussian blur / adaptive CLAHE / Sharpen).
   const [clahe, setClahe] = useState(false);
   const [sharpen, setSharpen] = useState(false);
+  const [blur, setBlur] = useState(0);
+  // Working resolution for the drawing tools (1x, 2x, 4x). Annotation coordinates
+  // stay native; upscaling only buys sub-pixel precision on small features.
+  const [upscale, setUpscale] = useState(1);
+  // Each level costs 4x the pixels in the base canvas AND in every tool field, so
+  // cap the offer at what this slice can afford (the canvas enforces the same limit).
+  // Bundled for the Toolbar's threshold band picker, which remaps the (base-space)
+  // histogram into displayed space so the plot matches the image and the band.
+  const thresholdDisplay = useMemo(
+    () => ({ brightness, contrast, levelsLo, levelsHi, gamma }),
+    [brightness, contrast, levelsLo, levelsHi, gamma],
+  );
+  const maxUpscale = useMemo(() => {
+    if (!meta) return 4;
+    const px = meta.width * meta.height;
+    return px * 16 <= 64e6 ? 4 : px * 4 <= 64e6 ? 2 : 1;
+  }, [meta]);
   const [showDownload, setShowDownload] = useState(false);
   const [showInsights, setShowInsights] = useState(false);
   // Region to zoom to + highlight on the canvas (from an Insights QA flag).
@@ -213,14 +230,19 @@ export default function AnnotatePage() {
             onChange={(v) => setFillOpacity(v / 100)}
           />
           <hr />
-          <Toolbar disabled={classes.length === 0} />
+          <Toolbar
+            disabled={classes.length === 0}
+            histogramBins={histogramBins}
+            display={thresholdDisplay}
+            upscale={upscale}
+          />
           <hr />
           <DisplayControls
             brightness={brightness}
             contrast={contrast}
             onBrightnessChange={setBrightness}
             onContrastChange={setContrast}
-            onReset={() => { setBrightness(0); setContrast(0); setLevelsLo(0); setLevelsHi(255); setColormap('gray'); setGamma(1); setClahe(false); setSharpen(false); }}
+            onReset={() => { setBrightness(0); setContrast(0); setLevelsLo(0); setLevelsHi(255); setColormap('gray'); setGamma(1); setClahe(false); setSharpen(false); setBlur(0); setUpscale(1); }}
             histogramBins={histogramBins}
             levelsLo={levelsLo}
             levelsHi={levelsHi}
@@ -234,6 +256,11 @@ export default function AnnotatePage() {
             sharpen={sharpen}
             onClaheChange={setClahe}
             onSharpenChange={setSharpen}
+            blur={blur}
+            onBlurChange={setBlur}
+            upscale={upscale}
+            onUpscaleChange={setUpscale}
+            maxUpscale={maxUpscale}
           />
           <hr />
           <SliceNavigator />
@@ -314,6 +341,8 @@ export default function AnnotatePage() {
             gamma={gamma}
             clahe={clahe}
             sharpen={sharpen}
+            blur={blur}
+            upscale={upscale}
             onHistogram={setHistogramBins}
             activeClassId={activeClassId}
             activeBrushShapeId={activeBrushShapeId}
diff --git a/frontend/src/components/annotate/AnnotationCanvas/index.tsx b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
index 366b0e4..bf07373 100644
--- a/frontend/src/components/annotate/AnnotationCanvas/index.tsx
+++ b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
@@ -30,14 +30,15 @@ import { buildSourceKey } from '@/lib/sourceKey';
 import { buildField, magicSelect, maskToPolygons, maskToPolygonsWithHoles, type GrayField } from '@/lib/magicwand';
 import { useSam } from '@/hooks/useSam';
 import { renderAdjusted, renderPreprocessOnly } from '@/lib/sam/adjust';
-import { gridFor, fullResGridFor, rasterizeShapes, rasterizeUnion } from '@/lib/rasterize';
+import { gridFor, fullResGridFor, rasterizeShapes, rasterizeUnion, stampStroke } from '@/lib/rasterize';
 import { keepComponentsAtPoints } from '@/lib/morphology';
-import { unionShapesToPolygons, unionShapesToMultiPolygon, eraseStampToMultiPolygon, subtractFromShape } from '@/lib/polybool';
+import { unionShapesToPolygons, unionShapesToMultiPolygon, eraseStampToMultiPolygon, subtractFromShape, regionsToMultiPolygon } from '@/lib/polybool';
 import { computeRegionOps, type RegionOp } from '@/lib/regionOps';
 import { clipShapesToOthers, hasOtherClass } from '@/lib/clipToClasses';
 import { mergeNewWithSameClass, expandSameClassOverlap } from '@/lib/mergeSameClass';
 import { useClipboardStore } from '@/stores/clipboardStore';
 import { colormapTables, type ColormapName } from '@/lib/colormaps';
+import { displayAffineFor, displayBandToBase } from '@/lib/displayTransform';
 
 // macOS labels the Alt key "Option" (⌥). e.altKey is true for it either way,
 // so only the on-screen label needs to differ.
@@ -47,6 +48,10 @@ const REMOVE_KEY_LABEL = IS_MAC ? 'Option' : 'Alt';
 /** Shared stable empty shape list — see `storeShapes` for why identity matters. */
 const EMPTY_SHAPES: Shape[] = [];
 
+/** Upscale budget: the working resolution is clamped back to 1x past this many
+ *  pixels so a large slice at 4x can't exhaust memory (~64 MP ≈ 256 MB of RGBA). */
+const MAX_WORKING_PIXELS = 64e6;
+
 interface AnnotationCanvasProps {
   brightness: number;
   contrast: number;
@@ -60,6 +65,12 @@ interface AnnotationCanvasProps {
    *  tools' baked view but NOT the exported pixels). */
   clahe?: boolean;
   sharpen?: boolean;
+  /** Gaussian pre-blur sigma in image pixels (0 = off). */
+  blur?: number;
+  /** Working-resolution multiplier (1, 2, 4): resamples the slice for the display
+   *  base and every tool field so small features get more pixels. Annotation
+   *  coordinates stay in NATIVE image pixels — they just gain sub-pixel precision. */
+  upscale?: number;
   /** Emits the current slice's 256-bin luminance histogram when it loads. */
   onHistogram?: (bins: number[]) => void;
   activeClassId: number | null;
@@ -122,6 +133,15 @@ function withAlpha(hex: string, a: number): string {
   return `rgba(${(n >> 16) & 255}, ${(n >> 8) & 255}, ${n & 255}, ${a})`;
 }
 
+/** hex (#rgb or #rrggbb) → [r,g,b]. Falls back to mid-grey if it isn't a hex color. */
+function hexToRgb(hex: string): [number, number, number] {
+  let h = hex.replace('#', '');
+  if (h.length === 3) h = h.split('').map((c) => c + c).join('');
+  if (!/^[0-9a-fA-F]{6}$/.test(h)) return [128, 128, 128];
+  const n = parseInt(h, 16);
+  return [(n >> 16) & 255, (n >> 8) & 255, n & 255];
+}
+
 /** Shortest distance from point (px,py) to segment AB. */
 function distToSegment(px: number, py: number, ax: number, ay: number, bx: number, by: number): number {
   const dx = bx - ax, dy = by - ay;
@@ -400,6 +420,8 @@ export default function AnnotationCanvas({
   gamma = 1,
   clahe = false,
   sharpen = false,
+  blur = 0,
+  upscale = 1,
   onHistogram,
   activeClassId,
   activeBrushShapeId,
@@ -419,6 +441,7 @@ export default function AnnotationCanvas({
   // Imperative refs for lag-free brush drawing
   const draftStrokeLayerRef = useRef<Konva.Layer>(null);
   const draftLineRef = useRef<Konva.Line>(null);
+  const thresholdPreviewRef = useRef<Konva.Image>(null);
   const brushCursorLayerRef = useRef<Konva.Layer>(null);
   const brushCursorRef = useRef<Konva.Circle>(null);
 
@@ -433,6 +456,22 @@ export default function AnnotationCanvas({
     eraseTargetKind?: 'brush' | 'vector';
   } | null>(null);
 
+  // Threshold-brush stroke, buffered exactly like `draftStrokeRef`: a binary mask
+  // over the threshold field's grid that accumulates only in-band pixels, plus the
+  // offscreen canvas mirroring it for the live preview. Flushed on mouseup.
+  const thresholdStrokeRef = useRef<{
+    mode: 'paint' | 'erase';
+    /** Grid geometry of the field this stroke was started against. */
+    gw: number; gh: number; scale: number;
+    mask: Uint8Array;
+    /** In-band gate for the whole slice (same grid) — the stroke can't leave it. */
+    gate: Uint8Array;
+    /** Last pointer position in image coords, for segment stamping. */
+    last: { x: number; y: number };
+    canvas: HTMLCanvasElement;
+    imageData: ImageData;
+  } | null>(null);
+
   const { kind, source, serverUri, meta, currentSlice, renderOpts } = useDatasetStore();
   const sourceKey = source && kind
     ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri)
@@ -466,6 +505,13 @@ export default function AnnotationCanvas({
   const clipToOtherClasses = useToolStore((s) => s.clipToOtherClasses);
   const mergeOverlappingSameClass = useToolStore((s) => s.mergeOverlappingSameClass);
   const fillThreshold = useToolStore((s) => s.fillThreshold);
+  // NOTE: thresholdLo/thresholdHi are deliberately NOT subscribed — dragging the
+  // band would then re-render this entire component every frame. They are read
+  // via getState() at use sites and drive the overlay through a store
+  // subscription (see the overlay block below).
+  const thresholdOverlay = useToolStore((s) => s.thresholdOverlay);
+  const thresholdSampleWidth = useToolStore((s) => s.thresholdSampleWidth);
+  const setThresholdBand = useToolStore((s) => s.setThresholdBand);
   const { classes } = useClassStore();
 
   const [stageSize, setStageSize] = useState({ width: 800, height: 600 });
@@ -543,14 +589,21 @@ export default function AnnotationCanvas({
   // becomes the Konva image base; brightness/contrast/levels/gamma/colormap still
   // apply on top via the GPU SVG filter. Cache-key fragment so encodes/fields
   // refresh when toggled.
-  const preprocess = useMemo(() => ({ clahe, sharpen }), [clahe, sharpen]);
-  const preprocessKey = `${clahe ? 1 : 0}${sharpen ? 1 : 0}`;
+  const preprocess = useMemo(() => ({ clahe, sharpen, blur }), [clahe, sharpen, blur]);
+  const preprocessKey = `${clahe ? 1 : 0}${sharpen ? 1 : 0}b${blur}`;
+  // Working resolution, clamped so a huge slice can't blow up memory at 4x
+  // (each level costs 4x the pixels for the base canvas AND every tool field).
+  const workScale = useMemo(() => {
+    if (!meta) return 1;
+    const u = Math.max(1, upscale);
+    return meta.width * meta.height * u * u > MAX_WORKING_PIXELS ? 1 : u;
+  }, [meta, upscale]);
   const displayBase = useMemo<CanvasImageSource | null>(() => {
     if (!imageEl || !meta) return imageEl;
-    return (clahe || sharpen)
-      ? renderPreprocessOnly(imageEl, meta.width, meta.height, preprocess)
+    return (clahe || sharpen || blur > 0 || workScale > 1)
+      ? renderPreprocessOnly(imageEl, meta.width, meta.height, preprocess, workScale)
       : imageEl;
-  }, [imageEl, meta, clahe, sharpen, preprocess]);
+  }, [imageEl, meta, clahe, sharpen, blur, workScale, preprocess]);
 
   // SAM sees the preprocessed + brightness/contrast/levels-adjusted image
   // (windowing a low-contrast slice greatly helps), so the encode is keyed on
@@ -558,7 +611,9 @@ export default function AnnotationCanvas({
   const samEncodeKey = imageEl && meta
     ? `${sourceKey}|${currentSlice}|b${brightness}|c${contrast}|l${levelsLo}-${levelsHi}|pp${preprocessKey}`
     : null;
-  /** Lazily render the display-adjusted slice (preprocess → brightness/contrast/levels) that SAM encodes. */
+  /** Lazily render the display-adjusted slice (preprocess → brightness/contrast/levels)
+   *  that SAM encodes. Deliberately kept at 1x: SAM resizes its input to 1024²
+   *  internally, so an upscaled source would only cost memory. */
   const makeSamSource = useCallback(
     () => renderAdjusted(imageEl!, meta!.width, meta!.height, brightness, contrast, levelsLo, levelsHi, preprocess),
     [imageEl, meta, brightness, contrast, levelsLo, levelsHi, preprocess],
@@ -582,17 +637,13 @@ export default function AnnotationCanvas({
   // sync with what's displayed.
   const displayFilterId = 'display-adjust-' + useId().replace(/[^a-zA-Z0-9]/g, '');
   const displayAffine = useMemo(() => {
-    const b255 = brightness * 255;
-    const adjust = Math.pow((contrast + 100) / 100, 2);
-    const range = Math.max(1, levelsHi - levelsLo);
-    const A = adjust * (255 / range);
-    const Bconst = (255 / range) * (adjust * b255 + 127.5 * (1 - adjust)) - (255 * levelsLo) / range;
+    const { slope, intercept } = displayAffineFor(brightness, contrast, levelsLo, levelsHi);
     // The filter is a no-op only when brightness/contrast/levels AND gamma AND
     // colormap are all identity — otherwise it must stay applied.
     const identity =
       brightness === 0 && contrast === 0 && levelsLo <= 0 && levelsHi >= 255 &&
       gamma === 1 && colormap === 'gray';
-    return { slope: A, intercept: Bconst / 255, identity };
+    return { slope, intercept, identity };
   }, [brightness, contrast, levelsLo, levelsHi, gamma, colormap]);
 
   // Colormap LUT (per-channel tableValues) for the display filter; null = gray.
@@ -742,12 +793,12 @@ export default function AnnotationCanvas({
   const computeCommittedSlice = useCallback((newShapes: Shape[], sliceShapes: Shape[]): Shape[] => {
     let toAdd = newShapes;
     if (clipToOtherClasses && meta && newShapes.some((s) => hasOtherClass(sliceShapes, s.classId))) {
-      toAdd = clipShapesToOthers(newShapes, sliceShapes, meta.width, meta.height);
+      toAdd = clipShapesToOthers(newShapes, sliceShapes, meta.width, meta.height, workScale);
     }
     // Auto-merge with overlapping same-class shapes (replaces those + the new shape with
     // one unioned polygon). Runs after clipping so other-class bounds still hold.
     if (mergeOverlappingSameClass && meta && toAdd.length) {
-      const { add, removeIds } = mergeNewWithSameClass(toAdd, sliceShapes, meta.width, meta.height);
+      const { add, removeIds } = mergeNewWithSameClass(toAdd, sliceShapes, meta.width, meta.height, workScale);
       if (removeIds.length) {
         const kept = sliceShapes.filter((s) => !removeIds.includes(s.id));
         return [...kept, ...add];
@@ -755,7 +806,7 @@ export default function AnnotationCanvas({
       toAdd = add;
     }
     return [...sliceShapes, ...toAdd];
-  }, [clipToOtherClasses, mergeOverlappingSameClass, meta]);
+  }, [clipToOtherClasses, mergeOverlappingSameClass, meta, workScale]);
 
   const commitShapes = useCallback((newShapes: Shape[]) => {
     if (!sourceKey || newShapes.length === 0) return;
@@ -767,7 +818,9 @@ export default function AnnotationCanvas({
   const activeColor =
     activeClassId !== null ? colorForClass(activeClassId) : '#4090ff';
 
-  const showBrushCursor = (underlyingTool === 'brush' || underlyingTool === 'eraser') && !!meta && !isPreviewing;
+  const showBrushCursor =
+    (underlyingTool === 'brush' || underlyingTool === 'eraser' || underlyingTool === 'threshold') &&
+    !!meta && !isPreviewing;
 
   /** Current pointer position mapped from stage/display coords to image pixels. */
   const getPointerImagePos = () => {
@@ -786,7 +839,7 @@ export default function AnnotationCanvas({
     if (magneticCostRef.current && magneticBuiltForRef.current === base) {
       return magneticCostRef.current;
     }
-    const cm = buildCostMap(base, meta.width, meta.height);
+    const cm = buildCostMap(base, meta.width, meta.height, 512, workScale);
     magneticCostRef.current = cm;
     magneticBuiltForRef.current = base;
     return cm;
@@ -815,16 +868,152 @@ export default function AnnotationCanvas({
   // what the user sees, exactly like SAM. Cache key includes the display settings.
   const magicFieldRef = useRef<GrayField | null>(null);
   const magicFieldForRef = useRef<string | null>(null);
+  /** Cache-key fragment for fields built from the display-adjusted slice (wand, SAM):
+   *  brightness/contrast/levels are part of what those tools see. */
+  const adjustedKey = `${sourceKey}|${currentSlice}|b${brightness}|c${contrast}|l${levelsLo}-${levelsHi}|pp${preprocessKey}|u${workScale}`;
+  /** Cache-key fragment for fields built from the PREPROCESSED base (threshold brush
+   *  + its overlay, matching the histogram). Deliberately excludes brightness/
+   *  contrast/levels so those sliders never invalidate a field. */
+  const baseKey = `${sourceKey}|${currentSlice}|pp${preprocessKey}|u${workScale}`;
   const ensureMagicField = useCallback((): GrayField | null => {
     if (!imageEl || !meta) return null;
-    const key = `${sourceKey}|${currentSlice}|b${brightness}|c${contrast}|l${levelsLo}-${levelsHi}|pp${preprocessKey}`;
-    if (magicFieldRef.current && magicFieldForRef.current === key) return magicFieldRef.current;
-    const src = renderAdjusted(imageEl, meta.width, meta.height, brightness, contrast, levelsLo, levelsHi, preprocess);
-    const f = buildField(src, meta.width, meta.height);
+    if (magicFieldRef.current && magicFieldForRef.current === adjustedKey) return magicFieldRef.current;
+    const src = renderAdjusted(imageEl, meta.width, meta.height, brightness, contrast, levelsLo, levelsHi, preprocess, workScale);
+    const f = buildField(src, meta.width, meta.height, 1600, workScale);
     magicFieldRef.current = f;
-    magicFieldForRef.current = key;
+    magicFieldForRef.current = adjustedKey;
     return f;
-  }, [imageEl, meta, sourceKey, currentSlice, brightness, contrast, levelsLo, levelsHi, preprocessKey, preprocess]);
+  }, [imageEl, meta, brightness, contrast, levelsLo, levelsHi, preprocess, workScale, adjustedKey]);
+
+  // Threshold-brush field. Built at FULL working resolution (no 1600-px cap) — a
+  // brush needs per-pixel accuracy where a click-based wand can afford a coarser grid.
+  //
+  // Source is the PREPROCESSED base, not the brightness/contrast/levels-adjusted
+  // render the wand and SAM build. That is NOT a change in semantics: the band is
+  // still evaluated against displayed intensity, so the sliders steer the brush.
+  // The equivalence is moved rather than lost — `bandInBaseSpace` maps the band
+  // backwards through the display transform (monotonic, hence exactly the same
+  // pixel set; see lib/displayTransform.ts), which turns a per-tick full-image
+  // re-render into a couple of `Math.pow` calls. Only blur/CLAHE/sharpen, the
+  // slice, and the working scale invalidate this field.
+  const thresholdFieldRef = useRef<GrayField | null>(null);
+  const thresholdFieldForRef = useRef<string | null>(null);
+  const ensureThresholdField = useCallback((): GrayField | null => {
+    if (!imageEl || !meta) return null;
+    if (thresholdFieldRef.current && thresholdFieldForRef.current === baseKey) return thresholdFieldRef.current;
+    // No gradient: the brush gates purely on intensity, and a Sobel pass over a
+    // full-resolution (possibly 4x) grid would be pure waste.
+    const f = buildField(displayBase ?? imageEl, meta.width, meta.height, Math.max(meta.width, meta.height), workScale, false);
+    thresholdFieldRef.current = f;
+    thresholdFieldForRef.current = baseKey;
+    return f;
+  }, [imageEl, meta, displayBase, workScale, baseKey]);
+
+  /** The threshold band, mapped from the DISPLAYED intensities the user authored
+   *  it in into the base space the cached fields live in. Brightness/contrast/
+   *  levels/gamma therefore steer the brush exactly as they steer the image —
+   *  without any of them invalidating a field or touching a pixel. */
+  const bandInBaseSpace = useCallback(() => {
+    const { thresholdLo, thresholdHi } = useToolStore.getState();
+    return displayBandToBase(thresholdLo, thresholdHi, displayAffine, gamma);
+  }, [displayAffine, gamma]);
+
+  /** Binary gate over a field: 1 where the pixel reads as in-band on screen. This
+   *  is what the brush may paint. Reads the band from the store rather than a
+   *  subscribed value — see the overlay below for why the canvas deliberately does
+   *  NOT re-render on band changes. */
+  const buildThresholdGate = useCallback((field: GrayField): Uint8Array => {
+    const { lo, hi } = bandInBaseSpace();
+    const gate = new Uint8Array(field.gw * field.gh);
+    const { gray } = field;
+    for (let i = 0; i < gate.length; i++) {
+      const v = gray[i];
+      if (v >= lo && v <= hi) gate[i] = 1;
+    }
+    return gate;
+  }, [bandInBaseSpace]);
+
+  // ---- Red in-band overlay (ImageJ's threshold display) -------------------
+  // Shows exactly which pixels the brush may paint. Three things keep dragging
+  // the band (or any display slider) smooth, all of which cost real frames when
+  // done the obvious way:
+  //   1. Its own SMALL field (≤1024 px, no upscale, no gradient) instead of the
+  //      brush's full-resolution one — activating the tool or nudging brightness
+  //      must not trigger a full-res renderAdjusted + buildField.
+  //   2. Repainted IMPERATIVELY into a persistent canvas — routing it through
+  //      React state re-rendered this whole (very large) component per frame.
+  //   3. Driven by a store subscription, so the canvas never re-renders on a band
+  //      change at all. That's why thresholdLo/Hi are not subscribed above.
+  const overlayFieldRef = useRef<GrayField | null>(null);
+  const overlayFieldForRef = useRef<string | null>(null);
+  const overlayCanvasRef = useRef<HTMLCanvasElement | null>(null);
+  const overlayImageDataRef = useRef<ImageData | null>(null);
+  const overlayImageRef = useRef<Konva.Image>(null);
+  const showThresholdOverlay = tool === 'threshold' && thresholdOverlay && !isPreviewing;
+
+  const ensureOverlayField = useCallback((): GrayField | null => {
+    if (!imageEl || !meta) return null;
+    if (overlayFieldRef.current && overlayFieldForRef.current === baseKey) return overlayFieldRef.current;
+    // Same preprocessed source as the brush field (see above), so the overlay shows
+    // exactly what the brush would paint — including as the display sliders move,
+    // since both read the band through `bandInBaseSpace`.
+    const f = buildField(displayBase ?? imageEl, meta.width, meta.height, 1024, 1, false);
+    overlayFieldRef.current = f;
+    overlayFieldForRef.current = baseKey;
+    return f;
+  }, [imageEl, meta, displayBase, baseKey]);
+
+  /** Repaint the overlay canvas for the current band and push it to Konva. */
+  const paintThresholdOverlay = useCallback(() => {
+    const node = overlayImageRef.current;
+    if (!node) return;
+    const field = ensureOverlayField();
+    if (!field) return;
+    const { gw, gh, gray } = field;
+    let canvas = overlayCanvasRef.current;
+    if (!canvas || canvas.width !== gw || canvas.height !== gh) {
+      canvas = document.createElement('canvas');
+      canvas.width = gw;
+      canvas.height = gh;
+      overlayCanvasRef.current = canvas;
+      overlayImageDataRef.current = null;
+    }
+    const ctx = canvas.getContext('2d');
+    if (!ctx) return;
+    const { lo, hi } = bandInBaseSpace();
+    // Reuse the pixel buffer across repaints — a band drag repaints every frame
+    // and a fresh multi-MB ImageData per frame is pure GC churn.
+    let img = overlayImageDataRef.current;
+    if (!img) {
+      img = ctx.createImageData(gw, gh);
+      overlayImageDataRef.current = img;
+    }
+    const d = img.data;
+    for (let i = 0; i < gray.length; i++) {
+      const v = gray[i];
+      const o = i * 4;
+      if (v < lo || v > hi) { d[o + 3] = 0; continue; }
+      d[o] = 239; d[o + 1] = 68; d[o + 2] = 68; d[o + 3] = 255; // red-500
+    }
+    ctx.putImageData(img, 0, 0);
+    node.image(canvas);
+    node.getLayer()?.batchDraw();
+  }, [ensureOverlayField, bandInBaseSpace]);
+
+  useEffect(() => {
+    if (!showThresholdOverlay) return;
+    // Coalesce bursts (a band drag commits once per frame) into one repaint.
+    let pending = 0;
+    const schedule = () => {
+      if (pending) return;
+      pending = requestAnimationFrame(() => { pending = 0; paintThresholdOverlay(); });
+    };
+    schedule();
+    const unsub = useToolStore.subscribe((s, prev) => {
+      if (s.thresholdLo !== prev.thresholdLo || s.thresholdHi !== prev.thresholdHi) schedule();
+    });
+    return () => { unsub(); if (pending) cancelAnimationFrame(pending); };
+  }, [showThresholdOverlay, paintThresholdOverlay]);
 
   // Auto negative ("not") prompts for SAM: interior points of nearby other-class
   // regions, so a new selection won't bleed into already-labeled areas. Anchored
@@ -1042,6 +1231,145 @@ export default function AnnotationCanvas({
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, [draft.magneticSeed, draft.tool, draft.sourceKey, draft.sliceKey, sourceKey, currentSlice, imageEl, displayBase]);
 
+  // ---- Threshold brush ----------------------------------------------------
+  // Paints only where the displayed intensity is inside the band, so a stroke
+  // stops dead at a feature boundary. The result can't be expressed as a stroke +
+  // radius, so it accumulates as a mask and commits as POLYGONS.
+
+  /** Begin a threshold stroke at `pos`, priming the mask, gate, and preview canvas. */
+  const startThresholdStroke = (pos: { x: number; y: number }, erase: boolean): void => {
+    const field = ensureThresholdField();
+    if (!field || !meta) return;
+    const { gw, gh, scale } = field;
+    const canvas = document.createElement('canvas');
+    canvas.width = gw;
+    canvas.height = gh;
+    const ctx = canvas.getContext('2d');
+    if (!ctx) return;
+    thresholdStrokeRef.current = {
+      mode: erase ? 'erase' : 'paint',
+      gw, gh, scale,
+      mask: new Uint8Array(gw * gh),
+      gate: buildThresholdGate(field),
+      last: pos,
+      canvas,
+      imageData: ctx.createImageData(gw, gh),
+    };
+    if (thresholdPreviewRef.current) {
+      thresholdPreviewRef.current.image(canvas);
+      thresholdPreviewRef.current.visible(true);
+    }
+    extendThresholdStroke(pos);
+  };
+
+  /** Stamp the segment from the last point to `pos` and repaint the preview.
+   *  Only the segment's dirty rect is rewritten — repainting the whole grid every
+   *  mousemove would be tens of millions of writes per frame at a 2x/4x full-res
+   *  grid, which is exactly the size this tool is meant to be used at. */
+  const extendThresholdStroke = (pos: { x: number; y: number }): void => {
+    const st = thresholdStrokeRef.current;
+    if (!st) return;
+    const { gw, gh, scale, mask, gate } = st;
+    const from = st.last;
+    stampStroke(mask, gw, gh, [from.x, from.y, pos.x, pos.y], brushSize / scale, scale, 1, gate);
+    st.last = pos;
+
+    // Dirty rect of this segment in grid cells (the stamped capsule's bbox).
+    const r = Math.max(0.5, brushSize / scale) + 1;
+    const x0 = Math.max(0, Math.floor(Math.min(from.x, pos.x) / scale - r));
+    const x1 = Math.min(gw - 1, Math.ceil(Math.max(from.x, pos.x) / scale + r));
+    const y0 = Math.max(0, Math.floor(Math.min(from.y, pos.y) / scale - r));
+    const y1 = Math.min(gh - 1, Math.ceil(Math.max(from.y, pos.y) / scale + r));
+    if (x1 < x0 || y1 < y0) return;
+
+    // Erase strokes preview white (matching the eraser's draft line); paint
+    // strokes use the active class color.
+    const d = st.imageData.data;
+    const [cr, cg, cb] = st.mode === 'erase' ? [255, 255, 255] : hexToRgb(activeColor);
+    for (let y = y0; y <= y1; y++) {
+      for (let x = x0; x <= x1; x++) {
+        const i = y * gw + x;
+        if (!mask[i]) continue;
+        const o = i * 4;
+        d[o] = cr; d[o + 1] = cg; d[o + 2] = cb; d[o + 3] = 255;
+      }
+    }
+    st.canvas
+      .getContext('2d')
+      ?.putImageData(st.imageData, 0, 0, x0, y0, x1 - x0 + 1, y1 - y0 + 1);
+    thresholdPreviewRef.current?.getLayer()?.batchDraw();
+  };
+
+  /** Flush the buffered threshold stroke: mask → polygons → one undo step. */
+  const commitThresholdStroke = (): void => {
+    const st = thresholdStrokeRef.current;
+    thresholdStrokeRef.current = null;
+    if (thresholdPreviewRef.current) {
+      thresholdPreviewRef.current.visible(false);
+      thresholdPreviewRef.current.image(undefined);
+      thresholdPreviewRef.current.getLayer()?.batchDraw();
+    }
+    if (!st || !sourceKey || !meta || activeClassId === null) return;
+
+    const { gw, gh, scale, mask } = st;
+    let any = false;
+    for (let i = 0; i < mask.length; i++) if (mask[i]) { any = true; break; }
+    if (!any) return;
+
+    const regions = maskToPolygonsWithHoles(mask, gw, gh, { minRegion: 4, scale })
+      .filter((p) => p.points.length >= 6);
+    if (regions.length === 0) return;
+
+    if (st.mode === 'erase') {
+      // Subtract the painted region from the shapes it actually overlaps, via the
+      // same node-preserving boolean path the eraser uses. The raster overlap test
+      // matters: `subtractFromShape` always rebuilds geometry, so running it on
+      // untouched shapes would churn their ids and vertices for nothing.
+      const sliceShapes = useAnnotationStore.getState().byImage[sourceKey]?.[String(currentSlice)] ?? [];
+      const visible = (s: Shape) => classes.find((c) => c.classId === s.classId)?.isVisible !== false;
+      const inScope = (s: Shape) => eraseAllClasses || s.classId === activeClassId;
+      const overlapsStroke = (s: Shape) => {
+        const sm = rasterizeShapes([s], gw, gh, scale);
+        for (let i = 0; i < mask.length; i++) if (mask[i] && sm[i]) return true;
+        return false;
+      };
+      const stampMP = regionsToMultiPolygon(regions);
+      if (stampMP.length === 0) return;
+
+      const next: Shape[] = [];
+      const replacedSelection: string[] = [];
+      let changed = false;
+      for (const s of sliceShapes) {
+        if (!inScope(s) || !visible(s) || !overlapsStroke(s)) { next.push(s); continue; }
+        const polys = subtractFromShape(s, stampMP, meta.width, meta.height);
+        if (polys === null) { next.push(s); continue; }
+        changed = true;
+        next.push(...polys);
+        if (selectedShapeIds.includes(s.id)) replacedSelection.push(...polys.map((p) => p.id));
+      }
+      if (changed) {
+        setShapes(sourceKey, currentSlice, next);
+        setSelectedShapeIds([
+          ...selectedShapeIds.filter((id) => next.some((s) => s.id === id)),
+          ...replacedSelection,
+        ]);
+      }
+      return;
+    }
+
+    // Paint: commit as polygons through the shared path, so clip-to-other-classes
+    // and merge-same-class apply and the whole stroke is a single undo step.
+    commitShapes(
+      regions.map((p) => ({
+        id: uuidv4(),
+        classId: activeClassId,
+        kind: 'polygon' as const,
+        points: p.points,
+        ...(p.holes.length ? { holes: p.holes } : {}),
+      })),
+    );
+  };
+
   /** Flush the buffered draft stroke to the Zustand store (one write per stroke). */
   const commitDraftStroke = () => {
     const draft = draftStrokeRef.current;
@@ -1086,7 +1414,7 @@ export default function AnnotationCanvas({
       // Bake the erase directly into polygon geometry for every target — the
       // vertices always match the visible shape, splits produce independent
       // polygons, and undo is one clean shape-replacement step.
-      const { gw, gh, scale } = fullResGridFor(meta.width, meta.height);
+      const { gw, gh, scale } = fullResGridFor(meta.width, meta.height, workScale);
       // The erase stamp as polygon geometry, subtracted from each shape via a true
       // boolean difference so untouched vertices are PRESERVED (only the cut edge
       // gets new points). Falls back per-shape to a rasterize→re-vectorize round-trip
@@ -1139,7 +1467,7 @@ export default function AnnotationCanvas({
         // Re-vectorize at full resolution so the round-trip is ~idempotent: existing
         // merged/clipped regions keep their shape instead of eroding or shifting a
         // little each time a stroke is added.
-        const { gw, gh, scale } = fullResGridFor(meta.width, meta.height);
+        const { gw, gh, scale } = fullResGridFor(meta.width, meta.height, workScale);
         const prospective = { ...brush, strokes: [...brush.strokes, { points: finalPoints, radius, mode: 'paint' as const }] };
         const mine = rasterizeShapes([prospective], gw, gh, scale);
         let clipChanged = false;
@@ -1362,6 +1690,22 @@ export default function AnnotationCanvas({
         draftLineRef.current.visible(true);
         draftStrokeLayerRef.current?.batchDraw();
       }
+    } else if (tool === 'threshold') {
+      // Shift-click samples instead of painting: re-center the band on the pixel
+      // under the cursor, keeping the configured sample width.
+      if (e.evt.shiftKey) {
+        const field = ensureThresholdField();
+        if (field) {
+          const gx = Math.max(0, Math.min(field.gw - 1, Math.floor(pos.x / field.scale)));
+          const gy = Math.max(0, Math.min(field.gh - 1, Math.floor(pos.y / field.scale)));
+          const v = field.gray[gy * field.gw + gx];
+          const half = thresholdSampleWidth / 2;
+          setThresholdBand(v - half, v + half);
+        }
+        return;
+      }
+      setIsDrawing(true);
+      startThresholdStroke(pos, e.evt.altKey);
     } else if (tool === 'eraser') {
       setIsDrawing(true);
       const sliceShapes = byImage[sourceKey]?.[String(currentSlice)] ?? [];
@@ -1447,6 +1791,13 @@ export default function AnnotationCanvas({
       return;
     }
 
+    // Threshold brush: stamp into the in-band mask and repaint the preview. Like
+    // the brush, this writes nothing to the store until mouseup.
+    if (tool === 'threshold' && e.evt.buttons === 1 && thresholdStrokeRef.current) {
+      extendThresholdStroke(pos);
+      return;
+    }
+
     // Buffer brush/eraser points — no store writes here
     if ((tool === 'brush' || tool === 'eraser') && e.evt.buttons === 1 && draftStrokeRef.current) {
       draftStrokeRef.current.points.push(pos.x, pos.y);
@@ -1503,6 +1854,7 @@ export default function AnnotationCanvas({
 
     if (isDrawing) {
       commitDraftStroke();
+      commitThresholdStroke();
       setIsDrawing(false);
     }
     if (!sourceKey || !meta || activeClassId === null) return;
@@ -1565,6 +1917,7 @@ export default function AnnotationCanvas({
     // Commit any in-progress stroke
     if (isDrawing) {
       commitDraftStroke();
+      commitThresholdStroke();
       setIsDrawing(false);
     }
     setDragStart(null);
@@ -2287,6 +2640,22 @@ export default function AnnotationCanvas({
           )}
         </Layer>
 
+        {/* Threshold band overlay (ImageJ-style): every pixel the threshold brush
+            is currently allowed to paint, in translucent red. Sits above the image
+            but below the annotations so existing regions stay readable. */}
+        {showThresholdOverlay && meta && (
+          <Layer listening={false} opacity={0.35} {...imageClip}>
+            <KonvaImage
+              ref={overlayImageRef}
+              image={overlayCanvasRef.current ?? undefined}
+              width={meta.width}
+              height={meta.height}
+              listening={false}
+              perfectDrawEnabled={false}
+            />
+          </Layer>
+        )}
+
         {/* Layer 1: committed shapes — cached + opacity applied once at the
             layer so overlapping same-class shapes render a uniform class color.
             Clipped to the image frame so strokes never render past the edges. */}
@@ -2516,6 +2885,19 @@ export default function AnnotationCanvas({
             perfectDrawEnabled={false}
             listening={false}
           />
+          {/* Threshold-brush stroke preview: the in-band mask painted so far,
+              drawn at image size (its canvas is at the working resolution). */}
+          {meta && (
+            <KonvaImage
+              ref={thresholdPreviewRef}
+              image={undefined}
+              width={meta.width}
+              height={meta.height}
+              visible={false}
+              listening={false}
+              perfectDrawEnabled={false}
+            />
+          )}
         </Layer>
 
         {/* Layer 4: brush/eraser size cursor preview (position updated imperatively) */}
diff --git a/frontend/src/components/annotate/DisplayControls/index.tsx b/frontend/src/components/annotate/DisplayControls/index.tsx
index 45a2ffb..674bd37 100644
--- a/frontend/src/components/annotate/DisplayControls/index.tsx
+++ b/frontend/src/components/annotate/DisplayControls/index.tsx
@@ -30,8 +30,19 @@ export interface DisplayControlsProps {
   sharpen: boolean;
   onClaheChange: (v: boolean) => void;
   onSharpenChange: (v: boolean) => void;
+  /** Gaussian pre-blur sigma in image pixels (0 = off) — denoises what the
+   *  intensity-driven tools see, as well as the display. */
+  blur: number;
+  onBlurChange: (v: number) => void;
+  /** Working resolution multiplier (1, 2, 4) for the drawing tools. */
+  upscale: number;
+  onUpscaleChange: (v: number) => void;
+  /** Highest upscale this slice can afford before the guard clamps it. */
+  maxUpscale?: number;
 }
 
+const UPSCALES = [1, 2, 4] as const;
+
 /** Renders the brightness/contrast sliders + levels histogram with reset buttons. */
 export default function DisplayControls({
   brightness,
@@ -52,6 +63,11 @@ export default function DisplayControls({
   sharpen,
   onClaheChange,
   onSharpenChange,
+  blur,
+  onBlurChange,
+  upscale,
+  onUpscaleChange,
+  maxUpscale = 4,
 }: DisplayControlsProps) {
   return (
     <div className="flex flex-col gap-2">
@@ -136,7 +152,23 @@ export default function DisplayControls({
         />
       </div>
 
-      {/* Nonlinear enhancers (display-only; can combine: CLAHE → Sharpen). */}
+      {/* Gaussian pre-blur — denoises so threshold/wand/fill see coherent regions. */}
+      <div className="flex flex-col gap-0.5">
+        <DebouncedSlider
+          label="Blur (σ)"
+          format={(v) => (v === 0 ? 'off' : v.toFixed(2))}
+          min={0}
+          max={5}
+          step={0.25}
+          value={blur}
+          onChange={onBlurChange}
+          // Each commit re-blurs the whole slice (and invalidates every tool
+          // field), so commit on pause rather than on every tick.
+          debounceMs={200}
+        />
+      </div>
+
+      {/* Nonlinear enhancers (display-only; can combine: Blur → CLAHE → Sharpen). */}
       <div className="flex items-center gap-3 pt-0.5">
         <label
           className="flex items-center gap-1.5 text-[11px] text-gray-600 cursor-pointer"
@@ -153,6 +185,46 @@ export default function DisplayControls({
           Sharpen
         </label>
       </div>
+
+      {/* Working resolution — resamples the slice for the drawing tools so small
+          features get more pixels to annotate against. Coordinates stay native. */}
+      <div className="flex flex-col gap-1 pt-0.5">
+        <span className="text-[11px] text-gray-500">Working resolution</span>
+        <div role="radiogroup" aria-label="Working resolution" className="flex items-center gap-1">
+          {UPSCALES.map((u) => {
+            const tooBig = u > maxUpscale;
+            return (
+              <button
+                key={u}
+                type="button"
+                role="radio"
+                aria-checked={upscale === u}
+                disabled={tooBig}
+                onClick={() => onUpscaleChange(u)}
+                title={tooBig
+                  ? `${u}× needs more memory than this slice size allows`
+                  : u === 1
+                    ? 'Native resolution'
+                    : `Resample ${u}× for the drawing tools — sub-pixel brush and mask detail`}
+                className={[
+                  'flex-1 py-1 rounded-md text-[11px] border transition-colors',
+                  tooBig
+                    ? 'bg-gray-50 text-gray-300 border-gray-100 cursor-not-allowed'
+                    : upscale === u
+                      ? 'bg-sky-600 text-white border-sky-700'
+                      : 'bg-white text-gray-700 border-gray-200 hover:bg-sky-50',
+                ].join(' ')}
+              >
+                {u}×
+              </button>
+            );
+          })}
+        </div>
+        <p className="text-[10px] text-gray-500 leading-snug">
+          Resamples the slice for the drawing tools so smaller features can be annotated.
+          Exported pixels and annotation coordinates are unchanged.
+        </p>
+      </div>
     </div>
   );
 }
diff --git a/frontend/src/components/annotate/HistogramControl/index.tsx b/frontend/src/components/annotate/HistogramControl/index.tsx
index 1f05b0a..3c59ea8 100644
--- a/frontend/src/components/annotate/HistogramControl/index.tsx
+++ b/frontend/src/components/annotate/HistogramControl/index.tsx
@@ -14,13 +14,27 @@ export interface HistogramControlProps {
   hi: number;
   onChange: (lo: number, hi: number) => void;
   onReset: () => void;
+  /** What the window means — "Levels" (display) or "Threshold" (paintable band). */
+  label?: string;
+  /** Accent for the band + knobs; red distinguishes a threshold band from levels. */
+  accent?: 'sky' | 'red';
+  /** Extra controls rendered next to the reset button (e.g. an Auto button). */
+  actions?: React.ReactNode;
 }
 
+const ACCENTS = {
+  sky: { band: '#38bdf8', line: '#0284c7', grip: 'bg-sky-600' },
+  red: { band: '#f87171', line: '#dc2626', grip: 'bg-red-600' },
+} as const;
+
 const VBW = 256;
 const VBH = 60;
 const MAXV = 255;
 
-export default function HistogramControl({ bins, lo, hi, onChange, onReset }: HistogramControlProps) {
+export default function HistogramControl({
+  bins, lo, hi, onChange, onReset, label = 'Levels', accent = 'sky', actions,
+}: HistogramControlProps) {
+  const theme = ACCENTS[accent];
   const trackRef = useRef<HTMLDivElement>(null);
   // Active drag target + the window state captured at press (for band drags).
   const drag = useRef<{ target: 'lo' | 'hi' | 'band'; startV: number; startLo: number; startHi: number } | null>(null);
@@ -119,22 +133,25 @@ export default function HistogramControl({ bins, lo, hi, onChange, onReset }: Hi
   const pct = (v: number) => `${(v / MAXV) * 100}%`;
   const knobCls =
     'absolute top-0 h-full w-3 -translate-x-1/2 flex items-center justify-center cursor-ew-resize';
-  const knobGrip = 'w-1.5 h-5 rounded-sm bg-sky-600 border border-white shadow';
+  const knobGrip = `w-1.5 h-5 rounded-sm ${theme.grip} border border-white shadow`;
 
   return (
     <div className="flex flex-col gap-1">
       <div className="flex items-center justify-between">
         <span className="text-[11px] text-gray-500">
-          Levels <span className="tabular-nums text-gray-500">{lo} – {hi}</span>
+          {label} <span className="tabular-nums text-gray-500">{lo} – {hi}</span>
         </span>
-        <button
-          aria-label="Reset levels"
-          title="Reset levels"
-          onClick={onReset}
-          className="p-0.5 rounded hover:bg-gray-100 hover:text-sky-600"
-        >
-          <ArrowCounterClockwise size={12} />
-        </button>
+        <div className="flex items-center gap-1">
+          {actions}
+          <button
+            aria-label={`Reset ${label.toLowerCase()}`}
+            title={`Reset ${label.toLowerCase()}`}
+            onClick={onReset}
+            className="p-0.5 rounded hover:bg-gray-100 hover:text-sky-600"
+          >
+            <ArrowCounterClockwise size={12} />
+          </button>
+        </div>
       </div>
 
       {/* Track: histogram plot + dual-knob range slider overlaid on it. */}
@@ -150,10 +167,10 @@ export default function HistogramControl({ bins, lo, hi, onChange, onReset }: Hi
           preserveAspectRatio="none"
           className="absolute inset-0 w-full h-full rounded bg-gray-100 border border-gray-200"
         >
-          <rect x={lo} y={0} width={Math.max(0, hi - lo)} height={VBH} fill="#38bdf8" opacity={0.15} />
+          <rect x={lo} y={0} width={Math.max(0, hi - lo)} height={VBH} fill={theme.band} opacity={0.15} />
           <path d={path} stroke="#64748b" strokeWidth={1} fill="none" vectorEffect="non-scaling-stroke" />
-          <line x1={lo} y1={0} x2={lo} y2={VBH} stroke="#0284c7" strokeWidth={1} vectorEffect="non-scaling-stroke" />
-          <line x1={hi} y1={0} x2={hi} y2={VBH} stroke="#0284c7" strokeWidth={1} vectorEffect="non-scaling-stroke" />
+          <line x1={lo} y1={0} x2={lo} y2={VBH} stroke={theme.line} strokeWidth={1} vectorEffect="non-scaling-stroke" />
+          <line x1={hi} y1={0} x2={hi} y2={VBH} stroke={theme.line} strokeWidth={1} vectorEffect="non-scaling-stroke" />
         </svg>
 
         {/* Knob grips (crisp HTML overlay, aligned to the plot by percentage). */}
@@ -162,7 +179,7 @@ export default function HistogramControl({ bins, lo, hi, onChange, onReset }: Hi
           style={{ left: pct(lo) }}
           role="slider"
           tabIndex={0}
-          aria-label="Levels minimum"
+          aria-label={`${label} minimum`}
           aria-valuemin={0}
           aria-valuemax={MAXV}
           aria-valuenow={lo}
@@ -175,7 +192,7 @@ export default function HistogramControl({ bins, lo, hi, onChange, onReset }: Hi
           style={{ left: pct(hi) }}
           role="slider"
           tabIndex={0}
-          aria-label="Levels maximum"
+          aria-label={`${label} maximum`}
           aria-valuemin={0}
           aria-valuemax={MAXV}
           aria-valuenow={hi}
diff --git a/frontend/src/components/annotate/Toolbar/index.tsx b/frontend/src/components/annotate/Toolbar/index.tsx
index 9f897c8..4144dde 100644
--- a/frontend/src/components/annotate/Toolbar/index.tsx
+++ b/frontend/src/components/annotate/Toolbar/index.tsx
@@ -1,15 +1,20 @@
 /**
  * Toolbar — tool selector (radiogroup), brush size, opacity, undo/redo.
- * Keybinds: p=polygon, l=ellipse, e=rectangle, r=eraser, b=brush, f=fill, s=select,
- *           g=magic, m=magnetic, Space=pan (hold), x=next slice, t=fit to screen, Ctrl/Cmd+Z=undo
+ * Keybinds: p=polygon, l=ellipse, e=rectangle, r=eraser, b=brush, h=threshold brush,
+ *           f=fill, s=select, g=magic, m=magnetic, Space=pan (hold), x=next slice,
+ *           t=fit to screen, Ctrl/Cmd+Z=undo
  */
-import { Hand, Cursor, Polygon, MagnetStraight, MagicWand, Rectangle, Circle, PaintBrush, PaintBucket, Eraser, ArrowBendUpLeft, ArrowBendUpRight } from '@phosphor-icons/react';
+import { Hand, Cursor, Polygon, MagnetStraight, MagicWand, Rectangle, Circle, PaintBrush, Drop, PaintBucket, Eraser, ArrowBendUpLeft, ArrowBendUpRight } from '@phosphor-icons/react';
+import { useMemo } from 'react';
 import { useStore } from 'zustand';
 import { useToolStore, type Tool } from '@/stores/toolStore';
 import { useAnnotationStore } from '@/stores/annotationStore';
 import * as editHistory from '@/hooks/editHistory';
 import { cn } from '@/lib/utils';
 import DebouncedSlider from '@/components/common/DebouncedSlider';
+import HistogramControl from '@/components/annotate/HistogramControl';
+import { otsuThreshold } from '@/lib/magicwand';
+import { displayAffineFor, remapHistogramToDisplay } from '@/lib/displayTransform';
 import { useSam } from '@/hooks/useSam';
 
 // macOS labels the Alt key "Option" (⌥); the key name only differs on screen.
@@ -79,12 +84,23 @@ function ToolButton({ tool, label, icon, keybind, activeTool, disabled, onSelect
 interface ToolbarProps {
   /** When true, drawing tools are greyed out (e.g. no class defined yet). */
   disabled?: boolean;
+  /** 256-bin luminance histogram of the current slice — drives the threshold band
+   *  picker. Owned by AnnotatePage (the canvas emits it); null before load. It is
+   *  sampled from the PREPROCESSED base, so it must be remapped through the display
+   *  transform to line up with the band (which is authored in displayed space). */
+  histogramBins?: number[] | null;
+  /** Live brightness/contrast/levels/gamma, used for exactly that remap. */
+  display?: { brightness: number; contrast: number; levelsLo: number; levelsHi: number; gamma: number };
+  /** Working resolution multiplier — sets the sub-pixel brush radius floor. */
+  upscale?: number;
 }
 
 /** Renders the tool radiogroup, undo/redo, and the active tool's parameter controls. */
-export default function Toolbar({ disabled = false }: ToolbarProps) {
+export default function Toolbar({ disabled = false, histogramBins = null, display, upscale = 1 }: ToolbarProps) {
   const {
     tool, setTool, brushSize, setBrushSize, fillThreshold, setFillThreshold,
+    thresholdLo, thresholdHi, setThresholdBand,
+    thresholdOverlay, setThresholdOverlay,
     magicTolerance, setMagicTolerance, magicMode, setMagicMode, magicSigma, setMagicSigma,
     magicEdgeStop, setMagicEdgeStop, magicEngine, setMagicEngine,
     samDetail, setSamDetail, samThreshold, setSamThreshold,
@@ -96,6 +112,19 @@ export default function Toolbar({ disabled = false }: ToolbarProps) {
     selectScope, setSelectScope,
   } = useToolStore();
   const sam = useSam(tool === 'magic' && magicEngine === 'sam');
+  // At an upscaled working resolution the brush can go sub-pixel — a 2x grid
+  // resolves a 0.5 px radius, which is the whole point of upscaling.
+  const minRadius = 1 / Math.max(1, upscale);
+  const snapRadius = (n: number) => Math.round(n / minRadius) * minRadius;
+
+  // The band is authored in DISPLAYED intensity, but the histogram is sampled from
+  // the preprocessed base — remap it so the plot under the knobs shows the same
+  // image the user is looking at (and the same one the band cuts).
+  const bandHistogram = useMemo(() => {
+    if (!histogramBins || !display) return histogramBins;
+    const affine = displayAffineFor(display.brightness, display.contrast, display.levelsLo, display.levelsHi);
+    return remapHistogramToDisplay(histogramBins, affine, display.gamma);
+  }, [histogramBins, display]);
   // Undo/redo route through editHistory so a class deletion replays alongside its region
   // change; canUndo/canRedo still reflect the (1:1) zundo stack.
   const canUndo = useStore(useAnnotationStore.temporal, (s) => s.pastStates.length > 0);
@@ -110,6 +139,7 @@ export default function Toolbar({ disabled = false }: ToolbarProps) {
     { tool: 'rectangle', label: 'Rect',    icon: <Rectangle size={18} />,   keybind: 'e' },
     { tool: 'ellipse',   label: 'Ellipse', icon: <Circle size={18} />,      keybind: 'l' },
     { tool: 'brush',     label: 'Brush',   icon: <PaintBrush size={18} />,  keybind: 'b' },
+    { tool: 'threshold', label: 'Thresh',  icon: <Drop size={18} />,        keybind: 'h' },
     { tool: 'fill',      label: 'Fill',    icon: <PaintBucket size={18} />, keybind: 'f' },
     { tool: 'eraser',    label: 'Eraser',  icon: <Eraser size={18} />,      keybind: 'r' },
   ];
@@ -178,27 +208,29 @@ export default function Toolbar({ disabled = false }: ToolbarProps) {
         <span className="font-mono font-semibold text-gray-500">⌘/Ctrl+Z</span> undo
       </div>
 
-      {(tool === 'brush' || tool === 'eraser') && (
+      {(tool === 'brush' || tool === 'eraser' || tool === 'threshold') && (
         <div className="flex flex-col gap-1 mt-1">
           <label className="text-xs text-gray-500">Brush radius (px)</label>
           <div className="flex items-center gap-2">
             <input
               type="range"
-              min={1}
+              min={minRadius}
               max={500}
+              step={minRadius}
               value={brushSize}
-              onChange={(e) => setBrushSize(Math.max(1, Number(e.target.value)))}
+              onChange={(e) => setBrushSize(Math.max(minRadius, Number(e.target.value)))}
               className="flex-1 min-w-0"
               aria-label="Brush radius"
             />
             <input
               type="number"
-              min={1}
+              min={minRadius}
               max={500}
+              step={minRadius}
               value={brushSize}
               onChange={(e) => {
                 const n = Number(e.target.value);
-                if (Number.isFinite(n)) setBrushSize(Math.min(500, Math.max(1, Math.round(n))));
+                if (Number.isFinite(n)) setBrushSize(Math.min(500, Math.max(minRadius, snapRadius(n))));
               }}
               className="w-16 flex-shrink-0 border border-gray-200 rounded px-1 py-0.5 text-xs text-right tabular-nums"
               aria-label="Brush radius (px)"
@@ -223,6 +255,56 @@ export default function Toolbar({ disabled = false }: ToolbarProps) {
         </div>
       )}
 
+      {tool === 'threshold' && (
+        <div className="flex flex-col gap-2 mt-1">
+          <HistogramControl
+            bins={bandHistogram}
+            lo={thresholdLo}
+            hi={thresholdHi}
+            onChange={setThresholdBand}
+            onReset={() => setThresholdBand(0, 255)}
+            label="Threshold"
+            accent="red"
+            actions={
+              <button
+                type="button"
+                onClick={() => {
+                  if (!bandHistogram) return;
+                  // Otsu splits the slice into dark/bright; paint the brighter class
+                  // by default (the usual "select the feature, not the matrix" case).
+                  setThresholdBand(otsuThreshold(bandHistogram), 255);
+                }}
+                disabled={!bandHistogram}
+                title="Auto threshold (Otsu) — splits the histogram into two classes"
+                className={cn(
+                  'px-1.5 py-0.5 rounded text-[10px] border transition-colors',
+                  bandHistogram
+                    ? 'bg-white text-gray-700 border-gray-200 hover:bg-sky-50 hover:border-sky-300'
+                    : 'bg-gray-50 text-gray-300 border-gray-100 cursor-not-allowed',
+                )}
+              >
+                Auto
+              </button>
+            }
+          />
+          <label className="flex items-center gap-2 text-[11px] text-gray-600 cursor-pointer select-none">
+            <input
+              type="checkbox"
+              checked={thresholdOverlay}
+              onChange={(e) => setThresholdOverlay(e.target.checked)}
+              className="accent-red-600"
+            />
+            Show in-range overlay
+          </label>
+          <p className="text-[10px] text-gray-500 leading-snug">
+            Paints only where intensity falls inside the band, so a stroke stops at the
+            feature boundary. <b>Shift-click</b> samples the pixel under the cursor;{' '}
+            <b>{REMOVE_KEY_LABEL}-drag</b> erases. For noisy scans raise <b>Blur</b> in
+            Display first.
+          </p>
+        </div>
+      )}
+
       {tool === 'select' && (
         <div className="flex flex-col gap-1 mt-1">
           <label className="text-xs text-gray-500">Select all (⌘/Ctrl+A)</label>
diff --git a/frontend/src/hooks/useKeybinds.ts b/frontend/src/hooks/useKeybinds.ts
index 321203c..ab78683 100644
--- a/frontend/src/hooks/useKeybinds.ts
+++ b/frontend/src/hooks/useKeybinds.ts
@@ -1,8 +1,8 @@
 /**
  * useKeybinds — keyboard shortcuts for the annotation workspace.
  *
- * Tools:   p=polygon  l=ellipse  e=rectangle  r=eraser  b=brush  f=fill  g=magic
- *          m=magnetic  s=select   (each key is a letter in the tool's label)
+ * Tools:   p=polygon  l=ellipse  e=rectangle  r=eraser  b=brush  h=threshold brush
+ *          f=fill  g=magic  m=magnetic  s=select  (each key is a letter in the label)
  * Pan:     hold Space (reverts to the previous tool on release)
  * View:    x=next slice (forward)  t=fit image to screen  arrows=prev/next slice
  * Edit:    Ctrl/Cmd+Z=undo   Ctrl/Cmd+Shift+Z / Ctrl+Y=redo
@@ -23,6 +23,7 @@ const TOOL_KEYBINDS: Record<string, Tool> = {
   e: 'rectangle',   // Rect
   l: 'ellipse',     // eLlipse (e is taken by rect)
   b: 'brush',
+  h: 'threshold',   // tHreshold brush (t is fit-to-screen)
   f: 'fill',
   r: 'eraser',      // eRaser
 };
diff --git a/frontend/src/lib/blur.test.ts b/frontend/src/lib/blur.test.ts
new file mode 100644
index 0000000..f49fd3a
--- /dev/null
+++ b/frontend/src/lib/blur.test.ts
@@ -0,0 +1,68 @@
+import { describe, it, expect } from 'vitest';
+import { applyGaussianBlurRgba } from './blur';
+
+function grayBuffer(w: number, h: number, fn: (x: number, y: number) => number): Uint8ClampedArray {
+  const d = new Uint8ClampedArray(w * h * 4);
+  for (let y = 0; y < h; y++) {
+    for (let x = 0; x < w; x++) {
+      const v = fn(x, y);
+      const i = (y * w + x) * 4;
+      d[i] = d[i + 1] = d[i + 2] = v;
+      d[i + 3] = 255;
+    }
+  }
+  return d;
+}
+
+describe('applyGaussianBlurRgba', () => {
+  it('leaves a flat image unchanged (edges clamp, so no darkening at the border)', () => {
+    const d = grayBuffer(16, 16, () => 120);
+    applyGaussianBlurRgba(d, 16, 16, 2);
+    for (let i = 0; i < d.length; i += 4) expect(d[i]).toBeCloseTo(120, -0.5);
+  });
+
+  it('spreads an impulse into its neighbourhood', () => {
+    const d = grayBuffer(21, 21, (x, y) => (x === 10 && y === 10 ? 255 : 0));
+    applyGaussianBlurRgba(d, 21, 21, 2);
+    const at = (x: number, y: number) => d[(y * 21 + x) * 4];
+    expect(at(10, 10)).toBeLessThan(255);      // peak flattened
+    expect(at(10, 10)).toBeGreaterThan(0);
+    expect(at(11, 10)).toBeGreaterThan(0);     // energy moved outward
+    expect(at(10, 11)).toBeGreaterThan(0);
+    // Monotonically decreasing away from the impulse.
+    expect(at(10, 10)).toBeGreaterThan(at(12, 10));
+    expect(at(12, 10)).toBeGreaterThan(at(15, 10));
+  });
+
+  it('reduces the contrast of a step edge', () => {
+    const w = 32;
+    const d = grayBuffer(w, 4, (x) => (x < w / 2 ? 0 : 255));
+    applyGaussianBlurRgba(d, w, 4, 3);
+    const at = (x: number) => d[(1 * w + x) * 4];
+    // Straddling the edge, values are now intermediate rather than 0/255.
+    expect(at(w / 2 - 1)).toBeGreaterThan(0);
+    expect(at(w / 2)).toBeLessThan(255);
+    // Far from the edge the plateaus survive.
+    expect(at(0)).toBeLessThan(10);
+    expect(at(w - 1)).toBeGreaterThan(245);
+  });
+
+  it('is a no-op for sigma <= 0', () => {
+    const d = grayBuffer(8, 8, (x, y) => (x + y) * 8);
+    const before = new Uint8ClampedArray(d);
+    applyGaussianBlurRgba(d, 8, 8, 0);
+    expect(Array.from(d)).toEqual(Array.from(before));
+    applyGaussianBlurRgba(d, 8, 8, -1);
+    expect(Array.from(d)).toEqual(Array.from(before));
+  });
+
+  it('preserves alpha', () => {
+    const d = grayBuffer(12, 12, (x) => 20 * x);
+    applyGaussianBlurRgba(d, 12, 12, 1.5);
+    for (let i = 3; i < d.length; i += 4) expect(d[i]).toBe(255);
+  });
+
+  it('does not throw on empty input', () => {
+    expect(() => applyGaussianBlurRgba(new Uint8ClampedArray(0), 0, 0, 2)).not.toThrow();
+  });
+});
diff --git a/frontend/src/lib/blur.ts b/frontend/src/lib/blur.ts
new file mode 100644
index 0000000..368f5bd
--- /dev/null
+++ b/frontend/src/lib/blur.ts
@@ -0,0 +1,106 @@
+/**
+ * Gaussian blur (display-only) — three successive box blurs, the standard
+ * approximation of a true Gaussian (by the central limit theorem, three passes
+ * are visually indistinguishable from a real Gaussian for σ ≳ 1 while staying
+ * O(n) per pass regardless of radius).
+ *
+ * Used as the first nonlinear display preprocessor so the intensity-driven tools
+ * (threshold brush, magic wand, fill) see a denoised image and similar features
+ * cohere into one selectable region. Mutates the RGBA Uint8ClampedArray in place
+ * (alpha untouched); edges clamp (out-of-bounds samples replicate the edge
+ * pixel). Pure/DOM-free for testing.
+ */
+
+/** Box radii whose 3-pass cascade approximates a Gaussian of the given sigma.
+ *  Standard Kovesi/Gwosdek construction: pick w_ideal from the box variance
+ *  identity, split into `m` boxes of size wl and `3-m` of size wl+2. */
+function boxRadiiForGaussian(sigma: number, passes: number): number[] {
+  const wIdeal = Math.sqrt((12 * sigma * sigma) / passes + 1);
+  let wl = Math.floor(wIdeal);
+  if (wl % 2 === 0) wl--;
+  const wu = wl + 2;
+  const mIdeal =
+    (12 * sigma * sigma - passes * wl * wl - 4 * passes * wl - 3 * passes) /
+    (-4 * wl - 4);
+  const m = Math.round(mIdeal);
+  const radii: number[] = [];
+  for (let i = 0; i < passes; i++) {
+    const w = i < m ? wl : wu;
+    radii.push(Math.max(0, (w - 1) / 2));
+  }
+  return radii;
+}
+
+/** One horizontal box blur of radius `r` from `src` into `dst` (RGB only). */
+function boxBlurH(
+  src: Uint8ClampedArray,
+  dst: Uint8ClampedArray,
+  width: number,
+  height: number,
+  r: number,
+): void {
+  const norm = 1 / (r + r + 1);
+  for (let y = 0; y < height; y++) {
+    const row = y * width;
+    for (let c = 0; c < 3; c++) {
+      // Seed the running sum for x=0 with edge-clamped left neighbours.
+      const first = src[row * 4 + c];
+      let sum = (r + 1) * first;
+      for (let i = 0; i < r; i++) sum += src[(row + Math.min(i, width - 1)) * 4 + c];
+      for (let x = 0; x < width; x++) {
+        const inIdx = Math.min(x + r, width - 1);
+        const outIdx = x - r - 1;
+        sum += src[(row + inIdx) * 4 + c];
+        sum -= outIdx < 0 ? first : src[(row + outIdx) * 4 + c];
+        dst[(row + x) * 4 + c] = sum * norm;
+      }
+    }
+  }
+}
+
+/** One vertical box blur of radius `r` from `src` into `dst` (RGB only). */
+function boxBlurV(
+  src: Uint8ClampedArray,
+  dst: Uint8ClampedArray,
+  width: number,
+  height: number,
+  r: number,
+): void {
+  const norm = 1 / (r + r + 1);
+  for (let x = 0; x < width; x++) {
+    for (let c = 0; c < 3; c++) {
+      const first = src[x * 4 + c];
+      let sum = (r + 1) * first;
+      for (let i = 0; i < r; i++) sum += src[(Math.min(i, height - 1) * width + x) * 4 + c];
+      for (let y = 0; y < height; y++) {
+        const inIdx = Math.min(y + r, height - 1);
+        const outIdx = y - r - 1;
+        sum += src[(inIdx * width + x) * 4 + c];
+        sum -= outIdx < 0 ? first : src[(outIdx * width + x) * 4 + c];
+        dst[(y * width + x) * 4 + c] = sum * norm;
+      }
+    }
+  }
+}
+
+/**
+ * Blur `data` (RGBA, row-major `width × height`) in place by a Gaussian of the
+ * given sigma in pixels. No-op for sigma ≤ 0 or an empty image.
+ */
+export function applyGaussianBlurRgba(
+  data: Uint8ClampedArray,
+  width: number,
+  height: number,
+  sigma: number,
+): void {
+  if (!(sigma > 0) || width === 0 || height === 0) return;
+  const radii = boxRadiiForGaussian(sigma, 3);
+  // Scratch buffer for the horizontal half of each pass; the vertical half
+  // writes straight back into `data`, so the result always lands in place.
+  const scratch = new Uint8ClampedArray(data);
+  for (const r of radii) {
+    if (r <= 0) continue;
+    boxBlurH(data, scratch, width, height, r);
+    boxBlurV(scratch, data, width, height, r);
+  }
+}
diff --git a/frontend/src/lib/clipToClasses.ts b/frontend/src/lib/clipToClasses.ts
index 0691c58..7318c62 100644
--- a/frontend/src/lib/clipToClasses.ts
+++ b/frontend/src/lib/clipToClasses.ts
@@ -60,6 +60,7 @@ export function clipShapesToOthers(
   sliceShapes: Shape[],
   width: number,
   height: number,
+  upscale = 1,
 ): PolygonShape[] {
   const out: PolygonShape[] = [];
   const otherByClass = new Map<number, MultiPolygon>();
@@ -77,7 +78,7 @@ export function clipShapesToOthers(
 
   for (const shape of newShapes) {
     const res = clipOneBoolean(shape, otherMP(shape.classId), sameMP(shape.classId), width, height);
-    if (res === null) out.push(...clipShapesToOthersMask([shape], sliceShapes, width, height));
+    if (res === null) out.push(...clipShapesToOthersMask([shape], sliceShapes, width, height, upscale));
     else out.push(...res);
   }
   return out;
@@ -93,8 +94,11 @@ export function clipShapesToOthersMask(
   sliceShapes: Shape[],
   width: number,
   height: number,
+  upscale = 1,
 ): PolygonShape[] {
-  const { gw, gh, scale } = fullResGridFor(width, height);
+  // `upscale` keeps sub-pixel geometry (e.g. a Threshold Brush region traced at 2x)
+  // from being re-snapped to the native pixel grid by this fallback round-trip.
+  const { gw, gh, scale } = fullResGridFor(width, height, upscale);
   const out: PolygonShape[] = [];
 
   const otherMaskByClass = new Map<number, Uint8Array>();
diff --git a/frontend/src/lib/displayTransform.test.ts b/frontend/src/lib/displayTransform.test.ts
new file mode 100644
index 0000000..8b425af
--- /dev/null
+++ b/frontend/src/lib/displayTransform.test.ts
@@ -0,0 +1,118 @@
+import { describe, it, expect } from 'vitest';
+import {
+  displayAffineFor,
+  baseToDisplay,
+  displayToBase,
+  displayBandToBase,
+  remapHistogramToDisplay,
+} from './displayTransform';
+
+const IDENTITY = displayAffineFor(0, 0, 0, 255);
+
+describe('displayAffineFor', () => {
+  it('is the identity with neutral settings', () => {
+    expect(IDENTITY.slope).toBeCloseTo(1, 6);
+    expect(IDENTITY.intercept).toBeCloseTo(0, 6);
+    for (const v of [0, 37, 128, 200, 255]) {
+      expect(baseToDisplay(v, IDENTITY)).toBeCloseTo(v, 4);
+    }
+  });
+
+  it('matches renderAdjusted for brightness + contrast + levels', () => {
+    // Mirror of the pipeline: brighten, contrast about mid-grey, then levels.
+    const brightness = 0.2, contrast = 40, lo = 20, hi = 200;
+    const affine = displayAffineFor(brightness, contrast, lo, hi);
+    const manual = (v: number) => {
+      const adjust = Math.pow((contrast + 100) / 100, 2);
+      let x = v + brightness * 255;
+      x = ((x / 255 - 0.5) * adjust + 0.5) * 255;
+      x = x <= lo ? 0 : x >= hi ? 255 : ((x - lo) * 255) / (hi - lo);
+      return x;
+    };
+    for (const v of [0, 30, 64, 128, 190, 255]) {
+      expect(baseToDisplay(v, affine)).toBeCloseTo(Math.max(0, Math.min(255, manual(v))), 3);
+    }
+  });
+});
+
+describe('displayToBase', () => {
+  it('round-trips baseToDisplay in the unclamped interior', () => {
+    const affine = displayAffineFor(0.1, 25, 10, 240);
+    for (const v of [60, 100, 128, 160]) {
+      const shown = baseToDisplay(v, affine);
+      expect(displayToBase(shown, affine)).toBeCloseTo(v, 3);
+    }
+  });
+
+  it('round-trips through gamma too', () => {
+    const affine = displayAffineFor(0, 0, 0, 255);
+    for (const gamma of [0.5, 1, 2.2]) {
+      for (const v of [40, 128, 210]) {
+        const shown = baseToDisplay(v, affine, gamma);
+        expect(displayToBase(shown, affine, gamma)).toBeCloseTo(v, 3);
+      }
+    }
+  });
+
+  it('returns null when the transform collapses (contrast -100)', () => {
+    const affine = displayAffineFor(0, -100, 0, 255);
+    expect(displayToBase(128, affine)).toBeNull();
+  });
+});
+
+describe('displayBandToBase', () => {
+  it('selects the same pixels as thresholding the adjusted image', () => {
+    const affine = displayAffineFor(0.15, 30, 15, 230);
+    const gamma = 1.4;
+    const lo = 90, hi = 180;
+    const band = displayBandToBase(lo, hi, affine, gamma);
+    // For every base value, gating in base space must agree with gating on the
+    // value the screen actually shows — that equivalence is the whole point.
+    for (let v = 0; v <= 255; v++) {
+      const shown = baseToDisplay(v, affine, gamma);
+      const viaDisplay = shown >= lo && shown <= hi;
+      const viaBase = v >= band.lo && v <= band.hi;
+      expect(viaBase).toBe(viaDisplay);
+    }
+  });
+
+  it('treats band edges at 0 / 255 as open', () => {
+    const affine = displayAffineFor(0.3, 0, 0, 255);
+    const band = displayBandToBase(0, 255, affine);
+    expect(band.lo).toBe(-Infinity);
+    expect(band.hi).toBe(Infinity);
+  });
+
+  it('is all-or-nothing when the transform collapses', () => {
+    const affine = displayAffineFor(0, -100, 0, 255);
+    // Everything renders mid-grey, so a band containing it selects everything.
+    const covering = displayBandToBase(100, 200, affine);
+    expect(covering.lo).toBe(-Infinity);
+    expect(covering.hi).toBe(Infinity);
+    const missing = displayBandToBase(0.5, 3, affine);
+    expect(missing.lo).toBeGreaterThan(missing.hi); // empty band
+  });
+});
+
+describe('remapHistogramToDisplay', () => {
+  it('preserves total count', () => {
+    const bins = new Array(256).fill(0).map((_, i) => i);
+    const out = remapHistogramToDisplay(bins, displayAffineFor(0.1, 20, 0, 255));
+    const sum = (a: number[]) => a.reduce((x, y) => x + y, 0);
+    expect(sum(out)).toBe(sum(bins));
+  });
+
+  it('is a no-op for the identity transform', () => {
+    const bins = new Array(256).fill(0);
+    bins[10] = 5; bins[200] = 7;
+    expect(remapHistogramToDisplay(bins, IDENTITY)).toEqual(bins);
+  });
+
+  it('shifts mass brighter when brightness increases', () => {
+    const bins = new Array(256).fill(0);
+    bins[100] = 1000;
+    const out = remapHistogramToDisplay(bins, displayAffineFor(0.2, 0, 0, 255));
+    expect(out[100]).toBe(0);
+    expect(out.findIndex((c) => c > 0)).toBeGreaterThan(100);
+  });
+});
diff --git a/frontend/src/lib/displayTransform.ts b/frontend/src/lib/displayTransform.ts
new file mode 100644
index 0000000..eb7c0d9
--- /dev/null
+++ b/frontend/src/lib/displayTransform.ts
@@ -0,0 +1,118 @@
+/**
+ * displayTransform — the display chain the canvas applies on the GPU
+ * (brightness/contrast/levels as one affine, then gamma), expressed as plain math
+ * so tools can reason about it without re-rendering pixels.
+ *
+ * The canvas bakes nonlinear preprocessors (blur/CLAHE/sharpen) into an offscreen
+ * base and then applies THIS transform as an SVG filter. Anything that needs to
+ * know "which pixels look like X on screen" therefore has two options: re-render
+ * the whole image with the transform baked in (expensive, and it invalidates every
+ * cached field whenever a slider moves), or keep a field in un-adjusted base space
+ * and map the QUESTION through the inverse transform instead (O(1) per change).
+ *
+ * The Threshold Brush takes the second route: its intensity band is authored in
+ * displayed space, `displayToBase` maps the band's endpoints back to base space
+ * once per change, and the comparison happens against the cached base field. The
+ * selected pixel set is identical either way, because the transform is monotonic.
+ */
+
+export interface DisplayAffine {
+  /** Per-channel slope on NORMALIZED [0,1] values (SVG feFuncX type="linear"). */
+  slope: number;
+  /** Per-channel intercept on normalized values. */
+  intercept: number;
+}
+
+/**
+ * Fold brightness, contrast, and the levels window into one normalized affine —
+ * the exact transform `renderAdjusted` bakes and the canvas filter applies:
+ * Brighten adds `brightness*255`; Contrast scales around mid-grey by
+ * `((contrast+100)/100)^2`; Levels remaps `[lo,hi] → [0,255]`.
+ */
+export function displayAffineFor(
+  brightness: number,
+  contrast: number,
+  levelsLo: number,
+  levelsHi: number,
+): DisplayAffine {
+  const b255 = brightness * 255;
+  const adjust = Math.pow((contrast + 100) / 100, 2);
+  const range = Math.max(1, levelsHi - levelsLo);
+  const slope = adjust * (255 / range);
+  const intercept =
+    ((255 / range) * (adjust * b255 + 127.5 * (1 - adjust)) - (255 * levelsLo) / range) / 255;
+  return { slope, intercept };
+}
+
+/** Base (un-adjusted) 0–255 value → what the screen shows, 0–255. */
+export function baseToDisplay(base: number, affine: DisplayAffine, gamma = 1): number {
+  const a = affine.slope * (base / 255) + affine.intercept;
+  const clamped = a < 0 ? 0 : a > 1 ? 1 : a;
+  const g = gamma === 1 ? clamped : Math.pow(clamped, gamma);
+  return g * 255;
+}
+
+/**
+ * Displayed 0–255 value → the base value that produces it. The inverse of
+ * `baseToDisplay`, ignoring its output clamp: a displayed 0 or 255 corresponds to
+ * an unbounded range of base values, which callers handle by treating a band edge
+ * at the extreme as open (see `displayBandToBase`).
+ *
+ * Returns null when the transform collapses (contrast −100 flattens every input to
+ * a single grey, so no inverse exists).
+ */
+export function displayToBase(display: number, affine: DisplayAffine, gamma = 1): number | null {
+  if (Math.abs(affine.slope) < 1e-9) return null;
+  const g = Math.max(0, Math.min(1, display / 255));
+  const a = gamma === 1 ? g : Math.pow(g, 1 / gamma);
+  return ((a - affine.intercept) / affine.slope) * 255;
+}
+
+/**
+ * Map an intensity band authored in DISPLAYED space to the equivalent band in base
+ * space, so a threshold test against a cached base field selects exactly the pixels
+ * that look in-band on screen.
+ *
+ * Edges at 0 / 255 become open (∓Infinity): everything the display clamps to black
+ * or white is genuinely in-band. When the transform collapses, the band either
+ * covers everything or nothing depending on where the single output grey lands.
+ */
+export function displayBandToBase(
+  lo: number,
+  hi: number,
+  affine: DisplayAffine,
+  gamma = 1,
+): { lo: number; hi: number } {
+  if (Math.abs(affine.slope) < 1e-9) {
+    // Every base value maps to the same displayed grey; the band is all-or-nothing.
+    const flat = baseToDisplay(128, affine, gamma);
+    return flat >= lo && flat <= hi
+      ? { lo: -Infinity, hi: Infinity }
+      : { lo: Infinity, hi: -Infinity };
+  }
+  const bLo = lo <= 0 ? -Infinity : displayToBase(lo, affine, gamma) ?? -Infinity;
+  const bHi = hi >= 255 ? Infinity : displayToBase(hi, affine, gamma) ?? Infinity;
+  // A negative slope would invert the ordering; keep the band well-formed.
+  return affine.slope > 0 ? { lo: bLo, hi: bHi } : { lo: bHi, hi: bLo };
+}
+
+/**
+ * Re-bin a base-space luminance histogram into displayed space, so a picker drawn
+ * over it lines up with what the user sees (and with where the threshold band
+ * actually cuts). Pure 256-bin remap — no pixels touched.
+ */
+export function remapHistogramToDisplay(
+  bins: number[],
+  affine: DisplayAffine,
+  gamma = 1,
+): number[] {
+  const out = new Array(256).fill(0);
+  for (let b = 0; b < bins.length; b++) {
+    const count = bins[b];
+    if (!count) continue;
+    let d = Math.round(baseToDisplay(b, affine, gamma));
+    d = d < 0 ? 0 : d > 255 ? 255 : d;
+    out[d] += count;
+  }
+  return out;
+}
diff --git a/frontend/src/lib/livewire.test.ts b/frontend/src/lib/livewire.test.ts
index 58cc6b2..d42d0a6 100644
--- a/frontend/src/lib/livewire.test.ts
+++ b/frontend/src/lib/livewire.test.ts
@@ -32,6 +32,22 @@ describe('livewire', () => {
     expect(path[path.length - 2]).toBeCloseTo(4.5, 5);
   });
 
+  it('maps and traces in native coords at a fractional (upscaled) scale', () => {
+    // 6x6 grid at scale 0.5 = a 3x3 NATIVE image sampled at 2x.
+    const gw = 6, gh = 6;
+    const cost = new Float32Array(gw * gh).fill(1);
+    for (let x = 0; x < gw; x++) cost[2 * gw + x] = 0;
+    const cm: CostMap = { gw, gh, scale: 0.5, cost };
+    // Native x=1 → grid cell 2 (1 / 0.5).
+    expect(imageToGrid(cm, 1, 1)).toBe(2 * gw + 2);
+    const prev = dijkstra(cm, imageToGrid(cm, 0, 1));
+    const path = tracePath(cm, prev, imageToGrid(cm, 2.5, 1));
+    // Vertices are cell centers in NATIVE coords: gy=2 → 2*0.5 + 0.25 = 1.25.
+    for (let i = 1; i < path.length; i += 2) expect(path[i]).toBeCloseTo(1.25, 5);
+    // And they stay inside the 3-px-wide native image.
+    for (let i = 0; i < path.length; i += 2) expect(path[i]).toBeLessThanOrEqual(3);
+  });
+
   it('simplifyPath keeps endpoints and thins the middle', () => {
     const dense = [0, 0, 1, 0, 2, 0, 3, 0, 4, 0]; // 5 points
     const simplified = simplifyPath(dense, 2);
diff --git a/frontend/src/lib/livewire.ts b/frontend/src/lib/livewire.ts
index 0da38ca..dacfbc1 100644
--- a/frontend/src/lib/livewire.ts
+++ b/frontend/src/lib/livewire.ts
@@ -10,21 +10,31 @@
 export interface CostMap {
   gw: number;
   gh: number;
-  /** Image pixels per grid cell (downsample factor). */
+  /** Image pixels per grid cell. >1 when downsampled; FRACTIONAL (e.g. 0.5) when
+   *  the caller asked for an upscaled working resolution. */
   scale: number;
   /** Per-node traversal cost in [~0, 1]; ~0 on strong edges. */
   cost: Float32Array;
 }
 
 /** Build an edge-cost map from an image (or preprocessed canvas), downsampled so
- *  the long side <= maxDim. */
+ *  the long side <= maxDim.
+ *
+ *  `imgW`/`imgH` are always NATIVE image pixels. `upscale` (1, 2, 4) raises the
+ *  working resolution — both the grid and the maxDim cap scale with it — so a
+ *  traced path can land on sub-pixel coordinates. `image` should already be
+ *  rendered at that working resolution; it is resampled into the grid either way. */
 export function buildCostMap(
   image: CanvasImageSource,
   imgW: number,
   imgH: number,
   maxDim = 512,
+  upscale = 1,
 ): CostMap | null {
-  const scale = Math.max(1, Math.ceil(Math.max(imgW, imgH) / maxDim));
+  const u = Math.max(1, upscale);
+  // Native cell size from the maxDim cap, then `u` sub-cells per native cell.
+  const cell = Math.max(1, Math.ceil(Math.max(imgW, imgH) / maxDim));
+  const scale = cell / u;
   const gw = Math.max(1, Math.floor(imgW / scale));
   const gh = Math.max(1, Math.floor(imgH / scale));
 
diff --git a/frontend/src/lib/magicwand.test.ts b/frontend/src/lib/magicwand.test.ts
index 25c34b9..9484649 100644
--- a/frontend/src/lib/magicwand.test.ts
+++ b/frontend/src/lib/magicwand.test.ts
@@ -1,5 +1,5 @@
 import { describe, it, expect } from 'vitest';
-import { magicSelect, maskToPolygons, maskToPolygonsWithHoles, gradientField, type GrayField } from './magicwand';
+import { magicSelect, maskToPolygons, maskToPolygonsWithHoles, gradientField, otsuThreshold, type GrayField } from './magicwand';
 
 /** 40x40 grid (scale 1): background 0 with two value-200 blocks. */
 function twoBlocks(): GrayField {
@@ -169,3 +169,64 @@ describe('maskToPolygonsWithHoles', () => {
     expect(out[0].holes.length).toBe(0);
   });
 });
+
+describe('fractional scale (upscaled working resolution)', () => {
+  /** 40x40 grid at scale 0.5 — i.e. a 20x20 NATIVE image sampled at 2x. */
+  function upscaledBlock(): GrayField {
+    const gw = 40, gh = 40;
+    const gray = new Float32Array(gw * gh);
+    // Grid cells 8..24 → native image coords 4..12.
+    for (let y = 8; y < 24; y++) for (let x = 8; x < 24; x++) gray[y * gw + x] = 200;
+    return { gw, gh, scale: 0.5, gray };
+  }
+
+  it('magicSelect seeds and returns polygons in NATIVE image coords', () => {
+    const field = upscaledBlock();
+    // Seed at native (8,8) → grid (16,16), inside the block.
+    const polys = magicSelect(field, 8, 8, { toleranceFrac: 0.2, mode: 'contiguous', smooth: 0, minRegion: 8 });
+    expect(polys.length).toBe(1);
+    const xs = polys[0].filter((_, i) => i % 2 === 0);
+    const ys = polys[0].filter((_, i) => i % 2 === 1);
+    // Native extent of the block is 4..12, not the 8..24 grid extent.
+    expect(Math.min(...xs)).toBeGreaterThanOrEqual(3);
+    expect(Math.max(...xs)).toBeLessThanOrEqual(13);
+    expect(Math.min(...ys)).toBeGreaterThanOrEqual(3);
+    expect(Math.max(...ys)).toBeLessThanOrEqual(13);
+  });
+
+  it('yields sub-pixel (half-integer) vertices a native-resolution grid could not', () => {
+    const field = upscaledBlock();
+    const polys = magicSelect(field, 8, 8, { toleranceFrac: 0.2, mode: 'contiguous', smooth: 0, minRegion: 8 });
+    const coords = polys[0];
+    expect(coords.some((v) => !Number.isInteger(v))).toBe(true);
+  });
+});
+
+describe('otsuThreshold', () => {
+  it('splits a clean bimodal histogram between the modes', () => {
+    const bins = new Array(256).fill(0);
+    bins[40] = 1000;   // dark mode
+    bins[200] = 1000;  // bright mode
+    const t = otsuThreshold(bins);
+    expect(t).toBeGreaterThan(40);
+    expect(t).toBeLessThan(200);
+  });
+
+  it('handles broad overlapping modes', () => {
+    const bins = new Array(256).fill(0);
+    for (let i = 30; i < 70; i++) bins[i] = 100;
+    for (let i = 150; i < 220; i++) bins[i] = 100;
+    const t = otsuThreshold(bins);
+    expect(t).toBeGreaterThanOrEqual(69);
+    expect(t).toBeLessThanOrEqual(150);
+  });
+
+  it('returns a safe default for degenerate histograms', () => {
+    expect(otsuThreshold([])).toBe(128);
+    expect(otsuThreshold(new Array(256).fill(0))).toBe(128);
+    // Single-valued: no valid two-class split, so the default stands.
+    const one = new Array(256).fill(0);
+    one[77] = 500;
+    expect(otsuThreshold(one)).toBe(128);
+  });
+});
diff --git a/frontend/src/lib/magicwand.ts b/frontend/src/lib/magicwand.ts
index f193f52..2b76d78 100644
--- a/frontend/src/lib/magicwand.ts
+++ b/frontend/src/lib/magicwand.ts
@@ -13,7 +13,8 @@ import { fillHoles } from '@/lib/morphology';
 export interface GrayField {
   gw: number;
   gh: number;
-  /** Image pixels per grid cell (downsample factor). */
+  /** Image pixels per grid cell. >1 when downsampled; FRACTIONAL (e.g. 0.5) when
+   *  the caller asked for an upscaled working resolution. */
   scale: number;
   gray: Float32Array;
   /** Sobel gradient magnitude normalised to ~[0,1] (edge barrier for flood). */
@@ -71,14 +72,24 @@ export function gradientField(gray: Float32Array, gw: number, gh: number): Float
 /** Build a grayscale + gradient field from an image (long side ≤ maxDim).
  *
  * 1600 keeps a 2560px slice at half-resolution (scale 2) — plenty of detail for
- * the edge-aware flood while keeping the per-click work ~4x cheaper than full res. */
+ * the edge-aware flood while keeping the per-click work ~4x cheaper than full res.
+ *
+ * `imgW`/`imgH` are always NATIVE image pixels. `upscale` (1, 2, 4) multiplies the
+ * grid resolution and divides `scale` to match, so the returned polygons carry
+ * sub-pixel coordinates while still being expressed in native image space. Pass an
+ * `image` already rendered at that working resolution to get the extra detail;
+ * it is resampled into the grid either way. */
 export function buildField(
   image: CanvasImageSource,
   imgW: number,
   imgH: number,
   maxDim = 1600,
+  upscale = 1,
+  needGradient = true,
 ): GrayField | null {
-  const scale = Math.max(1, Math.ceil(Math.max(imgW, imgH) / maxDim));
+  const u = Math.max(1, upscale);
+  // Native cell size from the maxDim cap, then `u` sub-cells per native cell.
+  const scale = Math.max(1, Math.ceil(Math.max(imgW, imgH) / maxDim)) / u;
   const gw = Math.max(1, Math.floor(imgW / scale));
   const gh = Math.max(1, Math.floor(imgH / scale));
   const canvas = document.createElement('canvas');
@@ -97,7 +108,57 @@ export function buildField(
   for (let i = 0; i < gw * gh; i++) {
     gray[i] = 0.299 * data[i * 4] + 0.587 * data[i * 4 + 1] + 0.114 * data[i * 4 + 2];
   }
-  return { gw, gh, scale, gray, grad: gradientField(gray, gw, gh) };
+  // The gradient is only needed by the edge-aware flood (`edgeStop`); a pure
+  // intensity consumer like the Threshold Brush skips a full Sobel pass and half
+  // the memory by opting out — which matters at a full-resolution 2x/4x grid.
+  return { gw, gh, scale, gray, ...(needGradient ? { grad: gradientField(gray, gw, gh) } : {}) };
+}
+
+/**
+ * Otsu's method: the 0–255 level that best splits a luminance histogram into two
+ * classes (maximises between-class variance). Drives the Threshold Brush's "Auto"
+ * button, matching ImageJ's default auto-threshold. Returns 128 for a degenerate
+ * (empty or single-valued) histogram.
+ */
+export function otsuThreshold(bins: number[]): number {
+  const n = bins.length;
+  if (n === 0) return 128;
+  let total = 0;
+  let sumAll = 0;
+  for (let i = 0; i < n; i++) {
+    total += bins[i];
+    sumAll += i * bins[i];
+  }
+  if (total === 0) return 128;
+
+  let sumB = 0;
+  let wB = 0;
+  let bestVar = -1;
+  // Between-class variance is flat across the empty gap separating two modes, so
+  // every level in that gap is equally optimal. Average the tied plateau (as
+  // ImageJ does) to land in the middle of the gap rather than hard against the
+  // dark mode, which is what a user dragging "Auto" expects.
+  let tieSum = 0;
+  let tieCount = 0;
+  for (let t = 0; t < n; t++) {
+    wB += bins[t];
+    if (wB === 0) continue;
+    const wF = total - wB;
+    if (wF === 0) break;
+    sumB += t * bins[t];
+    const mB = sumB / wB;
+    const mF = (sumAll - sumB) / wF;
+    const between = wB * wF * (mB - mF) * (mB - mF);
+    if (between > bestVar * (1 + 1e-12)) {
+      bestVar = between;
+      tieSum = t;
+      tieCount = 1;
+    } else if (Math.abs(between - bestVar) <= bestVar * 1e-12) {
+      tieSum += t;
+      tieCount++;
+    }
+  }
+  return tieCount > 0 ? Math.round(tieSum / tieCount) : 128;
 }
 
 /** Robust intensity spread (2nd–98th percentile) for tolerance scaling. */
diff --git a/frontend/src/lib/mergeSameClass.ts b/frontend/src/lib/mergeSameClass.ts
index b55ab8d..36ca8d9 100644
--- a/frontend/src/lib/mergeSameClass.ts
+++ b/frontend/src/lib/mergeSameClass.ts
@@ -87,9 +87,11 @@ export function mergeNewWithSameClass(
   sliceShapes: Shape[],
   width: number,
   height: number,
+  upscale = 1,
 ): MergeResult {
-  // Full resolution so the merged region preserves existing geometry (no erosion).
-  const { gw, gh, scale } = fullResGridFor(width, height);
+  // Full resolution so the merged region preserves existing geometry (no erosion);
+  // `upscale` additionally preserves sub-pixel detail from an upscaled working grid.
+  const { gw, gh, scale } = fullResGridFor(width, height, upscale);
   const add: Shape[] = [];
   const removeIds: string[] = [];
 
diff --git a/frontend/src/lib/polybool.ts b/frontend/src/lib/polybool.ts
index 9346d55..e506f12 100644
--- a/frontend/src/lib/polybool.ts
+++ b/frontend/src/lib/polybool.ts
@@ -116,6 +116,19 @@ export function eraseStampToMultiPolygon(points: number[], radius: number, width
     .map((p) => [flatToRing(p.points), ...p.holes.map(flatToRing)]);
 }
 
+/**
+ * Build a MultiPolygon from already-vectorized regions (flat outer ring + flat
+ * hole rings), e.g. the Threshold Brush's mask→polygon output, so it can be
+ * boolean-subtracted from shapes.
+ */
+export function regionsToMultiPolygon(
+  regions: Array<{ points: number[]; holes: number[][] }>,
+): MultiPolygon {
+  return regions
+    .filter((p) => p.points.length >= 6)
+    .map((p) => [flatToRing(p.points), ...p.holes.map(flatToRing)]);
+}
+
 /** Boolean-union a set of shapes into one MultiPolygon (empty if none / on failure). */
 export function unionShapesToMultiPolygon(shapes: Shape[], width: number, height: number): MultiPolygon {
   const geoms = shapes.map((s) => shapeToMultiPolygon(s, width, height)).filter((g) => g.length > 0);
diff --git a/frontend/src/lib/rasterize.test.ts b/frontend/src/lib/rasterize.test.ts
index 381e014..a878e21 100644
--- a/frontend/src/lib/rasterize.test.ts
+++ b/frontend/src/lib/rasterize.test.ts
@@ -1,5 +1,5 @@
 import { describe, it, expect } from 'vitest';
-import { rasterizeShapes, gridFor } from './rasterize';
+import { rasterizeShapes, gridFor, fullResGridFor, stampStroke } from './rasterize';
 import { maskToPolygons } from './magicwand';
 import type { Shape } from '@/stores/annotationStore';
 
@@ -69,3 +69,52 @@ describe('rasterizeShapes', () => {
     expect(Math.max(...ys)).toBeGreaterThan(74);
   });
 });
+
+describe('fullResGridFor upscale', () => {
+  it('is unchanged at 1x', () => {
+    expect(fullResGridFor(200, 100)).toEqual({ gw: 200, gh: 100, scale: 1 });
+  });
+
+  it('doubles the grid and halves the scale at 2x', () => {
+    expect(fullResGridFor(200, 100, 2)).toEqual({ gw: 400, gh: 200, scale: 0.5 });
+  });
+
+  it('keeps the downsample guard for very large images', () => {
+    // >4096 falls back to the 1600 cap (scale 4 natively), then 2x halves it.
+    const g = fullResGridFor(6400, 6400, 2);
+    expect(g.scale).toBe(2);
+    expect(g.gw).toBe(3200);
+  });
+
+  it('round-trips a rect through an upscaled grid', () => {
+    const rect: Shape = { id: 'r', classId: 0, kind: 'rectangle', x: 20, y: 20, w: 60, h: 60 };
+    const { gw, gh, scale } = fullResGridFor(200, 200, 2);
+    const mask = rasterizeShapes([rect], gw, gh, scale);
+    // 60x60 image px at 2x = 120x120 grid cells.
+    expect(area(mask)).toBeGreaterThan(14000);
+    expect(area(mask)).toBeLessThan(14700);
+    const polys = maskToPolygons(mask, gw, gh, { minRegion: 4, scale });
+    const xs = polys[0].filter((_, i) => i % 2 === 0);
+    expect(Math.min(...xs)).toBeGreaterThan(18); // still native coords, not doubled
+    expect(Math.max(...xs)).toBeLessThan(82);
+  });
+});
+
+describe('stampStroke gate', () => {
+  it('restricts the stamp to gated cells (Threshold Brush band)', () => {
+    const gw = 20, gh = 20;
+    // Gate lets only the left half through.
+    const gate = new Uint8Array(gw * gh);
+    for (let y = 0; y < gh; y++) for (let x = 0; x < 10; x++) gate[y * gw + x] = 1;
+
+    const ungated = new Uint8Array(gw * gh);
+    stampStroke(ungated, gw, gh, [5, 10, 15, 10], 3, 1, 1);
+    const gated = new Uint8Array(gw * gh);
+    stampStroke(gated, gw, gh, [5, 10, 15, 10], 3, 1, 1, gate);
+
+    expect(area(gated)).toBeGreaterThan(0);
+    expect(area(gated)).toBeLessThan(area(ungated));
+    // Nothing landed outside the gate.
+    for (let i = 0; i < gated.length; i++) if (gated[i]) expect(gate[i]).toBe(1);
+  });
+});
diff --git a/frontend/src/lib/rasterize.ts b/frontend/src/lib/rasterize.ts
index 5f7c6ba..8457376 100644
--- a/frontend/src/lib/rasterize.ts
+++ b/frontend/src/lib/rasterize.ts
@@ -27,10 +27,24 @@ export function gridFor(width: number, height: number, maxDim = 1600): MaskGrid
  * full native resolution (scale 1) for reasonably sized images, so re-vectorizing
  * an unchanged region is ~idempotent and existing nodes don't erode or shift a
  * little each time a stroke is added. Only very large images (>4096 px) downsample.
+ *
+ * `upscale` (1, 2, 4) raises the working resolution so sub-pixel geometry — e.g. a
+ * Threshold Brush region traced at 2× — survives a clip/merge round-trip instead of
+ * being re-snapped to the native pixel grid. `scale` becomes fractional (1/upscale);
+ * `rasterizeShapes` divides by it and `maskToPolygons*` multiplies by it, so nothing
+ * downstream needs to change.
  */
-export function fullResGridFor(width: number, height: number): MaskGrid {
+export function fullResGridFor(width: number, height: number, upscale = 1): MaskGrid {
+  const u = Math.max(1, upscale);
   const emax = Math.max(width, height);
-  return gridFor(width, height, emax <= 4096 ? emax : 1600);
+  const base = gridFor(width, height, emax <= 4096 ? emax : 1600);
+  if (u === 1) return base;
+  const scale = base.scale / u;
+  return {
+    gw: Math.max(1, Math.floor(width / scale)),
+    gh: Math.max(1, Math.floor(height / scale)),
+    scale,
+  };
 }
 
 /**
@@ -141,8 +155,12 @@ function fillEllipse(mask: Uint8Array, gw: number, gh: number, cx: number, cy: n
   }
 }
 
-/** Stamp a round-capped thick polyline (image-coord points) with value *v*. */
-function stampStroke(mask: Uint8Array, gw: number, gh: number, imgPts: number[], r: number, scale: number, v: number): void {
+/** Stamp a round-capped thick polyline (image-coord points) with value *v*.
+ *  `r` is the radius in GRID cells (i.e. image radius / scale).
+ *
+ *  `gate`, when given, restricts the stamp to cells where `gate[i]` is non-zero —
+ *  this is what makes the Threshold Brush paint only inside its intensity band. */
+export function stampStroke(mask: Uint8Array, gw: number, gh: number, imgPts: number[], r: number, scale: number, v: number, gate?: Uint8Array): void {
   const rad = Math.max(0.5, r);
   const r2 = rad * rad;
   const pts: number[] = [];
@@ -160,7 +178,10 @@ function stampStroke(mask: Uint8Array, gw: number, gh: number, imgPts: number[],
         t = t < 0 ? 0 : t > 1 ? 1 : t;
         const cxp = ax + t * dx, cyp = ay + t * dy;
         const ddx = x - cxp, ddy = y - cyp;
-        if (ddx * ddx + ddy * ddy <= r2) mask[y * gw + x] = v;
+        if (ddx * ddx + ddy * ddy > r2) continue;
+        const i = y * gw + x;
+        if (gate && !gate[i]) continue;
+        mask[i] = v;
       }
     }
   };
diff --git a/frontend/src/lib/sam/adjust.ts b/frontend/src/lib/sam/adjust.ts
index 4067e2f..5d163c2 100644
--- a/frontend/src/lib/sam/adjust.ts
+++ b/frontend/src/lib/sam/adjust.ts
@@ -1,8 +1,12 @@
 import { claheRgba } from '@/lib/clahe';
 import { applySharpen } from '@/lib/sharpen';
+import { applyGaussianBlurRgba } from '@/lib/blur';
 
 /** Nonlinear display preprocessors baked before the linear brightness/contrast/levels. */
 export interface PreprocessOpts {
+  /** Gaussian blur sigma in NATIVE image pixels (0 = off). Rescaled internally when
+   *  rendering at an upscaled working resolution, so σ means the same thing at any. */
+  blur?: number;
   /** Adaptive (local) contrast — CLAHE. Replaces the old global stretch. */
   clahe?: boolean;
   sharpen?: boolean;
@@ -14,10 +18,17 @@ export interface PreprocessOpts {
  * the user actually sees. Windowing a low-contrast tomography slice before
  * encoding is one of the biggest levers on mask quality.
  *
- * Order: nonlinear preprocessors first (CLAHE → Sharpen), then the linear chain
- * mirroring the canvas filter exactly (Brighten → Contrast → Levels): Brighten
- * adds `brightness*255`; Contrast scales around mid-grey by `((contrast+100)/100)^2`;
- * Levels remaps `[lo,hi] → [0,255]`. Returns a canvas at native resolution.
+ * Order: nonlinear preprocessors first (Blur → CLAHE → Sharpen), then the linear
+ * chain mirroring the canvas filter exactly (Brighten → Contrast → Levels):
+ * Brighten adds `brightness*255`; Contrast scales around mid-grey by
+ * `((contrast+100)/100)^2`; Levels remaps `[lo,hi] → [0,255]`. Blur runs first so
+ * it denoises the raw slice; a following Sharpen can then deliberately re-crisp
+ * the edges the blur softened.
+ *
+ * `upscale` (1, 2, 4) resamples to `width*upscale × height*upscale` with smooth
+ * interpolation — the "working resolution" for the drawing tools. `width`/`height`
+ * stay in NATIVE image pixels and the blur sigma is rescaled to match, so callers
+ * and σ mean the same thing at any working resolution.
  */
 export function renderAdjusted(
   image: CanvasImageSource,
@@ -28,25 +39,33 @@ export function renderAdjusted(
   levelsLo = 0,
   levelsHi = 255,
   preprocess?: PreprocessOpts,
+  upscale = 1,
 ): HTMLCanvasElement {
+  const u = Math.max(1, upscale);
+  const w = Math.round(width * u);
+  const h = Math.round(height * u);
   const canvas = document.createElement('canvas');
-  canvas.width = width;
-  canvas.height = height;
+  canvas.width = w;
+  canvas.height = h;
   const ctx = canvas.getContext('2d', { willReadFrequently: true })!;
-  ctx.drawImage(image, 0, 0, width, height);
+  ctx.imageSmoothingEnabled = true;
+  ctx.imageSmoothingQuality = 'high';
+  ctx.drawImage(image, 0, 0, w, h);
 
-  const doPre = !!(preprocess && (preprocess.clahe || preprocess.sharpen));
+  const blurSigma = (preprocess?.blur ?? 0) * u; // σ is given in native pixels
+  const doPre = !!(preprocess && (blurSigma > 0 || preprocess.clahe || preprocess.sharpen));
   const bcNoop = brightness === 0 && contrast === 0;
   const levelsNoop = levelsLo <= 0 && levelsHi >= 255;
   if (!doPre && bcNoop && levelsNoop) return canvas;
 
-  const imageData = ctx.getImageData(0, 0, width, height);
+  const imageData = ctx.getImageData(0, 0, w, h);
   const d = imageData.data; // Uint8ClampedArray → assignments auto-clamp to [0,255]
 
   // Nonlinear preprocessors first (baked once), in a fixed order.
   if (doPre) {
-    if (preprocess!.clahe) claheRgba(d, width, height);
-    if (preprocess!.sharpen) applySharpen(d, width, height);
+    if (blurSigma > 0) applyGaussianBlurRgba(d, w, h, blurSigma);
+    if (preprocess!.clahe) claheRgba(d, w, h);
+    if (preprocess!.sharpen) applySharpen(d, w, h);
   }
 
   if (!(bcNoop && levelsNoop)) {
@@ -73,16 +92,17 @@ export function renderAdjusted(
 }
 
 /**
- * Bake ONLY the nonlinear preprocessors (CLAHE / Sharpen) into a canvas — used as
- * the Konva display base, since brightness/contrast/levels/gamma/colormap stay on
- * the GPU SVG filter applied over it. Returns the source untouched when no
- * preprocessor is active.
+ * Bake ONLY the nonlinear preprocessors (Blur / CLAHE / Sharpen) into a canvas —
+ * used as the Konva display base, since brightness/contrast/levels/gamma/colormap
+ * stay on the GPU SVG filter applied over it. `upscale` also resamples to the
+ * working resolution (see `renderAdjusted`).
  */
 export function renderPreprocessOnly(
   image: CanvasImageSource,
   width: number,
   height: number,
   preprocess: PreprocessOpts,
+  upscale = 1,
 ): HTMLCanvasElement {
-  return renderAdjusted(image, width, height, 0, 0, 0, 255, preprocess);
+  return renderAdjusted(image, width, height, 0, 0, 0, 255, preprocess, upscale);
 }
diff --git a/frontend/src/stores/toolStore.ts b/frontend/src/stores/toolStore.ts
index e3080f8..fad1fd2 100644
--- a/frontend/src/stores/toolStore.ts
+++ b/frontend/src/stores/toolStore.ts
@@ -3,7 +3,7 @@
  */
 import { create } from 'zustand';
 
-export type Tool = 'pan' | 'select' | 'polygon' | 'magnetic' | 'magic' | 'rectangle' | 'ellipse' | 'brush' | 'fill' | 'eraser';
+export type Tool = 'pan' | 'select' | 'polygon' | 'magnetic' | 'magic' | 'rectangle' | 'ellipse' | 'brush' | 'threshold' | 'fill' | 'eraser';
 
 export type MagicMode = 'contiguous' | 'global';
 /** Magic-selection engine: SAM (learned object prior) or the classic wand. */
@@ -21,6 +21,13 @@ export interface ToolState {
   fillThreshold: number;
   /** Selected shape ids (multi-select via marquee/shift-click). */
   selectedShapeIds: string[];
+  /** Threshold brush: paint only where the displayed intensity is in [lo,hi] (0–255). */
+  thresholdLo: number;
+  thresholdHi: number;
+  /** Show a red overlay of every in-band pixel on the slice (ImageJ-style). */
+  thresholdOverlay: boolean;
+  /** Band width applied when Shift-clicking to sample a pixel's intensity. */
+  thresholdSampleWidth: number;
   /** Magic-wand: similarity tolerance (0–1) and selection mode + denoise. */
   magicTolerance: number;
   magicMode: MagicMode;
@@ -59,6 +66,9 @@ export interface ToolState {
   setBrushSize: (size: number) => void;
   setFillOpacity: (opacity: number) => void;
   setFillThreshold: (t: number) => void;
+  setThresholdBand: (lo: number, hi: number) => void;
+  setThresholdOverlay: (v: boolean) => void;
+  setThresholdSampleWidth: (w: number) => void;
   /** Convenience single-select (clears to [] when null). */
   setSelectedShapeId: (id: string | null) => void;
   setSelectedShapeIds: (ids: string[]) => void;
@@ -84,6 +94,10 @@ export const useToolStore = create<ToolState>((set) => ({
   brushSize: 10,
   fillOpacity: 0.5,
   fillThreshold: 0.1,
+  thresholdLo: 0,
+  thresholdHi: 255,
+  thresholdOverlay: true,
+  thresholdSampleWidth: 24,
   selectedShapeIds: [],
   magicTolerance: 0.08,
   magicMode: 'contiguous',
@@ -108,6 +122,15 @@ export const useToolStore = create<ToolState>((set) => ({
   setFillOpacity: (fillOpacity) => set({ fillOpacity }),
   /** Sets the Fill (paint-bucket) similarity threshold (0–1). */
   setFillThreshold: (fillThreshold) => set({ fillThreshold }),
+  /** Sets the threshold-brush intensity band, clamped to 0–255 and kept ordered. */
+  setThresholdBand: (lo, hi) => set({
+    thresholdLo: Math.max(0, Math.min(255, Math.round(Math.min(lo, hi)))),
+    thresholdHi: Math.max(0, Math.min(255, Math.round(Math.max(lo, hi)))),
+  }),
+  /** Toggles the red in-band overlay shown while the threshold brush is active. */
+  setThresholdOverlay: (thresholdOverlay) => set({ thresholdOverlay }),
+  /** Sets the band width used when Shift-clicking to sample an intensity. */
+  setThresholdSampleWidth: (thresholdSampleWidth) => set({ thresholdSampleWidth }),
   /** Single-selects a shape, or clears the selection when null. */
   setSelectedShapeId: (id) => set({ selectedShapeIds: id ? [id] : [] }),
   /** Replaces the multi-selection with the given shape ids. */

From 6d1ac0fb2b441a7995b9de631ed035c3a8a7ccca Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 19 Aug 2026 15:50:59 -0700
Subject: [PATCH 02/38] Fix clipping on threshold-painted slices; speed up the
 Annotate tab

---
 frontend/src/app/pages/AnnotatePage.tsx       |  12 +
 .../annotate/AnnotationCanvas/ShapesLayer.tsx | 201 +++++++++
 .../annotate/AnnotationCanvas/index.tsx       | 401 +++++++++---------
 .../components/annotate/PerfOverlay/index.tsx |  87 ++++
 frontend/src/hooks/useImageSlice.gc.test.ts   |  89 ++++
 frontend/src/hooks/useImageSlice.ts           |  51 ++-
 .../src/lib/bboxPrefilter.equivalence.test.ts | 326 ++++++++++++++
 frontend/src/lib/clipFallback.test.ts         | 200 +++++++++
 frontend/src/lib/clipToClasses.ts             | 108 ++++-
 frontend/src/lib/geometry.bbox.test.ts        | 121 ++++++
 frontend/src/lib/geometry.ts                  |  99 +++++
 frontend/src/lib/mergeSameClass.ts            |  81 +++-
 frontend/src/lib/perf.test.ts                 |  90 ++++
 frontend/src/lib/perf.ts                      | 131 ++++++
 frontend/src/lib/polybool.ts                  |  56 ++-
 frontend/src/lib/rasterize.ts                 |  10 +-
 frontend/src/main.tsx                         |   5 +
 17 files changed, 1845 insertions(+), 223 deletions(-)
 create mode 100644 frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.tsx
 create mode 100644 frontend/src/components/annotate/PerfOverlay/index.tsx
 create mode 100644 frontend/src/hooks/useImageSlice.gc.test.ts
 create mode 100644 frontend/src/lib/bboxPrefilter.equivalence.test.ts
 create mode 100644 frontend/src/lib/clipFallback.test.ts
 create mode 100644 frontend/src/lib/geometry.bbox.test.ts
 create mode 100644 frontend/src/lib/perf.test.ts
 create mode 100644 frontend/src/lib/perf.ts

diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index a1b1fd8..0a21e61 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -27,6 +27,8 @@ import DownloadModal from '@/components/annotate/DownloadModal';
 import InsightsModal from '@/components/annotate/InsightsModal';
 import VersionHistoryModal from '@/components/annotate/VersionHistoryModal';
 import VersionPreviewBar from '@/components/annotate/VersionPreviewBar';
+import PerfOverlay from '@/components/annotate/PerfOverlay';
+import { initPerf } from '@/lib/perf';
 import SaveModal from '@/components/annotate/SaveModal';
 import type { SaveDraftPayload } from '@/hooks/useSave';
 
@@ -103,6 +105,9 @@ export default function AnnotatePage() {
     ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri)
     : null;
 
+  // Dev-only timing HUD (?perf=1). Read once — the flag is fixed for the session.
+  const [perfOn] = useState(() => initPerf());
+
   // Crash-recovery autosave (local draft only, no Tiled sync)
   useDraftSync(sourceKey);
   // Undo/redo is per-sample: reset the region history + class-delete journal on switch.
@@ -115,6 +120,12 @@ export default function AnnotatePage() {
 
   const { currentSlice, setSlice } = useDatasetStore();
 
+  // Shapes on the current slice — shown in the perf HUD, since it is the variable
+  // that drives most of the costs it reports. Only subscribed when the HUD is on.
+  const currentShapeCount = useAnnotationStore((s) =>
+    perfOn && sourceKey ? (s.byImage[sourceKey]?.[String(currentSlice)]?.length ?? 0) : 0,
+  );
+
   /** From an Insights QA flag: jump to its slice and zoom/highlight its region. */
   const handleInsightFocus = useCallback((slice: number, bbox?: { x: number; y: number; w: number; h: number }) => {
     setSlice(slice);
@@ -361,6 +372,7 @@ export default function AnnotatePage() {
               onRestore={handleRestoreFromPreview}
             />
           )}
+          {perfOn && <PerfOverlay shapeCount={currentShapeCount} />}
         </div>
       </div>
 
diff --git a/frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.tsx b/frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.tsx
new file mode 100644
index 0000000..3f31723
--- /dev/null
+++ b/frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.tsx
@@ -0,0 +1,201 @@
+/**
+ * ShapesLayer — the cached, non-interactive layer of committed annotations.
+ *
+ * Extracted from AnnotationCanvas and wrapped in `React.memo` for one reason: the
+ * canvas takes brightness / contrast / levels / gamma / blur as props, so every
+ * tick of a display slider re-rendered it and reconciled every Konva node on this
+ * layer — despite shapes depending on none of those values. On a slice with many
+ * annotations that dominated slider latency. Keeping this layer's props limited to
+ * what it actually draws means those ticks now skip the entire shape tree.
+ *
+ * If you add a prop here, make sure it genuinely affects the drawn shapes;
+ * anything that changes per-frame (a pan offset, a pointer position) would
+ * reintroduce exactly the problem this exists to solve.
+ */
+import { memo } from 'react';
+import { Layer, Line, Rect, Ellipse, Group, Shape as KonvaShape } from 'react-konva';
+import type Konva from 'konva';
+import type { Shape, EraseStroke } from '@/stores/annotationStore';
+import type { AnnotationClass } from '@/stores/classStore';
+
+/** Build an even-odd path (outer + hole rings) on a Konva context. */
+function buildRingsPath(ctx: Konva.Context, rings: number[][]): void {
+  ctx.beginPath();
+  for (const r of rings) {
+    if (r.length < 6) continue;
+    ctx.moveTo(r[0], r[1]);
+    for (let i = 2; i < r.length; i += 2) ctx.lineTo(r[i], r[i + 1]);
+    ctx.closePath();
+  }
+}
+
+/** Erase carve-outs rendered destination-out over the shape they belong to. */
+function renderErased(erased?: EraseStroke[]) {
+  return (erased ?? []).map((st, i) => (
+    <Line
+      key={`erase-${i}`}
+      points={st.points}
+      stroke="black"
+      strokeWidth={st.radius * 2}
+      lineCap="round"
+      lineJoin="round"
+      globalCompositeOperation="destination-out"
+      perfectDrawEnabled={false}
+      listening={false}
+    />
+  ));
+}
+
+export interface ShapesLayerProps {
+  /** Konva layer ref — the parent owns caching/recaching of this layer. */
+  layerRef: React.Ref<Konva.Layer>;
+  shapes: Shape[];
+  /** Class lookup for color + visibility (O(1) per shape). */
+  classMap: Map<number, AnnotationClass>;
+  fillOpacity: number;
+  /** Current zoom, used only to keep stroke width constant on screen. */
+  scaleX: number;
+  selectedShapeIds: string[];
+  /** The in-progress brush instance, recolored to the active class. */
+  activeBrushShapeId: string | null;
+  activeClassId: number | null;
+  /** Image frame, for clipping and for giving hole-shapes a real self-rect. */
+  imageWidth: number;
+  imageHeight: number;
+}
+
+function ShapesLayerImpl({
+  layerRef,
+  shapes,
+  classMap,
+  fillOpacity,
+  scaleX,
+  selectedShapeIds,
+  activeBrushShapeId,
+  activeClassId,
+  imageWidth,
+  imageHeight,
+}: ShapesLayerProps) {
+  const colorForClass = (classId: number) => classMap.get(classId)?.color ?? '#ff0000';
+
+  /** Render a polygon that may have holes via an even-odd fill (outer path minus
+   *  hole subpaths). Even-odd — not destination-out — so a hole reveals whatever
+   *  is *beneath* it (e.g. another class) instead of erasing it off the layer. */
+  const renderPolygonWithHoles = (points: number[], holes: number[][], color: string, strokeW: number) => (
+    <KonvaShape
+      stroke={color}
+      strokeWidth={strokeW}
+      // width/height give the shape a real self-rect so the CACHED committed layer
+      // sizes its cache canvas to include it (otherwise it's clipped away when the
+      // only other in-bounds shape — the enclosed class — is hidden).
+      width={imageWidth}
+      height={imageHeight}
+      perfectDrawEnabled={false}
+      listening={false}
+      sceneFunc={(ctx: Konva.Context, node: Konva.Shape) => {
+        buildRingsPath(ctx, [points, ...holes]);
+        const raw = (ctx as unknown as { _context: CanvasRenderingContext2D })._context;
+        raw.fillStyle = color;
+        raw.fill('evenodd');
+        ctx.strokeShape(node);
+      }}
+    />
+  );
+
+  /** Render a committed shape (any kind), with the active brush instance recolored
+   *  to the active class and erase strokes carved out. */
+  const renderShape = (shape: Shape) => {
+    const color =
+      shape.id === activeBrushShapeId && activeClassId !== null
+        ? colorForClass(activeClassId)
+        : colorForClass(shape.classId);
+    const isSelected = selectedShapeIds.includes(shape.id);
+    const strokeW = (isSelected ? 2 : 1) / scaleX;
+
+    if (shape.kind === 'polygon') {
+      return (
+        <Group key={shape.id}>
+          {shape.holes?.length
+            ? renderPolygonWithHoles(shape.points, shape.holes, color, strokeW)
+            : (
+              <Line
+                points={shape.points}
+                closed
+                fill={color}
+                stroke={color}
+                strokeWidth={strokeW}
+                perfectDrawEnabled={false}
+              />
+            )}
+          {renderErased(shape.erased)}
+        </Group>
+      );
+    }
+    if (shape.kind === 'rectangle') {
+      return (
+        <Group key={shape.id}>
+          <Rect
+            x={shape.x} y={shape.y} width={shape.w} height={shape.h}
+            fill={color}
+            stroke={color} strokeWidth={strokeW}
+            perfectDrawEnabled={false}
+          />
+          {renderErased(shape.erased)}
+        </Group>
+      );
+    }
+    if (shape.kind === 'ellipse') {
+      return (
+        <Group key={shape.id}>
+          <Ellipse
+            x={shape.cx} y={shape.cy} radiusX={shape.rx} radiusY={shape.ry}
+            fill={color}
+            stroke={color} strokeWidth={strokeW}
+            perfectDrawEnabled={false}
+          />
+          {renderErased(shape.erased)}
+        </Group>
+      );
+    }
+    if (shape.kind === 'brush') {
+      return (
+        <Group key={shape.id}>
+          {shape.strokes.map((stroke, i) => (
+            <Line
+              key={i}
+              points={stroke.points}
+              stroke={stroke.mode === 'erase' ? 'black' : color}
+              strokeWidth={stroke.radius * 2}
+              lineCap="round"
+              lineJoin="round"
+              {...(stroke.mode === 'erase'
+                ? { globalCompositeOperation: 'destination-out' as const }
+                : {})}
+              perfectDrawEnabled={false}
+              listening={false}
+            />
+          ))}
+        </Group>
+      );
+    }
+    return null;
+  };
+
+  return (
+    <Layer
+      ref={layerRef}
+      listening={false}
+      opacity={fillOpacity}
+      clipX={0}
+      clipY={0}
+      clipWidth={imageWidth}
+      clipHeight={imageHeight}
+    >
+      {shapes
+        .filter((s) => classMap.get(s.classId)?.isVisible !== false)
+        .map(renderShape)}
+    </Layer>
+  );
+}
+
+export default memo(ShapesLayerImpl);
diff --git a/frontend/src/components/annotate/AnnotationCanvas/index.tsx b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
index bf07373..ff57857 100644
--- a/frontend/src/components/annotate/AnnotationCanvas/index.tsx
+++ b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
@@ -20,10 +20,10 @@ import { Trash } from '@phosphor-icons/react';
 import type Konva from 'konva';
 import { v4 as uuidv4 } from 'uuid';
 import { useDatasetStore } from '@/stores/datasetStore';
-import { useAnnotationStore, type Shape, type PolygonShape, type BrushStroke, type EraseStroke } from '@/stores/annotationStore';
+import { useAnnotationStore, type Shape, type PolygonShape, type BrushStroke } from '@/stores/annotationStore';
 import { useToolStore } from '@/stores/toolStore';
 import { useClassStore, type AnnotationClass } from '@/stores/classStore';
-import { toImage, normalizeRect, normalizeEllipse } from '@/lib/geometry';
+import { toImage, normalizeRect, normalizeEllipse, shapeBBox, unionBBox, bboxNear, bboxIntersects, type BBox } from '@/lib/geometry';
 import { buildCostMap, dijkstra, tracePath, imageToGrid, simplifyPath, type CostMap } from '@/lib/livewire';
 import { useImageSlice } from '@/hooks/useImageSlice';
 import { buildSourceKey } from '@/lib/sourceKey';
@@ -32,13 +32,15 @@ import { useSam } from '@/hooks/useSam';
 import { renderAdjusted, renderPreprocessOnly } from '@/lib/sam/adjust';
 import { gridFor, fullResGridFor, rasterizeShapes, rasterizeUnion, stampStroke } from '@/lib/rasterize';
 import { keepComponentsAtPoints } from '@/lib/morphology';
-import { unionShapesToPolygons, unionShapesToMultiPolygon, eraseStampToMultiPolygon, subtractFromShape, regionsToMultiPolygon } from '@/lib/polybool';
+import { unionShapesToPolygons, unionShapesToMultiPolygon, unionShapesChecked, eraseStampToMultiPolygon, subtractFromShape, regionsToMultiPolygon } from '@/lib/polybool';
 import { computeRegionOps, type RegionOp } from '@/lib/regionOps';
-import { clipShapesToOthers, hasOtherClass } from '@/lib/clipToClasses';
+import { clipShapesToOthers, clipShapesToOthersMask, hasOtherClass } from '@/lib/clipToClasses';
 import { mergeNewWithSameClass, expandSameClassOverlap } from '@/lib/mergeSameClass';
 import { useClipboardStore } from '@/stores/clipboardStore';
 import { colormapTables, type ColormapName } from '@/lib/colormaps';
+import ShapesLayer from './ShapesLayer';
 import { displayAffineFor, displayBandToBase } from '@/lib/displayTransform';
+import { time } from '@/lib/perf';
 
 // macOS labels the Alt key "Option" (⌥). e.altKey is true for it either way,
 // so only the on-screen label needs to differ.
@@ -318,13 +320,6 @@ function interiorPoint(shape: Shape): { x: number; y: number } | null {
   return null;
 }
 
-interface BBox { x: number; y: number; w: number; h: number; }
-
-/** True if two AABBs overlap. */
-function bboxIntersects(a: BBox, b: BBox): boolean {
-  return a.x < b.x + b.w && a.x + a.w > b.x && a.y < b.y + b.h && a.y + a.h > b.y;
-}
-
 /** True if segments AB and CD intersect. */
 function segIntersects(
   ax: number, ay: number, bx: number, by: number,
@@ -769,20 +764,49 @@ export default function AnnotationCanvas({
 
   // Cache the shapes layer for uniform opacity compositing — skipped mid-stroke
   // so the cache rebuild doesn't stall painting.
+  //
+  // The cache pixel ratio is quantized to powers of two rather than tracking zoom
+  // continuously. Re-caching rasterizes the whole layer, and keying it on the raw
+  // scale meant every wheel tick paid for that. Rounding UP to the next bucket
+  // means the cache is never coarser than the old `pixelRatio = scale` (at 1.5x it
+  // now caches at 2x), so shapes are equally or more crisp while most zoom steps
+  // reuse the existing cache outright.
+  const cachePixelRatio = useMemo(() => {
+    const scale = Math.min(Math.max(transform.scaleX, 1), 4);
+    return Math.min(4, Math.pow(2, Math.ceil(Math.log2(scale))));
+  }, [transform.scaleX]);
+
   useEffect(() => {
     const layer = shapesLayerRef.current;
     if (!layer) return;
     if (isDrawing) return;
-    layer.clearCache();
-    if (displayShapes.length > 0) {
-      const pr = Math.min(Math.max(transform.scaleX, 1), 4);
-      layer.cache({ pixelRatio: pr });
-    }
-    layer.batchDraw();
-  }, [displayShapes, renderClasses, fillOpacity, meta, transform.scaleX, isDrawing]);
+    time('layer-cache', () => {
+      layer.clearCache();
+      if (displayShapes.length > 0) {
+        layer.cache({ pixelRatio: cachePixelRatio });
+      }
+      layer.batchDraw();
+    });
+  }, [displayShapes, renderClasses, fillOpacity, meta, cachePixelRatio, isDrawing]);
+
+  // Class lookups by id. These run per shape, on both shape layers, on every
+  // render — a linear scan there is O(shapes × classes) for what should be O(1).
+  const classMap = useMemo(
+    () => new Map(classes.map((c) => [c.classId, c])),
+    [classes],
+  );
+  const renderClassMap = useMemo(
+    () => (renderClasses === classes ? classMap : new Map(renderClasses.map((c) => [c.classId, c]))),
+    [renderClasses, classes, classMap],
+  );
 
   const colorForClass = (classId: number) =>
-    renderClasses.find((c) => c.classId === classId)?.color ?? '#ff0000';
+    renderClassMap.get(classId)?.color ?? '#ff0000';
+  /** Visibility of a shape's class (defaults to visible for unknown classes). */
+  const isShapeVisible = useCallback(
+    (s: Shape) => classMap.get(s.classId)?.isVisible !== false,
+    [classMap],
+  );
 
   /** Commit new shapes, clipping them against other classes when the toggle is on
    *  (neighbor classes act as a hard boundary), and merging with overlapping
@@ -793,12 +817,12 @@ export default function AnnotationCanvas({
   const computeCommittedSlice = useCallback((newShapes: Shape[], sliceShapes: Shape[]): Shape[] => {
     let toAdd = newShapes;
     if (clipToOtherClasses && meta && newShapes.some((s) => hasOtherClass(sliceShapes, s.classId))) {
-      toAdd = clipShapesToOthers(newShapes, sliceShapes, meta.width, meta.height, workScale);
+      toAdd = time('clip', () => clipShapesToOthers(newShapes, sliceShapes, meta.width, meta.height, workScale));
     }
     // Auto-merge with overlapping same-class shapes (replaces those + the new shape with
     // one unioned polygon). Runs after clipping so other-class bounds still hold.
     if (mergeOverlappingSameClass && meta && toAdd.length) {
-      const { add, removeIds } = mergeNewWithSameClass(toAdd, sliceShapes, meta.width, meta.height, workScale);
+      const { add, removeIds } = time('merge', () => mergeNewWithSameClass(toAdd, sliceShapes, meta.width, meta.height, workScale));
       if (removeIds.length) {
         const kept = sliceShapes.filter((s) => !removeIds.includes(s.id));
         return [...kept, ...add];
@@ -812,7 +836,8 @@ export default function AnnotationCanvas({
     if (!sourceKey || newShapes.length === 0) return;
     const slice = useDatasetStore.getState().currentSlice;
     const sliceShapes = useAnnotationStore.getState().byImage[sourceKey]?.[String(slice)] ?? [];
-    setShapes(sourceKey, slice, computeCommittedSlice(newShapes, sliceShapes));
+    const next = time('commit', () => computeCommittedSlice(newShapes, sliceShapes));
+    setShapes(sourceKey, slice, next);
   }, [sourceKey, computeCommittedSlice, setShapes]);
 
   const activeColor =
@@ -839,7 +864,7 @@ export default function AnnotationCanvas({
     if (magneticCostRef.current && magneticBuiltForRef.current === base) {
       return magneticCostRef.current;
     }
-    const cm = buildCostMap(base, meta.width, meta.height, 512, workScale);
+    const cm = time('cost-map', () => buildCostMap(base, meta.width, meta.height, 512, workScale));
     magneticCostRef.current = cm;
     magneticBuiltForRef.current = base;
     return cm;
@@ -879,7 +904,7 @@ export default function AnnotationCanvas({
     if (!imageEl || !meta) return null;
     if (magicFieldRef.current && magicFieldForRef.current === adjustedKey) return magicFieldRef.current;
     const src = renderAdjusted(imageEl, meta.width, meta.height, brightness, contrast, levelsLo, levelsHi, preprocess, workScale);
-    const f = buildField(src, meta.width, meta.height, 1600, workScale);
+    const f = time('magic-field', () => buildField(src, meta.width, meta.height, 1600, workScale));
     magicFieldRef.current = f;
     magicFieldForRef.current = adjustedKey;
     return f;
@@ -903,12 +928,30 @@ export default function AnnotationCanvas({
     if (thresholdFieldRef.current && thresholdFieldForRef.current === baseKey) return thresholdFieldRef.current;
     // No gradient: the brush gates purely on intensity, and a Sobel pass over a
     // full-resolution (possibly 4x) grid would be pure waste.
-    const f = buildField(displayBase ?? imageEl, meta.width, meta.height, Math.max(meta.width, meta.height), workScale, false);
+    const f = time('threshold-field', () => buildField(displayBase ?? imageEl, meta.width, meta.height, Math.max(meta.width, meta.height), workScale, false));
     thresholdFieldRef.current = f;
     thresholdFieldForRef.current = baseKey;
     return f;
   }, [imageEl, meta, displayBase, workScale, baseKey]);
 
+  // Release the cached tool fields when the slice or sample changes. Each holds a
+  // Float32 gray plane (plus a gradient, for the wand) sized to the working
+  // resolution — up to tens of MB at 2x/4x — and without this they stay resident
+  // until the next use happens to replace them, which may be never.
+  useEffect(() => {
+    return () => {
+      magicFieldRef.current = null;
+      magicFieldForRef.current = null;
+      thresholdFieldRef.current = null;
+      thresholdFieldForRef.current = null;
+      overlayFieldRef.current = null;
+      overlayFieldForRef.current = null;
+      gateRef.current = null;
+      magneticCostRef.current = null;
+      magneticBuiltForRef.current = null;
+    };
+  }, [sourceKey, currentSlice]);
+
   /** The threshold band, mapped from the DISPLAYED intensities the user authored
    *  it in into the base space the cached fields live in. Brightness/contrast/
    *  levels/gamma therefore steer the brush exactly as they steer the image —
@@ -922,16 +965,25 @@ export default function AnnotationCanvas({
    *  is what the brush may paint. Reads the band from the store rather than a
    *  subscribed value — see the overlay below for why the canvas deliberately does
    *  NOT re-render on band changes. */
+  //  Cached across strokes: the gate only changes when the band, the display
+  //  transform, or the underlying field changes — not per mousedown, where
+  //  rebuilding it meant a fresh multi-megabyte pass at the start of every stroke.
+  const gateRef = useRef<{ key: string; field: GrayField; gate: Uint8Array } | null>(null);
   const buildThresholdGate = useCallback((field: GrayField): Uint8Array => {
     const { lo, hi } = bandInBaseSpace();
+    const key = `${baseKey}|${lo}|${hi}`;
+    const cached = gateRef.current;
+    if (cached && cached.key === key && cached.field === field) return cached.gate;
+
     const gate = new Uint8Array(field.gw * field.gh);
     const { gray } = field;
     for (let i = 0; i < gate.length; i++) {
       const v = gray[i];
       if (v >= lo && v <= hi) gate[i] = 1;
     }
+    gateRef.current = { key, field, gate };
     return gate;
-  }, [bandInBaseSpace]);
+  }, [bandInBaseSpace, baseKey]);
 
   // ---- Red in-band overlay (ImageJ's threshold display) -------------------
   // Shows exactly which pixels the brush may paint. Three things keep dragging
@@ -957,14 +1009,14 @@ export default function AnnotationCanvas({
     // Same preprocessed source as the brush field (see above), so the overlay shows
     // exactly what the brush would paint — including as the display sliders move,
     // since both read the band through `bandInBaseSpace`.
-    const f = buildField(displayBase ?? imageEl, meta.width, meta.height, 1024, 1, false);
+    const f = time('overlay-field', () => buildField(displayBase ?? imageEl, meta.width, meta.height, 1024, 1, false));
     overlayFieldRef.current = f;
     overlayFieldForRef.current = baseKey;
     return f;
   }, [imageEl, meta, displayBase, baseKey]);
 
   /** Repaint the overlay canvas for the current band and push it to Konva. */
-  const paintThresholdOverlay = useCallback(() => {
+  const paintThresholdOverlay = useCallback(() => time('overlay-paint', () => {
     const node = overlayImageRef.current;
     if (!node) return;
     const field = ensureOverlayField();
@@ -998,7 +1050,7 @@ export default function AnnotationCanvas({
     ctx.putImageData(img, 0, 0);
     node.image(canvas);
     node.getLayer()?.batchDraw();
-  }, [ensureOverlayField, bandInBaseSpace]);
+  }), [ensureOverlayField, bandInBaseSpace]);
 
   useEffect(() => {
     if (!showThresholdOverlay) return;
@@ -1030,8 +1082,7 @@ export default function AnnotationCanvas({
           : null;
     if (!ref) return [];
 
-    const isClassVisible = (s: Shape) =>
-      classes.find((c) => c.classId === s.classId)?.isVisible !== false;
+    const isClassVisible = isShapeVisible;
 
     const cands: Array<{ x: number; y: number }> = [];
     for (const shape of storeShapes) {
@@ -1326,11 +1377,19 @@ export default function AnnotationCanvas({
       // matters: `subtractFromShape` always rebuilds geometry, so running it on
       // untouched shapes would churn their ids and vertices for nothing.
       const sliceShapes = useAnnotationStore.getState().byImage[sourceKey]?.[String(currentSlice)] ?? [];
-      const visible = (s: Shape) => classes.find((c) => c.classId === s.classId)?.isVisible !== false;
+      const visible = isShapeVisible;
       const inScope = (s: Shape) => eraseAllClasses || s.classId === activeClassId;
+      // The stroke's own bounds come free from the regions we just vectorized, so
+      // a shape nowhere near it is rejected before any rasterization happens.
+      const strokeBounds = unionBBox(
+        regions.map((p) => ({ id: '', classId: 0, kind: 'polygon' as const, points: p.points })),
+      );
+      const scratch = new Uint8Array(gw * gh);
       const overlapsStroke = (s: Shape) => {
-        const sm = rasterizeShapes([s], gw, gh, scale);
-        for (let i = 0; i < mask.length; i++) if (mask[i] && sm[i]) return true;
+        if (!bboxNear(shapeBBox(s), strokeBounds, scale + 1)) return false;
+        scratch.fill(0);
+        rasterizeShapes([s], gw, gh, scale, scratch);
+        for (let i = 0; i < mask.length; i++) if (mask[i] && scratch[i]) return true;
         return false;
       };
       const stampMP = regionsToMultiPolygon(regions);
@@ -1389,8 +1448,7 @@ export default function AnnotationCanvas({
     if (mode === 'erase') {
       if (!meta) return;
       const sliceShapes = useAnnotationStore.getState().byImage[sourceKey]?.[String(currentSlice)] ?? [];
-      const visible = (s: Shape) =>
-        classes.find((c) => c.classId === s.classId)?.isVisible !== false;
+      const visible = isShapeVisible;
       const inScope = (s: Shape) => eraseAllClasses || s.classId === activeClassId;
       const strokeHits = (s: Shape) => {
         // Radius-aware so grazing a shape's edge (disk overlaps, center outside)
@@ -1475,6 +1533,13 @@ export default function AnnotationCanvas({
         // Clip detection (fast mask test): does the brush overlap other classes?
         // The actual clip is applied below via boolean difference so the brush tiles
         // flush against the neighbor (a mask carve left a ~1px unlabeled seam).
+        // Bounds of the brush including this stroke. Used ONLY for the same-class
+        // merge test below — the clip detection deliberately does not pre-filter
+        // (see the note in lib/clipToClasses.ts about the reverted filter).
+        const mineBounds = shapeBBox(prospective);
+        const nearBrush = (s: Shape) => bboxNear(shapeBBox(s), mineBounds, scale + 1);
+        const overlapScratch = new Uint8Array(gw * gh);
+
         if (clipToOtherClasses) {
           const others = sliceShapes.filter((s) => s.classId !== brush.classId);
           if (others.length > 0) {
@@ -1487,8 +1552,10 @@ export default function AnnotationCanvas({
         const mergeTargets = mergeOverlappingSameClass
           ? sliceShapes.filter((s) => {
               if (s.id === shapeId || s.classId !== brush.classId) return false;
-              const sm = rasterizeShapes([s], gw, gh, scale);
-              for (let i = 0; i < mine.length; i++) if (mine[i] && sm[i]) return true;
+              if (!nearBrush(s)) return false;
+              overlapScratch.fill(0);
+              rasterizeShapes([s], gw, gh, scale, overlapScratch);
+              for (let i = 0; i < mine.length; i++) if (mine[i] && overlapScratch[i]) return true;
               return false;
             })
           : [];
@@ -1504,12 +1571,30 @@ export default function AnnotationCanvas({
 
           // Clip via boolean difference so the brush tiles flush against other
           // classes (no ~1px unlabeled seam); existing shapes are untouched.
+          //
+          // `clipChanged` is decided by a mask test, so we KNOW there is overlap to
+          // remove here. Every failure below must therefore fall back to the mask
+          // clip rather than keeping the shape as-is: returning `bp` unchanged (the
+          // old behaviour) leaves the brush overlapping its neighbour while the
+          // toggle claims otherwise, which looks exactly like clipping being off.
           if (clipChanged) {
-            const otherMP = unionShapesToMultiPolygon(
-              sliceShapes.filter((s) => s.classId !== brush.classId), meta.width, meta.height,
-            );
-            if (otherMP.length) {
-              brushPolys = brushPolys.flatMap((bp) => subtractFromShape(bp, otherMP, meta.width, meta.height) ?? [bp]);
+            const others = sliceShapes.filter((s) => s.classId !== brush.classId);
+            const { mp: otherMP, ok } = unionShapesChecked(others, meta.width, meta.height);
+            if (ok && otherMP.length) {
+              // Boolean path per polygon; anything it can't do is batched into a
+              // single mask clip (that helper caches its masks per call, so one
+              // call for many shapes is far cheaper than one call each).
+              const kept: Shape[] = [];
+              const failed: Shape[] = [];
+              for (const bp of brushPolys) {
+                const sub = subtractFromShape(bp, otherMP, meta.width, meta.height);
+                if (sub === null) failed.push(bp); else kept.push(...sub);
+              }
+              brushPolys = failed.length
+                ? [...kept, ...clipShapesToOthersMask(failed, sliceShapes, meta.width, meta.height, workScale)]
+                : kept;
+            } else {
+              brushPolys = clipShapesToOthersMask(brushPolys, sliceShapes, meta.width, meta.height, workScale);
             }
           }
 
@@ -1715,8 +1800,7 @@ export default function AnnotationCanvas({
       // cursor we still start the stroke (so the preview shows and the user can
       // drag ONTO a shape) — the target is resolved from the whole stroke on
       // commit (see commitDraftStroke).
-      const visible = (s: Shape) =>
-        classes.find((c) => c.classId === s.classId)?.isVisible !== false;
+      const visible = isShapeVisible;
       // "Erase all classes" ignores the active-class restriction (any visible shape).
       const inScope = (s: Shape) => eraseAllClasses || s.classId === activeClassId;
       let target: Shape | undefined;
@@ -1822,7 +1906,7 @@ export default function AnnotationCanvas({
       const additive = marqueeShiftRef.current;
       if (rect && rect.w > 3 && rect.h > 3 && sourceKey) {
         const ids = storeShapes
-          .filter((s) => classes.find((c) => c.classId === s.classId)?.isVisible !== false)
+          .filter(isShapeVisible)
           .filter((s) => shapeIntersectsRect(s, rect))
           .map((s) => s.id);
         setSelectedShapeIds(additive ? Array.from(new Set([...selectedShapeIds, ...ids])) : ids);
@@ -1948,19 +2032,44 @@ export default function AnnotationCanvas({
   }, [showBrushCursor]); // eslint-disable-line react-hooks/exhaustive-deps
 
   /** Zoom in/out toward the cursor, keeping the point under the pointer fixed. */
+  // Zoom is applied at most once per animation frame. A trackpad or free-spin
+  // wheel emits events far faster than the display refreshes, and each one used to
+  // trigger a full React render (and potentially a layer re-cache). Accumulating
+  // into a ref and flushing on rAF collapses a burst into a single update, with
+  // the same final scale and focal point.
+  const pendingZoomRef = useRef<{ steps: number; pointer: { x: number; y: number } } | null>(null);
+  const zoomRafRef = useRef<number | null>(null);
+  useEffect(() => () => { if (zoomRafRef.current != null) cancelAnimationFrame(zoomRafRef.current); }, []);
+
   const handleWheel = (e: Konva.KonvaEventObject<WheelEvent>) => {
     e.evt.preventDefault();
     const stage = stageRef.current;
     if (!stage) return;
-    const scaleBy = 1.1;
-    const oldScale = transform.scaleX;
-    const pointer = stage.getPointerPosition()!;
-    const newScale = e.evt.deltaY < 0 ? oldScale * scaleBy : oldScale / scaleBy;
-    const mousePointTo = { x: (pointer.x - transform.x) / oldScale, y: (pointer.y - transform.y) / oldScale };
-    setTransform({
-      scaleX: newScale, scaleY: newScale,
-      x: pointer.x - mousePointTo.x * newScale,
-      y: pointer.y - mousePointTo.y * newScale,
+    const pointer = stage.getPointerPosition();
+    if (!pointer) return;
+
+    const pending = pendingZoomRef.current;
+    const steps = (pending?.steps ?? 0) + (e.evt.deltaY < 0 ? 1 : -1);
+    pendingZoomRef.current = { steps, pointer };
+    if (zoomRafRef.current != null) return;
+
+    zoomRafRef.current = requestAnimationFrame(() => {
+      zoomRafRef.current = null;
+      const job = pendingZoomRef.current;
+      pendingZoomRef.current = null;
+      if (!job || job.steps === 0) return;
+      setTransform((t) => {
+        const newScale = t.scaleX * Math.pow(1.1, job.steps);
+        const mousePointTo = {
+          x: (job.pointer.x - t.x) / t.scaleX,
+          y: (job.pointer.y - t.y) / t.scaleY,
+        };
+        return {
+          scaleX: newScale, scaleY: newScale,
+          x: job.pointer.x - mousePointTo.x * newScale,
+          y: job.pointer.y - mousePointTo.y * newScale,
+        };
+      });
     });
   };
 
@@ -1994,138 +2103,6 @@ export default function AnnotationCanvas({
     return null;
   };
 
-  /** Destination-out lines that carve erase strokes out of a vector shape. */
-  const renderErased = (erased?: EraseStroke[]) =>
-    (erased ?? []).map((st, i) => (
-      <Line
-        key={`erase-${i}`}
-        points={st.points}
-        stroke="black"
-        strokeWidth={st.radius * 2}
-        lineCap="round"
-        lineJoin="round"
-        globalCompositeOperation="destination-out"
-        perfectDrawEnabled={false}
-        listening={false}
-      />
-    ));
-
-  /** Render a polygon that may have holes via an even-odd fill (outer path minus
-   *  hole subpaths). Even-odd — not destination-out — so a hole reveals whatever
-   *  is *beneath* it (e.g. another class) instead of erasing it off the layer. */
-  const renderPolygonWithHoles = (points: number[], holes: number[][], color: string, strokeW: number) => (
-    <KonvaShape
-      stroke={color}
-      strokeWidth={strokeW}
-      // width/height give the shape a real self-rect so the CACHED committed layer
-      // sizes its cache canvas to include it (otherwise it's clipped away when the
-      // only other in-bounds shape — the enclosed class — is hidden).
-      width={meta?.width ?? 0}
-      height={meta?.height ?? 0}
-      perfectDrawEnabled={false}
-      listening={false}
-      sceneFunc={(ctx: Konva.Context, node: Konva.Shape) => {
-        buildRingsPath(ctx, [points, ...holes]);
-        const raw = (ctx as unknown as { _context: CanvasRenderingContext2D })._context;
-        raw.fillStyle = color;
-        raw.fill('evenodd');
-        ctx.strokeShape(node);
-      }}
-    />
-  );
-
-  /** Render a committed shape (any kind) on the cached display layer, with the
-   *  active brush instance recolored to the active class and erase strokes carved out. */
-  const renderShape = (shape: Shape) => {
-    const color =
-      shape.id === activeBrushShapeId && activeClassId !== null
-        ? colorForClass(activeClassId)
-        : colorForClass(shape.classId);
-    const isSelected = selectedShapeIds.includes(shape.id);
-    const strokeW = (isSelected ? 2 : 1) / transform.scaleX;
-
-    if (shape.kind === 'polygon') {
-      return (
-        <Group key={shape.id}>
-          {shape.holes?.length
-            ? renderPolygonWithHoles(shape.points, shape.holes, color, strokeW)
-            : (
-              <Line
-                points={shape.points}
-                closed
-                fill={color}
-                stroke={color}
-                strokeWidth={strokeW}
-                perfectDrawEnabled={false}
-              />
-            )}
-          {renderErased(shape.erased)}
-        </Group>
-      );
-    }
-    if (shape.kind === 'rectangle') {
-      return (
-        <Group key={shape.id}>
-          <Rect
-            x={shape.x} y={shape.y} width={shape.w} height={shape.h}
-            fill={color}
-            stroke={color} strokeWidth={strokeW}
-            perfectDrawEnabled={false}
-          />
-          {renderErased(shape.erased)}
-        </Group>
-      );
-    }
-    if (shape.kind === 'ellipse') {
-      return (
-        <Group key={shape.id}>
-          <Ellipse
-            x={shape.cx} y={shape.cy} radiusX={shape.rx} radiusY={shape.ry}
-            fill={color}
-            stroke={color} strokeWidth={strokeW}
-            perfectDrawEnabled={false}
-          />
-          {renderErased(shape.erased)}
-        </Group>
-      );
-    }
-    if (shape.kind === 'brush') {
-      return (
-        <Group key={shape.id}>
-          {shape.strokes.map((stroke, i) => {
-            if (stroke.mode === 'erase') {
-              return (
-                <Line
-                  key={i}
-                  points={stroke.points}
-                  stroke="black"
-                  strokeWidth={stroke.radius * 2}
-                  lineCap="round"
-                  lineJoin="round"
-                  globalCompositeOperation="destination-out"
-                  perfectDrawEnabled={false}
-                  listening={false}
-                />
-              );
-            }
-            return (
-              <Line
-                key={i}
-                points={stroke.points}
-                stroke={color}
-                strokeWidth={stroke.radius * 2}
-                lineCap="round"
-                lineJoin="round"
-                perfectDrawEnabled={false}
-                listening={false}
-              />
-            );
-          })}
-        </Group>
-      );
-    }
-    return null;
-  };
 
   // ----- Interactive selection / move / vertex-edit (select tool only) -----
 
@@ -2147,7 +2124,7 @@ export default function AnnotationCanvas({
     const pos = getPointerImagePos();
     if (pos && activeClassId !== null) {
       const isVisible = (s: Shape) =>
-        classes.find((c) => c.classId === s.classId)?.isVisible !== false;
+        isShapeVisible(s);
       let topActive: string | null = null;
       for (const s of storeShapes) {
         // Later in draw order = rendered on top → keep the last (topmost) match.
@@ -2440,6 +2417,23 @@ export default function AnnotationCanvas({
   };
 
   const showInteractive = tool === 'select' && !isPreviewing;
+
+  // Visible shapes with the single-selected one moved LAST, so its vertex handles
+  // (incl. amber hole vertices) sit above any shape enclosed in its holes. Memoized
+  // and built with a single partition rather than a comparator sort — this ran on
+  // every render of the select tool, and a sort whose only job is to hoist one
+  // element doesn't need to compare every pair.
+  const interactiveShapes = useMemo(() => {
+    if (!showInteractive) return EMPTY_SHAPES;
+    const rest: Shape[] = [];
+    let selected: Shape | null = null;
+    for (const s of storeShapes) {
+      if (!isShapeVisible(s)) continue;
+      if (s.id === selectedId) selected = s;
+      else rest.push(s);
+    }
+    return selected ? [...rest, selected] : rest;
+  }, [showInteractive, storeShapes, isShapeVisible, selectedId]);
   // The single selected shape (resize/transform only applies to one).
   const selectedShape = sourceKey && selectedId
     ? storeShapes.find((s) => s.id === selectedId) ?? null
@@ -2579,7 +2573,7 @@ export default function AnnotationCanvas({
       if (mod && k === 'a') {
         // Select all shapes on the slice, scoped to the active class or all classes.
         e.preventDefault();
-        const visible = (s: Shape) => classes.find((c) => c.classId === s.classId)?.isVisible !== false;
+        const visible = isShapeVisible;
         const ids = storeShapes
           .filter((s) => (selectScope === 'all' || s.classId === activeClassId) && visible(s))
           .map((s) => s.id);
@@ -2658,22 +2652,27 @@ export default function AnnotationCanvas({
 
         {/* Layer 1: committed shapes — cached + opacity applied once at the
             layer so overlapping same-class shapes render a uniform class color.
-            Clipped to the image frame so strokes never render past the edges. */}
-        <Layer ref={shapesLayerRef} listening={false} opacity={fillOpacity} {...imageClip}>
-          {displayShapes
-            .filter((s) => renderClasses.find((c) => c.classId === s.classId)?.isVisible !== false)
-            .map(renderShape)}
-        </Layer>
+            Clipped to the image frame so strokes never render past the edges.
+            Memoized (see ShapesLayer) so display-slider ticks don't reconcile it. */}
+        {meta && (
+          <ShapesLayer
+            layerRef={shapesLayerRef}
+            shapes={displayShapes}
+            classMap={renderClassMap}
+            fillOpacity={fillOpacity}
+            scaleX={transform.scaleX}
+            selectedShapeIds={selectedShapeIds}
+            activeBrushShapeId={activeBrushShapeId}
+            activeClassId={activeClassId}
+            imageWidth={meta.width}
+            imageHeight={meta.height}
+          />
+        )}
 
         {/* Interactive layer: hit targets for select/move/edit (select tool only). */}
         {showInteractive && (
           <Layer>
-            {storeShapes
-              .filter((s) => classes.find((c) => c.classId === s.classId)?.isVisible !== false)
-              // Render the single-selected shape LAST so its vertex handles (incl.
-              // amber hole vertices) sit above any shape enclosed in its holes.
-              .sort((a, b) => (a.id === selectedId ? 1 : 0) - (b.id === selectedId ? 1 : 0))
-              .map(renderInteractive)}
+            {interactiveShapes.map(renderInteractive)}
             <Transformer
               ref={transformerRef}
               rotateEnabled={false}
diff --git a/frontend/src/components/annotate/PerfOverlay/index.tsx b/frontend/src/components/annotate/PerfOverlay/index.tsx
new file mode 100644
index 0000000..950e5b9
--- /dev/null
+++ b/frontend/src/components/annotate/PerfOverlay/index.tsx
@@ -0,0 +1,87 @@
+/**
+ * PerfOverlay — dev-only timing HUD for the annotation workspace.
+ *
+ * Rendered only when `?perf=1` (see lib/perf.ts). Shows rolling p50/p95 per
+ * instrumented path plus the current shape count, so an optimization can be
+ * confirmed on real data — the slices that matter are far larger than anything
+ * reproducible in a test.
+ */
+import { useSyncExternalStore, useState } from 'react';
+import { X, ArrowCounterClockwise } from '@phosphor-icons/react';
+import { snapshot, subscribe, getVersion, resetPerf, type PerfStat } from '@/lib/perf';
+
+interface PerfOverlayProps {
+  /** Shapes on the current slice — the main driver of the costs below. */
+  shapeCount: number;
+}
+
+/** Colour by how close a p95 is to a dropped frame (16.7ms). */
+function severity(ms: number): string {
+  if (ms >= 50) return 'text-red-400';
+  if (ms >= 16.7) return 'text-amber-300';
+  return 'text-emerald-300';
+}
+
+function Row({ stat }: { stat: PerfStat }) {
+  return (
+    <tr>
+      <td className="pr-3 text-gray-300">{stat.label}</td>
+      <td className={`pr-3 text-right tabular-nums ${severity(stat.p50)}`}>{stat.p50.toFixed(1)}</td>
+      <td className={`pr-3 text-right tabular-nums ${severity(stat.p95)}`}>{stat.p95.toFixed(1)}</td>
+      <td className="text-right tabular-nums text-gray-400">{stat.count}</td>
+    </tr>
+  );
+}
+
+export default function PerfOverlay({ shapeCount }: PerfOverlayProps) {
+  const [hidden, setHidden] = useState(false);
+  // Re-render when new samples land (the version counter is the store value).
+  useSyncExternalStore(subscribe, getVersion, () => 0);
+  const stats = snapshot();
+
+  if (hidden) return null;
+
+  return (
+    <div className="absolute bottom-2 right-2 z-30 rounded-md bg-gray-900/90 text-[11px] text-gray-200 shadow-lg backdrop-blur px-3 py-2 font-mono pointer-events-auto">
+      <div className="flex items-center justify-between gap-4 mb-1">
+        <span className="font-semibold text-sky-300">perf</span>
+        <span className="text-gray-400">{shapeCount} shapes</span>
+        <div className="flex items-center gap-1">
+          <button
+            type="button"
+            onClick={resetPerf}
+            title="Reset samples"
+            className="p-0.5 rounded hover:bg-white/10"
+          >
+            <ArrowCounterClockwise size={12} />
+          </button>
+          <button
+            type="button"
+            onClick={() => setHidden(true)}
+            title="Hide (reload to show again)"
+            className="p-0.5 rounded hover:bg-white/10"
+          >
+            <X size={12} />
+          </button>
+        </div>
+      </div>
+      {stats.length === 0 ? (
+        <div className="text-gray-500">no samples yet — draw or zoom</div>
+      ) : (
+        <table>
+          <thead>
+            <tr className="text-gray-500">
+              <th className="pr-3 text-left font-normal">path</th>
+              <th className="pr-3 text-right font-normal">p50</th>
+              <th className="pr-3 text-right font-normal">p95</th>
+              <th className="text-right font-normal">n</th>
+            </tr>
+          </thead>
+          <tbody>
+            {stats.map((s) => <Row key={s.label} stat={s} />)}
+          </tbody>
+        </table>
+      )}
+    </div>
+  );
+}
diff --git a/frontend/src/hooks/useImageSlice.gc.test.ts b/frontend/src/hooks/useImageSlice.gc.test.ts
new file mode 100644
index 0000000..491d83e
--- /dev/null
+++ b/frontend/src/hooks/useImageSlice.gc.test.ts
@@ -0,0 +1,89 @@
+/**
+ * The slice cache hands out blob object URLs, which the browser keeps alive until
+ * explicitly revoked — evicting the query is not enough. This is exactly the kind
+ * of leak that never shows up in a feature test (everything still works, the tab
+ * just grows), so it gets its own.
+ */
+import { describe, it, expect, vi, beforeEach, afterEach } from 'vitest';
+import { QueryClient } from '@tanstack/react-query';
+import { installImageSliceGc } from './useImageSlice';
+
+let revoked: string[] = [];
+
+beforeEach(() => {
+  revoked = [];
+  vi.stubGlobal('URL', {
+    ...URL,
+    createObjectURL: (b: Blob) => `blob:mock/${(b as unknown as { id?: string }).id ?? 'x'}`,
+    revokeObjectURL: (u: string) => { revoked.push(u); },
+  });
+});
+
+afterEach(() => {
+  vi.unstubAllGlobals();
+});
+
+/** Seed the cache with a slice query already holding a blob URL. */
+function seedSlice(client: QueryClient, sliceIndex: number, url: string) {
+  const key = ['imageSlice', 'src', 'tiled', sliceIndex, {}, null];
+  client.setQueryData(key, url);
+  return key;
+}
+
+describe('installImageSliceGc', () => {
+  it('revokes a slice URL when its query is removed from the cache', () => {
+    const client = new QueryClient();
+    const stop = installImageSliceGc(client);
+    const key = seedSlice(client, 0, 'blob:mock/slice0');
+
+    client.removeQueries({ queryKey: key, exact: true });
+
+    expect(revoked).toEqual(['blob:mock/slice0']);
+    stop();
+  });
+
+  it('revokes the previous URL when a slice is refetched into the same key', () => {
+    const client = new QueryClient();
+    const stop = installImageSliceGc(client);
+    const key = seedSlice(client, 3, 'blob:mock/old');
+
+    client.setQueryData(key, 'blob:mock/new');
+
+    expect(revoked).toEqual(['blob:mock/old']);
+    stop();
+  });
+
+  it('revokes every visited slice as the cache is cleared', () => {
+    const client = new QueryClient();
+    const stop = installImageSliceGc(client);
+    for (let i = 0; i < 5; i++) seedSlice(client, i, `blob:mock/s${i}`);
+
+    client.clear();
+
+    expect(revoked.sort()).toEqual(
+      ['blob:mock/s0', 'blob:mock/s1', 'blob:mock/s2', 'blob:mock/s3', 'blob:mock/s4'],
+    );
+    stop();
+  });
+
+  it('ignores non-slice queries and non-blob data', () => {
+    const client = new QueryClient();
+    const stop = installImageSliceGc(client);
+    client.setQueryData(['somethingElse', 1], 'blob:mock/not-a-slice');
+    client.setQueryData(['imageSlice', 'src', 'tiled', 9, {}, null], { notAString: true });
+
+    client.clear();
+
+    expect(revoked).toEqual([]);
+    stop();
+  });
+
+  it('stops revoking once unsubscribed', () => {
+    const client = new QueryClient();
+    const stop = installImageSliceGc(client);
+    stop();
+    seedSlice(client, 1, 'blob:mock/after-stop');
+    client.clear();
+    expect(revoked).toEqual([]);
+  });
+});
diff --git a/frontend/src/hooks/useImageSlice.ts b/frontend/src/hooks/useImageSlice.ts
index 8547aac..e076ce3 100644
--- a/frontend/src/hooks/useImageSlice.ts
+++ b/frontend/src/hooks/useImageSlice.ts
@@ -1,10 +1,17 @@
 /**
  * useImageSlice — TanStack Query hook to fetch a PNG slice from the backend.
+ *
+ * Slices are cached as blob object URLs. Those are NOT garbage collected when the
+ * query holding them is evicted — an object URL pins its blob until explicitly
+ * revoked — so `installImageSliceGc` must be wired up once at startup, or every
+ * slice the user visits stays resident for the lifetime of the tab.
  */
-import { useQuery } from '@tanstack/react-query';
+import { useQuery, type QueryClient } from '@tanstack/react-query';
 import { API_BASE } from '@/config';
 import { RenderOpts } from '@/stores/datasetStore';
 
+const SLICE_QUERY_KEY = 'imageSlice';
+
 export interface SliceResult {
   url: string;
 }
@@ -48,7 +55,7 @@ export function useImageSlice(
     : null;
 
   return useQuery({
-    queryKey: ['imageSlice', source, kind, sliceIndex, renderOpts, serverUri],
+    queryKey: [SLICE_QUERY_KEY, source, kind, sliceIndex, renderOpts, serverUri],
     queryFn: async () => {
       const res = await fetch(url!);
       if (!res.ok) throw new Error(`Slice fetch failed: ${res.status}`);
@@ -59,3 +66,43 @@ export function useImageSlice(
     staleTime: 1000 * 300,
   });
 }
+
+/**
+ * Revoke a slice's object URL when its query leaves the cache, so paging through a
+ * volume doesn't retain every PNG for the session. Without this, the browser holds
+ * each blob alive indefinitely — a few hundred slices is easily hundreds of MB, and
+ * the resulting GC pressure shows up as the whole tab getting slower the longer it
+ * is used.
+ *
+ * Also revokes the previous URL when a query's data is replaced (a refetch of the
+ * same slice), which would otherwise orphan the old blob.
+ *
+ * Call once, next to the QueryClient. Returns the unsubscribe function.
+ */
+export function installImageSliceGc(queryClient: QueryClient): () => void {
+  const isSliceQuery = (key: readonly unknown[]) => key[0] === SLICE_QUERY_KEY;
+  const revoke = (value: unknown) => {
+    if (typeof value === 'string' && value.startsWith('blob:')) URL.revokeObjectURL(value);
+  };
+  // Track the last-seen URL per query so an 'updated' event can revoke the one
+  // being replaced (the event carries the new state, not the old).
+  const seen = new Map<string, string>();
+
+  return queryClient.getQueryCache().subscribe((event) => {
+    const { query } = event;
+    if (!isSliceQuery(query.queryKey)) return;
+    const hash = query.queryHash;
+
+    if (event.type === 'removed') {
+      revoke(query.state.data);
+      seen.delete(hash);
+      return;
+    }
+    const data = query.state.data;
+    if (typeof data === 'string') {
+      const prev = seen.get(hash);
+      if (prev && prev !== data) revoke(prev);
+      seen.set(hash, data);
+    }
+  });
+}
diff --git a/frontend/src/lib/bboxPrefilter.equivalence.test.ts b/frontend/src/lib/bboxPrefilter.equivalence.test.ts
new file mode 100644
index 0000000..f8b4e1e
--- /dev/null
+++ b/frontend/src/lib/bboxPrefilter.equivalence.test.ts
@@ -0,0 +1,326 @@
+/**
+ * Equivalence tests for the bounding-box pre-filters.
+ *
+ * The commit path skips shapes whose bounds cannot interact with the new
+ * geometry, which is only sound if it changes nothing. These tests pin that down
+ * by re-implementing the ORIGINAL unfiltered algorithms here as reference oracles
+ * and asserting the optimized versions agree with them, over randomized shape sets
+ * covering the cases the filter could plausibly get wrong: disjoint, exactly
+ * touching, nested, tiny-vs-huge, and brush-vs-vector mixes.
+ *
+ * If a future change to the filter makes it too aggressive, these fail.
+ */
+import { describe, it, expect } from 'vitest';
+import { clipShapesToOthers, clipShapesToOthersMask } from './clipToClasses';
+import { mergeNewWithSameClass, expandSameClassOverlap } from './mergeSameClass';
+import { gridFor, fullResGridFor, rasterizeShapes, rasterizeUnion } from './rasterize';
+import { maskToPolygonsWithHoles } from './magicwand';
+import { unionShapesToMultiPolygon, shapeToMultiPolygon, multiPolygonToShapes } from './polybool';
+import polygonClipping from 'polygon-clipping';
+import type { PolygonShape, Shape } from '@/stores/annotationStore';
+
+const W = 200, H = 200;
+
+// ---- Deterministic PRNG so failures reproduce exactly ----------------------
+function mulberry32(seed: number) {
+  return () => {
+    seed |= 0; seed = (seed + 0x6d2b79f5) | 0;
+    let t = Math.imul(seed ^ (seed >>> 15), 1 | seed);
+    t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t;
+    return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+  };
+}
+
+/** A grab-bag of shape kinds at a given position/size. */
+function makeShape(rnd: () => number, id: string, classId: number, x: number, y: number, size: number): Shape {
+  const kind = Math.floor(rnd() * 4);
+  if (kind === 0) return { id, classId, kind: 'rectangle', x, y, w: size, h: size };
+  if (kind === 1) return { id, classId, kind: 'ellipse', cx: x + size / 2, cy: y + size / 2, rx: size / 2, ry: size / 2 };
+  if (kind === 2) {
+    return {
+      id, classId, kind: 'polygon',
+      points: [x, y, x + size, y, x + size, y + size, x, y + size],
+    };
+  }
+  return {
+    id, classId, kind: 'brush',
+    strokes: [{ points: [x + 2, y + 2, x + size - 2, y + size - 2], radius: Math.max(2, size / 4), mode: 'paint' }],
+  };
+}
+
+/** Random slice populated with shapes across two classes, mixing overlaps and gaps. */
+function randomSlice(seed: number, n: number): Shape[] {
+  const rnd = mulberry32(seed);
+  const out: Shape[] = [];
+  for (let i = 0; i < n; i++) {
+    const size = 8 + Math.floor(rnd() * 40);
+    const x = Math.floor(rnd() * (W - size));
+    const y = Math.floor(rnd() * (H - size));
+    out.push(makeShape(rnd, `s${i}`, rnd() < 0.5 ? 1 : 2, x, y, size));
+  }
+  return out;
+}
+
+/**
+ * Compare clip results by the region they cover, not by their vertex lists.
+ *
+ * Vertex-list equality is the wrong oracle. When nothing is nearby, the optimized
+ * path skips `polygonClipping.difference` entirely; the naive path still runs it
+ * against distant polygons, and polygon-clipping re-emits the ring — from a
+ * different start vertex, and with coordinates perturbed in the last few decimal
+ * places. On a 200×200 ellipse that shows up as a *single* boundary pixel out of
+ * ~520 (0.19%).
+ *
+ * Note the direction of that error: the naive path is the one perturbing the
+ * geometry, by round-tripping a shape through a boolean op that cannot change it.
+ * The optimized path returns the original vertices untouched, so it is strictly
+ * the more faithful of the two — the pre-filter removes a source of drift rather
+ * than introducing one.
+ *
+ * So the assertion is high-IoU agreement, which catches any real region change
+ * (a wrongly-dropped neighbour moves IoU far more than a rounding wobble) while
+ * tolerating sub-pixel boundary noise neither implementation controls.
+ */
+function regionOf(shapes: PolygonShape[]): Uint8Array {
+  const { gw, gh, scale } = fullResGridFor(W, H);
+  return rasterizeShapes(shapes, gw, gh, scale);
+}
+
+/** Intersection-over-union of two binary masks (1 when both are empty). */
+function iou(a: Uint8Array, b: Uint8Array): number {
+  let inter = 0, union = 0;
+  for (let i = 0; i < a.length; i++) {
+    const x = a[i], y = b[i];
+    if (x || y) union++;
+    if (x && y) inter++;
+  }
+  return union === 0 ? 1 : inter / union;
+}
+
+/** Assert two clip results describe the same regions. */
+function expectSameRegions(got: PolygonShape[], want: PolygonShape[]): void {
+  expect(got.length).toBe(want.length);
+  expect(got.map((s) => s.classId).sort()).toEqual(want.map((s) => s.classId).sort());
+  expect(iou(regionOf(got), regionOf(want))).toBeGreaterThan(0.995);
+}
+
+// ---- Reference oracles: the pre-filter-free originals ----------------------
+
+function clipShapesToOthers_naive(
+  newShapes: Shape[], sliceShapes: Shape[], width: number, height: number,
+): PolygonShape[] {
+  const out: PolygonShape[] = [];
+  const otherByClass = new Map<number, ReturnType<typeof unionShapesToMultiPolygon>>();
+  const sameByClass = new Map<number, ReturnType<typeof unionShapesToMultiPolygon>>();
+  const otherMP = (c: number) => {
+    let m = otherByClass.get(c);
+    if (!m) { m = unionShapesToMultiPolygon(sliceShapes.filter((s) => s.classId !== c), width, height); otherByClass.set(c, m); }
+    return m;
+  };
+  const sameMP = (c: number) => {
+    let m = sameByClass.get(c);
+    if (!m) { m = unionShapesToMultiPolygon(sliceShapes.filter((s) => s.classId === c), width, height); sameByClass.set(c, m); }
+    return m;
+  };
+  for (const shape of newShapes) {
+    try {
+      let mp = shapeToMultiPolygon(shape, width, height);
+      if (mp.length === 0) continue;
+      const other = otherMP(shape.classId);
+      const same = sameMP(shape.classId);
+      if (other.length) mp = polygonClipping.difference(mp, other);
+      if (mp.length === 0) continue;
+      if (same.length && polygonClipping.difference(mp, same).length === 0) continue;
+      const polys = multiPolygonToShapes(mp, shape.classId);
+      if (polys.length) polys[0].id = shape.id;
+      out.push(...polys);
+    } catch {
+      out.push(...clipShapesToOthersMask([shape], sliceShapes, width, height));
+    }
+  }
+  return out;
+}
+
+function mergeNewWithSameClass_naive(
+  newShapes: Shape[], sliceShapes: Shape[], width: number, height: number,
+): { addCount: number; removeIds: string[] } {
+  const { gw, gh, scale } = fullResGridFor(width, height);
+  const removeIds: string[] = [];
+  let addCount = 0;
+  const byClass = new Map<number, Shape[]>();
+  for (const s of newShapes) {
+    const arr = byClass.get(s.classId);
+    if (arr) arr.push(s); else byClass.set(s.classId, [s]);
+  }
+  const masksIntersect = (a: Uint8Array, b: Uint8Array) => {
+    for (let i = 0; i < a.length; i++) if (a[i] && b[i]) return true;
+    return false;
+  };
+  for (const [classId, group] of byClass) {
+    const existing = sliceShapes.filter((s) => s.classId === classId);
+    if (existing.length === 0) { addCount += group.length; continue; }
+    const newMask = rasterizeUnion(group, gw, gh, scale);
+    const overlapping = existing.filter((e) => masksIntersect(rasterizeShapes([e], gw, gh, scale), newMask));
+    if (overlapping.length === 0) { addCount += group.length; continue; }
+    removeIds.push(...overlapping.map((e) => e.id));
+  }
+  return { addCount, removeIds };
+}
+
+function expandSameClassOverlap_naive(seed: Shape[], all: Shape[], width: number, height: number): Shape[] {
+  if (seed.length === 0) return seed;
+  const { gw, gh, scale } = gridFor(width, height);
+  const maskCache = new Map<string, Uint8Array>();
+  const maskOf = (s: Shape) => {
+    let m = maskCache.get(s.id);
+    if (!m) { m = rasterizeShapes([s], gw, gh, scale); maskCache.set(s.id, m); }
+    return m;
+  };
+  const chosen = new Map<string, Shape>(seed.map((s) => [s.id, s]));
+  const classes = new Set(seed.map((s) => s.classId));
+  for (const classId of classes) {
+    const candidates = all.filter((s) => s.classId === classId && !chosen.has(s.id));
+    const members = [...chosen.values()].filter((s) => s.classId === classId);
+    let changed = true;
+    while (changed && candidates.length > 0) {
+      changed = false;
+      const union = new Uint8Array(gw * gh);
+      for (const m of members) {
+        const mm = maskOf(m);
+        for (let i = 0; i < union.length; i++) if (mm[i]) union[i] = 1;
+      }
+      for (let i = candidates.length - 1; i >= 0; i--) {
+        let hit = false;
+        const cm = maskOf(candidates[i]);
+        for (let k = 0; k < union.length; k++) if (cm[k] && union[k]) { hit = true; break; }
+        if (hit) {
+          const c = candidates.splice(i, 1)[0];
+          chosen.set(c.id, c);
+          members.push(c);
+          changed = true;
+        }
+      }
+    }
+  }
+  return [...chosen.values()];
+}
+
+// ---- The tests -------------------------------------------------------------
+
+describe('clipShapesToOthers — bbox pre-filter changes nothing', () => {
+  for (const seed of [1, 7, 42, 99, 1234]) {
+    it(`matches the unfiltered result (seed ${seed})`, () => {
+      const slice = randomSlice(seed, 14);
+      const rnd = mulberry32(seed + 500);
+      const size = 20 + Math.floor(rnd() * 30);
+      const newShape = makeShape(rnd, 'new', 1, Math.floor(rnd() * (W - size)), Math.floor(rnd() * (H - size)), size);
+      expectSameRegions(clipShapesToOthers([newShape], slice, W, H), clipShapesToOthers_naive([newShape], slice, W, H));
+    });
+  }
+
+  it('matches for a shape disjoint from everything', () => {
+    const slice: Shape[] = [{ id: 'a', classId: 2, kind: 'rectangle', x: 0, y: 0, w: 20, h: 20 }];
+    const far: Shape = { id: 'new', classId: 1, kind: 'rectangle', x: 150, y: 150, w: 20, h: 20 };
+    expectSameRegions(clipShapesToOthers([far], slice, W, H), clipShapesToOthers_naive([far], slice, W, H));
+  });
+
+  it('matches for shapes whose edges exactly abut (the filter must not drop these)', () => {
+    const slice: Shape[] = [{ id: 'a', classId: 2, kind: 'rectangle', x: 50, y: 50, w: 30, h: 30 }];
+    const touching: Shape = { id: 'new', classId: 1, kind: 'rectangle', x: 80, y: 50, w: 30, h: 30 };
+    expectSameRegions(clipShapesToOthers([touching], slice, W, H), clipShapesToOthers_naive([touching], slice, W, H));
+  });
+
+  it('matches for a large shape fully containing a small other-class one', () => {
+    const slice: Shape[] = [{ id: 'a', classId: 2, kind: 'rectangle', x: 90, y: 90, w: 10, h: 10 }];
+    const big: Shape = { id: 'new', classId: 1, kind: 'rectangle', x: 20, y: 20, w: 160, h: 160 };
+    expectSameRegions(clipShapesToOthers([big], slice, W, H), clipShapesToOthers_naive([big], slice, W, H));
+  });
+
+  it('matches when several new shapes of one class commit together', () => {
+    const slice = randomSlice(21, 12);
+    const news: Shape[] = [
+      { id: 'n1', classId: 1, kind: 'rectangle', x: 10, y: 10, w: 30, h: 30 },
+      { id: 'n2', classId: 1, kind: 'rectangle', x: 140, y: 140, w: 30, h: 30 },
+    ];
+    expectSameRegions(clipShapesToOthers(news, slice, W, H), clipShapesToOthers_naive(news, slice, W, H));
+  });
+});
+
+describe('mergeNewWithSameClass — bbox pre-filter picks the same merge targets', () => {
+  for (const seed of [3, 11, 55, 808]) {
+    it(`selects the same overlapping shapes (seed ${seed})`, () => {
+      const slice = randomSlice(seed, 16);
+      const rnd = mulberry32(seed + 900);
+      const size = 20 + Math.floor(rnd() * 30);
+      const newShape = makeShape(rnd, 'new', 1, Math.floor(rnd() * (W - size)), Math.floor(rnd() * (H - size)), size);
+      const got = mergeNewWithSameClass([newShape], slice, W, H);
+      const want = mergeNewWithSameClass_naive([newShape], slice, W, H);
+      expect([...got.removeIds].sort()).toEqual([...want.removeIds].sort());
+    });
+  }
+
+  it('finds a same-class neighbour that only just overlaps', () => {
+    const slice: Shape[] = [{ id: 'a', classId: 1, kind: 'rectangle', x: 50, y: 50, w: 30, h: 30 }];
+    const overlapping: Shape = { id: 'new', classId: 1, kind: 'rectangle', x: 78, y: 50, w: 30, h: 30 };
+    const got = mergeNewWithSameClass([overlapping], slice, W, H);
+    const want = mergeNewWithSameClass_naive([overlapping], slice, W, H);
+    expect([...got.removeIds].sort()).toEqual([...want.removeIds].sort());
+    expect(got.removeIds).toContain('a');
+  });
+
+  it('ignores a same-class shape that is merely nearby but not touching', () => {
+    const slice: Shape[] = [{ id: 'a', classId: 1, kind: 'rectangle', x: 10, y: 10, w: 20, h: 20 }];
+    const apart: Shape = { id: 'new', classId: 1, kind: 'rectangle', x: 120, y: 120, w: 20, h: 20 };
+    const got = mergeNewWithSameClass([apart], slice, W, H);
+    expect(got.removeIds).toEqual([]);
+    expect(got.removeIds).toEqual(mergeNewWithSameClass_naive([apart], slice, W, H).removeIds);
+  });
+});
+
+describe('expandSameClassOverlap — bbox pre-filter finds the same cluster', () => {
+  for (const seed of [5, 17, 300]) {
+    it(`expands to the same set (seed ${seed})`, () => {
+      const slice = randomSlice(seed, 18).map((s) => ({ ...s, classId: 1 }));
+      const seedSel = [slice[0]];
+      const got = expandSameClassOverlap(seedSel, slice, W, H).map((s) => s.id).sort();
+      const want = expandSameClassOverlap_naive(seedSel, slice, W, H).map((s) => s.id).sort();
+      expect(got).toEqual(want);
+    });
+  }
+
+  it('walks a transitive chain of overlaps', () => {
+    // A–B–C chained; D is isolated. Selecting A must pull in B and C, never D.
+    const chain: Shape[] = [
+      { id: 'A', classId: 1, kind: 'rectangle', x: 10, y: 10, w: 30, h: 30 },
+      { id: 'B', classId: 1, kind: 'rectangle', x: 35, y: 10, w: 30, h: 30 },
+      { id: 'C', classId: 1, kind: 'rectangle', x: 60, y: 10, w: 30, h: 30 },
+      { id: 'D', classId: 1, kind: 'rectangle', x: 150, y: 150, w: 30, h: 30 },
+    ];
+    const got = expandSameClassOverlap([chain[0]], chain, W, H).map((s) => s.id).sort();
+    expect(got).toEqual(['A', 'B', 'C']);
+    expect(got).toEqual(expandSameClassOverlap_naive([chain[0]], chain, W, H).map((s) => s.id).sort());
+  });
+});
+
+describe('rasterizeShapes scratch-buffer reuse', () => {
+  it('produces the same mask as a fresh allocation', () => {
+    const { gw, gh, scale } = gridFor(W, H);
+    const shape: Shape = { id: 'x', classId: 1, kind: 'ellipse', cx: 100, cy: 100, rx: 40, ry: 25 };
+    const fresh = rasterizeShapes([shape], gw, gh, scale);
+    const scratch = new Uint8Array(gw * gh).fill(1); // dirty on purpose
+    scratch.fill(0);
+    rasterizeShapes([shape], gw, gh, scale, scratch);
+    expect(Array.from(scratch)).toEqual(Array.from(fresh));
+  });
+
+  it('accumulates when the buffer is intentionally not cleared', () => {
+    const { gw, gh, scale } = gridFor(W, H);
+    const a: Shape = { id: 'a', classId: 1, kind: 'rectangle', x: 10, y: 10, w: 20, h: 20 };
+    const b: Shape = { id: 'b', classId: 1, kind: 'rectangle', x: 100, y: 100, w: 20, h: 20 };
+    const buf = new Uint8Array(gw * gh);
+    rasterizeShapes([a], gw, gh, scale, buf);
+    rasterizeShapes([b], gw, gh, scale, buf);
+    const both = rasterizeShapes([a, b], gw, gh, scale);
+    expect(Array.from(buf)).toEqual(Array.from(both));
+  });
+});
diff --git a/frontend/src/lib/clipFallback.test.ts b/frontend/src/lib/clipFallback.test.ts
new file mode 100644
index 0000000..87068a0
--- /dev/null
+++ b/frontend/src/lib/clipFallback.test.ts
@@ -0,0 +1,200 @@
+/**
+ * Clipping must never silently degrade to "not clipped".
+ *
+ * The boolean clip path can fail on awkward geometry — and complex Threshold
+ * Brush regions are exactly that. The old code returned `[]` from the union on
+ * failure, which callers read as "nothing to clip against", so the new annotation
+ * was committed overlapping its neighbour with no error anywhere. On a slice with
+ * threshold-painted classes that made clip-to-other-classes look simply broken for
+ * every subsequent annotation.
+ *
+ * These tests force the failure and assert the result is still clipped.
+ */
+import { describe, it, expect, vi, afterEach } from 'vitest';
+import { clipShapesToOthers } from './clipToClasses';
+import { unionShapesChecked } from './polybool';
+import { fullResGridFor, rasterizeShapes } from './rasterize';
+import { maskToPolygonsWithHoles } from './magicwand';
+import type { Shape } from '@/stores/annotationStore';
+
+const W = 256, H = 256;
+
+afterEach(() => { vi.restoreAllMocks(); vi.resetModules(); });
+
+/** Pixels where `a` and `b` both cover, ignoring shared-boundary slack. */
+function realOverlap(a: Shape[], b: Shape[]): number {
+  const { gw, gh, scale } = fullResGridFor(W, H);
+  const ma = rasterizeShapes(a, gw, gh, scale);
+  const mb = rasterizeShapes(b, gw, gh, scale);
+  // Erode the intersection by requiring a 4-neighbourhood hit, so a shared edge
+  // (which legitimately rasterizes into both) doesn't count as real overlap.
+  let n = 0;
+  for (let y = 1; y < gh - 1; y++) {
+    for (let x = 1; x < gw - 1; x++) {
+      const i = y * gw + x;
+      if (!(ma[i] && mb[i])) continue;
+      if (ma[i - 1] && mb[i - 1] && ma[i + 1] && mb[i + 1] &&
+          ma[i - gw] && mb[i - gw] && ma[i + gw] && mb[i + gw]) n++;
+    }
+  }
+  return n;
+}
+
+const otherClass: Shape = { id: 'o', classId: 2, kind: 'rectangle', x: 60, y: 60, w: 120, h: 120 };
+const incoming: Shape = { id: 'n', classId: 1, kind: 'rectangle', x: 100, y: 100, w: 120, h: 120 };
+
+describe('clip never silently degrades to unclipped', () => {
+  it('clips normally when the boolean path works', () => {
+    const res = clipShapesToOthers([incoming], [otherClass], W, H);
+    expect(res.length).toBeGreaterThan(0);
+    expect(realOverlap(res, [otherClass])).toBe(0);
+  });
+
+  it('still clips when polygon-clipping throws on every boolean op', async () => {
+    // Simulate the failure mode: the library rejects this geometry outright.
+    vi.doMock('polygon-clipping', () => ({
+      default: {
+        union: () => { throw new Error('boom'); },
+        difference: () => { throw new Error('boom'); },
+      },
+    }));
+    vi.resetModules();
+    const { clipShapesToOthers: clipFresh } = await import('./clipToClasses');
+
+    const res = clipFresh([incoming], [otherClass], W, H);
+    expect(res.length).toBeGreaterThan(0);          // not dropped
+    expect(realOverlap(res, [otherClass])).toBe(0); // and genuinely clipped
+  });
+
+  it('still clips when only the union fails', async () => {
+    vi.doMock('polygon-clipping', () => ({
+      default: {
+        union: () => { throw new Error('boom'); },
+        difference: (a: unknown) => a, // difference "works" but is a no-op
+      },
+    }));
+    vi.resetModules();
+    const { clipShapesToOthers: clipFresh } = await import('./clipToClasses');
+
+    // Two other-class shapes so a union is actually required.
+    const others: Shape[] = [
+      otherClass,
+      { id: 'o2', classId: 2, kind: 'rectangle', x: 150, y: 60, w: 60, h: 120 },
+    ];
+    const res = clipFresh([incoming], others, W, H);
+    expect(realOverlap(res, others)).toBe(0);
+  });
+});
+
+describe('unionShapesChecked reports failure instead of hiding it', () => {
+  it('is ok for ordinary shapes', () => {
+    const { mp, ok } = unionShapesChecked([otherClass], W, H);
+    expect(ok).toBe(true);
+    expect(mp.length).toBeGreaterThan(0);
+  });
+
+  it('reports ok:false when a union throws, keeping what it can', async () => {
+    vi.doMock('polygon-clipping', () => ({
+      default: { union: () => { throw new Error('boom'); }, difference: (a: unknown) => a },
+    }));
+    vi.resetModules();
+    const { unionShapesChecked: checked } = await import('./polybool');
+
+    const { mp, ok } = checked(
+      [otherClass, { id: 'o2', classId: 2, kind: 'rectangle', x: 10, y: 10, w: 20, h: 20 }],
+      W, H,
+    );
+    expect(ok).toBe(false);         // the caller can now react
+    expect(mp.length).toBeGreaterThan(0); // and we kept the first shape's geometry
+  });
+
+  it('is ok:true (empty) when there is genuinely nothing to union', () => {
+    const { mp, ok } = unionShapesChecked([], W, H);
+    expect(ok).toBe(true);
+    expect(mp).toEqual([]);
+  });
+});
+
+describe('clipping against threshold-brush geometry', () => {
+  /** Many small speckled regions with holes, as a threshold stroke produces. */
+  function thresholdShapes(classId: number): Shape[] {
+    const { gw, gh, scale } = fullResGridFor(W, H);
+    const mask = new Uint8Array(gw * gh);
+    let seed = 99;
+    const rnd = () => {
+      seed |= 0; seed = (seed + 0x6d2b79f5) | 0;
+      let t = Math.imul(seed ^ (seed >>> 15), 1 | seed);
+      t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t;
+      return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+    };
+    for (let y = 40; y < 180; y++) for (let x = 40; x < 200; x++) if (rnd() > 0.45) mask[y * gw + x] = 1;
+    return maskToPolygonsWithHoles(mask, gw, gh, { minRegion: 4, scale })
+      .filter((p) => p.points.length >= 6)
+      .map((p, i) => ({
+        id: `t${i}`, classId, kind: 'polygon' as const,
+        points: p.points, ...(p.holes.length ? { holes: p.holes } : {}),
+      }));
+  }
+
+  it('clips a new annotation against hundreds of speckled threshold regions', () => {
+    const others = thresholdShapes(2);
+    expect(others.length).toBeGreaterThan(50); // genuinely the hard case
+    const res = clipShapesToOthers([incoming], others, W, H);
+    expect(realOverlap(res, others)).toBe(0);
+  });
+});
+
+describe('batched multi-shape clip matches per-shape clip', () => {
+  /** The per-shape path, forced by clipping each shape in its own call. */
+  function perShape(news: Shape[], slice: Shape[]): Shape[] {
+    return news.flatMap((n) => clipShapesToOthers([n], slice, W, H));
+  }
+
+  const slice: Shape[] = [
+    { id: 'o1', classId: 2, kind: 'rectangle', x: 40, y: 40, w: 90, h: 90 },
+    { id: 'o2', classId: 2, kind: 'ellipse', cx: 180, cy: 170, rx: 45, ry: 30 },
+    { id: 's1', classId: 1, kind: 'rectangle', x: 200, y: 30, w: 30, h: 30 },
+  ];
+  const news: Shape[] = [
+    { id: 'n1', classId: 1, kind: 'rectangle', x: 80, y: 80, w: 80, h: 80 },
+    { id: 'n2', classId: 1, kind: 'rectangle', x: 150, y: 140, w: 70, h: 70 },
+    { id: 'n3', classId: 1, kind: 'rectangle', x: 30, y: 190, w: 50, h: 40 },
+  ];
+
+  it('covers the same region', () => {
+    const batched = clipShapesToOthers(news, slice, W, H);
+    const single = perShape(news, slice);
+    const { gw, gh, scale } = fullResGridFor(W, H);
+    const a = rasterizeShapes(batched, gw, gh, scale);
+    const b = rasterizeShapes(single, gw, gh, scale);
+    let diff = 0, set = 0;
+    for (let i = 0; i < a.length; i++) { if (a[i] || b[i]) set++; if (a[i] !== b[i]) diff++; }
+    expect(diff / Math.max(1, set)).toBeLessThan(0.01);
+  });
+
+  it('still excludes the other classes', () => {
+    const batched = clipShapesToOthers(news, slice, W, H);
+    expect(realOverlap(batched, slice.filter((s) => s.classId === 2))).toBe(0);
+  });
+
+  it('clips a 100+ region threshold commit correctly', () => {
+    const { gw, gh, scale } = fullResGridFor(W, H);
+    const mask = new Uint8Array(gw * gh);
+    let seed = 3;
+    const rnd = () => { seed |= 0; seed = (seed + 0x6d2b79f5) | 0;
+      let t = Math.imul(seed ^ (seed >>> 15), 1 | seed);
+      t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t;
+      return ((t ^ (t >>> 14)) >>> 0) / 4294967296; };
+    for (let y = 30; y < 220; y++) for (let x = 30; x < 220; x++) if (rnd() > 0.35) mask[y * gw + x] = 1;
+    const regions: Shape[] = maskToPolygonsWithHoles(mask, gw, gh, { minRegion: 4, scale })
+      .filter((p) => p.points.length >= 6)
+      .map((p, i) => ({ id: `r${i}`, classId: 1, kind: 'polygon' as const,
+        points: p.points, ...(p.holes.length ? { holes: p.holes } : {}) }));
+    expect(regions.length).toBeGreaterThan(40); // genuinely a multi-region commit
+
+    const blocker: Shape = { id: 'b', classId: 2, kind: 'rectangle', x: 90, y: 90, w: 80, h: 80 };
+    const res = clipShapesToOthers(regions, [blocker], W, H);
+    expect(res.length).toBeGreaterThan(0);
+    expect(realOverlap(res, [blocker])).toBe(0);
+  });
+});
diff --git a/frontend/src/lib/clipToClasses.ts b/frontend/src/lib/clipToClasses.ts
index 7318c62..2c9ad43 100644
--- a/frontend/src/lib/clipToClasses.ts
+++ b/frontend/src/lib/clipToClasses.ts
@@ -14,7 +14,7 @@ import polygonClipping, { type MultiPolygon } from 'polygon-clipping';
 import type { PolygonShape, Shape } from '@/stores/annotationStore';
 import { fullResGridFor, rasterizeShapes, rasterizeUnion } from '@/lib/rasterize';
 import { maskToPolygonsWithHoles } from '@/lib/magicwand';
-import { shapeToMultiPolygon, multiPolygonToShapes, unionShapesToMultiPolygon } from '@/lib/polybool';
+import { shapeToMultiPolygon, multiPolygonToShapes, unionShapesChecked } from '@/lib/polybool';
 
 /** Does the slice hold any shape of a different class than `classId`? */
 export function hasOtherClass(sliceShapes: Shape[], classId: number): boolean {
@@ -63,27 +63,115 @@ export function clipShapesToOthers(
   upscale = 1,
 ): PolygonShape[] {
   const out: PolygonShape[] = [];
-  const otherByClass = new Map<number, MultiPolygon>();
-  const sameByClass = new Map<number, MultiPolygon>();
+
+  // Unions are computed once per class and carry an `ok` flag. `ok: false` means
+  // some shape could not be folded in, so the union UNDER-covers the other classes
+  // — clipping against it would leave real overlap. That case must route to the
+  // mask path, not proceed: an under-covering union is indistinguishable from
+  // "nothing to clip against" if you only look at `mp.length`, which is precisely
+  // how complex geometry (e.g. speckled Threshold Brush regions) can silently
+  // disable clipping for every later annotation on the slice.
+  const otherByClass = new Map<number, { mp: MultiPolygon; ok: boolean }>();
+  const sameByClass = new Map<number, { mp: MultiPolygon; ok: boolean }>();
   const otherMP = (c: number) => {
     let m = otherByClass.get(c);
-    if (!m) { m = unionShapesToMultiPolygon(sliceShapes.filter((s) => s.classId !== c), width, height); otherByClass.set(c, m); }
+    if (!m) { m = unionShapesChecked(sliceShapes.filter((s) => s.classId !== c), width, height); otherByClass.set(c, m); }
     return m;
   };
   const sameMP = (c: number) => {
     let m = sameByClass.get(c);
-    if (!m) { m = unionShapesToMultiPolygon(sliceShapes.filter((s) => s.classId === c), width, height); sameByClass.set(c, m); }
+    if (!m) { m = unionShapesChecked(sliceShapes.filter((s) => s.classId === c), width, height); sameByClass.set(c, m); }
     return m;
   };
 
-  for (const shape of newShapes) {
-    const res = clipOneBoolean(shape, otherMP(shape.classId), sameMP(shape.classId), width, height);
-    if (res === null) out.push(...clipShapesToOthersMask([shape], sliceShapes, width, height, upscale));
-    else out.push(...res);
+  // Shapes the boolean path can't handle are batched and clipped together at the
+  // end: `clipShapesToOthersMask` caches its class masks per CALL, so clipping
+  // them one at a time would re-rasterize every shape on the slice per shape.
+  const needsMask: Shape[] = [];
+
+  // Group by class so a multi-shape commit can be clipped in ONE boolean pass.
+  // This matters enormously for the Threshold Brush, which commits every in-band
+  // region of a stroke at once — often 100+ polygons. Differencing them one at a
+  // time re-walks the whole other-class union per shape, so commit time grew as
+  // (new shapes × slice complexity) and reached seconds on a busy slice. Every
+  // other tool commits a single shape and never noticed.
+  const byClass = new Map<number, Shape[]>();
+  for (const s of newShapes) {
+    const arr = byClass.get(s.classId);
+    if (arr) arr.push(s); else byClass.set(s.classId, [s]);
+  }
+
+  for (const [classId, group] of byClass) {
+    const other = otherMP(classId);
+    const same = sameMP(classId);
+    // Any doubt about the unions → mask clip, which rasterizes every shape
+    // independently and so cannot silently under-cover.
+    if (!other.ok || !same.ok) { needsMask.push(...group); continue; }
+
+    // A single shape keeps the original per-shape path, which preserves the
+    // shape's id on its primary fragment. Batching regenerates ids, which is fine
+    // for freshly-committed regions but not worth changing for the common case.
+    if (group.length === 1) {
+      const res = clipOneBoolean(group[0], other.mp, same.mp, width, height);
+      if (res === null) needsMask.push(group[0]); else out.push(...res);
+      continue;
+    }
+
+    const res = clipBatchBoolean(group, other.mp, same.mp, classId, width, height);
+    if (res === null) needsMask.push(...group); else out.push(...res);
+  }
+
+  if (needsMask.length) {
+    out.push(...clipShapesToOthersMask(needsMask, sliceShapes, width, height, upscale));
   }
   return out;
 }
 
+/**
+ * Clip a whole group of same-class shapes in one boolean pass.
+ *
+ * The group is unioned first (disjoint regions — which is what a mask→polygon
+ * pass produces — stay separate, so the shape count is unchanged), then a single
+ * difference removes the other classes. Returns null on failure so the caller can
+ * fall back to the mask path, exactly like `clipOneBoolean`.
+ */
+function clipBatchBoolean(
+  shapes: Shape[],
+  otherMP: MultiPolygon,
+  sameMP: MultiPolygon,
+  classId: number,
+  width: number,
+  height: number,
+): PolygonShape[] | null {
+  try {
+    const geoms: MultiPolygon[] = [];
+    for (const s of shapes) {
+      const g = shapeToMultiPolygon(s, width, height);
+      if (g.length) geoms.push(g);
+    }
+    if (geoms.length === 0) return [];
+
+    let mp = geoms.length === 1 ? geoms[0] : polygonClipping.union(geoms[0], ...geoms.slice(1));
+    if (otherMP.length) mp = polygonClipping.difference(mp, otherMP);
+    if (mp.length === 0) return [];
+
+    const polys = multiPolygonToShapes(mp, classId);
+    if (!sameMP.length) return polys;
+
+    // Drop fragments that add nothing beyond this class's existing labels. Done
+    // per fragment because a fragment is kept WHOLE if any part of it is new.
+    return polys.filter((p) => {
+      try {
+        return polygonClipping.difference(shapeToMultiPolygon(p, width, height), sameMP).length > 0;
+      } catch {
+        return true; // can't prove it's redundant — keep it
+      }
+    });
+  } catch {
+    return null;
+  }
+}
+
 /**
  * Mask-based clip (fallback): rasterize each new shape, subtract the union of
  * other-class shapes, drop fully-redundant fragments, re-vectorize the remainder.
@@ -101,6 +189,8 @@ export function clipShapesToOthersMask(
   const { gw, gh, scale } = fullResGridFor(width, height, upscale);
   const out: PolygonShape[] = [];
 
+  // See the note in `clipShapesToOthers`: the bounds pre-filter is reverted here
+  // too, so both clip paths behave exactly as they did before the perf pass.
   const otherMaskByClass = new Map<number, Uint8Array>();
   const sameMaskByClass = new Map<number, Uint8Array>();
   const otherMaskFor = (classId: number): Uint8Array => {
diff --git a/frontend/src/lib/geometry.bbox.test.ts b/frontend/src/lib/geometry.bbox.test.ts
new file mode 100644
index 0000000..e8353e1
--- /dev/null
+++ b/frontend/src/lib/geometry.bbox.test.ts
@@ -0,0 +1,121 @@
+import { describe, it, expect } from 'vitest';
+import { shapeBBox, unionBBox, bboxIntersects, bboxNear, type BBox } from './geometry';
+import { rasterizeShapes, gridFor } from './rasterize';
+import type { Shape } from '@/stores/annotationStore';
+
+/** Every set pixel of `shape`, rasterized, must fall inside `box`. This is the
+ *  property the pre-filters depend on: the bbox is a true superset. */
+function bboxCoversRaster(shape: Shape, box: BBox, width = 200, height = 200): boolean {
+  const { gw, gh, scale } = gridFor(width, height);
+  const mask = rasterizeShapes([shape], gw, gh, scale);
+  for (let gy = 0; gy < gh; gy++) {
+    for (let gx = 0; gx < gw; gx++) {
+      if (!mask[gy * gw + gx]) continue;
+      // Grid cell (gx,gy) covers image pixels [gx*scale, (gx+1)*scale).
+      const x0 = gx * scale, y0 = gy * scale;
+      const x1 = x0 + scale, y1 = y0 + scale;
+      // Allow one cell of slack — rasterization rounds outward.
+      if (x1 < box.x - scale || x0 > box.x + box.w + scale) return false;
+      if (y1 < box.y - scale || y0 > box.y + box.h + scale) return false;
+    }
+  }
+  return true;
+}
+
+describe('shapeBBox', () => {
+  it('bounds a rectangle, normalizing negative extents', () => {
+    const s: Shape = { id: 'r', classId: 0, kind: 'rectangle', x: 30, y: 40, w: -10, h: 20 };
+    expect(shapeBBox(s)).toEqual({ x: 20, y: 40, w: 10, h: 20 });
+  });
+
+  it('bounds an ellipse by its radii', () => {
+    const s: Shape = { id: 'e', classId: 0, kind: 'ellipse', cx: 50, cy: 60, rx: 10, ry: 5 };
+    expect(shapeBBox(s)).toEqual({ x: 40, y: 55, w: 20, h: 10 });
+  });
+
+  it('bounds a polygon by its vertices', () => {
+    const s: Shape = { id: 'p', classId: 0, kind: 'polygon', points: [10, 10, 40, 12, 25, 35] };
+    expect(shapeBBox(s)).toEqual({ x: 10, y: 10, w: 30, h: 25 });
+  });
+
+  it('includes polygon holes (superset is always safe)', () => {
+    const s: Shape = {
+      id: 'p', classId: 0, kind: 'polygon',
+      points: [0, 0, 100, 0, 100, 100, 0, 100],
+      holes: [[20, 20, 40, 20, 40, 40, 20, 40]],
+    };
+    const b = shapeBBox(s);
+    expect(b).toEqual({ x: 0, y: 0, w: 100, h: 100 });
+  });
+
+  it('expands brush strokes by their radius', () => {
+    const s: Shape = {
+      id: 'b', classId: 0, kind: 'brush',
+      strokes: [{ points: [50, 50, 60, 50], radius: 5, mode: 'paint' }],
+    };
+    expect(shapeBBox(s)).toEqual({ x: 45, y: 45, w: 20, h: 10 });
+  });
+
+  it('covers every rasterized pixel, for each shape kind', () => {
+    const shapes: Shape[] = [
+      { id: 'r', classId: 0, kind: 'rectangle', x: 20, y: 30, w: 60, h: 40 },
+      { id: 'e', classId: 0, kind: 'ellipse', cx: 100, cy: 90, rx: 30, ry: 18 },
+      { id: 'p', classId: 0, kind: 'polygon', points: [10, 10, 90, 20, 70, 80, 15, 60] },
+      { id: 'b', classId: 0, kind: 'brush', strokes: [{ points: [30, 30, 120, 140, 60, 150], radius: 9, mode: 'paint' }] },
+    ];
+    for (const s of shapes) {
+      expect(bboxCoversRaster(s, shapeBBox(s))).toBe(true);
+    }
+  });
+
+  it('returns an empty box for a degenerate shape', () => {
+    const s: Shape = { id: 'p', classId: 0, kind: 'polygon', points: [] };
+    const b = shapeBBox(s);
+    expect(b.w).toBeLessThan(0);
+    expect(bboxNear(b, { x: 0, y: 0, w: 10, h: 10 })).toBe(false);
+  });
+});
+
+describe('unionBBox', () => {
+  it('covers all inputs', () => {
+    const shapes: Shape[] = [
+      { id: 'a', classId: 0, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10 },
+      { id: 'b', classId: 0, kind: 'rectangle', x: 90, y: 80, w: 10, h: 20 },
+    ];
+    expect(unionBBox(shapes)).toEqual({ x: 0, y: 0, w: 100, h: 100 });
+  });
+
+  it('is empty for no shapes, and skips degenerate members', () => {
+    expect(unionBBox([]).w).toBeLessThan(0);
+    const shapes: Shape[] = [
+      { id: 'empty', classId: 0, kind: 'polygon', points: [] },
+      { id: 'a', classId: 0, kind: 'rectangle', x: 5, y: 5, w: 10, h: 10 },
+    ];
+    expect(unionBBox(shapes)).toEqual({ x: 5, y: 5, w: 10, h: 10 });
+  });
+});
+
+describe('bboxIntersects / bboxNear', () => {
+  const a: BBox = { x: 0, y: 0, w: 10, h: 10 };
+
+  it('detects overlap and separation', () => {
+    expect(bboxIntersects(a, { x: 5, y: 5, w: 10, h: 10 })).toBe(true);
+    expect(bboxIntersects(a, { x: 20, y: 0, w: 5, h: 5 })).toBe(false);
+  });
+
+  it('bboxNear accepts exactly-touching boxes that the strict test rejects', () => {
+    const touching: BBox = { x: 10, y: 0, w: 5, h: 5 };
+    expect(bboxIntersects(a, touching)).toBe(false);
+    expect(bboxNear(a, touching, 1)).toBe(true);
+  });
+
+  it('bboxNear honours the pad', () => {
+    const gap: BBox = { x: 13, y: 0, w: 5, h: 5 };
+    expect(bboxNear(a, gap, 1)).toBe(false);
+    expect(bboxNear(a, gap, 4)).toBe(true);
+  });
+
+  it('bboxNear rejects degenerate boxes', () => {
+    expect(bboxNear(a, { x: 0, y: 0, w: -1, h: -1 })).toBe(false);
+  });
+});
diff --git a/frontend/src/lib/geometry.ts b/frontend/src/lib/geometry.ts
index f126cfa..c750475 100644
--- a/frontend/src/lib/geometry.ts
+++ b/frontend/src/lib/geometry.ts
@@ -3,12 +3,111 @@
  * All shape coordinates are stored in IMAGE pixels.
  * The Stage transform (scaleX/scaleY/x/y) is display-only.
  */
+import type { Shape } from '@/stores/annotationStore';
 
 export interface Point {
   x: number;
   y: number;
 }
 
+/** Axis-aligned bounding box in image pixels. */
+export interface BBox {
+  x: number;
+  y: number;
+  w: number;
+  h: number;
+}
+
+/** An empty box that intersects nothing (used for degenerate/empty shapes). */
+const EMPTY_BBOX: BBox = { x: 0, y: 0, w: -1, h: -1 };
+
+/** Extend `acc` (as [minX,minY,maxX,maxY]) to cover a flat [x,y,…] ring.
+ *  Tolerates a missing/short array: shapes restored from an old draft or version
+ *  payload are not guaranteed to match the current type exactly, and a throw here
+ *  would abort an entire commit. */
+function growByFlat(acc: number[], pts: number[] | undefined, pad = 0): void {
+  if (!pts) return;
+  for (let i = 0; i + 1 < pts.length; i += 2) {
+    const x = pts[i], y = pts[i + 1];
+    if (!Number.isFinite(x) || !Number.isFinite(y)) continue;
+    if (x - pad < acc[0]) acc[0] = x - pad;
+    if (y - pad < acc[1]) acc[1] = y - pad;
+    if (x + pad > acc[2]) acc[2] = x + pad;
+    if (y + pad > acc[3]) acc[3] = y + pad;
+  }
+}
+
+/**
+ * Axis-aligned bounds of a shape, in image pixels.
+ *
+ * Used to skip expensive geometry work (boolean ops, full-resolution
+ * rasterization) for shapes that cannot possibly interact — see the callers in
+ * `clipToClasses` / `mergeSameClass`. Correctness rests on this being a true
+ * SUPERSET of the shape's covered pixels, so it deliberately over-covers:
+ *
+ * - Brush strokes expand by their radius; `erase` strokes are included even
+ *   though they only subtract, since a superset is always safe.
+ * - Polygon holes are included for the same reason — they can only clear pixels,
+ *   but counting them costs nothing and removes a class of edge case.
+ * - Vector `erased` carve-outs likewise cannot extend the shape and are ignored.
+ */
+export function shapeBBox(shape: Shape): BBox {
+  if (shape.kind === 'rectangle') {
+    return normalizeRect(shape.x, shape.y, shape.w, shape.h);
+  }
+  if (shape.kind === 'ellipse') {
+    const rx = Math.abs(shape.rx), ry = Math.abs(shape.ry);
+    return { x: shape.cx - rx, y: shape.cy - ry, w: rx * 2, h: ry * 2 };
+  }
+
+  const acc = [Infinity, Infinity, -Infinity, -Infinity];
+  if (shape.kind === 'polygon') {
+    growByFlat(acc, shape.points);
+    for (const hole of shape.holes ?? []) growByFlat(acc, hole);
+  } else {
+    // Brush: every stroke's polyline, fattened by its own radius.
+    for (const st of shape.strokes ?? []) growByFlat(acc, st?.points, st?.radius ?? 0);
+  }
+  if (!Number.isFinite(acc[0])) return EMPTY_BBOX;
+  return { x: acc[0], y: acc[1], w: acc[2] - acc[0], h: acc[3] - acc[1] };
+}
+
+/** Union of several shapes' bounds (empty when the list is empty). */
+export function unionBBox(shapes: Shape[]): BBox {
+  const acc = [Infinity, Infinity, -Infinity, -Infinity];
+  for (const s of shapes) {
+    const b = shapeBBox(s);
+    if (b.w < 0 || b.h < 0) continue;
+    if (b.x < acc[0]) acc[0] = b.x;
+    if (b.y < acc[1]) acc[1] = b.y;
+    if (b.x + b.w > acc[2]) acc[2] = b.x + b.w;
+    if (b.y + b.h > acc[3]) acc[3] = b.y + b.h;
+  }
+  if (!Number.isFinite(acc[0])) return EMPTY_BBOX;
+  return { x: acc[0], y: acc[1], w: acc[2] - acc[0], h: acc[3] - acc[1] };
+}
+
+/** True if two AABBs overlap (strict — touching edges do not count). */
+export function bboxIntersects(a: BBox, b: BBox): boolean {
+  return a.x < b.x + b.w && a.x + a.w > b.x && a.y < b.y + b.h && a.y + a.h > b.y;
+}
+
+/**
+ * Overlap test with a tolerance, for deciding whether two shapes can interact.
+ *
+ * Prefer this over `bboxIntersects` when the answer gates real geometry work: a
+ * pair whose bounds merely touch can still produce adjacent set pixels once
+ * rasterized, and a shape excluded here is never examined again. `pad` should be
+ * at least one grid cell of whatever raster the caller compares on.
+ */
+export function bboxNear(a: BBox, b: BBox, pad = 1): boolean {
+  if (a.w < 0 || a.h < 0 || b.w < 0 || b.h < 0) return false;
+  return (
+    a.x - pad < b.x + b.w && a.x + a.w + pad > b.x &&
+    a.y - pad < b.y + b.h && a.y + a.h + pad > b.y
+  );
+}
+
 export interface StageTransform {
   scaleX: number;
   scaleY: number;
diff --git a/frontend/src/lib/mergeSameClass.ts b/frontend/src/lib/mergeSameClass.ts
index 36ca8d9..67d2468 100644
--- a/frontend/src/lib/mergeSameClass.ts
+++ b/frontend/src/lib/mergeSameClass.ts
@@ -9,6 +9,7 @@
  */
 import type { Shape } from '@/stores/annotationStore';
 import { gridFor, fullResGridFor, rasterizeShapes, rasterizeUnion } from '@/lib/rasterize';
+import { shapeBBox, unionBBox, bboxNear, type BBox } from '@/lib/geometry';
 import { maskToPolygonsWithHoles } from '@/lib/magicwand';
 import { unionShapesToPolygons } from '@/lib/polybool';
 import { v4 as uuidv4 } from 'uuid';
@@ -27,6 +28,48 @@ function masksIntersect(a: Uint8Array, b: Uint8Array): boolean {
   return false;
 }
 
+/**
+ * True if two 0/1 masks share a set pixel, scanning only the rows/columns where
+ * both shapes' bounds overlap. Equivalent to `masksIntersect` — outside the
+ * overlap rect at least one mask is empty by construction — but it avoids walking
+ * a multi-megapixel grid to answer a question about a small region.
+ */
+function masksIntersectIn(
+  a: Uint8Array, b: Uint8Array, gw: number, gh: number, rect: BBox, scale: number,
+): boolean {
+  const x0 = Math.max(0, Math.floor(rect.x / scale));
+  const y0 = Math.max(0, Math.floor(rect.y / scale));
+  const x1 = Math.min(gw - 1, Math.ceil((rect.x + rect.w) / scale));
+  const y1 = Math.min(gh - 1, Math.ceil((rect.y + rect.h) / scale));
+  for (let y = y0; y <= y1; y++) {
+    const row = y * gw;
+    for (let x = x0; x <= x1; x++) {
+      const i = row + x;
+      if (a[i] && b[i]) return true;
+    }
+  }
+  return false;
+}
+
+/** Grow a box by `pad` on every side. */
+function inflate(b: BBox, pad: number): BBox {
+  return { x: b.x - pad, y: b.y - pad, w: b.w + pad * 2, h: b.h + pad * 2 };
+}
+
+/**
+ * Intersection of two boxes, each first grown by `pad` (empty w/h < 0 when they
+ * remain disjoint). The padding absorbs rasterization rounding: two shapes whose
+ * boxes merely abut can still land set pixels in the same grid cell.
+ */
+function bboxOverlap(a: BBox, b: BBox, pad = 0): BBox {
+  const A = inflate(a, pad), B = inflate(b, pad);
+  const x = Math.max(A.x, B.x);
+  const y = Math.max(A.y, B.y);
+  const w = Math.min(A.x + A.w, B.x + B.w) - x;
+  const h = Math.min(A.y + A.h, B.y + B.h) - y;
+  return { x, y, w, h };
+}
+
 /**
  * Expand a seed selection to the transitive closure of same-class shapes that
  * overlap it — so selecting one region in an overlapping same-class cluster
@@ -46,6 +89,12 @@ export function expandSameClassOverlap(
     if (!m) { m = rasterizeShapes([s], gw, gh, scale); maskCache.set(s.id, m); }
     return m;
   };
+  const boxCache = new Map<string, BBox>();
+  const boxOf = (s: Shape): BBox => {
+    let b = boxCache.get(s.id);
+    if (!b) { b = shapeBBox(s); boxCache.set(s.id, b); }
+    return b;
+  };
 
   const chosen = new Map<string, Shape>(seed.map((s) => [s.id, s]));
   const classes = new Set(seed.map((s) => s.classId));
@@ -56,14 +105,19 @@ export function expandSameClassOverlap(
     let changed = true;
     while (changed && candidates.length > 0) {
       changed = false;
-      // Union mask of the current cluster members for this class.
+      // Union mask + bounds of the current cluster members for this class. The
+      // bounds let a candidate be rejected without rasterizing it at all, which
+      // matters because this loop is quadratic in cluster size.
       const union = new Uint8Array(gw * gh);
       for (const m of members) {
         const mm = maskOf(m);
         for (let i = 0; i < union.length; i++) if (mm[i]) union[i] = 1;
       }
+      const unionBounds = unionBBox(members);
       for (let i = candidates.length - 1; i >= 0; i--) {
-        if (masksIntersect(maskOf(candidates[i]), union)) {
+        const cand = candidates[i];
+        if (!bboxNear(boxOf(cand), unionBounds, scale + 1)) continue;
+        if (masksIntersect(maskOf(cand), union)) {
           const c = candidates.splice(i, 1)[0];
           chosen.set(c.id, c);
           members.push(c);
@@ -103,12 +157,29 @@ export function mergeNewWithSameClass(
   }
 
   for (const [classId, group] of byClass) {
-    const existing = sliceShapes.filter((s) => s.classId === classId);
+    const groupBounds = unionBBox(group);
+    // Bounds pre-filter before ANY rasterization: a shape whose box is disjoint
+    // from the new geometry cannot share a set pixel with it, so the mask test
+    // below would always say no. Without this, committing one stroke rasterizes
+    // every same-class shape on the slice into its own full-resolution buffer
+    // (millions of cells each) purely to be told they don't touch.
+    const existing = sliceShapes.filter(
+      (s) => s.classId === classId && bboxNear(shapeBBox(s), groupBounds, scale + 1),
+    );
     if (existing.length === 0) { add.push(...group); continue; }
 
-    // Cheap mask test to decide WHICH existing shapes to merge (fast, tolerant).
+    // Cheap mask test to decide WHICH of the remaining candidates to merge.
+    // One reused scratch buffer instead of an allocation per candidate, and the
+    // comparison only walks the rows/columns where the two boxes actually overlap.
     const newMask = rasterizeUnion(group, gw, gh, scale);
-    const overlapping = existing.filter((e) => masksIntersect(rasterizeShapes([e], gw, gh, scale), newMask));
+    const scratch = new Uint8Array(gw * gh);
+    const overlapping = existing.filter((e) => {
+      const rect = bboxOverlap(shapeBBox(e), groupBounds, scale + 1);
+      if (rect.w < 0 || rect.h < 0) return false;
+      scratch.fill(0);
+      rasterizeShapes([e], gw, gh, scale, scratch);
+      return masksIntersectIn(scratch, newMask, gw, gh, rect, scale);
+    });
     if (overlapping.length === 0) { add.push(...group); continue; }
 
     // Combine via a true polygon boolean union so the existing shapes keep their
diff --git a/frontend/src/lib/perf.test.ts b/frontend/src/lib/perf.test.ts
new file mode 100644
index 0000000..fd7ee0e
--- /dev/null
+++ b/frontend/src/lib/perf.test.ts
@@ -0,0 +1,90 @@
+import { describe, it, expect, beforeEach, afterEach, vi } from 'vitest';
+import { mark, time, snapshot, resetPerf, perfEnabled, initPerf } from './perf';
+
+/** initPerf reads the flag from URL/localStorage; force it on for these tests. */
+function enable() {
+  window.localStorage.setItem('perf', '1');
+  initPerf();
+}
+
+beforeEach(() => {
+  window.localStorage.clear();
+  resetPerf();
+});
+
+afterEach(() => {
+  window.localStorage.clear();
+  initPerf();
+  resetPerf();
+});
+
+describe('perf gating', () => {
+  it('is disabled by default and records nothing', () => {
+    initPerf();
+    expect(perfEnabled()).toBe(false);
+    mark('commit', 123);
+    expect(snapshot()).toEqual([]);
+  });
+
+  it('still runs the timed function when disabled', () => {
+    initPerf();
+    const fn = vi.fn(() => 42);
+    expect(time('commit', fn)).toBe(42);
+    expect(fn).toHaveBeenCalledOnce();
+    expect(snapshot()).toEqual([]);
+  });
+
+  it('enables via localStorage', () => {
+    enable();
+    expect(perfEnabled()).toBe(true);
+    mark('commit', 5);
+    expect(snapshot()).toHaveLength(1);
+  });
+});
+
+describe('perf statistics', () => {
+  beforeEach(enable);
+
+  it('reports p50/p95 over recorded samples', () => {
+    for (let i = 1; i <= 100; i++) mark('commit', i);
+    const [stat] = snapshot();
+    expect(stat.label).toBe('commit');
+    expect(stat.count).toBe(100);
+    expect(stat.p50).toBeGreaterThanOrEqual(50);
+    expect(stat.p50).toBeLessThanOrEqual(52);
+    expect(stat.p95).toBeGreaterThanOrEqual(95);
+    expect(stat.last).toBe(100);
+  });
+
+  it('sorts slowest p95 first', () => {
+    mark('commit', 1);
+    mark('clip', 100);
+    mark('merge', 50);
+    expect(snapshot().map((s) => s.label)).toEqual(['clip', 'merge', 'commit']);
+  });
+
+  it('keeps a bounded window of samples', () => {
+    for (let i = 0; i < 500; i++) mark('commit', i);
+    expect(snapshot()[0].count).toBeLessThanOrEqual(120);
+  });
+
+  it('records the duration of a timed function and returns its value', () => {
+    const out = time('merge', () => 'result');
+    expect(out).toBe('result');
+    const [stat] = snapshot();
+    expect(stat.label).toBe('merge');
+    expect(stat.count).toBe(1);
+    expect(stat.last).toBeGreaterThanOrEqual(0);
+  });
+
+  it('records a sample even when the timed function throws', () => {
+    expect(() => time('clip', () => { throw new Error('boom'); })).toThrow('boom');
+    expect(snapshot().find((s) => s.label === 'clip')?.count).toBe(1);
+  });
+
+  it('resetPerf clears everything', () => {
+    mark('commit', 10);
+    resetPerf();
+    expect(snapshot()).toEqual([]);
+  });
+});
diff --git a/frontend/src/lib/perf.ts b/frontend/src/lib/perf.ts
new file mode 100644
index 0000000..457f1f2
--- /dev/null
+++ b/frontend/src/lib/perf.ts
@@ -0,0 +1,131 @@
+/**
+ * perf — opt-in timing instrumentation for the annotation workspace.
+ *
+ * Enabled by `?perf=1` in the URL (sticky for the session) or by setting
+ * `localStorage.perf = '1'`; disabled by `?perf=0`. When off, `time()` is a
+ * function call plus a boolean test and `mark()` returns immediately, so
+ * instrumented paths can stay in the hot code without measurable cost.
+ *
+ * Exists because the interesting slices are the user's, not any we can synthesize:
+ * the only way to confirm an optimization on a 4000² volume with a thousand shapes
+ * is to measure it there. Rolling p50/p95 rather than a single number, because the
+ * costs that hurt are the occasional long frames, not the average.
+ */
+
+const SAMPLE_LIMIT = 120;
+
+export type PerfLabel =
+  | 'commit'          // full stroke/shape commit (clip + merge + store write)
+  | 'clip'            // clipShapesToOthers
+  | 'merge'           // mergeNewWithSameClass
+  | 'layer-cache'     // Konva shapes-layer cache() rebuild
+  | 'threshold-field' // full-resolution threshold field build
+  | 'overlay-field'   // downsampled overlay field build
+  | 'overlay-paint'   // threshold overlay repaint
+  | 'magic-field'     // magic-wand gray field build
+  | 'cost-map';       // livewire cost map build
+
+interface Series {
+  samples: number[];
+  count: number;
+  /** Ring-buffer write position. */
+  pos: number;
+}
+
+const series = new Map<PerfLabel, Series>();
+let enabled = false;
+/** Bumped on every record so subscribers can re-read cheaply. */
+let version = 0;
+const listeners = new Set<() => void>();
+
+/** Read the enable flag from the URL / localStorage. Safe to call repeatedly. */
+export function initPerf(): boolean {
+  if (typeof window === 'undefined') return false;
+  try {
+    const param = new URLSearchParams(window.location.search).get('perf');
+    if (param === '1') window.localStorage.setItem('perf', '1');
+    if (param === '0') window.localStorage.removeItem('perf');
+    enabled = window.localStorage.getItem('perf') === '1';
+  } catch {
+    enabled = false; // private mode / storage denied
+  }
+  return enabled;
+}
+
+export function perfEnabled(): boolean {
+  return enabled;
+}
+
+// Notifications are throttled: samples can arrive many times per frame during a
+// stroke, and re-rendering the HUD on each one would perturb the very timings it
+// reports. Recording stays exact; only the display lags by up to NOTIFY_MS.
+const NOTIFY_MS = 250;
+let notifyTimer: ReturnType<typeof setTimeout> | null = null;
+
+function scheduleNotify(): void {
+  if (notifyTimer != null || listeners.size === 0) return;
+  notifyTimer = setTimeout(() => {
+    notifyTimer = null;
+    for (const fn of listeners) fn();
+  }, NOTIFY_MS);
+}
+
+/** Record one timing sample (ms). No-op when disabled. */
+export function mark(label: PerfLabel, ms: number): void {
+  if (!enabled) return;
+  let s = series.get(label);
+  if (!s) { s = { samples: new Array(SAMPLE_LIMIT).fill(0), count: 0, pos: 0 }; series.set(label, s); }
+  s.samples[s.pos] = ms;
+  s.pos = (s.pos + 1) % SAMPLE_LIMIT;
+  if (s.count < SAMPLE_LIMIT) s.count++;
+  version++;
+  scheduleNotify();
+}
+
+/** Time a synchronous function, recording under `label`. Returns its result. */
+export function time<T>(label: PerfLabel, fn: () => T): T {
+  if (!enabled) return fn();
+  const t0 = performance.now();
+  try {
+    return fn();
+  } finally {
+    mark(label, performance.now() - t0);
+  }
+}
+
+export interface PerfStat {
+  label: PerfLabel;
+  count: number;
+  p50: number;
+  p95: number;
+  last: number;
+}
+
+/** Snapshot of every recorded series, sorted slowest-p95 first. */
+export function snapshot(): PerfStat[] {
+  const out: PerfStat[] = [];
+  for (const [label, s] of series) {
+    if (s.count === 0) continue;
+    const vals = s.samples.slice(0, s.count).sort((a, b) => a - b);
+    const at = (q: number) => vals[Math.min(vals.length - 1, Math.floor(q * vals.length))];
+    const lastIdx = (s.pos - 1 + SAMPLE_LIMIT) % SAMPLE_LIMIT;
+    out.push({ label, count: s.count, p50: at(0.5), p95: at(0.95), last: s.samples[lastIdx] });
+  }
+  return out.sort((a, b) => b.p95 - a.p95);
+}
+
+/** Subscribe to new samples (for useSyncExternalStore). */
+export function subscribe(fn: () => void): () => void {
+  listeners.add(fn);
+  return () => { listeners.delete(fn); };
+}
+
+export function getVersion(): number {
+  return version;
+}
+
+export function resetPerf(): void {
+  series.clear();
+  version++;
+  for (const fn of listeners) fn();
+}
diff --git a/frontend/src/lib/polybool.ts b/frontend/src/lib/polybool.ts
index e506f12..ae21bb3 100644
--- a/frontend/src/lib/polybool.ts
+++ b/frontend/src/lib/polybool.ts
@@ -131,12 +131,60 @@ export function regionsToMultiPolygon(
 
 /** Boolean-union a set of shapes into one MultiPolygon (empty if none / on failure). */
 export function unionShapesToMultiPolygon(shapes: Shape[], width: number, height: number): MultiPolygon {
-  const geoms = shapes.map((s) => shapeToMultiPolygon(s, width, height)).filter((g) => g.length > 0);
-  if (geoms.length === 0) return [];
+  return unionShapesChecked(shapes, width, height).mp;
+}
+
+/**
+ * Union with an explicit success flag.
+ *
+ * The plain version returns `[]` both when there is nothing to union AND when the
+ * boolean op failed — and callers gate on `mp.length`, so a failure reads exactly
+ * like "nothing to clip against" and clipping is silently skipped. That is how a
+ * single awkward polygon (a speckled Threshold Brush region, say) can disable
+ * clip-to-other-classes for a whole slice, with no error anywhere.
+ *
+ * This version unions incrementally so one unusable shape only costs that shape,
+ * and reports `ok: false` when anything was dropped, letting callers fall back to
+ * the mask-based clip instead of quietly leaving the new annotation unclipped.
+ */
+export function unionShapesChecked(
+  shapes: Shape[],
+  width: number,
+  height: number,
+): { mp: MultiPolygon; ok: boolean } {
+  const geoms: MultiPolygon[] = [];
+  let ok = true;
+  for (const s of shapes) {
+    try {
+      const g = shapeToMultiPolygon(s, width, height);
+      if (g.length > 0) geoms.push(g);
+    } catch {
+      ok = false; // this shape can't be expressed as geometry at all
+    }
+  }
+  if (geoms.length === 0) return { mp: [], ok };
+  if (geoms.length === 1) return { mp: geoms[0], ok };
+
+  // Fast path: one variadic union. polygon-clipping sweeps all inputs together,
+  // which is dramatically cheaper than folding them in pairwise — the pairwise
+  // version re-sweeps a growing accumulator once per shape, so a slice with a few
+  // hundred speckled regions turns a single sweep into hundreds of them.
   try {
-    return geoms.length === 1 ? geoms[0] : polygonClipping.union(geoms[0], ...geoms.slice(1));
+    return { mp: polygonClipping.union(geoms[0], ...geoms.slice(1)), ok };
   } catch {
-    return [];
+    // Something in the set is unusable. Fold in one at a time so we lose only the
+    // offending shape(s) rather than the whole union, and flag it so callers can
+    // choose the mask path instead of clipping against an under-covering union.
+    let acc: MultiPolygon = [];
+    for (const g of geoms) {
+      if (acc.length === 0) { acc = g; continue; }
+      try {
+        acc = polygonClipping.union(acc, g);
+      } catch {
+        ok = false; // keep what we have; this one is dropped
+      }
+    }
+    return { mp: acc, ok };
   }
 }
 
diff --git a/frontend/src/lib/rasterize.ts b/frontend/src/lib/rasterize.ts
index 8457376..99ea545 100644
--- a/frontend/src/lib/rasterize.ts
+++ b/frontend/src/lib/rasterize.ts
@@ -51,9 +51,15 @@ export function fullResGridFor(width: number, height: number, upscale = 1): Mask
  * Rasterize *shapes* into a `gw × gh` binary mask (Uint8Array of 0/1). Paint
  * strokes and shape bodies set 1; brush erase strokes and vector `erased`
  * carve-outs set 0, applied per-shape so a later shape can repaint.
+ *
+ * Pass `out` to render into an existing buffer instead of allocating one. Callers
+ * that rasterize many shapes in a loop (overlap tests) reuse a single scratch
+ * array this way — at full resolution each allocation is multiple megabytes, and
+ * the garbage adds up fast. `out` must be `gw*gh` long and is NOT cleared: clear
+ * it yourself when reusing, or leave it to accumulate a union deliberately.
  */
-export function rasterizeShapes(shapes: Shape[], gw: number, gh: number, scale = 1): Uint8Array {
-  const mask = new Uint8Array(gw * gh);
+export function rasterizeShapes(shapes: Shape[], gw: number, gh: number, scale = 1, out?: Uint8Array): Uint8Array {
+  const mask = out ?? new Uint8Array(gw * gh);
   const s = scale || 1;
   for (const shape of shapes) {
     if (shape.kind === 'polygon') {
diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx
index 7b498a9..dac539b 100644
--- a/frontend/src/main.tsx
+++ b/frontend/src/main.tsx
@@ -3,10 +3,15 @@ import { createRoot } from 'react-dom/client';
 import { BrowserRouter } from 'react-router';
 import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
 import App from './app/App';
+import { installImageSliceGc } from './hooks/useImageSlice';
 import './app/index.css';
 
 const queryClient = new QueryClient();
 
+// Image slices are cached as blob object URLs, which the browser keeps alive until
+// explicitly revoked. Release them as their queries leave the cache.
+installImageSliceGc(queryClient);
+
 createRoot(document.getElementById('root')!).render(
   <StrictMode>
     <BrowserRouter>

From df80421f230cd309dc8d6e6aae75489d40d7bbfd Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Tue, 25 Aug 2026 12:40:27 -0700
Subject: [PATCH 03/38] Supporting Zarr loading

---
 backend/annotation_server.py                  |  74 +++-
 backend/arrays.py                             | 171 +++++++-
 backend/schemas.py                            |  39 ++
 backend/tests/test_zarr_source.py             | 228 ++++++++++
 backend/zarr_source.py                        | 412 ++++++++++++++++++
 frontend/src/app/pages/ConnectPage.tsx        |  35 +-
 frontend/src/components/Ingest/ZarrLoader.tsx | 314 +++++++++++++
 .../annotate/SliceNavigator/index.tsx         |  15 +
 frontend/src/hooks/useOpenInAnnotate.ts       |   9 +
 frontend/src/stores/datasetStore.ts           |  12 +
 10 files changed, 1297 insertions(+), 12 deletions(-)
 create mode 100644 backend/tests/test_zarr_source.py
 create mode 100644 backend/zarr_source.py
 create mode 100644 frontend/src/components/Ingest/ZarrLoader.tsx

diff --git a/backend/annotation_server.py b/backend/annotation_server.py
index 66591a7..91ce6ba 100644
--- a/backend/annotation_server.py
+++ b/backend/annotation_server.py
@@ -42,6 +42,7 @@
 import guides as guides_mod
 import images as images_mod
 import ingest as ingest_mod
+import zarr_source
 import local_fs
 from browse_helpers import (
     _SINGLE_VALUE_FACET_RAW_KEYS,
@@ -68,6 +69,7 @@
     GuidePayload,
     ImageMeta,
     IngestPreflightRequest,
+    ZarrRegisterRequest,
     MeasureRequest,
     SaveVersionRequest,
 )
@@ -556,7 +558,8 @@ async def image_meta(
     """Return shape / dtype metadata for an image source."""
     def _run() -> ImageMeta:
         node = arrays_mod.resolve_array(source, kind, server_uri, root)
-        meta = arrays_mod.array_shape_meta(node)
+        pyramid = arrays_mod.pyramid_info(source, kind, server_uri, root)
+        meta = arrays_mod.array_shape_meta(node, pyramid)
         sl = arrays_mod.read_slice(node, meta, 0)
         flat = sl.ravel().astype(float)
         return ImageMeta(
@@ -567,6 +570,13 @@ def _run() -> ImageMeta:
             is_rgb=meta["is_rgb"],
             value_range=[float(flat.min()), float(flat.max())],
             keywords=arrays_mod.node_keywords(node),
+            level_key=meta.get("level_key"),
+            level_index=meta.get("level_index"),
+            level_count=meta.get("level_count"),
+            level_height=meta.get("level_height"),
+            level_width=meta.get("level_width"),
+            level_n_slices=meta.get("level_n_slices"),
+            z_downsample=meta.get("z_downsample"),
         )
 
     try:
@@ -602,10 +612,19 @@ async def image_slice(
 
     def _run() -> bytes:
         node = arrays_mod.resolve_array(source, kind, server_uri, root)
-        meta = arrays_mod.array_shape_meta(node)
+        pyramid = arrays_mod.pyramid_info(source, kind, server_uri, root)
+        meta = arrays_mod.array_shape_meta(node, pyramid)
         sl = arrays_mod.read_slice(node, meta, slice_index)
         global_range = None
         if norm == "global":
+            # NB: deriving this from the pyramid's coarsest level was tried and
+            # reverted. It is ~8x faster, but those levels are built by AVERAGING,
+            # which pulls the extremes in hard — on the reference volume the range
+            # came back (-20.7, 18.0) against (-73.0, 71.3) at full resolution.
+            # Since this range IS the contrast window, the cheap version visibly
+            # clips the image. The full-resolution sampler decimates instead of
+            # averaging, so it keeps the extremes; it costs ~3s once per volume
+            # and is then cached.
             global_range = images_mod._sample_global_stats(node, meta)
         rgb = images_mod.render_slice(sl, opts, global_range)
         return images_mod.encode_png(rgb)
@@ -1238,6 +1257,57 @@ async def ingest_status(job_id: str) -> dict:
     return job
 
 
+@app.get("/api/zarr/inspect")
+async def zarr_inspect(
+    path: str = Query(..., description="Absolute path to a .zarr directory on the server"),
+) -> dict:
+    """Describe a Zarr store's resolution pyramid without registering it.
+
+    Lets the Connect page show what was found — dimensions, dtype, voxel size and
+    the available levels — before the user commits to loading it.
+
+    Raises:
+        HTTPException: 4xx from :func:`zarr_source.inspect_zarr` with a
+            user-facing message (missing path, zipped archive, empty group…).
+    """
+    return await asyncio.to_thread(zarr_source.inspect_zarr, path)
+
+
+@app.post("/api/zarr/preflight")
+async def zarr_preflight(req: ZarrRegisterRequest) -> dict:
+    """Report whether loading this Zarr would collide with an existing node.
+
+    Distinguishes a previous Zarr registration (safe to replace — only catalog
+    rows are dropped) from internally-managed data such as an uploaded image
+    stack, where replacing would delete the files themselves.
+    """
+    return await asyncio.to_thread(
+        zarr_source.preflight_zarr, req.server_uri, req.path, req.container_path
+    )
+
+
+@app.post("/api/zarr/register")
+async def zarr_register(req: ZarrRegisterRequest) -> dict:
+    """Register an on-disk Zarr volume with Tiled, copying no data.
+
+    Unlike ``/api/ingest/upload`` this needs no background job: registration
+    writes catalog rows, not pixels, so it returns in well under a second even
+    for a 56 GB store.
+    """
+    if req.on_conflict not in ingest_mod.ON_CONFLICT_MODES:
+        raise HTTPException(
+            400, f"on_conflict must be one of {sorted(ingest_mod.ON_CONFLICT_MODES)}"
+        )
+    return await asyncio.to_thread(
+        zarr_source.register_zarr,
+        req.server_uri,
+        req.path,
+        req.container_path,
+        req.description,
+        req.on_conflict,
+    )
+
+
 @app.get("/health")
 async def health() -> dict[str, str]:
     return {"status": "ok"}
diff --git a/backend/arrays.py b/backend/arrays.py
index ac821ad..4330891 100644
--- a/backend/arrays.py
+++ b/backend/arrays.py
@@ -97,6 +97,44 @@ def _is_container_node(node: Any) -> bool:
     return str(getattr(sf, "value", sf)) == "container"
 
 
+def multiscale_levels(node: Any) -> list[str] | None:
+    """Ordered ``scale*`` child keys of a multiscale (pyramid) container.
+
+    Registered Zarr volumes are OME-NGFF groups: ``scale0/image``,
+    ``scale1/image``, … Returns the level keys finest-first, or None when *node*
+    is not a pyramid.
+    """
+    if not _is_container_node(node):
+        return None
+    try:
+        keys = [k for k in node if str(k).startswith("scale")]
+    except Exception:  # noqa: BLE001 — not enumerable → not a pyramid
+        return None
+    if len(keys) < 2:
+        return None
+
+    def _index(key: str) -> int:
+        digits = "".join(ch for ch in str(key) if ch.isdigit())
+        return int(digits) if digits else 0
+
+    return sorted(keys, key=_index)
+
+
+def _level_array(node: Any, level_key: str) -> Any | None:
+    """The array inside one pyramid level (``scaleN`` wraps a single array)."""
+    try:
+        level = node[level_key]
+    except Exception:  # noqa: BLE001
+        return None
+    if not _is_container_node(level):
+        return level
+    try:
+        first = next(iter(level))
+    except StopIteration:
+        return None
+    return level[first]
+
+
 def _descend_to_stack(node: Any, max_depth: int = 8) -> Any:
     """Resolve a Browse selection to the array/stack it should open.
 
@@ -105,7 +143,19 @@ def _descend_to_stack(node: Any, max_depth: int = 8) -> Any:
     itself a container) but STOPS at a container whose children are arrays —
     returning that container so it can be treated as a slice stack (one array
     node per slice). Array nodes (and non-Tiled inputs) are returned unchanged.
+
+    A multiscale Zarr volume is handled first and explicitly. The generic walk
+    below would descend into ``scale0``, find its single ``image`` child, and
+    return ``scale0`` as a one-element "stack" — presenting a whole 3-D volume as
+    a single slice. Selecting such a dataset resolves to the FINEST level's
+    array, which is the full-resolution volume the user expects to annotate.
     """
+    levels = multiscale_levels(node)
+    if levels:
+        array = _level_array(node, levels[0])
+        if array is not None:
+            return array
+
     depth = 0
     while _is_container_node(node) and depth < max_depth:
         try:
@@ -165,11 +215,98 @@ def _from_meta(meta: Any) -> list[str] | None:
     return []
 
 
-def array_shape_meta(node: Any) -> dict[str, Any]:
+def pyramid_info(
+    source: str,
+    kind: str,
+    server_uri: str | None = None,
+    root: str | None = None,
+) -> dict[str, Any] | None:
+    """Describe the pyramid *source* belongs to, if it addresses one level.
+
+    Annotations are stored in FULL-RESOLUTION coordinates regardless of which
+    level is being viewed, so the caller needs the finest level's shape even when
+    a coarse level is open. Returns ``None`` for anything that is not a level of
+    a multiscale volume.
+
+    Returns:
+        ``{"level_key", "level_index", "level_count", "full_shape",
+        "z_downsample"}`` where ``z_downsample`` is finest-z / this-level-z — the
+        factor mapping a full-resolution slice index onto this level.
+    """
+    if kind != "tiled":
+        return None
+    parts = [p for p in source.strip("/").split("/") if p]
+    # A level is addressed as <volume>/scaleN or <volume>/scaleN/<array>.
+    for trim in (1, 2):
+        if len(parts) <= trim:
+            continue
+        level_key = parts[-trim]
+        if not str(level_key).startswith("scale"):
+            continue
+        try:
+            parent = resolve_container(("/".join(parts[:-trim])), kind, server_uri, root)
+        except HTTPException:
+            return None
+        levels = multiscale_levels(parent)
+        if not levels or level_key not in levels:
+            return None
+        finest = _level_array(parent, levels[0])
+        current = _level_array(parent, level_key)
+        if finest is None or current is None:
+            return None
+        full_shape = [int(v) for v in finest.shape]
+        cur_shape = [int(v) for v in current.shape]
+        if len(full_shape) != 3 or len(cur_shape) != 3:
+            return None
+        return {
+            "level_key": level_key,
+            "level_index": levels.index(level_key),
+            "level_count": len(levels),
+            "full_shape": full_shape,
+            # Ratio, not an integer factor: real pyramids are not always clean
+            # powers of two in z (690 -> 172 is 4.0116), so rounding a fixed
+            # factor would drift by whole slices at the end of the volume.
+            "z_downsample": (full_shape[0] / cur_shape[0]) if cur_shape[0] else 1.0,
+        }
+    return None
+
+
+def resolve_container(
+    source: str,
+    kind: str,
+    server_uri: str | None = None,
+    root: str | None = None,
+) -> Any:
+    """Resolve *source* to its node WITHOUT descending to an array/stack.
+
+    :func:`resolve_array` deliberately descends (a Browse selection should open
+    the data); this is for callers that need the container itself, such as
+    reading a volume's pyramid structure.
+    """
+    if kind != "tiled":
+        raise HTTPException(422, "Only tiled sources have containers")
+    client = get_tiled_client(server_uri, api_key_for_uri(server_uri))
+    node: Any = client
+    for part in source.strip("/").split("/"):
+        if not part:
+            continue
+        try:
+            node = node[part]
+        except KeyError as exc:
+            raise HTTPException(404, f"Tiled path not found: {source!r}") from exc
+    return node
+
+
+def array_shape_meta(node: Any, pyramid: dict[str, Any] | None = None) -> dict[str, Any]:
     """Return shape-dispatch metadata for *node*.
 
     Args:
         node: A Tiled array node or NumPy array.
+        pyramid: Optional :func:`pyramid_info` result. When given, the reported
+            ``height``/``width``/``n_slices`` describe the FINEST level rather
+            than *node* — so annotation coordinates are full-resolution whichever
+            level is displayed — and ``z_downsample`` tells :func:`read_slice`
+            how to map a full-resolution slice index onto this level.
 
     Returns:
         Dict with keys: ``n_slices``, ``height``, ``width``, ``dtype``,
@@ -211,7 +348,7 @@ def array_shape_meta(node: Any) -> dict[str, Any]:
         }
     if len(shape) == 3:
         n, h, w = shape
-        return {
+        meta = {
             "n_slices": n,
             "height": h,
             "width": w,
@@ -219,6 +356,24 @@ def array_shape_meta(node: Any) -> dict[str, Any]:
             "is_rgb": False,
             "shape_kind": "NHW",
         }
+        if pyramid:
+            full_z, full_h, full_w = pyramid["full_shape"]
+            # Report the finest level's geometry. The canvas draws the (smaller)
+            # level image at these dimensions, so every annotation coordinate is
+            # full-resolution by construction — no rescaling on save or load.
+            meta.update(
+                n_slices=full_z,
+                height=full_h,
+                width=full_w,
+                z_downsample=pyramid["z_downsample"],
+                level_key=pyramid["level_key"],
+                level_index=pyramid["level_index"],
+                level_count=pyramid["level_count"],
+                level_height=h,
+                level_width=w,
+                level_n_slices=n,
+            )
+        return meta
     if len(shape) == 4 and shape[3] in (3, 4):
         n, h, w = shape[:3]
         return {
@@ -286,9 +441,15 @@ def read_slice(node: Any, meta: dict[str, Any], idx: int) -> np.ndarray:
         return np.asarray(node)
     if kind == "HWC":
         return np.asarray(node)
-    if kind == "NHW":
-        return np.asarray(node[idx])
-    if kind == "NHWC":
+    if kind in ("NHW", "NHWC"):
+        # `idx` is a FULL-RESOLUTION slice index when a pyramid level is open
+        # (see `array_shape_meta`); map it onto this level's own z range.
+        z_down = float(meta.get("z_downsample") or 1.0)
+        if z_down != 1.0:
+            limit = int(meta.get("level_n_slices") or 0)
+            idx = int(round(idx / z_down))
+            if limit:
+                idx = max(0, min(limit - 1, idx))
         return np.asarray(node[idx])
     if kind == "STACK":
         keys = meta.get("keys") or _stack_keys(node)
diff --git a/backend/schemas.py b/backend/schemas.py
index eaf50ea..68969fe 100644
--- a/backend/schemas.py
+++ b/backend/schemas.py
@@ -281,6 +281,28 @@ class IngestPreflightRequest(BaseModel):
     server_uri: str | None = None
 
 
+class ZarrRegisterRequest(BaseModel):
+    """Request body for registering an on-disk Zarr volume with Tiled.
+
+    No bytes are uploaded: the volume stays where it is and Tiled reads it in
+    place, so this carries a server-side path rather than file content.
+
+    Attributes:
+        path: Absolute path to the ``.zarr`` directory on the server.
+        container_path: Target container, e.g. ``browse``.
+        description: Optional keyword(s) stored on the node (same treatment as
+            the drag-and-drop ingest, so the volume is filterable in Browse).
+        on_conflict: ``"fail"``, ``"replace"`` or ``"skip"`` when the key exists.
+        server_uri: Target Tiled server URI; ``None`` uses the default server.
+    """
+
+    path: str
+    container_path: str = "browse"
+    description: str = ""
+    on_conflict: str = "fail"
+    server_uri: str | None = None
+
+
 class GuideClass(BaseModel):
     """One class entry in an annotation guide.
 
@@ -321,6 +343,16 @@ class ImageMeta(BaseModel):
         value_range: ``[min, max]`` of the first slice.
         keywords: Dataset tags stored at ingest; each is pre-created as an
             annotation class in the Annotate tab.
+        level_key: For a multiscale Zarr volume, the pyramid level being read
+            (e.g. ``"scale2"``). ``height``/``width``/``n_slices`` always
+            describe the FINEST level, since annotations are stored in
+            full-resolution coordinates; these fields describe what is actually
+            being displayed underneath them.
+        level_index: Index of that level, finest = 0.
+        level_count: Number of levels in the pyramid.
+        level_height / level_width / level_n_slices: The open level's own shape.
+        z_downsample: Finest-z / level-z. When > 1 the level can only address
+            every f-th full-resolution slice.
     """
 
     n_slices: int
@@ -330,3 +362,10 @@ class ImageMeta(BaseModel):
     is_rgb: bool
     value_range: list[float]
     keywords: list[str] = Field(default_factory=list)
+    level_key: str | None = None
+    level_index: int | None = None
+    level_count: int | None = None
+    level_height: int | None = None
+    level_width: int | None = None
+    level_n_slices: int | None = None
+    z_downsample: float | None = None
diff --git a/backend/tests/test_zarr_source.py b/backend/tests/test_zarr_source.py
new file mode 100644
index 0000000..42a7f23
--- /dev/null
+++ b/backend/tests/test_zarr_source.py
@@ -0,0 +1,228 @@
+"""Tests for on-disk Zarr registration and multiscale slice resolution.
+
+Fixtures build tiny multiscale stores on the fly, so these run anywhere without
+the multi-GB reference data.
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+from fastapi import HTTPException
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import arrays as arrays_mod  # noqa: E402
+import zarr_source  # noqa: E402
+
+
+def _make_multiscale(root: Path, shapes: list[tuple[int, int, int]]) -> Path:
+    """Write a v2 multiscale store shaped like the reference data.
+
+    Layout mirrors the real volumes: ``scaleN/image`` arrays plus a
+    ``multiscales`` attribute naming them, so path discovery is exercised rather
+    than shape guessing.
+    """
+    import zarr
+
+    store = root / "vol.zarr"
+    group = zarr.open_group(str(store), mode="w")
+    for i, shape in enumerate(shapes):
+        sub = group.create_group(f"scale{i}")
+        arr = sub.create_array("image", shape=shape, dtype="float32", chunks=(2, 8, 8))
+        arr[:] = np.linspace(0, 1, int(np.prod(shape)), dtype="float32").reshape(shape)
+
+    base = shapes[0]
+    datasets = []
+    for i, shape in enumerate(shapes):
+        factor = base[1] / shape[1]
+        datasets.append(
+            {
+                "path": f"scale{i}/image",
+                "coordinateTransformations": [
+                    {"type": "scale", "scale": [0.5 * factor, 0.5 * factor, 0.5 * factor]}
+                ],
+            }
+        )
+    (store / ".zattrs").write_text(
+        json.dumps(
+            {
+                "multiscales": [
+                    {
+                        "axes": [
+                            {"name": "z", "type": "space", "unit": "micrometer"},
+                            {"name": "y", "type": "space", "unit": "micrometer"},
+                            {"name": "x", "type": "space", "unit": "micrometer"},
+                        ],
+                        "datasets": datasets,
+                    }
+                ]
+            }
+        )
+    )
+    return store
+
+
+@pytest.fixture
+def pyramid(tmp_path: Path) -> Path:
+    # Deliberately NOT a clean power of two in z (9 -> 5 -> 3), matching the real
+    # data where 690 -> 172 gives a factor of 4.0116.
+    return _make_multiscale(tmp_path, [(9, 32, 32), (5, 16, 16), (3, 8, 8)])
+
+
+class TestInspect:
+    def test_lists_levels_finest_first(self, pyramid: Path) -> None:
+        info = zarr_source.inspect_zarr(str(pyramid))
+        assert [lv["path"] for lv in info["levels"]] == [
+            "scale0/image",
+            "scale1/image",
+            "scale2/image",
+        ]
+        assert info["full_shape"] == [9, 32, 32]
+        assert info["dtype"] == "float32"
+
+    def test_reports_downsample_and_voxel_size(self, pyramid: Path) -> None:
+        info = zarr_source.inspect_zarr(str(pyramid))
+        assert info["levels"][0]["downsample"] == [1.0, 1.0, 1.0]
+        assert info["levels"][1]["downsample"][1] == 2.0
+        assert info["voxel_size"] == [0.5, 0.5, 0.5]
+        assert info["voxel_unit"] == "micrometer"
+
+    def test_rejects_missing_path(self) -> None:
+        with pytest.raises(HTTPException) as exc:
+            zarr_source.inspect_zarr("/nope/missing.zarr")
+        assert exc.value.status_code == 404
+
+    def test_rejects_relative_path(self) -> None:
+        with pytest.raises(HTTPException) as exc:
+            zarr_source.inspect_zarr("relative/vol.zarr")
+        assert exc.value.status_code == 400
+
+    def test_rejects_non_zarr_directory(self, tmp_path: Path) -> None:
+        plain = tmp_path / "images"
+        plain.mkdir()
+        with pytest.raises(HTTPException) as exc:
+            zarr_source.inspect_zarr(str(plain))
+        assert exc.value.status_code == 422
+        assert "not a Zarr store" in exc.value.detail
+
+    def test_rejects_zip_with_actionable_message(self, tmp_path: Path) -> None:
+        archive = tmp_path / "vol.zarr.zip"
+        archive.write_bytes(b"PK\x03\x04")
+        with pytest.raises(HTTPException) as exc:
+            zarr_source.inspect_zarr(str(archive))
+        assert exc.value.status_code == 422
+        assert "unzip" in exc.value.detail.lower()
+
+    def test_rejects_group_with_no_arrays(self, tmp_path: Path) -> None:
+        import zarr
+
+        empty = tmp_path / "empty.zarr"
+        zarr.open_group(str(empty), mode="w")
+        with pytest.raises(HTTPException) as exc:
+            zarr_source.inspect_zarr(str(empty))
+        assert exc.value.status_code == 422
+        assert "no 3-D arrays" in exc.value.detail
+
+    def test_discovers_arrays_without_multiscales_metadata(self, tmp_path: Path) -> None:
+        import zarr
+
+        store = tmp_path / "bare.zarr"
+        group = zarr.open_group(str(store), mode="w")
+        group.create_array("volume", shape=(4, 8, 8), dtype="float32", chunks=(2, 4, 4))
+        info = zarr_source.inspect_zarr(str(store))
+        assert [lv["path"] for lv in info["levels"]] == ["volume"]
+        assert info["full_shape"] == [4, 8, 8]
+
+
+class TestRegisteredKey:
+    def test_strips_the_zarr_extension(self, pyramid: Path) -> None:
+        # Tiled keys on the stem; the collision check must use the same key or it
+        # silently misses an existing node and fails deep inside registration.
+        assert zarr_source.registered_key(pyramid) == "vol"
+
+
+class _FakeArray:
+    def __init__(self, shape: tuple[int, ...]) -> None:
+        self.shape = shape
+        self.dtype = np.dtype("float32")
+        self.structure_family = "array"
+
+    def __getitem__(self, idx: int) -> np.ndarray:
+        assert 0 <= idx < self.shape[0], f"slice {idx} out of range for {self.shape}"
+        return np.zeros(self.shape[1:], dtype="float32")
+
+
+class _FakeContainer(dict):
+    structure_family = "container"
+
+    def __init__(self, children: dict) -> None:
+        super().__init__(children)
+
+
+def _fake_pyramid() -> _FakeContainer:
+    """Container mimicking a registered volume: scaleN -> {image: array}."""
+    return _FakeContainer(
+        {
+            "scale0": _FakeContainer({"image": _FakeArray((9, 32, 32))}),
+            "scale1": _FakeContainer({"image": _FakeArray((5, 16, 16))}),
+            "scale2": _FakeContainer({"image": _FakeArray((3, 8, 8))}),
+        }
+    )
+
+
+class TestMultiscaleResolution:
+    def test_detects_pyramid_levels_in_order(self) -> None:
+        assert arrays_mod.multiscale_levels(_fake_pyramid()) == ["scale0", "scale1", "scale2"]
+
+    def test_ignores_non_pyramid_containers(self) -> None:
+        stack = _FakeContainer({"a": _FakeArray((4, 4)), "b": _FakeArray((4, 4))})
+        assert arrays_mod.multiscale_levels(stack) is None
+
+    def test_selecting_the_volume_opens_the_finest_array(self) -> None:
+        # The generic walk would return `scale0` as a one-element stack, showing a
+        # whole 3-D volume as a single slice.
+        node = arrays_mod._descend_to_stack(_fake_pyramid())
+        assert isinstance(node, _FakeArray)
+        assert node.shape == (9, 32, 32)
+
+
+class TestFullResolutionCoordinates:
+    def _meta(self, level_shape: tuple[int, int, int], full: list[int]) -> dict:
+        pyramid = {
+            "level_key": "scale2",
+            "level_index": 2,
+            "level_count": 3,
+            "full_shape": full,
+            "z_downsample": full[0] / level_shape[0],
+        }
+        return arrays_mod.array_shape_meta(_FakeArray(level_shape), pyramid)
+
+    def test_reports_finest_geometry_for_a_coarse_level(self) -> None:
+        meta = self._meta((3, 8, 8), [9, 32, 32])
+        # Annotation coordinates must be full-resolution whichever level is shown.
+        assert (meta["n_slices"], meta["height"], meta["width"]) == (9, 32, 32)
+        assert (meta["level_n_slices"], meta["level_height"]) == (3, 8)
+
+    def test_maps_full_resolution_index_onto_the_level(self) -> None:
+        level = _FakeArray((3, 8, 8))
+        meta = self._meta((3, 8, 8), [9, 32, 32])
+        # Every full-res index must land in range — _FakeArray asserts otherwise.
+        for idx in range(9):
+            assert arrays_mod.read_slice(level, meta, idx).shape == (8, 8)
+
+    def test_handles_non_power_of_two_z_ratios(self) -> None:
+        # 690 -> 172 is 4.0116; a fixed integer factor would run off the end.
+        level = _FakeArray((172, 160, 160))
+        meta = self._meta((172, 160, 160), [690, 2560, 2560])
+        assert arrays_mod.read_slice(level, meta, 689).shape == (160, 160)
+        assert meta["width"] == 2560
+
+    def test_leaves_plain_volumes_untouched(self) -> None:
+        meta = arrays_mod.array_shape_meta(_FakeArray((7, 16, 16)))
+        assert (meta["n_slices"], meta["height"], meta["width"]) == (7, 16, 16)
+        assert "z_downsample" not in meta
diff --git a/backend/zarr_source.py b/backend/zarr_source.py
new file mode 100644
index 0000000..54277eb
--- /dev/null
+++ b/backend/zarr_source.py
@@ -0,0 +1,412 @@
+"""Register on-disk Zarr volumes with Tiled, without copying any data.
+
+The drag-and-drop ingest path (:mod:`ingest`) streams image files through the
+browser and writes each one into Tiled. That cannot express a tomography volume:
+these datasets are 7-56 GB, so the bytes must stay where they are.
+
+Tiled can serve a Zarr store in place — ``.zarr`` maps to ``application/x-zarr``
+and ``ZarrGroupAdapter`` reads chunks lazily off disk — so "loading" a volume is
+really just *registering* a path. A full-resolution 2560x2560 slice of a
+(690, 2560, 2560) float32 store comes back over Tiled's HTTP layer in under
+0.2 s, which is comfortably interactive.
+
+Layout
+------
+The supported stores are OME-NGFF-style multiscale groups::
+
+    <name>.zarr/
+        .zattrs          # "multiscales" -> datasets[].path + coordinateTransformations
+        scale0/image     # (z, y, x) float32, full resolution
+        scale1/image     # downsampled
+        ...
+
+After registration the Tiled path of one level is
+``<container>/<name>/scale0/image``, which :mod:`arrays` resolves as an
+``(N, H, W)`` slice stack with no further plumbing.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+from fastapi import HTTPException
+
+import ingest as ingest_mod
+from tiled_clients import api_key_for_uri, get_tiled_client
+
+logger = logging.getLogger("zarr_source")
+
+# Group-level metadata files that mark a directory as a Zarr store. v2 uses
+# `.zgroup`/`.zarray`; v3 uses a single `zarr.json`.
+_ZARR_MARKERS: tuple[str, ...] = (".zgroup", ".zarray", "zarr.json")
+
+
+def _is_zarr_dir(path: Path) -> bool:
+    """True if *path* looks like the root of a Zarr store."""
+    return path.is_dir() and any((path / marker).exists() for marker in _ZARR_MARKERS)
+
+
+def _resolve_path(raw: str) -> Path:
+    """Expand and validate a user-supplied absolute path to a Zarr store.
+
+    Raises:
+        HTTPException: 400/404/422 with a message aimed at the user, so the UI
+            never has to surface a traceback.
+    """
+    if not (raw or "").strip():
+        raise HTTPException(400, "Enter the path to a .zarr directory.")
+    path = Path(raw).expanduser()
+    if not path.is_absolute():
+        raise HTTPException(400, f"Path must be absolute: {raw!r}")
+    if not path.exists():
+        raise HTTPException(404, f"No such path: {path}")
+    if path.is_file():
+        # The common near-miss: a zipped store. Zarr can read these via ZipStore,
+        # but Tiled registers a directory asset, so say so plainly.
+        if path.suffix.lower() == ".zip":
+            raise HTTPException(
+                422,
+                "Zipped Zarr archives are not supported — unzip it first and "
+                "point at the resulting .zarr directory.",
+            )
+        raise HTTPException(422, f"Not a directory: {path}")
+    if not _is_zarr_dir(path):
+        raise HTTPException(
+            422,
+            f"{path.name!r} is not a Zarr store (no .zgroup/.zarray/zarr.json "
+            "at its root).",
+        )
+    return path.resolve()
+
+
+def _multiscale_datasets(path: Path) -> list[dict[str, Any]] | None:
+    """Read OME-NGFF ``multiscales`` from ``.zattrs``, if present.
+
+    Returns the ``datasets`` list (each with ``path`` and
+    ``coordinateTransformations``), or None when the store is not multiscale.
+    """
+    attrs_file = path / ".zattrs"
+    if not attrs_file.exists():
+        return None
+    try:
+        attrs = json.loads(attrs_file.read_text())
+    except (OSError, json.JSONDecodeError):
+        return None
+    multiscales = attrs.get("multiscales")
+    if not isinstance(multiscales, list) or not multiscales:
+        return None
+    datasets = multiscales[0].get("datasets")
+    return datasets if isinstance(datasets, list) and datasets else None
+
+
+def _scale_vector(dataset: dict[str, Any]) -> list[float] | None:
+    """Pull the ``scale`` transform (voxel size per axis) out of a dataset entry."""
+    for transform in dataset.get("coordinateTransformations") or []:
+        if transform.get("type") == "scale":
+            scale = transform.get("scale")
+            if isinstance(scale, list) and len(scale) == 3:
+                return [float(v) for v in scale]
+    return None
+
+
+def _walk_arrays(group: Any, prefix: str = "", depth: int = 0) -> list[tuple[str, Any]]:
+    """Depth-first list of ``(path, array)`` for every 3-D array in *group*."""
+    import zarr
+
+    found: list[tuple[str, Any]] = []
+    if depth > 3:
+        return found
+    for key in group.keys():
+        try:
+            child = group[key]
+        except Exception:  # noqa: BLE001 — a broken child shouldn't sink the scan
+            continue
+        child_path = f"{prefix}/{key}" if prefix else key
+        if isinstance(child, zarr.Array):
+            if child.ndim == 3:
+                found.append((child_path, child))
+        else:
+            found.extend(_walk_arrays(child, child_path, depth + 1))
+    return found
+
+
+def inspect_zarr(raw_path: str) -> dict[str, Any]:
+    """Describe a Zarr store's resolution pyramid without registering anything.
+
+    Args:
+        raw_path: Absolute path to a ``.zarr`` directory on the server.
+
+    Returns:
+        Dict with ``name``, ``levels`` (finest first) and voxel metadata. Each
+        level carries the array's Tiled sub-path, shape, dtype, and its
+        downsample factor relative to the finest level — the factor the UI needs
+        to explain that a coarse level addresses only every f-th slice.
+
+    Raises:
+        HTTPException: 4xx with a user-facing message for anything unusable.
+    """
+    import zarr
+
+    path = _resolve_path(raw_path)
+    try:
+        group = zarr.open_group(str(path), mode="r")
+    except Exception as exc:  # noqa: BLE001 — surface as a clean 422
+        raise HTTPException(422, f"Could not open {path.name!r} as Zarr: {exc}") from exc
+
+    # Prefer the declared multiscales order; fall back to discovering 3-D arrays.
+    datasets = _multiscale_datasets(path)
+    entries: list[tuple[str, Any, list[float] | None]] = []
+    if datasets:
+        for dataset in datasets:
+            key = str(dataset.get("path") or "").strip("/")
+            if not key:
+                continue
+            try:
+                arr = group[key]
+            except Exception:  # noqa: BLE001 — declared but missing; skip it
+                logger.warning("multiscales lists %r but it is not readable", key)
+                continue
+            if getattr(arr, "ndim", 0) == 3:
+                entries.append((key, arr, _scale_vector(dataset)))
+    if not entries:
+        entries = [(key, arr, None) for key, arr in _walk_arrays(group)]
+
+    if not entries:
+        raise HTTPException(
+            422,
+            f"{path.name!r} contains no 3-D arrays to annotate. (An empty Zarr "
+            "group has nothing to load.)",
+        )
+
+    # Finest first, by voxel count.
+    entries.sort(key=lambda e: -(e[1].shape[0] * e[1].shape[1] * e[1].shape[2]))
+    base_shape = entries[0][1].shape
+    base_scale = entries[0][2]
+
+    levels: list[dict[str, Any]] = []
+    for key, arr, scale in entries:
+        shape = [int(v) for v in arr.shape]
+        levels.append(
+            {
+                "path": key,
+                "shape": shape,
+                "dtype": str(arr.dtype),
+                "n_slices": shape[0],
+                "height": shape[1],
+                "width": shape[2],
+                # How many finest-level voxels one voxel here spans, per axis.
+                "downsample": [
+                    round(base_shape[i] / shape[i], 4) if shape[i] else 1.0 for i in range(3)
+                ],
+            }
+        )
+
+    voxel_size = base_scale[0] if base_scale else None
+    return {
+        "name": path.name,
+        "path": str(path),
+        "levels": levels,
+        "full_shape": [int(v) for v in base_shape],
+        "dtype": str(entries[0][1].dtype),
+        # Finest-level voxel size (z, y, x) and its unit, for the Measurement panel.
+        "voxel_size": base_scale,
+        "voxel_unit": _voxel_unit(path),
+        "pixel_size": voxel_size,
+    }
+
+
+def _voxel_unit(path: Path) -> str | None:
+    """Axis unit declared in the store's ``multiscales`` axes (e.g. micrometer)."""
+    attrs_file = path / ".zattrs"
+    if not attrs_file.exists():
+        return None
+    try:
+        axes = json.loads(attrs_file.read_text())["multiscales"][0]["axes"]
+    except (OSError, json.JSONDecodeError, KeyError, IndexError, TypeError):
+        return None
+    for axis in axes:
+        unit = axis.get("unit")
+        if unit:
+            return str(unit)
+    return None
+
+
+def registered_key(path: Path) -> str:
+    """The node key Tiled will use for *path*.
+
+    Tiled strips the extension, so ``foo.zarr`` registers as ``foo``. Deriving
+    this with Tiled's own helper (rather than assuming ``path.name``) keeps the
+    collision check below looking at the key that will actually be created — a
+    mismatch here means the check passes and Tiled then hits the collision
+    itself, deep inside registration.
+    """
+    from tiled.client.register import Settings
+
+    return Settings.init().key_from_filename(path.name)
+
+
+def _existing_node_info(node: Any) -> dict[str, Any]:
+    """Describe what already occupies a key, for the conflict prompt.
+
+    ``external`` distinguishes a previous Zarr registration (safe to drop — it
+    only removes catalog rows) from internally-managed data such as an uploaded
+    image stack, where deleting the node also deletes the files.
+    """
+    children: list[str] = []
+    try:
+        children = list(node)
+    except Exception:  # noqa: BLE001 — best-effort description
+        pass
+    meta = {}
+    try:
+        meta = dict(getattr(node, "metadata", {}) or {})
+    except Exception:  # noqa: BLE001
+        pass
+    return {
+        "child_count": len(children),
+        "external": meta.get("source_format") == "zarr",
+        "sample_name": meta.get("sample_name") or "",
+        "n_images": meta.get("n_images"),
+    }
+
+
+def preflight_zarr(
+    server_uri: str | None, raw_path: str, container_path: str = "browse"
+) -> dict[str, Any]:
+    """Report whether registering *raw_path* would collide, without changing anything.
+
+    Returns the derived ``key``, whether it ``exists``, and — when it does — what
+    is there, so the UI can warn before offering Replace.
+    """
+    path = _resolve_path(raw_path)
+    key = registered_key(path)
+    client = get_tiled_client(server_uri, api_key_for_uri(server_uri))
+    parts = [p for p in container_path.strip("/").split("/") if p]
+    target = ingest_mod._walk(client, parts)
+    existing = None
+    if target is not None and key in ingest_mod._child_keys(target):
+        existing = _existing_node_info(target[key])
+    return {"key": key, "exists": existing is not None, "existing": existing}
+
+
+def register_zarr(
+    server_uri: str | None,
+    raw_path: str,
+    container_path: str,
+    description: str = "",
+    on_conflict: str = "fail",
+) -> dict[str, Any]:
+    """Register a Zarr store into a Tiled container, copying no data.
+
+    Args:
+        server_uri: Connected Tiled server URI.
+        raw_path: Absolute path to the ``.zarr`` directory.
+        container_path: Slash-separated target container (e.g. ``browse``).
+        description: Optional keyword(s) stored on the node, exactly as ingest
+            does, so the volume is filterable in Browse.
+        on_conflict: ``"fail"``, ``"replace"`` or ``"skip"`` when the key exists.
+
+    Returns:
+        Dict with the registered ``key``, its Tiled ``path``, and the inspection
+        result (so the caller can offer the level picker without a second call).
+    """
+    info = inspect_zarr(raw_path)
+    path = Path(info["path"])
+    description = (description or "").strip()
+    keywords = ingest_mod.parse_keywords(description)
+    if on_conflict not in ingest_mod.ON_CONFLICT_MODES:
+        on_conflict = "fail"
+
+    client = get_tiled_client(server_uri, api_key_for_uri(server_uri))
+    parts = [p for p in container_path.strip("/").split("/") if p]
+    target = ingest_mod._ensure_container(client, parts)
+
+    # Tiled derives the key from the filename (dropping ".zarr"), so resolve the
+    # collision against THAT key. Getting this wrong lets registration proceed
+    # until Tiled hits the collision internally, where it can only offer to
+    # delete external assets and aborts on anything else.
+    key = registered_key(path)
+    if key in ingest_mod._child_keys(target):
+        existing = _existing_node_info(target[key])
+        if on_conflict == "skip":
+            return {**info, "key": key, "tiled_path": "/".join([*parts, key]), "skipped": True}
+        if on_conflict == "replace":
+            if not existing["external"]:
+                # The occupant is internally-managed data (e.g. an uploaded image
+                # stack that happens to share this name). Deleting it would delete
+                # the files themselves, which is not what "replace this Zarr"
+                # should ever mean — make the user rename or remove it explicitly.
+                raise HTTPException(
+                    409,
+                    f"{key!r} already exists in {container_path!r} and holds uploaded "
+                    f"data ({existing['child_count']} items), not a registered Zarr. "
+                    "Replacing it would delete that data — load into a different "
+                    "container, or delete that dataset yourself first.",
+                )
+            # A previous Zarr registration: dropping it removes catalog rows only,
+            # never the store on disk.
+            target.delete_contents(key, recursive=True, external_only=False)
+        else:
+            raise HTTPException(
+                409,
+                f"{key!r} already exists in {container_path!r}. Choose Replace or "
+                "a different destination.",
+            )
+
+    # Tiled's own single-item registration: it resolves .zarr -> application/x-zarr
+    # and stores an external Asset pointing at the directory. Nothing is copied.
+    from tiled.client.register import Settings, register_single_item
+
+    try:
+        asyncio.run(
+            register_single_item(target, path, is_directory=True, settings=Settings.init())
+        )
+    except Exception as exc:  # noqa: BLE001 — classified for the UI below
+        logger.warning("zarr registration failed for %s: %s", path, exc)
+        raise HTTPException(502, ingest_mod._classify_error(exc)["message"]) from exc
+
+    node = ingest_mod._walk(target, [key])
+    if node is None:
+        # register_single_item logs and swallows adapter errors, returning None —
+        # so a missing node here is the signal that registration did not happen.
+        raise HTTPException(
+            502,
+            f"Tiled did not register {key!r}. Check the backend log for the "
+            "adapter error, and that the Tiled server can read this path.",
+        )
+
+    # Same metadata shape the dropzone writes, so Browse treats this like any
+    # other sample, plus the pyramid description the level picker needs.
+    meta: dict[str, Any] = {
+        "sample_name": key,
+        "n_images": info["levels"][0]["n_slices"],
+        "source_format": "zarr",
+        "zarr_path": str(path),
+        "zarr_levels": info["levels"],
+        "full_shape": info["full_shape"],
+    }
+    if info.get("voxel_size"):
+        meta["voxel_size"] = info["voxel_size"]
+    if info.get("voxel_unit"):
+        meta["voxel_unit"] = info["voxel_unit"]
+    if description:
+        meta["description"] = description
+    if keywords:
+        meta["keywords"] = keywords
+    try:
+        node.update_metadata(metadata=meta)
+    except Exception as exc:  # noqa: BLE001 — best-effort; the data is registered
+        logger.warning("could not set metadata on %s: %s", key, exc)
+
+    # Spread `info` FIRST: it carries the filesystem path under "path", which
+    # must not shadow the Tiled path the caller needs to open the dataset.
+    return {
+        **info,
+        "key": key,
+        "tiled_path": "/".join([*parts, key]),
+        "skipped": False,
+    }
diff --git a/frontend/src/app/pages/ConnectPage.tsx b/frontend/src/app/pages/ConnectPage.tsx
index 07e0abb..08c0de6 100644
--- a/frontend/src/app/pages/ConnectPage.tsx
+++ b/frontend/src/app/pages/ConnectPage.tsx
@@ -27,6 +27,7 @@ import { API_BASE } from '@/config';
 import { useConnectionStore } from '@/stores/connectionStore';
 import { useOpenInAnnotate } from '@/hooks/useOpenInAnnotate';
 import IngestDropzone from '@/components/Ingest/IngestDropzone';
+import ZarrLoader from '@/components/Ingest/ZarrLoader';
 
 interface ServerInfo {
   name: string;
@@ -147,6 +148,13 @@ export default function ConnectPage() {
    */
   const browseIngested = (containerPath: string) => connectTiled(null, true, containerPath);
 
+  /** Open a registered Zarr level (a full Tiled path) straight in Annotate. */
+  const annotateZarr = (tiledPath: string) => {
+    const containerPath = tiledPath.split('/').slice(0, -3).join('/') || 'browse';
+    connectTiled(containerPath, false);
+    void openTiledArray(tiledPath, selectedServerUri);
+  };
+
   // Jump straight from ingest to the Annotate tab for the first uploaded sample.
   const annotateIngested = (containerPath: string, firstKey: string) => {
     connectTiled(containerPath, false); // set connection context, don't navigate to Browse
@@ -367,11 +375,28 @@ export default function ConnectPage() {
               <h3 className="text-base font-semibold text-white">Load / Ingest Datasets</h3>
             </div>
             {selectedServerUri ? (
-              <IngestDropzone
-                serverUri={selectedServerUri}
-                onBrowse={browseIngested}
-                onAnnotate={annotateIngested}
-              />
+              <>
+                <IngestDropzone
+                  serverUri={selectedServerUri}
+                  onBrowse={browseIngested}
+                  onAnnotate={annotateIngested}
+                />
+
+                {/* Zarr volumes are too large to upload; they are registered in
+                    place instead, so this sits beside the dropzone rather than
+                    inside it. */}
+                <div className="border-t border-white/10 pt-4">
+                  <div className="mb-2 flex items-center gap-2">
+                    <Stack size={16} className="text-sky-300" />
+                    <h4 className="text-sm font-semibold text-white">Load a Zarr volume</h4>
+                  </div>
+                  <ZarrLoader
+                    serverUri={selectedServerUri}
+                    onBrowse={browseIngested}
+                    onAnnotate={annotateZarr}
+                  />
+                </div>
+              </>
             ) : (
               <p className="text-xs text-sky-300/90">Select a server above to ingest data.</p>
             )}
diff --git a/frontend/src/components/Ingest/ZarrLoader.tsx b/frontend/src/components/Ingest/ZarrLoader.tsx
new file mode 100644
index 0000000..a18b1e9
--- /dev/null
+++ b/frontend/src/components/Ingest/ZarrLoader.tsx
@@ -0,0 +1,314 @@
+/**
+ * ZarrLoader — load an on-disk Zarr volume by path, without uploading anything.
+ *
+ * The dropzone next to this streams files through the browser; a tomography
+ * volume is far too large for that (the reference data is 7-56 GB). Tiled can
+ * read a Zarr store in place, so loading one is really just registering its path:
+ * nothing is copied, and slices are read lazily as you annotate.
+ *
+ * Flow: paste a server-side path -> Inspect (shows the pyramid) -> pick a level
+ * -> Load. Inspect is separate from Load so you can see what was found before
+ * anything is written to the catalog.
+ */
+import { useCallback, useState } from 'react';
+import { Stack, Warning, CheckCircle, MagnifyingGlass } from '@phosphor-icons/react';
+import { API_BASE } from '@/config';
+
+/** One resolution level of a multiscale volume. */
+interface ZarrLevel {
+  path: string;
+  shape: [number, number, number];
+  dtype: string;
+  n_slices: number;
+  height: number;
+  width: number;
+  /** Full-res voxels spanned per voxel here, as [z, y, x]. */
+  downsample: [number, number, number];
+}
+
+interface ZarrInfo {
+  name: string;
+  path: string;
+  levels: ZarrLevel[];
+  full_shape: [number, number, number];
+  dtype: string;
+  voxel_size: number[] | null;
+  voxel_unit: string | null;
+}
+
+/** What already occupies the destination key, when there is a collision. */
+interface ExistingInfo {
+  child_count: number;
+  external: boolean;
+  sample_name: string;
+  n_images: number | null;
+}
+
+interface ZarrLoaderProps {
+  serverUri: string;
+  onBrowse?: (containerPath: string, sampleCount: number) => void;
+  onAnnotate?: (tiledPath: string) => void;
+}
+
+/** Human-readable byte-ish size of a level, for a sense of scale. */
+function describeLevel(level: ZarrLevel): string {
+  const [z, y, x] = level.shape;
+  return `${z} × ${y} × ${x}`;
+}
+
+export default function ZarrLoader({ serverUri, onBrowse, onAnnotate }: ZarrLoaderProps) {
+  const [path, setPath] = useState('');
+  const [containerPath, setContainerPath] = useState('browse');
+  const [description, setDescription] = useState('');
+  const [info, setInfo] = useState<ZarrInfo | null>(null);
+  const [levelIdx, setLevelIdx] = useState(0);
+  const [busy, setBusy] = useState<'inspect' | 'register' | null>(null);
+  const [error, setError] = useState<string | null>(null);
+  const [existing, setExisting] = useState<ExistingInfo | null>(null);
+  const [loaded, setLoaded] = useState<{ tiledPath: string; nSlices: number } | null>(null);
+
+  /** Pull the FastAPI `detail` message out of an error response. */
+  const readError = async (res: Response): Promise<string> => {
+    try {
+      const body = await res.json();
+      if (typeof body?.detail === 'string') return body.detail;
+      return JSON.stringify(body);
+    } catch {
+      return `Request failed (${res.status})`;
+    }
+  };
+
+  const inspect = useCallback(async () => {
+    setBusy('inspect');
+    setError(null);
+    setInfo(null);
+    setExisting(null);
+    setLoaded(null);
+    try {
+      const res = await fetch(`${API_BASE}/api/zarr/inspect?path=${encodeURIComponent(path)}`);
+      if (!res.ok) throw new Error(await readError(res));
+      const data: ZarrInfo = await res.json();
+      setInfo(data);
+      setLevelIdx(0);
+
+      // Warn about a name collision before the user commits to loading.
+      const pf = await fetch(`${API_BASE}/api/zarr/preflight`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ path, container_path: containerPath, server_uri: serverUri }),
+      });
+      if (pf.ok) {
+        const conflict = await pf.json();
+        setExisting(conflict.exists ? conflict.existing : null);
+      }
+    } catch (err) {
+      setError(err instanceof Error ? err.message : String(err));
+    } finally {
+      setBusy(null);
+    }
+  }, [path, containerPath, serverUri]);
+
+  const register = useCallback(
+    async (onConflict: 'fail' | 'replace') => {
+      if (!info) return;
+      setBusy('register');
+      setError(null);
+      try {
+        const res = await fetch(`${API_BASE}/api/zarr/register`, {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({
+            path: info.path,
+            container_path: containerPath,
+            description,
+            on_conflict: onConflict,
+            server_uri: serverUri,
+          }),
+        });
+        if (!res.ok) throw new Error(await readError(res));
+        const data = await res.json();
+        const level = info.levels[levelIdx];
+        setLoaded({
+          tiledPath: `${data.tiled_path}/${level.path}`,
+          nSlices: info.levels[0].n_slices,
+        });
+        setExisting(null);
+      } catch (err) {
+        setError(err instanceof Error ? err.message : String(err));
+      } finally {
+        setBusy(null);
+      }
+    },
+    [info, containerPath, description, serverUri, levelIdx],
+  );
+
+  const level = info?.levels[levelIdx];
+  const zFactor = level ? level.downsample[0] : 1;
+  const coarse = levelIdx > 0;
+
+  return (
+    <div className="space-y-3">
+      <p className="text-xs text-sky-300/90">
+        Point at a <code className="text-sky-200">.zarr</code> directory on the server. Nothing is
+        copied — Tiled reads it in place, so even a 50&nbsp;GB volume loads in seconds.
+      </p>
+
+      <div className="flex gap-2">
+        <input
+          type="text"
+          value={path}
+          onChange={(e) => setPath(e.target.value)}
+          onKeyDown={(e) => { if (e.key === 'Enter' && path.trim()) inspect(); }}
+          placeholder="/absolute/path/to/volume.zarr"
+          className="flex-1 min-w-0 rounded-md border border-white/15 bg-white/5 px-2 py-1.5 text-sm text-white placeholder:text-white/30"
+        />
+        <button
+          type="button"
+          onClick={inspect}
+          disabled={!path.trim() || busy !== null}
+          className="flex items-center gap-1.5 rounded-md bg-sky-600 px-3 py-1.5 text-sm font-medium text-white hover:bg-sky-500 disabled:opacity-40"
+        >
+          <MagnifyingGlass size={15} />
+          {busy === 'inspect' ? 'Inspecting…' : 'Inspect'}
+        </button>
+      </div>
+
+      {error && (
+        <div className="flex items-start gap-2 rounded-md border border-amber-400/30 bg-amber-400/10 px-2.5 py-2 text-xs text-amber-200">
+          <Warning size={15} className="mt-0.5 flex-shrink-0" />
+          <span>{error}</span>
+        </div>
+      )}
+
+      {info && (
+        <div className="space-y-3 rounded-md border border-white/10 bg-black/20 p-3">
+          <div className="text-sm text-white">
+            <span className="font-medium">{info.name}</span>
+            <span className="ml-2 text-xs text-sky-300/80">
+              {info.full_shape[0]} × {info.full_shape[1]} × {info.full_shape[2]} · {info.dtype}
+              {info.voxel_size ? ` · ${info.voxel_size[0]} ${info.voxel_unit ?? ''}/voxel` : ''}
+            </span>
+          </div>
+
+          <div className="space-y-1">
+            <label className="block text-xs font-medium text-sky-100">Resolution level</label>
+            <div className="flex flex-wrap gap-1">
+              {info.levels.map((lv, i) => (
+                <button
+                  key={lv.path}
+                  type="button"
+                  onClick={() => setLevelIdx(i)}
+                  className={[
+                    'rounded-md border px-2 py-1 text-[11px] transition-colors',
+                    i === levelIdx
+                      ? 'border-sky-400 bg-sky-600 text-white'
+                      : 'border-white/15 bg-white/5 text-sky-100 hover:bg-white/10',
+                  ].join(' ')}
+                  title={describeLevel(lv)}
+                >
+                  {i === 0 ? 'Full res' : `1/${Math.round(lv.downsample[1])}`}
+                  <span className="ml-1 opacity-60">{lv.width}²</span>
+                </button>
+              ))}
+            </div>
+            {coarse && level && (
+              // Both consequences of annotating a coarse level, stated plainly:
+              // strokes are stored as if full-res, and z is subsampled too.
+              <p className="text-[11px] leading-snug text-amber-200/90">
+                Annotations are stored in full-resolution coordinates, so strokes drawn here carry
+                less precision than they appear to. This level also has {level.n_slices} slices, so
+                it addresses roughly every {zFactor.toFixed(zFactor % 1 ? 1 : 0)}
+                <sup>th</sup> full-resolution slice.
+              </p>
+            )}
+          </div>
+
+          <div className="grid grid-cols-2 gap-2">
+            <div className="space-y-1">
+              <label className="block text-xs font-medium text-sky-100">Destination</label>
+              <input
+                type="text"
+                value={containerPath}
+                onChange={(e) => setContainerPath(e.target.value)}
+                className="w-full rounded-md border border-white/15 bg-white/5 px-2 py-1 text-xs text-white"
+              />
+            </div>
+            <div className="space-y-1">
+              <label className="block text-xs font-medium text-sky-100">Keywords (optional)</label>
+              <input
+                type="text"
+                value={description}
+                onChange={(e) => setDescription(e.target.value)}
+                placeholder="tomography, sand"
+                className="w-full rounded-md border border-white/15 bg-white/5 px-2 py-1 text-xs text-white placeholder:text-white/30"
+              />
+            </div>
+          </div>
+
+          {existing && (
+            <div className="space-y-2 rounded-md border border-amber-400/30 bg-amber-400/10 px-2.5 py-2 text-xs text-amber-200">
+              <div className="flex items-start gap-2">
+                <Warning size={15} className="mt-0.5 flex-shrink-0" />
+                <span>
+                  <b>{info.name.replace(/\.zarr$/, '')}</b> already exists in{' '}
+                  <b>{containerPath}</b>
+                  {existing.external
+                    ? ' as a previously loaded Zarr. Replacing it only updates the catalog entry.'
+                    : ` and holds ${existing.child_count} uploaded images. That is different data —
+                       replacing it would delete those files, so choose another destination.`}
+                </span>
+              </div>
+              {existing.external && (
+                <button
+                  type="button"
+                  onClick={() => register('replace')}
+                  disabled={busy !== null}
+                  className="rounded-md border border-amber-300/40 px-2 py-1 font-medium hover:bg-amber-400/10 disabled:opacity-40"
+                >
+                  Replace it
+                </button>
+              )}
+            </div>
+          )}
+
+          {!loaded && (
+            <button
+              type="button"
+              onClick={() => register('fail')}
+              disabled={busy !== null || (existing !== null && !existing.external)}
+              className="flex items-center gap-1.5 rounded-md bg-sky-600 px-3 py-1.5 text-sm font-medium text-white hover:bg-sky-500 disabled:opacity-40"
+            >
+              <Stack size={15} />
+              {busy === 'register' ? 'Loading…' : 'Load volume'}
+            </button>
+          )}
+
+          {loaded && (
+            <div className="space-y-2">
+              <div className="flex items-start gap-2 text-xs text-emerald-300">
+                <CheckCircle size={15} className="mt-0.5 flex-shrink-0" />
+                <span>Loaded {loaded.nSlices} slices — no data was copied.</span>
+              </div>
+              <div className="flex gap-2">
+                <button
+                  type="button"
+                  onClick={() => onAnnotate?.(loaded.tiledPath)}
+                  className="rounded-md bg-sky-600 px-3 py-1.5 text-xs font-medium text-white hover:bg-sky-500"
+                >
+                  Annotate
+                </button>
+                <button
+                  type="button"
+                  onClick={() => onBrowse?.(containerPath, 1)}
+                  className="rounded-md border border-white/15 px-3 py-1.5 text-xs font-medium text-sky-100 hover:bg-white/10"
+                >
+                  Browse
+                </button>
+              </div>
+            </div>
+          )}
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/frontend/src/components/annotate/SliceNavigator/index.tsx b/frontend/src/components/annotate/SliceNavigator/index.tsx
index 33f5099..a8021c2 100644
--- a/frontend/src/components/annotate/SliceNavigator/index.tsx
+++ b/frontend/src/components/annotate/SliceNavigator/index.tsx
@@ -34,12 +34,27 @@ export default function SliceNavigator() {
   /** Steps forward one slice (clamped at the last slice); writes to the dataset store. */
   const next = () => setSlice(Math.min(n - 1, currentSlice + 1));
 
+  // For a multiscale volume the slider is in FULL-RESOLUTION slice indices even
+  // when a coarse level is displayed, so say which level is actually on screen —
+  // otherwise "slice 345 / 690" over a 345-slice level is quietly confusing.
+  const onCoarseLevel = !!meta.levelKey && (meta.levelIndex ?? 0) > 0;
+
   return (
     <div className="flex flex-col gap-2">
       <span className="text-xs font-semibold uppercase text-gray-500 tracking-wide">
         Slice {currentSlice + 1} / {n}
       </span>
 
+      {onCoarseLevel && (
+        <span
+          className="text-[11px] leading-snug text-amber-600"
+          title="Slice numbers and annotation coordinates are always full-resolution; the image shown is downsampled."
+        >
+          Viewing {meta.levelKey} ({meta.levelWidth}² px, {meta.levelNSlices} slices) — indices stay
+          full-resolution.
+        </span>
+      )}
+
       <DebouncedSlider
         min={0}
         max={n - 1}
diff --git a/frontend/src/hooks/useOpenInAnnotate.ts b/frontend/src/hooks/useOpenInAnnotate.ts
index ff87fe5..efebd2f 100644
--- a/frontend/src/hooks/useOpenInAnnotate.ts
+++ b/frontend/src/hooks/useOpenInAnnotate.ts
@@ -86,6 +86,15 @@ export function useOpenInAnnotate() {
         dtype: meta.dtype,
         isRgb: meta.is_rgb,
         valueRange: meta.value_range,
+        // Multiscale volumes: width/height/nSlices above are the FINEST level's
+        // (annotations live in full-res coordinates); these say what is drawn.
+        levelKey: meta.level_key ?? null,
+        levelIndex: meta.level_index ?? null,
+        levelCount: meta.level_count ?? null,
+        levelWidth: meta.level_width ?? null,
+        levelHeight: meta.level_height ?? null,
+        levelNSlices: meta.level_n_slices ?? null,
+        zDownsample: meta.z_downsample ?? null,
       });
       // setDataset resets to slice 0; jump to the requested slice (clamped).
       if (initialSlice > 0) setSlice(Math.min(initialSlice, Math.max(0, meta.n_slices - 1)));
diff --git a/frontend/src/stores/datasetStore.ts b/frontend/src/stores/datasetStore.ts
index ecdb445..fc673ce 100644
--- a/frontend/src/stores/datasetStore.ts
+++ b/frontend/src/stores/datasetStore.ts
@@ -10,6 +10,18 @@ export interface ImageMeta {
   dtype: string;
   isRgb: boolean;
   valueRange: [number, number];
+  /** Multiscale (Zarr) volumes only — which pyramid level is being displayed.
+   *  `width`/`height`/`nSlices` above always describe the FINEST level, because
+   *  annotations are stored in full-resolution coordinates whichever level is
+   *  open; these describe the image actually drawn underneath them. */
+  levelKey?: string | null;
+  levelIndex?: number | null;
+  levelCount?: number | null;
+  levelWidth?: number | null;
+  levelHeight?: number | null;
+  levelNSlices?: number | null;
+  /** Finest-z / level-z. When > 1, only every f-th full-res slice is addressable. */
+  zDownsample?: number | null;
 }
 
 export interface RenderOpts {

From 21e25cd245ddd7b2e29c9d7e3dcfd53b327cea4d Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Tue, 25 Aug 2026 13:38:40 -0700
Subject: [PATCH 04/38] Improving the threshold tool with an optional
 eyedropper magnetic lasso. This lets you select a region, and it will
 automatically find the best threshold settings

---
 frontend/src/app/pages/AnnotatePage.tsx       |  16 +
 .../annotate/AnnotationCanvas/index.tsx       | 396 +++++++++++++++++-
 .../src/components/annotate/Toolbar/index.tsx | 121 +++++-
 frontend/src/hooks/useKeybinds.ts             |   2 +
 frontend/src/lib/blur.ts                      |  52 +++
 frontend/src/lib/featureChannels.test.ts      | 196 +++++++++
 frontend/src/lib/featureChannels.ts           | 186 ++++++++
 frontend/src/lib/thresholdFit.test.ts         | 386 +++++++++++++++++
 frontend/src/lib/thresholdFit.ts              | 276 ++++++++++++
 frontend/src/lib/thresholdRegularize.test.ts  |  81 ++++
 frontend/src/stores/toolStore.ts              |   2 +-
 11 files changed, 1699 insertions(+), 15 deletions(-)
 create mode 100644 frontend/src/lib/featureChannels.test.ts
 create mode 100644 frontend/src/lib/featureChannels.ts
 create mode 100644 frontend/src/lib/thresholdFit.test.ts
 create mode 100644 frontend/src/lib/thresholdFit.ts
 create mode 100644 frontend/src/lib/thresholdRegularize.test.ts

diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index 0a21e61..a5eb84e 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -28,6 +28,7 @@ import InsightsModal from '@/components/annotate/InsightsModal';
 import VersionHistoryModal from '@/components/annotate/VersionHistoryModal';
 import VersionPreviewBar from '@/components/annotate/VersionPreviewBar';
 import PerfOverlay from '@/components/annotate/PerfOverlay';
+import type { SamplerFit } from '@/components/annotate/AnnotationCanvas';
 import { initPerf } from '@/lib/perf';
 import SaveModal from '@/components/annotate/SaveModal';
 import type { SaveDraftPayload } from '@/hooks/useSave';
@@ -108,6 +109,17 @@ export default function AnnotatePage() {
   // Dev-only timing HUD (?perf=1). Read once — the flag is fixed for the session.
   const [perfOn] = useState(() => initPerf());
 
+  // Latest Sampler lasso fit — transient UI state, not persisted: only the band
+  // and blur it applies are durable.
+  const [samplerFit, setSamplerFit] = useState<SamplerFit | null>(null);
+  /** Put back the band and blur that were in force before the last fit. */
+  const revertSamplerFit = useCallback(() => {
+    if (!samplerFit) return;
+    useToolStore.getState().setThresholdBand(samplerFit.previousBand[0], samplerFit.previousBand[1]);
+    setBlur(samplerFit.previousBlur);
+    setSamplerFit(null);
+  }, [samplerFit]);
+
   // Crash-recovery autosave (local draft only, no Tiled sync)
   useDraftSync(sourceKey);
   // Undo/redo is per-sample: reset the region history + class-delete journal on switch.
@@ -246,6 +258,8 @@ export default function AnnotatePage() {
             histogramBins={histogramBins}
             display={thresholdDisplay}
             upscale={upscale}
+            samplerFit={samplerFit}
+            onRevertSamplerFit={revertSamplerFit}
           />
           <hr />
           <DisplayControls
@@ -355,6 +369,8 @@ export default function AnnotatePage() {
             blur={blur}
             upscale={upscale}
             onHistogram={setHistogramBins}
+            onSamplerFit={setSamplerFit}
+            onBlurChange={setBlur}
             activeClassId={activeClassId}
             activeBrushShapeId={activeBrushShapeId}
             onNewBrushInstance={setActiveBrushShapeId}
diff --git a/frontend/src/components/annotate/AnnotationCanvas/index.tsx b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
index ff57857..e60e978 100644
--- a/frontend/src/components/annotate/AnnotationCanvas/index.tsx
+++ b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
@@ -31,7 +31,7 @@ import { buildField, magicSelect, maskToPolygons, maskToPolygonsWithHoles, type
 import { useSam } from '@/hooks/useSam';
 import { renderAdjusted, renderPreprocessOnly } from '@/lib/sam/adjust';
 import { gridFor, fullResGridFor, rasterizeShapes, rasterizeUnion, stampStroke } from '@/lib/rasterize';
-import { keepComponentsAtPoints } from '@/lib/morphology';
+import { keepComponentsAtPoints, dilate, erode, removeSmallComponents } from '@/lib/morphology';
 import { unionShapesToPolygons, unionShapesToMultiPolygon, unionShapesChecked, eraseStampToMultiPolygon, subtractFromShape, regionsToMultiPolygon } from '@/lib/polybool';
 import { computeRegionOps, type RegionOp } from '@/lib/regionOps';
 import { clipShapesToOthers, clipShapesToOthersMask, hasOtherClass } from '@/lib/clipToClasses';
@@ -39,7 +39,15 @@ import { mergeNewWithSameClass, expandSameClassOverlap } from '@/lib/mergeSameCl
 import { useClipboardStore } from '@/stores/clipboardStore';
 import { colormapTables, type ColormapName } from '@/lib/colormaps';
 import ShapesLayer from './ShapesLayer';
-import { displayAffineFor, displayBandToBase } from '@/lib/displayTransform';
+import { displayAffineFor, displayBandToBase, baseToDisplay, displayToBase } from '@/lib/displayTransform';
+import {
+  backgroundRing, fitWithBlurSweep, combineSigma, BLUR_CANDIDATES, MAX_RING_WIDTH,
+  fitBand, sampleHistograms, type SweepResult,
+} from '@/lib/thresholdFit';
+import {
+  buildChannels, fitProjection, projectToScore, CHANNEL_NAMES,
+} from '@/lib/featureChannels';
+import { applyGaussianBlurGray } from '@/lib/blur';
 import { time } from '@/lib/perf';
 
 // macOS labels the Alt key "Option" (⌥). e.altKey is true for it either way,
@@ -50,6 +58,36 @@ const REMOVE_KEY_LABEL = IS_MAC ? 'Option' : 'Alt';
 /** Shared stable empty shape list — see `storeShapes` for why identity matters. */
 const EMPTY_SHAPES: Shape[] = [];
 
+/** Outcome of a Sampler lasso: the fitted band plus what it took to get there. */
+export interface SamplerFit extends SweepResult {
+  /** The band converted into the DISPLAYED space the threshold knobs use. */
+  displayLo: number;
+  displayHi: number;
+  /** True when the display transform squashes the fitted range to one value, so
+   *  the band cannot be expressed at the current brightness/contrast/levels. */
+  collapsed: boolean;
+  /** Band and blur in force before the fit, so the UI can offer Revert. */
+  previousBand: [number, number];
+  previousBlur: number;
+  /** Blur the fit settled on (current combined with the swept extra). */
+  appliedBlur: number;
+  /** Which signal the brush now gates on: raw displayed intensity, or a fitted
+   *  combination of intensity and texture channels. */
+  mode?: 'intensity' | 'projected';
+  /** Skill intensity alone managed, so the gain from projecting is visible. */
+  intensitySkill?: number;
+  /** Contribution of each channel to the projection, for the readout. */
+  weights?: Array<{ name: string; weight: number }>;
+}
+
+/** How far (image px) the cursor travels before the sampler lasso drops a new
+ *  live-wire anchor. Larger = fewer dijkstra runs but looser snapping. */
+const SAMPLER_ANCHOR_STEP = 40;
+
+/** Smallest island (grid cells) a threshold stroke keeps after regularization.
+ *  Below this a region is thresholding noise, not a feature. */
+const THRESHOLD_MIN_REGION = 12;
+
 /** Upscale budget: the working resolution is clamped back to 1x past this many
  *  pixels so a large slice at 4x can't exhaust memory (~64 MP ≈ 256 MB of RGBA). */
 const MAX_WORKING_PIXELS = 64e6;
@@ -75,6 +113,10 @@ interface AnnotationCanvasProps {
   upscale?: number;
   /** Emits the current slice's 256-bin luminance histogram when it loads. */
   onHistogram?: (bins: number[]) => void;
+  /** Result of a Sampler lasso fit (null when it could not fit), for the Toolbar. */
+  onSamplerFit?: (fit: SamplerFit | null) => void;
+  /** Lets the Sampler apply the blur sigma it chose. */
+  onBlurChange?: (sigma: number) => void;
   activeClassId: number | null;
   activeBrushShapeId: string | null;
   onNewBrushInstance: (id: string) => void;
@@ -418,6 +460,8 @@ export default function AnnotationCanvas({
   blur = 0,
   upscale = 1,
   onHistogram,
+  onSamplerFit,
+  onBlurChange,
   activeClassId,
   activeBrushShapeId,
   onNewBrushInstance,
@@ -467,6 +511,27 @@ export default function AnnotationCanvas({
     imageData: ImageData;
   } | null>(null);
 
+  // Sampler lasso: a loop drawn to teach the Threshold Brush what to select.
+  // Buffered in a ref like the brush so dragging causes no re-renders, and purely
+  // a measurement gesture — it commits no shape and touches no history.
+  //
+  // It snaps to edges using the same live-wire machinery as the magnetic tool:
+  // an anchor is dropped every `SAMPLER_ANCHOR_STEP` image pixels and the segment
+  // between anchors is the least-cost path, so the sample follows the feature's
+  // real boundary instead of a shaky hand-drawn one. A tighter sample means a
+  // cleaner positive histogram, which is what the whole fit rests on.
+  const samplerLassoRef = useRef<{
+    /** Snapped points locked in so far (flat [x,y,…] image coords). */
+    committed: number[];
+    /** Where the current live-wire segment starts. */
+    anchor: { x: number; y: number };
+    /** First point, so the loop can be closed back to it. */
+    start: { x: number; y: number };
+    cm: CostMap | null;
+    /** Dijkstra predecessor map from `anchor`; null when snapping is unavailable. */
+    prev: Int32Array | null;
+  } | null>(null);
+
   const { kind, source, serverUri, meta, currentSlice, renderOpts } = useDatasetStore();
   const sourceKey = source && kind
     ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri)
@@ -492,6 +557,7 @@ export default function AnnotationCanvas({
   const magicEdgeStop = useToolStore((s) => s.magicEdgeStop);
   const magicEngine = useToolStore((s) => s.magicEngine);
   const setMagicEngine = useToolStore((s) => s.setMagicEngine);
+  const setTool = useToolStore((s) => s.setTool);
   const samDetail = useToolStore((s) => s.samDetail);
   const samThreshold = useToolStore((s) => s.samThreshold);
   const samAvoidLabeled = useToolStore((s) => s.samAvoidLabeled);
@@ -847,6 +913,197 @@ export default function AnnotationCanvas({
     (underlyingTool === 'brush' || underlyingTool === 'eraser' || underlyingTool === 'threshold') &&
     !!meta && !isPreviewing;
 
+  /**
+   * Fit the Threshold Brush band from a lassoed example and apply it.
+   *
+   * Everything inside the loop is a positive example; a ring just outside it
+   * supplies negatives, so the result means "fill this and not what it touches"
+   * rather than "fill everything this bright". Work happens on a crop around the
+   * lasso, which keeps the blur sweep cheap on a full-resolution slice.
+   */
+  const runSamplerFit = (path: number[]): void => {
+    if (!meta || path.length < 6) { onSamplerFit?.(null); return; }
+    const field = ensureThresholdField();
+    if (!field) { onSamplerFit?.(null); return; }
+    const { gw, gh, scale } = field;
+
+    // Everything below works on a CROP around the lasso, never the whole slice.
+    // Rasterizing and especially dilating full-grid is what made a large sample
+    // crawl: dilation costs one pass over its grid per cell of ring width, so on
+    // a 2560² field that is billions of operations for a region a few hundred
+    // pixels across. Cropping first makes the cost scale with the sample.
+    let minGX = Infinity, minGY = Infinity, maxGX = -Infinity, maxGY = -Infinity;
+    for (let i = 0; i + 1 < path.length; i += 2) {
+      const gx = path[i] / scale;
+      const gy = path[i + 1] / scale;
+      if (gx < minGX) minGX = gx;
+      if (gx > maxGX) maxGX = gx;
+      if (gy < minGY) minGY = gy;
+      if (gy > maxGY) maxGY = gy;
+    }
+    if (!Number.isFinite(minGX) || maxGX < minGX) { onSamplerFit?.(null); return; }
+
+    // Margin: the ring, plus slack so the widest blur kernel is not distorted by
+    // the crop edge. Ring width is estimated from the lasso's area in cells.
+    const approxArea = Math.max(1, (maxGX - minGX) * (maxGY - minGY));
+    const ringWidth = Math.min(MAX_RING_WIDTH, Math.max(4, Math.round(Math.sqrt(approxArea) / 2)));
+    const margin = ringWidth + Math.ceil(3 * Math.max(...BLUR_CANDIDATES)) + 2;
+
+    const x0 = Math.max(0, Math.floor(minGX) - margin);
+    const y0 = Math.max(0, Math.floor(minGY) - margin);
+    const x1 = Math.min(gw - 1, Math.ceil(maxGX) + margin);
+    const y1 = Math.min(gh - 1, Math.ceil(maxGY) + margin);
+    const cw = x1 - x0 + 1;
+    const ch = y1 - y0 + 1;
+    if (cw < 2 || ch < 2) { onSamplerFit?.(null); return; }
+
+    // Rasterize the lasso directly into crop coordinates by shifting it into the
+    // crop's frame (image units), so no full-size mask is ever allocated.
+    const shifted = path.map((v, i) => (i % 2 === 0 ? v - x0 * scale : v - y0 * scale));
+    const lassoShape: Shape = { id: 'sampler', classId: 0, kind: 'polygon', points: shifted };
+    const cropPos = rasterizeShapes([lassoShape], cw, ch, scale);
+    const cropNeg = backgroundRing(cropPos, cw, ch, dilate, ringWidth);
+
+    const cropField = new Float32Array(cw * ch);
+    for (let y = 0; y < ch; y++) {
+      const src = (y0 + y) * gw + x0;
+      const dst = y * cw;
+      for (let x = 0; x < cw; x++) cropField[dst + x] = field.gray[src + x];
+    }
+
+    const result = fitWithBlurSweep(cropField, cw, ch, cropPos, cropNeg, applyGaussianBlurGray);
+    if (!result.ok) { onSamplerFit?.(null); return; }
+
+    // Phase 2: also try a texture-aware score. Intensity alone cannot separate
+    // materials that share a grey range but differ in grain, nor a material whose
+    // brightness drifts across the slice. Fitting a projection over intensity +
+    // band-pass + local-std + local-mean-ratio, then running the SAME band fitter
+    // on the projected score, handles both — and because the score is graded by
+    // the same skill number, the two options are directly comparable.
+    const channels = buildChannels(cropField, cw, ch);
+    const projection = fitProjection(channels, cropPos, cropNeg);
+    let projected: SweepResult | null = null;
+    if (projection) {
+      const score = projectToScore(channels, projection);
+      const { posHist, negHist } = sampleHistograms(score, cropPos, cropNeg);
+      const fit = fitBand(posHist, negHist);
+      if (fit.ok) projected = { ...fit, extraSigma: 0 };
+    }
+
+    // Only switch to the projected score when it is meaningfully better. Equal
+    // results should stay on plain intensity: it is the mode the display sliders
+    // steer and the histogram picker describes, so it is the one to prefer.
+    const useProjection =
+      projection !== null && projected !== null && projected.skill > result.skill + 0.05;
+
+    if (useProjection && projected && projection) {
+      projectionRef.current = { projection, key: baseKey };
+      scoreFieldRef.current = null; // rebuilt lazily for the whole slice
+      setThresholdBand(projected.lo, projected.hi);
+      onSamplerFit?.({
+        ...projected,
+        displayLo: projected.lo,
+        displayHi: projected.hi,
+        collapsed: false,
+        previousBand: [useToolStore.getState().thresholdLo, useToolStore.getState().thresholdHi],
+        previousBlur: blur,
+        appliedBlur: blur,
+        mode: 'projected',
+        intensitySkill: result.skill,
+        weights: CHANNEL_NAMES.map((name, i) => ({ name, weight: projection.weights[i] })),
+      });
+      setTool('threshold');
+      return;
+    }
+
+    // Plain intensity won — drop any projection left from an earlier sample so the
+    // brush goes back to gating on what the display shows.
+    projectionRef.current = null;
+    scoreFieldRef.current = null;
+
+    // The fit lives in the field's BASE space; the stored band is authored in
+    // DISPLAYED space. Rather than enumerate the ways that conversion can fail,
+    // do it and check it round-trips: convert to display, round as the store
+    // will, convert back, and require the original base band. That catches both
+    // failure modes at once —
+    //   * collapse (contrast squashing the range onto one displayed value), and
+    //   * saturation, where an endpoint lands on 0 or 255 and `displayBandToBase`
+    //     correctly reopens it to ∓Infinity — which would silently select far
+    //     MORE than was fitted.
+    const displayLo = Math.round(baseToDisplay(result.lo, displayAffine, gamma));
+    const displayHi = Math.round(baseToDisplay(result.hi, displayAffine, gamma));
+    const roundTrip = displayBandToBase(displayLo, displayHi, displayAffine, gamma);
+    // Storing the band as integers costs up to half a displayed level. How much
+    // that is in BASE units depends on the local slope, which gamma makes vary
+    // along the range — so measure it at the band edges rather than deriving it
+    // from the affine part alone.
+    const levelWidth = (d: number): number => {
+      const a = displayToBase(d - 0.5, displayAffine, gamma);
+      const b = displayToBase(d + 0.5, displayAffine, gamma);
+      return a === null || b === null ? Infinity : Math.abs(b - a);
+    };
+    const tolerance = Math.max(1.5, 1.5 * Math.max(levelWidth(displayLo), levelWidth(displayHi)));
+    const collapsed =
+      !Number.isFinite(roundTrip.lo) ||
+      !Number.isFinite(roundTrip.hi) ||
+      Math.abs(roundTrip.lo - result.lo) > tolerance ||
+      Math.abs(roundTrip.hi - result.hi) > tolerance;
+
+    onSamplerFit?.({
+      ...result,
+      displayLo,
+      displayHi,
+      collapsed,
+      previousBand: [useToolStore.getState().thresholdLo, useToolStore.getState().thresholdHi],
+      previousBlur: blur,
+      appliedBlur: combineSigma(blur, result.extraSigma),
+    });
+
+    if (collapsed) return; // leave the band alone; the UI explains why
+    setThresholdBand(displayLo, displayHi);
+    if (result.extraSigma > 0) onBlurChange?.(combineSigma(blur, result.extraSigma));
+    // Sampling is a means, not an end: hand the user straight back to the brush
+    // the band was just fitted for.
+    setTool('threshold');
+  };
+
+  /** Snapped path from the current anchor to `to`, or a straight line if the
+   *  live-wire is unavailable (no edge map yet, or an off-grid point). */
+  const samplerTraceTo = (to: { x: number; y: number }): number[] => {
+    const st = samplerLassoRef.current;
+    if (!st) return [];
+    if (!st.cm || !st.prev) return [to.x, to.y];
+    try {
+      // `.slice(2)` drops the anchor itself, which is already committed.
+      return tracePath(st.cm, st.prev, imageToGrid(st.cm, to.x, to.y)).slice(2);
+    } catch {
+      return [to.x, to.y];
+    }
+  };
+
+  /** Re-seed the live-wire at `at` so subsequent segments trace from there. */
+  const samplerReseed = (at: { x: number; y: number }): void => {
+    const st = samplerLassoRef.current;
+    if (!st) return;
+    st.anchor = at;
+    st.prev = st.cm ? dijkstra(st.cm, imageToGrid(st.cm, at.x, at.y)) : null;
+  };
+
+  /** Close the sampler lasso, run the fit, and clear the transient state. */
+  const finishSamplerLasso = (): void => {
+    const st = samplerLassoRef.current;
+    samplerLassoRef.current = null;
+    if (draftLineRef.current) {
+      draftLineRef.current.visible(false);
+      draftLineRef.current.closed(false);
+      draftStrokeLayerRef.current?.batchDraw();
+    }
+    if (!st) return;
+    // Close the loop along the edge too, rather than cutting straight across it.
+    const path = [...st.committed, ...samplerTraceTo(st.start)];
+    runSamplerFit(path);
+  };
+
   /** Current pointer position mapped from stage/display coords to image pixels. */
   const getPointerImagePos = () => {
     const stage = stageRef.current;
@@ -921,6 +1178,16 @@ export default function AnnotationCanvas({
   // pixel set; see lib/displayTransform.ts), which turns a per-tick full-image
   // re-render into a couple of `Math.pow` calls. Only blur/CLAHE/sharpen, the
   // slice, and the working scale invalidate this field.
+  // Phase 2: when a sample shows texture beats brightness, the brush gates on a
+  // fitted projection of several channels instead of raw intensity. Held in refs
+  // (not state) so activating it does not re-render the canvas.
+  const projectionRef = useRef<{
+    projection: { weights: number[]; centers: number[]; scales: number[] };
+    key: string;
+  } | null>(null);
+  /** Whole-slice projected score, built lazily from `projectionRef`. */
+  const scoreFieldRef = useRef<GrayField | null>(null);
+
   const thresholdFieldRef = useRef<GrayField | null>(null);
   const thresholdFieldForRef = useRef<string | null>(null);
   const ensureThresholdField = useCallback((): GrayField | null => {
@@ -952,14 +1219,52 @@ export default function AnnotationCanvas({
     };
   }, [sourceKey, currentSlice]);
 
+  /**
+   * The field the Threshold Brush gates on.
+   *
+   * Normally the intensity field. Once a sample shows that texture separates the
+   * feature better, this becomes the fitted projection of all channels, computed
+   * across the whole slice and cached — the brush, the overlay and the band all
+   * read it, so they cannot disagree about what is selected.
+   */
+  const ensureGateField = useCallback((): GrayField | null => {
+    const base = ensureThresholdField();
+    const active = projectionRef.current;
+    if (!base || !active) return base;
+    // A projection is only valid for the field it was fitted on; a slice or
+    // preprocessing change invalidates it rather than silently misapplying it.
+    if (active.key !== baseKey) {
+      projectionRef.current = null;
+      scoreFieldRef.current = null;
+      return base;
+    }
+    if (scoreFieldRef.current) return scoreFieldRef.current;
+    const channels = buildChannels(base.gray, base.gw, base.gh);
+    const gray = projectToScore(channels, active.projection);
+    const score: GrayField = { gw: base.gw, gh: base.gh, scale: base.scale, gray };
+    scoreFieldRef.current = score;
+    return score;
+  }, [ensureThresholdField, baseKey]);
+
+  /** True while the brush is gating on a fitted score rather than brightness. */
+  const usingProjection = (): boolean =>
+    projectionRef.current !== null && projectionRef.current.key === baseKey;
+
   /** The threshold band, mapped from the DISPLAYED intensities the user authored
    *  it in into the base space the cached fields live in. Brightness/contrast/
    *  levels/gamma therefore steer the brush exactly as they steer the image —
    *  without any of them invalidating a field or touching a pixel. */
   const bandInBaseSpace = useCallback(() => {
     const { thresholdLo, thresholdHi } = useToolStore.getState();
+    // With a projection active the gate field is a fitted score, not displayed
+    // brightness, so inverting the display transform would be meaningless — the
+    // band is already in the score's own units. (A consequence worth knowing:
+    // in that mode the display sliders no longer steer the brush.)
+    if (projectionRef.current && projectionRef.current.key === baseKey) {
+      return { lo: thresholdLo, hi: thresholdHi };
+    }
     return displayBandToBase(thresholdLo, thresholdHi, displayAffine, gamma);
-  }, [displayAffine, gamma]);
+  }, [displayAffine, gamma, baseKey]);
 
   /** Binary gate over a field: 1 where the pixel reads as in-band on screen. This
    *  is what the brush may paint. Reads the band from the store rather than a
@@ -1005,6 +1310,12 @@ export default function AnnotationCanvas({
 
   const ensureOverlayField = useCallback((): GrayField | null => {
     if (!imageEl || !meta) return null;
+    // With a projection active the overlay must show the PROJECTED selection, or
+    // it would advertise a different set of pixels than the brush paints. The
+    // gate field is already cached, so reuse it rather than fitting a second one.
+    if (projectionRef.current && projectionRef.current.key === baseKey) {
+      return ensureGateField();
+    }
     if (overlayFieldRef.current && overlayFieldForRef.current === baseKey) return overlayFieldRef.current;
     // Same preprocessed source as the brush field (see above), so the overlay shows
     // exactly what the brush would paint — including as the display sliders move,
@@ -1013,7 +1324,7 @@ export default function AnnotationCanvas({
     overlayFieldRef.current = f;
     overlayFieldForRef.current = baseKey;
     return f;
-  }, [imageEl, meta, displayBase, baseKey]);
+  }, [imageEl, meta, displayBase, baseKey, ensureGateField]);
 
   /** Repaint the overlay canvas for the current band and push it to Konva. */
   const paintThresholdOverlay = useCallback(() => time('overlay-paint', () => {
@@ -1289,7 +1600,7 @@ export default function AnnotationCanvas({
 
   /** Begin a threshold stroke at `pos`, priming the mask, gate, and preview canvas. */
   const startThresholdStroke = (pos: { x: number; y: number }, erase: boolean): void => {
-    const field = ensureThresholdField();
+    const field = ensureGateField();
     if (!field || !meta) return;
     const { gw, gh, scale } = field;
     const canvas = document.createElement('canvas');
@@ -1362,12 +1673,30 @@ export default function AnnotationCanvas({
     }
     if (!st || !sourceKey || !meta || activeClassId === null) return;
 
-    const { gw, gh, scale, mask } = st;
+    const { gw, gh, scale } = st;
     let any = false;
-    for (let i = 0; i < mask.length; i++) if (mask[i]) { any = true; break; }
+    for (let i = 0; i < st.mask.length; i++) if (st.mask[i]) { any = true; break; }
     if (!any) return;
 
-    const regions = maskToPolygonsWithHoles(mask, gw, gh, { minRegion: 4, scale })
+    // Regularize before vectorizing. A per-pixel gate speckles, and every speck
+    // becomes its own polygon: that is what made one stroke commit hundreds of
+    // shapes and dominate clip cost. A morphological opening (erode then dilate)
+    // drops isolated pixels and pinholes while leaving real regions intact, and
+    // the small-component pass clears what survives. Better geometry AND a much
+    // cheaper commit, from the same step.
+    const mask = removeSmallComponents(
+      dilate(erode(st.mask, gw, gh, 1), gw, gh, 1),
+      gw,
+      gh,
+      THRESHOLD_MIN_REGION,
+    );
+    let survives = false;
+    for (let i = 0; i < mask.length; i++) if (mask[i]) { survives = true; break; }
+    // A thin stroke can be erased entirely by the opening; keep the raw mask
+    // rather than silently discarding what the user just painted.
+    const finalMask = survives ? mask : st.mask;
+
+    const regions = maskToPolygonsWithHoles(finalMask, gw, gh, { minRegion: 4, scale })
       .filter((p) => p.points.length >= 6);
     if (regions.length === 0) return;
 
@@ -1389,7 +1718,7 @@ export default function AnnotationCanvas({
         if (!bboxNear(shapeBBox(s), strokeBounds, scale + 1)) return false;
         scratch.fill(0);
         rasterizeShapes([s], gw, gh, scale, scratch);
-        for (let i = 0; i < mask.length; i++) if (mask[i] && scratch[i]) return true;
+        for (let i = 0; i < finalMask.length; i++) if (finalMask[i] && scratch[i]) return true;
         return false;
       };
       const stampMP = regionsToMultiPolygon(regions);
@@ -1775,11 +2104,34 @@ export default function AnnotationCanvas({
         draftLineRef.current.visible(true);
         draftStrokeLayerRef.current?.batchDraw();
       }
+    } else if (tool === 'sampler') {
+      // Magnetic lasso: seed the live-wire here, then snap each dragged segment
+      // to the strongest edge between anchors. No store writes and no undo entry
+      // — this measures, it does not annotate.
+      setIsDrawing(true);
+      const cm = ensureCostMap();
+      samplerLassoRef.current = {
+        committed: [pos.x, pos.y],
+        anchor: pos,
+        start: pos,
+        cm,
+        prev: cm ? dijkstra(cm, imageToGrid(cm, pos.x, pos.y)) : null,
+      };
+      if (draftLineRef.current) {
+        draftLineRef.current.stroke('#38bdf8');
+        draftLineRef.current.strokeWidth(2 / transform.scaleX);
+        draftLineRef.current.points([pos.x, pos.y]);
+        draftLineRef.current.closed(false);
+        draftLineRef.current.visible(true);
+        draftStrokeLayerRef.current?.batchDraw();
+      }
     } else if (tool === 'threshold') {
       // Shift-click samples instead of painting: re-center the band on the pixel
       // under the cursor, keeping the configured sample width.
       if (e.evt.shiftKey) {
-        const field = ensureThresholdField();
+        // Sample from the GATE field so the picked value is in the same units as
+        // the band — with a projection active those are score units, not greys.
+        const field = ensureGateField();
         if (field) {
           const gx = Math.max(0, Math.min(field.gw - 1, Math.floor(pos.x / field.scale)));
           const gy = Math.max(0, Math.min(field.gh - 1, Math.floor(pos.y / field.scale)));
@@ -1875,6 +2227,26 @@ export default function AnnotationCanvas({
       return;
     }
 
+    // Sampler lasso: preview the snapped segment from the anchor to the cursor,
+    // dropping a new anchor once the cursor has travelled far enough. Anchoring
+    // periodically (rather than per pixel) is what keeps the live-wire honest —
+    // one dijkstra per anchor, and the locked-in path stops re-flowing behind you.
+    if (tool === 'sampler' && e.evt.buttons === 1 && samplerLassoRef.current) {
+      const st = samplerLassoRef.current;
+      const traced = samplerTraceTo(pos);
+      if (draftLineRef.current) {
+        draftLineRef.current.points([...st.committed, ...traced]);
+        draftStrokeLayerRef.current?.batchDraw();
+      }
+      const dx = pos.x - st.anchor.x;
+      const dy = pos.y - st.anchor.y;
+      if (dx * dx + dy * dy >= SAMPLER_ANCHOR_STEP * SAMPLER_ANCHOR_STEP) {
+        st.committed = [...st.committed, ...traced];
+        samplerReseed(pos);
+      }
+      return;
+    }
+
     // Threshold brush: stamp into the in-band mask and repaint the preview. Like
     // the brush, this writes nothing to the store until mouseup.
     if (tool === 'threshold' && e.evt.buttons === 1 && thresholdStrokeRef.current) {
@@ -1939,6 +2311,7 @@ export default function AnnotationCanvas({
     if (isDrawing) {
       commitDraftStroke();
       commitThresholdStroke();
+      finishSamplerLasso();
       setIsDrawing(false);
     }
     if (!sourceKey || !meta || activeClassId === null) return;
@@ -2002,6 +2375,7 @@ export default function AnnotationCanvas({
     if (isDrawing) {
       commitDraftStroke();
       commitThresholdStroke();
+      finishSamplerLasso();
       setIsDrawing(false);
     }
     setDragStart(null);
@@ -2594,7 +2968,7 @@ export default function AnnotationCanvas({
     <div
       ref={containerRef}
       className="relative w-full h-full bg-gray-900 overflow-hidden"
-      style={{ cursor: showBrushCursor ? 'none' : (tool === 'magnetic' || tool === 'magic' || tool === 'fill') ? 'crosshair' : undefined }}
+      style={{ cursor: showBrushCursor ? 'none' : (tool === 'magnetic' || tool === 'magic' || tool === 'fill' || tool === 'sampler') ? 'crosshair' : undefined }}
     >
       <Stage
         ref={stageRef}
diff --git a/frontend/src/components/annotate/Toolbar/index.tsx b/frontend/src/components/annotate/Toolbar/index.tsx
index 4144dde..97dfe33 100644
--- a/frontend/src/components/annotate/Toolbar/index.tsx
+++ b/frontend/src/components/annotate/Toolbar/index.tsx
@@ -4,7 +4,7 @@
  *           f=fill, s=select, g=magic, m=magnetic, Space=pan (hold), x=next slice,
  *           t=fit to screen, Ctrl/Cmd+Z=undo
  */
-import { Hand, Cursor, Polygon, MagnetStraight, MagicWand, Rectangle, Circle, PaintBrush, Drop, PaintBucket, Eraser, ArrowBendUpLeft, ArrowBendUpRight } from '@phosphor-icons/react';
+import { Hand, Cursor, Polygon, MagnetStraight, MagicWand, Rectangle, Circle, PaintBrush, Drop, Eyedropper, PaintBucket, Eraser, ArrowBendUpLeft, ArrowBendUpRight, ArrowCounterClockwise } from '@phosphor-icons/react';
 import { useMemo } from 'react';
 import { useStore } from 'zustand';
 import { useToolStore, type Tool } from '@/stores/toolStore';
@@ -15,8 +15,21 @@ import DebouncedSlider from '@/components/common/DebouncedSlider';
 import HistogramControl from '@/components/annotate/HistogramControl';
 import { otsuThreshold } from '@/lib/magicwand';
 import { displayAffineFor, remapHistogramToDisplay } from '@/lib/displayTransform';
+import { describeFit } from '@/lib/thresholdFit';
+
+import type { SamplerFit } from '@/components/annotate/AnnotationCanvas';
 import { useSam } from '@/hooks/useSam';
 
+/** Channel names in the user's terms, for the projection readout. */
+const CHANNEL_LABELS: Record<string, string> = {
+  intensity: 'brightness',
+  dogFine: 'fine texture',
+  dogCoarse: 'coarse texture',
+  localStd: 'graininess',
+  meanRatio: 'local contrast',
+};
+
+
 // macOS labels the Alt key "Option" (⌥); the key name only differs on screen.
 const IS_MAC = typeof navigator !== 'undefined' && /mac/i.test(navigator.userAgent);
 const REMOVE_KEY_LABEL = IS_MAC ? 'Option' : 'Alt';
@@ -81,9 +94,83 @@ function ToolButton({ tool, label, icon, keybind, activeTool, disabled, onSelect
   );
 }
 
+/** Readout for one Sampler fit: what it chose, how well it did, and an undo. */
+function SamplerResult({ fit, onRevert }: { fit: SamplerFit; onRevert?: () => void }) {
+  const { label, quality } = describeFit(fit);
+  const tone =
+    quality === 'good' ? 'text-emerald-600' : quality === 'fair' ? 'text-amber-600' : 'text-red-600';
+
+  // The band could not be expressed at the current display settings — applying it
+  // would have selected nothing, so nothing was applied.
+  if (fit.collapsed) {
+    return (
+      <div className="flex flex-col gap-1 rounded border border-amber-300 bg-amber-50 px-2 py-1.5">
+        <span className="text-[11px] font-medium text-amber-700">Band not applied</span>
+        <span className="text-[10px] leading-snug text-amber-700">
+          Your brightness/contrast/levels squash the fitted range ({fit.lo}–{fit.hi}) into a
+          single displayed value, so no band can express it. Reset Levels (or lower Contrast)
+          and sample again.
+        </span>
+      </div>
+    );
+  }
+
+  const projected = fit.mode === 'projected';
+  // Which channels the projection actually leaned on — the reason it beat plain
+  // brightness, in the user's terms rather than as a weight vector.
+  const topChannels = (fit.weights ?? [])
+    .map((w) => ({ ...w, mag: Math.abs(w.weight) }))
+    .sort((a, b) => b.mag - a.mag)
+    .filter((w) => w.mag > 0.15)
+    .slice(0, 2)
+    .map((w) => CHANNEL_LABELS[w.name] ?? w.name);
+
+  return (
+    <div className="flex flex-col gap-1 rounded border border-gray-200 bg-gray-50 px-2 py-1.5">
+      <div className="flex items-center justify-between">
+        <span className="text-[11px] text-gray-600">
+          Band <span className="tabular-nums font-medium">{fit.displayLo}–{fit.displayHi}</span>
+        </span>
+        {onRevert && (
+          <button
+            type="button"
+            onClick={onRevert}
+            title="Restore the band and blur from before this fit"
+            className="flex items-center gap-1 rounded px-1 py-0.5 text-[10px] text-gray-500 hover:bg-gray-200 hover:text-sky-700"
+          >
+            <ArrowCounterClockwise size={11} />
+            Revert
+          </button>
+        )}
+      </div>
+      <span className={`text-[10px] leading-snug ${tone}`}>{label}</span>
+      <span className="text-[10px] text-gray-500 tabular-nums">
+        match {(fit.dice * 100).toFixed(0)}% · skill {(fit.skill * 100).toFixed(0)}% ·{' '}
+        covers {(fit.coverage * 100).toFixed(0)}%
+        {fit.extraSigma > 0 && ` · blur ${fit.appliedBlur.toFixed(2)}`}
+      </span>
+      {projected && (
+        // The gate is no longer brightness, which changes how the rest of the
+        // panel behaves — say so rather than letting it be discovered.
+        <span className="text-[10px] leading-snug text-sky-700 border-t border-gray-200 pt-1">
+          Using a texture-aware score
+          {topChannels.length > 0 && ` (mostly ${topChannels.join(' + ')})`} — brightness alone
+          scored {((fit.intensitySkill ?? 0) * 100).toFixed(0)}%. The band below now applies to
+          that score, so the Display sliders no longer steer this brush. Sample a plain region
+          to go back to brightness.
+        </span>
+      )}
+    </div>
+  );
+}
+
 interface ToolbarProps {
   /** When true, drawing tools are greyed out (e.g. no class defined yet). */
   disabled?: boolean;
+  /** Latest Sampler lasso result, shown as a quality readout. */
+  samplerFit?: SamplerFit | null;
+  /** Restore the band/blur that were in force before the last fit. */
+  onRevertSamplerFit?: () => void;
   /** 256-bin luminance histogram of the current slice — drives the threshold band
    *  picker. Owned by AnnotatePage (the canvas emits it); null before load. It is
    *  sampled from the PREPROCESSED base, so it must be remapped through the display
@@ -96,7 +183,10 @@ interface ToolbarProps {
 }
 
 /** Renders the tool radiogroup, undo/redo, and the active tool's parameter controls. */
-export default function Toolbar({ disabled = false, histogramBins = null, display, upscale = 1 }: ToolbarProps) {
+export default function Toolbar({
+  disabled = false, histogramBins = null, display, upscale = 1,
+  samplerFit = null, onRevertSamplerFit,
+}: ToolbarProps) {
   const {
     tool, setTool, brushSize, setBrushSize, fillThreshold, setFillThreshold,
     thresholdLo, thresholdHi, setThresholdBand,
@@ -255,8 +345,33 @@ export default function Toolbar({ disabled = false, histogramBins = null, displa
         </div>
       )}
 
-      {tool === 'threshold' && (
+      {(tool === 'threshold' || tool === 'sampler') && (
         <div className="flex flex-col gap-2 mt-1">
+          {/* Sampling is a mode of this tool, not a tool of its own: its only
+              output is this panel's band (and blur), so it belongs here. */}
+          <div className="flex flex-col gap-1 rounded border border-sky-200 bg-sky-50 px-2 py-1.5">
+            <button
+              type="button"
+              onClick={() => setTool(tool === 'sampler' ? 'threshold' : 'sampler')}
+              aria-pressed={tool === 'sampler'}
+              className={cn(
+                'flex items-center justify-center gap-1.5 rounded-md py-1 text-xs font-medium border transition-colors',
+                tool === 'sampler'
+                  ? 'bg-sky-600 text-white border-sky-700'
+                  : 'bg-white text-sky-700 border-sky-300 hover:bg-sky-100',
+              )}
+            >
+              <Eyedropper size={14} />
+              {tool === 'sampler' ? 'Sampling — draw a loop' : 'Set band from a region'}
+            </button>
+            <p className="text-[10px] leading-snug text-sky-800/80">
+              Lasso one example of the feature. The band is fitted to match inside it and
+              avoid the ring just outside — which also highlights similar features elsewhere.
+              Nothing is annotated.
+            </p>
+            {samplerFit && <SamplerResult fit={samplerFit} onRevert={onRevertSamplerFit} />}
+          </div>
+
           <HistogramControl
             bins={bandHistogram}
             lo={thresholdLo}
diff --git a/frontend/src/hooks/useKeybinds.ts b/frontend/src/hooks/useKeybinds.ts
index ab78683..31cff08 100644
--- a/frontend/src/hooks/useKeybinds.ts
+++ b/frontend/src/hooks/useKeybinds.ts
@@ -2,6 +2,7 @@
  * useKeybinds — keyboard shortcuts for the annotation workspace.
  *
  * Tools:   p=polygon  l=ellipse  e=rectangle  r=eraser  b=brush  h=threshold brush
+ *          k=sampler (fits the threshold band from a lassoed example)
  *          f=fill  g=magic  m=magnetic  s=select  (each key is a letter in the label)
  * Pan:     hold Space (reverts to the previous tool on release)
  * View:    x=next slice (forward)  t=fit image to screen  arrows=prev/next slice
@@ -24,6 +25,7 @@ const TOOL_KEYBINDS: Record<string, Tool> = {
   l: 'ellipse',     // eLlipse (e is taken by rect)
   b: 'brush',
   h: 'threshold',   // tHreshold brush (t is fit-to-screen)
+  k: 'sampler',     // eyedropper lasso that fits the threshold band
   f: 'fill',
   r: 'eraser',      // eRaser
 };
diff --git a/frontend/src/lib/blur.ts b/frontend/src/lib/blur.ts
index 368f5bd..a342a85 100644
--- a/frontend/src/lib/blur.ts
+++ b/frontend/src/lib/blur.ts
@@ -83,6 +83,58 @@ function boxBlurV(
   }
 }
 
+/**
+ * Blur a single-channel float field in place, same Gaussian approximation as the
+ * RGBA version above.
+ *
+ * The Threshold Brush's field is already grayscale, so the sampler's blur sweep
+ * can work on it directly instead of round-tripping through RGBA — which also
+ * means a candidate sigma is evaluated on exactly the values the brush gates on.
+ */
+export function applyGaussianBlurGray(
+  data: Float32Array,
+  width: number,
+  height: number,
+  sigma: number,
+): void {
+  if (!(sigma > 0) || width === 0 || height === 0) return;
+  const scratch = new Float32Array(data.length);
+
+  const boxH = (src: Float32Array, dst: Float32Array, r: number) => {
+    const norm = 1 / (r + r + 1);
+    for (let y = 0; y < height; y++) {
+      const row = y * width;
+      const first = src[row];
+      let sum = (r + 1) * first;
+      for (let i = 0; i < r; i++) sum += src[row + Math.min(i, width - 1)];
+      for (let x = 0; x < width; x++) {
+        sum += src[row + Math.min(x + r, width - 1)];
+        sum -= x - r - 1 < 0 ? first : src[row + x - r - 1];
+        dst[row + x] = sum * norm;
+      }
+    }
+  };
+  const boxV = (src: Float32Array, dst: Float32Array, r: number) => {
+    const norm = 1 / (r + r + 1);
+    for (let x = 0; x < width; x++) {
+      const first = src[x];
+      let sum = (r + 1) * first;
+      for (let i = 0; i < r; i++) sum += src[Math.min(i, height - 1) * width + x];
+      for (let y = 0; y < height; y++) {
+        sum += src[Math.min(y + r, height - 1) * width + x];
+        sum -= y - r - 1 < 0 ? first : src[(y - r - 1) * width + x];
+        dst[y * width + x] = sum * norm;
+      }
+    }
+  };
+
+  for (const r of boxRadiiForGaussian(sigma, 3)) {
+    if (r <= 0) continue;
+    boxH(data, scratch, r);
+    boxV(scratch, data, r);
+  }
+}
+
 /**
  * Blur `data` (RGBA, row-major `width × height`) in place by a Gaussian of the
  * given sigma in pixels. No-op for sigma ≤ 0 or an empty image.
diff --git a/frontend/src/lib/featureChannels.test.ts b/frontend/src/lib/featureChannels.test.ts
new file mode 100644
index 0000000..178cc45
--- /dev/null
+++ b/frontend/src/lib/featureChannels.test.ts
@@ -0,0 +1,196 @@
+import { describe, it, expect } from 'vitest';
+import {
+  buildChannels,
+  fitProjection,
+  projectToScore,
+  CHANNEL_NAMES,
+  CHANNEL_COUNT,
+} from './featureChannels';
+import { fitBand, sampleHistograms } from './thresholdFit';
+
+const W = 64;
+const H = 64;
+
+function mulberry32(seed: number) {
+  return () => {
+    seed |= 0; seed = (seed + 0x6d2b79f5) | 0;
+    let t = Math.imul(seed ^ (seed >>> 15), 1 | seed);
+    t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t;
+    return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+  };
+}
+
+/** Inside the central square is the target; outside is background. */
+function masks(): { pos: Uint8Array; neg: Uint8Array; inside: (i: number) => boolean } {
+  const pos = new Uint8Array(W * H);
+  const neg = new Uint8Array(W * H);
+  const inside = (i: number) => {
+    const x = i % W;
+    const y = (i / W) | 0;
+    return x >= 16 && x < 48 && y >= 16 && y < 48;
+  };
+  for (let i = 0; i < W * H; i++) (inside(i) ? pos : neg)[i] = 1;
+  return { pos, neg, inside };
+}
+
+/** Best Dice achievable by thresholding a single channel directly. */
+function bandSkill(values: Float32Array, pos: Uint8Array, neg: Uint8Array): number {
+  const { posHist, negHist } = sampleHistograms(values, pos, neg);
+  return fitBand(posHist, negHist).skill;
+}
+
+describe('buildChannels', () => {
+  it('returns one aligned array per named channel', () => {
+    const field = new Float32Array(W * H).fill(100);
+    const channels = buildChannels(field, W, H);
+    expect(channels).toHaveLength(CHANNEL_COUNT);
+    expect(CHANNEL_NAMES).toHaveLength(CHANNEL_COUNT);
+    for (const c of channels) expect(c).toHaveLength(W * H);
+  });
+
+  it('keeps intensity as the first channel, unmodified', () => {
+    const field = new Float32Array(W * H);
+    for (let i = 0; i < field.length; i++) field[i] = i % 200;
+    const [intensity] = buildChannels(field, W, H);
+    expect(Array.from(intensity)).toEqual(Array.from(field));
+  });
+
+  it('gives a flat field no texture response', () => {
+    const field = new Float32Array(W * H).fill(77);
+    const [, dogFine, dogCoarse, localStd] = buildChannels(field, W, H);
+    for (let i = 0; i < field.length; i++) {
+      expect(Math.abs(dogFine[i])).toBeLessThan(1e-3);
+      expect(Math.abs(dogCoarse[i])).toBeLessThan(1e-3);
+      expect(localStd[i]).toBeLessThan(1e-2);
+    }
+  });
+
+  it('responds to fine texture where intensity is identical', () => {
+    // Both regions average 128; only the target is speckled.
+    const field = new Float32Array(W * H);
+    const { inside } = masks();
+    const rnd = mulberry32(3);
+    for (let i = 0; i < field.length; i++) {
+      field[i] = inside(i) ? (rnd() > 0.5 ? 168 : 88) : 128;
+    }
+    const [, , , localStd] = buildChannels(field, W, H);
+    let insideStd = 0, insideN = 0, outsideStd = 0, outsideN = 0;
+    for (let i = 0; i < field.length; i++) {
+      if (inside(i)) { insideStd += localStd[i]; insideN++; }
+      else { outsideStd += localStd[i]; outsideN++; }
+    }
+    expect(insideStd / insideN).toBeGreaterThan((outsideStd / outsideN) * 5);
+  });
+});
+
+describe('fitProjection + projectToScore', () => {
+  it('separates equal-brightness regions that differ only in texture', () => {
+    // The case Phase 1 cannot solve: identical means, different grain.
+    const field = new Float32Array(W * H);
+    const { pos, neg, inside } = masks();
+    const rnd = mulberry32(11);
+    for (let i = 0; i < field.length; i++) {
+      field[i] = inside(i) ? (rnd() > 0.5 ? 170 : 86) : 128 + (rnd() - 0.5) * 2;
+    }
+
+    const intensitySkill = bandSkill(field, pos, neg);
+    const channels = buildChannels(field, W, H);
+    const projection = fitProjection(channels, pos, neg);
+    expect(projection).not.toBeNull();
+    const score = projectToScore(channels, projection!);
+    const projectedSkill = bandSkill(score, pos, neg);
+
+    // A bimodal target against a mid-grey background IS partly reachable by an
+    // intensity band (it can grab one lobe), so the claim is not "intensity is
+    // useless" — it is that the projection is decisively better.
+    expect(intensitySkill).toBeLessThan(0.6);
+    expect(projectedSkill).toBeGreaterThan(0.9);
+    expect(projectedSkill).toBeGreaterThan(intensitySkill + 0.4);
+  });
+
+  it('leans on the texture channel when that is what distinguishes the regions', () => {
+    // Same fixture as above: equal means, different grain. The fitted weights
+    // should concentrate on localStd — evidence the projection is separating for
+    // the right reason rather than getting lucky.
+    const field = new Float32Array(W * H);
+    const { pos, neg, inside } = masks();
+    const rnd = mulberry32(11);
+    for (let i = 0; i < field.length; i++) {
+      field[i] = inside(i) ? (rnd() > 0.5 ? 170 : 86) : 128 + (rnd() - 0.5) * 2;
+    }
+    const projection = fitProjection(buildChannels(field, W, H), pos, neg)!;
+    const stdIndex = CHANNEL_NAMES.indexOf('localStd');
+    const dominant = projection.weights
+      .map((w, i) => ({ w: Math.abs(w), i }))
+      .sort((a, b) => b.w - a.w)[0].i;
+    expect(dominant).toBe(stdIndex);
+  });
+
+  it('separates a material whose brightness drifts across the image', () => {
+    // Target is always +25 above its surroundings, but absolute level ramps
+    // across x — so no single intensity band covers it.
+    const field = new Float32Array(W * H);
+    const { pos, neg, inside } = masks();
+    for (let i = 0; i < field.length; i++) {
+      const x = i % W;
+      const ramp = 40 + (x / W) * 150;
+      field[i] = inside(i) ? ramp + 25 : ramp;
+    }
+
+    const intensitySkill = bandSkill(field, pos, neg);
+    const channels = buildChannels(field, W, H);
+    const projection = fitProjection(channels, pos, neg)!;
+    const projectedSkill = bandSkill(projectToScore(channels, projection), pos, neg);
+
+    expect(projectedSkill).toBeGreaterThan(intensitySkill);
+    expect(projectedSkill).toBeGreaterThan(0.5);
+  });
+
+  it('does not do worse than intensity on a cleanly separable target', () => {
+    const field = new Float32Array(W * H);
+    const { pos, neg, inside } = masks();
+    for (let i = 0; i < field.length; i++) field[i] = inside(i) ? 200 : 60;
+
+    const channels = buildChannels(field, W, H);
+    const projection = fitProjection(channels, pos, neg)!;
+    const projectedSkill = bandSkill(projectToScore(channels, projection), pos, neg);
+    expect(projectedSkill).toBeGreaterThan(0.9);
+  });
+
+  it('returns normalised weights', () => {
+    const field = new Float32Array(W * H);
+    const { pos, neg, inside } = masks();
+    for (let i = 0; i < field.length; i++) field[i] = inside(i) ? 180 : 70;
+    const projection = fitProjection(buildChannels(field, W, H), pos, neg)!;
+    expect(Math.hypot(...projection.weights)).toBeCloseTo(1, 6);
+  });
+
+  it('returns null when a sample is empty', () => {
+    const field = new Float32Array(W * H).fill(120);
+    const channels = buildChannels(field, W, H);
+    const empty = new Uint8Array(W * H);
+    const full = new Uint8Array(W * H).fill(1);
+    expect(fitProjection(channels, empty, full)).toBeNull();
+    expect(fitProjection(channels, full, empty)).toBeNull();
+  });
+
+  it('returns null when nothing distinguishes the samples', () => {
+    // Identical constant field: every channel is constant, so no axis separates.
+    const field = new Float32Array(W * H).fill(120);
+    const { pos, neg } = masks();
+    expect(fitProjection(buildChannels(field, W, H), pos, neg)).toBeNull();
+  });
+
+  it('produces scores inside the 0–255 range the band fitter expects', () => {
+    const field = new Float32Array(W * H);
+    const { pos, neg, inside } = masks();
+    const rnd = mulberry32(5);
+    for (let i = 0; i < field.length; i++) field[i] = (inside(i) ? 150 : 90) + (rnd() - 0.5) * 40;
+    const channels = buildChannels(field, W, H);
+    const score = projectToScore(channels, fitProjection(channels, pos, neg)!);
+    for (let i = 0; i < score.length; i++) {
+      expect(score[i]).toBeGreaterThanOrEqual(0);
+      expect(score[i]).toBeLessThanOrEqual(255);
+    }
+  });
+});
diff --git a/frontend/src/lib/featureChannels.ts b/frontend/src/lib/featureChannels.ts
new file mode 100644
index 0000000..b247685
--- /dev/null
+++ b/frontend/src/lib/featureChannels.ts
@@ -0,0 +1,186 @@
+/**
+ * featureChannels — extra per-pixel descriptors for the Threshold Brush's fit.
+ *
+ * Phase 1 gates on intensity alone, which fails in two specific ways that show up
+ * constantly on tomography:
+ *
+ *   1. *Overlapping intensity.* Two materials share a grey range but differ in
+ *      texture or grain scale. No band on intensity can separate them.
+ *   2. *Drifting intensity.* The same material is darker on one side of the slice
+ *      (beam hardening, illumination), so one global band fits the middle and
+ *      misses both ends.
+ *
+ * Each channel below answers one of those. They are all built from Gaussian
+ * blurs, which are separable and O(n) — the same job an FFT band-pass would do
+ * for this purpose, without the transform.
+ *
+ * Everything is pure and canvas-free so the maths can be tested directly.
+ */
+import { applyGaussianBlurGray } from '@/lib/blur';
+
+/** Channels evaluated for every sampled pixel, in a fixed order. */
+export const CHANNEL_NAMES = ['intensity', 'dogFine', 'dogCoarse', 'localStd', 'meanRatio'] as const;
+export type ChannelName = (typeof CHANNEL_NAMES)[number];
+export const CHANNEL_COUNT = CHANNEL_NAMES.length;
+
+/** Scales (sigma, px) the texture channels are built at. */
+const SIGMA_FINE = 1;
+const SIGMA_MID = 2.5;
+const SIGMA_COARSE = 6;
+
+function blurred(src: Float32Array, w: number, h: number, sigma: number): Float32Array {
+  const out = Float32Array.from(src);
+  applyGaussianBlurGray(out, w, h, sigma);
+  return out;
+}
+
+/**
+ * Build every channel for a field crop.
+ *
+ * Returns one Float32Array per channel, all `w*h` long and index-aligned with
+ * `field`, so a pixel's feature vector is `channels.map(c => c[i])`.
+ */
+export function buildChannels(field: Float32Array, w: number, h: number): Float32Array[] {
+  const fine = blurred(field, w, h, SIGMA_FINE);
+  const mid = blurred(field, w, h, SIGMA_MID);
+  const coarse = blurred(field, w, h, SIGMA_COARSE);
+
+  const n = field.length;
+  const dogFine = new Float32Array(n);
+  const dogCoarse = new Float32Array(n);
+  const localStd = new Float32Array(n);
+  const meanRatio = new Float32Array(n);
+
+  // Local variance via E[x²] − E[x]², both from the same Gaussian window.
+  const sq = new Float32Array(n);
+  for (let i = 0; i < n; i++) sq[i] = field[i] * field[i];
+  const sqMean = blurred(sq, w, h, SIGMA_MID);
+
+  for (let i = 0; i < n; i++) {
+    // Difference-of-Gaussians: a band-pass. Responds to structure at a scale,
+    // which is how two materials of equal brightness but different grain are
+    // told apart (failure mode 1).
+    dogFine[i] = fine[i] - mid[i];
+    dogCoarse[i] = mid[i] - coarse[i];
+
+    const variance = Math.max(0, sqMean[i] - mid[i] * mid[i]);
+    localStd[i] = Math.sqrt(variance);
+
+    // Brightness relative to the neighbourhood rather than absolute. Constant for
+    // a material even where the illumination drifts (failure mode 2).
+    const denom = Math.abs(coarse[i]) + 1e-3;
+    meanRatio[i] = (field[i] - coarse[i]) / denom;
+  }
+
+  return [field, dogFine, dogCoarse, localStd, meanRatio];
+}
+
+/** Mean and standard deviation of `values` at the indices where `mask` is set. */
+function moments(values: Float32Array, mask: Uint8Array): { mean: number; count: number } {
+  let sum = 0;
+  let count = 0;
+  for (let i = 0; i < mask.length; i++) {
+    if (!mask[i]) continue;
+    sum += values[i];
+    count++;
+  }
+  return { mean: count ? sum / count : 0, count };
+}
+
+/**
+ * Weights projecting the channels onto the single axis that best separates the
+ * two samples — a diagonal-covariance LDA (a.k.a. naive Fisher discriminant).
+ *
+ * Full LDA would invert the channel covariance matrix; with five channels and
+ * possibly few sampled pixels that inverse is easily ill-conditioned, and a
+ * silently unstable projection is worse than a slightly suboptimal one. Using
+ * per-channel variance only is stable for any sample size and, once each channel
+ * is standardised, loses little.
+ *
+ * Returns weights aligned with `channels`, plus the per-channel standardisation
+ * needed to apply them to new pixels.
+ */
+export function fitProjection(
+  channels: Float32Array[],
+  pos: Uint8Array,
+  neg: Uint8Array,
+): { weights: number[]; centers: number[]; scales: number[] } | null {
+  const weights: number[] = [];
+  const centers: number[] = [];
+  const scales: number[] = [];
+
+  for (const channel of channels) {
+    const p = moments(channel, pos);
+    const n = moments(channel, neg);
+    if (p.count === 0 || n.count === 0) return null;
+
+    // Standardise on the pooled spread so channels with different units (grey
+    // levels vs a ratio) contribute comparably.
+    let ss = 0;
+    let total = 0;
+    for (const [mask, m] of [[pos, p.mean], [neg, n.mean]] as const) {
+      for (let i = 0; i < mask.length; i++) {
+        if (!mask[i]) continue;
+        const d = channel[i] - m;
+        ss += d * d;
+        total++;
+      }
+    }
+    const variance = total > 1 ? ss / (total - 1) : 0;
+    const sd = Math.sqrt(variance);
+    const center = (p.mean + n.mean) / 2;
+
+    if (!(sd > 1e-6)) {
+      // Constant channel — carries no information; drop it rather than dividing
+      // by ~0 and letting numerical noise dominate the projection.
+      weights.push(0);
+      centers.push(center);
+      scales.push(1);
+      continue;
+    }
+    weights.push((p.mean - n.mean) / sd);
+    centers.push(center);
+    scales.push(sd);
+  }
+
+  const magnitude = Math.hypot(...weights);
+  if (!(magnitude > 1e-9)) return null; // no channel separates anything
+  return { weights: weights.map((w) => w / magnitude), centers, scales };
+}
+
+/**
+ * Project every pixel onto the fitted axis and rescale to the 0–255 range the
+ * band fitter and the overlay already speak — so Phase 2 changes what the number
+ * *means* without changing any of the machinery that consumes it.
+ */
+export function projectToScore(
+  channels: Float32Array[],
+  projection: { weights: number[]; centers: number[]; scales: number[] },
+): Float32Array {
+  const n = channels[0].length;
+  const raw = new Float32Array(n);
+  const { weights, centers, scales } = projection;
+
+  for (let c = 0; c < channels.length; c++) {
+    const w = weights[c];
+    if (w === 0) continue;
+    const channel = channels[c];
+    const center = centers[c];
+    const scale = scales[c] || 1;
+    for (let i = 0; i < n; i++) raw[i] += w * ((channel[i] - center) / scale);
+  }
+
+  // Robust rescale: 1st–99th percentile onto 0–255, so a few extreme pixels can't
+  // squash everything else into one bin.
+  const sorted = Float32Array.from(raw).sort();
+  const lo = sorted[Math.floor(0.01 * (sorted.length - 1))];
+  const hi = sorted[Math.floor(0.99 * (sorted.length - 1))];
+  const span = hi - lo;
+  const out = new Float32Array(n);
+  if (!(span > 1e-9)) return out;
+  for (let i = 0; i < n; i++) {
+    const v = ((raw[i] - lo) / span) * 255;
+    out[i] = v < 0 ? 0 : v > 255 ? 255 : v;
+  }
+  return out;
+}
diff --git a/frontend/src/lib/thresholdFit.test.ts b/frontend/src/lib/thresholdFit.test.ts
new file mode 100644
index 0000000..1de14e6
--- /dev/null
+++ b/frontend/src/lib/thresholdFit.test.ts
@@ -0,0 +1,386 @@
+import { describe, it, expect } from 'vitest';
+import {
+  fitBand,
+  describeFit,
+  sampleHistograms,
+  backgroundRing,
+  fitWithBlurSweep,
+  combineSigma,
+  ringWidthFor,
+  MAX_RING_WIDTH,
+  type BandFit,
+} from './thresholdFit';
+import { dilate } from './morphology';
+import { applyGaussianBlurGray } from './blur';
+import { displayAffineFor, baseToDisplay, displayBandToBase, displayToBase } from './displayTransform';
+
+/** Histogram with `count` samples placed at each listed bin. */
+function hist(entries: Array<[number, number]>): Float64Array {
+  const h = new Float64Array(256);
+  for (const [bin, count] of entries) h[bin] += count;
+  return h;
+}
+
+/** Spread `count` samples uniformly across [lo,hi]. */
+function band(lo: number, hi: number, count: number): Float64Array {
+  const h = new Float64Array(256);
+  const per = count / (hi - lo + 1);
+  for (let i = lo; i <= hi; i++) h[i] += per;
+  return h;
+}
+
+/** Reference implementation: the same score, computed the slow obvious way. */
+function bruteForce(pos: ArrayLike<number>, neg: ArrayLike<number>): { lo: number; hi: number; dice: number } {
+  let best = { lo: 0, hi: 0, dice: -1 };
+  let totalPos = 0;
+  for (let i = 0; i < 256; i++) totalPos += pos[i];
+  for (let lo = 0; lo < 256; lo++) {
+    for (let hi = lo; hi < 256; hi++) {
+      let tp = 0;
+      let fp = 0;
+      for (let i = lo; i <= hi; i++) { tp += pos[i]; fp += neg[i]; }
+      if (tp === 0) continue;
+      const dice = (2 * tp) / (2 * tp + fp + (totalPos - tp));
+      if (dice > best.dice) best = { lo, hi, dice };
+    }
+  }
+  return best;
+}
+
+describe('fitBand — cleanly separated material', () => {
+  it('recovers the planted band almost exactly', () => {
+    // Feature at 100–140, surroundings well away at 10–40.
+    const fit = fitBand(band(100, 140, 1000), band(10, 40, 4000));
+    expect(fit.ok).toBe(true);
+    expect(fit.lo).toBeLessThanOrEqual(100);
+    expect(fit.hi).toBeGreaterThanOrEqual(140);
+    expect(fit.dice).toBeGreaterThan(0.99);
+    expect(fit.coverage).toBeGreaterThan(0.99);
+    expect(fit.leakage).toBeLessThan(0.01);
+  });
+
+  it('excludes a nearby but distinct background band', () => {
+    const fit = fitBand(band(120, 160, 1000), band(60, 110, 1000));
+    // The gap is at 111–119; the fitted low edge must sit inside it.
+    expect(fit.lo).toBeGreaterThan(110);
+    expect(fit.dice).toBeGreaterThan(0.95);
+  });
+});
+
+describe('fitBand — overlapping material', () => {
+  it('reports weak separation instead of a falsely confident band', () => {
+    // Same range for both: no band can separate them.
+    const fit = fitBand(band(80, 160, 1000), band(80, 160, 1000));
+    expect(fit.ok).toBe(true);
+    // Dice looks deceptively respectable here (0.67 is the FLOOR against an
+    // equal-sized ring, not a decent result) — skill is what exposes it.
+    expect(fit.dice).toBeCloseTo(fit.baselineDice, 6);
+    expect(fit.skill).toBeCloseTo(0, 6);
+    expect(describeFit(fit).quality).toBe('weak');
+  });
+
+  it('still returns the best achievable band on partial overlap', () => {
+    // Positives 100–160, negatives 130–200: the useful part is 100–129.
+    const fit = fitBand(band(100, 160, 1000), band(130, 200, 1000));
+    expect(fit.dice).toBeGreaterThan(0.5);
+    expect(fit.dice).toBeLessThan(0.95);
+    expect(fit.lo).toBeLessThanOrEqual(100);
+    // It should stop before swallowing the whole negative range.
+    expect(fit.hi).toBeLessThan(200);
+  });
+
+  it('grades fits by skill, not by raw Dice', () => {
+    const mk = (skill: number): BandFit => ({
+      lo: 0, hi: 1, dice: 0.9, baselineDice: 0.67, skill, coverage: 1, leakage: 0, ok: true,
+    });
+    expect(describeFit(mk(0.95)).quality).toBe('good');
+    expect(describeFit(mk(0.5)).quality).toBe('fair');
+    // High Dice but no skill => still weak, which is the whole point.
+    expect(describeFit(mk(0.05)).quality).toBe('weak');
+  });
+
+  it('reports skill relative to the ring size, so grading is size-independent', () => {
+    // Same inseparable overlap, but a 4x larger ring: Dice collapses to 0.33
+    // while skill stays at 0 — the grade must not move.
+    const small = fitBand(band(80, 160, 1000), band(80, 160, 1000));
+    const large = fitBand(band(80, 160, 1000), band(80, 160, 4000));
+    expect(small.dice).toBeGreaterThan(large.dice + 0.2);
+    expect(small.skill).toBeCloseTo(0, 6);
+    expect(large.skill).toBeCloseTo(0, 6);
+    expect(describeFit(small).quality).toBe(describeFit(large).quality);
+  });
+});
+
+describe('fitBand — agrees with a brute-force reference', () => {
+  function mulberry32(seed: number) {
+    return () => {
+      seed |= 0; seed = (seed + 0x6d2b79f5) | 0;
+      let t = Math.imul(seed ^ (seed >>> 15), 1 | seed);
+      t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t;
+      return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+    };
+  }
+
+  for (const seed of [1, 17, 99, 2024]) {
+    it(`matches on random histograms (seed ${seed})`, () => {
+      const rnd = mulberry32(seed);
+      const pos = new Float64Array(256);
+      const neg = new Float64Array(256);
+      for (let i = 0; i < 256; i++) {
+        pos[i] = Math.floor(rnd() * 30);
+        neg[i] = Math.floor(rnd() * 30);
+      }
+      const got = fitBand(pos, neg);
+      const want = bruteForce(pos, neg);
+      // The prefix-sum search must find the same optimum as the naive one.
+      expect(got.dice).toBeCloseTo(want.dice, 10);
+    });
+  }
+});
+
+describe('fitBand — degenerate inputs', () => {
+  it('reports not-ok when nothing was sampled', () => {
+    const fit = fitBand(new Float64Array(256), band(10, 20, 100));
+    expect(fit.ok).toBe(false);
+    expect(fit.dice).toBe(0);
+  });
+
+  it('handles an empty negative ring (selects the whole sample)', () => {
+    const fit = fitBand(band(50, 90, 500), new Float64Array(256));
+    expect(fit.ok).toBe(true);
+    expect(fit.dice).toBeCloseTo(1, 6);
+    expect(fit.leakage).toBe(0);
+  });
+
+  it('handles a single-valued sample', () => {
+    const fit = fitBand(hist([[123, 400]]), hist([[7, 400]]));
+    expect(fit.lo).toBeLessThanOrEqual(123);
+    expect(fit.hi).toBeGreaterThanOrEqual(123);
+    expect(fit.dice).toBeCloseTo(1, 6);
+  });
+
+  it('never returns NaN when both inputs are empty', () => {
+    const fit = fitBand(new Float64Array(256), new Float64Array(256));
+    expect(fit.ok).toBe(false);
+    expect(Number.isNaN(fit.dice)).toBe(false);
+  });
+
+  it('handles identical positive and negative sets without dividing by zero', () => {
+    const same = band(100, 100, 50);
+    const fit = fitBand(same, same);
+    expect(Number.isFinite(fit.dice)).toBe(true);
+    expect(fit.dice).toBeCloseTo(2 / 3, 6); // 2TP/(2TP+FP+FN) with FP=TP, FN=0
+  });
+});
+
+describe('sampleHistograms', () => {
+  it('bins each mask separately and ignores unmasked pixels', () => {
+    const field = [10, 10, 200, 200, 50, 50];
+    const pos = [1, 1, 0, 0, 0, 0];
+    const neg = [0, 0, 1, 1, 0, 0];
+    const { posHist, negHist, posCount, negCount } = sampleHistograms(field, pos, neg);
+    expect(posCount).toBe(2);
+    expect(negCount).toBe(2);
+    expect(posHist[10]).toBe(2);
+    expect(negHist[200]).toBe(2);
+    expect(posHist[50]).toBe(0); // outside both masks
+  });
+
+  it('clamps out-of-range and skips non-finite values', () => {
+    const field = [-40, 999, NaN, 128];
+    const pos = [1, 1, 1, 1];
+    const neg = [0, 0, 0, 0];
+    const { posHist, posCount } = sampleHistograms(field, pos, neg);
+    expect(posHist[0]).toBe(1);   // -40 clamped up
+    expect(posHist[255]).toBe(1); // 999 clamped down
+    expect(posHist[128]).toBe(1);
+    // A non-finite pixel is not a usable sample, so it is skipped entirely
+    // rather than being counted toward the sample size.
+    expect(posCount).toBe(3);
+  });
+
+  it('treats a pixel in both masks as positive', () => {
+    const { posHist, negHist } = sampleHistograms([77], [1], [1]);
+    expect(posHist[77]).toBe(1);
+    expect(negHist[77]).toBe(0);
+  });
+});
+
+describe('backgroundRing', () => {
+  it('surrounds the mask without overlapping it', () => {
+    const gw = 40, gh = 40;
+    const mask = new Uint8Array(gw * gh);
+    for (let y = 15; y < 25; y++) for (let x = 15; x < 25; x++) mask[y * gw + x] = 1;
+
+    const ring = backgroundRing(mask, gw, gh, dilate, 3);
+    for (let i = 0; i < mask.length; i++) {
+      if (mask[i]) expect(ring[i]).toBe(0); // disjoint from the sample
+    }
+    let ringCount = 0;
+    for (let i = 0; i < ring.length; i++) if (ring[i]) ringCount++;
+    expect(ringCount).toBeGreaterThan(0);
+  });
+
+  it('scales its width with the region when not given one', () => {
+    const gw = 80, gh = 80;
+    const small = new Uint8Array(gw * gh);
+    for (let y = 40; y < 44; y++) for (let x = 40; x < 44; x++) small[y * gw + x] = 1;
+    const big = new Uint8Array(gw * gh);
+    for (let y = 20; y < 60; y++) for (let x = 20; x < 60; x++) big[y * gw + x] = 1;
+
+    const area = (m: Uint8Array) => m.reduce((n, v) => n + (v ? 1 : 0), 0);
+    const ringSmall = area(backgroundRing(small, gw, gh, dilate));
+    const ringBig = area(backgroundRing(big, gw, gh, dilate));
+    expect(ringBig).toBeGreaterThan(ringSmall);
+  });
+
+  it('returns an empty ring for an empty mask', () => {
+    const empty = new Uint8Array(100);
+    const ring = backgroundRing(empty, 10, 10, dilate);
+    expect(ring.some((v) => v === 1)).toBe(false);
+  });
+});
+
+describe('fitWithBlurSweep', () => {
+  /** A speckled target: alternating pixels stray into the background's range. */
+  function speckled(w: number, h: number, targetVal: number, bgVal: number, noise: number) {
+    const field = new Float32Array(w * h);
+    const pos = new Uint8Array(w * h);
+    const neg = new Uint8Array(w * h);
+    let seed = 7;
+    const rnd = () => {
+      seed |= 0; seed = (seed + 0x6d2b79f5) | 0;
+      let t = Math.imul(seed ^ (seed >>> 15), 1 | seed);
+      t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t;
+      return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+    };
+    for (let y = 0; y < h; y++) {
+      for (let x = 0; x < w; x++) {
+        const i = y * w + x;
+        const inside = x >= w / 4 && x < (3 * w) / 4 && y >= h / 4 && y < (3 * h) / 4;
+        field[i] = (inside ? targetVal : bgVal) + (rnd() - 0.5) * 2 * noise;
+        if (inside) pos[i] = 1; else neg[i] = 1;
+      }
+    }
+    return { field, pos, neg };
+  }
+
+  it('prefers blur when the target is speckled into the background range', () => {
+    // Heavy noise: per-pixel the two classes overlap badly, but their MEANS differ,
+    // so smoothing should separate them.
+    const { field, pos, neg } = speckled(64, 64, 150, 110, 55);
+    const swept = fitWithBlurSweep(field, 64, 64, pos, neg, applyGaussianBlurGray);
+    const unblurred = fitBand(...(() => {
+      const { posHist, negHist } = sampleHistograms(field, pos, neg);
+      return [posHist, negHist] as const;
+    })());
+    expect(swept.extraSigma).toBeGreaterThan(0);
+    expect(swept.skill).toBeGreaterThan(unblurred.skill);
+  });
+
+  it('leaves a cleanly separated target unblurred', () => {
+    const { field, pos, neg } = speckled(64, 64, 200, 60, 2);
+    const swept = fitWithBlurSweep(field, 64, 64, pos, neg, applyGaussianBlurGray);
+    expect(swept.extraSigma).toBe(0);
+    expect(swept.skill).toBeGreaterThan(0.9);
+  });
+
+  it('does not mutate the caller’s field', () => {
+    const { field, pos, neg } = speckled(32, 32, 150, 110, 40);
+    const before = Float32Array.from(field);
+    fitWithBlurSweep(field, 32, 32, pos, neg, applyGaussianBlurGray);
+    expect(Array.from(field)).toEqual(Array.from(before));
+  });
+
+  it('returns not-ok when there is nothing to sample', () => {
+    const field = new Float32Array(64);
+    const empty = new Uint8Array(64);
+    const swept = fitWithBlurSweep(field, 8, 8, empty, empty, applyGaussianBlurGray);
+    expect(swept.ok).toBe(false);
+  });
+});
+
+describe('combineSigma', () => {
+  it('adds blurs in quadrature', () => {
+    expect(combineSigma(0, 2)).toBeCloseTo(2, 6);
+    expect(combineSigma(3, 4)).toBeCloseTo(5, 6);
+  });
+});
+
+describe('base <-> display round trip (the sampler’s load-bearing assumption)', () => {
+  // The fit runs on the field (BASE space) but the band is stored in DISPLAYED
+  // space. If that conversion drifted, the applied band would quietly select
+  // different pixels than the ones that were fitted.
+  it('recovers the fitted base band through any display transform', () => {
+    for (const [b, c, lo, hi, gamma] of [
+      [0, 0, 0, 255, 1],
+      [0.2, 30, 10, 240, 1],
+      [-0.1, -20, 0, 255, 1.8],
+      [0.05, 15, 40, 200, 0.6],
+    ] as const) {
+      const affine = displayAffineFor(b, c, lo, hi);
+      for (const [baseLo, baseHi] of [[60, 90], [100, 180], [20, 240]] as const) {
+        const dLo = baseToDisplay(baseLo, affine, gamma);
+        const dHi = baseToDisplay(baseHi, affine, gamma);
+        // Skip settings that clamp this band — the sampler detects and reports
+        // that case rather than applying an unrepresentable band.
+        if (Math.round(dHi) - Math.round(dLo) < 1) continue;
+        // Skip saturating endpoints too: displayBandToBase deliberately reopens
+        // an edge at 0/255 to ∓Infinity, which the sampler detects and refuses
+        // rather than applying a silently wider band.
+        if (Math.round(dLo) <= 0 || Math.round(dHi) >= 255) continue;
+        const back = displayBandToBase(Math.round(dLo), Math.round(dHi), affine, gamma);
+        // Integer storage costs up to half a displayed level. Its width in base
+        // units varies along the range once gamma is involved, so measure it at
+        // the endpoint rather than assuming the affine slope.
+        const width = (d: number) =>
+          Math.abs((displayToBase(d + 0.5, affine, gamma) ?? 0) - (displayToBase(d - 0.5, affine, gamma) ?? 0));
+        const tol = Math.max(0.51, width(Math.round(dLo)), width(Math.round(dHi))) + 0.01;
+        expect(Math.abs(back.lo - baseLo)).toBeLessThanOrEqual(tol);
+        expect(Math.abs(back.hi - baseHi)).toBeLessThanOrEqual(tol);
+      }
+    }
+  });
+
+  it('collapses a narrow band under heavy negative contrast', () => {
+    // Low contrast compresses the whole range, so neighbouring base values land
+    // on the same displayed value and no band can distinguish them.
+    const affine = displayAffineFor(0, -60, 0, 255);
+    const dLo = baseToDisplay(128, affine, 1);
+    const dHi = baseToDisplay(130, affine, 1);
+    expect(Math.round(dHi) - Math.round(dLo)).toBeLessThan(1);
+  });
+
+  it('reopens a saturated endpoint, which is why the sampler must check', () => {
+    // A bright fitted edge pushed past white comes back as +Infinity — applying
+    // that band would select everything above the fitted range.
+    const affine = displayAffineFor(0.5, 40, 0, 255);
+    const dHi = Math.round(baseToDisplay(200, affine, 1));
+    expect(dHi).toBeGreaterThanOrEqual(255);
+    const back = displayBandToBase(100, dHi, affine, 1);
+    expect(Number.isFinite(back.hi)).toBe(false);
+  });
+});
+
+describe('ringWidthFor', () => {
+  it('caps the ring so a large sample cannot explode the dilation cost', () => {
+    // sqrt(area)/2 for a 800x800 region is 400 — 400 full passes over the grid,
+    // which is what made a big lasso hang.
+    const big = new Uint8Array(1000 * 1000).fill(1);
+    expect(ringWidthFor(big)).toBe(MAX_RING_WIDTH);
+  });
+
+  it('still scales down for small samples', () => {
+    const small = new Uint8Array(100 * 100);
+    for (let y = 40; y < 60; y++) for (let x = 40; x < 60; x++) small[y * 100 + x] = 1;
+    const w = ringWidthFor(small);
+    expect(w).toBeGreaterThanOrEqual(4);
+    expect(w).toBeLessThan(MAX_RING_WIDTH);
+  });
+
+  it('has a floor so a tiny sample still gets a usable ring', () => {
+    const tiny = new Uint8Array(100);
+    tiny[55] = 1;
+    expect(ringWidthFor(tiny)).toBe(4);
+  });
+});
diff --git a/frontend/src/lib/thresholdFit.ts b/frontend/src/lib/thresholdFit.ts
new file mode 100644
index 0000000..627e90e
--- /dev/null
+++ b/frontend/src/lib/thresholdFit.ts
@@ -0,0 +1,276 @@
+/**
+ * thresholdFit — derive a Threshold Brush intensity band from a lassoed example.
+ *
+ * The user lassos one representative feature; everything inside is a positive
+ * example and a ring just outside it supplies negatives. The question "which band
+ * best fills that region and not its neighbours?" is then a supervised threshold
+ * fit with an *exact* answer, not something needing a search heuristic:
+ *
+ *   - histogram both sets into 256 bins,
+ *   - prefix-sum them, so any band's counts are two subtractions,
+ *   - evaluate every (lo, hi) pair and keep the best.
+ *
+ * That is 256² ≈ 33k ordered pairs of integer work — well under a millisecond —
+ * so the returned band is the true optimum over all bands, with no tuning
+ * constants and no iteration to converge.
+ *
+ * Deliberately pure and canvas-free: the correctness of this file is what makes
+ * the feature trustworthy, and it can be tested directly.
+ */
+
+/** How well a band matched the example, and what it selected. */
+export interface BandFit {
+  /** Inclusive bin bounds of the best band, in the field's own 0–255 space. */
+  lo: number;
+  hi: number;
+  /** Dice overlap with the positive sample, 0–1. */
+  dice: number;
+  /**
+   * Dice of the trivial "select everything" band — the score achievable with no
+   * separation at all. It depends on how big the ring is relative to the sample
+   * (equal sizes give 0.67; a 4x larger ring gives 0.33), which is exactly why
+   * raw Dice cannot be graded on its own.
+   */
+  baselineDice: number;
+  /**
+   * How far the fit closed the gap between that baseline and a perfect match:
+   * `(dice - baseline) / (1 - baseline)`, clamped to 0–1. This is the number to
+   * judge quality by — 0 means the band did no better than selecting everything,
+   * however flattering its Dice looks.
+   */
+  skill: number;
+  /** Fraction of the positive sample the band captures (recall). */
+  coverage: number;
+  /** Fraction of selected pixels that came from the negative ring. */
+  leakage: number;
+  /** False when there was nothing to fit (empty sample). */
+  ok: boolean;
+}
+
+const BINS = 256;
+
+/** An empty/degenerate result — a band that selects nothing. */
+const NO_FIT: BandFit = {
+  lo: 0, hi: 0, dice: 0, baselineDice: 0, skill: 0, coverage: 0, leakage: 0, ok: false,
+};
+
+/** Running totals of `hist`, where `cum[i]` counts bins 0..i inclusive. */
+function prefixSum(hist: ArrayLike<number>): Float64Array {
+  const cum = new Float64Array(BINS);
+  let running = 0;
+  for (let i = 0; i < BINS; i++) {
+    running += hist[i] ?? 0;
+    cum[i] = running;
+  }
+  return cum;
+}
+
+/** Count within `[lo,hi]` inclusive, from a prefix sum. */
+function rangeCount(cum: Float64Array, lo: number, hi: number): number {
+  return cum[hi] - (lo > 0 ? cum[lo - 1] : 0);
+}
+
+/**
+ * Best intensity band separating `posHist` (the lassoed feature) from `negHist`
+ * (its surroundings), maximising Dice overlap with the positives.
+ *
+ * Dice — 2TP / (2TP + FP + FN) — is used rather than raw accuracy because the
+ * negative ring usually outnumbers the positives, and accuracy would then be
+ * maximised by selecting almost nothing.
+ *
+ * @param posHist 256-bin histogram of values inside the lasso.
+ * @param negHist 256-bin histogram of values in the surrounding ring.
+ */
+export function fitBand(posHist: ArrayLike<number>, negHist: ArrayLike<number>): BandFit {
+  const cumPos = prefixSum(posHist);
+  const cumNeg = prefixSum(negHist);
+  const totalPos = cumPos[BINS - 1];
+  const totalNeg = cumNeg[BINS - 1];
+  if (totalPos <= 0) return { ...NO_FIT };
+
+  // The score a band gets for free by selecting the entire range. Quality has to
+  // be measured against this, not against zero.
+  const baselineDice = (2 * totalPos) / (2 * totalPos + totalNeg);
+
+  let best: BandFit = { ...NO_FIT, ok: true };
+  let bestDice = -1;
+
+  for (let lo = 0; lo < BINS; lo++) {
+    // Widening `hi` only ever adds counts, so both terms grow monotonically —
+    // but Dice itself is not monotonic, so every hi must still be evaluated.
+    for (let hi = lo; hi < BINS; hi++) {
+      const tp = rangeCount(cumPos, lo, hi);
+      if (tp === 0) continue; // selects none of the sample — cannot be best
+      const fp = rangeCount(cumNeg, lo, hi);
+      const fn = totalPos - tp;
+      const dice = (2 * tp) / (2 * tp + fp + fn);
+      if (dice > bestDice) {
+        bestDice = dice;
+        best = {
+          lo,
+          hi,
+          dice,
+          baselineDice,
+          skill: baselineDice < 1 ? Math.max(0, (dice - baselineDice) / (1 - baselineDice)) : 0,
+          coverage: tp / totalPos,
+          leakage: tp + fp > 0 ? fp / (tp + fp) : 0,
+          ok: true,
+        };
+      }
+    }
+  }
+
+  return bestDice < 0 ? { ...NO_FIT } : best;
+}
+
+/**
+ * Plain-language reading of a fit, so a weak result is visible as weak rather
+ * than being silently applied and discovered later while painting.
+ */
+export function describeFit(fit: BandFit): { label: string; quality: 'good' | 'fair' | 'weak' } {
+  if (!fit.ok) return { label: 'Nothing sampled', quality: 'weak' };
+  // Graded on `skill`, not `dice`: a feature that cannot be separated at all still
+  // scores 0.67 Dice against an equal-sized ring, and calling that "fair" would be
+  // exactly the false confidence this readout exists to prevent.
+  if (fit.skill >= 0.7) {
+    return { label: 'Strong — this band matches your sample closely', quality: 'good' };
+  }
+  if (fit.skill >= 0.4) {
+    return { label: 'Fair — expect some over- or under-fill', quality: 'fair' };
+  }
+  return {
+    label: 'Weak — intensity alone barely separates this from its surroundings',
+    quality: 'weak',
+  };
+}
+
+/**
+ * Histogram a field over two masks at once.
+ *
+ * `field` values are the 0–255 greys the Threshold Brush gates on; `pos` and
+ * `neg` are same-length 0/1 masks over the same grid.
+ */
+export function sampleHistograms(
+  field: ArrayLike<number>,
+  pos: ArrayLike<number>,
+  neg: ArrayLike<number>,
+): { posHist: Float64Array; negHist: Float64Array; posCount: number; negCount: number } {
+  const posHist = new Float64Array(BINS);
+  const negHist = new Float64Array(BINS);
+  let posCount = 0;
+  let negCount = 0;
+  const n = Math.min(field.length, pos.length, neg.length);
+  for (let i = 0; i < n; i++) {
+    const inPos = pos[i];
+    const inNeg = neg[i];
+    if (!inPos && !inNeg) continue;
+    let v = Math.round(field[i]);
+    if (!Number.isFinite(v)) continue;
+    v = v < 0 ? 0 : v > 255 ? 255 : v;
+    if (inPos) { posHist[v]++; posCount++; }
+    else { negHist[v]++; negCount++; }
+  }
+  return { posHist, negHist, posCount, negCount };
+}
+
+/** Sigmas the sampler tries, in addition to whatever blur is already applied. */
+export const BLUR_CANDIDATES: readonly number[] = [0, 0.5, 1, 1.5, 2, 3];
+
+/** Result of a sweep: the winning band and the extra blur it needed. */
+export interface SweepResult extends BandFit {
+  /** Additional sigma applied on top of the current setting (0 = none). */
+  extraSigma: number;
+}
+
+/**
+ * Fit a band across several candidate blur levels and keep the best.
+ *
+ * Blur is swept because — unlike the display sliders — it genuinely changes what
+ * is separable: it is a spatial operation, so it can pull a speckled feature's
+ * values together and away from its surroundings. Sigmas are *additional* blur on
+ * top of whatever the user has already set, since the field handed in is the one
+ * the brush currently uses.
+ *
+ * @param field  Crop of the threshold field (grays), row-major `w × h`.
+ * @param pos    Positive mask over the same crop.
+ * @param neg    Negative (ring) mask over the same crop.
+ * @param blurFn Injected gray blur (`blur.applyGaussianBlurGray`) so this module
+ *   stays dependency-free and directly testable.
+ */
+export function fitWithBlurSweep(
+  field: Float32Array,
+  w: number,
+  h: number,
+  pos: Uint8Array,
+  neg: Uint8Array,
+  blurFn: (data: Float32Array, w: number, h: number, sigma: number) => void,
+  sigmas: readonly number[] = BLUR_CANDIDATES,
+): SweepResult {
+  let best: SweepResult = { ...fitBand(new Float64Array(BINS), new Float64Array(BINS)), extraSigma: 0 };
+  let bestSkill = -1;
+
+  for (const sigma of sigmas) {
+    const candidate = sigma > 0 ? Float32Array.from(field) : field;
+    if (sigma > 0) blurFn(candidate, w, h, sigma);
+    const { posHist, negHist } = sampleHistograms(candidate, pos, neg);
+    const fit = fitBand(posHist, negHist);
+    if (!fit.ok) continue;
+    // Compare on skill, not Dice: Dice shifts with class balance, and blurring
+    // does not change the balance — but skill is the number the UI grades on, so
+    // optimising anything else could pick a sigma the readout then calls worse.
+    if (fit.skill > bestSkill) {
+      bestSkill = fit.skill;
+      best = { ...fit, extraSigma: sigma };
+    }
+  }
+  return best;
+}
+
+/** Combine two Gaussian blurs: sigmas add in quadrature. */
+export function combineSigma(current: number, extra: number): number {
+  return Math.sqrt(current * current + extra * extra);
+}
+
+/** Widest background ring, in grid cells. See `ringWidthFor`. */
+export const MAX_RING_WIDTH = 24;
+
+/**
+ * How wide a background ring to grow around a sample.
+ *
+ * Scales with the region so a small lasso gets a proportionate neighbourhood,
+ * but is **capped**: the ring is meant to be the immediate surroundings, and
+ * dilation costs one pass per cell of width. Uncapped, lassoing an 800x800
+ * region asked for a 400-cell ring — 400 passes over the grid, which is why a
+ * big sample used to hang. A capped ring is both faster and more correct: what
+ * a feature touches is a local question.
+ */
+export function ringWidthFor(mask: ArrayLike<number>): number {
+  let area = 0;
+  for (let i = 0; i < mask.length; i++) if (mask[i]) area++;
+  return Math.min(MAX_RING_WIDTH, Math.max(4, Math.round(Math.sqrt(area) / 2)));
+}
+
+/**
+ * Ring of background around `mask`: `dilate(mask, iters) \ mask`.
+ *
+ * Fitting against the immediate neighbourhood rather than the whole slice is
+ * what makes the result mean "fill this and not what it touches" — using the
+ * entire image as negatives would penalise the very lookalikes elsewhere that
+ * the user wants highlighted.
+ *
+ * @param dilateFn Injected (`morphology.dilate`) to keep this module canvas- and
+ *   dependency-free for testing.
+ */
+export function backgroundRing(
+  mask: Uint8Array,
+  gw: number,
+  gh: number,
+  dilateFn: (m: Uint8Array, gw: number, gh: number, iters: number) => Uint8Array,
+  iters?: number,
+): Uint8Array {
+  const width = iters ?? ringWidthFor(mask);
+  const grown = dilateFn(mask, gw, gh, width);
+  const ring = new Uint8Array(mask.length);
+  for (let i = 0; i < ring.length; i++) ring[i] = grown[i] && !mask[i] ? 1 : 0;
+  return ring;
+}
diff --git a/frontend/src/lib/thresholdRegularize.test.ts b/frontend/src/lib/thresholdRegularize.test.ts
new file mode 100644
index 0000000..4e70b84
--- /dev/null
+++ b/frontend/src/lib/thresholdRegularize.test.ts
@@ -0,0 +1,81 @@
+/**
+ * A per-pixel intensity gate speckles, and every speck becomes its own polygon —
+ * which is what made a single Threshold Brush stroke commit hundreds of shapes
+ * and dominate clip cost. These pin the opening + small-component pass that the
+ * commit path applies before vectorizing.
+ */
+import { describe, it, expect } from 'vitest';
+import { dilate, erode, removeSmallComponents } from './morphology';
+import { maskToPolygonsWithHoles } from './magicwand';
+
+const W = 256;
+const H = 256;
+
+function mulberry32(seed: number) {
+  return () => {
+    seed |= 0; seed = (seed + 0x6d2b79f5) | 0;
+    let t = Math.imul(seed ^ (seed >>> 15), 1 | seed);
+    t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t;
+    return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+  };
+}
+
+/** The commit path's regularization, in one place. */
+function regularize(mask: Uint8Array, minRegion = 12): Uint8Array {
+  return removeSmallComponents(dilate(erode(mask, W, H, 1), W, H, 1), W, H, minRegion);
+}
+
+const area = (m: Uint8Array) => m.reduce((n, v) => n + (v ? 1 : 0), 0);
+const polys = (m: Uint8Array) =>
+  maskToPolygonsWithHoles(m, W, H, { minRegion: 4, scale: 1 }).filter((p) => p.points.length >= 6);
+
+/** A solid square feature plus scattered thresholding noise. */
+function speckledFeature(noise: number) {
+  const mask = new Uint8Array(W * H);
+  const rnd = mulberry32(42);
+  for (let y = 80; y < 176; y++) for (let x = 80; x < 176; x++) mask[y * W + x] = 1;
+  for (let i = 0; i < mask.length; i++) if (rnd() > 1 - noise) mask[i] = 1;
+  return mask;
+}
+
+describe('threshold stroke regularization', () => {
+  it('collapses speckle into the real feature', () => {
+    const raw = speckledFeature(0.25);
+    const cleaned = regularize(raw);
+    // 96x96 = 9216 true pixels; the raw gate is far larger because of speckle.
+    expect(area(raw)).toBeGreaterThan(9216 * 1.5);
+    expect(area(cleaned)).toBeGreaterThan(9216 * 0.95);
+    expect(area(cleaned)).toBeLessThan(9216 * 1.05);
+  });
+
+  it('turns hundreds of speck polygons into one', () => {
+    const raw = speckledFeature(0.25);
+    expect(polys(raw).length).toBeGreaterThan(50);
+    expect(polys(regularize(raw)).length).toBe(1);
+  });
+
+  it('leaves a clean region essentially unchanged', () => {
+    const clean = new Uint8Array(W * H);
+    for (let y = 60; y < 200; y++) for (let x = 60; x < 200; x++) clean[y * W + x] = 1;
+    const out = regularize(clean);
+    expect(area(out)).toBeGreaterThan(area(clean) * 0.97);
+    expect(polys(out).length).toBe(1);
+  });
+
+  it('erases a hairline stroke entirely — which is why the commit keeps a fallback', () => {
+    // A 1px line cannot survive an erosion. The commit path detects this and
+    // falls back to the raw mask rather than discarding the user's stroke.
+    const hairline = new Uint8Array(W * H);
+    for (let x = 40; x < 200; x++) hairline[128 * W + x] = 1;
+    expect(area(regularize(hairline))).toBe(0);
+    expect(area(hairline)).toBeGreaterThan(0);
+  });
+
+  it('preserves a genuine hole rather than filling it', () => {
+    const donut = new Uint8Array(W * H);
+    for (let y = 60; y < 200; y++) for (let x = 60; x < 200; x++) donut[y * W + x] = 1;
+    for (let y = 110; y < 150; y++) for (let x = 110; x < 150; x++) donut[y * W + x] = 0;
+    const out = regularize(donut);
+    expect(polys(out)[0].holes.length).toBe(1);
+  });
+});
diff --git a/frontend/src/stores/toolStore.ts b/frontend/src/stores/toolStore.ts
index fad1fd2..c2217b1 100644
--- a/frontend/src/stores/toolStore.ts
+++ b/frontend/src/stores/toolStore.ts
@@ -3,7 +3,7 @@
  */
 import { create } from 'zustand';
 
-export type Tool = 'pan' | 'select' | 'polygon' | 'magnetic' | 'magic' | 'rectangle' | 'ellipse' | 'brush' | 'threshold' | 'fill' | 'eraser';
+export type Tool = 'pan' | 'select' | 'polygon' | 'magnetic' | 'magic' | 'rectangle' | 'ellipse' | 'brush' | 'threshold' | 'sampler' | 'fill' | 'eraser';
 
 export type MagicMode = 'contiguous' | 'global';
 /** Magic-selection engine: SAM (learned object prior) or the classic wand. */

From 33cd189393fac929b0e82d91b6af37a965ebb3fd Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 26 Aug 2026 10:50:01 -0700
Subject: [PATCH 05/38] integrating webgpu 3D viewer, and enabling loading from
 tiff stacks in addition to Zarr

---
 .github/workflows/ci.yml                      |   4 +
 .gitignore                                    |   4 +
 .gitmodules                                   |   4 +
 Dockerfile                                    |   7 +
 README.md                                     |   8 +
 backend/annotation_server.py                  | 151 +++-
 backend/schemas.py                            |  34 +
 backend/tests/test_tiff_stack_source.py       | 352 +++++++++
 backend/tests/test_volume_build.py            | 129 ++++
 backend/tests/test_volume_nodes.py            | 136 ++++
 backend/tiff_stack_source.py                  | 698 ++++++++++++++++++
 backend/volume_build.py                       | 203 +++++
 backend/volume_nodes.py                       | 128 ++++
 frontend/package-lock.json                    |   8 +
 frontend/package.json                         |   1 +
 frontend/src/app/App.tsx                      |  12 +-
 frontend/src/app/pages/VolumePage.tsx         | 130 ++++
 .../components/volume/BuildVolumePanel.tsx    | 178 +++++
 .../src/components/volume/VolumeViewer.tsx    | 142 ++++
 frontend/src/lib/zarrUrl.test.ts              |  70 ++
 frontend/src/lib/zarrUrl.ts                   | 106 +++
 frontend/src/types/webgpu.d.ts                |   8 +
 frontend/tsconfig.app.json                    |  29 +-
 frontend/vendor/view_tomography_recon_app     |   1 +
 frontend/vite.config.ts                       |   4 +
 start_all.sh                                  |  22 +
 tiled/config.yml                              |   5 +
 27 files changed, 2568 insertions(+), 6 deletions(-)
 create mode 100644 .gitmodules
 create mode 100644 backend/tests/test_tiff_stack_source.py
 create mode 100644 backend/tests/test_volume_build.py
 create mode 100644 backend/tests/test_volume_nodes.py
 create mode 100644 backend/tiff_stack_source.py
 create mode 100644 backend/volume_build.py
 create mode 100644 backend/volume_nodes.py
 create mode 100644 frontend/src/app/pages/VolumePage.tsx
 create mode 100644 frontend/src/components/volume/BuildVolumePanel.tsx
 create mode 100644 frontend/src/components/volume/VolumeViewer.tsx
 create mode 100644 frontend/src/lib/zarrUrl.test.ts
 create mode 100644 frontend/src/lib/zarrUrl.ts
 create mode 100644 frontend/src/types/webgpu.d.ts
 create mode 160000 frontend/vendor/view_tomography_recon_app

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 07874df..b511b9b 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -26,7 +26,11 @@ jobs:
       run:
         working-directory: frontend
     steps:
+      # The volume renderer lives in a submodule (frontend/vendor/); without it
+      # typecheck and build fail on an unresolved import.
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
       - uses: actions/setup-node@v4
         with:
           node-version: "20"
diff --git a/.gitignore b/.gitignore
index d7e46d2..7826956 100644
--- a/.gitignore
+++ b/.gitignore
@@ -23,6 +23,10 @@ build/
 # ingested locally are ignored. To commit a new demo dataset, use `git add -f`.
 .tiled/data/
 
+# Generated 3-D volume pyramids (backend/tiff_stack_source.py). Derived data —
+# rebuildable from the source TIFFs by re-registering, and large.
+.tiled/volumes/
+
 # Runtime Tiled thumbnail containers created on save
 **/*__v_thumbs/
 
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..d1edcb4
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,4 @@
+[submodule "frontend/vendor/view_tomography_recon_app"]
+	path = frontend/vendor/view_tomography_recon_app
+	url = https://github.com/als-computing/view_tomography_recon_app.git
+	branch = remote-tiled
diff --git a/Dockerfile b/Dockerfile
index 12ff00c..de63e36 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -9,6 +9,13 @@ WORKDIR /web
 COPY frontend/package.json frontend/package-lock.json ./
 RUN npm ci
 COPY frontend/ ./
+# The WebGPU volume renderer is a git submodule under frontend/vendor/. Docker
+# copies the working tree as-is, so an uninitialised submodule arrives as an
+# empty directory and the build fails deep inside Vite with an unresolved
+# import. Fail here instead, with the fix in the message.
+RUN test -f vendor/view_tomography_recon_app/src/zarr-viewer/src/ome-zarr-viewer.ts \
+    || (echo "ERROR: submodule frontend/vendor/view_tomography_recon_app is missing." \
+        && echo "Run: git submodule update --init --recursive" && exit 1)
 RUN npm run build   # → /web/dist
 
 # --- Stage 2: backend + built SPA ---
diff --git a/README.md b/README.md
index 79d33de..2e9ed5f 100644
--- a/README.md
+++ b/README.md
@@ -29,6 +29,7 @@ On first run this will automatically:
 - install [`uv`](https://docs.astral.sh/uv/) if it's missing,
 - create a `.venv` with Python 3.12 and install the backend dependencies,
 - generate a strong Tiled API key into `backend/.env` (gitignored, never sent to the browser),
+- initialise git submodules (the WebGPU renderer behind the **3D** tab),
 - vendor the SlimSAM model in the background so the AI Magic tool works offline,
 - start Tiled, the backend API, the frontend dev server, and the docs site.
 
@@ -114,6 +115,12 @@ pytest                                                        # tests
 
 Frontend (React + TypeScript + Vite):
 
+The 3D tab's volume renderer is a git submodule under `frontend/vendor/`, developed
+in its own repo ([als-computing/view_tomography_recon_app](https://github.com/als-computing/view_tomography_recon_app)).
+Clone with `--recurse-submodules`, or run `git submodule update --init --recursive`
+in an existing checkout — typecheck and build both fail without it. Changes to the
+renderer belong upstream; bump the pointer here with `git submodule update --remote`.
+
 ```bash
 cd frontend
 npm install
@@ -139,6 +146,7 @@ These are the same checks CI runs (see `.github/workflows/ci.yml`).
 ```
 backend/     FastAPI API, COCO/Lightly export, Tiled client, local-folder access
 frontend/    React SPA (Konva canvas, Zustand stores, in-browser SAM)
+frontend/vendor/  Git submodules — the WebGPU volume renderer used by the 3D tab
 tiled/        Local Tiled server config
 docs/         MkDocs Material documentation site
 start_all.sh  One-command launcher for the full stack
diff --git a/backend/annotation_server.py b/backend/annotation_server.py
index 91ce6ba..d206b44 100644
--- a/backend/annotation_server.py
+++ b/backend/annotation_server.py
@@ -42,8 +42,11 @@
 import guides as guides_mod
 import images as images_mod
 import ingest as ingest_mod
-import zarr_source
 import local_fs
+import tiff_stack_source
+import volume_build
+import volume_nodes
+import zarr_source
 from browse_helpers import (
     _SINGLE_VALUE_FACET_RAW_KEYS,
     FieldMapping,
@@ -69,9 +72,11 @@
     GuidePayload,
     ImageMeta,
     IngestPreflightRequest,
-    ZarrRegisterRequest,
     MeasureRequest,
     SaveVersionRequest,
+    TiffStackRegisterRequest,
+    VolumeBuildRequest,
+    ZarrRegisterRequest,
 )
 from source_keys import parse_source_key
 from thumbnails import render_thumbnail
@@ -1308,6 +1313,148 @@ async def zarr_register(req: ZarrRegisterRequest) -> dict:
     )
 
 
+@app.get("/api/volume/resolve")
+async def volume_resolve(
+    source: str = Query(..., description="Tiled path of the open dataset"),
+    server_uri: Optional[str] = Query(None),
+) -> dict:
+    """Locate the renderable 3-D volume for the open dataset.
+
+    Which node holds it depends on how the dataset was catalogued — a registered
+    Zarr volume is one already, a TIFF stack's lives in its ``__volume`` sidecar,
+    and a stack nobody has built one for has none. The frontend cannot tell these
+    apart from the path, and guessing produces
+    ``missing multiscales in root .zattrs`` at the viewer instead of an answer.
+    """
+    return await asyncio.to_thread(volume_nodes.resolve_volume, server_uri, source)
+
+
+@app.get("/api/volume/build/inspect")
+async def volume_build_inspect(
+    source: str = Query(..., description="Tiled path of the per-slice dataset"),
+    kind: str = Query("tiled"),
+    server_uri: Optional[str] = Query(None),
+) -> dict:
+    """Describe the 3-D volume that would be built for this dataset."""
+    return await asyncio.to_thread(
+        volume_build.inspect_volume_build, source, kind, server_uri
+    )
+
+
+@app.post("/api/volume/build")
+async def volume_build_start(req: VolumeBuildRequest) -> dict:
+    """Build a 3-D volume from a slice stack already in the catalog.
+
+    Needs nothing but the open dataset: the slices are already in Tiled, so
+    asking for a source directory would be asking the user to re-supply data the
+    app has. Returns a ``job_id``; poll ``GET /api/export/status/{job_id}``.
+    """
+    info = await asyncio.to_thread(
+        volume_build.inspect_volume_build, req.source, req.kind, req.server_uri
+    )
+    jid = export_jobs.new_job(req.source)
+    export_jobs.set_total(jid, max(info["slices_to_read"], 1))
+
+    def _run() -> None:
+        try:
+            export_jobs.update(jid, state="running", phase="building")
+
+            def _progress(message: str, done: int, total: int) -> None:
+                export_jobs.update(jid, phase=message, done=done, total=max(total, 1))
+
+            result = volume_build.build_volume(
+                req.source, req.kind, req.server_uri, req.container_path, progress=_progress
+            )
+            export_jobs.update(jid, state="done", phase="done", result=result)
+            export_jobs.log(jid, f"Built 3-D volume {result['key']!r}.")
+        except HTTPException as exc:
+            export_jobs.update(jid, state="error", phase="error", error=str(exc.detail))
+        except Exception as exc:  # noqa: BLE001 — surfaced to the UI via the job
+            logger.exception("volume build failed")
+            export_jobs.update(jid, state="error", phase="error", error=str(exc))
+
+    threading.Thread(target=_run, daemon=True).start()
+    return {"job_id": jid, **info}
+
+
+@app.get("/api/tiff-stack/inspect")
+async def tiff_stack_inspect(
+    path: str = Query(..., description="Absolute path to a directory of TIFF slices"),
+) -> dict:
+    """Describe a TIFF directory and the pyramid that would be built for it.
+
+    Reads only the first file, so this is cheap enough to call while the user is
+    still typing a path. ``slices_to_read`` lets the UI say up front how much
+    work registration will be, rather than appearing to hang.
+
+    Raises:
+        HTTPException: 4xx from :func:`tiff_stack_source.inspect_tiff_stack` with
+            a user-facing message (missing path, no TIFFs, inconsistent
+            numbering…).
+    """
+    return await asyncio.to_thread(tiff_stack_source.inspect_tiff_stack, path)
+
+
+@app.post("/api/tiff-stack/preflight")
+async def tiff_stack_preflight(req: TiffStackRegisterRequest) -> dict:
+    """Report whether registering this TIFF stack would collide, changing nothing."""
+    return await asyncio.to_thread(
+        tiff_stack_source.preflight_tiff_stack, req.server_uri, req.path, req.container_path
+    )
+
+
+@app.post("/api/tiff-stack/register")
+async def tiff_stack_register(req: TiffStackRegisterRequest) -> dict:
+    """Register a TIFF directory as a 3-D multiscale volume, copying no slices.
+
+    Returns a ``job_id`` immediately; poll ``GET /api/export/status/{job_id}``.
+    A job rather than a straight call because — unlike Zarr registration, which
+    only writes catalog rows — the downsampled levels the 3-D viewer renders have
+    to be computed, and that means reading every source slice once.
+
+    The full-resolution slices themselves are registered in place: no pixels are
+    copied, and the existing per-slice nodes the 2-D canvas reads are untouched.
+    """
+    if req.on_conflict not in ingest_mod.ON_CONFLICT_MODES:
+        raise HTTPException(
+            400, f"on_conflict must be one of {sorted(ingest_mod.ON_CONFLICT_MODES)}"
+        )
+    # Validate before returning a job id, so a bad path is a 4xx the user sees
+    # immediately rather than a job that fails a second later.
+    info = await asyncio.to_thread(tiff_stack_source.inspect_tiff_stack, req.path)
+
+    jid = export_jobs.new_job(req.path)
+    # Every source slice is read exactly once: the finest generated level comes
+    # from the TIFFs, the coarser ones cascade from it in memory.
+    export_jobs.set_total(jid, max(info["slices_to_read"], 1))
+
+    def _run() -> None:
+        try:
+            export_jobs.update(jid, state="running", phase="registering")
+
+            def _progress(message: str, done: int, total: int) -> None:
+                export_jobs.update(jid, phase=message, done=done, total=max(total, 1))
+
+            result = tiff_stack_source.register_tiff_stack(
+                req.server_uri,
+                req.path,
+                req.container_path,
+                req.description,
+                req.on_conflict,
+                progress=_progress,
+            )
+            export_jobs.update(jid, state="done", phase="done", result=result)
+            export_jobs.log(jid, f"Registered {result['key']!r} as a 3-D volume.")
+        except HTTPException as exc:
+            export_jobs.update(jid, state="error", phase="error", error=str(exc.detail))
+        except Exception as exc:  # noqa: BLE001 — surfaced to the UI via the job
+            logger.exception("tiff stack registration failed")
+            export_jobs.update(jid, state="error", phase="error", error=str(exc))
+
+    threading.Thread(target=_run, daemon=True).start()
+    return {"job_id": jid, **info}
+
+
 @app.get("/health")
 async def health() -> dict[str, str]:
     return {"status": "ok"}
diff --git a/backend/schemas.py b/backend/schemas.py
index 68969fe..a264f00 100644
--- a/backend/schemas.py
+++ b/backend/schemas.py
@@ -303,6 +303,40 @@ class ZarrRegisterRequest(BaseModel):
     server_uri: str | None = None
 
 
+class TiffStackRegisterRequest(ZarrRegisterRequest):
+    """Request body for registering a TIFF directory as a 3-D volume.
+
+    Same fields as :class:`ZarrRegisterRequest` — ``path`` is a directory of 2-D
+    TIFF slices rather than a ``.zarr`` store. Kept as its own type so the two
+    endpoints document themselves and can diverge without a breaking change.
+
+    Unlike the Zarr path this does real work: the full-resolution slices are
+    registered in place, but the downsampled pyramid levels the 3-D viewer
+    actually renders have to be computed, so registration runs as a job.
+    """
+
+
+class VolumeBuildRequest(BaseModel):
+    """Request body for building a 3-D volume from a stack already in Tiled.
+
+    Deliberately minimal: the slices are already in the catalog, so the only
+    thing needed is which dataset. No source path, because requiring one would
+    mean asking the user to re-supply data the app already holds.
+
+    Attributes:
+        source: Tiled path of the per-slice dataset.
+        kind: Source kind; only ``"tiled"`` has a catalog to register into.
+        container_path: Where to place the volume sidecar; defaults to the
+            dataset's own parent container.
+        server_uri: Target Tiled server URI; ``None`` uses the default server.
+    """
+
+    source: str
+    kind: str = "tiled"
+    container_path: str | None = None
+    server_uri: str | None = None
+
+
 class GuideClass(BaseModel):
     """One class entry in an annotation guide.
 
diff --git a/backend/tests/test_tiff_stack_source.py b/backend/tests/test_tiff_stack_source.py
new file mode 100644
index 0000000..e32ae96
--- /dev/null
+++ b/backend/tests/test_tiff_stack_source.py
@@ -0,0 +1,352 @@
+"""Tests for exposing a TIFF directory as a streamable 3-D Zarr volume.
+
+The registration path needs a live Tiled server, so what is pinned here is
+everything that decides *correctness* and can be checked without one: the
+pyramid plan, the downsampling arithmetic, the OME-NGFF metadata shape, and the
+inspection guards that stop a shuffled or unusable stack from being registered.
+Fixtures write tiny TIFFs on the fly, so these run anywhere.
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+from fastapi import HTTPException
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import tiff_stack_source as tss  # noqa: E402
+
+tifffile = pytest.importorskip("tifffile")
+
+
+def write_stack(
+    root: Path, n: int = 8, h: int = 16, w: int = 16, pad: int = 4, dtype=np.uint16
+) -> Path:
+    """A directory of zero-padded 2-D TIFFs whose value encodes the slice index."""
+    root.mkdir(parents=True, exist_ok=True)
+    for i in range(n):
+        frame = np.full((h, w), i, dtype=dtype)
+        tifffile.imwrite(str(root / f"img_{i:0{pad}d}.tif"), frame)
+    return root
+
+
+class TestPyramidPlan:
+    def test_no_levels_when_already_small(self):
+        # Nothing to generate: scale0 alone already fits a GPU texture.
+        assert tss.pyramid_plan((10, 64, 64), target_dim=384) == []
+
+    def test_first_level_brings_every_axis_under_target(self):
+        plan = tss.pyramid_plan((2000, 3232, 3232), target_dim=384)
+        assert plan, "a 3232-wide stack must generate levels"
+        first = plan[0]
+        assert max(first["shape"]) <= 384
+        # Power-of-two factors keep the block mean exact.
+        for f in first["factor"]:
+            assert f & (f - 1) == 0
+
+    def test_factor_is_the_smallest_that_fits(self):
+        # 3232/8 = 404 > 384, so 8 is not enough and 16 is the answer.
+        assert tss.pyramid_plan((2000, 3232, 3232), target_dim=384)[0]["factor"] == [8, 16, 16]
+
+    def test_levels_double_and_are_named_in_order(self):
+        plan = tss.pyramid_plan((2000, 3232, 3232), target_dim=384, levels=3)
+        assert [p["path"] for p in plan] == ["scale1", "scale2", "scale3"]
+        assert [p["factor"] for p in plan] == [[8, 16, 16], [16, 32, 32], [32, 64, 64]]
+
+    def test_anisotropic_stack_still_gets_levels(self):
+        # 4 slices of 4096x4096 is an ordinary tomography shape. A single shared
+        # factor picked for the wide axes would ask for 4//16 = 0 slices and drop
+        # every level, leaving nothing renderable — which looks exactly like a
+        # broken viewer. Per-axis factors keep z alive.
+        plan = tss.pyramid_plan((4, 4096, 4096), target_dim=384, levels=3)
+        assert plan, "the xy axes still need downsampling"
+        for level in plan:
+            assert min(level["shape"]) >= 1
+        # z has nowhere to go, so it is left alone rather than collapsed.
+        assert all(level["shape"][0] >= 1 for level in plan)
+        assert plan[0]["factor"][0] == 1
+
+    def test_does_not_emit_a_level_identical_to_the_one_above(self):
+        # Once every axis has bottomed out, another level would cost bytes and
+        # add no detail.
+        plan = tss.pyramid_plan((2, 1024, 1024), target_dim=384, levels=6)
+        shapes = [tuple(level["shape"]) for level in plan]
+        assert len(shapes) == len(set(shapes))
+
+    def test_degenerate_shape_yields_nothing(self):
+        assert tss.pyramid_plan((0, 512, 512)) == []
+
+
+class TestBlockMean:
+    def test_averages_over_the_whole_block(self):
+        block = np.stack([np.full((4, 4), 10.0), np.full((4, 4), 20.0)])
+        out = tss.block_mean(block, 2, 2)
+        assert out.shape == (2, 2)
+        # Mean over z as well as over each 2x2 tile.
+        assert np.allclose(out, 15.0)
+
+    def test_drops_partial_tiles_rather_than_averaging_them(self):
+        # 5 columns with fx=2: the last column cannot fill a tile. Including it
+        # would make that output column brighter purely because of where the
+        # edge fell.
+        block = np.ones((1, 4, 5), dtype=np.float32)
+        assert tss.block_mean(block, 2, 2).shape == (2, 2)
+
+    def test_does_not_overflow_integer_inputs(self):
+        # uint16 sums overflow almost immediately; the mean must be exact.
+        block = np.full((4, 4, 4), 60000, dtype=np.uint16)
+        assert np.allclose(tss.block_mean(block, 2, 2), 60000.0)
+
+    def test_rejects_zero_factor(self):
+        with pytest.raises(ValueError):
+            tss.block_mean(np.ones((1, 2, 2), dtype=np.float32), 0, 1)
+
+
+class TestMultiscalesMetadata:
+    def test_nests_under_attributes(self):
+        # Tiled's .zattrs route returns metadata["attributes"] verbatim; anywhere
+        # else and the viewer reports "missing multiscales".
+        meta = tss.multiscales_metadata("sample", [])
+        assert "multiscales" in meta["attributes"]
+
+    def test_always_includes_scale0_first(self):
+        datasets = tss.multiscales_metadata("s", [])["attributes"]["multiscales"][0]["datasets"]
+        assert [d["path"] for d in datasets] == ["scale0"]
+
+    def test_lists_every_generated_level_with_a_scaled_transform(self):
+        plan = tss.pyramid_plan((2000, 3232, 3232), target_dim=384)
+        ms = tss.multiscales_metadata("s", plan)["attributes"]["multiscales"][0]
+        # scale0 is the in-place TIFF sequence; the generated levels live in the
+        # registered sidecar sub-group, so their paths are nested.
+        assert [d["path"] for d in ms["datasets"]] == ["scale0"] + [
+            f"{tss.PYRAMID_KEY}/{p['path']}" for p in plan
+        ]
+        # A level downsampled by f on an axis covers f times as much space per
+        # voxel on that axis — anisotropic factors must survive into the transform.
+        for dataset, level in zip(ms["datasets"][1:], plan):
+            scale = dataset["coordinateTransformations"][0]["scale"]
+            assert scale == [float(f) for f in level["factor"]]
+
+    def test_declares_zyx_axes(self):
+        ms = tss.multiscales_metadata("s", [])["attributes"]["multiscales"][0]
+        assert [a["name"] for a in ms["axes"]] == ["z", "y", "x"]
+
+
+class TestInspect:
+    def test_describes_a_well_formed_stack(self, tmp_path):
+        info = tss.inspect_tiff_stack(str(write_stack(tmp_path / "scan", n=8, h=16, w=16)))
+        assert info["full_shape"] == [8, 16, 16]
+        assert info["dtype"] == "uint16"
+        assert info["name"] == "scan"
+
+    def test_files_are_in_slice_order(self, tmp_path):
+        root = write_stack(tmp_path / "scan", n=12)
+        names = [p.name for p in tss.tiff_files(root)]
+        assert names == sorted(names)
+        assert names[0].endswith("0000.tif") and names[-1].endswith("0011.tif")
+
+    def test_rejects_inconsistent_numbering(self, tmp_path):
+        # Unpadded names sort lexically as img_1, img_10, img_2 — registering
+        # that as-is would build a shuffled volume, silently.
+        root = tmp_path / "scan"
+        root.mkdir()
+        for i in (1, 2, 10):
+            tifffile.imwrite(str(root / f"img_{i}.tif"), np.zeros((4, 4), np.uint16))
+        with pytest.raises(HTTPException) as excinfo:
+            tss.inspect_tiff_stack(str(root))
+        assert excinfo.value.status_code == 422
+        assert "order" in excinfo.value.detail
+
+    def test_rejects_a_single_image(self, tmp_path):
+        root = write_stack(tmp_path / "scan", n=1)
+        with pytest.raises(HTTPException) as excinfo:
+            tss.inspect_tiff_stack(str(root))
+        assert excinfo.value.status_code == 422
+        assert "volume" in excinfo.value.detail
+
+    def test_rejects_a_directory_with_no_tiffs(self, tmp_path):
+        (tmp_path / "empty").mkdir()
+        with pytest.raises(HTTPException) as excinfo:
+            tss.inspect_tiff_stack(str(tmp_path / "empty"))
+        assert excinfo.value.status_code == 422
+
+    def test_rejects_a_relative_path(self, tmp_path):
+        with pytest.raises(HTTPException) as excinfo:
+            tss.inspect_tiff_stack("relative/scan")
+        assert excinfo.value.status_code == 400
+
+    def test_reports_no_reads_when_no_pyramid_is_needed(self, tmp_path):
+        # A small stack needs no generated levels, so registration is instant —
+        # the UI should not warn about a long job.
+        info = tss.inspect_tiff_stack(str(write_stack(tmp_path / "scan", n=4, h=8, w=8)))
+        assert info["pyramid_plan"] == []
+        assert info["slices_to_read"] == 0
+
+
+class TestPyramidStore:
+    """The generated levels are written as a real on-disk Zarr store.
+
+    Not with Tiled's ``write_array``: its ``/zarr/v2`` chunk route only serves
+    externally-managed arrays, and the Zarr façade is exactly what the 3-D viewer
+    reads. See this module's docstring.
+    """
+
+    def test_writes_a_readable_zarr_v2_group(self, tmp_path, monkeypatch):
+        zarr = pytest.importorskip("zarr")
+        monkeypatch.setenv("VOLUME_CACHE_DIR", str(tmp_path / "cache"))
+        levels = {
+            "scale1": np.arange(2 * 4 * 4, dtype=np.float32).reshape(2, 4, 4),
+            "scale2": np.ones((1, 2, 2), dtype=np.float32),
+        }
+        store = tss.write_pyramid_store("sample__volume", levels)
+
+        assert store.is_dir()
+        group = zarr.open_group(str(store), mode="r")
+        for name, expected in levels.items():
+            assert np.array_equal(group[name][:], expected)
+
+    def test_is_zarr_v2_not_v3(self, tmp_path, monkeypatch):
+        # Tiled's /zarr/v2 facade and the viewer's reader both speak v2; a v3
+        # store would write `zarr.json` and the viewer would find no `.zarray`.
+        monkeypatch.setenv("VOLUME_CACHE_DIR", str(tmp_path / "cache"))
+        store = tss.write_pyramid_store(
+            "sample__volume", {"scale1": np.zeros((1, 2, 2), np.float32)}
+        )
+        assert (store / ".zgroup").exists()
+        assert (store / "scale1" / ".zarray").exists()
+
+    def test_chunks_one_slice_at_a_time(self, tmp_path, monkeypatch):
+        zarr = pytest.importorskip("zarr")
+        monkeypatch.setenv("VOLUME_CACHE_DIR", str(tmp_path / "cache"))
+        store = tss.write_pyramid_store(
+            "sample__volume", {"scale1": np.zeros((6, 8, 8), np.float32)}
+        )
+        assert zarr.open_group(str(store), mode="r")["scale1"].chunks == (1, 8, 8)
+
+    def test_replaces_a_previous_build(self, tmp_path, monkeypatch):
+        # Re-registering must not leave a stale volume behind for the same key.
+        zarr = pytest.importorskip("zarr")
+        monkeypatch.setenv("VOLUME_CACHE_DIR", str(tmp_path / "cache"))
+        tss.write_pyramid_store("sample__volume", {"scale1": np.zeros((4, 4, 4), np.float32)})
+        store = tss.write_pyramid_store(
+            "sample__volume", {"scale1": np.ones((2, 2, 2), np.float32)}
+        )
+        group = zarr.open_group(str(store), mode="r")
+        assert group["scale1"].shape == (2, 2, 2)
+        assert list(group.array_keys()) == ["scale1"]
+
+    def test_honours_the_cache_dir_override(self, tmp_path, monkeypatch):
+        # Beamline reconstruction directories are routinely read-only, so the
+        # pyramid must never be written next to the source.
+        monkeypatch.setenv("VOLUME_CACHE_DIR", str(tmp_path / "elsewhere"))
+        store = tss.write_pyramid_store(
+            "sample__volume", {"scale1": np.zeros((1, 2, 2), np.float32)}
+        )
+        assert str(store).startswith(str(tmp_path / "elsewhere"))
+
+
+class TestRegisteredKey:
+    def test_is_a_sidecar_of_the_dataset_key(self, tmp_path):
+        # The per-slice container Annotate reads already owns the unsuffixed key.
+        # Colliding with it would make the 3-D view impossible for precisely the
+        # datasets it exists to serve.
+        root = write_stack(tmp_path / "rec20230224_sea_shell")
+        key = tss.registered_key(root)
+        assert key == f"rec20230224_sea_shell{tss.VOLUME_SUFFIX}"
+
+    def test_matches_the_existing_sidecar_convention(self, tmp_path):
+        # Same shape as __v_thumbs / __masks elsewhere in the catalog.
+        assert tss.VOLUME_SUFFIX.startswith("__")
+
+
+class TestBuildLevel:
+    def test_downsamples_a_real_stack_correctly(self, tmp_path):
+        # Slice i has value 2i, so a factor-2 level's slice j is the mean of
+        # source slices 2j and 2j+1 — i.e. 4j+1. Even values keep the expected
+        # result an exact integer, so this checks the averaging rather than the
+        # rounding (which has its own test below).
+        root = tmp_path / "scan"
+        root.mkdir()
+        for i in range(8):
+            tifffile.imwrite(str(root / f"img_{i:04d}.tif"), np.full((8, 8), 2 * i, np.uint16))
+        out = tss._build_level(tss.tiff_files(root), [2, 2, 2], [4, 4, 4], np.dtype(np.uint16))
+        assert out.shape == (4, 4, 4)
+        assert out.dtype == np.uint16
+        for j in range(4):
+            assert np.all(out[j] == 4 * j + 1)
+
+    def test_rounds_rather_than_truncating_on_integer_output(self, tmp_path):
+        # Mean of 0 and 3 is 1.5. Truncation would give 1; the cast must round.
+        root = tmp_path / "scan"
+        root.mkdir()
+        for i, value in enumerate((0, 3)):
+            tifffile.imwrite(str(root / f"img_{i:04d}.tif"), np.full((4, 4), value, np.uint16))
+        out = tss._build_level(tss.tiff_files(root), [2, 1, 1], [1, 4, 4], np.dtype(np.uint16))
+        assert np.all(out == 2)
+
+    def test_counts_every_source_slice_once(self, tmp_path):
+        root = write_stack(tmp_path / "scan", n=8, h=8, w=8)
+        reads = []
+        tss._build_level(
+            tss.tiff_files(root), [2, 2, 2], [4, 4, 4], np.dtype(np.uint16), lambda: reads.append(1)
+        )
+        assert len(reads) == 8
+
+    def test_supports_anisotropic_factors(self, tmp_path):
+        # z left alone, xy halved — the shape a short, wide stack produces.
+        root = write_stack(tmp_path / "scan", n=4, h=8, w=8, dtype=np.float32)
+        out = tss._build_level(tss.tiff_files(root), [1, 2, 2], [4, 4, 4], np.dtype(np.float32))
+        assert out.shape == (4, 4, 4)
+        for z in range(4):
+            assert np.allclose(out[z], z)  # no z averaging, so values survive
+
+    def test_preserves_float_input_without_rounding(self, tmp_path):
+        root = write_stack(tmp_path / "scan", n=4, h=8, w=8, dtype=np.float32)
+        out = tss._build_level(tss.tiff_files(root), [2, 2, 2], [2, 4, 4], np.dtype(np.float32))
+        assert out.dtype == np.float32
+        assert np.allclose(out[0], 0.5)  # mean of slices valued 0 and 1
+
+
+class TestCascade:
+    """Coarse levels are built from the level above, not from the source.
+
+    That is what makes registration read every source slice once instead of once
+    per level. It is only a valid shortcut because the factors are powers of two,
+    so averaging an average equals averaging the source — these tests pin that.
+    """
+
+    def test_matches_a_direct_downsample(self):
+        rng = np.random.default_rng(7)
+        source = rng.random((16, 32, 32), dtype=np.float32)
+
+        direct = tss.downsample_array(source, [4, 4, 4])
+        cascaded = tss.downsample_array(tss.downsample_array(source, [2, 2, 2]), [2, 2, 2])
+
+        assert direct.shape == cascaded.shape
+        assert np.allclose(direct, cascaded, atol=1e-5)
+
+    def test_matches_a_direct_downsample_with_anisotropic_factors(self):
+        rng = np.random.default_rng(11)
+        source = rng.random((4, 32, 32), dtype=np.float32)
+
+        direct = tss.downsample_array(source, [1, 4, 4])
+        cascaded = tss.downsample_array(tss.downsample_array(source, [1, 2, 2]), [1, 2, 2])
+
+        assert np.allclose(direct, cascaded, atol=1e-5)
+
+    def test_relative_factors_between_plan_levels_are_whole_numbers(self):
+        # The cascade divides each level's factor by the previous level's; a
+        # non-integer ratio would silently truncate and misalign the level.
+        plan = tss.pyramid_plan((604, 2560, 2560), target_dim=384, levels=3)
+        for previous, level in zip(plan, plan[1:]):
+            for axis in range(3):
+                assert level["factor"][axis] % previous["factor"][axis] == 0
+
+    def test_shrinks_on_every_axis_that_still_can(self):
+        out = tss.downsample_array(np.ones((8, 8, 8), dtype=np.float32), [2, 2, 2])
+        assert out.shape == (4, 4, 4)
+        assert np.allclose(out, 1.0)  # a constant volume stays constant
diff --git a/backend/tests/test_volume_build.py b/backend/tests/test_volume_build.py
new file mode 100644
index 0000000..789162f
--- /dev/null
+++ b/backend/tests/test_volume_build.py
@@ -0,0 +1,129 @@
+"""Tests for building a 3-D volume from a slice stack already in Tiled.
+
+The registration half needs a live catalog, so what is pinned here is the part
+that decides whether the feature is usable at all: what gets inspected, which
+inputs are refused (and with a message that says what to do instead), and that
+full resolution is deliberately not duplicated.
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+from fastapi import HTTPException
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import tiff_stack_source as tss  # noqa: E402
+import volume_build  # noqa: E402
+
+
+@pytest.fixture
+def fake_stack(monkeypatch):
+    """Stand in for a Tiled stack: `resolve_array` + `array_shape_meta` + `read_slice`."""
+
+    def install(n_slices=64, height=1024, width=1024, dtype="uint16", is_rgb=False):
+        meta = {
+            "n_slices": n_slices,
+            "height": height,
+            "width": width,
+            "dtype": dtype,
+            "is_rgb": is_rgb,
+        }
+        monkeypatch.setattr(volume_build.arrays_mod, "resolve_array", lambda *a, **k: object())
+        monkeypatch.setattr(volume_build.arrays_mod, "array_shape_meta", lambda *a, **k: meta)
+        # Slice i is filled with i, so a downsampled level's values are checkable.
+        monkeypatch.setattr(
+            volume_build.arrays_mod,
+            "read_slice",
+            lambda node, m, idx: np.full((height, width), idx, dtype=np.dtype(dtype)),
+        )
+        return meta
+
+    return install
+
+
+class TestInspect:
+    def test_describes_the_pyramid_that_would_be_built(self, fake_stack):
+        fake_stack(n_slices=64, height=1024, width=1024)
+        info = volume_build.inspect_volume_build("browse/stack")
+        assert info["full_shape"] == [64, 1024, 1024]
+        assert info["pyramid_plan"]
+        assert info["already_small"] is False
+
+    def test_reads_every_source_slice_once(self, fake_stack):
+        # The coarse levels cascade in memory, so the cost is one pass.
+        fake_stack(n_slices=690)
+        assert volume_build.inspect_volume_build("browse/stack")["slices_to_read"] == 690
+
+    def test_flags_a_stack_that_needs_no_downsampling(self, fake_stack):
+        fake_stack(n_slices=8, height=64, width=64)
+        info = volume_build.inspect_volume_build("browse/stack")
+        assert info["already_small"] is True
+        assert info["slices_to_read"] == 0
+
+    def test_refuses_a_single_image(self, fake_stack):
+        fake_stack(n_slices=1)
+        with pytest.raises(HTTPException) as excinfo:
+            volume_build.inspect_volume_build("browse/one")
+        assert excinfo.value.status_code == 422
+        assert "single image" in excinfo.value.detail
+
+    def test_refuses_colour_images(self, fake_stack):
+        # The renderer draws one scalar volume; RGB has no meaning there.
+        fake_stack(is_rgb=True)
+        with pytest.raises(HTTPException) as excinfo:
+            volume_build.inspect_volume_build("browse/rgb")
+        assert excinfo.value.status_code == 422
+        assert "Colour" in excinfo.value.detail
+
+
+class TestBuildGuards:
+    def test_refuses_non_tiled_sources(self):
+        with pytest.raises(HTTPException) as excinfo:
+            volume_build.build_volume("foo.tif", kind="local")
+        assert excinfo.value.status_code == 422
+
+    def test_refuses_an_already_small_stack_with_advice(self, fake_stack):
+        # No pyramid is needed, but the data is still per-slice and unstreamable.
+        # Saying only "nothing to do" would leave the user stuck.
+        fake_stack(n_slices=8, height=64, width=64)
+        with pytest.raises(HTTPException) as excinfo:
+            volume_build.build_volume("browse/small")
+        assert excinfo.value.status_code == 422
+        assert "source images" in excinfo.value.detail
+
+    def test_refuses_to_place_a_volume_at_the_catalog_root(self, fake_stack):
+        fake_stack()
+        with pytest.raises(HTTPException) as excinfo:
+            volume_build.build_volume("stack")
+        assert excinfo.value.status_code == 422
+        assert "root" in excinfo.value.detail
+
+
+class TestMetadataShape:
+    def test_omits_scale0_when_full_resolution_is_not_copied(self):
+        # Full resolution stays in the per-slice nodes. Declaring a scale0 that
+        # does not exist would make the viewer request a missing level.
+        plan = tss.pyramid_plan((690, 2560, 2560))
+        ms = tss.multiscales_metadata("v", plan, include_scale0=False)["attributes"][
+            "multiscales"
+        ][0]
+        paths = [d["path"] for d in ms["datasets"]]
+        assert all(p.startswith(f"{tss.PYRAMID_KEY}/") for p in paths)
+        assert "scale0" not in paths
+
+    def test_scale_transforms_stay_relative_to_full_resolution(self):
+        # Dropping scale0 must not renumber the others: a level downsampled 16x
+        # still describes 16 full-res voxels per voxel, or the volume renders at
+        # the wrong physical size.
+        plan = tss.pyramid_plan((690, 2560, 2560))
+        ms = tss.multiscales_metadata("v", plan, include_scale0=False)["attributes"][
+            "multiscales"
+        ][0]
+        for dataset, level in zip(ms["datasets"], plan):
+            scale = dataset["coordinateTransformations"][0]["scale"]
+            assert scale == [float(f) for f in level["factor"]]
diff --git a/backend/tests/test_volume_nodes.py b/backend/tests/test_volume_nodes.py
new file mode 100644
index 0000000..f9b017b
--- /dev/null
+++ b/backend/tests/test_volume_nodes.py
@@ -0,0 +1,136 @@
+"""Tests for locating a dataset's renderable 3-D volume.
+
+This is the module that answers "which node do I point the viewer at?". Getting
+it wrong is not a subtle failure — the viewer reports
+``openOmeZarr: missing multiscales in root .zattrs``, which tells the user
+nothing about the real answer ("look at the sidecar" / "none has been built").
+
+A fake catalog stands in for Tiled: the logic under test is metadata inspection
+and path walking, both of which a real server would only slow down.
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import volume_nodes  # noqa: E402
+from tiff_stack_source import VOLUME_SUFFIX  # noqa: E402
+
+MULTISCALES = {"attributes": {"multiscales": [{"datasets": [{"path": "scale0"}]}]}}
+
+
+class FakeNode:
+    """A catalog node: metadata plus children, indexable like a Tiled container."""
+
+    def __init__(self, metadata=None, children=None):
+        self.metadata = metadata or {}
+        self._children = children or {}
+
+    def __getitem__(self, key):
+        return self._children[key]
+
+
+@pytest.fixture
+def catalog(monkeypatch):
+    """A catalog with one of each shape the resolver has to tell apart."""
+    root = FakeNode(children={
+        "browse": FakeNode(children={
+            # A registered Zarr volume: multiscale itself, with level children.
+            "zarr_vol": FakeNode(
+                metadata=dict(MULTISCALES, zarr_path="/data/zarr_vol.zarr"),
+                children={"scale0": FakeNode(children={"image": FakeNode()})},
+            ),
+            # A TIFF stack: per-slice 2-D arrays, no multiscales anywhere.
+            "tiff_stack": FakeNode(metadata={"n_images": 604}),
+            # ...and the sidecar built for it.
+            f"tiff_stack{VOLUME_SUFFIX}": FakeNode(
+                metadata=dict(MULTISCALES, tiff_dir="/data/tiff_stack"),
+            ),
+            # A stack nobody has built a volume for.
+            "lonely_stack": FakeNode(metadata={"n_images": 12}),
+        })
+    })
+    monkeypatch.setattr(volume_nodes, "get_tiled_client", lambda *a, **k: root)
+    monkeypatch.setattr(volume_nodes, "api_key_for_uri", lambda *a, **k: None)
+    return root
+
+
+class TestHasMultiscales:
+    def test_true_where_tiled_will_serve_it(self):
+        # Tiled's .zattrs route returns metadata["attributes"] verbatim, so that
+        # is the only place the viewer can see multiscales.
+        assert volume_nodes.has_multiscales(FakeNode(metadata=MULTISCALES))
+
+    def test_false_when_nested_anywhere_else(self):
+        # A common near-miss: right key, wrong level. It would never reach .zattrs.
+        assert not volume_nodes.has_multiscales(
+            FakeNode(metadata={"multiscales": [{"datasets": []}]})
+        )
+
+    def test_false_for_empty_or_missing(self):
+        assert not volume_nodes.has_multiscales(FakeNode(metadata={}))
+        assert not volume_nodes.has_multiscales(FakeNode(metadata={"attributes": {}}))
+        assert not volume_nodes.has_multiscales(
+            FakeNode(metadata={"attributes": {"multiscales": []}})
+        )
+
+
+class TestResolveVolume:
+    def test_open_node_is_already_a_volume(self, catalog):
+        result = volume_nodes.resolve_volume(None, "browse/zarr_vol")
+        assert result["mode"] == "self"
+        assert result["path"] == "browse/zarr_vol"
+
+    def test_walks_up_from_a_pyramid_level(self, catalog):
+        # Opening `<volume>/scale0/image` is ordinary — that array has no
+        # multiscales of its own, but its grandparent does.
+        result = volume_nodes.resolve_volume(None, "browse/zarr_vol/scale0/image")
+        assert result["mode"] == "ancestor"
+        assert result["path"] == "browse/zarr_vol"
+
+    def test_finds_the_sidecar_for_a_tiff_stack(self, catalog):
+        # The bug this module exists for: the open node is a container of 2-D
+        # slices, and the volume is its __volume sibling.
+        result = volume_nodes.resolve_volume(None, "browse/tiff_stack")
+        assert result["mode"] == "sidecar"
+        assert result["path"] == f"browse/tiff_stack{VOLUME_SUFFIX}"
+
+    def test_reports_none_with_an_actionable_message(self, catalog):
+        result = volume_nodes.resolve_volume(None, "browse/lonely_stack")
+        assert result["mode"] == "none"
+        assert result["path"] is None
+        assert "built" in result["message"]
+
+    def test_reports_none_for_a_missing_node(self, catalog):
+        result = volume_nodes.resolve_volume(None, "browse/does_not_exist")
+        assert result["mode"] == "none"
+
+    def test_handles_an_empty_source(self, catalog):
+        result = volume_nodes.resolve_volume(None, "")
+        assert result["mode"] == "none"
+        assert result["message"]
+
+    def test_tolerates_surrounding_slashes(self, catalog):
+        assert volume_nodes.resolve_volume(None, "/browse/zarr_vol/")["mode"] == "self"
+
+    def test_never_ascends_past_the_catalog_root(self, catalog):
+        # Walking up must not wander off the top and resolve some unrelated node.
+        assert volume_nodes.resolve_volume(None, "browse")["mode"] == "none"
+
+
+class TestSourceDir:
+    def test_surfaces_the_registered_tiff_directory(self, catalog):
+        result = volume_nodes.resolve_volume(None, "browse/tiff_stack")
+        assert result["source_dir"] == "/data/tiff_stack"
+
+    def test_surfaces_the_registered_zarr_path(self, catalog):
+        result = volume_nodes.resolve_volume(None, "browse/zarr_vol")
+        assert result["source_dir"] == "/data/zarr_vol.zarr"
+
+    def test_is_none_when_nothing_was_recorded(self):
+        assert volume_nodes.source_dir_of(FakeNode(metadata={})) is None
diff --git a/backend/tiff_stack_source.py b/backend/tiff_stack_source.py
new file mode 100644
index 0000000..6e9ad18
--- /dev/null
+++ b/backend/tiff_stack_source.py
@@ -0,0 +1,698 @@
+"""Expose a directory of TIFF slices as a streamable 3-D Zarr volume.
+
+Why this exists
+---------------
+Tiled 0.2.12 mounts a Zarr v2 router at ``/zarr/v2``: every array node is
+already readable as a Zarr store, with ``.zarray`` synthesized from the Tiled
+structure and ``/{i.j.k}`` serving a chunk. The 3-D viewer streams from that
+directly — no export, no second endpoint.
+
+Two things stop a TIFF stack from working that way today:
+
+1. :mod:`ingest` writes one 2-D array **per file**, so ``/zarr/v2/<stack>`` is a
+   *group of N 2-D arrays* — not a volume. That layout is load-bearing for the
+   2-D canvas (``arrays._stack_keys`` / ``read_slice``) and for per-frame Browse
+   metadata, so it stays exactly as it is. This module registers a 3-D **view
+   alongside** it; nothing here changes how slices are read.
+2. Tiled's ``.zattrs`` returns ``metadata["attributes"]`` verbatim, so OME-NGFF
+   ``multiscales`` appears only if something writes it. This module writes it.
+
+Layout produced
+---------------
+The same OME-NGFF shape :mod:`zarr_source` already documents, so both paths look
+identical to the viewer. The volume is a **sidecar** of the dataset, keyed with a
+``__volume`` suffix in the style of the existing ``__v_thumbs`` and ``__masks``
+siblings, because the per-slice container occupies the unsuffixed key::
+
+    <stem>/                # untouched: the per-slice 2-D nodes Annotate reads
+    <stem>__volume/        # container; .zattrs carries "multiscales"
+        scale0             # (N, H, W) — the TIFF files, registered IN PLACE
+        pyramid/scale1     # downsampled, in an on-disk Zarr sidecar
+        pyramid/scale2     # ...
+
+Why the coarse levels go to disk rather than into Tiled
+-------------------------------------------------------
+Writing them with ``write_array`` would be less machinery, but Tiled 0.2.12's
+``/zarr/v2`` chunk route only works for **externally-managed** arrays: for one it
+writes itself, ``entry.read(slice=<tuple>)`` reaches ``NDSlice.__getitem__`` with
+a tuple and raises ``TypeError: tuple indices must be integers or slices``. The
+``/api/v1`` block route serves the same array fine, so this is specific to the
+Zarr façade — and the Zarr façade is the whole point here. Measured on a real
+604 x 2560 x 2560 stack: ``scale0`` (external TIFF sequence) served a 19 MB chunk
+in 0.2 s while every ``write_array`` level returned HTTP 500.
+
+Writing a normal Zarr store and registering it in place sidesteps that, and has
+the same shape as :mod:`zarr_source`'s path — one fewer special case, not one
+more. Revisit if the upstream route is fixed.
+
+Why a pyramid is not optional
+-----------------------------
+The renderer only lists levels that fit ``maxTextureDimension3D`` (commonly
+2048) and its voxel budget. A single-level ``multiscales`` over a
+2000x3232x3232 stack therefore offers *nothing renderable*, which looks
+identical to a broken viewer. The coarse levels are the ones actually drawn;
+``scale0`` is never fully read for the 3-D view, and earns its place by making
+full-resolution ROI work possible later.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import shutil
+from pathlib import Path
+from typing import Any, Callable
+
+import numpy as np
+from fastapi import HTTPException
+
+import ingest as ingest_mod
+from tiled_clients import api_key_for_uri, get_tiled_client
+
+logger = logging.getLogger("tiff_stack_source")
+
+TIFF_SUFFIXES: tuple[str, ...] = (".tif", ".tiff")
+
+# Longest edge (voxels) the finest GENERATED level may have. Chosen so a level
+# comfortably fits a 2048 3-D texture with room for the renderer's own budget,
+# and so building one stays within a few hundred MB of RAM: this module assembles
+# each generated level in memory before writing it (worst case 384**3 * 4 B ~=
+# 226 MB, and z is normally far smaller than 384).
+TARGET_DIM = 384
+
+# How many levels to generate below scale0. Three gives the renderer a choice of
+# detail without the cost growing: each is 1/8 the voxels of the one above.
+GENERATED_LEVELS = 3
+
+#: Key of the sub-group holding the generated levels, inside the volume node.
+PYRAMID_KEY = "pyramid"
+
+
+def pyramid_cache_root() -> Path:
+    """Directory the generated Zarr pyramids are written to.
+
+    Deliberately *not* next to the source TIFFs: beamline reconstruction
+    directories are routinely read-only or on shared storage, and failing to
+    register a volume because the source's parent cannot be written to would be a
+    confusing way to discover that. Override with ``VOLUME_CACHE_DIR``.
+    """
+    default = Path(__file__).resolve().parent.parent / ".tiled" / "volumes"
+    return Path(os.getenv("VOLUME_CACHE_DIR", str(default))).expanduser().resolve()
+
+
+def _resolve_dir(raw: str) -> Path:
+    """Expand and validate a user-supplied absolute path to a TIFF directory.
+
+    Raises:
+        HTTPException: 4xx with a user-facing message, so the UI never has to
+            surface a traceback.
+    """
+    if not (raw or "").strip():
+        raise HTTPException(400, "Enter the path to a directory of TIFF slices.")
+    path = Path(raw).expanduser()
+    if not path.is_absolute():
+        raise HTTPException(400, f"Path must be absolute: {raw!r}")
+    if not path.exists():
+        raise HTTPException(404, f"No such path: {path}")
+    if not path.is_dir():
+        raise HTTPException(422, f"Not a directory: {path}")
+    return path.resolve()
+
+
+def tiff_files(path: Path) -> list[Path]:
+    """Sorted TIFF files directly inside *path*.
+
+    Sorted lexically, which is slice order for the zero-padded names tomography
+    reconstruction writes. Non-padded names (``img_2`` before ``img_10``) would
+    sort wrong — :func:`inspect_tiff_stack` rejects those rather than silently
+    building a shuffled volume.
+    """
+    return sorted(
+        p for p in path.iterdir() if p.is_file() and p.suffix.lower() in TIFF_SUFFIXES
+    )
+
+
+def _zero_padding_is_consistent(files: list[Path]) -> bool:
+    """True when lexical order is numeric order.
+
+    Only meaningful when the names carry numbers at all; a set of names with no
+    digits has nothing to get wrong, so it passes.
+    """
+    import re
+
+    numbers: list[int] = []
+    for f in files:
+        match = re.search(r"(\d+)(?!.*\d)", f.stem)
+        if not match:
+            return True  # no numbering scheme to violate
+        numbers.append(int(match.group(1)))
+    return numbers == sorted(numbers)
+
+
+def pyramid_plan(
+    shape: tuple[int, int, int],
+    target_dim: int = TARGET_DIM,
+    levels: int = GENERATED_LEVELS,
+) -> list[dict[str, Any]]:
+    """Downsample factors and shapes for the levels to generate below ``scale0``.
+
+    Factors are **per axis**, and each is the smallest power of two bringing that
+    axis to ``target_dim`` or below. Powers of two keep the block mean exact and
+    the coordinate transforms simple.
+
+    Per-axis rather than one shared factor because tomography stacks are often
+    strongly anisotropic — 4 slices of 4096x4096 is an ordinary shape. A single
+    factor chosen for the wide axes would ask for 4//16 = 0 slices, so *every*
+    level would be dropped as degenerate and the volume would end up with no
+    renderable level at all: indistinguishable, from the outside, from a broken
+    viewer. An axis therefore stops halving once it reaches 1.
+
+    Args:
+        shape: ``(n_slices, height, width)`` of the full-resolution stack.
+        target_dim: Longest edge allowed for the finest generated level.
+        levels: How many levels to generate.
+
+    Returns:
+        One dict per level with ``path``, ``factor`` (a 3-list, z/y/x) and
+        ``shape``. Empty when the source is already at or below ``target_dim`` on
+        every axis — there is then nothing to generate and ``scale0`` alone is
+        renderable.
+    """
+    if any(s <= 0 for s in shape):
+        return []
+    if max(shape) <= target_dim:
+        return []
+
+    def axis_factor(size: int, cap: int) -> int:
+        f = 1
+        # `size // (f * 2) >= 1` is the guard that keeps a short axis alive.
+        while size / f > cap and size // (f * 2) >= 1:
+            f *= 2
+        return f
+
+    factors = [axis_factor(s, target_dim) for s in shape]
+
+    plan: list[dict[str, Any]] = []
+    for index in range(levels):
+        level_factors = [
+            # Halve again only where the axis can still spare it; an axis already
+            # at 1 stays at 1 rather than dragging the level into degeneracy.
+            f * 2 if index and shape[axis] // (f * 2) >= 1 else f
+            for axis, f in enumerate(factors)
+        ]
+        factors = level_factors
+        level_shape = [shape[axis] // f for axis, f in enumerate(factors)]
+        if min(level_shape) < 1:
+            break
+        # A level identical to the one above adds bytes and no detail.
+        if plan and level_shape == plan[-1]["shape"]:
+            break
+        plan.append(
+            {
+                "path": f"scale{len(plan) + 1}",
+                "factor": list(factors),
+                "shape": level_shape,
+            }
+        )
+    return plan
+
+
+def multiscales_metadata(
+    name: str,
+    plan: list[dict[str, Any]],
+    voxel_size: tuple[float, float, float] = (1.0, 1.0, 1.0),
+    include_scale0: bool = True,
+) -> dict[str, Any]:
+    """OME-NGFF ``multiscales`` for the generated levels, and optionally ``scale0``.
+
+    Returned under an ``attributes`` key because Tiled's ``.zattrs`` route
+    returns ``metadata["attributes"]`` verbatim — put it anywhere else and the
+    viewer sees an empty attribute set and reports "missing multiscales".
+
+    Args:
+        include_scale0: True when the full-resolution data is registered in place
+            as a ``scale0`` sibling (the TIFF-directory path). False when the
+            volume was built from data already in Tiled, where full resolution
+            stays in the per-slice nodes and is not duplicated — the viewer never
+            loads a level that large anyway.
+    """
+    datasets = []
+    if include_scale0:
+        datasets.append(
+            {
+                "path": "scale0",
+                "coordinateTransformations": [
+                    {"type": "scale", "scale": [float(v) for v in voxel_size]}
+                ],
+            }
+        )
+    for level in plan:
+        factors = level["factor"]
+        datasets.append(
+            {
+                # Generated levels live in the sidecar sub-group, so the path the
+                # viewer follows is nested. Tiled serves nested groups fine — the
+                # Zarr volumes `zarr_source` registers use `scale0/image`.
+                "path": f"{PYRAMID_KEY}/{level['path']}",
+                "coordinateTransformations": [
+                    {
+                        "type": "scale",
+                        "scale": [float(v) * float(f) for v, f in zip(voxel_size, factors)],
+                    }
+                ],
+            }
+        )
+    return {
+        "attributes": {
+            "multiscales": [
+                {
+                    "version": "0.4",
+                    "name": name,
+                    "axes": [
+                        {"name": "z", "type": "space"},
+                        {"name": "y", "type": "space"},
+                        {"name": "x", "type": "space"},
+                    ],
+                    "datasets": datasets,
+                }
+            ]
+        }
+    }
+
+
+def block_mean(block: np.ndarray, fy: int, fx: int) -> np.ndarray:
+    """Mean-downsample a ``(k, H, W)`` block to ``(H//fy, W//fx)``.
+
+    Averages across the whole block in z as well as over each ``fy x fx`` tile,
+    so one output voxel is the mean of every input voxel it covers. Trailing
+    rows/columns that do not fill a tile are dropped rather than partially
+    averaged — a partial tile would be brighter or darker than its neighbours
+    purely because of where the edge fell.
+
+    Computed in float32 regardless of input dtype (uint16 sums overflow fast),
+    then cast back by the caller.
+    """
+    if fy < 1 or fx < 1:
+        raise ValueError("downsample factors must be >= 1")
+    h = (block.shape[1] // fy) * fy
+    w = (block.shape[2] // fx) * fx
+    trimmed = block[:, :h, :w].astype(np.float32, copy=False)
+    return trimmed.reshape(trimmed.shape[0], h // fy, fy, w // fx, fx).mean(axis=(0, 2, 4))
+
+
+def inspect_tiff_stack(raw_path: str) -> dict[str, Any]:
+    """Describe a TIFF directory and the pyramid that would be built for it.
+
+    Reads only the first file, so this is cheap enough to call on every keystroke
+    in a path field.
+
+    Raises:
+        HTTPException: 4xx with a user-facing message for anything unusable.
+    """
+    import tifffile
+
+    path = _resolve_dir(raw_path)
+    files = tiff_files(path)
+    if not files:
+        raise HTTPException(422, f"No .tif/.tiff files directly inside {path.name!r}.")
+    if len(files) < 2:
+        raise HTTPException(
+            422,
+            f"{path.name!r} holds a single image, not a volume. The 3-D view needs a stack.",
+        )
+    if not _zero_padding_is_consistent(files):
+        raise HTTPException(
+            422,
+            f"{path.name!r} has inconsistently numbered files (e.g. 'img_2' next to "
+            "'img_10'), so filename order is not slice order. Zero-pad the numbers "
+            "and try again — registering as-is would build a shuffled volume.",
+        )
+
+    try:
+        first = tifffile.imread(str(files[0]))
+    except Exception as exc:  # noqa: BLE001 — surface as a clean 422
+        raise HTTPException(422, f"Could not read {files[0].name!r}: {exc}") from exc
+    if first.ndim != 2:
+        raise HTTPException(
+            422,
+            f"{files[0].name!r} is {first.ndim}-D; this path expects one 2-D slice per file.",
+        )
+
+    shape = (len(files), int(first.shape[0]), int(first.shape[1]))
+    plan = pyramid_plan(shape)
+    return {
+        "name": path.name,
+        "path": str(path),
+        "n_slices": shape[0],
+        "height": shape[1],
+        "width": shape[2],
+        "dtype": str(first.dtype),
+        "full_shape": list(shape),
+        "pyramid_plan": plan,
+        # Surfaced so the UI can say "this will take a while" honestly: every
+        # source slice is read once to build the levels.
+        "slices_to_read": shape[0] if plan else 0,
+    }
+
+
+def _build_level(
+    files: list[Path],
+    factor: list[int],
+    out_shape: list[int],
+    dtype: np.dtype,
+    on_slice: Callable[[], None] | None = None,
+) -> np.ndarray:
+    """Read the source stack and mean-downsample it by the per-axis *factor*.
+
+    Reads at most ``factor[0]`` slices at a time, so peak memory is set by the
+    output level plus a handful of input slices — never by the whole stack,
+    which can be tens of GB.
+    """
+    import tifffile
+
+    fz, fy, fx = (int(f) for f in factor)
+    out = np.empty(out_shape, dtype=np.float32)
+    for z in range(out_shape[0]):
+        block = []
+        for k in range(fz):
+            index = z * fz + k
+            if index >= len(files):
+                break
+            block.append(tifffile.imread(str(files[index])))
+            if on_slice:
+                on_slice()
+        out[z] = block_mean(np.stack(block), fy, fx)
+    return _cast_like(out, dtype)
+
+
+def downsample_array(volume: np.ndarray, factor: list[int]) -> np.ndarray:
+    """Mean-downsample an in-memory volume by the per-axis *factor*.
+
+    Used to build each coarse level from the level above rather than from the
+    source. Because the factors are powers of two, averaging an average over
+    matching block sizes gives the same result as averaging the source directly,
+    while reading every source slice **once** instead of once per level. On a
+    604 x 2560 x 2560 float32 stack that is the difference between ~16 GB of
+    reads and ~48 GB.
+
+    (The two agree exactly only where the divisions are exact; a trailing partial
+    tile is dropped at each step, so the very last row/column of a level may
+    differ from a direct downsample. That is a sub-voxel edge effect on a preview
+    level, not something a viewer can show.)
+    """
+    fz, fy, fx = (int(f) for f in factor)
+    z = (volume.shape[0] // fz) * fz
+    y = (volume.shape[1] // fy) * fy
+    x = (volume.shape[2] // fx) * fx
+    trimmed = volume[:z, :y, :x].astype(np.float32, copy=False)
+    return trimmed.reshape(z // fz, fz, y // fy, fy, x // fx, fx).mean(axis=(1, 3, 5))
+
+
+def write_pyramid_store(key: str, levels: dict[str, np.ndarray]) -> Path:
+    """Write the generated levels as a Zarr v2 group and return its path.
+
+    One store per dataset, under :func:`pyramid_cache_root`, replacing any
+    previous build for the same key — a re-registration should not accumulate
+    stale copies of a volume.
+
+    Written as Zarr **v2** to match what Tiled's ``/zarr/v2`` façade and the
+    viewer's OME-NGFF reader both speak.
+    """
+    import zarr
+
+    root = pyramid_cache_root() / key
+    root.mkdir(parents=True, exist_ok=True)
+    store_path = root / f"{PYRAMID_KEY}.zarr"
+    if store_path.exists():
+        shutil.rmtree(store_path)
+
+    group = zarr.open_group(str(store_path), mode="w", zarr_format=2)
+    for name, array in levels.items():
+        group.create_array(
+            name,
+            shape=array.shape,
+            dtype=array.dtype,
+            # One chunk per output slice: matches how the viewer streams, and
+            # keeps any single request small.
+            chunks=(1, *array.shape[1:]),
+        )[:] = array
+    return store_path
+
+
+def _cast_like(values: np.ndarray, dtype: np.dtype) -> np.ndarray:
+    """Cast a float working array back to the source dtype, rounding integers.
+
+    Keeps the rendered volume in the same intensity units as the 2-D canvas
+    instead of silently becoming float — and rounds rather than truncating, so a
+    mean of 1.5 does not become 1.
+    """
+    if np.issubdtype(dtype, np.integer):
+        info = np.iinfo(dtype)
+        return np.clip(np.rint(values), info.min, info.max).astype(dtype)
+    return values.astype(dtype)
+
+
+#: Suffix marking the 3-D volume node as a sidecar of the dataset it describes.
+#: Matches the existing ``__v_thumbs`` / ``__masks`` convention.
+VOLUME_SUFFIX = "__volume"
+
+
+def registered_key(path: Path) -> str:
+    """The node key for *path*'s 3-D volume.
+
+    Suffixed, because the 2-D per-slice container that Annotate reads is keyed on
+    the very same directory name. Without the suffix the volume would land on top
+    of it — and since that container holds ingested data, the collision check
+    below would (correctly) refuse, making the 3-D view impossible for exactly
+    the datasets it is for. A sidecar key lets the two coexist, which is the
+    whole design: this is additive, and the 2-D read path is untouched.
+
+    The stem comes from Tiled's own helper rather than assuming ``path.name`` —
+    the same care :mod:`zarr_source` takes, and for the same reason: a key
+    mismatch makes the collision check inspect a node that is not the one about
+    to be created, and Tiled then resolves the real collision itself, deep inside
+    registration where its only remedy is deletion.
+    """
+    from tiled.client.register import Settings
+
+    return f"{Settings.init().key_from_filename(path.name)}{VOLUME_SUFFIX}"
+
+
+def preflight_tiff_stack(
+    server_uri: str | None, raw_path: str, container_path: str = "browse"
+) -> dict[str, Any]:
+    """Report whether registering *raw_path* would collide, changing nothing."""
+    path = _resolve_dir(raw_path)
+    key = registered_key(path)
+    client = get_tiled_client(server_uri, api_key_for_uri(server_uri))
+    parts = [p for p in container_path.strip("/").split("/") if p]
+    target = ingest_mod._walk(client, parts)
+    existing = None
+    if target is not None and key in ingest_mod._child_keys(target):
+        node = target[key]
+        meta: dict[str, Any] = {}
+        try:
+            meta = dict(getattr(node, "metadata", {}) or {})
+        except Exception:  # noqa: BLE001 — best-effort description
+            pass
+        children: list[str] = []
+        try:
+            children = list(node)
+        except Exception:  # noqa: BLE001
+            pass
+        existing = {
+            "child_count": len(children),
+            # Only a previous registration by THIS module is safe to replace:
+            # dropping it removes catalog rows and the generated levels, never
+            # the source TIFFs. Anything else may be internally-managed data,
+            # where deleting the node deletes the files.
+            "external": meta.get("source_format") == "tiff-stack-3d",
+            "sample_name": meta.get("sample_name") or "",
+        }
+    return {"key": key, "exists": existing is not None, "existing": existing}
+
+
+def register_tiff_stack(
+    server_uri: str | None,
+    raw_path: str,
+    container_path: str = "browse",
+    description: str = "",
+    on_conflict: str = "fail",
+    progress: Callable[[str, int, int], None] | None = None,
+) -> dict[str, Any]:
+    """Register a TIFF directory as a 3-D multiscale volume, copying no slices.
+
+    ``scale0`` points at the TIFF files where they already are. Only the
+    generated (small) levels are written into Tiled.
+
+    Args:
+        server_uri: Connected Tiled server URI.
+        raw_path: Absolute path to the directory of TIFF slices.
+        container_path: Slash-separated target container (e.g. ``browse``).
+        description: Optional keyword(s), as ingest stores them, so the volume is
+            filterable in Browse.
+        on_conflict: ``"fail"``, ``"replace"`` or ``"skip"`` when the key exists.
+        progress: Optional ``(message, done, total)`` callback for the job UI.
+
+    Returns:
+        The inspection result plus the registered ``key`` and ``tiled_path``.
+    """
+    info = inspect_tiff_stack(raw_path)
+    path = Path(info["path"])
+    files = tiff_files(path)
+    plan: list[dict[str, Any]] = info["pyramid_plan"]
+    description = (description or "").strip()
+    keywords = ingest_mod.parse_keywords(description)
+    if on_conflict not in ingest_mod.ON_CONFLICT_MODES:
+        on_conflict = "fail"
+
+    client = get_tiled_client(server_uri, api_key_for_uri(server_uri))
+    parts = [p for p in container_path.strip("/").split("/") if p]
+    target = ingest_mod._ensure_container(client, parts)
+
+    key = registered_key(path)
+    if key in ingest_mod._child_keys(target):
+        existing = preflight_tiff_stack(server_uri, raw_path, container_path)["existing"]
+        if on_conflict == "skip":
+            return {**info, "key": key, "tiled_path": "/".join([*parts, key]), "skipped": True}
+        if on_conflict == "replace":
+            if not (existing or {}).get("external"):
+                raise HTTPException(
+                    409,
+                    f"{key!r} already exists in {container_path!r} and does not hold a "
+                    "registered TIFF volume. Replacing it could delete uploaded data — "
+                    "load into a different container, or remove that dataset yourself first.",
+                )
+            target.delete_contents(key, recursive=True, external_only=False)
+        else:
+            raise HTTPException(
+                409,
+                f"{key!r} already exists in {container_path!r}. Choose Replace or a "
+                "different destination.",
+            )
+
+    volume = target.create_container(key=key, metadata={})
+
+    # scale0: the files themselves, registered in place as ONE 3-D array.
+    # `register_image_sequence` builds a TiffSequenceAdapter over the sorted list
+    # and stores external Assets — shape (N, H, W), one chunk per slice, which is
+    # exactly the granularity a streaming volume viewer wants.
+    from tiled.client.register import Settings, register_image_sequence
+
+    if progress:
+        progress("Registering full-resolution slices", 0, info["slices_to_read"])
+    try:
+        asyncio.run(register_image_sequence(volume, "scale0", files, Settings.init()))
+    except Exception as exc:  # noqa: BLE001 — classified for the UI
+        logger.warning("tiff sequence registration failed for %s: %s", path, exc)
+        raise HTTPException(502, ingest_mod._classify_error(exc)["message"]) from exc
+
+    if ingest_mod._walk(volume, ["scale0"]) is None:
+        # register_image_sequence logs and swallows adapter errors, so a missing
+        # node here is the signal that registration did not actually happen.
+        raise HTTPException(
+            502,
+            f"Tiled did not register the slices of {key!r}. Check the backend log for "
+            "the adapter error, and that the Tiled server can read this path.",
+        )
+
+    # Generated levels, built as a cascade: the finest from the TIFF files, each
+    # coarser one from the level above (already in memory, and tiny). Reading the
+    # source once instead of once per level is the difference between ~16 GB and
+    # ~48 GB of I/O on a 604 x 2560 x 2560 float32 stack.
+    dtype = np.dtype(info["dtype"])
+    total_reads = info["slices_to_read"]
+    done = 0
+    previous: np.ndarray | None = None
+    previous_factor: list[int] | None = None
+    built: dict[str, np.ndarray] = {}
+
+    for level in plan:
+        label = f"Building {level['path']}"
+        if progress:
+            progress(label, done, total_reads)
+
+        if previous is None or previous_factor is None:
+            def _tick() -> None:
+                nonlocal done
+                done += 1
+                # Throttled: one update per 25 slices is smooth enough for a
+                # progress bar and keeps the job lock uncontended.
+                if progress and done % 25 == 0:
+                    progress(label, done, total_reads)
+
+            working = _build_level(files, level["factor"], level["shape"], dtype, _tick).astype(
+                np.float32, copy=False
+            )
+        else:
+            relative = [
+                int(level["factor"][axis] // previous_factor[axis]) for axis in range(3)
+            ]
+            working = downsample_array(previous, relative)
+
+        built[level["path"]] = _cast_like(working, dtype)
+        previous, previous_factor = working, list(level["factor"])
+
+    # Write the generated levels as a plain on-disk Zarr store and register it in
+    # place, exactly as an externally-supplied Zarr volume would be — see this
+    # module's docstring for why `write_array` cannot be used here.
+    if built:
+        if progress:
+            progress("Writing pyramid", total_reads, total_reads)
+        sidecar = write_pyramid_store(key, built)
+        from tiled.client.register import register_single_item
+
+        try:
+            asyncio.run(
+                register_single_item(
+                    volume, sidecar, is_directory=True, settings=Settings.init()
+                )
+            )
+        except Exception as exc:  # noqa: BLE001 — classified for the UI
+            logger.warning("pyramid registration failed for %s: %s", sidecar, exc)
+            raise HTTPException(502, ingest_mod._classify_error(exc)["message"]) from exc
+
+        # A node existing is not enough. When the store sits outside Tiled's
+        # `readable_storage`, registration still creates the node — it just has
+        # no children, and every chunk request then 500s at read time, far from
+        # the cause. Check for contents, and name the likely fix.
+        pyramid_node = ingest_mod._walk(volume, [PYRAMID_KEY])
+        if pyramid_node is None or not list(pyramid_node):
+            raise HTTPException(
+                502,
+                f"Tiled registered no levels for {key!r} from {sidecar}. The usual "
+                "cause is that this path is not in the Tiled server's "
+                "`readable_storage` (see tiled/config.yml) — add it, or point "
+                "VOLUME_CACHE_DIR somewhere already readable.",
+            )
+
+    meta: dict[str, Any] = {
+        "sample_name": key,
+        "n_images": info["n_slices"],
+        "source_format": "tiff-stack-3d",
+        "tiff_dir": str(path),
+        "full_shape": info["full_shape"],
+        "pyramid_plan": plan,
+        **multiscales_metadata(key, plan),
+    }
+    if description:
+        meta["description"] = description
+    if keywords:
+        meta["keywords"] = keywords
+    try:
+        volume.update_metadata(metadata=meta)
+    except Exception as exc:  # noqa: BLE001 — best-effort; the data is registered
+        logger.warning("could not set metadata on %s: %s", key, exc)
+
+    if progress:
+        progress("Done", total_reads, total_reads)
+
+    # Spread `info` FIRST so its filesystem "path" cannot shadow the Tiled path
+    # the caller needs to open the dataset.
+    return {
+        **info,
+        "key": key,
+        "tiled_path": "/".join([*parts, key]),
+        "skipped": False,
+    }
diff --git a/backend/volume_build.py b/backend/volume_build.py
new file mode 100644
index 0000000..b84c336
--- /dev/null
+++ b/backend/volume_build.py
@@ -0,0 +1,203 @@
+"""Build a renderable 3-D volume from a slice stack already in Tiled.
+
+:mod:`tiff_stack_source` registers a *directory of TIFFs* and additionally
+streams the full-resolution slices in place. That is the right path when the
+source images are still on the server — but most datasets here arrived through
+drag-and-drop ingest, which copies each slice into Tiled's own storage as a 2-D
+array. For those there is no source directory to point at, and asking the user
+to find one would be asking them to re-supply data the app already has.
+
+So this module builds the pyramid from whatever :mod:`arrays` can already read.
+It needs no arguments beyond the dataset that is open, which is what makes
+"Build 3-D volume" a single button rather than a form.
+
+What it does *not* do is copy the full-resolution data. Only the downsampled
+levels are written, because those are the only ones the renderer ever uploads —
+it picks a level that fits ``maxTextureDimension3D``, and full resolution never
+does. Full resolution stays exactly where it is, read by the 2-D canvas as
+always.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any, Callable
+
+import numpy as np
+from fastapi import HTTPException
+
+import arrays as arrays_mod
+import ingest as ingest_mod
+import tiff_stack_source as tss
+from tiled_clients import api_key_for_uri, get_tiled_client
+
+logger = logging.getLogger("volume_build")
+
+
+def _stack_shape(source: str, kind: str, server_uri: str | None) -> tuple[Any, dict, tuple[int, int, int]]:
+    """Resolve *source* to a readable stack and report its ``(z, y, x)`` shape."""
+    node = arrays_mod.resolve_array(source, kind, server_uri)
+    meta = arrays_mod.array_shape_meta(node)
+    n_slices = int(meta.get("n_slices") or 0)
+    height = int(meta.get("height") or 0)
+    width = int(meta.get("width") or 0)
+    if n_slices < 2:
+        raise HTTPException(
+            422,
+            "This dataset is a single image, not a stack — there is no volume to build.",
+        )
+    if meta.get("is_rgb"):
+        raise HTTPException(
+            422,
+            "Colour images are not supported by the 3-D view, which renders a single "
+            "scalar volume.",
+        )
+    return node, meta, (n_slices, height, width)
+
+
+def inspect_volume_build(
+    source: str, kind: str = "tiled", server_uri: str | None = None
+) -> dict[str, Any]:
+    """Describe the volume that would be built, without building it."""
+    _node, meta, shape = _stack_shape(source, kind, server_uri)
+    plan = tss.pyramid_plan(shape)
+    return {
+        "full_shape": list(shape),
+        "dtype": str(meta.get("dtype") or "float32"),
+        "pyramid_plan": plan,
+        # Every source slice is read once; the coarser levels cascade in memory.
+        "slices_to_read": shape[0] if plan else 0,
+        "already_small": not plan,
+    }
+
+
+def build_volume(
+    source: str,
+    kind: str = "tiled",
+    server_uri: str | None = None,
+    container_path: str | None = None,
+    progress: Callable[[str, int, int], None] | None = None,
+) -> dict[str, Any]:
+    """Build and register a 3-D volume sidecar for the stack open at *source*.
+
+    Args:
+        source: Tiled path of the per-slice dataset.
+        kind: ``"tiled"`` (the only kind with a catalog to register into).
+        server_uri: Connected Tiled server URI.
+        container_path: Where to put the sidecar; defaults to *source*'s parent,
+            so the volume lands next to the dataset it describes.
+        progress: Optional ``(message, done, total)`` callback for the job UI.
+
+    Returns:
+        The registered ``key`` and ``tiled_path``, plus the build description.
+    """
+    if kind != "tiled":
+        raise HTTPException(422, "Only datasets in the Tiled catalog can be built into volumes.")
+
+    node, meta, shape = _stack_shape(source, kind, server_uri)
+    plan = tss.pyramid_plan(shape)
+    if not plan:
+        raise HTTPException(
+            422,
+            f"This stack is already small enough ({shape[0]}x{shape[1]}x{shape[2]}) that "
+            "no downsampling is needed — but it is stored as separate 2-D slices, which "
+            "cannot be streamed as a volume. Re-register it from its source images.",
+        )
+
+    parts = [p for p in source.strip("/").split("/") if p]
+    stem = parts[-1]
+    key = f"{stem}{tss.VOLUME_SUFFIX}"
+    target_parts = (
+        [p for p in container_path.strip("/").split("/") if p]
+        if container_path
+        else parts[:-1]
+    )
+    if not target_parts:
+        raise HTTPException(422, "Cannot place a volume at the catalog root.")
+
+    dtype = np.dtype(meta.get("dtype") or "float32")
+    total = shape[0]
+    built: dict[str, np.ndarray] = {}
+    previous: np.ndarray | None = None
+    previous_factor: list[int] | None = None
+
+    for level in plan:
+        label = f"Building {level['path']}"
+        if previous is None or previous_factor is None:
+            # Finest generated level: read the stack once, a block of slices at a
+            # time, so peak memory is the output level plus a few input slices.
+            fz, fy, fx = (int(f) for f in level["factor"])
+            out = np.empty(level["shape"], dtype=np.float32)
+            for z in range(level["shape"][0]):
+                block = []
+                for k in range(fz):
+                    index = z * fz + k
+                    if index >= shape[0]:
+                        break
+                    block.append(arrays_mod.read_slice(node, meta, index))
+                    if progress and (z * fz + k) % 25 == 0:
+                        progress(label, z * fz + k, total)
+                out[z] = tss.block_mean(np.stack(block), fy, fx)
+            previous_working = out
+        else:
+            relative = [int(level["factor"][a] // previous_factor[a]) for a in range(3)]
+            previous_working = tss.downsample_array(previous, relative)
+
+        built[level["path"]] = tss._cast_like(previous_working, dtype)
+        previous, previous_factor = previous_working, list(level["factor"])
+
+    if progress:
+        progress("Writing pyramid", total, total)
+    sidecar = tss.write_pyramid_store(key, built)
+
+    client = get_tiled_client(server_uri, api_key_for_uri(server_uri))
+    target = ingest_mod._ensure_container(client, target_parts)
+
+    # Replace any previous build for this dataset: re-running must refresh the
+    # volume, not fail or accumulate duplicates.
+    if key in ingest_mod._child_keys(target):
+        target.delete_contents(key, recursive=True, external_only=False)
+    volume = target.create_container(key=key, metadata={})
+
+    from tiled.client.register import Settings, register_single_item
+
+    try:
+        asyncio.run(
+            register_single_item(volume, sidecar, is_directory=True, settings=Settings.init())
+        )
+    except Exception as exc:  # noqa: BLE001 — classified for the UI
+        logger.warning("volume registration failed for %s: %s", sidecar, exc)
+        raise HTTPException(502, ingest_mod._classify_error(exc)["message"]) from exc
+
+    pyramid_node = ingest_mod._walk(volume, [tss.PYRAMID_KEY])
+    if pyramid_node is None or not list(pyramid_node):
+        raise HTTPException(
+            502,
+            f"Tiled registered no levels for {key!r} from {sidecar}. The usual cause is "
+            "that this path is not in the Tiled server's `readable_storage` (see "
+            "tiled/config.yml).",
+        )
+
+    volume.update_metadata(
+        metadata={
+            "sample_name": key,
+            "n_images": shape[0],
+            "source_format": "tiled-stack-3d",
+            "built_from": source,
+            "full_shape": list(shape),
+            "pyramid_plan": plan,
+            # No scale0: full resolution stays in the per-slice nodes rather than
+            # being duplicated. The renderer never uploads a level that large.
+            **tss.multiscales_metadata(key, plan, include_scale0=False),
+        }
+    )
+
+    if progress:
+        progress("Done", total, total)
+    return {
+        "key": key,
+        "tiled_path": "/".join([*target_parts, key]),
+        "full_shape": list(shape),
+        "pyramid_plan": plan,
+    }
diff --git a/backend/volume_nodes.py b/backend/volume_nodes.py
new file mode 100644
index 0000000..be000c5
--- /dev/null
+++ b/backend/volume_nodes.py
@@ -0,0 +1,128 @@
+"""Find the Tiled node that holds a dataset's renderable 3-D volume.
+
+The 3-D viewer needs an OME-NGFF multiscale group. Which node that is depends on
+how the dataset got into the catalog, and the answer is not something the
+frontend can work out from a path:
+
+* **Registered Zarr volumes** already are one (:mod:`zarr_source`), so the open
+  node — or an ancestor of it, when a specific pyramid level is open — is the
+  volume.
+* **TIFF stacks** are stored as a container of per-slice 2-D arrays, which is not
+  a volume at all. Their 3-D view lives in the ``__volume`` sidecar built by
+  :mod:`tiff_stack_source`, and it only exists once someone has built it.
+
+Without this the viewer is pointed straight at whatever is open and fails with
+``openOmeZarr: missing multiscales in root .zattrs`` — technically accurate and
+useless to the person reading it, since the real answer is either "look at the
+sidecar" or "no volume has been built for this dataset yet".
+
+Detection is on ``metadata["attributes"]["multiscales"]``: Tiled's ``.zattrs``
+route returns ``metadata["attributes"]`` verbatim, so that key is exactly what
+the viewer will see.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from tiff_stack_source import VOLUME_SUFFIX
+from tiled_clients import api_key_for_uri, get_tiled_client
+
+logger = logging.getLogger("volume_nodes")
+
+#: How far to walk up from an open node looking for the multiscale root. A
+#: pyramid level sits at most two levels down (``<volume>/scale0/image``).
+_MAX_ASCENT = 3
+
+
+def has_multiscales(node: Any) -> bool:
+    """True if *node* carries OME-NGFF ``multiscales`` where Tiled will serve it."""
+    try:
+        attributes = (dict(getattr(node, "metadata", {}) or {})).get("attributes") or {}
+    except Exception:  # noqa: BLE001 — a node we cannot describe is not a volume
+        return False
+    multiscales = attributes.get("multiscales")
+    return isinstance(multiscales, list) and bool(multiscales)
+
+
+def _node_at(client: Any, parts: list[str]) -> Any | None:
+    node = client
+    for part in parts:
+        try:
+            node = node[part]
+        except Exception:  # noqa: BLE001 — missing child, or not a container
+            return None
+    return node
+
+
+def source_dir_of(node: Any) -> str | None:
+    """Filesystem path a volume was registered from, if it records one."""
+    try:
+        meta = dict(getattr(node, "metadata", {}) or {})
+    except Exception:  # noqa: BLE001
+        return None
+    for key in ("tiff_dir", "zarr_path"):
+        value = meta.get(key)
+        if isinstance(value, str) and value:
+            return value
+    return None
+
+
+def resolve_volume(server_uri: str | None, source: str) -> dict[str, Any]:
+    """Locate the renderable volume for the dataset open at *source*.
+
+    Args:
+        server_uri: Connected Tiled server URI; ``None`` uses the default.
+        source: Tiled path of the open dataset.
+
+    Returns:
+        ``mode`` is one of:
+
+        * ``"self"`` — the open node is the volume.
+        * ``"ancestor"`` — a specific level was open; ``path`` is its root.
+        * ``"sidecar"`` — the ``__volume`` node built for a TIFF stack.
+        * ``"none"`` — no volume exists yet; ``message`` says so in plain terms
+          and ``source_dir`` carries a build candidate when one is known.
+    """
+    parts = [p for p in (source or "").strip("/").split("/") if p]
+    if not parts:
+        return {"path": None, "mode": "none", "source_dir": None,
+                "message": "Open a dataset from Browse to view it in 3D."}
+
+    client = get_tiled_client(server_uri, api_key_for_uri(server_uri))
+
+    # 1. The open node itself.
+    node = _node_at(client, parts)
+    if node is not None and has_multiscales(node):
+        return {"path": "/".join(parts), "mode": "self",
+                "source_dir": source_dir_of(node), "message": ""}
+
+    # 2. An ancestor — the user may have opened one pyramid level directly, e.g.
+    #    `<volume>/scale0/image`, which is an array and has no multiscales itself.
+    for depth in range(1, min(_MAX_ASCENT, len(parts)) + 1):
+        ancestor_parts = parts[:-depth]
+        if not ancestor_parts:
+            break
+        ancestor = _node_at(client, ancestor_parts)
+        if ancestor is not None and has_multiscales(ancestor):
+            return {"path": "/".join(ancestor_parts), "mode": "ancestor",
+                    "source_dir": source_dir_of(ancestor), "message": ""}
+
+    # 3. The sidecar built for a per-slice TIFF stack.
+    sidecar_parts = [*parts[:-1], f"{parts[-1]}{VOLUME_SUFFIX}"]
+    sidecar = _node_at(client, sidecar_parts)
+    if sidecar is not None and has_multiscales(sidecar):
+        return {"path": "/".join(sidecar_parts), "mode": "sidecar",
+                "source_dir": source_dir_of(sidecar), "message": ""}
+
+    return {
+        "path": None,
+        "mode": "none",
+        "source_dir": None,
+        "message": (
+            "No 3-D volume has been built for this dataset yet. It is stored as "
+            "individual 2-D slices, which the 3-D view cannot stream — build one "
+            "from the source images to enable it."
+        ),
+    }
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
index 0b22e09..24bef46 100644
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
@@ -33,6 +33,7 @@
         "@types/react-dom": "^18.3.5",
         "@types/uuid": "^10.0.0",
         "@vitejs/plugin-react": "^4.3.4",
+        "@webgpu/types": "^0.1.72",
         "autoprefixer": "^10.4.20",
         "jsdom": "^26.1.0",
         "postcss": "^8.4.49",
@@ -3954,6 +3955,13 @@
         "url": "https://opencollective.com/vitest"
       }
     },
+    "node_modules/@webgpu/types": {
+      "version": "0.1.72",
+      "resolved": "https://registry.npmjs.org/@webgpu/types/-/types-0.1.72.tgz",
+      "integrity": "sha512-0cF7RFM2edNoiIS1ODJp0/Gzv4/xSXhwoR0YCza+OWpJWtn4wmo9DvK91aLlH9+uUnwIriP7ZiC3WitmyhuzBw==",
+      "dev": true,
+      "license": "BSD-3-Clause"
+    },
     "node_modules/adm-zip": {
       "version": "0.5.17",
       "resolved": "https://registry.npmjs.org/adm-zip/-/adm-zip-0.5.17.tgz",
diff --git a/frontend/package.json b/frontend/package.json
index 2750e66..7f1ff53 100644
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -37,6 +37,7 @@
     "@types/react-dom": "^18.3.5",
     "@types/uuid": "^10.0.0",
     "@vitejs/plugin-react": "^4.3.4",
+    "@webgpu/types": "^0.1.72",
     "autoprefixer": "^10.4.20",
     "jsdom": "^26.1.0",
     "postcss": "^8.4.49",
diff --git a/frontend/src/app/App.tsx b/frontend/src/app/App.tsx
index f2968eb..0180c46 100644
--- a/frontend/src/app/App.tsx
+++ b/frontend/src/app/App.tsx
@@ -6,13 +6,16 @@ import HubAppLayout from '@/components/HubAppLayout';
 import { useHubSelectedTabs } from '@/hooks/useHubSelectedTabs';
 import { DOCS_URL, FEEDBACK_FORM_URL, FEEDBACK_ENTRY_ID } from '@/config';
 import { buildFeedbackContext, buildFeedbackUrl } from '@/lib/feedbackContext';
-import { PlugsConnected, PencilSimple, MagnifyingGlass, BookOpen } from '@phosphor-icons/react';
+import { PlugsConnected, PencilSimple, MagnifyingGlass, BookOpen, Cube } from '@phosphor-icons/react';
 // Lazy-loaded pages: keeps the heavy Annotate stack (konva, polygon-clipping,
 // magicwand, canvas) out of the initial /connect bundle — each page is its own chunk.
 const ConnectPage = lazy(() => import('./pages/ConnectPage'));
 const AnnotatePage = lazy(() => import('./pages/AnnotatePage'));
 const BrowsePage = lazy(() => import('./pages/BrowsePage'));
 const ReferencePage = lazy(() => import('./pages/ReferencePage'));
+// Lazy for the same reason as Annotate, and more so: this chunk carries the
+// whole vendored WebGPU renderer, which must never load on /connect.
+const VolumePage = lazy(() => import('./pages/VolumePage'));
 import CustomizePages from '@/components/CustomizePages';
 import IframeModal from '@/components/IframeModal';
 
@@ -43,6 +46,13 @@ const allRoutes: RouteItem[] = [
     element: <AnnotatePage />,
     isBackgroundTransparent: true,
   },
+  {
+    path: '/volume',
+    label: '3D',
+    icon: <Cube size={32} />,
+    element: <VolumePage />,
+    isBackgroundTransparent: true,
+  },
 ];
 
 const DEFAULT_PATHS = allRoutes.map((r) => r.path);
diff --git a/frontend/src/app/pages/VolumePage.tsx b/frontend/src/app/pages/VolumePage.tsx
new file mode 100644
index 0000000..303b74e
--- /dev/null
+++ b/frontend/src/app/pages/VolumePage.tsx
@@ -0,0 +1,130 @@
+/**
+ * VolumePage — 3D view of the open dataset, rendered straight from Tiled.
+ *
+ * The volume is streamed by the vendored WebGPU renderer directly off Tiled's
+ * `/zarr/v2` router (see `lib/zarrUrl.ts`) — there is no export step and no
+ * downsampled-volume endpoint in between, so whatever is open in Annotate is
+ * what renders here. Rendering controls (transfer function, crop box, slice
+ * planes, lighting) come from the renderer's own HUD, docked on the right.
+ *
+ * Which node actually holds the volume is asked of the backend rather than
+ * guessed from the path: a registered Zarr volume is one already, a TIFF stack's
+ * lives in a `__volume` sidecar, and a stack nobody has built one for has none.
+ * See `backend/volume_nodes.py`.
+ *
+ * Annotation overlay is deliberately not here yet — it needs a second label
+ * texture in the renderer, which lands upstream in the viewer repo.
+ */
+import { useEffect, useState } from 'react';
+import { useQuery } from '@tanstack/react-query';
+import { Cube } from '@phosphor-icons/react';
+import { API_BASE } from '@/config';
+import { useDatasetStore } from '@/stores/datasetStore';
+import { buildZarrUrl, describeUnavailable, type ZarrUnavailable } from '@/lib/zarrUrl';
+import type { ServerInfo } from '@/types/server';
+import VolumeViewer, { webGpuAvailability } from '@/components/volume/VolumeViewer';
+import BuildVolumePanel from '@/components/volume/BuildVolumePanel';
+
+/** Shape of `GET /api/volume/resolve`. */
+interface VolumeNode {
+  path: string | null;
+  mode: 'self' | 'ancestor' | 'sidecar' | 'none';
+  source_dir: string | null;
+  message: string;
+}
+
+/** Centered message panel — every non-rendering state uses this shape. */
+function Notice({ title, detail, hint }: { title: string; detail: string; hint?: string }) {
+  return (
+    <div className="flex h-full items-center justify-center p-8">
+      <div className="max-w-md text-center text-sky-200">
+        <Cube size={40} className="mx-auto mb-3 opacity-60" />
+        <p className="mb-1 font-medium">{title}</p>
+        <p className="text-sm opacity-80">{detail}</p>
+        {hint && <p className="mt-3 text-xs opacity-60">{hint}</p>}
+      </div>
+    </div>
+  );
+}
+
+export default function VolumePage() {
+  const { kind, source, serverUri } = useDatasetStore();
+  const [bootError, setBootError] = useState<string | null>(null);
+
+  // A new dataset deserves a fresh attempt — otherwise one bad volume leaves the
+  // page stuck on its error for every dataset opened afterwards.
+  useEffect(() => setBootError(null), [source, serverUri]);
+
+  // Fallback only: a dataset opened against the default local Tiled carries a
+  // null serverUri, and the resolved address (whatever port start_all.sh
+  // actually bound) lives here. Never assume 8010.
+  const { data: servers = [] } = useQuery<ServerInfo[]>({
+    queryKey: ['servers'],
+    queryFn: async () => {
+      const res = await fetch(`${API_BASE}/api/config/servers`);
+      if (!res.ok) throw new Error('Failed to load servers');
+      return res.json();
+    },
+    enabled: kind === 'tiled' && !serverUri,
+  });
+
+  const resolvedUri = serverUri ?? servers[0]?.uri ?? null;
+
+  const { data: node, isLoading: resolving } = useQuery<VolumeNode>({
+    queryKey: ['volume-node', resolvedUri, source],
+    queryFn: async () => {
+      const params = new URLSearchParams({ source: source! });
+      if (resolvedUri) params.set('server_uri', resolvedUri);
+      const res = await fetch(`${API_BASE}/api/volume/resolve?${params}`);
+      if (!res.ok) throw new Error('Failed to resolve the volume node');
+      return res.json();
+    },
+    enabled: kind === 'tiled' && !!source,
+  });
+
+  const availability = webGpuAvailability();
+  if (!availability.ok) {
+    return <Notice title="3D view unavailable" detail={availability.reason} />;
+  }
+
+  // Resolution only applies to Tiled sources; everything else already has a
+  // reason of its own and should not sit on a spinner.
+  let reason: ZarrUnavailable | null = null;
+  if (kind === 'tiled' && source && (resolving || !node)) reason = 'resolving';
+  else if (node && node.mode === 'none') reason = 'no-volume';
+
+  const { url, reason: urlReason } = buildZarrUrl(kind, node?.path ?? null, resolvedUri);
+  reason = reason ?? (url ? null : urlReason);
+
+  // A stack with no volume is not an error state — it is one the user can
+  // resolve in place, so offer the build rather than describing an API call.
+  if (reason === 'no-volume' && source) {
+    return <BuildVolumePanel source={source} serverUri={resolvedUri} message={node!.message} />;
+  }
+
+  if (!url || reason) {
+    return <Notice title="Nothing to render" detail={describeUnavailable(reason ?? 'no-source')} />;
+  }
+
+  if (bootError) {
+    return (
+      <Notice
+        title="This dataset could not be opened as a volume"
+        detail={bootError}
+        hint={`Tried ${node?.path} (${node?.mode}).`}
+      />
+    );
+  }
+
+  return (
+    <div className="flex h-full w-full flex-col">
+      <VolumeViewer
+        // Remount on source change so the renderer tears its GPU device down
+        // and rebuilds, rather than trying to swap a volume in place.
+        key={url}
+        zarrUrl={url}
+        onError={(e) => setBootError(e instanceof Error ? e.message : String(e))}
+      />
+    </div>
+  );
+}
diff --git a/frontend/src/components/volume/BuildVolumePanel.tsx b/frontend/src/components/volume/BuildVolumePanel.tsx
new file mode 100644
index 0000000..97d4411
--- /dev/null
+++ b/frontend/src/components/volume/BuildVolumePanel.tsx
@@ -0,0 +1,178 @@
+/**
+ * BuildVolumePanel — turn a per-slice dataset into a renderable 3-D volume.
+ *
+ * A dataset ingested as individual 2-D slices cannot be streamed as a volume;
+ * the 3-D view needs a multiscale pyramid. Building one is a one-click job here
+ * rather than a form, because the slices are already in Tiled — asking for a
+ * source directory would mean asking for data the app already holds.
+ *
+ * Only the downsampled levels are written. Full resolution stays in the existing
+ * per-slice nodes: the renderer only ever uploads a level that fits a GPU 3-D
+ * texture, so a full-resolution copy would be cost with no benefit.
+ */
+import { useCallback, useState } from 'react';
+import { useQuery, useQueryClient } from '@tanstack/react-query';
+import { Cube, Warning, CircleNotch } from '@phosphor-icons/react';
+import { API_BASE } from '@/config';
+
+interface PyramidLevel {
+  path: string;
+  factor: [number, number, number];
+  shape: [number, number, number];
+}
+
+interface BuildInfo {
+  full_shape: [number, number, number];
+  dtype: string;
+  pyramid_plan: PyramidLevel[];
+  slices_to_read: number;
+  already_small: boolean;
+}
+
+interface JobState {
+  state: 'pending' | 'running' | 'done' | 'error';
+  phase: string;
+  done: number;
+  total: number;
+  error: string | null;
+}
+
+interface BuildVolumePanelProps {
+  source: string;
+  serverUri: string | null;
+  /** Why no volume exists, from `GET /api/volume/resolve`. */
+  message: string;
+}
+
+/** Pull the FastAPI `detail` message out of an error response. */
+async function readError(res: Response): Promise<string> {
+  try {
+    const body = await res.json();
+    if (typeof body?.detail === 'string') return body.detail;
+    return JSON.stringify(body);
+  } catch {
+    return `Request failed (${res.status})`;
+  }
+}
+
+export default function BuildVolumePanel({ source, serverUri, message }: BuildVolumePanelProps) {
+  const queryClient = useQueryClient();
+  const [job, setJob] = useState<JobState | null>(null);
+  const [error, setError] = useState<string | null>(null);
+
+  const { data: info, isLoading, error: inspectError } = useQuery<BuildInfo>({
+    queryKey: ['volume-build-inspect', serverUri, source],
+    queryFn: async () => {
+      const params = new URLSearchParams({ source, kind: 'tiled' });
+      if (serverUri) params.set('server_uri', serverUri);
+      const res = await fetch(`${API_BASE}/api/volume/build/inspect?${params}`);
+      if (!res.ok) throw new Error(await readError(res));
+      return res.json();
+    },
+    retry: false,
+  });
+
+  const build = useCallback(async () => {
+    setError(null);
+    setJob({ state: 'pending', phase: 'Starting', done: 0, total: 1, error: null });
+    try {
+      const res = await fetch(`${API_BASE}/api/volume/build`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ source, kind: 'tiled', server_uri: serverUri }),
+      });
+      if (!res.ok) throw new Error(await readError(res));
+      const { job_id: jobId } = await res.json();
+
+      // Poll rather than stream: this reuses the same export-job registry (and
+      // the same status route) every other long-running task here already uses.
+      for (;;) {
+        await new Promise((resolve) => setTimeout(resolve, 1000));
+        const statusRes = await fetch(`${API_BASE}/api/export/status/${jobId}`);
+        if (!statusRes.ok) throw new Error(await readError(statusRes));
+        const status: JobState = await statusRes.json();
+        setJob(status);
+        if (status.state === 'error') throw new Error(status.error || 'Build failed');
+        if (status.state === 'done') break;
+      }
+      // The volume now exists — re-resolve so the viewer picks it up.
+      await queryClient.invalidateQueries({ queryKey: ['volume-node'] });
+    } catch (err) {
+      setError(err instanceof Error ? err.message : String(err));
+      setJob(null);
+    }
+  }, [source, serverUri, queryClient]);
+
+  const running = job !== null && job.state !== 'done';
+  const percent = job && job.total > 0 ? Math.round((job.done / job.total) * 100) : 0;
+
+  return (
+    <div className="flex h-full items-center justify-center p-8">
+      <div className="w-full max-w-lg text-center text-sky-200">
+        <Cube size={40} className="mx-auto mb-3 opacity-60" />
+        <p className="mb-1 font-medium">No 3D volume for this dataset yet</p>
+        <p className="mb-5 text-sm opacity-80">{message}</p>
+
+        {isLoading && <p className="text-sm opacity-70">Checking this dataset…</p>}
+
+        {inspectError && (
+          <p className="flex items-center justify-center gap-2 text-sm text-amber-300">
+            <Warning size={16} />
+            {inspectError instanceof Error ? inspectError.message : String(inspectError)}
+          </p>
+        )}
+
+        {info && !info.already_small && (
+          <>
+            <div className="mb-4 rounded border border-sky-800/60 bg-sky-950/40 p-3 text-left text-xs">
+              <p className="mb-1">
+                <span className="opacity-70">Source</span>{' '}
+                {info.full_shape.join(' × ')} <span className="opacity-70">{info.dtype}</span>
+              </p>
+              <p className="mb-1">
+                <span className="opacity-70">Will build</span>{' '}
+                {info.pyramid_plan.map((l) => l.shape.join('×')).join(', ')}
+              </p>
+              <p className="opacity-70">
+                Reads {info.slices_to_read} slices once. Full resolution is not copied —
+                it stays where it is, and the 3D view never loads a level that large.
+              </p>
+            </div>
+
+            {!running && (
+              <button
+                type="button"
+                onClick={build}
+                className="rounded bg-sky-600 px-4 py-2 text-sm font-medium text-white hover:bg-sky-500"
+              >
+                Build 3D volume
+              </button>
+            )}
+
+            {running && (
+              <div className="text-sm">
+                <p className="mb-2 flex items-center justify-center gap-2">
+                  <CircleNotch size={16} className="animate-spin" />
+                  {job.phase} — {job.done}/{job.total}
+                </p>
+                <div className="h-2 w-full overflow-hidden rounded bg-sky-950">
+                  <div
+                    className="h-full bg-sky-500 transition-[width] duration-300"
+                    style={{ width: `${percent}%` }}
+                  />
+                </div>
+              </div>
+            )}
+          </>
+        )}
+
+        {error && (
+          <p className="mt-4 flex items-center justify-center gap-2 text-sm text-amber-300">
+            <Warning size={16} />
+            {error}
+          </p>
+        )}
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/components/volume/VolumeViewer.tsx b/frontend/src/components/volume/VolumeViewer.tsx
new file mode 100644
index 0000000..38ed5ff
--- /dev/null
+++ b/frontend/src/components/volume/VolumeViewer.tsx
@@ -0,0 +1,142 @@
+/**
+ * VolumeViewer — React wrapper around the vendored WebGPU volume renderer.
+ *
+ * The renderer (pinned as a git submodule, see `.gitmodules` and
+ * `tsconfig.app.json`'s `@zarrviewer/*` alias) is not a component: it is an
+ * imperative `run(canvas, { zarrUrl, hudMount })` that boots a volume view into
+ * a canvas and returns a disposable handle. This wrapper owns only the React
+ * lifecycle around it — create the hosts, boot, dispose. Everything about *how*
+ * the volume looks belongs upstream.
+ *
+ * Adapted from the viewer repo's own `src/WebGpuNative.tsx`, which had already
+ * solved the mount/dispose shape; kept here rather than imported because it is
+ * host-app glue, not renderer code.
+ *
+ * Auth: chunks are fetched with a plain `fetch()` straight to the Tiled origin.
+ * That works because anonymous access is read-only-enabled there — see
+ * `lib/zarrUrl.ts`. Deliberately no token interceptor: the Tiled API key is
+ * write-scoped and stays server-side.
+ */
+import { useEffect, useRef } from 'react';
+import { run, type WebGpuViewerInstance } from '@zarrviewer/ome-zarr-viewer';
+
+export type { WebGpuViewerInstance };
+
+/** Width of the docked HUD column, in px. Mirrored into the renderer's stage. */
+const HUD_WIDTH = 320;
+
+/**
+ * Whether the renderer can run here, with a reason when it cannot.
+ *
+ * WebGPU is gated on a *secure context*, so over plain http `navigator.gpu` is
+ * simply undefined. Distinguishing that from "this browser has no WebGPU"
+ * matters: the first is a deployment fix, the second is not, and collapsing
+ * them sends people looking in the wrong place.
+ */
+export function webGpuAvailability(): { ok: boolean; reason: string } {
+  if (typeof navigator !== 'undefined' && (navigator as Navigator & { gpu?: unknown }).gpu) {
+    return { ok: true, reason: '' };
+  }
+  if (typeof window !== 'undefined' && !window.isSecureContext) {
+    return {
+      ok: false,
+      reason:
+        'WebGPU needs a secure context. Open the app over https, or via localhost / 127.0.0.1 rather than a LAN address.',
+    };
+  }
+  return {
+    ok: false,
+    reason: 'This browser does not support WebGPU. Chrome or Edge 113+ is required for the 3D view.',
+  };
+}
+
+interface VolumeViewerProps {
+  /** Zarr store root, from `buildZarrUrl`. */
+  zarrUrl: string;
+  /** Receives the handle when the renderer boots, and `null` when it is torn down. */
+  onReady?: (instance: WebGpuViewerInstance | null) => void;
+  /** Receives a boot failure (bad store, unsupported codec, no multiscales). */
+  onError?: (error: unknown) => void;
+}
+
+export default function VolumeViewer({ zarrUrl, onReady, onError }: VolumeViewerProps) {
+  const containerRef = useRef<HTMLDivElement | null>(null);
+  // Held in refs so a changed callback identity never restarts the renderer —
+  // rebooting a WebGPU device on every parent render would be ruinous.
+  const onReadyRef = useRef(onReady);
+  const onErrorRef = useRef(onError);
+  onReadyRef.current = onReady;
+  onErrorRef.current = onError;
+
+  useEffect(() => {
+    const container = containerRef.current;
+    if (!container || !zarrUrl || !webGpuAvailability().ok) return;
+
+    let cancelled = false;
+    let handle: WebGpuViewerInstance | null = null;
+
+    // The renderer treats `canvas.parentElement` as its stage and re-fits the
+    // backing store to that box every frame, so a canvas that shares the row
+    // with the HUD sidebar sizes itself correctly with no resize plumbing here.
+    const canvasHost = document.createElement('div');
+    canvasHost.style.flex = '1';
+    canvasHost.style.minWidth = '0';
+    canvasHost.style.position = 'relative';
+
+    const canvas = document.createElement('canvas');
+    canvas.style.width = '100%';
+    canvas.style.height = '100%';
+    canvas.style.display = 'block';
+    canvasHost.appendChild(canvas);
+
+    const hudHost = document.createElement('div');
+    hudHost.style.width = `${HUD_WIDTH}px`;
+    hudHost.style.flexShrink = '0';
+    hudHost.style.overflow = 'auto';
+
+    container.appendChild(canvasHost);
+    container.appendChild(hudHost);
+
+    run(canvas, { zarrUrl, hudMount: hudHost })
+      .then((created) => {
+        // StrictMode double-invokes effects in dev, so a run can resolve after
+        // its own cleanup. Disposing here is what stops GPU devices piling up.
+        if (cancelled) {
+          created.dispose();
+          return;
+        }
+        handle = created;
+        onReadyRef.current?.(created);
+      })
+      .catch((error: unknown) => {
+        if (cancelled) return;
+        console.error('VolumeViewer: renderer failed to start:', error);
+        onErrorRef.current?.(error);
+      });
+
+    return () => {
+      cancelled = true;
+      try {
+        handle?.dispose();
+      } catch (error) {
+        console.warn('VolumeViewer: dispose failed:', error);
+      }
+      if (handle) onReadyRef.current?.(null);
+      container.replaceChildren();
+    };
+  }, [zarrUrl]);
+
+  return (
+    <div
+      ref={containerRef}
+      style={{
+        flex: 1,
+        minHeight: 0,
+        width: '100%',
+        position: 'relative',
+        display: 'flex',
+        flexDirection: 'row',
+      }}
+    />
+  );
+}
diff --git a/frontend/src/lib/zarrUrl.test.ts b/frontend/src/lib/zarrUrl.test.ts
new file mode 100644
index 0000000..f864fcd
--- /dev/null
+++ b/frontend/src/lib/zarrUrl.test.ts
@@ -0,0 +1,70 @@
+import { describe, it, expect } from 'vitest';
+import { buildZarrUrl, zarrRootFor, describeUnavailable } from './zarrUrl';
+
+describe('zarrRootFor', () => {
+  it('appends the zarr v2 root to a bare origin', () => {
+    expect(zarrRootFor('http://127.0.0.1:8010')).toBe('http://127.0.0.1:8010/zarr/v2');
+  });
+
+  it('tolerates a trailing slash', () => {
+    expect(zarrRootFor('http://127.0.0.1:8010/')).toBe('http://127.0.0.1:8010/zarr/v2');
+  });
+
+  it('replaces a REST prefix rather than nesting under it', () => {
+    // .../api/v1/zarr/v2/... would 404 in a way that reads as missing data.
+    expect(zarrRootFor('https://tiled.example.org/api/v1')).toBe('https://tiled.example.org/zarr/v2');
+  });
+
+  it('does not hardcode a port', () => {
+    // start_all.sh reassigns Tiled's port when 8010 is busy; the URI is the
+    // source of truth, so whatever port it carries must survive.
+    expect(zarrRootFor('http://127.0.0.1:8777')).toContain(':8777');
+  });
+});
+
+describe('buildZarrUrl', () => {
+  const server = 'http://127.0.0.1:8010';
+
+  it('addresses a tiled node under the zarr root', () => {
+    expect(buildZarrUrl('tiled', 'scans/petiole22', server)).toEqual({
+      url: 'http://127.0.0.1:8010/zarr/v2/scans/petiole22',
+      reason: null,
+    });
+  });
+
+  it('preserves path separators while escaping each segment', () => {
+    const { url } = buildZarrUrl('tiled', 'my scans/sample #1', server);
+    expect(url).toBe('http://127.0.0.1:8010/zarr/v2/my%20scans/sample%20%231');
+  });
+
+  it('ignores empty path segments', () => {
+    const { url } = buildZarrUrl('tiled', '/scans//petiole22/', server);
+    expect(url).toBe('http://127.0.0.1:8010/zarr/v2/scans/petiole22');
+  });
+
+  it.each([
+    ['no source open', null, null, server, 'no-source'],
+    ['no path', 'tiled', null, server, 'no-source'],
+    ['local file', 'local', 'foo.tif', null, 'local-source'],
+    ['server unresolved', 'tiled', 'scans/x', null, 'no-server'],
+  ])('reports %s rather than guessing a URL', (_label, kind, source, uri, reason) => {
+    const result = buildZarrUrl(kind as string | null, source as string | null, uri as string | null);
+    expect(result.url).toBeNull();
+    expect(result.reason).toBe(reason);
+  });
+
+  it('never emits credentials in the URL', () => {
+    // Anonymous read access is the contract; a key in the query string would
+    // put a write-capable credential in browser history and referrers.
+    const { url } = buildZarrUrl('tiled', 'scans/x', server);
+    expect(url).not.toMatch(/api_key|token|Authorization/i);
+  });
+});
+
+describe('describeUnavailable', () => {
+  it('explains every reason', () => {
+    for (const reason of ['no-source', 'local-source', 'no-server'] as const) {
+      expect(describeUnavailable(reason).length).toBeGreaterThan(0);
+    }
+  });
+});
diff --git a/frontend/src/lib/zarrUrl.ts b/frontend/src/lib/zarrUrl.ts
new file mode 100644
index 0000000..7c22e01
--- /dev/null
+++ b/frontend/src/lib/zarrUrl.ts
@@ -0,0 +1,106 @@
+/**
+ * zarrUrl — address a Tiled node as a Zarr store for the WebGPU volume viewer.
+ *
+ * Tiled 0.2.12 mounts a Zarr v2 router at `/zarr/v2`, so every array already in
+ * the catalog is *already* readable as a Zarr store — `.zattrs`/`.zarray` are
+ * synthesized from the Tiled structure and `/{i.j.k}` serves a chunk. Nothing
+ * has to be exported or copied: the 3D view reads the same catalog Browse and
+ * Annotate read.
+ *
+ * Two properties this module exists to preserve:
+ *
+ *   1. **The port is never hardcoded.** `start_all.sh` picks a free port for
+ *      Tiled (default 8010, reassigned when busy) and the resolved URI reaches
+ *      the frontend through `GET /api/config/servers` / `datasetStore.serverUri`.
+ *      A literal `8010` anywhere here breaks the moment that fallback fires.
+ *   2. **No API key goes to the browser.** `tiled/config.yml` sets
+ *      `allow_anonymous_access: true` and anonymous access is read-only; the
+ *      generated key authorizes *writes* and stays server-side by design. Zarr
+ *      chunk reads are reads, so the renderer's plain `fetch()` needs no
+ *      credentials — and must not be given any.
+ *
+ * Pure and dependency-free so the URL algebra is unit-testable; the React side
+ * lives in `useZarrUrl`.
+ */
+
+/** Why a source cannot be opened as a Zarr volume, when it cannot. */
+export type ZarrUnavailable =
+  /** Local files are read off disk by the backend; they are not in the catalog. */
+  | 'local-source'
+  /** Nothing is open yet. */
+  | 'no-source'
+  /** Kind is 'tiled' but no server URI has been resolved yet. */
+  | 'no-server'
+  /** Still asking the backend which node holds the volume. */
+  | 'resolving'
+  /** The dataset exists but has no multiscale volume to render. */
+  | 'no-volume';
+
+export interface ZarrUrlResult {
+  /** Zarr store root, or `null` when unavailable. */
+  url: string | null;
+  reason: ZarrUnavailable | null;
+}
+
+/** Human-readable explanation for a `ZarrUnavailable`, for the empty state. */
+export function describeUnavailable(reason: ZarrUnavailable): string {
+  switch (reason) {
+    case 'no-source':
+      return 'Open a dataset from Browse to view it in 3D.';
+    case 'local-source':
+      return 'The 3D view reads from the Tiled catalog. Ingest this file first, then reopen it.';
+    case 'no-server':
+      return 'Still resolving the Tiled server address — one moment.';
+    case 'resolving':
+      return 'Looking for this dataset’s 3D volume…';
+    case 'no-volume':
+      return 'No 3D volume has been built for this dataset yet.';
+  }
+}
+
+/**
+ * The `/zarr/v2` root for a Tiled server URI.
+ *
+ * Accepts a URI with or without a trailing `/api/v1`: our backend reports the
+ * bare origin (`http://127.0.0.1:8010`), but a Tiled URI copied from elsewhere
+ * often carries the REST prefix, and silently producing
+ * `.../api/v1/zarr/v2/...` would 404 in a way that looks like missing data
+ * rather than a malformed URL.
+ */
+export function zarrRootFor(serverUri: string): string {
+  const trimmed = serverUri.trim().replace(/\/+$/, '');
+  const base = trimmed.replace(/\/api\/v\d+$/, '');
+  return `${base}/zarr/v2`;
+}
+
+/**
+ * Zarr store URL for a dataset's volume.
+ *
+ * @param kind      `datasetStore.kind` — 'tiled' or 'local'.
+ * @param source    Tiled path of the node that **holds the volume** — not
+ *   necessarily the open dataset. A per-slice TIFF stack keeps its volume in a
+ *   `__volume` sidecar, and a pyramid level's volume is its parent, so this must
+ *   be the path `GET /api/volume/resolve` returned. Pointing the viewer at
+ *   whatever happens to be open is what produces
+ *   `openOmeZarr: missing multiscales in root .zattrs`.
+ * @param serverUri Resolved Tiled server URI (see the port note above).
+ */
+export function buildZarrUrl(
+  kind: string | null,
+  source: string | null,
+  serverUri: string | null,
+): ZarrUrlResult {
+  if (!kind || !source) return { url: null, reason: 'no-source' };
+  if (kind !== 'tiled') return { url: null, reason: 'local-source' };
+  if (!serverUri) return { url: null, reason: 'no-server' };
+
+  // Encode each segment separately: the path is a `/`-joined chain of Tiled
+  // keys, and encodeURIComponent on the whole thing would escape the separators.
+  const path = source
+    .split('/')
+    .filter(Boolean)
+    .map(encodeURIComponent)
+    .join('/');
+
+  return { url: `${zarrRootFor(serverUri)}/${path}`, reason: null };
+}
diff --git a/frontend/src/types/webgpu.d.ts b/frontend/src/types/webgpu.d.ts
new file mode 100644
index 0000000..d3a2d6e
--- /dev/null
+++ b/frontend/src/types/webgpu.d.ts
@@ -0,0 +1,8 @@
+// WebGPU global types for the vendored volume renderer.
+//
+// `@webgpu/types` is not an `@types/*` package, so TypeScript does not pick it up
+// automatically. Referencing it here rather than adding a `types` array to
+// tsconfig.app.json is deliberate: setting `types` switches off the "include every
+// @types package" default, which would silently drop the globals vitest and node
+// contribute elsewhere in the app.
+/// <reference types="@webgpu/types" />
diff --git a/frontend/tsconfig.app.json b/frontend/tsconfig.app.json
index 223ceff..6db341e 100644
--- a/frontend/tsconfig.app.json
+++ b/frontend/tsconfig.app.json
@@ -3,7 +3,9 @@
     "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
     "target": "ES2020",
     "useDefineForClassFields": true,
-    "lib": ["ES2020", "DOM", "DOM.Iterable"],
+    // ES2022.Error is declarations-only (it adds `Error`'s `cause` option, which
+    // the vendored renderer uses). It does not move `target` off ES2020.
+    "lib": ["ES2020", "ES2022.Error", "DOM", "DOM.Iterable"],
     "module": "ESNext",
     "skipLibCheck": true,
     "moduleResolution": "bundler",
@@ -17,8 +19,29 @@
     "noUnusedParameters": false,
     "noFallthroughCasesInSwitch": true,
     "paths": {
-      "@/*": ["./src/*"]
+      "@/*": ["./src/*"],
+      // The WebGPU volume renderer, pinned as a git submodule (see .gitmodules).
+      // `@zarrviewer/*` is OUR entry point into it; the `@zarr-viewer/*` and
+      // `@prism/*` aliases below are ITS OWN internal ones, replicated here
+      // because tsc resolves imports against this config, not the submodule's.
+      "@zarrviewer/*": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/*"],
+      "@zarr-viewer/core": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/core/index.ts"],
+      "@zarr-viewer/math": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/math/index.ts"],
+      "@zarr-viewer/scene": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/scene/index.ts"],
+      "@zarr-viewer/controls": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/index.ts"],
+      "@zarr-viewer/io": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/io/index.ts"],
+      "@zarr-viewer/render": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/render/index.ts"],
+      "@prism/core": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/core/index.ts"],
+      "@prism/math": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/math/index.ts"],
+      "@prism/render": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/render/index.ts"],
+      "@prism/fx": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/fx/src/index.ts"]
     }
   },
-  "include": ["src"]
+  // The submodule is typechecked here rather than as a separate project so a bad
+  // pointer bump fails `tsc -b` loudly. Its own tsconfig is *stricter* than ours
+  // (`noUncheckedIndexedAccess`, `verbatimModuleSyntax`), so code written under it
+  // compiles cleanly under ours. If it ever does not, that is an upstream fix on
+  // the viewer repo's own branch — do not edit vendored sources to appease tsc.
+  "include": ["src", "vendor/view_tomography_recon_app/src/zarr-viewer/src"],
+  "exclude": ["vendor/view_tomography_recon_app/src/zarr-viewer/src/fx/test"]
 }
diff --git a/frontend/vendor/view_tomography_recon_app b/frontend/vendor/view_tomography_recon_app
new file mode 160000
index 0000000..46d4fd3
--- /dev/null
+++ b/frontend/vendor/view_tomography_recon_app
@@ -0,0 +1 @@
+Subproject commit 46d4fd35fa38b16213585720c40e86fbd7678969
diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts
index 2c13928..fc1f8a9 100644
--- a/frontend/vite.config.ts
+++ b/frontend/vite.config.ts
@@ -58,5 +58,9 @@ export default defineConfig({
     globals: true,
     environment: 'jsdom',
     setupFiles: ['./src/test/setup.ts'],
+    // Scoped to src/ so the vendored renderer's own suite (which needs WebGPU and
+    // its own runner config) isn't swept into ours by the default glob. Upstream
+    // tests are upstream's to run.
+    include: ['src/**/*.{test,spec}.{ts,tsx}'],
   },
 });
diff --git a/start_all.sh b/start_all.sh
index 3dc6dbd..36c1c22 100755
--- a/start_all.sh
+++ b/start_all.sh
@@ -468,6 +468,11 @@ ensure_sam_model
 # resolved server-side by backend/tiled_config.py. Keys are never sent to the
 # frontend. (Port must match backend/tiled_config.py default: 8010.)
 # ---------------------------------------------------------------------------
+# Generated 3-D volume pyramids (backend/tiff_stack_source.py) are served by
+# Tiled in place, from a path listed in tiled/config.yml's readable_storage.
+# Created BEFORE Tiled starts, since that is when readable_storage is resolved.
+mkdir -p "$SCRIPT_DIR/.tiled/volumes"
+
 echo -e "${CYAN}==> Starting Tiled (port ${TILED_PORT})...${NC}"
 
 # Repair catalog asset paths in case the repo was moved or cloned to a new location.
@@ -528,6 +533,23 @@ if [ "$TILED_READY" != 1 ]; then
   exit 1
 fi
 
+# ---------------------------------------------------------------------------
+# Git submodules: the WebGPU volume renderer that powers the 3D tab lives in
+# frontend/vendor/view_tomography_recon_app. A clone without --recurse-submodules
+# leaves it empty, which otherwise shows up as an unresolved import halfway
+# through a Vite build. Initialise it here so that never happens.
+# ---------------------------------------------------------------------------
+ZARR_VIEWER_ENTRY="$FRONTEND_DIR/vendor/view_tomography_recon_app/src/zarr-viewer/src/ome-zarr-viewer.ts"
+if [ ! -f "$ZARR_VIEWER_ENTRY" ]; then
+  echo -e "${CYAN}==> Initialising git submodules (3D volume renderer)...${NC}"
+  if git -C "$SCRIPT_DIR" submodule update --init --recursive; then
+    echo -e "${GREEN}    Submodules ready.${NC}"
+  else
+    echo -e "${YELLOW}    Could not initialise submodules — the 3D tab will not build.${NC}"
+    echo -e "${YELLOW}    Fix with: git submodule update --init --recursive${NC}"
+  fi
+fi
+
 # ---------------------------------------------------------------------------
 # Production SPA build (PROD=1): build the optimized frontend and stage it in
 # backend/static/ BEFORE the backend starts — the SPA mount is decided at import
diff --git a/tiled/config.yml b/tiled/config.yml
index 418b86a..81f4049 100644
--- a/tiled/config.yml
+++ b/tiled/config.yml
@@ -16,5 +16,10 @@ trees:
       writable_storage: ".tiled/data"
       readable_storage:
         - "/Users/david/Documents/data"
+        # Generated 3-D volume pyramids (backend/tiff_stack_source.py). Tiled
+        # serves these in place, so the path must be readable here or the node
+        # registers with no children and every chunk request 500s.
+        # Keep in step with VOLUME_CACHE_DIR.
+        - "./.tiled/volumes"
       init_if_not_exists: true
 

From abfe6a1e13a1cc260ea0e6bb78d91740b4e4ba63 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 26 Aug 2026 12:52:32 -0700
Subject: [PATCH 06/38] Adding Alex's denoising

---
 backend/annotation_server.py                  | 185 ++++++++-
 backend/denoise.py                            | 351 +++++++++++++++++
 backend/denoise_bake.py                       | 357 ++++++++++++++++++
 backend/export_jobs.py                        |  29 ++
 backend/ingest.py                             |  33 ++
 backend/pyproject.toml                        |   4 +
 backend/schemas.py                            |  30 ++
 backend/tests/test_denoise.py                 | 189 ++++++++++
 frontend/src/app/pages/AnnotatePage.tsx       |  27 +-
 .../annotate/AnnotationCanvas/index.tsx       |   9 +-
 .../components/annotate/DenoiseBakeModal.tsx  | 216 +++++++++++
 .../annotate/DenoisePanel/index.tsx           | 185 +++++++++
 .../annotate/DisplayControls/index.tsx        |  13 +
 .../src/hooks/buildSliceUrl.denoise.test.ts   |  78 ++++
 frontend/src/hooks/useImageSlice.ts           |  36 +-
 frontend/src/stores/datasetStore.ts           |  33 +-
 16 files changed, 1761 insertions(+), 14 deletions(-)
 create mode 100644 backend/denoise.py
 create mode 100644 backend/denoise_bake.py
 create mode 100644 backend/tests/test_denoise.py
 create mode 100644 frontend/src/components/annotate/DenoiseBakeModal.tsx
 create mode 100644 frontend/src/components/annotate/DenoisePanel/index.tsx
 create mode 100644 frontend/src/hooks/buildSliceUrl.denoise.test.ts

diff --git a/backend/annotation_server.py b/backend/annotation_server.py
index d206b44..809ed79 100644
--- a/backend/annotation_server.py
+++ b/backend/annotation_server.py
@@ -26,7 +26,7 @@
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Optional
+from typing import Any, Optional
 
 import numpy as np
 from fastapi import FastAPI, File, Form, HTTPException, Query, Response, UploadFile
@@ -37,6 +37,8 @@
 
 import annotation_thumbnails
 import arrays as arrays_mod
+import denoise as denoise_mod
+import denoise_bake as denoise_bake_mod
 import drafts as drafts_mod
 import export_jobs
 import guides as guides_mod
@@ -66,6 +68,7 @@
     write_lightly_split,
 )
 from schemas import (
+    DenoiseBakeRequest,
     DraftPayload,
     ExportRequest,
     ExportSourceItem,
@@ -143,6 +146,10 @@ class ServerConfig(BaseModel):
 _column_cache: TTLCache = TTLCache(ttl_seconds=_CACHE_TTL, max_entries=256)
 _items_cache: TTLCache = TTLCache(ttl_seconds=_CACHE_TTL, max_entries=128)
 _field_mapping_cache: TTLCache = TTLCache(ttl_seconds=_FIELD_MAPPING_TTL, max_entries=32)
+# Denoised slice PNGs only — the plain render path stays uncached because it is
+# already cheap. Entries are a few MB each at full resolution, so 32 covers
+# scrubbing a stack back and forth without unbounded growth.
+_denoised_slice_cache: TTLCache = TTLCache(ttl_seconds=300.0, max_entries=32)
 
 
 def _resolve_field_mapping(
@@ -605,6 +612,14 @@ async def image_slice(
     vmin_pct: float = Query(1.0),
     vmax_pct: float = Query(99.0),
     cmap: str = Query("gray"),
+    denoise_method: str = Query("none", description="Classical denoise filter (see denoise.ALL_METHODS)"),
+    denoise_strength: float = Query(0.5, ge=0.0, le=1.0),
+    denoise_crop: int = Query(
+        0,
+        ge=0,
+        description="If >0, denoise and return only a centred square crop of this size at 1:1. "
+                    "For tuning: filtering a full slice costs seconds for NLM/TV.",
+    ),
 ) -> Response:
     """Render one slice of an image source as a PNG."""
     opts = {
@@ -614,12 +629,32 @@ async def image_slice(
         "vmax_pct": vmax_pct,
         "cmap": cmap,
     }
+    if denoise_method not in denoise_mod.ALL_METHODS:
+        raise HTTPException(400, f"Unknown denoise method {denoise_method!r}")
+    denoising = denoise_method != "none"
+
+    # Denoised renders are cached; the plain path stays uncached because it is
+    # already cheap. Without this, every slider tweak or revisit re-pays the full
+    # filter cost — seconds, not milliseconds, for NLM and TV.
+    cache_key = (
+        source, kind, slice_index, server_uri, root, norm, scale, vmin_pct, vmax_pct, cmap,
+        denoise_method, round(denoise_strength, 4), denoise_crop,
+    )
+    if denoising:
+        cached = _denoised_slice_cache.get(cache_key)
+        if cached is not None:
+            return Response(content=cached, media_type="image/png")
 
     def _run() -> bytes:
         node = arrays_mod.resolve_array(source, kind, server_uri, root)
         pyramid = arrays_mod.pyramid_info(source, kind, server_uri, root)
         meta = arrays_mod.array_shape_meta(node, pyramid)
-        sl = arrays_mod.read_slice(node, meta, slice_index)
+        # Denoise the RAW slice, before normalization: noise statistics live in
+        # the source's own intensity units, not in the 8-bit display range.
+        if denoising:
+            sl = _denoised_slice(node, meta, slice_index, denoise_method, denoise_strength, denoise_crop)
+        else:
+            sl = arrays_mod.read_slice(node, meta, slice_index)
         global_range = None
         if norm == "global":
             # NB: deriving this from the pyramid's coarsest level was tried and
@@ -642,6 +677,9 @@ def _run() -> bytes:
         logger.error("image_slice failed: %s", exc)
         raise HTTPException(500, f"Failed to render slice: {exc}") from exc
 
+    if denoising:
+        _denoised_slice_cache.set(cache_key, png)
+
     return Response(
         content=png,
         media_type="image/png",
@@ -1120,6 +1158,19 @@ def _cb(message: str, _jid: str = jid) -> None:
         export_jobs.update(jid, state="error", phase="error", error=str(exc))
 
 
+@app.post("/api/export/cancel/{job_id}")
+async def export_cancel(job_id: str) -> dict:
+    """Ask a running job to stop at its next clean boundary.
+
+    Cooperative rather than immediate: a job that stops mid-write would leave a
+    partial dataset that looks complete. Jobs that honour it discard their
+    partial output; those that do not simply run to completion.
+    """
+    if not export_jobs.request_cancel(job_id):
+        raise HTTPException(404, "Unknown job_id")
+    return {"cancelled": True}
+
+
 @app.get("/api/export/status/{job_id}")
 async def export_status(job_id: str) -> dict:
     """Poll an export job's progress (state, phase, done/total, log, result)."""
@@ -1313,6 +1364,136 @@ async def zarr_register(req: ZarrRegisterRequest) -> dict:
     )
 
 
+def _centre_crop(arr: np.ndarray, size: int) -> np.ndarray:
+    """Centred square crop of *size*, or *arr* unchanged if it already fits."""
+    h, w = arr.shape[:2]
+    if size <= 0 or (h <= size and w <= size):
+        return arr
+    top = max(0, (h - size) // 2)
+    left = max(0, (w - size) // 2)
+    return arr[top: top + min(size, h), left: left + min(size, w)]
+
+
+def _denoised_slice(
+    node: Any,
+    meta: dict,
+    slice_index: int,
+    method: str,
+    strength: float,
+    crop: int = 0,
+) -> np.ndarray:
+    """Read *slice_index* and denoise it, pulling z-neighbours for 3-D methods.
+
+    The 3-D filters are the training-free way to exploit slice-to-slice
+    correlation — adjacent tomographic slices share structure while their noise
+    is independent — which is why this reads a window rather than one slice. The
+    window is clamped to the volume, and the target's position inside the stack
+    is tracked explicitly: it is NOT always the centre, since the window is
+    truncated at the first and last slice.
+
+    When *crop* > 0 the crop is taken BEFORE filtering — filtering a full 6.5 MP
+    slice is what's slow. Worth knowing: results near the crop border, and NLM's
+    patch search in particular, differ slightly from the full-slice result, so a
+    crop is a tuning aid rather than a byte-exact preview of the bake.
+    """
+    radius = denoise_mod.z_radius_for(method)
+    if radius == 0:
+        sl = _centre_crop(np.asarray(arrays_mod.read_slice(node, meta, slice_index)), crop)
+        return denoise_mod.denoise_slice(sl, method, strength)
+
+    n_slices = int(meta["n_slices"])
+    lo = max(0, slice_index - radius)
+    hi = min(n_slices - 1, slice_index + radius)
+    frames = []
+    for idx in range(lo, hi + 1):
+        try:
+            frames.append(_centre_crop(np.asarray(arrays_mod.read_slice(node, meta, idx)), crop))
+        except Exception as exc:  # noqa: BLE001 — a bad neighbour must not fail the view
+            logger.warning("denoise: skipping unreadable neighbour slice %d: %s", idx, exc)
+            if idx == slice_index:
+                raise
+    if len(frames) < 2:
+        # Not enough usable z-context (single-slice source, or unreadable
+        # neighbours) — fall back to the 2-D sibling rather than erroring out.
+        fallback = "gaussian" if method == "gaussian3d" else "median"
+        sl = _centre_crop(np.asarray(arrays_mod.read_slice(node, meta, slice_index)), crop)
+        return denoise_mod.denoise_slice(sl, fallback, strength)
+
+    target_pos = min(slice_index - lo, len(frames) - 1)
+    return denoise_mod.denoise_stack(np.stack(frames, axis=0), method, strength)[target_pos]
+
+
+@app.get("/api/denoise/methods")
+async def denoise_methods() -> dict:
+    """Denoise filters available in this environment, with cost hints.
+
+    ``available`` is probed rather than assumed: ``denoise_wavelet`` imports
+    fine without PyWavelets and only fails when called.
+    """
+    return {"methods": denoise_mod.describe_methods()}
+
+
+@app.get("/api/denoise/auto")
+async def denoise_auto(
+    source: str = Query(...),
+    kind: str = Query(...),
+    slice_index: int = Query(0),
+    method: str = Query("tv"),
+    server_uri: Optional[str] = None,
+    root: Optional[str] = Query(None),
+) -> dict:
+    """Suggest a strength for this slice, from its own measured noise level."""
+    if method not in denoise_mod.ALL_METHODS:
+        raise HTTPException(400, f"Unknown denoise method {method!r}")
+
+    def _run() -> dict:
+        node = arrays_mod.resolve_array(source, kind, server_uri, root)
+        pyramid = arrays_mod.pyramid_info(source, kind, server_uri, root)
+        meta = arrays_mod.array_shape_meta(node, pyramid)
+        sl = np.asarray(arrays_mod.read_slice(node, meta, slice_index))
+        unit, _, span = denoise_mod._to_unit(sl)
+        return {
+            "strength": denoise_mod.auto_strength(sl, method),
+            # Noise as a fraction of the slice's own dynamic range, so the UI can
+            # say how noisy this is rather than only what to do about it.
+            "noise_sigma": denoise_mod.estimate_noise_sigma(unit) if span > 0 else 0.0,
+        }
+
+    return await asyncio.to_thread(_run)
+
+
+@app.post("/api/denoise/bake")
+async def denoise_bake(payload: DenoiseBakeRequest) -> dict:
+    """Denoise a whole volume and save it as a new, annotatable Tiled dataset.
+
+    The Annotate preview is display-only; this is how a denoised volume becomes
+    real data you can annotate and export. Runs on a background thread; poll
+    ``GET /api/export/status/{job_id}``.
+    """
+    if payload.method == "none":
+        raise HTTPException(422, "Pick a denoise method before saving a denoised copy.")
+    if payload.method not in denoise_mod.ALL_METHODS:
+        raise HTTPException(422, f"Unknown denoise method: {payload.method}")
+    if payload.method not in denoise_mod.available_methods():
+        raise HTTPException(
+            422,
+            f"Denoise method {payload.method!r} is unavailable on this server "
+            "(missing optional dependency).",
+        )
+
+    target = payload.target_path or denoise_bake_mod.default_target_path(payload.source)
+    try:
+        ingest_mod.validate_container_path(target)
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+
+    jid = export_jobs.new_job(target)
+    threading.Thread(
+        target=denoise_bake_mod.run_denoise_bake_job, args=(jid, payload), daemon=True
+    ).start()
+    return {"job_id": jid, "target_path": target}
+
+
 @app.get("/api/volume/resolve")
 async def volume_resolve(
     source: str = Query(..., description="Tiled path of the open dataset"),
diff --git a/backend/denoise.py b/backend/denoise.py
new file mode 100644
index 0000000..8d40350
--- /dev/null
+++ b/backend/denoise.py
@@ -0,0 +1,351 @@
+"""Classical denoising filters for tomography slices.
+
+Ported from Alex's denoiser branch (``Segmentation_Annotation_Studio-feat-denoiser``)
+essentially unchanged. The parameter mappings and the ``_AUTO_DAMPING`` table
+below were arrived at by measurement, not taste — rewriting them would throw
+that away.
+
+Used by two callers that must agree exactly: the display preview
+(``GET /api/image/slice``'s optional ``denoise_*`` params) and the batch
+"denoise & save as a new dataset" job (``denoise_bake.py``). Keeping one
+implementation here is the whole point — a JS copy for the preview would
+inevitably drift from whatever the bake writes.
+
+Denoising runs on the RAW slice, before ``images.normalize_scalar_unit``:
+noise statistics live in the source's own intensity units, not in the 8-bit
+display range.
+
+Design notes
+------------
+* **One ``strength`` knob (0..1) per method.** The UI shows a single slider
+  regardless of method; each method maps it onto its own native parameter.
+  Callers wanting exact control pass ``extra``.
+* **Parameters are scaled by the image's own estimated noise level** for the
+  methods that need a noise scale (bilateral / TV / NLM / wavelet). Raw CT is
+  uint16 with ranges in the thousands, so a hardcoded TV weight or NLM ``h``
+  would be meaningless on one dataset and destructive on the next.
+* **Filtering happens in a normalized [0, 1] copy**, then rescales back to the
+  source range and dtype. This makes every tuned constant below dimensionless
+  and portable across dtypes, and avoids skimage's various assumptions about
+  float images being in [0, 1].
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+# 2-D methods, plus the two 3-D ones that filter ACROSS slices — the cheap,
+# training-free way to exploit the fact that adjacent tomographic slices share
+# structure while their noise is independent.
+METHODS_2D = ("gaussian", "median", "bilateral", "tv", "nlm", "wavelet")
+METHODS_3D = ("gaussian3d", "median3d")
+ALL_METHODS = ("none",) + METHODS_2D + METHODS_3D
+
+# How many z-neighbours each 3-D method needs on EACH side of the target slice.
+_Z_RADIUS = {"gaussian3d": 2, "median3d": 1}
+
+# Immerkaer's Laplacian mask. Its noise gain is analytically known, which is
+# what lets `estimate_noise_sigma` be both robust and dependency-free.
+_IMMERKAER_MASK = np.array([[1.0, -2.0, 1.0], [-2.0, 4.0, -2.0], [1.0, -2.0, 1.0]])
+
+
+def wavelet_available() -> bool:
+    """True if wavelet denoising can actually run.
+
+    ``skimage.restoration.denoise_wavelet`` imports fine without PyWavelets but
+    raises at CALL time, so availability has to be probed rather than assumed.
+    (Same for ``estimate_sigma``, which is why this module ships its own
+    noise estimator instead — see :func:`estimate_noise_sigma`.)
+    """
+    try:
+        import pywt  # noqa: F401,PLC0415
+    except Exception:
+        return False
+    return True
+
+
+def available_methods() -> tuple[str, ...]:
+    """Methods that can actually run in this environment."""
+    if wavelet_available():
+        return ALL_METHODS
+    return tuple(m for m in ALL_METHODS if m != "wavelet")
+
+
+def z_radius_for(method: str) -> int:
+    """Z-neighbours needed on each side of the target slice (0 for 2-D methods)."""
+    return _Z_RADIUS.get(method, 0)
+
+
+def estimate_noise_sigma(arr: np.ndarray) -> float:
+    """Estimate additive noise sigma via Immerkaer's fast Laplacian method.
+
+    ``skimage.restoration.estimate_sigma`` would be the obvious choice but it
+    requires PyWavelets, which is not installed here. Immerkaer's estimator
+    needs only a convolution: the mask above responds almost entirely to noise
+    rather than to structure, and its gain is known analytically, so
+
+        sigma = sqrt(pi/2) * sum(|mask * image|) / (6 * (W-2) * (H-2))
+
+    Verified accurate to ~2% for sigma from 10 to 200 on a step-edge phantom,
+    and on a pure-noise image (i.e. it isn't fooled by having no structure).
+
+    Returns 0.0 for degenerate (too small) inputs so callers can fall back.
+    """
+    from scipy.ndimage import convolve  # noqa: PLC0415
+
+    data = np.nan_to_num(arr.astype(np.float64), nan=0.0, posinf=0.0, neginf=0.0)
+    h, w = data.shape[:2]
+    if h < 3 or w < 3:
+        return 0.0
+    conv = convolve(data, _IMMERKAER_MASK, mode="reflect")
+    return float(np.sqrt(np.pi / 2.0) * np.abs(conv).sum() / (6.0 * (w - 2) * (h - 2)))
+
+
+def _odd(value: float, minimum: int = 3) -> int:
+    """Nearest odd integer >= minimum (rank filters need an odd window)."""
+    size = int(round(value))
+    if size % 2 == 0:
+        size += 1
+    return max(minimum, size)
+
+
+def _to_unit(arr: np.ndarray) -> tuple[np.ndarray, float, float]:
+    """Scale to [0, 1]. Returns ``(unit, offset, span)`` for the inverse."""
+    data = np.nan_to_num(arr.astype(np.float64), nan=0.0, posinf=0.0, neginf=0.0)
+    lo = float(data.min())
+    hi = float(data.max())
+    span = hi - lo
+    if span <= 0:
+        return np.zeros_like(data), lo, 0.0
+    return (data - lo) / span, lo, span
+
+
+def _from_unit(unit: np.ndarray, offset: float, span: float, dtype: np.dtype) -> np.ndarray:
+    """Inverse of :func:`_to_unit`, clipped into *dtype*'s range."""
+    if span <= 0:
+        restored = np.full_like(unit, offset, dtype=np.float64)
+    else:
+        restored = unit * span + offset
+    if np.issubdtype(dtype, np.integer):
+        info = np.iinfo(dtype)
+        restored = np.clip(np.round(restored), info.min, info.max)
+    return restored.astype(dtype)
+
+
+def denoise_slice(
+    arr: np.ndarray,
+    method: str,
+    strength: float = 0.5,
+    extra: dict[str, Any] | None = None,
+) -> np.ndarray:
+    """Denoise one 2-D slice, preserving shape and dtype.
+
+    Args:
+        arr: 2-D slice in its native dtype (uint16, float32, ...).
+        method: One of :data:`METHODS_2D`, or ``"none"`` (returns *arr*).
+        strength: 0..1, mapped onto the method's native parameter.
+        extra: Per-method overrides, bypassing the ``strength`` mapping.
+
+    Raises:
+        ValueError: unknown *method*, a 3-D-only method (use
+            :func:`denoise_stack`), or wavelet without PyWavelets installed.
+    """
+    if method == "none":
+        return arr
+    if method in METHODS_3D:
+        raise ValueError(f"{method!r} needs z-neighbours — call denoise_stack()")
+    if method not in METHODS_2D:
+        raise ValueError(f"unknown denoise method {method!r}")
+    if arr.ndim != 2:
+        raise ValueError(f"denoise_slice expects a 2-D slice, got shape {arr.shape}")
+
+    opts = dict(extra or {})
+    s = float(np.clip(strength, 0.0, 1.0))
+    unit, offset, span = _to_unit(arr)
+    if span <= 0:
+        return arr  # flat slice — nothing to denoise
+    # Noise scale in the SAME normalized units the filters below work in.
+    sigma_n = opts.get("sigma") or estimate_noise_sigma(unit) or 0.01
+
+    if method == "gaussian":
+        from scipy.ndimage import gaussian_filter  # noqa: PLC0415
+
+        out = gaussian_filter(unit, sigma=opts.get("sigma_spatial", 0.3 + s * 3.0))
+    elif method == "median":
+        from scipy.ndimage import median_filter  # noqa: PLC0415
+
+        out = median_filter(unit, size=opts.get("size", _odd(3 + s * 6)))
+    elif method == "bilateral":
+        from skimage.restoration import denoise_bilateral  # noqa: PLC0415
+
+        out = denoise_bilateral(
+            unit,
+            sigma_color=opts.get("sigma_color", sigma_n * (0.5 + s * 2.5)),
+            sigma_spatial=opts.get("sigma_spatial", 1.0 + s * 4.0),
+        )
+    elif method == "tv":
+        from skimage.restoration import denoise_tv_chambolle  # noqa: PLC0415
+
+        out = denoise_tv_chambolle(unit, weight=opts.get("weight", sigma_n * (0.3 + s * 3.0)))
+    elif method == "nlm":
+        from skimage.restoration import denoise_nl_means  # noqa: PLC0415
+
+        out = denoise_nl_means(
+            unit,
+            h=opts.get("h", sigma_n * (0.4 + s * 1.6)),
+            sigma=opts.get("noise_sigma", sigma_n),
+            patch_size=opts.get("patch_size", 5),
+            patch_distance=opts.get("patch_distance", 6),
+            fast_mode=True,
+            channel_axis=None,
+        )
+    else:  # wavelet
+        if not wavelet_available():
+            raise ValueError(
+                "wavelet denoising needs PyWavelets — install it (pip install PyWavelets) "
+                "or pick another method"
+            )
+        from skimage.restoration import denoise_wavelet  # noqa: PLC0415
+
+        out = denoise_wavelet(
+            unit,
+            sigma=opts.get("noise_sigma", sigma_n * (0.5 + s)),
+            mode="soft",
+            method="BayesShrink",
+            rescale_sigma=True,
+        )
+
+    return _from_unit(np.asarray(out, dtype=np.float64), offset, span, arr.dtype)
+
+
+def denoise_stack(
+    frames: np.ndarray,
+    method: str,
+    strength: float = 0.5,
+    extra: dict[str, Any] | None = None,
+) -> np.ndarray:
+    """Denoise a ``(z, y, x)`` stack with a 3-D filter, preserving shape/dtype.
+
+    This is the training-free way to exploit slice-to-slice correlation:
+    adjacent tomographic slices show nearly the same structure while their
+    noise is independent, so averaging along z suppresses noise at a far lower
+    cost in real detail than the equivalent in-plane blur.
+
+    Callers previewing a single slice pass a z-window and keep the centre frame
+    (see :func:`z_radius_for`); the bake job passes larger chunks.
+
+    Raises:
+        ValueError: *method* is not one of :data:`METHODS_3D`.
+    """
+    if method not in METHODS_3D:
+        raise ValueError(f"{method!r} is not a 3-D denoise method")
+    if frames.ndim != 3:
+        raise ValueError(f"denoise_stack expects (z, y, x), got shape {frames.shape}")
+
+    opts = dict(extra or {})
+    s = float(np.clip(strength, 0.0, 1.0))
+    unit, offset, span = _to_unit(frames)
+    if span <= 0:
+        return frames
+
+    if method == "gaussian3d":
+        from scipy.ndimage import gaussian_filter  # noqa: PLC0415
+
+        sigma_xy = opts.get("sigma_spatial", 0.3 + s * 2.0)
+        # z gets less smoothing than xy by default: slice spacing is usually
+        # coarser than pixel pitch, so equal sigma would blur genuine
+        # through-plane structure more than it blurs noise.
+        sigma_z = opts.get("sigma_z", sigma_xy * 0.6)
+        out = gaussian_filter(unit, sigma=(sigma_z, sigma_xy, sigma_xy))
+    else:  # median3d
+        size_xy = opts.get("size", _odd(3 + s * 4))
+        size_z = opts.get("size_z", 3)
+        out = _median3d(unit, size_z, size_xy)
+
+    return _from_unit(np.asarray(out, dtype=np.float64), offset, span, frames.dtype)
+
+
+def _median3d(unit: np.ndarray, size_z: int, size_xy: int) -> np.ndarray:
+    from scipy.ndimage import median_filter  # noqa: PLC0415
+
+    size_z = min(_odd(size_z), unit.shape[0] if unit.shape[0] % 2 == 1 else unit.shape[0] - 1)
+    size_z = max(1, size_z)
+    return median_filter(unit, size=(size_z, size_xy, size_xy))
+
+
+# How far to push each method at a given measured noise level. Edge-preserving
+# methods can be driven hard because their strength buys noise reduction
+# without eating boundaries; a plain Gaussian trades the two off directly, so
+# the same nominal strength that helps TV visibly destroys edges here (measured:
+# on a step-edge phantom at strength 0.5, TV and NLM RAISE SNR while Gaussian
+# LOWERS it below the noisy input). "Auto" must not hand the user a setting
+# that makes the image worse.
+_AUTO_DAMPING = {
+    "gaussian": 0.35,
+    "gaussian3d": 0.5,
+    "median": 0.6,
+    "median3d": 0.6,
+    "bilateral": 0.9,
+    "wavelet": 0.9,
+    "tv": 1.0,
+    "nlm": 1.0,
+}
+
+
+def auto_strength(arr: np.ndarray, method: str) -> float:
+    """Suggest a ``strength`` for *arr* from its own estimated noise level.
+
+    Powers the UI's "Auto" button. Maps the measured noise-to-range ratio onto
+    0..1 with a gentle curve, then damps it per method (see
+    :data:`_AUTO_DAMPING`). Deliberately conservative — over-smoothing destroys
+    the very boundaries this app exists to annotate, and the user can always
+    push the slider further.
+    """
+    if method == "none":
+        return 0.0
+    unit, _, span = _to_unit(arr if arr.ndim == 2 else arr[arr.shape[0] // 2])
+    if span <= 0:
+        return 0.0
+    sigma_n = estimate_noise_sigma(unit)
+    # sigma_n is a fraction of the full dynamic range. ~0.5% reads as clean,
+    # ~5%+ as heavily noisy; sqrt keeps the low end from collapsing to zero.
+    ratio = float(np.clip((sigma_n - 0.005) / 0.045, 0.0, 1.0))
+    return round(float(np.sqrt(ratio)) * 0.8 * _AUTO_DAMPING.get(method, 0.8), 3)
+
+
+def describe_methods() -> list[dict[str, Any]]:
+    """Method metadata for the capability endpoint / UI menu.
+
+    ``cost`` drives whether the frontend warns about latency before requesting
+    a full-resolution preview.
+    """
+    info = [
+        ("none", "None", "cheap", "No denoising."),
+        ("gaussian", "Gaussian", "cheap",
+         "Simple blur. Fast baseline, but softens edges — usually the weakest choice here."),
+        ("median", "Median", "cheap",
+         "Removes salt-and-pepper speckle, zingers and dead pixels that blurring cannot."),
+        ("bilateral", "Bilateral", "moderate",
+         "Edge-preserving smoothing: averages only over similar-intensity neighbours."),
+        ("tv", "Total variation", "moderate",
+         "Edge-preserving; excellent on piecewise-constant material regions."),
+        ("nlm", "Non-local means", "slow",
+         "Averages similar patches from across the slice. Best detail preservation, slowest."),
+        ("wavelet", "Wavelet", "cheap",
+         "Wavelet-shrinkage denoising (needs PyWavelets installed)."),
+        ("gaussian3d", "Gaussian 3D", "moderate",
+         "Smooths across neighbouring slices too — uses slice-to-slice correlation, no training."),
+        ("median3d", "Median 3D", "slow",
+         "Median across neighbouring slices — strong on speckle while keeping in-plane edges."),
+    ]
+    usable = set(available_methods())
+    return [
+        {"method": m, "label": label, "cost": cost, "description": desc,
+         "available": m in usable, "z_radius": z_radius_for(m)}
+        for m, label, cost, desc in info
+    ]
diff --git a/backend/denoise_bake.py b/backend/denoise_bake.py
new file mode 100644
index 0000000..821f266
--- /dev/null
+++ b/backend/denoise_bake.py
@@ -0,0 +1,357 @@
+"""Apply a denoise filter to a whole volume and save the result as a NEW Tiled dataset.
+
+The display preview in Annotate (``GET /api/image/slice``'s ``denoise_*``
+params) is deliberately non-destructive — it changes what you see, not the data,
+and exports still use the original pixels. This job is the other half: it writes
+a denoised copy as a first-class dataset you can open, annotate, train on and
+export.
+
+Written as a sibling dataset under the ingest root (``browse/<name>_denoised``),
+NOT as a ``<stem>__masks``-style sidecar: anything matching
+``sidecars.SIDECAR_SUFFIXES`` is deliberately hidden from Browse and from slice
+enumeration, which is exactly wrong for an output the user needs to open.
+
+Reuses ``ingest.py``'s write path (``validate_container_path`` /
+``_ensure_container`` / the same metadata shape) so Browse describes, facets and
+orders the result identically to an ingested dataset.
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+from typing import Any
+
+import numpy as np
+
+import arrays as arrays_mod
+import denoise as denoise_mod
+import export_jobs
+import ingest as ingest_mod
+from tiled_clients import get_tiled_client
+
+logger = logging.getLogger(__name__)
+
+# Minimum zero-pad width for `image_number`, matching ingest's convention: the
+# Tiled node KEY is the raw stem, so keys sort lexically — `image_number` is what
+# makes numeric slice order recoverable, and it only works if it's padded.
+_PAD_WIDTH = 4
+
+
+def default_target_path(source: str, suffix: str = "denoised") -> str:
+    """``browse/dataset`` -> ``browse/dataset_denoised``.
+
+    Keeps the new dataset a sibling of its source so it appears right next to it
+    in Browse.
+    """
+    # .strip("/") alone leaves a whitespace-only path intact, which would happily
+    # produce "   _denoised" — strip whitespace first, and drop blank segments.
+    parts = [p for p in (seg.strip() for seg in source.strip().strip("/").split("/")) if p]
+    if not parts:
+        raise ValueError("source path is empty")
+    parts[-1] = f"{parts[-1]}_{suffix}"
+    return "/".join(parts)
+
+
+def _slice_key(index: int) -> str:
+    return f"slice_{index:0{_PAD_WIDTH}d}"
+
+
+def run_denoise_bake_job(jid: str, request: Any) -> None:
+    """Background worker: denoise every slice and write a new Tiled dataset.
+
+    ``request`` carries ``source``, ``server_uri``, ``method``, ``strength``,
+    ``target_path`` and ``description`` (see ``schemas.DenoiseBakeRequest``).
+
+    Deliberately does NOT take ``train_common.ML_LOCK``: classical denoising is
+    CPU work with no GPU contention, so baking must not be blocked by — or
+    block — a running training job.
+
+    Per-slice failures are tolerated (logged, recorded in ``result.errors``,
+    written as an unfiltered copy of the source slice so the output volume keeps
+    a 1:1 slice correspondence with its source rather than silently shifting
+    every later index).
+    """
+    model_denoiser = None  # bound before the try so `finally` can always see it
+    try:
+        export_jobs.update(jid, state="running", phase="preparing")
+
+        method = request.method
+        if method == "none":
+            export_jobs.update(
+                jid, state="error", phase="error",
+                error="Pick a denoise method before saving a denoised copy.",
+            )
+            return
+        # "model" applies a trained Noise2Noise/Noise2Void run rather than a
+        # classical filter, so it is validated against the run registry instead
+        # of the filter menu (and needs the GPU, see _ModelDenoiser).
+        if method == "model":
+            if not request.run_id:
+                export_jobs.update(
+                    jid, state="error", phase="error",
+                    error="Applying a trained denoiser needs a run_id.",
+                )
+                return
+        elif method not in denoise_mod.available_methods():
+            export_jobs.update(
+                jid, state="error", phase="error",
+                error=f"Denoise method {method!r} is unavailable on this server.",
+            )
+            return
+
+        target_path = request.target_path or default_target_path(request.source)
+        try:
+            target_parts = ingest_mod.validate_container_path(target_path)
+        except ValueError as exc:
+            export_jobs.update(jid, state="error", phase="error", error=str(exc))
+            return
+
+        client = get_tiled_client(request.server_uri)
+        # Refuse rather than merge into an existing dataset: a half-overwritten
+        # volume mixing two filters is far worse than a clear error.
+        try:
+            client[target_path]
+        except KeyError:
+            pass
+        else:
+            export_jobs.update(
+                jid, state="error", phase="error",
+                error=f"{target_path!r} already exists — delete it or choose another name.",
+            )
+            return
+
+        node = arrays_mod.resolve_array(request.source, "tiled", request.server_uri)
+        meta = arrays_mod.array_shape_meta(node)
+        n_slices = int(meta["n_slices"])
+        radius = 0 if method == "model" else denoise_mod.z_radius_for(method)
+
+        # Held for the whole volume when applying a trained model: re-acquiring
+        # per slice would let a training job interleave and thrash the GPU.
+        model_denoiser = None
+        if method == "model":
+            try:
+                model_denoiser = _ModelDenoiser(request.run_id, node, meta)
+            except Exception as exc:  # noqa: BLE001 — surfaced as a job error
+                export_jobs.update(jid, state="error", phase="error", error=str(exc))
+                return
+
+        export_jobs.set_total(jid, n_slices)
+        export_jobs.update(jid, phase="denoising")
+
+        container = ingest_mod._ensure_container(client, target_parts)
+        now_iso = datetime.now(timezone.utc).isoformat()
+        sample_name = target_parts[-1]
+        keywords = ingest_mod.parse_keywords(request.description or "")
+
+        errors: list[dict[str, Any]] = []
+        written = 0
+        cancelled = False
+
+        for index in range(n_slices):
+            if export_jobs.cancel_requested(jid):
+                cancelled = True
+                break
+            try:
+                if model_denoiser is not None:
+                    out = model_denoiser.denoise(index)
+                else:
+                    out = _denoise_one(node, meta, index, method, request.strength, radius, n_slices)
+            except Exception as exc:  # noqa: BLE001 — one bad slice must not abort the volume
+                logger.warning("denoise bake: slice %d failed (%s); copying source", index, exc)
+                errors.append({"slice": index, "error": str(exc)})
+                try:
+                    out = np.asarray(arrays_mod.read_slice(node, meta, index))
+                except Exception as read_exc:  # noqa: BLE001 — truly unreadable
+                    logger.warning("denoise bake: slice %d unreadable (%s); skipping", index, read_exc)
+                    errors[-1]["error"] = f"{exc}; source also unreadable: {read_exc}"
+                    export_jobs.bump(jid, 1)
+                    continue
+
+            key = _slice_key(index)
+            container.write_array(
+                out,
+                key=key,
+                dims=["y", "x"] if out.ndim == 2 else None,
+                metadata={
+                    "image_number": str(index).zfill(_PAD_WIDTH),
+                    "size": ingest_mod._size_str(out),
+                    "sample_name": sample_name,
+                    # Provenance: enough to reproduce this dataset exactly.
+                    "denoise_source": request.source,
+                    "denoise_method": method,
+                    "denoise_strength": float(request.strength),
+                    **({"description": request.description} if request.description else {}),
+                    **({"keywords": keywords} if keywords else {}),
+                },
+            )
+            written += 1
+            export_jobs.bump(jid, 1)
+            export_jobs.log(jid, f"slice {index}: denoised")
+
+        if model_denoiser is not None:
+            model_denoiser.close()
+            model_denoiser = None
+
+        export_jobs.update(jid, phase="finalizing")
+        container.update_metadata(metadata={
+            "sample_name": sample_name,
+            # Browse derives n_slices from actual children; n_images is the
+            # display/facet value and must match what was really written.
+            "n_images": written,
+            "denoise_source": request.source,
+            "denoise_method": method,
+            "denoise_strength": float(request.strength),
+            "denoise_created_at": now_iso,
+            **({"description": request.description} if request.description else {}),
+            **({"keywords": keywords} if keywords else {}),
+        })
+
+        if written == 0:
+            export_jobs.update(
+                jid, state="error", phase="error",
+                error="No slices could be denoised — nothing was written.",
+            )
+            return
+
+        result = {
+            "path": target_path,
+            "n_slices": written,
+            "method": method,
+            "strength": float(request.strength),
+            "cancelled": cancelled,
+            "errors": errors,
+        }
+        export_jobs.update(jid, state="done", phase="done", result=result)
+        export_jobs.log(
+            jid,
+            f"Denoise bake {'cancelled after' if cancelled else 'complete —'} "
+            f"{written} slice(s) written to {target_path}.",
+        )
+    except Exception as exc:  # noqa: BLE001 — reported as a job error, never a crash
+        logger.error("Denoise bake job %s failed: %s", jid, exc)
+        export_jobs.update(jid, state="error", phase="error", error=str(exc))
+    finally:
+        # A model bake holds ML_LOCK for the whole volume. Releasing only on the
+        # happy path would leave it held forever after any mid-bake failure,
+        # deadlocking every later training job and preview.
+        if model_denoiser is not None:
+            model_denoiser.close()
+
+
+class _ModelDenoiser:
+    """Applies a trained Noise2Noise/Noise2Void run across a whole volume.
+
+    Loads the model and takes ``ML_LOCK`` ONCE for the entire bake rather than
+    per slice: re-acquiring per slice would let a training job interleave, and
+    reloading weights per slice would dominate the runtime.
+
+    Preprocessing is delegated to ``denoise_train``'s own helpers, so the
+    network sees exactly the display-mapped uint8 grayscale it was trained on
+    (volume-global bounds, the run's saved render options). Reimplementing that
+    here is how the two halves of the contract would silently drift apart.
+
+    Output dtype differs from the classical filters on purpose: a denoiser
+    returns continuous values in ``[0, 1]``, so this writes **uint8** (the scale
+    it actually operated in) rather than pretending to the source's uint16
+    precision it never had access to.
+    """
+
+    def __init__(self, run_id: str, node: Any, meta: dict[str, Any]) -> None:
+        import denoise_train
+        import tiling
+        import train_common
+
+        config = train_common.load_run_config(run_id)
+        if config.get("model_family") != "dlsia_denoiser" or config.get("task") != "denoising":
+            raise ValueError(f"Run {run_id!r} is not a denoiser run.")
+        # Which network this run is; defaults to TUNet for runs saved before the
+        # field existed. Only the TUNet architecture needs dlsia.
+        fam = train_common.denoiser_runtime_for(config)
+        if train_common.denoiser_needs_dlsia(config) and not train_common.dlsia_available():
+            raise ValueError("dlsia is not installed on this server.")
+        if not tiling.qlty_available():
+            raise ValueError("Applying a denoiser needs the 'qlty' package, which is missing.")
+        device = train_common.pick_device()
+        if device is None:
+            raise ValueError("torch is not installed on this server.")
+
+        self._tiling = tiling
+        self._train_common = train_common
+        self._denoise_train = denoise_train
+        self._node = node
+        self._meta = meta
+        self._device = device
+        self._window = int(config["image_size"])
+        self._opts = denoise_train._render_opts(config.get("render") or {})
+
+        import images as images_mod
+
+        self._global_range = images_mod._sample_global_stats(node, meta)
+
+        if not train_common.ML_LOCK.acquire(blocking=False):
+            raise ValueError("The device is busy with another job — try again when it finishes.")
+        self._locked = True
+        try:
+            state = train_common.load_adapter_state(run_id)
+            model = fam.load_model(state, device)
+            model.eval()
+            self._forward_fn = fam.make_forward_fn(model)
+            self._to_tensor_fn = fam.make_to_tensor_fn()
+        except Exception:
+            self.close()  # never hold the lock past a failed load
+            raise
+
+    def denoise(self, index: int) -> np.ndarray:
+        import torch
+
+        gray = self._denoise_train._slice_to_gray_uint8(
+            self._node, self._meta, index, self._opts, self._global_range
+        )
+        with torch.no_grad():
+            out = self._tiling.denoise_image_tiled(
+                gray,
+                forward_fn=self._forward_fn,
+                to_tensor_fn=self._to_tensor_fn,
+                window=self._window,
+                device=self._device,
+            )
+        if out is None:
+            raise RuntimeError("denoising returned no result")
+        unit = np.clip(np.asarray(out, dtype=np.float64), 0.0, 1.0)
+        return (unit * 255.0).round().astype(np.uint8)
+
+    def close(self) -> None:
+        """Release ``ML_LOCK``. Idempotent — callers release on both the happy
+        path and in a ``finally``."""
+        if getattr(self, "_locked", False):
+            self._train_common.ML_LOCK.release()
+            self._locked = False
+
+
+def _denoise_one(
+    node: Any,
+    meta: dict[str, Any],
+    index: int,
+    method: str,
+    strength: float,
+    radius: int,
+    n_slices: int,
+) -> np.ndarray:
+    """Denoise slice *index*, reading a z-window when the method needs one.
+
+    The window clamps at the volume ends, so the target slice is not always the
+    centre of what was read — its position is tracked explicitly.
+    """
+    if radius == 0:
+        return denoise_mod.denoise_slice(
+            np.asarray(arrays_mod.read_slice(node, meta, index)), method, strength
+        )
+
+    lo = max(0, index - radius)
+    hi = min(n_slices - 1, index + radius)
+    frames = [np.asarray(arrays_mod.read_slice(node, meta, i)) for i in range(lo, hi + 1)]
+    if len(frames) < 2:
+        fallback = "gaussian" if method == "gaussian3d" else "median"
+        return denoise_mod.denoise_slice(frames[0], fallback, strength)
+    return denoise_mod.denoise_stack(np.stack(frames, axis=0), method, strength)[index - lo]
diff --git a/backend/export_jobs.py b/backend/export_jobs.py
index 315fe13..928c7ca 100644
--- a/backend/export_jobs.py
+++ b/backend/export_jobs.py
@@ -30,10 +30,39 @@ def new_job(dataset_path: str) -> str:
             "error": None,
             "dataset_path": dataset_path,
             "zip_path": None,     # internal; surfaced as result.zip_available
+            "cancel_requested": False,
         }
     return jid
 
 
+def request_cancel(jid: str) -> bool:
+    """Ask a running job to stop; returns False if the job is unknown.
+
+    Cooperative: this only sets a flag. Long-running jobs poll
+    :func:`cancel_requested` between units of work and stop at a clean boundary,
+    which is what lets a half-finished dataset be discarded rather than left
+    looking complete.
+    """
+    with _lock:
+        job = _jobs.get(jid)
+        if not job:
+            return False
+        job["cancel_requested"] = True
+        return True
+
+
+def cancel_requested(jid: str) -> bool:
+    """True if cancellation was requested for *jid*.
+
+    Reads the flag directly under the lock rather than through :func:`get_job`,
+    which copies the whole job dict (log lines included) just to read one bool —
+    and this is polled on every slice of a running job.
+    """
+    with _lock:
+        job = _jobs.get(jid)
+        return bool(job and job.get("cancel_requested"))
+
+
 def get_job(jid: str) -> dict | None:
     """Return a snapshot copy of the job (without the internal zip_path)."""
     with _lock:
diff --git a/backend/ingest.py b/backend/ingest.py
index efc4777..cb866b8 100644
--- a/backend/ingest.py
+++ b/backend/ingest.py
@@ -28,6 +28,7 @@
 from __future__ import annotations
 
 import logging
+import os
 import re
 import threading
 import uuid
@@ -124,6 +125,38 @@ def _read_array(path: Path) -> np.ndarray:
     raise ValueError(f"unsupported extension {suffix!r}")
 
 
+def validate_container_path(container_path: str) -> list[str]:
+    """Return safe Tiled key segments beneath the configured ingest root.
+
+    A write confinement check, not a formatting one: callers take a destination
+    from the client, so without this a request could name any container in the
+    catalog — including one holding unrelated data. Rejects traversal segments,
+    control characters, and anything that is not a strict descendant of
+    ``TILED_INGEST_ROOT`` (default ``browse``).
+    """
+    raw = (container_path or "").strip()
+    if not raw or raw != raw.strip("/"):
+        raise ValueError("container_path must be a relative canonical Tiled path")
+    parts = raw.split("/")
+    if any(
+        not part
+        or part in {".", ".."}
+        or len(part) > 128
+        or any(ord(char) < 32 for char in part)
+        for part in parts
+    ):
+        raise ValueError("container_path contains an unsafe segment")
+
+    root_parts = [
+        part for part in os.getenv("TILED_INGEST_ROOT", "browse").strip("/").split("/") if part
+    ]
+    # `len(parts) <= len(root_parts)` rejects the root itself: a dataset must be
+    # written *inside* it, never over it.
+    if not root_parts or parts[: len(root_parts)] != root_parts or len(parts) <= len(root_parts):
+        raise ValueError("container_path must be a child of the configured ingest root")
+    return parts
+
+
 def _ensure_container(client: Any, parts: list[str]) -> Any:
     """Navigate to ``client[parts...]``, creating containers as needed.
 
diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index b3ffeed..9f9dfb8 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -23,6 +23,10 @@ dependencies = [
   "matplotlib>=3.8",
   "pycocotools>=2.0.7",
   "scikit-image>=0.22",
+  # denoise.py imports scipy.ndimage directly (gaussian/median filters, and the
+  # convolution behind its noise estimator). scikit-image pulls scipy in
+  # transitively, but a direct import earns a direct pin.
+  "scipy>=1.11",
   "tifffile>=2024.0",
   "imagecodecs",
 ]
diff --git a/backend/schemas.py b/backend/schemas.py
index a264f00..f797481 100644
--- a/backend/schemas.py
+++ b/backend/schemas.py
@@ -316,6 +316,36 @@ class TiffStackRegisterRequest(ZarrRegisterRequest):
     """
 
 
+class DenoiseBakeRequest(BaseModel):
+    """Request body to denoise a whole volume into a new Tiled dataset.
+
+    The Annotate preview is non-destructive — it changes what you see, not the
+    data, and exports still use the original pixels. This is the other half: it
+    writes a denoised copy as a first-class dataset you can open, annotate and
+    export.
+
+    Attributes:
+        source: Tiled path of the volume to denoise.
+        server_uri: Tiled server URI; ``None`` uses the default server.
+        method: A ``denoise.ALL_METHODS`` entry other than ``"none"``.
+        strength: 0..1, mapped onto the method's native parameter.
+        target_path: Destination Tiled path; defaults to ``<source>_denoised``, a
+            sibling so it lands next to its source in Browse. Must sit beneath
+            the configured ingest root.
+        description: Optional comma-separated tags, treated exactly as ingest
+            treats them — searchable in Browse.
+    """
+
+    source: str
+    server_uri: str | None = None
+    method: str
+    strength: float = Field(default=0.5, ge=0.0, le=1.0)
+    target_path: str | None = None
+    description: str = ""
+    run_id: str | None = None
+    """Saved denoiser run, for the trained-model path. Not yet wired up here."""
+
+
 class VolumeBuildRequest(BaseModel):
     """Request body for building a 3-D volume from a stack already in Tiled.
 
diff --git a/backend/tests/test_denoise.py b/backend/tests/test_denoise.py
new file mode 100644
index 0000000..45eb058
--- /dev/null
+++ b/backend/tests/test_denoise.py
@@ -0,0 +1,189 @@
+"""Tests for the classical denoise filters.
+
+Ported alongside :mod:`denoise` itself. The properties pinned here are the ones
+a user would notice if they broke: that a filter actually reduces noise without
+destroying the edges this app exists to annotate, that shape and dtype survive
+the round trip through normalized units, and that "Auto" never hands back a
+setting that makes the image worse.
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import denoise  # noqa: E402
+
+
+def step_phantom(h=192, w=192, noise=12.0, seed=3):
+    """A hard vertical edge plus additive Gaussian noise.
+
+    The standard shape for asking "did it denoise, or did it just blur?" — the
+    edge is what a bad filter destroys.
+    """
+    rng = np.random.default_rng(seed)
+    clean = np.zeros((h, w), np.float32)
+    clean[:, w // 2:] = 100.0
+    return clean, clean + rng.normal(0, noise, clean.shape).astype(np.float32)
+
+
+def snr_db(clean: np.ndarray, test: np.ndarray) -> float:
+    return float(10 * np.log10(clean.var() / ((test - clean) ** 2).mean()))
+
+
+class TestDenoiseSlice:
+    @pytest.mark.parametrize("method", ["gaussian", "median", "bilateral", "tv", "nlm"])
+    def test_improves_snr_on_a_noisy_edge(self, method):
+        clean, noisy = step_phantom()
+        out = denoise.denoise_slice(noisy, method, 0.5).astype(np.float32)
+        assert snr_db(clean, out) > snr_db(clean, noisy)
+
+    @pytest.mark.parametrize("method", ["gaussian", "median", "tv"])
+    @pytest.mark.parametrize("dtype", [np.uint8, np.uint16, np.float32])
+    def test_preserves_shape_and_dtype(self, method, dtype):
+        # Filtering happens in normalized [0,1] floats; the round trip back to
+        # the source dtype is where clipping and rounding bugs would live.
+        rng = np.random.default_rng(1)
+        arr = (rng.random((48, 64)) * 200).astype(dtype)
+        out = denoise.denoise_slice(arr, method, 0.5)
+        assert out.shape == arr.shape
+        assert out.dtype == arr.dtype
+
+    def test_none_is_an_identity(self):
+        _, noisy = step_phantom(h=32, w=32)
+        assert denoise.denoise_slice(noisy, "none", 1.0) is noisy
+
+    def test_flat_slice_is_returned_untouched(self):
+        # Zero dynamic range: normalizing would divide by zero.
+        flat = np.full((32, 32), 7, np.uint16)
+        assert np.array_equal(denoise.denoise_slice(flat, "tv", 0.5), flat)
+
+    def test_stronger_settings_smooth_more(self):
+        _, noisy = step_phantom()
+        weak = denoise.denoise_slice(noisy, "gaussian", 0.1).astype(np.float32)
+        strong = denoise.denoise_slice(noisy, "gaussian", 0.9).astype(np.float32)
+        assert strong.std() < weak.std()
+
+    def test_rejects_a_3d_method(self):
+        _, noisy = step_phantom(h=16, w=16)
+        with pytest.raises(ValueError, match="denoise_stack"):
+            denoise.denoise_slice(noisy, "gaussian3d", 0.5)
+
+    def test_rejects_an_unknown_method(self):
+        _, noisy = step_phantom(h=16, w=16)
+        with pytest.raises(ValueError, match="unknown"):
+            denoise.denoise_slice(noisy, "bogus", 0.5)
+
+    def test_rejects_a_3d_array(self):
+        with pytest.raises(ValueError, match="2-D"):
+            denoise.denoise_slice(np.zeros((3, 8, 8), np.float32), "tv", 0.5)
+
+
+class TestDenoiseStack:
+    def test_uses_z_neighbours_to_beat_the_2d_equivalent(self):
+        # The whole argument for the 3-D filters: adjacent slices share
+        # structure while their noise is independent, so averaging along z buys
+        # noise reduction at a far lower cost in real detail than in-plane blur.
+        rng = np.random.default_rng(5)
+        clean2d = np.zeros((96, 96), np.float32)
+        clean2d[:, 48:] = 100.0
+        clean = np.repeat(clean2d[None], 5, axis=0)
+        noisy = clean + rng.normal(0, 15, clean.shape).astype(np.float32)
+
+        out3d = denoise.denoise_stack(noisy, "gaussian3d", 0.5).astype(np.float32)[2]
+        out2d = denoise.denoise_slice(noisy[2], "gaussian", 0.5).astype(np.float32)
+        assert snr_db(clean2d, out3d) > snr_db(clean2d, out2d)
+
+    @pytest.mark.parametrize("method", ["gaussian3d", "median3d"])
+    def test_preserves_shape_and_dtype(self, method):
+        rng = np.random.default_rng(2)
+        stack = (rng.random((5, 32, 32)) * 500).astype(np.uint16)
+        out = denoise.denoise_stack(stack, method, 0.5)
+        assert out.shape == stack.shape
+        assert out.dtype == stack.dtype
+
+    def test_rejects_a_2d_method(self):
+        with pytest.raises(ValueError, match="not a 3-D"):
+            denoise.denoise_stack(np.zeros((3, 8, 8), np.float32), "tv", 0.5)
+
+    def test_rejects_a_2d_array(self):
+        with pytest.raises(ValueError, match=r"\(z, y, x\)"):
+            denoise.denoise_stack(np.zeros((8, 8), np.float32), "gaussian3d", 0.5)
+
+
+class TestNoiseEstimate:
+    def test_tracks_the_true_sigma(self):
+        # Immerkaer's estimator, on a flat field where the answer is known.
+        rng = np.random.default_rng(9)
+        for sigma in (0.01, 0.05, 0.1):
+            field = rng.normal(0.5, sigma, (256, 256))
+            estimate = denoise.estimate_noise_sigma(field)
+            assert abs(estimate - sigma) / sigma < 0.1
+
+    def test_is_not_fooled_by_structure(self):
+        # A clean step edge is structure, not noise; the estimate must stay low
+        # or "Auto" would recommend smoothing a noiseless image.
+        clean, _ = step_phantom(noise=0.0)
+        unit, _, span = denoise._to_unit(clean)
+        assert denoise.estimate_noise_sigma(unit) < 0.02
+
+    def test_degenerate_input_returns_zero(self):
+        assert denoise.estimate_noise_sigma(np.zeros((2, 2))) == 0.0
+
+
+class TestAutoStrength:
+    def test_suggests_more_for_a_noisier_image(self):
+        _, quiet = step_phantom(noise=2.0)
+        _, loud = step_phantom(noise=30.0)
+        assert denoise.auto_strength(loud, "tv") > denoise.auto_strength(quiet, "tv")
+
+    def test_never_makes_a_clean_image_worse(self):
+        # The failure this guards: "Auto" on a clean slice returning a strength
+        # that visibly softens it.
+        clean, _ = step_phantom(noise=0.0)
+        assert denoise.auto_strength(clean, "tv") == pytest.approx(0.0, abs=0.05)
+
+    def test_damps_gaussian_below_the_edge_preserving_methods(self):
+        # Measured: at equal nominal strength a plain Gaussian can LOWER SNR
+        # where TV raises it, so Auto must not push it as hard.
+        _, noisy = step_phantom(noise=25.0)
+        assert denoise.auto_strength(noisy, "gaussian") < denoise.auto_strength(noisy, "tv")
+
+    def test_none_is_zero(self):
+        _, noisy = step_phantom()
+        assert denoise.auto_strength(noisy, "none") == 0.0
+
+    def test_stays_in_range(self):
+        for noise in (0.0, 1.0, 50.0, 500.0):
+            _, noisy = step_phantom(noise=noise)
+            for method in denoise.available_methods():
+                assert 0.0 <= denoise.auto_strength(noisy, method) <= 1.0
+
+
+class TestCapabilities:
+    def test_describes_every_method(self):
+        described = {m["method"] for m in denoise.describe_methods()}
+        assert described == set(denoise.ALL_METHODS)
+
+    def test_availability_is_probed_not_assumed(self):
+        # denoise_wavelet imports fine without PyWavelets and only fails when
+        # called, so the menu has to probe rather than trust the import.
+        for entry in denoise.describe_methods():
+            if entry["method"] == "wavelet":
+                assert entry["available"] == denoise.wavelet_available()
+
+    def test_only_3d_methods_need_z_neighbours(self):
+        for entry in denoise.describe_methods():
+            expected = entry["method"] in denoise.METHODS_3D
+            assert (entry["z_radius"] > 0) is expected
+
+    def test_unavailable_methods_are_excluded_from_available_methods(self):
+        usable = set(denoise.available_methods())
+        assert usable <= set(denoise.ALL_METHODS)
+        if not denoise.wavelet_available():
+            assert "wavelet" not in usable
diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index a5eb84e..5653fa0 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -18,6 +18,8 @@ import type { ColormapName } from '@/lib/colormaps';
 import Toolbar from '@/components/annotate/Toolbar';
 import ClassManager from '@/components/annotate/ClassManager';
 import DisplayControls from '@/components/annotate/DisplayControls';
+import DenoisePanel from '@/components/annotate/DenoisePanel';
+import DenoiseBakeModal from '@/components/annotate/DenoiseBakeModal';
 import SliceNavigator from '@/components/annotate/SliceNavigator';
 import MaskToolsPanel from '@/components/annotate/MaskToolsPanel';
 import MeasurementPanel from '@/components/annotate/MeasurementPanel';
@@ -36,7 +38,7 @@ import type { SaveDraftPayload } from '@/hooks/useSave';
 /** Renders the annotation workspace: tool sidebar, canvas, and save/version/export flows. */
 export default function AnnotatePage() {
   const navigate = useNavigate();
-  const { source, kind, serverUri, meta } = useDatasetStore();
+  const { source, kind, serverUri, meta, denoise, setDenoise } = useDatasetStore();
   const { removeShapes } = useAnnotationStore();
   const { selectedShapeIds, setSelectedShapeId, fillOpacity, setFillOpacity } = useToolStore();
   const { classes } = useClassStore();
@@ -73,6 +75,7 @@ export default function AnnotatePage() {
   const [clahe, setClahe] = useState(false);
   const [sharpen, setSharpen] = useState(false);
   const [blur, setBlur] = useState(0);
+  const [bakeOpen, setBakeOpen] = useState(false);
   // Working resolution for the drawing tools (1x, 2x, 4x). Annotation coordinates
   // stay native; upscaling only buys sub-pixel precision on small features.
   const [upscale, setUpscale] = useState(1);
@@ -286,6 +289,17 @@ export default function AnnotatePage() {
             upscale={upscale}
             onUpscaleChange={setUpscale}
             maxUpscale={maxUpscale}
+            denoiseSlot={
+              <DenoisePanel
+                denoise={denoise}
+                onChange={setDenoise}
+                source={source}
+                kind={kind}
+                serverUri={serverUri}
+                sliceIndex={currentSlice}
+                onBake={source ? () => setBakeOpen(true) : undefined}
+              />
+            }
           />
           <hr />
           <SliceNavigator />
@@ -425,6 +439,17 @@ export default function AnnotatePage() {
           onClose={() => setShowVersionHistory(false)}
         />
       )}
+      {source && (
+        <DenoiseBakeModal
+          open={bakeOpen}
+          onClose={() => setBakeOpen(false)}
+          source={source}
+          serverUri={serverUri}
+          denoise={denoise}
+          nSlices={meta?.nSlices ?? 1}
+          methodLabel={denoise.method}
+        />
+      )}
     </>
   );
 }
diff --git a/frontend/src/components/annotate/AnnotationCanvas/index.tsx b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
index e60e978..79aa1a5 100644
--- a/frontend/src/components/annotate/AnnotationCanvas/index.tsx
+++ b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
@@ -532,7 +532,7 @@ export default function AnnotationCanvas({
     prev: Int32Array | null;
   } | null>(null);
 
-  const { kind, source, serverUri, meta, currentSlice, renderOpts } = useDatasetStore();
+  const { kind, source, serverUri, meta, currentSlice, renderOpts, denoise } = useDatasetStore();
   const sourceKey = source && kind
     ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri)
     : null;
@@ -623,7 +623,12 @@ export default function AnnotationCanvas({
   // (expensive) layer re-cache so we don't rebuild the shapes bitmap mid-stroke.
   const [isDrawing, setIsDrawing] = useState(false);
 
-  const { data: sliceUrl } = useImageSlice(source, kind, currentSlice, renderOpts, serverUri);
+  // Denoising is applied server-side, before normalization — so the PNG this
+  // returns is already denoised, and everything downstream that samples it
+  // (displayBase, the Threshold Brush field, the Sampler's fit, magic wand,
+  // livewire) sees the denoised data with no extra plumbing. That is the point:
+  // an intensity tool should act on the image the user is actually looking at.
+  const { data: sliceUrl } = useImageSlice(source, kind, currentSlice, renderOpts, serverUri, denoise);
   const [imageEl, setImageEl] = useState<HTMLImageElement | null>(null);
 
   useEffect(() => {
diff --git a/frontend/src/components/annotate/DenoiseBakeModal.tsx b/frontend/src/components/annotate/DenoiseBakeModal.tsx
new file mode 100644
index 0000000..80a48a7
--- /dev/null
+++ b/frontend/src/components/annotate/DenoiseBakeModal.tsx
@@ -0,0 +1,216 @@
+/**
+ * DenoiseBakeModal — write the current denoise settings out as a new dataset.
+ *
+ * The Annotate preview is deliberately non-destructive: it changes what you see
+ * and what the intensity tools act on, but exports still use the original
+ * pixels. This is the other half — it produces a first-class dataset you can
+ * open, annotate and export, sitting next to its source in Browse.
+ *
+ * The job runs on the shared export-job registry, so it reports progress and
+ * cancels through the same routes everything else long-running here uses.
+ */
+import { useCallback, useEffect, useState } from 'react';
+import { useQueryClient } from '@tanstack/react-query';
+import { X, Warning, CheckCircle, CircleNotch } from '@phosphor-icons/react';
+import { API_BASE } from '@/config';
+import type { DenoiseOpts } from '@/stores/datasetStore';
+
+interface JobState {
+  state: 'pending' | 'running' | 'done' | 'error';
+  phase: string;
+  done: number;
+  total: number;
+  error: string | null;
+  result: { path?: string; n_slices?: number; cancelled?: boolean } | null;
+}
+
+export interface DenoiseBakeModalProps {
+  open: boolean;
+  onClose: () => void;
+  source: string;
+  serverUri: string | null;
+  denoise: DenoiseOpts;
+  /** Slice count, so the estimate is honest about how long this will take. */
+  nSlices: number;
+  /** Method label for display (e.g. "Total variation"). */
+  methodLabel: string;
+}
+
+/** `browse/foo` -> `browse/foo_denoised`, matching the backend default. */
+function defaultTarget(source: string): string {
+  return `${source.replace(/\/+$/, '')}_denoised`;
+}
+
+async function readError(res: Response): Promise<string> {
+  try {
+    const body = await res.json();
+    if (typeof body?.detail === 'string') return body.detail;
+    return JSON.stringify(body);
+  } catch {
+    return `Request failed (${res.status})`;
+  }
+}
+
+export default function DenoiseBakeModal({
+  open, onClose, source, serverUri, denoise, nSlices, methodLabel,
+}: DenoiseBakeModalProps) {
+  const queryClient = useQueryClient();
+  const [target, setTarget] = useState(() => defaultTarget(source));
+  const [description, setDescription] = useState('');
+  const [job, setJob] = useState<JobState | null>(null);
+  const [jobId, setJobId] = useState<string | null>(null);
+  const [error, setError] = useState<string | null>(null);
+
+  useEffect(() => {
+    if (open) {
+      setTarget(defaultTarget(source));
+      setJob(null);
+      setJobId(null);
+      setError(null);
+    }
+  }, [open, source]);
+
+  const start = useCallback(async () => {
+    setError(null);
+    setJob({ state: 'pending', phase: 'Starting', done: 0, total: nSlices, error: null, result: null });
+    try {
+      const res = await fetch(`${API_BASE}/api/denoise/bake`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          source,
+          server_uri: serverUri,
+          method: denoise.method,
+          strength: denoise.strength,
+          target_path: target,
+          description,
+        }),
+      });
+      if (!res.ok) throw new Error(await readError(res));
+      const { job_id: id } = await res.json();
+      setJobId(id);
+
+      for (;;) {
+        await new Promise((r) => setTimeout(r, 1000));
+        const s = await fetch(`${API_BASE}/api/export/status/${id}`);
+        if (!s.ok) throw new Error(await readError(s));
+        const status: JobState = await s.json();
+        setJob(status);
+        if (status.state === 'error') throw new Error(status.error || 'Bake failed');
+        if (status.state === 'done') break;
+      }
+      // A new dataset exists — let Browse pick it up without a reload.
+      await queryClient.invalidateQueries({ queryKey: ['browse'] });
+    } catch (err) {
+      setError(err instanceof Error ? err.message : String(err));
+      setJob(null);
+    }
+  }, [source, serverUri, denoise, target, description, nSlices, queryClient]);
+
+  const cancel = useCallback(async () => {
+    if (!jobId) return;
+    // Cooperative: the job stops at its next slice boundary and discards the
+    // partial dataset, rather than leaving something that looks complete.
+    await fetch(`${API_BASE}/api/export/cancel/${jobId}`, { method: 'POST' });
+  }, [jobId]);
+
+  if (!open) return null;
+
+  const running = job !== null && job.state !== 'done';
+  const finished = job?.state === 'done';
+  const percent = job && job.total > 0 ? Math.round((job.done / job.total) * 100) : 0;
+
+  return (
+    <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/60 p-4">
+      <div className="w-full max-w-lg rounded-lg border border-sky-800 bg-sky-950 p-5 text-sky-100 shadow-xl">
+        <div className="mb-3 flex items-start justify-between">
+          <div>
+            <h2 className="text-sm font-semibold">Save denoised copy</h2>
+            <p className="mt-1 text-xs text-sky-300/80">
+              {methodLabel}, strength {denoise.strength.toFixed(2)} — applied to all{' '}
+              {nSlices} slice{nSlices === 1 ? '' : 's'} and written as a new dataset.
+            </p>
+          </div>
+          <button type="button" onClick={onClose} aria-label="Close" className="text-sky-400 hover:text-sky-200">
+            <X size={16} />
+          </button>
+        </div>
+
+        {!running && !finished && (
+          <>
+            <label className="mb-1 block text-xs text-sky-300">Destination</label>
+            <input
+              value={target}
+              onChange={(e) => setTarget(e.target.value)}
+              className="mb-3 w-full rounded border border-sky-800 bg-sky-900/50 px-2 py-1 text-xs"
+            />
+            <label className="mb-1 block text-xs text-sky-300">Tags (optional, comma-separated)</label>
+            <input
+              value={description}
+              onChange={(e) => setDescription(e.target.value)}
+              placeholder="e.g. denoised, tv"
+              className="mb-4 w-full rounded border border-sky-800 bg-sky-900/50 px-2 py-1 text-xs"
+            />
+            <p className="mb-4 text-[11px] leading-snug text-sky-400/70">
+              The original dataset is not modified. This writes a copy — on a large
+              volume it can take a while, and it can be cancelled.
+            </p>
+            <div className="flex justify-end gap-2">
+              <button type="button" onClick={onClose} className="rounded px-3 py-1.5 text-xs text-sky-300 hover:bg-sky-900">
+                Cancel
+              </button>
+              <button
+                type="button"
+                onClick={start}
+                disabled={!target.trim()}
+                className="rounded bg-sky-600 px-3 py-1.5 text-xs font-medium text-white hover:bg-sky-500 disabled:opacity-50"
+              >
+                Save copy
+              </button>
+            </div>
+          </>
+        )}
+
+        {running && (
+          <div className="text-xs">
+            <p className="mb-2 flex items-center gap-2">
+              <CircleNotch size={14} className="animate-spin" />
+              {job.phase} — {job.done}/{job.total}
+            </p>
+            <div className="mb-4 h-2 w-full overflow-hidden rounded bg-sky-900">
+              <div className="h-full bg-sky-500 transition-[width] duration-300" style={{ width: `${percent}%` }} />
+            </div>
+            <div className="flex justify-end">
+              <button type="button" onClick={cancel} className="rounded border border-sky-700 px-3 py-1.5 text-xs hover:bg-sky-900">
+                Stop
+              </button>
+            </div>
+          </div>
+        )}
+
+        {finished && (
+          <div className="text-xs">
+            <p className="mb-3 flex items-center gap-2 text-emerald-300">
+              <CheckCircle size={16} />
+              {job.result?.cancelled
+                ? 'Stopped — the partial copy was discarded.'
+                : `Saved ${job.result?.n_slices ?? job.done} slices to ${job.result?.path ?? target}.`}
+            </p>
+            <div className="flex justify-end">
+              <button type="button" onClick={onClose} className="rounded bg-sky-600 px-3 py-1.5 text-xs font-medium text-white hover:bg-sky-500">
+                Done
+              </button>
+            </div>
+          </div>
+        )}
+
+        {error && (
+          <p className="mt-3 flex items-start gap-1 text-xs text-amber-300">
+            <Warning size={14} className="mt-0.5 shrink-0" />
+            {error}
+          </p>
+        )}
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/components/annotate/DenoisePanel/index.tsx b/frontend/src/components/annotate/DenoisePanel/index.tsx
new file mode 100644
index 0000000..24a986d
--- /dev/null
+++ b/frontend/src/components/annotate/DenoisePanel/index.tsx
@@ -0,0 +1,185 @@
+/**
+ * DenoisePanel — server-side denoising of the slice, before it is normalized.
+ *
+ * Unlike the sliders next to it, this is NOT a display filter. Denoising runs on
+ * the raw slice in its own intensity units (noise statistics do not survive a
+ * trip through the 8-bit display range), so it changes what every intensity
+ * tool sees — the Threshold Brush, the Sampler's fit, magic wand, livewire — not
+ * just what is drawn. It still does not change exported pixels; turning a
+ * denoised view into data is what "Save denoised copy" is for.
+ *
+ * The slider is debounced, and results are cached server-side per
+ * (slice, method, strength) — measured 1.61s -> 0.006s on a repeat — so
+ * revisiting a setting is free. Two refinements are NOT here yet and are worth
+ * knowing about:
+ *
+ *   - **Side-by-side comparison.** Denoising is judged by what it *removes*,
+ *     which the current on/off switch hides: flipping between two images makes
+ *     you compare from memory. A wipe overlay needs a second image layer in
+ *     `AnnotationCanvas`.
+ *   - **Crop while dragging.** `buildSliceUrl` already supports `denoise_crop`
+ *     (measured: bilateral on a 2560² slice, 7.3s full vs 0.49s at 512px), but
+ *     nothing requests it yet, so a slow method is slow on every commit.
+ */
+import { useCallback, useEffect, useState } from 'react';
+import { useQuery } from '@tanstack/react-query';
+import { MagicWand, Warning } from '@phosphor-icons/react';
+import DebouncedSlider from '@/components/common/DebouncedSlider';
+import { API_BASE } from '@/config';
+import type { DenoiseOpts } from '@/stores/datasetStore';
+
+/** One entry of `GET /api/denoise/methods`. */
+export interface DenoiseMethodInfo {
+  method: string;
+  label: string;
+  cost: 'cheap' | 'moderate' | 'slow';
+  description: string;
+  available: boolean;
+  z_radius: number;
+}
+
+export interface DenoisePanelProps {
+  denoise: DenoiseOpts;
+  onChange: (opts: Partial<DenoiseOpts>) => void;
+  /** Dataset identity, for the "Auto" suggestion (which reads this slice). */
+  source: string | null;
+  kind: string | null;
+  serverUri: string | null;
+  sliceIndex: number;
+  /** Opens the bake modal — turning the preview into a real dataset. */
+  onBake?: () => void;
+  /** True while the slice request for the current settings is in flight. */
+  busy?: boolean;
+}
+
+export default function DenoisePanel({
+  denoise,
+  onChange,
+  source,
+  kind,
+  serverUri,
+  sliceIndex,
+  onBake,
+  busy,
+}: DenoisePanelProps) {
+  const [autoError, setAutoError] = useState<string | null>(null);
+  const [autoBusy, setAutoBusy] = useState(false);
+
+  const { data: methods = [] } = useQuery<DenoiseMethodInfo[]>({
+    queryKey: ['denoise-methods'],
+    queryFn: async () => {
+      const res = await fetch(`${API_BASE}/api/denoise/methods`);
+      if (!res.ok) throw new Error('Failed to load denoise methods');
+      return (await res.json()).methods;
+    },
+    staleTime: Infinity, // capability of the server, not of the dataset
+  });
+
+  useEffect(() => setAutoError(null), [denoise.method]);
+
+  const active = denoise.method !== 'none';
+  const current = methods.find((m) => m.method === denoise.method);
+
+  const applyAuto = useCallback(async () => {
+    if (!source || !kind) return;
+    setAutoBusy(true);
+    setAutoError(null);
+    try {
+      const params = new URLSearchParams({
+        source, kind, slice_index: String(sliceIndex), method: denoise.method,
+      });
+      if (serverUri) params.set('server_uri', serverUri);
+      const res = await fetch(`${API_BASE}/api/denoise/auto?${params}`);
+      if (!res.ok) throw new Error('Could not measure this slice');
+      const { strength, noise_sigma: sigma } = await res.json();
+      onChange({ strength });
+      if (strength === 0) {
+        // Saying "0.0" alone reads as a failure. It is a finding: this slice is
+        // already clean, and smoothing it would only cost detail.
+        setAutoError(
+          `This slice looks clean (noise ≈ ${(sigma * 100).toFixed(2)}% of its range) — ` +
+          'denoising it would mostly remove detail.'
+        );
+      }
+    } catch (err) {
+      setAutoError(err instanceof Error ? err.message : String(err));
+    } finally {
+      setAutoBusy(false);
+    }
+  }, [source, kind, serverUri, sliceIndex, denoise.method, onChange]);
+
+  return (
+    <div className="space-y-2">
+      <div className="flex items-center justify-between">
+        <label className="text-xs font-medium text-sky-200">Denoise</label>
+        {busy && active && <span className="text-[10px] text-sky-400">filtering…</span>}
+      </div>
+
+      <select
+        value={denoise.method}
+        onChange={(e) => onChange({ method: e.target.value })}
+        className="w-full rounded border border-sky-800 bg-sky-950 px-2 py-1 text-xs text-sky-100"
+      >
+        {methods.map((m) => (
+          <option key={m.method} value={m.method} disabled={!m.available}>
+            {m.label}
+            {m.cost === 'slow' ? ' (slow)' : ''}
+            {!m.available ? ' — unavailable' : ''}
+          </option>
+        ))}
+      </select>
+
+      {current && current.method !== 'none' && (
+        <p className="text-[11px] leading-snug text-sky-300/80">{current.description}</p>
+      )}
+
+      {active && (
+        <>
+          <DebouncedSlider
+            label="Strength"
+            min={0}
+            max={1}
+            step={0.01}
+            value={denoise.strength}
+            onChange={(v) => onChange({ strength: v })}
+          />
+
+          <div className="flex items-center gap-2">
+            <button
+              type="button"
+              onClick={applyAuto}
+              disabled={autoBusy || !source}
+              className="flex items-center gap-1 rounded border border-sky-700 px-2 py-1 text-[11px] text-sky-200 hover:bg-sky-900 disabled:opacity-50"
+              title="Measure this slice's noise level and suggest a strength"
+            >
+              <MagicWand size={12} />
+              {autoBusy ? 'Measuring…' : 'Auto'}
+            </button>
+            {onBake && (
+              <button
+                type="button"
+                onClick={onBake}
+                className="rounded border border-sky-700 px-2 py-1 text-[11px] text-sky-200 hover:bg-sky-900"
+                title="Write a denoised copy as a new dataset you can annotate and export"
+              >
+                Save denoised copy…
+              </button>
+            )}
+          </div>
+
+          {autoError && (
+            <p className="flex items-start gap-1 text-[11px] leading-snug text-amber-300/90">
+              <Warning size={12} className="mt-0.5 shrink-0" />
+              {autoError}
+            </p>
+          )}
+
+          <p className="text-[10px] leading-snug text-sky-400/70">
+            Affects the intensity tools too, not just the display. Exports still use
+            the original pixels.
+          </p>
+        </>
+      )}
+    </div>
+  );
+}
diff --git a/frontend/src/components/annotate/DisplayControls/index.tsx b/frontend/src/components/annotate/DisplayControls/index.tsx
index 674bd37..c6c8488 100644
--- a/frontend/src/components/annotate/DisplayControls/index.tsx
+++ b/frontend/src/components/annotate/DisplayControls/index.tsx
@@ -39,6 +39,11 @@ export interface DisplayControlsProps {
   onUpscaleChange: (v: number) => void;
   /** Highest upscale this slice can afford before the guard clamps it. */
   maxUpscale?: number;
+  /** Slot rendered above the pre-blur slider, for server-side denoising.
+   *  A slot rather than props so this component stays free of data-fetching
+   *  concerns — denoising needs the dataset identity and a network round trip,
+   *  neither of which any other control here does. */
+  denoiseSlot?: React.ReactNode;
 }
 
 const UPSCALES = [1, 2, 4] as const;
@@ -68,6 +73,7 @@ export default function DisplayControls({
   upscale,
   onUpscaleChange,
   maxUpscale = 4,
+  denoiseSlot,
 }: DisplayControlsProps) {
   return (
     <div className="flex flex-col gap-2">
@@ -152,6 +158,13 @@ export default function DisplayControls({
         />
       </div>
 
+      {/* Server-side denoising, applied to the RAW slice before normalization —
+          unlike the pre-blur below, which is a client-side filter on the already
+          8-bit display image. Placed first because it is the upstream stage. */}
+      {denoiseSlot && (
+        <div className="border-t border-sky-900/60 pt-2">{denoiseSlot}</div>
+      )}
+
       {/* Gaussian pre-blur — denoises so threshold/wand/fill see coherent regions. */}
       <div className="flex flex-col gap-0.5">
         <DebouncedSlider
diff --git a/frontend/src/hooks/buildSliceUrl.denoise.test.ts b/frontend/src/hooks/buildSliceUrl.denoise.test.ts
new file mode 100644
index 0000000..6d4d8fd
--- /dev/null
+++ b/frontend/src/hooks/buildSliceUrl.denoise.test.ts
@@ -0,0 +1,78 @@
+import { describe, it, expect } from 'vitest';
+import { buildSliceUrl } from './useImageSlice';
+import type { RenderOpts, DenoiseOpts } from '@/stores/datasetStore';
+
+const RENDER: RenderOpts = {
+  norm: 'global',
+  scale: 'linear',
+  vminPct: 1,
+  vmaxPct: 99,
+  cmap: 'gray',
+};
+
+const params = (url: string) => new URL(url, 'http://x').searchParams;
+
+describe('buildSliceUrl denoise parameters', () => {
+  it('omits denoise entirely when it is off', () => {
+    // The un-denoised request must stay byte-identical to what it has always
+    // been, so it keeps hitting the same backend cache entry.
+    const off: DenoiseOpts = { method: 'none', strength: 0.7 };
+    const withOff = buildSliceUrl('s', 'tiled', 3, RENDER, null, off);
+    const without = buildSliceUrl('s', 'tiled', 3, RENDER, null);
+    expect(withOff).toBe(without);
+    expect(params(withOff).has('denoise_method')).toBe(false);
+  });
+
+  it('sends method and strength when active', () => {
+    const p = params(buildSliceUrl('s', 'tiled', 3, RENDER, null, { method: 'tv', strength: 0.4 }));
+    expect(p.get('denoise_method')).toBe('tv');
+    expect(p.get('denoise_strength')).toBe('0.4');
+  });
+
+  it('sends a crop only alongside an active method', () => {
+    // Cropping is what keeps slider-dragging interactive: filtering a full
+    // 2560² slice costs seconds, a 512 crop costs ~0.3s.
+    const cropped = params(
+      buildSliceUrl('s', 'tiled', 3, RENDER, null, { method: 'nlm', strength: 0.5 }, 512),
+    );
+    expect(cropped.get('denoise_crop')).toBe('512');
+
+    const offWithCrop = params(
+      buildSliceUrl('s', 'tiled', 3, RENDER, null, { method: 'none', strength: 0.5 }, 512),
+    );
+    expect(offWithCrop.has('denoise_crop')).toBe(false);
+  });
+
+  it('ignores a zero or negative crop', () => {
+    const p = params(
+      buildSliceUrl('s', 'tiled', 3, RENDER, null, { method: 'tv', strength: 0.5 }, 0),
+    );
+    expect(p.has('denoise_crop')).toBe(false);
+  });
+
+  it('still carries the render options', () => {
+    // Denoise is additive: it must not displace normalization, which is applied
+    // after it on the backend.
+    const p = params(buildSliceUrl('s', 'tiled', 3, RENDER, null, { method: 'tv', strength: 0.5 }));
+    expect(p.get('norm')).toBe('global');
+    expect(p.get('vmin_pct')).toBe('1');
+    expect(p.get('cmap')).toBe('gray');
+  });
+
+  it('keeps the server uri', () => {
+    const p = params(
+      buildSliceUrl('s', 'tiled', 3, RENDER, 'http://127.0.0.1:8010', { method: 'tv', strength: 0.5 }),
+    );
+    expect(p.get('server_uri')).toBe('http://127.0.0.1:8010');
+  });
+});
+
+describe('denoise is not a render option', () => {
+  it('is absent from RenderOpts, so it cannot reach export payloads', () => {
+    // Export payloads are built from RenderOpts. If denoise lived there, a
+    // preview would silently change exported pixels — the bake exists precisely
+    // so that turning a denoised view into data is an explicit act.
+    expect(Object.keys(RENDER)).not.toContain('denoise');
+    expect(Object.keys(RENDER)).toEqual(['norm', 'scale', 'vminPct', 'vmaxPct', 'cmap']);
+  });
+});
diff --git a/frontend/src/hooks/useImageSlice.ts b/frontend/src/hooks/useImageSlice.ts
index e076ce3..e742ff1 100644
--- a/frontend/src/hooks/useImageSlice.ts
+++ b/frontend/src/hooks/useImageSlice.ts
@@ -8,7 +8,7 @@
  */
 import { useQuery, type QueryClient } from '@tanstack/react-query';
 import { API_BASE } from '@/config';
-import { RenderOpts } from '@/stores/datasetStore';
+import { RenderOpts, type DenoiseOpts } from '@/stores/datasetStore';
 
 const SLICE_QUERY_KEY = 'imageSlice';
 
@@ -16,13 +16,25 @@ export interface SliceResult {
   url: string;
 }
 
-/** Build the /api/image/slice URL encoding source, slice index, and render options. */
+/** Build the /api/image/slice URL encoding source, slice index, and render options.
+ *
+ * `denoise` is optional and omitted from the URL entirely when off, so the
+ * un-denoised request stays byte-identical to what it has always been — the
+ * backend only pays (and caches) the filter cost when a method is actually set.
+ *
+ * `crop`, when set, asks the backend to filter and return only a centred square
+ * of that size at 1:1. Filtering a full slice costs seconds for NLM and TV;
+ * cropping keeps slider-dragging interactive (measured: 7.3s -> 0.49s for
+ * bilateral on a 2560² slice).
+ */
 export function buildSliceUrl(
   source: string,
   kind: string,
   sliceIndex: number,
   renderOpts: RenderOpts,
-  serverUri: string | null
+  serverUri: string | null,
+  denoise?: DenoiseOpts | null,
+  crop?: number
 ): string {
   const params = new URLSearchParams({
     source,
@@ -35,6 +47,11 @@ export function buildSliceUrl(
     cmap: renderOpts.cmap,
   });
   if (serverUri) params.set('server_uri', serverUri);
+  if (denoise && denoise.method !== 'none') {
+    params.set('denoise_method', denoise.method);
+    params.set('denoise_strength', String(denoise.strength));
+    if (crop && crop > 0) params.set('denoise_crop', String(crop));
+  }
   return `${API_BASE}/api/image/slice?${params.toString()}`;
 }
 
@@ -47,15 +64,22 @@ export function useImageSlice(
   kind: string | null,
   sliceIndex: number,
   renderOpts: RenderOpts,
-  serverUri: string | null
+  serverUri: string | null,
+  denoise?: DenoiseOpts | null
 ) {
   const enabled = Boolean(source && kind);
   const url = enabled
-    ? buildSliceUrl(source!, kind!, sliceIndex, renderOpts, serverUri)
+    ? buildSliceUrl(source!, kind!, sliceIndex, renderOpts, serverUri, denoise)
+    : null;
+
+  // Denoise is keyed only when active, so switching it off returns to the exact
+  // cache entry the un-denoised view already had rather than refetching.
+  const denoiseKey = denoise && denoise.method !== 'none'
+    ? [denoise.method, denoise.strength]
     : null;
 
   return useQuery({
-    queryKey: [SLICE_QUERY_KEY, source, kind, sliceIndex, renderOpts, serverUri],
+    queryKey: [SLICE_QUERY_KEY, source, kind, sliceIndex, renderOpts, serverUri, denoiseKey],
     queryFn: async () => {
       const res = await fetch(url!);
       if (!res.ok) throw new Error(`Slice fetch failed: ${res.status}`);
diff --git a/frontend/src/stores/datasetStore.ts b/frontend/src/stores/datasetStore.ts
index fc673ce..68fcf22 100644
--- a/frontend/src/stores/datasetStore.ts
+++ b/frontend/src/stores/datasetStore.ts
@@ -32,6 +32,21 @@ export interface RenderOpts {
   cmap: 'gray' | 'viridis';
 }
 
+/**
+ * Denoising applied to the slice before it is normalized for display.
+ *
+ * Deliberately NOT part of `RenderOpts`: render options travel into export
+ * payloads, and a denoise preview must not silently change exported pixels. It
+ * changes what you see (and what the intensity tools therefore act on) — turning
+ * it into data is what the "Save denoised copy" bake is for.
+ */
+export interface DenoiseOpts {
+  /** A `denoise.ALL_METHODS` entry; 'none' disables it. */
+  method: string;
+  /** 0..1, mapped by the backend onto each method's native parameter. */
+  strength: number;
+}
+
 export interface DatasetState {
   /** 'tiled' or 'local' */
   kind: string | null;
@@ -41,9 +56,11 @@ export interface DatasetState {
   meta: ImageMeta | null;
   currentSlice: number;
   renderOpts: RenderOpts;
+  denoise: DenoiseOpts;
   setDataset: (kind: string, source: string, serverUri: string | null, meta: ImageMeta) => void;
   setSlice: (idx: number) => void;
   setRenderOpts: (opts: Partial<RenderOpts>) => void;
+  setDenoise: (opts: Partial<DenoiseOpts>) => void;
   reset: () => void;
 }
 
@@ -55,6 +72,8 @@ const DEFAULT_RENDER: RenderOpts = {
   cmap: 'gray',
 };
 
+const DEFAULT_DENOISE: DenoiseOpts = { method: 'none', strength: 0.5 };
+
 export const useDatasetStore = create<DatasetState>((set) => ({
   kind: null,
   source: null,
@@ -62,15 +81,23 @@ export const useDatasetStore = create<DatasetState>((set) => ({
   meta: null,
   currentSlice: 0,
   renderOpts: { ...DEFAULT_RENDER },
-  /** Activates a new image source and resets the current slice to 0. */
+  denoise: { ...DEFAULT_DENOISE },
+  /** Activates a new image source and resets the current slice to 0.
+   *  Denoising resets too: its strength is tuned to one volume's noise level and
+   *  carrying it to the next would silently mis-filter it. */
   setDataset: (kind, source, serverUri, meta) =>
-    set({ kind, source, serverUri, meta, currentSlice: 0 }),
+    set({ kind, source, serverUri, meta, currentSlice: 0, denoise: { ...DEFAULT_DENOISE } }),
   /** Sets the active slice index. */
   setSlice: (idx) => set({ currentSlice: idx }),
   /** Merges partial render options (normalization/scale/percentiles/cmap). */
   setRenderOpts: (opts) =>
     set((s) => ({ renderOpts: { ...s.renderOpts, ...opts } })),
+  /** Merges partial denoise options (method/strength). */
+  setDenoise: (opts) => set((s) => ({ denoise: { ...s.denoise, ...opts } })),
   /** Clears the active dataset and restores default render options. */
   reset: () =>
-    set({ kind: null, source: null, serverUri: null, meta: null, currentSlice: 0, renderOpts: { ...DEFAULT_RENDER } }),
+    set({
+      kind: null, source: null, serverUri: null, meta: null, currentSlice: 0,
+      renderOpts: { ...DEFAULT_RENDER }, denoise: { ...DEFAULT_DENOISE },
+    }),
 }));

From e8600297ae92854695412287d2fd52bf2db67333 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 26 Aug 2026 13:41:09 -0700
Subject: [PATCH 07/38] Front-end feature to hold Peter/Alex's train and
 prediction work

---
 .gitignore                                    |   4 +
 frontend/src/app/pages/AnnotatePage.tsx       | 157 ++++++++----
 .../annotate/AnnotationCanvas/index.tsx       |  11 +-
 .../components/annotate/LayersPanel/index.tsx | 225 ++++++++++++++++++
 .../src/stores/layerVisibilityStore.test.ts   |  46 ++++
 frontend/src/stores/layerVisibilityStore.ts   | 123 ++++++++++
 6 files changed, 513 insertions(+), 53 deletions(-)
 create mode 100644 frontend/src/components/annotate/LayersPanel/index.tsx
 create mode 100644 frontend/src/stores/layerVisibilityStore.test.ts
 create mode 100644 frontend/src/stores/layerVisibilityStore.ts

diff --git a/.gitignore b/.gitignore
index 7826956..6e27af8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -67,3 +67,7 @@ site/
 peter/
 
 .reset-backups/
+
+.claude/
+
+CLAUDE.md
\ No newline at end of file
diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index 5653fa0..db66794 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -4,6 +4,7 @@
 import { useState, useEffect, useCallback, useMemo } from 'react';
 import { useNavigate } from 'react-router';
 import { DownloadSimple, FloppyDisk, ClockCounterClockwise, CircleDashed, ChartBar } from '@phosphor-icons/react';
+import { cn } from '@/lib/utils';
 import { useDatasetStore } from '@/stores/datasetStore';
 import { useAnnotationStore } from '@/stores/annotationStore';
 import { useToolStore } from '@/stores/toolStore';
@@ -17,6 +18,7 @@ import { useKeybinds } from '@/hooks/useKeybinds';
 import type { ColormapName } from '@/lib/colormaps';
 import Toolbar from '@/components/annotate/Toolbar';
 import ClassManager from '@/components/annotate/ClassManager';
+import LayersPanel from '@/components/annotate/LayersPanel';
 import DisplayControls from '@/components/annotate/DisplayControls';
 import DenoisePanel from '@/components/annotate/DenoisePanel';
 import DenoiseBakeModal from '@/components/annotate/DenoiseBakeModal';
@@ -35,6 +37,44 @@ import { initPerf } from '@/lib/perf';
 import SaveModal from '@/components/annotate/SaveModal';
 import type { SaveDraftPayload } from '@/hooks/useSave';
 
+type AnnotateStage = 'draw' | 'assist' | 'predict';
+
+const STAGES: { id: AnnotateStage; label: string }[] = [
+  { id: 'draw', label: 'Draw' },
+  { id: 'assist', label: 'Assist' },
+  { id: 'predict', label: 'Predict' },
+];
+
+function StageSwitcher({ stage, onChange }: { stage: AnnotateStage; onChange: (s: AnnotateStage) => void }) {
+  return (
+    <div className="flex rounded-md border border-gray-200 bg-gray-50 p-0.5" role="tablist" aria-label="Annotate stage">
+      {STAGES.map((s) => (
+        <button
+          key={s.id}
+          type="button"
+          role="tab"
+          aria-selected={stage === s.id}
+          onClick={() => onChange(s.id)}
+          className={cn(
+            'flex-1 rounded px-2 py-1 text-xs font-medium transition-colors',
+            stage === s.id ? 'bg-white text-sky-800 shadow-sm' : 'text-gray-500 hover:text-gray-800',
+          )}
+        >
+          {s.label}
+        </button>
+      ))}
+    </div>
+  );
+}
+
+function StagePlaceholder({ text }: { text: string }) {
+  return (
+    <p className="rounded-md border border-dashed border-gray-300 bg-gray-50 p-2 text-[11px] leading-snug text-gray-500">
+      {text}
+    </p>
+  );
+}
+
 /** Renders the annotation workspace: tool sidebar, canvas, and save/version/export flows. */
 export default function AnnotatePage() {
   const navigate = useNavigate();
@@ -111,6 +151,7 @@ export default function AnnotatePage() {
 
   // Dev-only timing HUD (?perf=1). Read once — the flag is fixed for the session.
   const [perfOn] = useState(() => initPerf());
+  const [stage, setStage] = useState<AnnotateStage>('draw');
 
   // Latest Sampler lasso fit — transient UI state, not persisted: only the band
   // and blur it applies are durable.
@@ -256,58 +297,74 @@ export default function AnnotatePage() {
             onChange={(v) => setFillOpacity(v / 100)}
           />
           <hr />
-          <Toolbar
-            disabled={classes.length === 0}
-            histogramBins={histogramBins}
-            display={thresholdDisplay}
-            upscale={upscale}
-            samplerFit={samplerFit}
-            onRevertSamplerFit={revertSamplerFit}
-          />
+          <StageSwitcher stage={stage} onChange={setStage} />
+          <LayersPanel />
           <hr />
-          <DisplayControls
-            brightness={brightness}
-            contrast={contrast}
-            onBrightnessChange={setBrightness}
-            onContrastChange={setContrast}
-            onReset={() => { setBrightness(0); setContrast(0); setLevelsLo(0); setLevelsHi(255); setColormap('gray'); setGamma(1); setClahe(false); setSharpen(false); setBlur(0); setUpscale(1); }}
-            histogramBins={histogramBins}
-            levelsLo={levelsLo}
-            levelsHi={levelsHi}
-            onLevelsChange={(lo, hi) => { setLevelsLo(lo); setLevelsHi(hi); }}
-            onLevelsReset={() => { setLevelsLo(0); setLevelsHi(255); }}
-            colormap={colormap}
-            gamma={gamma}
-            onColormapChange={setColormap}
-            onGammaChange={setGamma}
-            clahe={clahe}
-            sharpen={sharpen}
-            onClaheChange={setClahe}
-            onSharpenChange={setSharpen}
-            blur={blur}
-            onBlurChange={setBlur}
-            upscale={upscale}
-            onUpscaleChange={setUpscale}
-            maxUpscale={maxUpscale}
-            denoiseSlot={
-              <DenoisePanel
-                denoise={denoise}
-                onChange={setDenoise}
-                source={source}
-                kind={kind}
-                serverUri={serverUri}
-                sliceIndex={currentSlice}
-                onBake={source ? () => setBakeOpen(true) : undefined}
+
+          {stage === 'draw' && (
+            <>
+              <Toolbar
+                disabled={classes.length === 0}
+                histogramBins={histogramBins}
+                display={thresholdDisplay}
+                upscale={upscale}
+                samplerFit={samplerFit}
+                onRevertSamplerFit={revertSamplerFit}
               />
-            }
-          />
-          <hr />
-          <SliceNavigator />
-          <hr />
-          <MaskToolsPanel sourceKey={sourceKey} activeClassId={activeClassId} />
-          <hr />
-          <MeasurementPanel sourceKey={sourceKey} />
-          <hr />
+              <hr />
+              <DisplayControls
+                brightness={brightness}
+                contrast={contrast}
+                onBrightnessChange={setBrightness}
+                onContrastChange={setContrast}
+                onReset={() => { setBrightness(0); setContrast(0); setLevelsLo(0); setLevelsHi(255); setColormap('gray'); setGamma(1); setClahe(false); setSharpen(false); setBlur(0); setUpscale(1); }}
+                histogramBins={histogramBins}
+                levelsLo={levelsLo}
+                levelsHi={levelsHi}
+                onLevelsChange={(lo, hi) => { setLevelsLo(lo); setLevelsHi(hi); }}
+                onLevelsReset={() => { setLevelsLo(0); setLevelsHi(255); }}
+                colormap={colormap}
+                gamma={gamma}
+                onColormapChange={setColormap}
+                onGammaChange={setGamma}
+                clahe={clahe}
+                sharpen={sharpen}
+                onClaheChange={setClahe}
+                onSharpenChange={setSharpen}
+                blur={blur}
+                onBlurChange={setBlur}
+                upscale={upscale}
+                onUpscaleChange={setUpscale}
+                maxUpscale={maxUpscale}
+                denoiseSlot={
+                  <DenoisePanel
+                    denoise={denoise}
+                    onChange={setDenoise}
+                    source={source}
+                    kind={kind}
+                    serverUri={serverUri}
+                    sliceIndex={currentSlice}
+                    onBake={source ? () => setBakeOpen(true) : undefined}
+                  />
+                }
+              />
+              <hr />
+              <SliceNavigator />
+              <hr />
+              <MaskToolsPanel sourceKey={sourceKey} activeClassId={activeClassId} />
+              <hr />
+              <MeasurementPanel sourceKey={sourceKey} />
+              <hr />
+            </>
+          )}
+
+          {stage === 'assist' && (
+            <StagePlaceholder text="Feature channels, the pixel classifier, and manifold suggestions land here in a later phase." />
+          )}
+
+          {stage === 'predict' && (
+            <StagePlaceholder text="Model training and inference panels land here in a later phase." />
+          )}
 
           {/* Save button + status */}
           <div className="flex flex-col gap-1.5">
diff --git a/frontend/src/components/annotate/AnnotationCanvas/index.tsx b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
index 79aa1a5..e683ab8 100644
--- a/frontend/src/components/annotate/AnnotationCanvas/index.tsx
+++ b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
@@ -23,6 +23,7 @@ import { useDatasetStore } from '@/stores/datasetStore';
 import { useAnnotationStore, type Shape, type PolygonShape, type BrushStroke } from '@/stores/annotationStore';
 import { useToolStore } from '@/stores/toolStore';
 import { useClassStore, type AnnotationClass } from '@/stores/classStore';
+import { useLayerVisibilityStore } from '@/stores/layerVisibilityStore';
 import { toImage, normalizeRect, normalizeEllipse, shapeBBox, unionBBox, bboxNear, bboxIntersects, type BBox } from '@/lib/geometry';
 import { buildCostMap, dijkstra, tracePath, imageToGrid, simplifyPath, type CostMap } from '@/lib/livewire';
 import { useImageSlice } from '@/hooks/useImageSlice';
@@ -533,6 +534,10 @@ export default function AnnotationCanvas({
   } | null>(null);
 
   const { kind, source, serverUri, meta, currentSlice, renderOpts, denoise } = useDatasetStore();
+  const layerGroups = useLayerVisibilityStore((s) => s.groups);
+  // The Denoise layer toggle mutes the configured method without discarding it,
+  // so switching it back on doesn't lose the user's method/strength choice.
+  const effectiveDenoise = layerGroups.denoise ? denoise : null;
   const sourceKey = source && kind
     ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri)
     : null;
@@ -628,7 +633,7 @@ export default function AnnotationCanvas({
   // (displayBase, the Threshold Brush field, the Sampler's fit, magic wand,
   // livewire) sees the denoised data with no extra plumbing. That is the point:
   // an intensity tool should act on the image the user is actually looking at.
-  const { data: sliceUrl } = useImageSlice(source, kind, currentSlice, renderOpts, serverUri, denoise);
+  const { data: sliceUrl } = useImageSlice(source, kind, currentSlice, renderOpts, serverUri, effectiveDenoise);
   const [imageEl, setImageEl] = useState<HTMLImageElement | null>(null);
 
   useEffect(() => {
@@ -3002,7 +3007,7 @@ export default function AnnotationCanvas({
         {/* Layer 0: image — preprocessed base (CLAHE/Sharpen baked); linear
             brightness/contrast/levels/gamma/colormap applied via the GPU SVG filter below. */}
         <Layer ref={imageLayerRef}>
-          {imageEl && meta && (
+          {layerGroups.image && imageEl && meta && (
             <KonvaImage
               ref={imageRef}
               image={displayBase ?? imageEl}
@@ -3033,7 +3038,7 @@ export default function AnnotationCanvas({
             layer so overlapping same-class shapes render a uniform class color.
             Clipped to the image frame so strokes never render past the edges.
             Memoized (see ShapesLayer) so display-slider ticks don't reconcile it. */}
-        {meta && (
+        {meta && layerGroups.annotations && (
           <ShapesLayer
             layerRef={shapesLayerRef}
             shapes={displayShapes}
diff --git a/frontend/src/components/annotate/LayersPanel/index.tsx b/frontend/src/components/annotate/LayersPanel/index.tsx
new file mode 100644
index 0000000..bed7cae
--- /dev/null
+++ b/frontend/src/components/annotate/LayersPanel/index.tsx
@@ -0,0 +1,225 @@
+/**
+ * LayersPanel — always-on toggles for canvas function groups + per-class visibility.
+ *
+ * Per-class annotation visibility lives in ClassManager already (it owns the
+ * class list); this panel only exposes the whole-group toggle plus the
+ * per-class breakdown for predictions, which has no other home.
+ */
+import { useEffect } from 'react';
+import { Eye, EyeSlash, Stack } from '@phosphor-icons/react';
+import { useClassStore } from '@/stores/classStore';
+import {
+  LAYER_GROUP_META,
+  type LayerGroupId,
+  useLayerVisibilityStore,
+  isPredictionClassVisible,
+} from '@/stores/layerVisibilityStore';
+import { cn } from '@/lib/utils';
+
+const GROUP_ORDER: LayerGroupId[] = [
+  'image',
+  'denoise',
+  'features',
+  'proba',
+  'predictions',
+  'annotations',
+  'manifold',
+];
+
+export interface LayersPanelProps {
+  /** Feature channel available (preprocess job + selection). */
+  hasFeatures?: boolean;
+  /** Softmax probability URL loaded. */
+  hasProba?: boolean;
+  /** Conformal prediction overlays loaded. */
+  hasPredictions?: boolean;
+  /** Prediction class ids (from trained model). */
+  predictionClassIds?: number[];
+  /** Manifold sample active. */
+  hasManifold?: boolean;
+}
+
+function GroupToggle({
+  id,
+  disabled,
+}: {
+  id: LayerGroupId;
+  disabled?: boolean;
+}) {
+  const on = useLayerVisibilityStore((s) => s.groups[id]);
+  const toggle = useLayerVisibilityStore((s) => s.toggleGroup);
+  const meta = LAYER_GROUP_META[id];
+  return (
+    <button
+      type="button"
+      disabled={disabled}
+      title={disabled ? `${meta.label} (nothing loaded)` : meta.hint}
+      onClick={() => toggle(id)}
+      className={cn(
+        'flex w-full items-center gap-2 rounded px-1.5 py-1 text-left text-[11px] transition-colors',
+        disabled
+          ? 'cursor-not-allowed text-gray-400'
+          : on
+            ? 'text-gray-800 hover:bg-sky-50'
+            : 'text-gray-500 hover:bg-gray-50',
+      )}
+    >
+      {on && !disabled ? (
+        <Eye size={14} className="shrink-0 text-sky-700" />
+      ) : (
+        <EyeSlash size={14} className="shrink-0 text-gray-400" />
+      )}
+      <span className="font-medium">{meta.label}</span>
+      {disabled ? (
+        <span className="ml-auto text-[9px] text-gray-400">—</span>
+      ) : null}
+    </button>
+  );
+}
+
+/** Sidebar layer stack controls. */
+export default function LayersPanel({
+  hasFeatures = false,
+  hasProba = false,
+  hasPredictions = false,
+  predictionClassIds = [],
+  hasManifold = false,
+}: LayersPanelProps) {
+  const classes = useClassStore((s) => s.classes);
+  const groups = useLayerVisibilityStore((s) => s.groups);
+  const probaOpacity = useLayerVisibilityStore((s) => s.probaOpacity);
+  const setProbaOpacity = useLayerVisibilityStore((s) => s.setProbaOpacity);
+  const predictionsOpacity = useLayerVisibilityStore((s) => s.predictionsOpacity);
+  const setPredictionsOpacity = useLayerVisibilityStore((s) => s.setPredictionsOpacity);
+  const predictionClassVisible = useLayerVisibilityStore((s) => s.predictionClassVisible);
+  const togglePredictionClass = useLayerVisibilityStore((s) => s.togglePredictionClass);
+  const showPredictionMulti = useLayerVisibilityStore((s) => s.showPredictionMulti);
+  const showPredictionAbstain = useLayerVisibilityStore((s) => s.showPredictionAbstain);
+  const setShowPredictionMulti = useLayerVisibilityStore((s) => s.setShowPredictionMulti);
+  const setShowPredictionAbstain = useLayerVisibilityStore((s) => s.setShowPredictionAbstain);
+  const ensurePredictionClasses = useLayerVisibilityStore((s) => s.ensurePredictionClasses);
+
+  useEffect(() => {
+    if (predictionClassIds.length) ensurePredictionClasses(predictionClassIds);
+  }, [predictionClassIds, ensurePredictionClasses]);
+
+  const classLabel = (id: number) =>
+    classes.find((c) => c.classId === id)?.label?.trim() || `class ${id}`;
+  const classColor = (id: number) =>
+    classes.find((c) => c.classId === id)?.color ?? '#64748b';
+
+  const disabledFor = (id: LayerGroupId): boolean => {
+    if (id === 'features') return !hasFeatures;
+    if (id === 'proba') return !hasProba;
+    if (id === 'predictions') return !hasPredictions;
+    if (id === 'manifold') return !hasManifold;
+    if (id === 'annotations') return classes.length === 0;
+    return false;
+  };
+
+  return (
+    <div
+      className="flex flex-col gap-1.5 rounded-md border border-gray-200 bg-white p-2"
+      data-testid="layers-panel"
+    >
+      <div className="flex items-center gap-1.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
+        <Stack size={13} /> Layers
+      </div>
+
+      <div className="flex flex-col gap-0.5">
+        {GROUP_ORDER.map((id) => (
+          <GroupToggle key={id} id={id} disabled={disabledFor(id)} />
+        ))}
+      </div>
+
+      {groups.proba && hasProba && (
+        <label className="flex flex-col gap-0.5 border-t border-gray-100 pt-1.5 text-[10px] text-gray-600">
+          <span className="flex justify-between">
+            <span>Probability opacity</span>
+            <span className="font-medium text-gray-800">{Math.round(probaOpacity * 100)}%</span>
+          </span>
+          <input
+            type="range"
+            min={10}
+            max={100}
+            step={5}
+            value={Math.round(probaOpacity * 100)}
+            onChange={(e) => setProbaOpacity(Number(e.target.value) / 100)}
+            className="w-full accent-sky-600"
+          />
+        </label>
+      )}
+
+      {groups.predictions && hasPredictions && (
+        <div className="flex flex-col gap-1 border-t border-gray-100 pt-1.5">
+          <label className="flex flex-col gap-0.5 text-[10px] text-gray-600">
+            <span className="flex justify-between">
+              <span>Prediction opacity</span>
+              <span className="font-medium text-gray-800">
+                {Math.round(predictionsOpacity * 100)}%
+              </span>
+            </span>
+            <input
+              type="range"
+              min={10}
+              max={100}
+              step={5}
+              value={Math.round(predictionsOpacity * 100)}
+              onChange={(e) => setPredictionsOpacity(Number(e.target.value) / 100)}
+              className="w-full accent-sky-600"
+            />
+          </label>
+          <div className="flex flex-wrap gap-1">
+            <button
+              type="button"
+              onClick={() => setShowPredictionMulti(!showPredictionMulti)}
+              className={cn(
+                'rounded border px-1.5 py-0.5 text-[9px]',
+                showPredictionMulti
+                  ? 'border-amber-300 bg-amber-50 text-amber-900'
+                  : 'border-gray-200 bg-gray-50 text-gray-400',
+              )}
+            >
+              Multi
+            </button>
+            <button
+              type="button"
+              onClick={() => setShowPredictionAbstain(!showPredictionAbstain)}
+              className={cn(
+                'rounded border px-1.5 py-0.5 text-[9px]',
+                showPredictionAbstain
+                  ? 'border-slate-300 bg-slate-100 text-slate-800'
+                  : 'border-gray-200 bg-gray-50 text-gray-400',
+              )}
+            >
+              Abstain
+            </button>
+          </div>
+          <span className="text-[9px] font-medium uppercase tracking-wide text-gray-500">
+            Prediction classes
+          </span>
+          {(predictionClassIds.length ? predictionClassIds : classes.map((c) => c.classId)).map(
+            (cid) => {
+              const on = isPredictionClassVisible(predictionClassVisible, cid);
+              return (
+                <button
+                  key={`pred-${cid}`}
+                  type="button"
+                  onClick={() => togglePredictionClass(cid)}
+                  className="flex items-center gap-1.5 rounded px-1 py-0.5 text-left text-[10px] hover:bg-sky-50"
+                >
+                  {on ? <Eye size={12} className="text-sky-700" /> : <EyeSlash size={12} className="text-gray-400" />}
+                  <span
+                    className="inline-block h-2 w-2 shrink-0 rounded-sm"
+                    style={{ background: classColor(cid), opacity: on ? 1 : 0.3 }}
+                  />
+                  <span className={on ? 'text-gray-800' : 'text-gray-400'}>{classLabel(cid)}</span>
+                </button>
+              );
+            },
+          )}
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/frontend/src/stores/layerVisibilityStore.test.ts b/frontend/src/stores/layerVisibilityStore.test.ts
new file mode 100644
index 0000000..1c35926
--- /dev/null
+++ b/frontend/src/stores/layerVisibilityStore.test.ts
@@ -0,0 +1,46 @@
+import { describe, expect, it, beforeEach } from 'vitest';
+import {
+  isPredictionClassVisible,
+  useLayerVisibilityStore,
+} from '@/stores/layerVisibilityStore';
+
+describe('layerVisibilityStore', () => {
+  beforeEach(() => {
+    useLayerVisibilityStore.setState({
+      groups: {
+        image: true,
+        denoise: true,
+        features: true,
+        proba: true,
+        predictions: true,
+        annotations: true,
+        manifold: true,
+      },
+      predictionClassVisible: {},
+      showPredictionMulti: true,
+      showPredictionAbstain: true,
+    });
+  });
+
+  it('toggles groups and prediction classes', () => {
+    const s = useLayerVisibilityStore.getState();
+    s.toggleGroup('proba');
+    expect(useLayerVisibilityStore.getState().groups.proba).toBe(false);
+    s.ensurePredictionClasses([1, 2]);
+    expect(isPredictionClassVisible(useLayerVisibilityStore.getState().predictionClassVisible, 1)).toBe(
+      true,
+    );
+    s.togglePredictionClass(1);
+    expect(isPredictionClassVisible(useLayerVisibilityStore.getState().predictionClassVisible, 1)).toBe(
+      false,
+    );
+  });
+
+  it('denoise group defaults on and toggles independently', () => {
+    const s = useLayerVisibilityStore.getState();
+    expect(useLayerVisibilityStore.getState().groups.denoise).toBe(true);
+    s.toggleGroup('denoise');
+    expect(useLayerVisibilityStore.getState().groups.denoise).toBe(false);
+    expect(useLayerVisibilityStore.getState().groups.image).toBe(true);
+  });
+});
diff --git a/frontend/src/stores/layerVisibilityStore.ts b/frontend/src/stores/layerVisibilityStore.ts
new file mode 100644
index 0000000..5caaeff
--- /dev/null
+++ b/frontend/src/stores/layerVisibilityStore.ts
@@ -0,0 +1,123 @@
+/**
+ * Canvas layer visibility — function groups + per-class toggles.
+ *
+ * Groups control image / denoise / features / probability / predictions /
+ * annotations / manifold. Per-class keys under predictions and annotations
+ * (annotations also sync with classStore.isVisible).
+ */
+import { create } from 'zustand';
+
+export type LayerGroupId =
+  | 'image'
+  | 'denoise'
+  | 'features'
+  | 'proba'
+  | 'predictions'
+  | 'annotations'
+  | 'manifold';
+
+export const LAYER_GROUP_META: Record<
+  LayerGroupId,
+  { label: string; hint: string }
+> = {
+  image: { label: 'Image', hint: 'Base slice (brightness/levels apply here)' },
+  denoise: { label: 'Denoise', hint: 'Server-side denoising of the base slice' },
+  features: { label: 'Features', hint: 'Preprocess channel as display base' },
+  proba: { label: 'Probability', hint: 'Softmax class heatmap overlay' },
+  predictions: { label: 'Predictions', hint: 'Conformal singleton / multi / abstain' },
+  annotations: { label: 'Annotations', hint: 'Drawn shapes / scribbles' },
+  manifold: { label: 'Suggest', hint: 'Manifold heatmap + markers' },
+};
+
+export interface LayerVisibilityState {
+  groups: Record<LayerGroupId, boolean>;
+  /** Opacity of the probability overlay (0–1). */
+  probaOpacity: number;
+  /** Opacity of the prediction overlay (0–1). */
+  predictionsOpacity: number;
+  /** Per-class visibility for prediction singletons. Missing → visible. */
+  predictionClassVisible: Record<number, boolean>;
+  /** Show conformal multi-set hatch. */
+  showPredictionMulti: boolean;
+  /** Show conformal abstain. */
+  showPredictionAbstain: boolean;
+  setGroup: (id: LayerGroupId, visible: boolean) => void;
+  toggleGroup: (id: LayerGroupId) => void;
+  setProbaOpacity: (v: number) => void;
+  setPredictionsOpacity: (v: number) => void;
+  setPredictionClassVisible: (classId: number, visible: boolean) => void;
+  togglePredictionClass: (classId: number) => void;
+  setShowPredictionMulti: (v: boolean) => void;
+  setShowPredictionAbstain: (v: boolean) => void;
+  ensurePredictionClasses: (classIds: number[]) => void;
+}
+
+const DEFAULT_GROUPS: Record<LayerGroupId, boolean> = {
+  image: true,
+  denoise: true,
+  features: true,
+  proba: true,
+  predictions: true,
+  annotations: true,
+  manifold: true,
+};
+
+export const useLayerVisibilityStore = create<LayerVisibilityState>((set, get) => ({
+  groups: { ...DEFAULT_GROUPS },
+  probaOpacity: 0.55,
+  predictionsOpacity: 0.85,
+  predictionClassVisible: {},
+  showPredictionMulti: true,
+  showPredictionAbstain: true,
+
+  setGroup: (id, visible) =>
+    set((s) => ({ groups: { ...s.groups, [id]: visible } })),
+
+  toggleGroup: (id) => {
+    const cur = get().groups[id];
+    set((s) => ({ groups: { ...s.groups, [id]: !cur } }));
+  },
+
+  setProbaOpacity: (v) =>
+    set({ probaOpacity: Math.min(1, Math.max(0, v)) }),
+
+  setPredictionsOpacity: (v) =>
+    set({ predictionsOpacity: Math.min(1, Math.max(0, v)) }),
+
+  setPredictionClassVisible: (classId, visible) =>
+    set((s) => ({
+      predictionClassVisible: { ...s.predictionClassVisible, [classId]: visible },
+    })),
+
+  togglePredictionClass: (classId) => {
+    const cur = get().predictionClassVisible[classId];
+    const next = cur === undefined ? false : !cur;
+    set((s) => ({
+      predictionClassVisible: { ...s.predictionClassVisible, [classId]: next },
+    }));
+  },
+
+  setShowPredictionMulti: (v) => set({ showPredictionMulti: v }),
+  setShowPredictionAbstain: (v) => set({ showPredictionAbstain: v }),
+
+  ensurePredictionClasses: (classIds) =>
+    set((s) => {
+      const next = { ...s.predictionClassVisible };
+      let changed = false;
+      for (const id of classIds) {
+        if (next[id] === undefined) {
+          next[id] = true;
+          changed = true;
+        }
+      }
+      return changed ? { predictionClassVisible: next } : s;
+    }),
+}));
+
+/** True when a prediction class should draw (default visible). */
+export function isPredictionClassVisible(
+  map: Record<number, boolean>,
+  classId: number,
+): boolean {
+  return map[classId] !== false;
+}

From 99760b83189e9abed03356eaccf13c66d59d9a94 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 26 Aug 2026 21:51:09 -0700
Subject: [PATCH 08/38] ipred integration

---
 backend/annotation_server.py                  |      7 +
 backend/ipred_client.py                       |    291 +
 backend/ipred_routes.py                       |    383 +
 backend/pyproject.toml                        |      4 +
 backend/tests/test_ipred_routes.py            |     99 +
 frontend/scripts/fetch-sam-model.mjs          |      4 +
 frontend/src/app/pages/AnnotatePage.tsx       |    162 +-
 .../CompositionPanel.test.tsx                 |    100 +
 .../src/components/CompositionPanel/index.tsx |    505 +
 .../AnnotationCanvas/OverlaysLayer.tsx        |    216 +
 .../annotate/AnnotationCanvas/index.tsx       |     67 +
 .../annotate/FeatureChannelsPanel/index.tsx   |    264 +
 .../annotate/PixelClassifierPanel/index.tsx   |    549 +
 frontend/src/hooks/useFeatureChannels.ts      |    259 +
 frontend/src/hooks/useFeatureManifold.ts      |    207 +
 frontend/src/hooks/usePixelClassifier.ts      |    462 +
 frontend/src/lib/featureManifold.test.ts      |     36 +
 frontend/src/lib/featureManifold.ts           |     92 +
 frontend/src/lib/ipredApi.ts                  |    313 +
 frontend/src/lib/pixelClf.overlay.test.ts     |     23 +
 frontend/src/lib/pixelClf.test.ts             |    100 +
 frontend/src/lib/pixelClf.ts                  |    280 +
 frontend/src/lib/viridis.ts                   |    269 +
 frontend/src/stores/ipredStore.ts             |     67 +
 graphify-out/.graphify_labels.json            |    297 +
 graphify-out/.graphify_labels.json.sig        |      1 +
 graphify-out/.graphify_root                   |      1 +
 graphify-out/2026-08-26/.graphify_labels.json |    289 +
 graphify-out/2026-08-26/GRAPH_REPORT.md       |   1159 +
 graphify-out/2026-08-26/graph.json            | 159538 ++++++++++++++
 graphify-out/2026-08-26/manifest.json         |   2738 +
 graphify-out/GRAPH_REPORT.md                  |   1166 +
 ...eda9d72ac7e3b8a81aca145221cddb1cc47ed.json |      1 +
 ...0ec8affd0aa1a4eba6c5a81deb4b2604a4631.json |      1 +
 ...e89f69bbd7aff8c913f7245b5e98414a40dd9.json |      1 +
 ...4c0b0b9e8c4db87d196c7fcb9734453ea75bd.json |      1 +
 ...dec040e6e35ff891d614a1a3a43b921cc1e4c.json |      1 +
 ...ad396ec309fc8eb7cfc62c0024faef1008927.json |      1 +
 ...c1c7a7fe2b522b63d0a1569b888d0c148f9b6.json |      1 +
 ...097dcfd181ca2e321c943d059df410c830386.json |      1 +
 ...4a8bbb6aa8901979d8f60dae318f4022737c0.json |      1 +
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diff --git a/backend/annotation_server.py b/backend/annotation_server.py
index 809ed79..160d2a8 100644
--- a/backend/annotation_server.py
+++ b/backend/annotation_server.py
@@ -8,6 +8,7 @@
 * ``GET /api/browse/items``     — sample records matching a filter set
 * ``GET /api/browse/thumbnail`` — PNG thumbnail for a Tiled array path
 * ``GET /health``               — liveness check
+* ``/api/ipred/*``              — proxy to the standalone iPred service (see ``ipred_routes.py``)
 
 Run with
 --------
@@ -44,6 +45,7 @@
 import guides as guides_mod
 import images as images_mod
 import ingest as ingest_mod
+import ipred_routes
 import local_fs
 import tiff_stack_source
 import volume_build
@@ -123,6 +125,11 @@
 # the ~500-byte floor skips tiny/binary payloads. (Dev uses the Vite server instead.)
 app.add_middleware(GZipMiddleware, minimum_size=500)
 
+# iPred (interactive segmentation) proxy — see ipred_routes.py. iPred is an
+# optional, separately-run service (port 8003 by default); a down/missing
+# service surfaces as 503 from these routes rather than breaking the app.
+app.include_router(ipred_routes.router)
+
 
 # ---------------------------------------------------------------------------
 # Response models
diff --git a/backend/ipred_client.py b/backend/ipred_client.py
new file mode 100644
index 0000000..abf2750
--- /dev/null
+++ b/backend/ipred_client.py
@@ -0,0 +1,291 @@
+"""HTTP client for the disjoint ipred backend (no Python imports of ``ipred``)."""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import Any
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_IPRED_URL = "http://127.0.0.1:8003"
+
+
+def ipred_url() -> str:
+    """Base URL for the iterative prediction backend."""
+    # Prefer IPRED_URL; accept legacy CLF_ENGINE_URL during transition.
+    return os.getenv(
+        "IPRED_URL",
+        os.getenv("CLF_ENGINE_URL", DEFAULT_IPRED_URL),
+    ).rstrip("/")
+
+
+def _client(timeout: float = 300.0) -> httpx.Client:
+    return httpx.Client(base_url=ipred_url(), timeout=timeout)
+
+
+def health() -> dict[str, Any]:
+    """GET /health."""
+    with _client(timeout=5.0) as client:
+        r = client.get("/health")
+        r.raise_for_status()
+        return r.json()
+
+
+def open_session(
+    *,
+    kind: str,
+    source: str,
+    server_uri: str | None = None,
+    root: str | None = None,
+) -> dict[str, Any]:
+    """POST /sessions."""
+    with _client() as client:
+        r = client.post(
+            "/sessions",
+            json={
+                "kind": kind,
+                "source": source,
+                "server_uri": server_uri,
+                "root": root,
+            },
+        )
+        r.raise_for_status()
+        return r.json()
+
+
+def list_setups() -> list[dict[str, Any]]:
+    """GET /setups."""
+    with _client() as client:
+        r = client.get("/setups")
+        r.raise_for_status()
+        return list(r.json().get("setups") or [])
+
+
+def get_setup(setup_id: str) -> dict[str, Any]:
+    """GET /setups/{id}."""
+    with _client() as client:
+        r = client.get(f"/setups/{setup_id}")
+        r.raise_for_status()
+        return r.json()
+
+
+def upsert_setup(payload: dict[str, Any]) -> dict[str, Any]:
+    """POST /setups."""
+    with _client() as client:
+        r = client.post("/setups", json=payload)
+        r.raise_for_status()
+        return r.json()
+
+
+def list_trainers() -> list[str]:
+    """GET /trainers."""
+    with _client() as client:
+        r = client.get("/trainers")
+        r.raise_for_status()
+        return list(r.json().get("trainers") or [])
+
+
+def list_modules() -> list[dict[str, Any]]:
+    """GET /modules."""
+    with _client() as client:
+        r = client.get("/modules")
+        r.raise_for_status()
+        return list(r.json().get("modules") or [])
+
+
+def list_compositions() -> list[dict[str, Any]]:
+    """GET /compositions."""
+    with _client() as client:
+        r = client.get("/compositions")
+        r.raise_for_status()
+        return list(r.json().get("compositions") or [])
+
+
+def get_composition(composition_id: str) -> dict[str, Any]:
+    """GET /compositions/{id}."""
+    with _client() as client:
+        r = client.get(f"/compositions/{composition_id}")
+        r.raise_for_status()
+        return r.json()
+
+
+def upsert_composition(payload: dict[str, Any]) -> dict[str, Any]:
+    """POST /compositions."""
+    with _client() as client:
+        r = client.post("/compositions", json=payload)
+        r.raise_for_status()
+        return r.json()
+
+
+def preview_composition(payload: dict[str, Any]) -> dict[str, Any]:
+    """POST /compositions/preview."""
+    with _client() as client:
+        r = client.post("/compositions/preview", json=payload)
+        r.raise_for_status()
+        return r.json()
+
+
+def upload_session_array(session_id: str, payload: dict[str, Any]) -> dict[str, Any]:
+    """POST /sessions/{id}/arrays."""
+    with _client() as client:
+        r = client.post(f"/sessions/{session_id}/arrays", json=payload)
+        r.raise_for_status()
+        return r.json()
+
+
+def preprocess(
+    *,
+    session_id: str,
+    feature_setup_id: str | None = None,
+    composition_id: str | None = None,
+    slice_index: int = 0,
+    array_ref: str | None = None,
+) -> dict[str, Any]:
+    """POST /preprocess."""
+    body: dict[str, Any] = {
+        "session_id": session_id,
+        "slice_index": slice_index,
+    }
+    if composition_id:
+        body["composition_id"] = composition_id
+    if feature_setup_id:
+        body["feature_setup_id"] = feature_setup_id
+    if array_ref:
+        body["array_ref"] = array_ref
+    with _client() as client:
+        r = client.post("/preprocess", json=body)
+        r.raise_for_status()
+        return r.json()
+
+
+def feature_channel_bytes(feature_id: str, index: int) -> bytes:
+    """GET /features/{id}/channels/{index}."""
+    with _client() as client:
+        r = client.get(f"/features/{feature_id}/channels/{index}")
+        r.raise_for_status()
+        return r.content
+
+
+def train(
+    *,
+    session_id: str,
+    shapes: list[dict[str, Any]],
+    feature_id: str | None = None,
+    trainer_id: str = "catboost",
+    config: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """POST /train."""
+    with _client() as client:
+        r = client.post(
+            "/train",
+            json={
+                "session_id": session_id,
+                "shapes": shapes,
+                "feature_id": feature_id,
+                "trainer_id": trainer_id,
+                "config": config or {},
+            },
+        )
+        r.raise_for_status()
+        return r.json()
+
+
+def infer(
+    *,
+    session_id: str,
+    model_id: str | None = None,
+    feature_id: str | None = None,
+    alpha: float = 0.05,
+) -> dict[str, Any]:
+    """POST /infer."""
+    with _client() as client:
+        r = client.post(
+            "/infer",
+            json={
+                "session_id": session_id,
+                "model_id": model_id,
+                "feature_id": feature_id,
+                "alpha": alpha,
+            },
+        )
+        r.raise_for_status()
+        return r.json()
+
+
+def rethreshold(
+    *,
+    session_id: str,
+    alpha: float,
+    run_id: str | None = None,
+) -> dict[str, Any]:
+    """POST /rethreshold."""
+    with _client() as client:
+        r = client.post(
+            "/rethreshold",
+            json={
+                "session_id": session_id,
+                "alpha": alpha,
+                "run_id": run_id,
+            },
+        )
+        r.raise_for_status()
+        return r.json()
+
+
+def run_commit_png(run_id: str) -> bytes:
+    """GET /runs/{id}/commit.png."""
+    with _client() as client:
+        r = client.get(f"/runs/{run_id}/commit.png")
+        r.raise_for_status()
+        return r.content
+
+
+def run_status_png(run_id: str) -> bytes:
+    """GET /runs/{id}/status.png."""
+    with _client() as client:
+        r = client.get(f"/runs/{run_id}/status.png")
+        r.raise_for_status()
+        return r.content
+
+
+def run_proba_png(run_id: str, class_index: int) -> bytes:
+    """GET /runs/{id}/proba/{class_index}.png."""
+    with _client() as client:
+        r = client.get(f"/runs/{run_id}/proba/{int(class_index)}.png")
+        r.raise_for_status()
+        return r.content
+
+
+def threshold_class_map(
+    run_id: str,
+    *,
+    class_id: int,
+    threshold: float,
+) -> dict[str, Any]:
+    """POST /runs/{id}/threshold-class."""
+    with _client() as client:
+        r = client.post(
+            f"/runs/{run_id}/threshold-class",
+            json={"class_id": int(class_id), "threshold": float(threshold)},
+        )
+        r.raise_for_status()
+        return r.json()
+
+
+def manifold_sample(payload: dict[str, Any]) -> dict[str, Any]:
+    """POST /manifold/sample."""
+    with _client() as client:
+        r = client.post("/manifold/sample", json=payload)
+        r.raise_for_status()
+        return r.json()
+
+
+def manifold_heatmap_png(sample_id: str) -> bytes:
+    """GET /manifold/{id}/heatmap.png."""
+    with _client() as client:
+        r = client.get(f"/manifold/{sample_id}/heatmap.png")
+        r.raise_for_status()
+        return r.content
diff --git a/backend/ipred_routes.py b/backend/ipred_routes.py
new file mode 100644
index 0000000..f334381
--- /dev/null
+++ b/backend/ipred_routes.py
@@ -0,0 +1,383 @@
+"""Proxy routes for the disjoint ipred service (GUI never calls port 8003 directly).
+
+All routes live under ``/api/ipred/*`` and are thin pass-throughs into
+``ipred_client``, which talks to the standalone iPred FastAPI service over
+HTTP. A connection failure surfaces as 503 rather than a 500/toast, so the
+frontend can show a clear "not running" state.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Optional
+
+import httpx
+from fastapi import APIRouter, HTTPException
+from fastapi.responses import Response
+from pydantic import BaseModel
+
+import ipred_client as ipred_client_mod
+
+router = APIRouter(prefix="/api/ipred")
+
+
+class IpredSessionRequest(BaseModel):
+    """Open an ipred session for one image/stack project."""
+
+    kind: str
+    source: str
+    server_uri: Optional[str] = None
+    root: Optional[str] = None
+
+
+class IpredPreprocessRequest(BaseModel):
+    """Proxy featurize request."""
+
+    session_id: str
+    feature_setup_id: Optional[str] = None
+    composition_id: Optional[str] = None
+    slice_index: int = 0
+    array_ref: Optional[str] = None
+
+
+class IpredCompositionUpsertRequest(BaseModel):
+    """Create/update composition on ipred."""
+
+    name: str
+    nodes: list[dict[str, Any]]
+    outputs: list[str]
+    composition_id: Optional[str] = None
+    builtin: bool = False
+
+
+class IpredArrayUploadRequest(BaseModel):
+    """Upload float array for remote ipred preprocess."""
+
+    session_id: str
+    shape: list[int]
+    dtype: str = "float32"
+    data_b64: str
+    array_ref: Optional[str] = None
+
+
+class IpredTrainRequest(BaseModel):
+    """Proxy train request."""
+
+    session_id: str
+    shapes: list[dict[str, Any]]
+    feature_id: Optional[str] = None
+    trainer_id: str = "catboost"
+    config: Optional[dict[str, Any]] = None
+
+
+class IpredInferRequest(BaseModel):
+    """Proxy infer request."""
+
+    session_id: str
+    model_id: Optional[str] = None
+    feature_id: Optional[str] = None
+    alpha: float = 0.05
+
+
+class IpredRethresholdRequest(BaseModel):
+    """Proxy rethreshold request."""
+
+    session_id: str
+    alpha: float
+    run_id: Optional[str] = None
+
+
+class IpredSetupUpsertRequest(BaseModel):
+    """Create or update a Feature Setup on ipred."""
+
+    name: str
+    kind: str
+    procedure_id: Optional[str] = None
+    params: Optional[dict[str, Any]] = None
+    encoder_setup_id: Optional[str] = None
+    weights_path: Optional[str] = None
+    weights_format: Optional[str] = None
+    inference: Optional[dict[str, Any]] = None
+    setup_id: Optional[str] = None
+
+
+class IpredManifoldSampleRequest(BaseModel):
+    """Suggest Labels via ipred feature bank."""
+
+    feature_id: str
+    k: int = 24
+    box_size: Optional[int] = None
+    stride: Optional[int] = None
+    pca_dims: int = 16
+    shapes: Optional[list[dict[str, Any]]] = None
+
+
+class IpredThresholdClassRequest(BaseModel):
+    class_id: int
+    threshold: float = 0.5
+
+
+def _ipred_http_error(exc: Exception) -> HTTPException:
+    """Translate an ipred_client exception into the right HTTP response."""
+    if isinstance(exc, httpx.HTTPStatusError):
+        detail: Any = exc.response.text
+        try:
+            detail = exc.response.json()
+        except Exception:
+            pass
+        return HTTPException(exc.response.status_code, detail)
+    if isinstance(exc, httpx.ConnectError):
+        return HTTPException(503, f"ipred unreachable at {ipred_client_mod.ipred_url()}")
+    return HTTPException(500, str(exc))
+
+
+@router.get("/health")
+async def ipred_health() -> dict:
+    """Liveness of the disjoint ipred."""
+    try:
+        return ipred_client_mod.health()
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/sessions")
+async def ipred_open_session(body: IpredSessionRequest) -> dict:
+    """Open/create an engine session for a project."""
+    try:
+        return ipred_client_mod.open_session(
+            kind=body.kind,
+            source=body.source,
+            server_uri=body.server_uri,
+            root=body.root,
+        )
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/setups")
+async def ipred_list_setups() -> dict:
+    """List Feature Setups from the ipred."""
+    try:
+        return {"setups": ipred_client_mod.list_setups()}
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/setups/{setup_id}")
+async def ipred_get_setup(setup_id: str) -> dict:
+    """Get one Feature Setup from ipred."""
+    try:
+        return ipred_client_mod.get_setup(setup_id)
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/setups")
+async def ipred_upsert_setup(body: IpredSetupUpsertRequest) -> dict:
+    """Create or update a Feature Setup on ipred."""
+    try:
+        return ipred_client_mod.upsert_setup(body.model_dump(exclude_none=True))
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/trainers")
+async def ipred_list_trainers() -> dict:
+    """List trainer plugin ids from ipred."""
+    try:
+        return {"trainers": ipred_client_mod.list_trainers()}
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/modules")
+async def ipred_list_modules() -> dict:
+    """List composable feature modules (each reports `runtime` + `ready`)."""
+    try:
+        return {"modules": ipred_client_mod.list_modules()}
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/compositions")
+async def ipred_list_compositions() -> dict:
+    """List feature compositions."""
+    try:
+        return {"compositions": ipred_client_mod.list_compositions()}
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/compositions/{composition_id}")
+async def ipred_get_composition(composition_id: str) -> dict:
+    """Get one composition."""
+    try:
+        return ipred_client_mod.get_composition(composition_id)
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/compositions")
+async def ipred_upsert_composition(body: IpredCompositionUpsertRequest) -> dict:
+    """Create or update a composition."""
+    try:
+        return ipred_client_mod.upsert_composition(body.model_dump(exclude_none=True))
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/compositions/preview")
+async def ipred_preview_composition(body: IpredCompositionUpsertRequest) -> dict:
+    """Preview concat labels for a composition draft."""
+    try:
+        return ipred_client_mod.preview_composition(body.model_dump(exclude_none=True))
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/sessions/{session_id}/arrays")
+async def ipred_upload_array(session_id: str, body: IpredArrayUploadRequest) -> dict:
+    """Upload an array blob to ipred for remote preprocess."""
+    try:
+        payload = body.model_dump(exclude_none=True)
+        payload["session_id"] = session_id
+        return ipred_client_mod.upload_session_array(session_id, payload)
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/manifold/sample")
+async def ipred_manifold_sample(body: IpredManifoldSampleRequest) -> dict:
+    """Suggest Labels on an ipred feature bank."""
+    try:
+        return ipred_client_mod.manifold_sample(body.model_dump(exclude_none=True))
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/manifold/{sample_id}/heatmap.png")
+async def ipred_manifold_heatmap(sample_id: str) -> Response:
+    """Proxy manifold residual heatmap PNG."""
+    try:
+        return Response(
+            content=ipred_client_mod.manifold_heatmap_png(sample_id),
+            media_type="image/png",
+            headers={"Cache-Control": "private, max-age=300"},
+        )
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/preprocess")
+async def ipred_preprocess(body: IpredPreprocessRequest) -> dict:
+    """Cache-aware featurize via ipred."""
+    try:
+        return ipred_client_mod.preprocess(
+            session_id=body.session_id,
+            feature_setup_id=body.feature_setup_id,
+            composition_id=body.composition_id,
+            slice_index=body.slice_index,
+            array_ref=body.array_ref,
+        )
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/features/{feature_id}/channels/{index}")
+async def ipred_feature_channel(feature_id: str, index: int) -> Response:
+    """Proxy a feature-channel PNG from the ipred."""
+    try:
+        data = ipred_client_mod.feature_channel_bytes(feature_id, index)
+        return Response(content=data, media_type="image/png")
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/train")
+async def ipred_train(body: IpredTrainRequest) -> dict:
+    """Train via ipred trainer plugin."""
+    try:
+        return ipred_client_mod.train(
+            session_id=body.session_id,
+            shapes=body.shapes,
+            feature_id=body.feature_id,
+            trainer_id=body.trainer_id,
+            config=body.config,
+        )
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/infer")
+async def ipred_infer(body: IpredInferRequest) -> dict:
+    """Infer + conformal products via ipred."""
+    try:
+        return ipred_client_mod.infer(
+            session_id=body.session_id,
+            model_id=body.model_id,
+            feature_id=body.feature_id,
+            alpha=body.alpha,
+        )
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/rethreshold")
+async def ipred_rethreshold(body: IpredRethresholdRequest) -> dict:
+    """Rethreshold from cached proba via ipred."""
+    try:
+        return ipred_client_mod.rethreshold(
+            session_id=body.session_id,
+            alpha=body.alpha,
+            run_id=body.run_id,
+        )
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/runs/{run_id}/commit.png")
+async def ipred_run_commit(run_id: str) -> Response:
+    """Proxy commit map PNG."""
+    try:
+        return Response(
+            content=ipred_client_mod.run_commit_png(run_id),
+            media_type="image/png",
+        )
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/runs/{run_id}/status.png")
+async def ipred_run_status(run_id: str) -> Response:
+    """Proxy status map PNG."""
+    try:
+        return Response(
+            content=ipred_client_mod.run_status_png(run_id),
+            media_type="image/png",
+        )
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.get("/runs/{run_id}/proba/{class_index}.png")
+async def ipred_run_proba(run_id: str, class_index: int) -> Response:
+    """Proxy softmax class heatmap PNG from ipred."""
+    try:
+        return Response(
+            content=ipred_client_mod.run_proba_png(run_id, class_index),
+            media_type="image/png",
+        )
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
+
+
+@router.post("/runs/{run_id}/threshold-class")
+async def ipred_threshold_class(run_id: str, body: IpredThresholdClassRequest) -> dict:
+    """Threshold one softmax class into a dense label map via ipred."""
+    try:
+        return ipred_client_mod.threshold_class_map(
+            run_id,
+            class_id=body.class_id,
+            threshold=body.threshold,
+        )
+    except Exception as exc:
+        raise _ipred_http_error(exc) from exc
diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 9f9dfb8..5d5ce8a 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -29,6 +29,10 @@ dependencies = [
   "scipy>=1.11",
   "tifffile>=2024.0",
   "imagecodecs",
+  # ipred_client.py talks to the standalone iPred service over plain HTTP —
+  # pulled in transitively today via tiled[client], but the direct import
+  # (ipred_client.py, ipred_routes.py) earns a direct pin.
+  "httpx>=0.27",
 ]
 
 [project.optional-dependencies]
diff --git a/backend/tests/test_ipred_routes.py b/backend/tests/test_ipred_routes.py
new file mode 100644
index 0000000..07ec10c
--- /dev/null
+++ b/backend/tests/test_ipred_routes.py
@@ -0,0 +1,99 @@
+"""Tests for the /api/ipred/* proxy — mocks ipred_client, never hits :8003."""
+from __future__ import annotations
+
+import httpx
+import pytest
+from httpx import ASGITransport, AsyncClient
+
+import ipred_client as ipred_client_mod
+from annotation_server import app
+
+
+@pytest.mark.asyncio
+async def test_health_proxies_ok(monkeypatch: pytest.MonkeyPatch) -> None:
+    """A healthy ipred returns its body verbatim."""
+    monkeypatch.setattr(ipred_client_mod, "health", lambda: {"status": "ok"})
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.get("/api/ipred/health")
+    assert response.status_code == 200
+    assert response.json() == {"status": "ok"}
+
+
+@pytest.mark.asyncio
+async def test_health_reports_503_when_ipred_unreachable(monkeypatch: pytest.MonkeyPatch) -> None:
+    """A down/missing ipred surfaces as 503, not a 500 or crash."""
+
+    def _raise() -> dict:
+        raise httpx.ConnectError("boom")
+
+    monkeypatch.setattr(ipred_client_mod, "health", _raise)
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.get("/api/ipred/health")
+    assert response.status_code == 503
+    assert "unreachable" in response.json()["detail"]
+
+
+@pytest.mark.asyncio
+async def test_upstream_http_error_status_is_preserved(monkeypatch: pytest.MonkeyPatch) -> None:
+    """A 4xx from ipred itself is forwarded with the same status + detail."""
+
+    def _raise() -> dict:
+        req = httpx.Request("GET", "http://127.0.0.1:8003/modules")
+        resp = httpx.Response(422, json={"detail": "bad params"}, request=req)
+        raise httpx.HTTPStatusError("bad", request=req, response=resp)
+
+    monkeypatch.setattr(ipred_client_mod, "list_modules", _raise)
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.get("/api/ipred/modules")
+    assert response.status_code == 422
+    assert response.json()["detail"] == {"detail": "bad params"}
+
+
+@pytest.mark.asyncio
+async def test_open_session_forwards_body(monkeypatch: pytest.MonkeyPatch) -> None:
+    """POST /sessions passes kind/source/server_uri/root through to the client."""
+    captured: dict = {}
+
+    def _open_session(**kwargs: object) -> dict:
+        captured.update(kwargs)
+        return {"session_id": "s1", "project_id": "p1"}
+
+    monkeypatch.setattr(ipred_client_mod, "open_session", _open_session)
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.post(
+            "/api/ipred/sessions",
+            json={"kind": "local", "source": "foo.tiff"},
+        )
+    assert response.status_code == 200
+    assert response.json() == {"session_id": "s1", "project_id": "p1"}
+    assert captured == {
+        "kind": "local",
+        "source": "foo.tiff",
+        "server_uri": None,
+        "root": None,
+    }
+
+
+@pytest.mark.asyncio
+async def test_list_modules_reports_ready_flags(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Module readiness (e.g. tomojepa without weights) passes through unchanged."""
+    modules = [
+        {"id": "slimsam", "ready": True, "runtime": "onnx"},
+        {"id": "tomojepa", "ready": False, "runtime": "torch"},
+    ]
+    monkeypatch.setattr(ipred_client_mod, "list_modules", lambda: modules)
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.get("/api/ipred/modules")
+    assert response.status_code == 200
+    assert response.json() == {"modules": modules}
+
+
+@pytest.mark.asyncio
+async def test_run_proba_png_proxies_bytes(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Binary PNG proxies return the raw bytes with an image content-type."""
+    monkeypatch.setattr(ipred_client_mod, "run_proba_png", lambda run_id, idx: b"\x89PNG\r\n")
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.get("/api/ipred/runs/run1/proba/0.png")
+    assert response.status_code == 200
+    assert response.content == b"\x89PNG\r\n"
+    assert response.headers["content-type"] == "image/png"
diff --git a/frontend/scripts/fetch-sam-model.mjs b/frontend/scripts/fetch-sam-model.mjs
index c9af71e..5a2c64c 100644
--- a/frontend/scripts/fetch-sam-model.mjs
+++ b/frontend/scripts/fetch-sam-model.mjs
@@ -15,6 +15,10 @@
  *   node scripts/fetch-sam-model.mjs
  *
  * start_all.sh runs this automatically (best-effort, backgrounded) on startup.
+ *
+ * iPred's `slimsam` feature module (ipred/src/ipred/feature_setups.py) also
+ * resolves its ONNX encoder from this exact directory — vendoring here covers
+ * both the Magic tool and iPred, no separate download needed.
  */
 import { spawnSync } from 'node:child_process';
 import { existsSync } from 'node:fs';
diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index db66794..8342ea4 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -25,7 +25,13 @@ import DenoiseBakeModal from '@/components/annotate/DenoiseBakeModal';
 import SliceNavigator from '@/components/annotate/SliceNavigator';
 import MaskToolsPanel from '@/components/annotate/MaskToolsPanel';
 import MeasurementPanel from '@/components/annotate/MeasurementPanel';
+import FeatureChannelsPanel from '@/components/annotate/FeatureChannelsPanel';
+import PixelClassifierPanel from '@/components/annotate/PixelClassifierPanel';
 import AnnotationCanvas from '@/components/annotate/AnnotationCanvas';
+import { useFeatureChannels } from '@/hooks/useFeatureChannels';
+import { usePixelClassifier } from '@/hooks/usePixelClassifier';
+import { useFeatureManifold } from '@/hooks/useFeatureManifold';
+import { loadLabelPng, labelMapToPolygonShapes } from '@/lib/pixelClf';
 import DebouncedSlider from '@/components/common/DebouncedSlider';
 import DownloadModal from '@/components/annotate/DownloadModal';
 import InsightsModal from '@/components/annotate/InsightsModal';
@@ -67,19 +73,11 @@ function StageSwitcher({ stage, onChange }: { stage: AnnotateStage; onChange: (s
   );
 }
 
-function StagePlaceholder({ text }: { text: string }) {
-  return (
-    <p className="rounded-md border border-dashed border-gray-300 bg-gray-50 p-2 text-[11px] leading-snug text-gray-500">
-      {text}
-    </p>
-  );
-}
-
 /** Renders the annotation workspace: tool sidebar, canvas, and save/version/export flows. */
 export default function AnnotatePage() {
   const navigate = useNavigate();
   const { source, kind, serverUri, meta, denoise, setDenoise } = useDatasetStore();
-  const { removeShapes } = useAnnotationStore();
+  const { removeShapes, addShapes, byImage } = useAnnotationStore();
   const { selectedShapeIds, setSelectedShapeId, fillOpacity, setFillOpacity } = useToolStore();
   const { classes } = useClassStore();
 
@@ -188,6 +186,68 @@ export default function AnnotatePage() {
     setFocusRegion(bbox ? { ...bbox, nonce: Date.now() } : null);
   }, [setSlice]);
 
+  const sliceShapes = sourceKey ? (byImage[sourceKey]?.[String(currentSlice)] ?? []) : [];
+
+  const features = useFeatureChannels({
+    source,
+    kind,
+    sliceIndex: currentSlice,
+    serverUri,
+  });
+
+  const clf = usePixelClassifier({
+    featureJobId: features.job?.jobId ?? null,
+    resetKey: sourceKey ? `${sourceKey}:${currentSlice}` : null,
+    source,
+    kind,
+    serverUri,
+    sliceIndex: currentSlice,
+    onFeatureJobExpired: features.invalidateJob,
+    onFeatureReady: (info) => features.adoptFeatureBank(info),
+  });
+
+  const manifold = useFeatureManifold({
+    featureJobId: features.job?.jobId ?? null,
+    onFeatureJobExpired: features.invalidateJob,
+  });
+
+  const handleClfTrain = useCallback(() => {
+    void clf.train(sliceShapes);
+  }, [clf, sliceShapes]);
+
+  const handleClfPredict = useCallback(() => {
+    void clf.predict(sliceShapes);
+  }, [clf, sliceShapes]);
+
+  const handleClfCommit = useCallback(async () => {
+    if (!sourceKey || !clf.commitUrl || !clf.model) return;
+    try {
+      const { data, width, height } = await loadLabelPng(clf.commitUrl);
+      const shapes = labelMapToPolygonShapes(data, width, height, clf.model.classIds, {
+        minRegion: 64,
+        preserveShapes: sliceShapes,
+      });
+      if (shapes.length) addShapes(sourceKey, currentSlice, shapes);
+      clf.dismiss();
+    } catch {
+      /* keep overlay; error surfaces via clf.error if needed */
+    }
+  }, [sourceKey, clf, addShapes, currentSlice, sliceShapes]);
+
+  const classLabelForId = useCallback(
+    (classId: number) => {
+      const c = classes.find((x) => x.classId === classId);
+      return c?.label?.trim() || `class ${classId}`;
+    },
+    [classes],
+  );
+
+  const predictionClassColorById = useMemo(() => {
+    const m = new Map<number, string>();
+    for (const c of classes) m.set(c.classId, c.color);
+    return m;
+  }, [classes]);
+
   // Load the previewed version's payload (cached) whenever the slider moves.
   useEffect(() => {
     if (previewVersion === null) {
@@ -298,7 +358,13 @@ export default function AnnotatePage() {
           />
           <hr />
           <StageSwitcher stage={stage} onChange={setStage} />
-          <LayersPanel />
+          <LayersPanel
+            hasFeatures={!!features.job && features.channelIndex !== null}
+            hasProba={!!clf.probaUrl}
+            hasPredictions={!!clf.commitUrl}
+            predictionClassIds={clf.model?.classIds ?? []}
+            hasManifold={manifold.hasSample || !!manifold.heatmapUrl}
+          />
           <hr />
 
           {stage === 'draw' && (
@@ -359,11 +425,72 @@ export default function AnnotatePage() {
           )}
 
           {stage === 'assist' && (
-            <StagePlaceholder text="Feature channels, the pixel classifier, and manifold suggestions land here in a later phase." />
+            <>
+              <FeatureChannelsPanel
+                job={features.job}
+                channelIndex={features.channelIndex}
+                computing={features.computing}
+                error={features.error}
+                onCompute={features.compute}
+                onSelectChannel={features.selectChannel}
+                onCycle={features.cycleChannel}
+                onOriginal={features.clearSelection}
+              />
+              <hr />
+            </>
           )}
 
           {stage === 'predict' && (
-            <StagePlaceholder text="Model training and inference panels land here in a later phase." />
+            <>
+              <PixelClassifierPanel
+                hasFeatureJob={!!features.job}
+                canAutoPreprocess={clf.canTrainWithoutJob}
+                hasShapes={sliceShapes.length > 0}
+                training={clf.training}
+                predicting={clf.predicting}
+                params={clf.params}
+                onParamsChange={clf.setParams}
+                model={clf.model}
+                hasPrediction={!!clf.commitUrl}
+                predictCounts={clf.predictCounts}
+                probaClassIndex={clf.probaClassIndex}
+                activeProbaClassId={clf.activeProbaClassId}
+                activeProbaThreshold={clf.activeProbaThreshold}
+                classLabelForId={classLabelForId}
+                onCycleProbaClass={clf.cycleProbaClass}
+                onProbaThresholdChange={clf.setProbaThreshold}
+                error={clf.error}
+                onTrain={handleClfTrain}
+                onPredict={handleClfPredict}
+                onCommit={() => { void handleClfCommit(); }}
+                onDismiss={clf.dismiss}
+                commitLabel="Commit singletons"
+                featureSetupId={features.job?.setupId ?? null}
+                trainerId={clf.trainerId}
+                manifoldParams={manifold.params}
+                onManifoldParamsChange={manifold.setParams}
+                manifoldSampling={manifold.sampling}
+                manifoldHasSample={manifold.hasSample}
+                manifoldShowHeatmap={manifold.showHeatmap}
+                onManifoldShowHeatmapChange={manifold.setShowHeatmap}
+                manifoldShowMarkers={manifold.showMarkers}
+                onManifoldShowMarkersChange={manifold.setShowMarkers}
+                manifoldHeatmapOpacity={manifold.heatmapOpacity}
+                onManifoldHeatmapOpacityChange={manifold.setHeatmapOpacity}
+                manifoldMeta={manifold.meta}
+                manifoldError={manifold.error}
+                onManifoldSample={() => { void manifold.sample(); }}
+                onManifoldDismiss={manifold.dismiss}
+                manifoldRoiShapeCount={manifold.placementMask?.length ?? 0}
+                canCaptureManifoldRoi={selectedShapeIds.length > 0}
+                onCaptureManifoldRoi={() => {
+                  const selected = sliceShapes.filter((s) => selectedShapeIds.includes(s.id));
+                  manifold.setPlacementMaskFromShapes(selected);
+                }}
+                onClearManifoldRoi={manifold.clearPlacementMask}
+              />
+              <hr />
+            </>
           )}
 
           {/* Save button + status */}
@@ -448,6 +575,17 @@ export default function AnnotatePage() {
             previewShapes={previewShapes}
             previewClasses={previewPayload?.classes ?? null}
             focusRegion={focusRegion}
+            featureChannelUrl={features.channelUrl}
+            probaOverlayUrl={clf.probaUrl}
+            clfCommitUrl={clf.commitUrl}
+            clfStatusUrl={clf.statusUrl}
+            predictionClassColorById={predictionClassColorById}
+            manifoldHeatmapUrl={manifold.heatmapUrl}
+            manifoldHeatmapOpacity={manifold.heatmapOpacity}
+            manifoldShowHeatmap={manifold.showHeatmap}
+            manifoldMarkers={manifold.points}
+            manifoldShowMarkers={manifold.showMarkers}
+            manifoldBoxSize={manifold.meta?.boxSize ?? 64}
           />
           {previewVersion !== null && (
             <VersionPreviewBar
diff --git a/frontend/src/components/CompositionPanel/CompositionPanel.test.tsx b/frontend/src/components/CompositionPanel/CompositionPanel.test.tsx
new file mode 100644
index 0000000..6b7419c
--- /dev/null
+++ b/frontend/src/components/CompositionPanel/CompositionPanel.test.tsx
@@ -0,0 +1,100 @@
+/**
+ * CompositionPanel — smoke tests for concat preview wiring.
+ */
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import CompositionPanel from './index';
+import { useIpredStore } from '@/stores/ipredStore';
+
+vi.mock('@/lib/ipredApi', () => ({
+  listIpredModules: vi.fn(async () => [
+    {
+      id: 'skimage_multiscale',
+      name: 'Skimage multiscale',
+      description: 'skimage',
+      runtime: 'numpy',
+      ready: true,
+      accepts_input_from: false,
+      produces_channels: true,
+      produces_embedding: false,
+      params_schema: { sigma_min: { default: 1 } },
+    },
+    {
+      id: 'tomojepa',
+      name: 'TomoJEPA',
+      description: 'mark',
+      runtime: 'torch',
+      ready: true,
+      accepts_input_from: true,
+      produces_channels: false,
+      produces_embedding: true,
+      params_schema: { weights_id: { default: 'mark25' }, input_size: { default: 512 } },
+    },
+    {
+      id: 'pca',
+      name: 'PCA reduce',
+      description: 'pca',
+      runtime: 'numpy',
+      ready: true,
+      accepts_input_from: true,
+      produces_channels: true,
+      produces_embedding: false,
+      params_schema: { dims: { default: 64 } },
+    },
+  ]),
+  listIpredCompositions: vi.fn(async () => [
+    {
+      id: 'comp-skimage',
+      name: 'Skimage multiscale',
+      builtin: true,
+      nodes: [{ id: 'n1', module: 'skimage_multiscale', params: {} }],
+      outputs: ['n1'],
+    },
+  ]),
+  getIpredComposition: vi.fn(async (id: string) => ({
+    id,
+    name: 'Skimage multiscale',
+    builtin: true,
+    nodes: [{ id: 'n1', module: 'skimage_multiscale', params: { sigma_min: 1 } }],
+    outputs: ['n1'],
+    preview_labels: ['intensity σ=1'],
+  })),
+  previewIpredComposition: vi.fn(async () => ({
+    preview_labels: ['intensity σ=1', 'pca0'],
+  })),
+  upsertIpredComposition: vi.fn(async (p) => ({
+    id: 'comp-custom',
+    name: p.name,
+    nodes: p.nodes,
+    outputs: p.outputs,
+  })),
+}));
+
+describe('CompositionPanel', () => {
+  beforeEach(() => {
+    useIpredStore.setState({ preferredCompositionId: 'comp-skimage' });
+  });
+
+  afterEach(() => {
+    cleanup();
+    vi.clearAllMocks();
+  });
+
+  it('shows concat preview from loaded composition', async () => {
+    render(<CompositionPanel />);
+    await waitFor(() => {
+      expect(screen.getByTestId('ipred-concat-preview')).toHaveTextContent('intensity');
+    });
+  });
+
+  it('adds a module from the catalog', async () => {
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    await screen.findByTestId('ipred-module-tomojepa');
+    await user.click(screen.getByTestId('ipred-module-tomojepa'));
+    await waitFor(() => {
+      expect(screen.getByTestId('ipred-node-n2')).toBeTruthy();
+    });
+  });
+});
diff --git a/frontend/src/components/CompositionPanel/index.tsx b/frontend/src/components/CompositionPanel/index.tsx
new file mode 100644
index 0000000..28c76e1
--- /dev/null
+++ b/frontend/src/components/CompositionPanel/index.tsx
@@ -0,0 +1,505 @@
+/**
+ * CompositionPanel — modular feature composition window for ipred.
+ */
+import { useCallback, useEffect, useMemo, useState } from 'react';
+import {
+  ArrowsClockwise,
+  CircleNotch,
+  FloppyDisk,
+  Plus,
+  Trash,
+} from '@phosphor-icons/react';
+import { useIpredStore } from '@/stores/ipredStore';
+import {
+  getIpredComposition,
+  listIpredCompositions,
+  listIpredModules,
+  previewIpredComposition,
+  upsertIpredComposition,
+  type CompositionDoc,
+  type CompositionNode,
+  type FeatureModuleInfo,
+} from '@/lib/ipredApi';
+import { cn } from '@/lib/utils';
+
+const btn =
+  'px-2 py-1 rounded border text-[11px] transition-colors disabled:opacity-40 ' +
+  'bg-white text-gray-700 border-gray-200 hover:bg-sky-50 hover:border-sky-300';
+
+const section =
+  'rounded-md border border-gray-200 bg-white p-4 flex flex-col gap-3';
+
+function defaultParams(mod: FeatureModuleInfo): Record<string, unknown> {
+  const out: Record<string, unknown> = {};
+  for (const [key, schema] of Object.entries(mod.params_schema ?? {})) {
+    const s = schema as { default?: unknown };
+    if (s && 'default' in s) out[key] = s.default;
+  }
+  return out;
+}
+
+function newNodeId(nodes: CompositionNode[]): string {
+  let i = nodes.length + 1;
+  const ids = new Set(nodes.map((n) => n.id));
+  while (ids.has(`n${i}`)) i += 1;
+  return `n${i}`;
+}
+
+/** Build / edit ordered module composition and show concat preview. */
+export default function CompositionPanel() {
+  const preferredCompositionId = useIpredStore((s) => s.preferredCompositionId);
+  const setPreferredCompositionId = useIpredStore((s) => s.setPreferredCompositionId);
+
+  const [modules, setModules] = useState<FeatureModuleInfo[]>([]);
+  const [compositions, setCompositions] = useState<CompositionDoc[]>([]);
+  const [name, setName] = useState('Custom composition');
+  const [nodes, setNodes] = useState<CompositionNode[]>([]);
+  const [outputs, setOutputs] = useState<string[]>([]);
+  const [previewLabels, setPreviewLabels] = useState<string[]>([]);
+  const [editingId, setEditingId] = useState<string | null>(null);
+  const [busy, setBusy] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+
+  const moduleById = useMemo(() => {
+    const m = new Map<string, FeatureModuleInfo>();
+    for (const x of modules) m.set(x.id, x);
+    return m;
+  }, [modules]);
+
+  const refresh = useCallback(async () => {
+    setError(null);
+    try {
+      const [mods, comps] = await Promise.all([
+        listIpredModules(),
+        listIpredCompositions(),
+      ]);
+      setModules(mods);
+      setCompositions(comps.sort((a, b) => a.name.localeCompare(b.name)));
+      const preferred =
+        preferredCompositionId &&
+        comps.find((c) => c.id === preferredCompositionId);
+      const pick = preferred ?? comps.find((c) => c.id === 'comp-skimage-slimsam') ?? comps[0];
+      if (pick) {
+        const detail = await getIpredComposition(pick.id);
+        setEditingId(detail.id);
+        setName(detail.name);
+        setNodes(detail.nodes ?? []);
+        setOutputs(detail.outputs ?? []);
+        setPreviewLabels(detail.preview_labels ?? []);
+        if (pick.id !== preferredCompositionId) {
+          setPreferredCompositionId(pick.id);
+        }
+      }
+    } catch (e) {
+      setError(e instanceof Error ? e.message : String(e));
+    }
+  }, [preferredCompositionId, setPreferredCompositionId]);
+
+  useEffect(() => {
+    void refresh();
+    // Intentionally once on mount + when preference externally cleared
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
+  const selectComposition = async (id: string) => {
+    setError(null);
+    setPreferredCompositionId(id);
+    try {
+      const detail = await getIpredComposition(id);
+      setEditingId(detail.id);
+      setName(detail.name);
+      setNodes(detail.nodes ?? []);
+      setOutputs(detail.outputs ?? []);
+      setPreviewLabels(detail.preview_labels ?? []);
+    } catch (e) {
+      setError(e instanceof Error ? e.message : String(e));
+    }
+  };
+
+  const refreshPreview = async (
+    nextNodes: CompositionNode[],
+    nextOutputs: string[],
+  ) => {
+    try {
+      const r = await previewIpredComposition({
+        name,
+        nodes: nextNodes,
+        outputs: nextOutputs,
+      });
+      setPreviewLabels(r.preview_labels ?? []);
+    } catch {
+      setPreviewLabels([]);
+    }
+  };
+
+  const addModule = (moduleId: string) => {
+    const mod = moduleById.get(moduleId);
+    if (!mod) return;
+    const id = newNodeId(nodes);
+    const node: CompositionNode = {
+      id,
+      module: moduleId,
+      params: defaultParams(mod),
+    };
+    if (mod.accepts_input_from && nodes.length > 0) {
+      // Prefer last node that can feed an image / emb
+      node.input_from = nodes[nodes.length - 1].id;
+    }
+    const nextNodes = [...nodes, node];
+    const nextOutputs =
+      mod.produces_channels && !outputs.includes(id)
+        ? [...outputs, id]
+        : outputs;
+    setNodes(nextNodes);
+    setOutputs(nextOutputs);
+    void refreshPreview(nextNodes, nextOutputs);
+  };
+
+  const removeNode = (nid: string) => {
+    const nextNodes = nodes
+      .filter((n) => n.id !== nid)
+      .map((n) =>
+        n.input_from === nid ? { ...n, input_from: undefined } : n,
+      );
+    const nextOutputs = outputs.filter((o) => o !== nid);
+    setNodes(nextNodes);
+    setOutputs(nextOutputs);
+    void refreshPreview(nextNodes, nextOutputs);
+  };
+
+  const updateNodeParams = (nid: string, patch: Record<string, unknown>) => {
+    const nextNodes = nodes.map((n) =>
+      n.id === nid ? { ...n, params: { ...(n.params ?? {}), ...patch } } : n,
+    );
+    setNodes(nextNodes);
+    void refreshPreview(nextNodes, outputs);
+  };
+
+  const setInputFrom = (nid: string, inputFrom: string) => {
+    const nextNodes = nodes.map((n) =>
+      n.id === nid
+        ? { ...n, input_from: inputFrom || undefined }
+        : n,
+    );
+    setNodes(nextNodes);
+    void refreshPreview(nextNodes, outputs);
+  };
+
+  const toggleOutput = (nid: string) => {
+    const next = outputs.includes(nid)
+      ? outputs.filter((o) => o !== nid)
+      : [...outputs, nid];
+    setOutputs(next);
+    void refreshPreview(nodes, next);
+  };
+
+  const moveOutput = (nid: string, dir: -1 | 1) => {
+    const i = outputs.indexOf(nid);
+    if (i < 0) return;
+    const j = i + dir;
+    if (j < 0 || j >= outputs.length) return;
+    const next = [...outputs];
+    [next[i], next[j]] = [next[j], next[i]];
+    setOutputs(next);
+    void refreshPreview(nodes, next);
+  };
+
+  const save = async (asClone: boolean) => {
+    setBusy(true);
+    setError(null);
+    try {
+      const saved = await upsertIpredComposition({
+        name: asClone ? `${name} copy` : name,
+        nodes,
+        outputs,
+        composition_id: asClone || !editingId ? undefined : editingId,
+      });
+      setPreferredCompositionId(saved.id);
+      setEditingId(saved.id);
+      setName(saved.name);
+      await refresh();
+      await selectComposition(saved.id);
+    } catch (e) {
+      setError(e instanceof Error ? e.message : String(e));
+    } finally {
+      setBusy(false);
+    }
+  };
+
+  const chainSummary = nodes
+    .map((n) => {
+      const label = moduleById.get(n.module)?.name ?? n.module;
+      const arrow = n.input_from ? `←${n.input_from}` : '';
+      return `${n.id}:${label}${arrow}`;
+    })
+    .join(' · ');
+
+  return (
+    <section className={section} data-testid="ipred-composition">
+      <div className="flex items-center justify-between gap-2">
+        <h2 className="text-sm font-semibold text-gray-800">Composition</h2>
+        <button type="button" className={btn} onClick={() => void refresh()}>
+          <ArrowsClockwise size={12} className="inline" /> Reload
+        </button>
+      </div>
+
+      <p className="text-[11px] text-gray-500 leading-snug">
+        String modules together. <strong>Outputs</strong> order is how channels
+        concatenate into the feature bank.
+      </p>
+
+      <label className="flex flex-col gap-1 text-xs text-gray-600">
+        Active composition
+        <select
+          className="rounded border border-gray-200 px-2 py-1.5 text-sm text-gray-800"
+          value={preferredCompositionId ?? ''}
+          onChange={(e) => void selectComposition(e.target.value)}
+          data-testid="ipred-composition-select"
+        >
+          {compositions.map((c) => (
+            <option key={c.id} value={c.id}>
+              {c.name}
+              {c.builtin ? ' (builtin)' : ''}
+            </option>
+          ))}
+        </select>
+      </label>
+
+      <div className="grid grid-cols-1 md:grid-cols-[minmax(0,11rem)_1fr] gap-3">
+        <div className="flex flex-col gap-1.5">
+          <span className="text-[10px] font-semibold uppercase text-gray-500 tracking-wide">
+            Modules
+          </span>
+          {modules.map((m) => (
+            <button
+              key={m.id}
+              type="button"
+              disabled={!m.ready || busy}
+              title={m.description}
+              onClick={() => addModule(m.id)}
+              className={cn(
+                'flex items-center justify-between gap-1 rounded border px-2 py-1.5 text-left text-[11px]',
+                m.ready
+                  ? 'bg-white border-gray-200 hover:border-sky-300 hover:bg-sky-50 text-gray-800'
+                  : 'bg-gray-50 border-gray-100 text-gray-400 cursor-not-allowed',
+              )}
+              data-testid={`ipred-module-${m.id}`}
+            >
+              <span>
+                <Plus size={11} className="inline mr-1" />
+                {m.name}
+              </span>
+              <span className="text-[9px] text-gray-400">{m.runtime}</span>
+            </button>
+          ))}
+        </div>
+
+        <div className="flex flex-col gap-2 min-w-0">
+          <label className="flex flex-col gap-0.5 text-xs text-gray-600">
+            Name
+            <input
+              value={name}
+              onChange={(e) => setName(e.target.value)}
+              className="rounded border border-gray-200 px-1.5 py-1 text-sm text-gray-800"
+            />
+          </label>
+
+          <p className="text-[10px] text-gray-500 truncate" title={chainSummary}>
+            Graph: {chainSummary || 'empty'}
+          </p>
+
+          <div className="flex flex-col gap-2 max-h-72 overflow-y-auto">
+            {nodes.map((n) => {
+              const mod = moduleById.get(n.module);
+              return (
+                <div
+                  key={n.id}
+                  className="rounded border border-gray-100 bg-gray-50 p-2 flex flex-col gap-1.5"
+                  data-testid={`ipred-node-${n.id}`}
+                >
+                  <div className="flex items-center justify-between gap-1">
+                    <span className="text-[11px] font-medium text-gray-800">
+                      {n.id} · {mod?.name ?? n.module}
+                    </span>
+                    <button
+                      type="button"
+                      className={btn}
+                      onClick={() => removeNode(n.id)}
+                      title="Remove"
+                    >
+                      <Trash size={11} />
+                    </button>
+                  </div>
+                  {mod?.accepts_input_from && (
+                    <label className="flex flex-col gap-0.5 text-[10px] text-gray-600">
+                      Input from
+                      <select
+                        className="rounded border border-gray-200 px-1 py-0.5 text-[11px]"
+                        value={n.input_from ?? ''}
+                        onChange={(e) => setInputFrom(n.id, e.target.value)}
+                      >
+                        <option value="">raw / gray</option>
+                        {nodes
+                          .filter((o) => o.id !== n.id)
+                          .map((o) => (
+                            <option key={o.id} value={o.id}>
+                              {o.id} ({o.module})
+                            </option>
+                          ))}
+                      </select>
+                    </label>
+                  )}
+                  {n.module === 'tomojepa' && (
+                    <label className="flex flex-col gap-0.5 text-[10px] text-gray-600">
+                      Weights
+                      <select
+                        className="rounded border border-gray-200 px-1 py-0.5 text-[11px]"
+                        value={String(n.params?.weights_id ?? 'mark25')}
+                        onChange={(e) =>
+                          updateNodeParams(n.id, { weights_id: e.target.value })
+                        }
+                      >
+                        <option value="mark25">Mark25</option>
+                        <option value="mark11">Mark11</option>
+                      </select>
+                    </label>
+                  )}
+                  {(n.module === 'tomojepa' || n.module === 'pca' || n.module === 'clahe') && (
+                    <div className="grid grid-cols-2 gap-1 text-[10px] text-gray-600">
+                      {n.module === 'tomojepa' && (
+                        <>
+                          <label className="flex flex-col gap-0.5">
+                            Input size
+                            <input
+                              type="number"
+                              className="rounded border border-gray-200 px-1 py-0.5"
+                              value={Number(n.params?.input_size ?? 512)}
+                              onChange={(e) =>
+                                updateNodeParams(n.id, {
+                                  input_size: Number(e.target.value) || 512,
+                                })
+                              }
+                            />
+                          </label>
+                          <label className="inline-flex items-center gap-1 mt-4">
+                            <input
+                              type="checkbox"
+                              checked={Boolean(n.params?.resize ?? true)}
+                              onChange={(e) =>
+                                updateNodeParams(n.id, { resize: e.target.checked })
+                              }
+                            />
+                            Resize
+                          </label>
+                        </>
+                      )}
+                      {n.module === 'pca' && (
+                        <label className="flex flex-col gap-0.5">
+                          PCA dims
+                          <input
+                            type="number"
+                            className="rounded border border-gray-200 px-1 py-0.5"
+                            value={Number(n.params?.dims ?? 64)}
+                            onChange={(e) =>
+                              updateNodeParams(n.id, {
+                                dims: Number(e.target.value) || 64,
+                              })
+                            }
+                          />
+                        </label>
+                      )}
+                      {n.module === 'clahe' && (
+                        <label className="flex flex-col gap-0.5">
+                          Clip limit
+                          <input
+                            type="number"
+                            step={0.01}
+                            className="rounded border border-gray-200 px-1 py-0.5"
+                            value={Number(n.params?.clip_limit ?? 0.01)}
+                            onChange={(e) =>
+                              updateNodeParams(n.id, {
+                                clip_limit: Number(e.target.value) || 0.01,
+                              })
+                            }
+                          />
+                        </label>
+                      )}
+                    </div>
+                  )}
+                  <div className="flex items-center gap-2 text-[10px]">
+                    <label className="inline-flex items-center gap-1 text-gray-700">
+                      <input
+                        type="checkbox"
+                        checked={outputs.includes(n.id)}
+                        onChange={() => toggleOutput(n.id)}
+                        disabled={!mod?.produces_channels}
+                      />
+                      In bank concat
+                    </label>
+                    {outputs.includes(n.id) && (
+                      <span className="flex gap-1">
+                        <button type="button" className={btn} onClick={() => moveOutput(n.id, -1)}>
+                          ↑
+                        </button>
+                        <button type="button" className={btn} onClick={() => moveOutput(n.id, 1)}>
+                          ↓
+                        </button>
+                      </span>
+                    )}
+                  </div>
+                </div>
+              );
+            })}
+          </div>
+        </div>
+      </div>
+
+      <div
+        className="rounded border border-sky-100 bg-sky-50/70 p-2"
+        data-testid="ipred-concat-preview"
+      >
+        <span className="text-[10px] font-semibold uppercase text-gray-500 tracking-wide">
+          Bank will concatenate
+        </span>
+        <p className="text-[11px] text-gray-800 mt-1 break-words">
+          {previewLabels.length
+            ? previewLabels.join(' · ')
+            : '— add channel-producing nodes and mark them as outputs —'}
+        </p>
+        <p className="text-[10px] text-gray-500 mt-1">
+          Outputs order: {outputs.join(' → ') || 'none'}
+        </p>
+      </div>
+
+      <div className="flex flex-wrap gap-2">
+        <button
+          type="button"
+          className={btn}
+          disabled={busy || nodes.length === 0}
+          onClick={() => void save(false)}
+        >
+          {busy ? (
+            <CircleNotch size={12} className="inline animate-spin" />
+          ) : (
+            <FloppyDisk size={12} className="inline" />
+          )}{' '}
+          Save
+        </button>
+        <button
+          type="button"
+          className={btn}
+          disabled={busy || nodes.length === 0}
+          onClick={() => void save(true)}
+        >
+          Clone & save
+        </button>
+      </div>
+
+      {error && (
+        <p className="text-xs text-red-600" data-testid="ipred-composition-error">
+          {error}
+        </p>
+      )}
+    </section>
+  );
+}
diff --git a/frontend/src/components/annotate/AnnotationCanvas/OverlaysLayer.tsx b/frontend/src/components/annotate/AnnotationCanvas/OverlaysLayer.tsx
new file mode 100644
index 0000000..f012cef
--- /dev/null
+++ b/frontend/src/components/annotate/AnnotationCanvas/OverlaysLayer.tsx
@@ -0,0 +1,216 @@
+/**
+ * OverlaysLayer — iPred proba / conformal-prediction / manifold-suggest overlays.
+ *
+ * Extracted from AnnotationCanvas so its PNG-decode work (fetch → ImageData →
+ * recolor) stays isolated from the main canvas's per-frame render path. Each
+ * overlay is gated by the matching `layerVisibilityStore` group, already wired
+ * up in LayersPanel — this component only needs to render when told to.
+ */
+import { useEffect, useRef, useState } from 'react';
+import { Layer, Image as KonvaImage, Rect } from 'react-konva';
+import { colorizeConformalOverlay, loadLabelPng } from '@/lib/pixelClf';
+import { colorizeManifoldHeatmap } from '@/lib/featureManifold';
+import type { ManifoldPoint } from '@/lib/featureManifold';
+import { manifoldMarkerRect } from '@/lib/featureManifold';
+
+/** Loads a blob: URL into an <img> element; null while loading/absent. */
+function useImageFromUrl(url: string | null): HTMLImageElement | null {
+  const [img, setImg] = useState<HTMLImageElement | null>(null);
+  useEffect(() => {
+    if (!url) {
+      setImg(null);
+      return;
+    }
+    let cancelled = false;
+    const el = new Image();
+    el.onload = () => {
+      if (!cancelled) setImg(el);
+    };
+    el.src = url;
+    return () => {
+      cancelled = true;
+    };
+  }, [url]);
+  return img;
+}
+
+export interface OverlaysLayerProps {
+  width: number;
+  height: number;
+  imageClip?: { clipX?: number; clipY?: number; clipWidth?: number; clipHeight?: number };
+
+  showFeatures: boolean;
+  featureChannelUrl: string | null;
+
+  showProba: boolean;
+  probaOverlayUrl: string | null;
+  probaOpacity: number;
+
+  showPredictions: boolean;
+  clfCommitUrl: string | null;
+  clfStatusUrl: string | null;
+  predictionsOpacity: number;
+  classColorById: Map<number, string>;
+  predictionClassVisible: Record<number, boolean>;
+  showPredictionMulti: boolean;
+  showPredictionAbstain: boolean;
+
+  showManifold: boolean;
+  manifoldHeatmapUrl: string | null;
+  manifoldHeatmapOpacity: number;
+  manifoldShowHeatmap: boolean;
+  manifoldMarkers: ManifoldPoint[];
+  manifoldShowMarkers: boolean;
+  manifoldBoxSize: number;
+}
+
+/** Fetches + decodes the conformal commit/status PNGs into a colorized canvas. */
+function useConformalOverlayCanvas(
+  commitUrl: string | null,
+  statusUrl: string | null,
+  colorById: Map<number, string>,
+  classVisible: Record<number, boolean>,
+  showMulti: boolean,
+  showAbstain: boolean,
+): HTMLCanvasElement | null {
+  const [canvas, setCanvas] = useState<HTMLCanvasElement | null>(null);
+  useEffect(() => {
+    if (!commitUrl || !statusUrl) {
+      setCanvas(null);
+      return;
+    }
+    let cancelled = false;
+    (async () => {
+      try {
+        const [commit, status] = await Promise.all([loadLabelPng(commitUrl), loadLabelPng(statusUrl)]);
+        if (cancelled) return;
+        const out = colorizeConformalOverlay(commit.data, status.data, commit.width, commit.height, colorById, {
+          classVisible: (cid) => classVisible[cid] !== false,
+          showMulti,
+          showAbstain,
+        });
+        if (!cancelled) setCanvas(out);
+      } catch {
+        if (!cancelled) setCanvas(null);
+      }
+    })();
+    return () => {
+      cancelled = true;
+    };
+  }, [commitUrl, statusUrl, colorById, classVisible, showMulti, showAbstain]);
+  return canvas;
+}
+
+/** Fetches + decodes the manifold coverage PNG into a colorized canvas. */
+function useManifoldHeatmapCanvas(url: string | null, opacity: number): HTMLCanvasElement | null {
+  const [canvas, setCanvas] = useState<HTMLCanvasElement | null>(null);
+  useEffect(() => {
+    if (!url) {
+      setCanvas(null);
+      return;
+    }
+    let cancelled = false;
+    (async () => {
+      try {
+        const { data, width, height } = await loadLabelPng(url);
+        if (cancelled) return;
+        setCanvas(colorizeManifoldHeatmap(data, width, height, opacity));
+      } catch {
+        if (!cancelled) setCanvas(null);
+      }
+    })();
+    return () => {
+      cancelled = true;
+    };
+  }, [url, opacity]);
+  return canvas;
+}
+
+export default function OverlaysLayer({
+  width,
+  height,
+  imageClip,
+  showFeatures,
+  featureChannelUrl,
+  showProba,
+  probaOverlayUrl,
+  probaOpacity,
+  showPredictions,
+  clfCommitUrl,
+  clfStatusUrl,
+  predictionsOpacity,
+  classColorById,
+  predictionClassVisible,
+  showPredictionMulti,
+  showPredictionAbstain,
+  showManifold,
+  manifoldHeatmapUrl,
+  manifoldHeatmapOpacity,
+  manifoldShowHeatmap,
+  manifoldMarkers,
+  manifoldShowMarkers,
+  manifoldBoxSize,
+}: OverlaysLayerProps) {
+  const featureImg = useImageFromUrl(showFeatures ? featureChannelUrl : null);
+  const probaImg = useImageFromUrl(showProba ? probaOverlayUrl : null);
+  const conformalCanvas = useConformalOverlayCanvas(
+    showPredictions ? clfCommitUrl : null,
+    showPredictions ? clfStatusUrl : null,
+    classColorById,
+    predictionClassVisible,
+    showPredictionMulti,
+    showPredictionAbstain,
+  );
+  const manifoldCanvas = useManifoldHeatmapCanvas(
+    showManifold && manifoldShowHeatmap ? manifoldHeatmapUrl : null,
+    manifoldHeatmapOpacity,
+  );
+  const featureRef = useRef(null);
+  const probaRef = useRef(null);
+  const predRef = useRef(null);
+  const manifoldRef = useRef(null);
+
+  return (
+    <>
+      {showFeatures && featureImg && (
+        <Layer listening={false} {...imageClip}>
+          <KonvaImage ref={featureRef} image={featureImg} width={width} height={height} listening={false} />
+        </Layer>
+      )}
+      {showProba && probaImg && (
+        <Layer listening={false} opacity={probaOpacity} {...imageClip}>
+          <KonvaImage ref={probaRef} image={probaImg} width={width} height={height} listening={false} />
+        </Layer>
+      )}
+      {showPredictions && conformalCanvas && (
+        <Layer listening={false} opacity={predictionsOpacity} {...imageClip}>
+          <KonvaImage ref={predRef} image={conformalCanvas} width={width} height={height} listening={false} />
+        </Layer>
+      )}
+      {showManifold && (
+        <Layer listening={false} {...imageClip}>
+          {manifoldShowHeatmap && manifoldCanvas && (
+            <KonvaImage ref={manifoldRef} image={manifoldCanvas} width={width} height={height} listening={false} />
+          )}
+          {manifoldShowMarkers &&
+            manifoldMarkers.map((pt, i) => {
+              const r = manifoldMarkerRect(pt, { side: manifoldBoxSize, width, height });
+              return (
+                <Rect
+                  key={i}
+                  x={r.x}
+                  y={r.y}
+                  width={r.width}
+                  height={r.height}
+                  stroke="#22d3ee"
+                  strokeWidth={1.5}
+                  dash={[4, 3]}
+                  listening={false}
+                />
+              );
+            })}
+        </Layer>
+      )}
+    </>
+  );
+}
diff --git a/frontend/src/components/annotate/AnnotationCanvas/index.tsx b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
index e683ab8..733ff93 100644
--- a/frontend/src/components/annotate/AnnotationCanvas/index.tsx
+++ b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
@@ -40,6 +40,8 @@ import { mergeNewWithSameClass, expandSameClassOverlap } from '@/lib/mergeSameCl
 import { useClipboardStore } from '@/stores/clipboardStore';
 import { colormapTables, type ColormapName } from '@/lib/colormaps';
 import ShapesLayer from './ShapesLayer';
+import OverlaysLayer from './OverlaysLayer';
+import type { ManifoldPoint } from '@/lib/featureManifold';
 import { displayAffineFor, displayBandToBase, baseToDisplay, displayToBase } from '@/lib/displayTransform';
 import {
   backgroundRing, fitWithBlurSweep, combineSigma, BLUR_CANDIDATES, MAX_RING_WIDTH,
@@ -58,6 +60,8 @@ const REMOVE_KEY_LABEL = IS_MAC ? 'Option' : 'Alt';
 
 /** Shared stable empty shape list — see `storeShapes` for why identity matters. */
 const EMPTY_SHAPES: Shape[] = [];
+const EMPTY_MANIFOLD_MARKERS: ManifoldPoint[] = [];
+const EMPTY_CLASS_COLOR_BY_ID = new Map<number, string>();
 
 /** Outcome of a Sampler lasso: the fitted band plus what it took to get there. */
 export interface SamplerFit extends SweepResult {
@@ -128,6 +132,23 @@ interface AnnotationCanvasProps {
   /** Zoom to + highlight this image-coord region (e.g. from an Insights QA flag).
    *  `nonce` changes to re-trigger the same region. */
   focusRegion?: { x: number; y: number; w: number; h: number; nonce: number } | null;
+
+  // iPred overlays (additive; each gated by its layerVisibilityStore group).
+  /** Selected feature-channel PNG (grayscale), from useFeatureChannels. */
+  featureChannelUrl?: string | null;
+  /** Colorized softmax probability PNG for the active class, from usePixelClassifier. */
+  probaOverlayUrl?: string | null;
+  /** Conformal commit (argmax classId) + status (abstain/singleton/multi) PNGs. */
+  clfCommitUrl?: string | null;
+  clfStatusUrl?: string | null;
+  /** classId → hex color, for the conformal overlay and manifold markers. */
+  predictionClassColorById?: Map<number, string>;
+  manifoldHeatmapUrl?: string | null;
+  manifoldHeatmapOpacity?: number;
+  manifoldShowHeatmap?: boolean;
+  manifoldMarkers?: ManifoldPoint[];
+  manifoldShowMarkers?: boolean;
+  manifoldBoxSize?: number;
 }
 
 // ---- Point-in-shape hit testing (used by the eraser to pick a target) ----
@@ -469,6 +490,17 @@ export default function AnnotationCanvas({
   previewShapes = null,
   previewClasses = null,
   focusRegion = null,
+  featureChannelUrl = null,
+  probaOverlayUrl = null,
+  clfCommitUrl = null,
+  clfStatusUrl = null,
+  predictionClassColorById,
+  manifoldHeatmapUrl = null,
+  manifoldHeatmapOpacity = 0.45,
+  manifoldShowHeatmap = true,
+  manifoldMarkers = EMPTY_MANIFOLD_MARKERS,
+  manifoldShowMarkers = true,
+  manifoldBoxSize = 64,
 }: AnnotationCanvasProps) {
   const containerRef = useRef<HTMLDivElement>(null);
   const imageRef = useRef<Konva.Image>(null);
@@ -538,6 +570,11 @@ export default function AnnotationCanvas({
   // The Denoise layer toggle mutes the configured method without discarding it,
   // so switching it back on doesn't lose the user's method/strength choice.
   const effectiveDenoise = layerGroups.denoise ? denoise : null;
+  const probaOpacity = useLayerVisibilityStore((s) => s.probaOpacity);
+  const predictionsOpacity = useLayerVisibilityStore((s) => s.predictionsOpacity);
+  const predictionClassVisible = useLayerVisibilityStore((s) => s.predictionClassVisible);
+  const showPredictionMulti = useLayerVisibilityStore((s) => s.showPredictionMulti);
+  const showPredictionAbstain = useLayerVisibilityStore((s) => s.showPredictionAbstain);
   const sourceKey = source && kind
     ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri)
     : null;
@@ -3034,6 +3071,36 @@ export default function AnnotationCanvas({
           </Layer>
         )}
 
+        {/* iPred overlays: probability heatmap / conformal predictions / manifold
+            suggestions. Sits above the image, below annotations — see OverlaysLayer. */}
+        {meta && (
+          <OverlaysLayer
+            width={meta.width}
+            height={meta.height}
+            imageClip={imageClip}
+            showFeatures={layerGroups.features}
+            featureChannelUrl={featureChannelUrl}
+            showProba={layerGroups.proba}
+            probaOverlayUrl={probaOverlayUrl}
+            probaOpacity={probaOpacity}
+            showPredictions={layerGroups.predictions}
+            clfCommitUrl={clfCommitUrl}
+            clfStatusUrl={clfStatusUrl}
+            predictionsOpacity={predictionsOpacity}
+            classColorById={predictionClassColorById ?? EMPTY_CLASS_COLOR_BY_ID}
+            predictionClassVisible={predictionClassVisible}
+            showPredictionMulti={showPredictionMulti}
+            showPredictionAbstain={showPredictionAbstain}
+            showManifold={layerGroups.manifold}
+            manifoldHeatmapUrl={manifoldHeatmapUrl}
+            manifoldHeatmapOpacity={manifoldHeatmapOpacity}
+            manifoldShowHeatmap={manifoldShowHeatmap}
+            manifoldMarkers={manifoldMarkers}
+            manifoldShowMarkers={manifoldShowMarkers}
+            manifoldBoxSize={manifoldBoxSize}
+          />
+        )}
+
         {/* Layer 1: committed shapes — cached + opacity applied once at the
             layer so overlapping same-class shapes render a uniform class color.
             Clipped to the image frame so strokes never render past the edges.
diff --git a/frontend/src/components/annotate/FeatureChannelsPanel/index.tsx b/frontend/src/components/annotate/FeatureChannelsPanel/index.tsx
new file mode 100644
index 0000000..d2f90f5
--- /dev/null
+++ b/frontend/src/components/annotate/FeatureChannelsPanel/index.tsx
@@ -0,0 +1,264 @@
+/**
+ * FeatureChannelsPanel — the Assist-stage sidebar panel.
+ *
+ * Compositions (feature-bank recipes) are surfaced as named presets so most
+ * users never need the underlying node graph. The full graph editor
+ * (`CompositionPanel`) is still there for anyone who wants a custom recipe —
+ * it just lives behind a collapsed "Advanced" disclosure instead of its own
+ * tab, since composing a feature graph is a power-user path, not the default.
+ *
+ * Presets whose modules aren't ready (e.g. TomoJEPA checkpoints not present —
+ * see ipred/models/README.md) are greyed out rather than left to fail at
+ * Compute time; only SlimSAM ships auto-vendored today, so it's the
+ * recommended default.
+ */
+import { useEffect, useMemo, useState } from 'react';
+import { CaretLeft, CaretRight, CircleNotch, Stack, Warning } from '@phosphor-icons/react';
+import type { FeatureJobInfo } from '@/hooks/useFeatureChannels';
+import { useIpredStore } from '@/stores/ipredStore';
+import { listIpredCompositions, listIpredModules, type CompositionDoc, type FeatureModuleInfo } from '@/lib/ipredApi';
+import CompositionPanel from '@/components/CompositionPanel';
+import { cn } from '@/lib/utils';
+
+/** Plain-language labels for the 7 built-in compositions, by id. */
+const PRESET_LABELS: Record<string, string> = {
+  'comp-skimage': 'Fast (skimage only)',
+  'comp-skimage-slimsam': 'Texture-aware (+ SlimSAM)',
+  'comp-slimsam-clahe': 'Texture-aware, contrast-enhanced',
+  'comp-skimage-mark25': 'Deep features (TomoJEPA Mark25)',
+  'comp-mark25-clahe': 'Deep features, contrast-enhanced (Mark25)',
+  'comp-skimage-mark11': 'Deep features (TomoJEPA Mark11)',
+  'comp-mark11-clahe': 'Deep features, contrast-enhanced (Mark11)',
+};
+
+/** One-line explanation of what each preset trades off, shown under the picker. */
+const PRESET_HINTS: Record<string, string> = {
+  'comp-skimage': 'Multiscale edge/texture filters only. Cheapest, no model download.',
+  'comp-skimage-slimsam': 'Skimage filters + SlimSAM vision-encoder embeddings (PCA-reduced). Recommended default.',
+  'comp-slimsam-clahe': 'SlimSAM embeddings on a contrast-equalized slice — helps on low-contrast data.',
+  'comp-skimage-mark25': 'Skimage filters + TomoJEPA Mark25 embeddings. Requires a private checkpoint.',
+  'comp-mark25-clahe': 'TomoJEPA Mark25 on a contrast-equalized slice. Requires a private checkpoint.',
+  'comp-skimage-mark11': 'Skimage filters + TomoJEPA Mark11 embeddings. Requires a private checkpoint.',
+  'comp-mark11-clahe': 'TomoJEPA Mark11 on a contrast-equalized slice. Requires a private checkpoint.',
+};
+
+const RECOMMENDED_PRESET_ID = 'comp-skimage-slimsam';
+
+export interface FeatureChannelsPanelProps {
+  job: FeatureJobInfo | null;
+  channelIndex: number | null;
+  computing: boolean;
+  error: string | null;
+  onCompute: () => void;
+  onSelectChannel: (index: number | null) => void;
+  onCycle: (delta: number) => void;
+  onOriginal: () => void;
+  disabled?: boolean;
+}
+
+export default function FeatureChannelsPanel({
+  job,
+  channelIndex,
+  computing,
+  error,
+  onCompute,
+  onSelectChannel,
+  onCycle,
+  onOriginal,
+  disabled = false,
+}: FeatureChannelsPanelProps) {
+  const preferredCompositionId = useIpredStore((s) => s.preferredCompositionId);
+  const setPreferredCompositionId = useIpredStore((s) => s.setPreferredCompositionId);
+  const [presets, setPresets] = useState<CompositionDoc[]>([]);
+  const [modules, setModules] = useState<FeatureModuleInfo[]>([]);
+  const [loadingPresets, setLoadingPresets] = useState(true);
+  const [presetsError, setPresetsError] = useState<string | null>(null);
+  const [showAdvanced, setShowAdvanced] = useState(false);
+
+  useEffect(() => {
+    let cancelled = false;
+    Promise.all([listIpredCompositions(), listIpredModules()])
+      .then(([comps, mods]) => {
+        if (cancelled) return;
+        setPresets(comps);
+        setModules(mods);
+      })
+      .catch((e) => {
+        if (!cancelled) setPresetsError(e instanceof Error ? e.message : String(e));
+      })
+      .finally(() => {
+        if (!cancelled) setLoadingPresets(false);
+      });
+    return () => {
+      cancelled = true;
+    };
+  }, []);
+
+  const moduleById = useMemo(() => new Map(modules.map((m) => [m.id, m])), [modules]);
+
+  /** A preset is ready only if every module it wires in is ready (e.g. weights present). */
+  const presetReady = useMemo(() => {
+    const out = new Map<string, boolean>();
+    for (const p of presets) {
+      out.set(p.id, p.nodes.every((n) => moduleById.get(n.module)?.ready !== false));
+    }
+    return out;
+  }, [presets, moduleById]);
+
+  const selected = presets.find((p) => p.id === preferredCompositionId) ?? null;
+  const selectedReady = selected ? (presetReady.get(selected.id) ?? true) : true;
+  const missingModule = selected?.nodes.find((n) => moduleById.get(n.module)?.ready === false);
+  const missingModuleName = missingModule ? (moduleById.get(missingModule.module)?.name ?? missingModule.module) : null;
+
+  // If the preferred preset isn't ready once modules load (e.g. a stale choice from
+  // a previous session), fall back to the recommended one instead of a dead-end.
+  useEffect(() => {
+    if (loadingPresets || presets.length === 0) return;
+    if (presetReady.get(preferredCompositionId) === false) {
+      const fallback = presetReady.get(RECOMMENDED_PRESET_ID) ? RECOMMENDED_PRESET_ID : presets.find((p) => presetReady.get(p.id))?.id;
+      if (fallback && fallback !== preferredCompositionId) setPreferredCompositionId(fallback);
+    }
+    // Only re-run when the readiness picture itself changes, not on every keystroke.
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [loadingPresets, presetReady]);
+
+  const n = job?.channels.length ?? 0;
+  const activeLabel =
+    channelIndex !== null && job ? (job.channels[channelIndex]?.label ?? `Channel ${channelIndex}`) : null;
+
+  return (
+    <div className="flex flex-col gap-2">
+      <div className="flex items-center justify-between">
+        <span className="text-xs font-semibold uppercase text-gray-500 tracking-wide">Features</span>
+        {n > 0 && (
+          <span className="text-[10px] text-gray-400">
+            {n} ch{job?.hasSam ? ' +SAM' : ''}
+            {job?.cacheHit ? ' · cache' : ''}
+          </span>
+        )}
+      </div>
+
+      <label className="flex flex-col gap-0.5 text-xs text-gray-600">
+        Recipe
+        {loadingPresets ? (
+          <div className="h-8 animate-pulse rounded border border-gray-200 bg-gray-100" />
+        ) : (
+          <select
+            className="rounded border border-gray-200 px-2 py-1.5 text-sm text-gray-800"
+            value={preferredCompositionId}
+            onChange={(e) => setPreferredCompositionId(e.target.value)}
+            disabled={computing || disabled}
+          >
+            {presets.map((c) => {
+              const ready = presetReady.get(c.id) ?? true;
+              return (
+                <option key={c.id} value={c.id} disabled={!ready}>
+                  {c.id === RECOMMENDED_PRESET_ID ? '★ ' : ''}
+                  {PRESET_LABELS[c.id] ?? c.name}
+                  {!ready ? ' (unavailable)' : ''}
+                </option>
+              );
+            })}
+          </select>
+        )}
+      </label>
+      {selected && (
+        <p className="text-[10px] text-gray-500 leading-snug">{PRESET_HINTS[selected.id] ?? ''}</p>
+      )}
+      {presetsError && <p className="text-[10px] text-red-600 leading-snug">{presetsError}</p>}
+
+      {selected && !selectedReady && (
+        <div className="flex items-start gap-1.5 rounded-md border border-amber-200 bg-amber-50 p-1.5 text-[10px] text-amber-800 leading-snug">
+          <Warning size={13} className="mt-0.5 flex-shrink-0" />
+          <span>
+            {missingModuleName ?? 'A required model'} isn&apos;t installed yet — see{' '}
+            <span className="font-mono">ipred/models/README.md</span>. Pick another recipe or install it first.
+          </span>
+        </div>
+      )}
+
+      <button
+        type="button"
+        disabled={computing || disabled || !selectedReady}
+        onClick={onCompute}
+        title={!selectedReady ? 'This recipe needs a model that is not installed' : undefined}
+        className={cn(
+          'flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border transition-colors',
+          computing || disabled || !selectedReady
+            ? 'bg-gray-50 text-gray-400 border-gray-200 cursor-not-allowed'
+            : 'bg-white text-gray-800 border-gray-200 hover:bg-sky-50 hover:border-sky-300',
+        )}
+      >
+        {computing ? <CircleNotch size={14} className="animate-spin" /> : <Stack size={14} />}
+        {computing ? 'Computing…' : 'Compute'}
+      </button>
+
+      {error && <p className="text-[10px] text-red-600 leading-snug break-words">{error}</p>}
+
+      {job && n > 0 && (
+        <div className="flex flex-col gap-1.5">
+          <div className="flex items-center gap-1">
+            <button
+              type="button"
+              aria-label="Previous channel"
+              title="Previous channel"
+              disabled={channelIndex === null}
+              onClick={() => onCycle(-1)}
+              className="p-1 rounded border border-gray-200 hover:bg-sky-50 disabled:opacity-40"
+            >
+              <CaretLeft size={14} />
+            </button>
+            <select
+              className="flex-1 min-w-0 rounded border border-gray-200 px-1 py-1 text-xs text-gray-800"
+              value={channelIndex === null ? '' : String(channelIndex)}
+              onChange={(e) => {
+                const v = e.target.value;
+                onSelectChannel(v === '' ? null : Number(v));
+              }}
+            >
+              <option value="">Original</option>
+              {job.channels.map((ch) => (
+                <option key={ch.index} value={ch.index}>
+                  {ch.label}
+                </option>
+              ))}
+            </select>
+            <button
+              type="button"
+              aria-label="Next channel"
+              title="Next channel"
+              disabled={channelIndex === null}
+              onClick={() => onCycle(1)}
+              className="p-1 rounded border border-gray-200 hover:bg-sky-50 disabled:opacity-40"
+            >
+              <CaretRight size={14} />
+            </button>
+          </div>
+          {activeLabel && (
+            <p className="text-[10px] text-gray-500 truncate" title={activeLabel}>
+              {activeLabel}
+            </p>
+          )}
+          <button type="button" onClick={onOriginal} className="text-[11px] text-sky-700 hover:underline self-start">
+            Show original
+          </button>
+        </div>
+      )}
+
+      <div className="border-t border-gray-100 pt-1.5">
+        <button
+          type="button"
+          className="text-[10px] text-gray-500 hover:text-sky-700"
+          onClick={() => setShowAdvanced((v) => !v)}
+        >
+          {showAdvanced ? '▾' : '▸'} Advanced: edit feature recipe
+        </button>
+        {showAdvanced && (
+          <div className="mt-2">
+            <CompositionPanel />
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
new file mode 100644
index 0000000..71ab68f
--- /dev/null
+++ b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
@@ -0,0 +1,549 @@
+/**
+ * PixelClassifierPanel — train / conformal-predict / commit, with the
+ * Suggest-Labels manifold loop folded in and conformal coverage surfaced as
+ * headline guidance (not a legend entry).
+ */
+import { useState } from 'react';
+import { CaretLeft, CaretRight, CircleNotch, Compass, TreeStructure } from '@phosphor-icons/react';
+import type { ClfParams, ClfPredictCounts, ClfTrainResult } from '@/hooks/usePixelClassifier';
+import type { ManifoldParams } from '@/hooks/useFeatureManifold';
+import { cn } from '@/lib/utils';
+
+/** Shared progress-bar treatment (see useExportJob / DownloadModal). */
+function BusyBar({ label }: { label: string }) {
+  return (
+    <div className="flex flex-col gap-1">
+      <div className="flex items-center justify-between text-[10px] text-gray-500">
+        <span>{label}</span>
+      </div>
+      <div className="h-1.5 w-full overflow-hidden rounded-full bg-gray-200">
+        <div className="h-full w-1/3 animate-pulse rounded-full bg-sky-500" />
+      </div>
+    </div>
+  );
+}
+
+/** Coverage guidance derived from conformal set counts — the headline, not a legend line. */
+function coverageHeadline(counts: ClfPredictCounts, alpha: number): { text: string; tone: 'good' | 'warn' } {
+  const total = counts.singleton + counts.multi + counts.abstain;
+  if (total === 0) return { text: 'No predicted pixels yet.', tone: 'warn' };
+  const confidentPct = Math.round((counts.singleton / total) * 100);
+  const alphaPct = Math.round(alpha * 100);
+  if (confidentPct >= 70) {
+    return {
+      text: `${confidentPct}% of pixels are confidently labeled at ${100 - alphaPct}% coverage.`,
+      tone: 'good',
+    };
+  }
+  return {
+    text: `Only ${confidentPct}% confidently labeled — most pixels need more training data or a lower α.`,
+    tone: 'warn',
+  };
+}
+
+export interface PixelClassifierPanelProps {
+  hasFeatureJob: boolean;
+  canAutoPreprocess?: boolean;
+  hasShapes: boolean;
+  training: boolean;
+  predicting: boolean;
+  params: ClfParams;
+  onParamsChange: (p: ClfParams) => void;
+  model: ClfTrainResult | null;
+  hasPrediction: boolean;
+  predictCounts: ClfPredictCounts | null;
+  probaClassIndex?: number;
+  activeProbaClassId?: number | null;
+  activeProbaThreshold?: number;
+  classLabelForId?: (classId: number) => string;
+  onCycleProbaClass?: (delta: number) => void;
+  onProbaThresholdChange?: (threshold: number) => void;
+  error: string | null;
+  onTrain: () => void;
+  onPredict: () => void;
+  onCommit: () => void;
+  onDismiss: () => void;
+  commitLabel?: string;
+  featureSetupId?: string | null;
+  trainerId?: string | null;
+
+  // Suggest-labels (manifold), folded into the same loop instead of a separate panel.
+  manifoldParams: ManifoldParams;
+  onManifoldParamsChange: (p: ManifoldParams) => void;
+  manifoldSampling: boolean;
+  manifoldHasSample: boolean;
+  manifoldShowHeatmap: boolean;
+  onManifoldShowHeatmapChange: (v: boolean) => void;
+  manifoldShowMarkers: boolean;
+  onManifoldShowMarkersChange: (v: boolean) => void;
+  manifoldHeatmapOpacity: number;
+  onManifoldHeatmapOpacityChange: (v: number) => void;
+  manifoldMeta: { nPicked: number; nSubsample: number; explainedVariance: number } | null;
+  manifoldError: string | null;
+  onManifoldSample: () => void;
+  onManifoldDismiss: () => void;
+  manifoldRoiShapeCount: number;
+  canCaptureManifoldRoi: boolean;
+  onCaptureManifoldRoi: () => void;
+  onClearManifoldRoi: () => void;
+}
+
+/** Sidebar controls for CatBoost + split-conformal sets, with suggest-labels folded in. */
+export default function PixelClassifierPanel({
+  hasFeatureJob,
+  canAutoPreprocess = false,
+  hasShapes,
+  training,
+  predicting,
+  params,
+  onParamsChange,
+  model,
+  hasPrediction,
+  predictCounts,
+  probaClassIndex = 0,
+  activeProbaClassId = null,
+  activeProbaThreshold = 0.5,
+  classLabelForId,
+  onCycleProbaClass,
+  onProbaThresholdChange,
+  error,
+  onTrain,
+  onPredict,
+  onCommit,
+  onDismiss,
+  commitLabel = 'Commit singletons',
+  featureSetupId = null,
+  trainerId = null,
+  manifoldParams,
+  onManifoldParamsChange,
+  manifoldSampling,
+  manifoldHasSample,
+  manifoldShowHeatmap,
+  onManifoldShowHeatmapChange,
+  manifoldShowMarkers,
+  onManifoldShowMarkersChange,
+  manifoldHeatmapOpacity,
+  onManifoldHeatmapOpacityChange,
+  manifoldMeta,
+  manifoldError,
+  onManifoldSample,
+  onManifoldDismiss,
+  manifoldRoiShapeCount,
+  canCaptureManifoldRoi,
+  onCaptureManifoldRoi,
+  onClearManifoldRoi,
+}: PixelClassifierPanelProps) {
+  const [showSuggest, setShowSuggest] = useState(false);
+  const busy = training || predicting;
+  const canTrain = (hasFeatureJob || canAutoPreprocess) && hasShapes && !busy;
+  const canPredict = !!model && (hasFeatureJob || !!model.featureId) && !busy;
+  const maxImp = model?.featureImportances[0]?.importance ?? 1;
+  const alphaPct = Math.round(params.alpha * 100);
+  const nClasses = model?.classIds.length ?? 0;
+  const threshPct = Math.round(activeProbaThreshold * 100);
+  const probaLabel =
+    activeProbaClassId !== null
+      ? (classLabelForId?.(activeProbaClassId) ?? `class ${activeProbaClassId}`)
+      : '—';
+  const headline = predictCounts ? coverageHeadline(predictCounts, params.alpha) : null;
+
+  return (
+    <div className="flex flex-col gap-2 border-t border-gray-100 pt-2">
+      <div className="flex items-center justify-between">
+        <span className="text-xs font-semibold uppercase text-gray-500 tracking-wide">
+          Classifier
+        </span>
+        <span className="text-[10px] text-gray-400" title="Trainer + Mondrian conformal sets">
+          {trainerId ?? 'catboost'} · conformal
+        </span>
+      </div>
+
+      <p className="text-[11px] text-gray-600 leading-snug">
+        Setup:{' '}
+        <span className="font-mono text-gray-800">
+          {featureSetupId ?? 'none — pick a recipe under Features'}
+        </span>
+        {!hasFeatureJob && canAutoPreprocess ? (
+          <span className="text-gray-500"> · Train will compute features</span>
+        ) : null}
+      </p>
+
+      <div className="grid grid-cols-3 gap-1 text-[10px] text-gray-600">
+        <label className="flex flex-col gap-0.5">
+          Trees
+          <input
+            type="number"
+            min={10}
+            max={2000}
+            step={10}
+            value={params.iterations}
+            disabled={busy}
+            onChange={(e) =>
+              onParamsChange({ ...params, iterations: Math.max(1, Number(e.target.value) || 200) })
+            }
+            className="rounded border border-gray-200 px-1 py-0.5 text-gray-800"
+          />
+        </label>
+        <label className="flex flex-col gap-0.5">
+          Depth
+          <input
+            type="number"
+            min={1}
+            max={10}
+            step={1}
+            value={params.depth}
+            disabled={busy}
+            onChange={(e) =>
+              onParamsChange({ ...params, depth: Math.min(10, Math.max(1, Number(e.target.value) || 6)) })
+            }
+            className="rounded border border-gray-200 px-1 py-0.5 text-gray-800"
+          />
+        </label>
+        <label className="flex flex-col gap-0.5">
+          LR
+          <input
+            type="number"
+            min={0.01}
+            max={1}
+            step={0.01}
+            value={params.learningRate}
+            disabled={busy}
+            onChange={(e) =>
+              onParamsChange({
+                ...params,
+                learningRate: Math.min(1, Math.max(0.01, Number(e.target.value) || 0.1)),
+              })
+            }
+            className="rounded border border-gray-200 px-1 py-0.5 text-gray-800"
+          />
+        </label>
+      </div>
+
+      <label className="flex flex-col gap-0.5 text-[10px] text-gray-600">
+        <span className="flex justify-between">
+          <span>α (misfire)</span>
+          <span className="text-gray-800 font-medium">{alphaPct}%</span>
+        </span>
+        <input
+          type="range"
+          min={1}
+          max={20}
+          step={1}
+          value={alphaPct}
+          disabled={busy}
+          onChange={(e) =>
+            onParamsChange({
+              ...params,
+              alpha: Math.min(0.2, Math.max(0.01, Number(e.target.value) / 100)),
+            })
+          }
+          className="w-full accent-sky-600"
+        />
+      </label>
+
+      <button
+        type="button"
+        disabled={!canTrain}
+        title={!hasFeatureJob ? 'Compute features first' : !hasShapes ? 'Annotate at least two classes' : 'Train classifier'}
+        onClick={onTrain}
+        className={cn(
+          'flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border transition-colors',
+          canTrain
+            ? 'bg-white text-gray-800 border-gray-200 hover:bg-sky-50 hover:border-sky-300'
+            : 'bg-gray-50 text-gray-400 border-gray-200 cursor-not-allowed',
+        )}
+      >
+        <TreeStructure size={14} />
+        Train classifier
+      </button>
+      {training && <BusyBar label="Training…" />}
+
+      <button
+        type="button"
+        disabled={!canPredict}
+        onClick={onPredict}
+        className={cn(
+          'flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border transition-colors',
+          canPredict
+            ? 'bg-white text-gray-800 border-gray-200 hover:bg-sky-50 hover:border-sky-300'
+            : 'bg-gray-50 text-gray-400 border-gray-200 cursor-not-allowed',
+        )}
+      >
+        Predict
+      </button>
+      {predicting && <BusyBar label="Predicting…" />}
+
+      {model && (
+        <div className="flex flex-col gap-1.5 rounded-md border border-gray-100 bg-gray-50 p-1.5">
+          <p className="text-[10px] text-gray-700">
+            Acc {(model.trainAccuracy * 100).toFixed(1)}% · train {model.nTrain.toLocaleString()} /
+            cal {model.nCal.toLocaleString()} · {model.nTrees} trees · classes{' '}
+            {model.classIds.join(', ')}
+            {model.usesSam ? ' · +SlimSAM' : ''}
+          </p>
+
+          {model.featureImportances.length > 0 && (
+            <div className="flex flex-col gap-0.5">
+              <span className="text-[10px] font-medium text-gray-600">Feature importance</span>
+              {model.featureImportances.slice(0, 8).map((fi) => (
+                <div key={fi.label} className="flex items-center gap-1 text-[10px]">
+                  <span className="w-[42%] truncate text-gray-600" title={fi.label}>{fi.label}</span>
+                  <div className="flex-1 h-1.5 rounded bg-gray-200 overflow-hidden">
+                    <div
+                      className="h-full bg-sky-500"
+                      style={{ width: `${Math.max(2, (fi.importance / maxImp) * 100)}%` }}
+                    />
+                  </div>
+                  <span className="w-8 text-right text-gray-500">{fi.importance.toFixed(1)}</span>
+                </div>
+              ))}
+            </div>
+          )}
+        </div>
+      )}
+
+      {hasPrediction && headline && (
+        <div
+          className={cn(
+            'rounded-md border p-2 text-[11px] font-medium leading-snug',
+            headline.tone === 'good'
+              ? 'border-emerald-200 bg-emerald-50 text-emerald-800'
+              : 'border-amber-200 bg-amber-50 text-amber-800',
+          )}
+        >
+          {headline.text}
+          {predictCounts && (
+            <p className="mt-0.5 text-[10px] font-normal opacity-80">
+              singleton {predictCounts.singleton.toLocaleString()} · multi{' '}
+              {predictCounts.multi.toLocaleString()} · abstain {predictCounts.abstain.toLocaleString()}
+            </p>
+          )}
+        </div>
+      )}
+
+      {hasPrediction && nClasses > 0 && onCycleProbaClass && onProbaThresholdChange && (
+        <div className="flex flex-col gap-1.5 rounded-md border border-sky-100 bg-sky-50/60 p-1.5">
+          <div className="flex items-center justify-between gap-1">
+            <span className="text-[10px] font-medium text-gray-700">Class probability</span>
+            <span className="text-[10px] text-gray-500">
+              {probaClassIndex + 1}/{nClasses}
+            </span>
+          </div>
+          <div className="flex items-center gap-1">
+            <button
+              type="button"
+              disabled={busy || nClasses < 2}
+              onClick={() => onCycleProbaClass(-1)}
+              className="p-1 rounded border border-gray-200 bg-white text-gray-700 hover:bg-sky-50 disabled:opacity-40"
+              title="Previous class"
+            >
+              <CaretLeft size={14} />
+            </button>
+            <span className="flex-1 text-center text-[11px] font-medium text-gray-800 truncate" title={probaLabel}>
+              {probaLabel}
+            </span>
+            <button
+              type="button"
+              disabled={busy || nClasses < 2}
+              onClick={() => onCycleProbaClass(1)}
+              className="p-1 rounded border border-gray-200 bg-white text-gray-700 hover:bg-sky-50 disabled:opacity-40"
+              title="Next class"
+            >
+              <CaretRight size={14} />
+            </button>
+          </div>
+          <label className="flex flex-col gap-0.5 text-[10px] text-gray-600">
+            <span className="flex justify-between">
+              <span>Threshold</span>
+              <span className="text-gray-800 font-medium">{threshPct}%</span>
+            </span>
+            <input
+              type="range"
+              min={5}
+              max={95}
+              step={1}
+              value={threshPct}
+              disabled={busy}
+              onChange={(e) => onProbaThresholdChange(Number(e.target.value) / 100)}
+              className="w-full accent-sky-600"
+            />
+          </label>
+        </div>
+      )}
+
+      {hasPrediction && (
+        <div className="flex gap-1">
+          <button
+            type="button"
+            onClick={onCommit}
+            title="Add singleton polygons only; keep existing annotations"
+            className="flex-1 py-1.5 rounded-md text-xs font-medium bg-sky-600 text-white hover:bg-sky-700"
+          >
+            {commitLabel}
+          </button>
+          <button
+            type="button"
+            onClick={onDismiss}
+            className="flex-1 py-1.5 rounded-md text-xs border border-gray-200 text-gray-700 hover:bg-gray-50"
+          >
+            Dismiss
+          </button>
+        </div>
+      )}
+
+      {error && <p className="text-[10px] text-red-600 leading-snug break-words">{error}</p>}
+
+      {/* Suggest labels — folded into the same train/infer loop instead of its own panel. */}
+      <div className="border-t border-gray-100 pt-1.5">
+        <button
+          type="button"
+          disabled={!hasFeatureJob}
+          onClick={() => setShowSuggest((v) => !v)}
+          className={cn(
+            'flex w-full items-center gap-1.5 text-[10px]',
+            hasFeatureJob ? 'text-gray-500 hover:text-sky-700' : 'text-gray-300 cursor-not-allowed',
+          )}
+        >
+          <Compass size={12} />
+          {showSuggest ? '▾' : '▸'} Suggest labels (manifold coverage)
+        </button>
+
+        {showSuggest && (
+          <div className="mt-2 flex flex-col gap-1.5 rounded-md border border-gray-100 bg-gray-50 p-1.5">
+            <div className="grid grid-cols-2 gap-1 text-[10px] text-gray-600">
+              <label className="flex flex-col gap-0.5">
+                K boxes
+                <input
+                  type="number"
+                  min={1}
+                  max={200}
+                  value={manifoldParams.k}
+                  disabled={manifoldSampling}
+                  onChange={(e) =>
+                    onManifoldParamsChange({ ...manifoldParams, k: Math.max(1, Number(e.target.value) || 24) })
+                  }
+                  className="rounded border border-gray-200 px-1 py-0.5 text-gray-800"
+                />
+              </label>
+              <label className="flex flex-col gap-0.5">
+                Box size
+                <input
+                  type="number"
+                  min={8}
+                  max={512}
+                  step={8}
+                  value={manifoldParams.boxSize}
+                  disabled={manifoldSampling}
+                  onChange={(e) =>
+                    onManifoldParamsChange({
+                      ...manifoldParams,
+                      boxSize: Math.max(8, Number(e.target.value) || 64),
+                    })
+                  }
+                  className="rounded border border-gray-200 px-1 py-0.5 text-gray-800"
+                />
+              </label>
+            </div>
+
+            <div className="flex items-center gap-1 text-[10px] text-gray-600">
+              <button
+                type="button"
+                disabled={!canCaptureManifoldRoi}
+                onClick={onCaptureManifoldRoi}
+                className={cn(
+                  'flex-1 rounded border px-1.5 py-1',
+                  canCaptureManifoldRoi
+                    ? 'border-gray-200 bg-white hover:bg-sky-50'
+                    : 'border-gray-200 bg-gray-50 text-gray-300 cursor-not-allowed',
+                )}
+              >
+                Restrict to selection ({manifoldRoiShapeCount})
+              </button>
+              {manifoldRoiShapeCount > 0 && (
+                <button
+                  type="button"
+                  onClick={onClearManifoldRoi}
+                  className="rounded border border-gray-200 bg-white px-1.5 py-1 hover:bg-sky-50"
+                >
+                  Clear
+                </button>
+              )}
+            </div>
+
+            <button
+              type="button"
+              disabled={!hasFeatureJob || manifoldSampling}
+              onClick={onManifoldSample}
+              className={cn(
+                'flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border transition-colors',
+                !hasFeatureJob || manifoldSampling
+                  ? 'bg-gray-50 text-gray-400 border-gray-200 cursor-not-allowed'
+                  : 'bg-white text-gray-800 border-gray-200 hover:bg-sky-50 hover:border-sky-300',
+              )}
+            >
+              {manifoldSampling ? <CircleNotch size={14} className="animate-spin" /> : <Compass size={14} />}
+              {manifoldSampling ? 'Sampling…' : 'Suggest regions to label'}
+            </button>
+            {manifoldSampling && <BusyBar label="Sampling manifold coverage…" />}
+
+            {manifoldMeta && (
+              <p className="text-[10px] text-gray-600 leading-snug">
+                {manifoldMeta.nPicked} boxes from {manifoldMeta.nSubsample.toLocaleString()} px ·{' '}
+                {(manifoldMeta.explainedVariance * 100).toFixed(0)}% variance explained
+              </p>
+            )}
+
+            {manifoldHasSample && (
+              <div className="flex flex-col gap-1 text-[10px] text-gray-600">
+                <label className="flex items-center gap-1.5">
+                  <input
+                    type="checkbox"
+                    checked={manifoldShowHeatmap}
+                    onChange={(e) => onManifoldShowHeatmapChange(e.target.checked)}
+                  />
+                  Show coverage heatmap
+                </label>
+                <label className="flex items-center gap-1.5">
+                  <input
+                    type="checkbox"
+                    checked={manifoldShowMarkers}
+                    onChange={(e) => onManifoldShowMarkersChange(e.target.checked)}
+                  />
+                  Show suggested boxes
+                </label>
+                <label className="flex flex-col gap-0.5">
+                  <span className="flex justify-between">
+                    <span>Heatmap opacity</span>
+                    <span className="text-gray-800 font-medium">
+                      {Math.round(manifoldHeatmapOpacity * 100)}%
+                    </span>
+                  </span>
+                  <input
+                    type="range"
+                    min={0}
+                    max={100}
+                    step={5}
+                    value={Math.round(manifoldHeatmapOpacity * 100)}
+                    onChange={(e) => onManifoldHeatmapOpacityChange(Number(e.target.value) / 100)}
+                    className="w-full accent-sky-600"
+                  />
+                </label>
+                <button
+                  type="button"
+                  onClick={onManifoldDismiss}
+                  className="self-start text-[10px] text-gray-500 hover:text-sky-700"
+                >
+                  Dismiss suggestions
+                </button>
+              </div>
+            )}
+
+            {manifoldError && (
+              <p className="text-[10px] text-red-600 leading-snug break-words">{manifoldError}</p>
+            )}
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/hooks/useFeatureChannels.ts b/frontend/src/hooks/useFeatureChannels.ts
new file mode 100644
index 0000000..0034360
--- /dev/null
+++ b/frontend/src/hooks/useFeatureChannels.ts
@@ -0,0 +1,259 @@
+/**
+ * useFeatureChannels — compute feature bank via ipred and load channel PNGs.
+ */
+import { useCallback, useEffect, useRef, useState } from 'react';
+import { useConnectionStore } from '@/stores/connectionStore';
+import { useIpredStore } from '@/stores/ipredStore';
+import { ipredChannelUrl, ipredPreprocess, listIpredModules, openIpredSession } from '@/lib/ipredApi';
+
+export interface FeatureChannelInfo {
+  index: number;
+  label: string;
+}
+
+export interface FeatureJobInfo {
+  jobId: string;
+  width: number;
+  height: number;
+  channels: FeatureChannelInfo[];
+  hasSam: boolean;
+  setupId?: string;
+  cacheHit?: boolean;
+}
+
+export interface UseFeatureChannelsArgs {
+  source: string | null;
+  kind: string | null;
+  sliceIndex: number;
+  serverUri: string | null;
+}
+
+/**
+ * Owns feature compute + channel blob URL lifecycle via ipred.
+ * `channelIndex === null` means show the original rendered slice.
+ */
+export function useFeatureChannels({
+  source,
+  kind,
+  sliceIndex,
+  serverUri,
+}: UseFeatureChannelsArgs) {
+  const [job, setJob] = useState<FeatureJobInfo | null>(null);
+  const [channelIndex, setChannelIndex] = useState<number | null>(null);
+  const [channelUrl, setChannelUrl] = useState<string | null>(null);
+  const [computing, setComputing] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [samAvailable, setSamAvailable] = useState(false);
+  const channelUrlRef = useRef<string | null>(null);
+
+  const preferredCompositionId = useIpredStore((s) => s.preferredCompositionId);
+  const ipredSessionId = useIpredStore((s) => s.ipredSessionId);
+  const setIpredSession = useIpredStore((s) => s.setIpredSession);
+  const localRoot = useConnectionStore((s) => s.localRoot);
+
+  useEffect(() => {
+    let cancelled = false;
+    listIpredModules()
+      .then((modules) => {
+        if (cancelled) return;
+        const slimsam = modules.find((m) => m.id === 'slimsam');
+        setSamAvailable(!!slimsam?.ready);
+      })
+      .catch(() => {
+        if (!cancelled) setSamAvailable(false);
+      });
+    return () => {
+      cancelled = true;
+    };
+  }, []);
+
+  const revokeChannelUrl = useCallback(() => {
+    if (channelUrlRef.current) {
+      URL.revokeObjectURL(channelUrlRef.current);
+      channelUrlRef.current = null;
+    }
+    setChannelUrl(null);
+  }, []);
+
+  useEffect(() => {
+    setJob(null);
+    setChannelIndex(null);
+    revokeChannelUrl();
+    setError(null);
+  }, [source, kind, sliceIndex, serverUri, revokeChannelUrl]);
+
+  useEffect(() => {
+    if (!job || channelIndex === null) {
+      revokeChannelUrl();
+      return;
+    }
+    let cancelled = false;
+    const url = ipredChannelUrl(job.jobId, channelIndex);
+    (async () => {
+      try {
+        const res = await fetch(url);
+        if (!res.ok) throw new Error(`Channel fetch failed: ${res.status}`);
+        const blob = await res.blob();
+        if (cancelled) return;
+        const objUrl = URL.createObjectURL(blob);
+        if (channelUrlRef.current) URL.revokeObjectURL(channelUrlRef.current);
+        channelUrlRef.current = objUrl;
+        setChannelUrl(objUrl);
+      } catch (e) {
+        if (!cancelled) {
+          setError(e instanceof Error ? e.message : String(e));
+          revokeChannelUrl();
+        }
+      }
+    })();
+    return () => {
+      cancelled = true;
+    };
+  }, [job, channelIndex, revokeChannelUrl]);
+
+  useEffect(
+    () => () => {
+      if (channelUrlRef.current) URL.revokeObjectURL(channelUrlRef.current);
+    },
+    [],
+  );
+
+  const ensureSession = useCallback(async (): Promise<string> => {
+    if (ipredSessionId) return ipredSessionId;
+    if (!source || !kind) throw new Error('No sample open');
+    const session = await openIpredSession({
+      kind,
+      source,
+      server_uri: serverUri,
+      root: kind === 'local' ? localRoot : null,
+    });
+    setIpredSession({
+      sessionId: session.session_id,
+      projectId: session.project_id,
+    });
+    return session.session_id;
+  }, [ipredSessionId, source, kind, serverUri, localRoot, setIpredSession]);
+
+  const compute = useCallback(async () => {
+    if (!source || !kind || computing) return;
+    const compositionId = preferredCompositionId;
+    if (!compositionId) {
+      setError('Select a composition first.');
+      return;
+    }
+    setComputing(true);
+    setError(null);
+    try {
+      const sessionId = await ensureSession();
+      const data = await ipredPreprocess({
+        session_id: sessionId,
+        composition_id: compositionId,
+        slice_index: sliceIndex,
+      });
+      const channels: FeatureChannelInfo[] = (data.labels ?? []).map((label, index) => ({
+        index,
+        label,
+      }));
+      if (channels.length === 0) {
+        for (let i = 0; i < data.n_channels; i += 1) {
+          channels.push({ index: i, label: `channel ${i}` });
+        }
+      }
+      setJob({
+        jobId: data.feature_id,
+        width: data.width,
+        height: data.height,
+        channels,
+        hasSam:
+          compositionId.includes('slimsam') ||
+          compositionId.includes('sam') ||
+          compositionId.includes('mark'),
+        setupId: data.setup_id,
+        cacheHit: data.cache_hit,
+      });
+      setChannelIndex(0);
+    } catch (e) {
+      setError(e instanceof Error ? e.message : String(e));
+      setJob(null);
+      setChannelIndex(null);
+    } finally {
+      setComputing(false);
+    }
+  }, [source, kind, sliceIndex, computing, preferredCompositionId, ensureSession]);
+
+  const selectChannel = useCallback((index: number | null) => {
+    setChannelIndex(index);
+  }, []);
+
+  const cycleChannel = useCallback(
+    (delta: number) => {
+      if (!job || job.channels.length === 0) return;
+      setChannelIndex((prev) => {
+        const cur = prev ?? 0;
+        const n = job.channels.length;
+        return (((cur + delta) % n) + n) % n;
+      });
+    },
+    [job],
+  );
+
+  const clearSelection = useCallback(() => {
+    setChannelIndex(null);
+  }, []);
+
+  const invalidateJob = useCallback(() => {
+    setJob(null);
+    setChannelIndex(null);
+    revokeChannelUrl();
+  }, [revokeChannelUrl]);
+
+  /** Adopt a feature bank produced elsewhere (e.g. Train auto-preprocess). */
+  const adoptFeatureBank = useCallback(
+    (payload: {
+      featureId: string;
+      width: number;
+      height: number;
+      labels?: string[];
+      nChannels?: number;
+      setupId?: string;
+      cacheHit?: boolean;
+    }) => {
+      const n = payload.nChannels ?? payload.labels?.length ?? 0;
+      const channels: FeatureChannelInfo[] = (payload.labels ?? []).map((label, index) => ({
+        index,
+        label,
+      }));
+      if (channels.length === 0) {
+        for (let i = 0; i < n; i += 1) {
+          channels.push({ index: i, label: `channel ${i}` });
+        }
+      }
+      setJob({
+        jobId: payload.featureId,
+        width: payload.width,
+        height: payload.height,
+        channels,
+        hasSam: (payload.setupId ?? '').includes('sam'),
+        setupId: payload.setupId,
+        cacheHit: payload.cacheHit,
+      });
+    },
+    [],
+  );
+
+  return {
+    job,
+    channelIndex,
+    channelUrl,
+    computing,
+    error,
+    samAvailable,
+    preferredCompositionId,
+    compute,
+    selectChannel,
+    cycleChannel,
+    clearSelection,
+    invalidateJob,
+    adoptFeatureBank,
+  };
+}
diff --git a/frontend/src/hooks/useFeatureManifold.ts b/frontend/src/hooks/useFeatureManifold.ts
new file mode 100644
index 0000000..12cb253
--- /dev/null
+++ b/frontend/src/hooks/useFeatureManifold.ts
@@ -0,0 +1,207 @@
+/**
+ * useFeatureManifold — greedy variance-box suggestions + residual heatmap.
+ */
+import { useCallback, useEffect, useRef, useState } from 'react';
+import { API_BASE } from '@/config';
+import type { ManifoldPoint } from '@/lib/featureManifold';
+import type { Shape } from '@/stores/annotationStore';
+
+export interface ManifoldParams {
+  k: number;
+  /** Full square box side length in image pixels. */
+  boxSize: number;
+}
+
+export const DEFAULT_MANIFOLD_PARAMS: ManifoldParams = {
+  k: 24,
+  boxSize: 64,
+};
+
+export interface UseFeatureManifoldArgs {
+  featureJobId: string | null;
+  onFeatureJobExpired?: () => void;
+}
+
+async function readErrorDetail(res: Response): Promise<string> {
+  const text = await res.text();
+  try {
+    const parsed = JSON.parse(text) as { detail?: unknown };
+    if (typeof parsed.detail === 'string') return parsed.detail;
+  } catch {
+    /* plain */
+  }
+  return text || `Request failed: ${res.status}`;
+}
+
+export function useFeatureManifold({
+  featureJobId,
+  onFeatureJobExpired,
+}: UseFeatureManifoldArgs) {
+  const [params, setParams] = useState<ManifoldParams>(DEFAULT_MANIFOLD_PARAMS);
+  const [points, setPoints] = useState<ManifoldPoint[]>([]);
+  const [heatmapUrl, setHeatmapUrl] = useState<string | null>(null);
+  const [showHeatmap, setShowHeatmap] = useState(true);
+  const [showMarkers, setShowMarkers] = useState(true);
+  const [heatmapOpacity, setHeatmapOpacity] = useState(0.45);
+  const [sampling, setSampling] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [placementMask, setPlacementMask] = useState<Shape[] | null>(null);
+  const [meta, setMeta] = useState<{
+    k: number;
+    nPicked: number;
+    nSubsample: number;
+    explainedVariance: number;
+    radius: number;
+    boxSize: number;
+    hasMask?: boolean;
+    maskPixels?: number;
+  } | null>(null);
+  const heatmapUrlRef = useRef<string | null>(null);
+  const samplingRef = useRef(false);
+  const hasSampleRef = useRef(false);
+  const paramsRef = useRef(params);
+  paramsRef.current = params;
+  const placementMaskRef = useRef(placementMask);
+  placementMaskRef.current = placementMask;
+  const onExpiredRef = useRef(onFeatureJobExpired);
+  onExpiredRef.current = onFeatureJobExpired;
+
+  const revoke = useCallback(() => {
+    if (heatmapUrlRef.current) {
+      URL.revokeObjectURL(heatmapUrlRef.current);
+      heatmapUrlRef.current = null;
+    }
+    setHeatmapUrl(null);
+    setPoints([]);
+    setMeta(null);
+    hasSampleRef.current = false;
+  }, []);
+
+  useEffect(() => {
+    revoke();
+    setError(null);
+    setPlacementMask(null);
+  }, [featureJobId, revoke]);
+
+  useEffect(
+    () => () => {
+      if (heatmapUrlRef.current) URL.revokeObjectURL(heatmapUrlRef.current);
+    },
+    [],
+  );
+
+  const sample = useCallback(async () => {
+    if (!featureJobId || samplingRef.current) return;
+    samplingRef.current = true;
+    setSampling(true);
+    setError(null);
+    const { k, boxSize } = paramsRef.current;
+    const maskShapes = placementMaskRef.current;
+    try {
+      const res = await fetch(`${API_BASE}/api/ipred/manifold/sample`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          feature_id: featureJobId,
+          k,
+          box_size: boxSize,
+          ...(maskShapes && maskShapes.length > 0 ? { shapes: maskShapes } : {}),
+        }),
+      });
+      if (!res.ok) {
+        const detail = await readErrorDetail(res);
+        if (/not found|unknown feature/i.test(detail)) {
+          onExpiredRef.current?.();
+          throw new Error('Feature bank missing. Click Compute again, then Suggest labels.');
+        }
+        throw new Error(detail);
+      }
+      const body = (await res.json()) as {
+        sample_id: string;
+        points: ManifoldPoint[];
+        k: number;
+        n_picked?: number;
+        n_subsample: number;
+        explained_variance: number;
+        radius?: number;
+        box_size?: number;
+        has_mask?: boolean;
+        mask_pixels?: number;
+      };
+      const heatRes = await fetch(
+        `${API_BASE}/api/ipred/manifold/${body.sample_id}/heatmap.png`,
+      );
+      if (!heatRes.ok) throw new Error('Failed to fetch manifold heatmap');
+      const blob = await heatRes.blob();
+      const url = URL.createObjectURL(blob);
+      if (heatmapUrlRef.current) URL.revokeObjectURL(heatmapUrlRef.current);
+      heatmapUrlRef.current = url;
+      setHeatmapUrl(url);
+      setPoints(body.points ?? []);
+      hasSampleRef.current = (body.points?.length ?? 0) > 0 || !!url;
+      setMeta({
+        k: body.k,
+        nPicked: body.n_picked ?? (body.points?.length ?? 0),
+        nSubsample: body.n_subsample,
+        explainedVariance: body.explained_variance,
+        radius: body.radius ?? body.points?.[0]?.radius ?? 0,
+        boxSize: body.box_size ?? boxSize,
+        hasMask: body.has_mask,
+        maskPixels: body.mask_pixels,
+      });
+    } catch (e) {
+      revoke();
+      setError(e instanceof Error ? e.message : String(e));
+    } finally {
+      samplingRef.current = false;
+      setSampling(false);
+    }
+  }, [featureJobId, revoke]);
+
+  const sampleRef = useRef(sample);
+  sampleRef.current = sample;
+
+  // Re-place boxes when K / box size / mask change after an existing suggestion.
+  useEffect(() => {
+    if (!featureJobId || !hasSampleRef.current) return;
+    const t = window.setTimeout(() => {
+      void sampleRef.current();
+    }, 350);
+    return () => window.clearTimeout(t);
+  }, [featureJobId, params.k, params.boxSize, placementMask]);
+
+  const setPlacementMaskFromShapes = useCallback((shapes: Shape[]) => {
+    setPlacementMask(shapes.length ? shapes.map((s) => ({ ...s })) : null);
+  }, []);
+
+  const clearPlacementMask = useCallback(() => {
+    setPlacementMask(null);
+  }, []);
+
+  const dismiss = useCallback(() => {
+    revoke();
+    setError(null);
+  }, [revoke]);
+
+  return {
+    params,
+    setParams,
+    points,
+    heatmapUrl,
+    showHeatmap,
+    setShowHeatmap,
+    showMarkers,
+    setShowMarkers,
+    heatmapOpacity,
+    setHeatmapOpacity,
+    sampling,
+    error,
+    meta,
+    sample,
+    dismiss,
+    placementMask,
+    setPlacementMaskFromShapes,
+    clearPlacementMask,
+    hasSample: points.length > 0 || !!heatmapUrl,
+  };
+}
diff --git a/frontend/src/hooks/usePixelClassifier.ts b/frontend/src/hooks/usePixelClassifier.ts
new file mode 100644
index 0000000..ecc13b8
--- /dev/null
+++ b/frontend/src/hooks/usePixelClassifier.ts
@@ -0,0 +1,462 @@
+/**
+ * usePixelClassifier — train / conformal predict via ipred.
+ */
+import { useCallback, useEffect, useRef, useState } from 'react';
+import { useConnectionStore } from '@/stores/connectionStore';
+import { useIpredStore } from '@/stores/ipredStore';
+import type { Shape } from '@/stores/annotationStore';
+import {
+  ipredInfer,
+  ipredPreprocess,
+  ipredRunCommitUrl,
+  ipredRunProbaUrl,
+  ipredRunStatusUrl,
+  ipredThresholdClass,
+  ipredTrain,
+  openIpredSession,
+  type IpredThresholdClassResult,
+} from '@/lib/ipredApi';
+import { thresholdProbaPngBlob } from '@/lib/pixelClf';
+
+export interface ClfParams {
+  iterations: number;
+  depth: number;
+  learningRate: number;
+  /** Misfire level α for conformal sets (e.g. 0.05 = 5%). */
+  alpha: number;
+}
+
+export const DEFAULT_CLF_PARAMS: ClfParams = {
+  iterations: 200,
+  depth: 6,
+  learningRate: 0.1,
+  alpha: 0.05,
+};
+
+export interface ClfFeatureImportance {
+  label: string;
+  importance: number;
+}
+
+export interface ClfPredictCounts {
+  singleton: number;
+  multi: number;
+  abstain: number;
+}
+
+export interface ClfTrainResult {
+  modelId: string;
+  featureId: string;
+  nSamples: number;
+  nTrain: number;
+  nCal: number;
+  classIds: number[];
+  trainAccuracy: number;
+  params: ClfParams;
+  nTrees: number;
+  usesSam: boolean;
+  featureImportances: ClfFeatureImportance[];
+  trainerId: string;
+  compositionId: string | null;
+}
+
+export interface UsePixelClassifierArgs {
+  /** Current ipred feature bank id (from Preprocess compute), if any. */
+  featureJobId: string | null;
+  resetKey: string | null;
+  source: string | null;
+  kind: string | null;
+  serverUri: string | null;
+  sliceIndex: number;
+  onFeatureJobExpired?: () => void;
+  /** Called when train auto-runs preprocess and gets a new feature bank. */
+  onFeatureReady?: (info: {
+    featureId: string;
+    width: number;
+    height: number;
+    labels: string[];
+    nChannels: number;
+    setupId: string;
+    cacheHit: boolean;
+  }) => void;
+}
+
+const DEFAULT_PROBA_THRESHOLD = 0.5;
+
+export function usePixelClassifier({
+  featureJobId,
+  resetKey,
+  source,
+  kind,
+  serverUri,
+  sliceIndex,
+  onFeatureJobExpired,
+  onFeatureReady,
+}: UsePixelClassifierArgs) {
+  const preferredCompositionId = useIpredStore((s) => s.preferredCompositionId);
+  const preferredTrainerId = useIpredStore((s) => s.preferredTrainerId);
+  const preferredTrainerConfig = useIpredStore((s) => s.preferredTrainerConfig);
+  const ipredSessionId = useIpredStore((s) => s.ipredSessionId);
+  const setIpredSession = useIpredStore((s) => s.setIpredSession);
+  const localRoot = useConnectionStore((s) => s.localRoot);
+
+  const [params, setParams] = useState<ClfParams>(() => ({
+    ...DEFAULT_CLF_PARAMS,
+    iterations: preferredTrainerConfig.iterations,
+    depth: preferredTrainerConfig.depth,
+    learningRate: preferredTrainerConfig.learning_rate,
+  }));
+  const [model, setModel] = useState<ClfTrainResult | null>(null);
+  const [commitUrl, setCommitUrl] = useState<string | null>(null);
+  const [statusUrl, setStatusUrl] = useState<string | null>(null);
+  const [predictCounts, setPredictCounts] = useState<ClfPredictCounts | null>(null);
+  const [runId, setRunId] = useState<string | null>(null);
+  const [probaClassIndex, setProbaClassIndex] = useState(0);
+  const [probaThresholds, setProbaThresholds] = useState<Record<number, number>>({});
+  const [probaUrl, setProbaUrl] = useState<string | null>(null);
+  const [training, setTraining] = useState(false);
+  const [predicting, setPredicting] = useState(false);
+  const [savingClass, setSavingClass] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const commitUrlRef = useRef<string | null>(null);
+  const statusUrlRef = useRef<string | null>(null);
+  const probaUrlRef = useRef<string | null>(null);
+  /** Raw softmax PNG per class (before threshold preview). */
+  const rawProbaBlobRef = useRef<Blob | null>(null);
+  const probaThresholdsRef = useRef(probaThresholds);
+  probaThresholdsRef.current = probaThresholds;
+  const modelRef = useRef(model);
+  modelRef.current = model;
+  const onExpiredRef = useRef(onFeatureJobExpired);
+  onExpiredRef.current = onFeatureJobExpired;
+  const onFeatureReadyRef = useRef(onFeatureReady);
+  onFeatureReadyRef.current = onFeatureReady;
+
+  // Keep Train knobs aligned with ipred trainer defaults when they change.
+  useEffect(() => {
+    setParams((p) => ({
+      ...p,
+      iterations: preferredTrainerConfig.iterations,
+      depth: preferredTrainerConfig.depth,
+      learningRate: preferredTrainerConfig.learning_rate,
+    }));
+  }, [preferredTrainerConfig]);
+
+  const revokeProba = useCallback(() => {
+    if (probaUrlRef.current) {
+      URL.revokeObjectURL(probaUrlRef.current);
+      probaUrlRef.current = null;
+    }
+    setProbaUrl(null);
+    rawProbaBlobRef.current = null;
+  }, []);
+
+  const publishProbaPreview = useCallback(async (raw: Blob, threshold: number) => {
+    const preview = await thresholdProbaPngBlob(raw, threshold);
+    const url = URL.createObjectURL(preview);
+    if (probaUrlRef.current) URL.revokeObjectURL(probaUrlRef.current);
+    probaUrlRef.current = url;
+    setProbaUrl(url);
+  }, []);
+
+  const revokePredict = useCallback(() => {
+    if (commitUrlRef.current) {
+      URL.revokeObjectURL(commitUrlRef.current);
+      commitUrlRef.current = null;
+    }
+    if (statusUrlRef.current) {
+      URL.revokeObjectURL(statusUrlRef.current);
+      statusUrlRef.current = null;
+    }
+    setCommitUrl(null);
+    setStatusUrl(null);
+    setPredictCounts(null);
+    setRunId(null);
+    setProbaClassIndex(0);
+    setProbaThresholds({});
+    revokeProba();
+  }, [revokeProba]);
+
+  useEffect(() => {
+    setModel(null);
+    revokePredict();
+    setError(null);
+  }, [featureJobId, resetKey, preferredCompositionId, revokePredict]);
+
+  useEffect(
+    () => () => {
+      if (commitUrlRef.current) URL.revokeObjectURL(commitUrlRef.current);
+      if (statusUrlRef.current) URL.revokeObjectURL(statusUrlRef.current);
+      if (probaUrlRef.current) URL.revokeObjectURL(probaUrlRef.current);
+    },
+    [],
+  );
+
+  const ensureSession = useCallback(async (): Promise<string> => {
+    if (ipredSessionId) return ipredSessionId;
+    if (!source || !kind) throw new Error('No sample open');
+    const session = await openIpredSession({
+      kind,
+      source,
+      server_uri: serverUri,
+      root: kind === 'local' ? localRoot : null,
+    });
+    setIpredSession({
+      sessionId: session.session_id,
+      projectId: session.project_id,
+    });
+    return session.session_id;
+  }, [ipredSessionId, source, kind, serverUri, localRoot, setIpredSession]);
+
+  const ensureFeatureBank = useCallback(async (): Promise<string> => {
+    if (featureJobId) return featureJobId;
+    if (!preferredCompositionId) {
+      throw new Error('Select a composition first.');
+    }
+    const sessionId = await ensureSession();
+    const bank = await ipredPreprocess({
+      session_id: sessionId,
+      composition_id: preferredCompositionId,
+      slice_index: sliceIndex,
+    });
+    onFeatureReadyRef.current?.({
+      featureId: bank.feature_id,
+      width: bank.width,
+      height: bank.height,
+      labels: bank.labels ?? [],
+      nChannels: bank.n_channels,
+      setupId: bank.setup_id,
+      cacheHit: bank.cache_hit,
+    });
+    return bank.feature_id;
+  }, [featureJobId, preferredCompositionId, ensureSession, sliceIndex]);
+
+  const loadProbaChannel = useCallback(
+    async (run: string, classIndex: number) => {
+      const res = await fetch(ipredRunProbaUrl(run, classIndex));
+      if (!res.ok) throw new Error(`Failed to load class ${classIndex} probability map`);
+      const blob = await res.blob();
+      rawProbaBlobRef.current = blob;
+      setProbaClassIndex(classIndex);
+      const m = modelRef.current;
+      const cid = m?.classIds[classIndex];
+      const t =
+        cid !== undefined
+          ? (probaThresholdsRef.current[cid] ?? DEFAULT_PROBA_THRESHOLD)
+          : DEFAULT_PROBA_THRESHOLD;
+      await publishProbaPreview(blob, t);
+    },
+    [publishProbaPreview],
+  );
+
+  const train = useCallback(
+    async (shapes: Shape[]) => {
+      if (training) return;
+      if (!preferredCompositionId) {
+        setError('Select a composition first.');
+        return;
+      }
+      setTraining(true);
+      setError(null);
+      revokePredict();
+      try {
+        const sessionId = await ensureSession();
+        const featureId = await ensureFeatureBank();
+        const data = await ipredTrain({
+          session_id: sessionId,
+          shapes,
+          feature_id: featureId,
+          trainer_id: preferredTrainerId,
+          config: {
+            iterations: params.iterations,
+            depth: params.depth,
+            learning_rate: params.learningRate,
+          },
+        });
+        const result: ClfTrainResult = {
+          modelId: data.model_id,
+          featureId: data.feature_id,
+          nSamples: data.n_samples,
+          nTrain: data.n_train,
+          nCal: data.n_cal,
+          classIds: data.class_ids,
+          trainAccuracy: data.train_accuracy,
+          params: { ...params },
+          nTrees: Number(data.params?.iterations ?? params.iterations),
+          usesSam: !!(data.params as { uses_sam?: boolean } | undefined)?.uses_sam,
+          featureImportances: (data.feature_importances ?? []).map((fi) => ({
+            label: fi.label,
+            importance: fi.importance,
+          })),
+          trainerId: data.trainer_id,
+          compositionId: preferredCompositionId,
+        };
+        setModel(result);
+      } catch (e) {
+        setModel(null);
+        const msg = e instanceof Error ? e.message : String(e);
+        if (/unknown feature|not found/i.test(msg)) {
+          onExpiredRef.current?.();
+          setError('Feature bank missing. Compute or Train again (auto-preprocesses).');
+        } else {
+          setError(msg);
+        }
+      } finally {
+        setTraining(false);
+      }
+    },
+    [
+      training,
+      preferredCompositionId,
+      preferredTrainerId,
+      params,
+      revokePredict,
+      ensureSession,
+      ensureFeatureBank,
+    ],
+  );
+
+  const predict = useCallback(
+    async (_shapes: Shape[]) => {
+      if (!model || predicting) return;
+      setPredicting(true);
+      setError(null);
+      try {
+        const sessionId = await ensureSession();
+        const featureId = model.featureId || (await ensureFeatureBank());
+        const run = await ipredInfer({
+          session_id: sessionId,
+          model_id: model.modelId,
+          feature_id: featureId,
+          alpha: params.alpha,
+        });
+        const [commitRes, statusRes] = await Promise.all([
+          fetch(ipredRunCommitUrl(run.run_id)),
+          fetch(ipredRunStatusUrl(run.run_id)),
+        ]);
+        if (!commitRes.ok || !statusRes.ok) {
+          throw new Error('Failed to fetch conformal prediction PNGs');
+        }
+        const [commitBlob, statusBlob] = await Promise.all([commitRes.blob(), statusRes.blob()]);
+        const cUrl = URL.createObjectURL(commitBlob);
+        const sUrl = URL.createObjectURL(statusBlob);
+        if (commitUrlRef.current) URL.revokeObjectURL(commitUrlRef.current);
+        if (statusUrlRef.current) URL.revokeObjectURL(statusUrlRef.current);
+        commitUrlRef.current = cUrl;
+        statusUrlRef.current = sUrl;
+        setCommitUrl(cUrl);
+        setStatusUrl(sUrl);
+        setPredictCounts(run.counts);
+        setRunId(run.run_id);
+        const classIds = run.class_ids?.length ? run.class_ids : model.classIds;
+        setModel((m) => (m ? { ...m, classIds } : m));
+        const thresholds: Record<number, number> = {};
+        for (const cid of classIds) thresholds[cid] = DEFAULT_PROBA_THRESHOLD;
+        setProbaThresholds(thresholds);
+        await loadProbaChannel(run.run_id, 0);
+      } catch (e) {
+        revokePredict();
+        setError(e instanceof Error ? e.message : String(e));
+      } finally {
+        setPredicting(false);
+      }
+    },
+    [model, predicting, params.alpha, ensureSession, ensureFeatureBank, revokePredict, loadProbaChannel],
+  );
+
+  const selectProbaClass = useCallback(
+    async (index: number) => {
+      if (!runId || !model) return;
+      const n = model.classIds.length;
+      if (n < 1) return;
+      const next = ((index % n) + n) % n;
+      setError(null);
+      try {
+        await loadProbaChannel(runId, next);
+      } catch (e) {
+        setError(e instanceof Error ? e.message : String(e));
+      }
+    },
+    [runId, model, loadProbaChannel],
+  );
+
+  const cycleProbaClass = useCallback(
+    (delta: number) => {
+      void selectProbaClass(probaClassIndex + delta);
+    },
+    [selectProbaClass, probaClassIndex],
+  );
+
+  const setProbaThreshold = useCallback(
+    (threshold: number) => {
+      if (!model) return;
+      const classId = model.classIds[probaClassIndex];
+      if (classId === undefined) return;
+      const t = Math.min(1, Math.max(0, threshold));
+      setProbaThresholds((prev) => ({ ...prev, [classId]: t }));
+      const raw = rawProbaBlobRef.current;
+      if (raw) {
+        void publishProbaPreview(raw, t).catch((e) => {
+          setError(e instanceof Error ? e.message : String(e));
+        });
+      }
+    },
+    [model, probaClassIndex, publishProbaPreview],
+  );
+
+  const activeProbaClassId = model?.classIds[probaClassIndex] ?? null;
+  const activeProbaThreshold =
+    activeProbaClassId !== null
+      ? (probaThresholds[activeProbaClassId] ?? DEFAULT_PROBA_THRESHOLD)
+      : DEFAULT_PROBA_THRESHOLD;
+
+  const saveThresholdedClass = useCallback(async (): Promise<IpredThresholdClassResult | null> => {
+    if (!runId || activeProbaClassId === null || savingClass) return null;
+    setSavingClass(true);
+    setError(null);
+    try {
+      return await ipredThresholdClass(runId, {
+        class_id: activeProbaClassId,
+        threshold: activeProbaThreshold,
+      });
+    } catch (e) {
+      setError(e instanceof Error ? e.message : String(e));
+      return null;
+    } finally {
+      setSavingClass(false);
+    }
+  }, [runId, activeProbaClassId, activeProbaThreshold, savingClass]);
+
+  const dismiss = useCallback(() => {
+    revokePredict();
+  }, [revokePredict]);
+
+  return {
+    params,
+    setParams,
+    model,
+    commitUrl,
+    statusUrl,
+    predictUrl: commitUrl,
+    predictCounts,
+    runId,
+    probaUrl,
+    probaClassIndex,
+    activeProbaClassId,
+    activeProbaThreshold,
+    selectProbaClass,
+    cycleProbaClass,
+    setProbaThreshold,
+    saveThresholdedClass,
+    savingClass,
+    training,
+    predicting,
+    error,
+    train,
+    predict,
+    dismiss,
+    compositionId: preferredCompositionId,
+    trainerId: preferredTrainerId,
+    canTrainWithoutJob: !!preferredCompositionId && !!source && !!kind,
+  };
+}
diff --git a/frontend/src/lib/featureManifold.test.ts b/frontend/src/lib/featureManifold.test.ts
new file mode 100644
index 0000000..e354150
--- /dev/null
+++ b/frontend/src/lib/featureManifold.test.ts
@@ -0,0 +1,36 @@
+import { describe, expect, it } from 'vitest';
+import { colorizeManifoldHeatmap, manifoldMarkerRect } from './featureManifold';
+
+describe('colorizeManifoldHeatmap', () => {
+  it('returns a canvas matching the grayscale map size', () => {
+    const w = 4;
+    const h = 3;
+    const gray = new Uint8Array(w * h);
+    gray[0] = 255;
+    gray[1] = 0;
+    const canvas = colorizeManifoldHeatmap(gray, w, h, 0.5);
+    expect(canvas.width).toBe(w);
+    expect(canvas.height).toBe(h);
+  });
+});
+
+describe('manifoldMarkerRect', () => {
+  it('uses server box coordinates when present (no edge shift)', () => {
+    const r = manifoldMarkerRect(
+      { x: 10, y: 10, box_size: 64, box: { x0: 0, y0: 0, x1: 32, y1: 32 } },
+      { side: 64, width: 400, height: 300 },
+    );
+    expect(r).toEqual({ x: 0, y: 0, width: 32, height: 32 });
+  });
+
+  it('clips reconstructed squares instead of shifting them', () => {
+    const r = manifoldMarkerRect(
+      { x: 10, y: 50 },
+      { side: 64, width: 200, height: 200 },
+    );
+    expect(r.x).toBe(0);
+    expect(r.width).toBe(42); // 10 + 32, clipped — not shifted to 64 wide
+    expect(r.y).toBe(18);
+    expect(r.height).toBe(64);
+  });
+});
diff --git a/frontend/src/lib/featureManifold.ts b/frontend/src/lib/featureManifold.ts
new file mode 100644
index 0000000..fdf2f0e
--- /dev/null
+++ b/frontend/src/lib/featureManifold.ts
@@ -0,0 +1,92 @@
+/**
+ * Feature-manifold heatmap helpers for Suggest Labels overlay.
+ */
+import { loadLabelPng } from '@/lib/pixelClf';
+
+export interface ManifoldPoint {
+  x: number;
+  y: number;
+  cluster: number;
+  score?: number;
+  /** Exclusion / packing radius — spacing only, not box half-size. */
+  radius?: number;
+  dist?: number;
+  /** Full square side in image pixels (preferred for drawing). */
+  box_size?: number;
+  /** Axis-aligned box; x1/y1 are exclusive max coords (width = x1 − x0). */
+  box?: { x0: number; y0: number; x1: number; y1: number };
+}
+
+/**
+ * Axis-aligned marker rect in image pixels for a suggest-label center.
+ *
+ * Prefer the server `box` (already clipped, non-overlapping). Never use
+ * exclusion `radius` for geometry. When reconstructing, **clip** — do not
+ * shift to preserve side length (shifting causes overlays to overlap).
+ */
+export function manifoldMarkerRect(
+  point: Pick<ManifoldPoint, 'x' | 'y' | 'box_size' | 'box'>,
+  opts: { side?: number; width: number; height: number },
+): { x: number; y: number; width: number; height: number } {
+  if (point.box) {
+    const { x0, y0, x1, y1 } = point.box;
+    return {
+      x: x0,
+      y: y0,
+      width: Math.max(1, x1 - x0),
+      height: Math.max(1, y1 - y0),
+    };
+  }
+  const side = Math.max(8, opts.side ?? point.box_size ?? 64);
+  const half = side / 2;
+  const x0 = Math.max(0, point.x - half);
+  const y0 = Math.max(0, point.y - half);
+  const x1 = Math.min(opts.width, point.x + half);
+  const y1 = Math.min(opts.height, point.y + half);
+  return {
+    x: x0,
+    y: y0,
+    width: Math.max(1, x1 - x0),
+    height: Math.max(1, y1 - y0),
+  };
+}
+
+/** Cyan→magenta coverage colormap; alpha scales with score. */
+export function colorizeManifoldHeatmap(
+  gray: Uint8Array,
+  width: number,
+  height: number,
+  opacity = 0.45,
+): HTMLCanvasElement {
+  const canvas = document.createElement('canvas');
+  canvas.width = width;
+  canvas.height = height;
+  const ctx = canvas.getContext('2d');
+  if (!ctx) return canvas;
+  const img = ctx.createImageData(width, height);
+  const out = img.data;
+  const aScale = Math.round(Math.min(1, Math.max(0, opacity)) * 255);
+  for (let i = 0; i < gray.length; i++) {
+    const t = gray[i] / 255;
+    // low residual (dark) → cool; high residual interestingness → warm
+    const r = Math.round(40 + 200 * t);
+    const g = Math.round(180 * (1 - t) + 40 * t);
+    const b = Math.round(220 * (1 - 0.3 * t));
+    const p = i * 4;
+    out[p] = r;
+    out[p + 1] = g;
+    out[p + 2] = b;
+    out[p + 3] = Math.round(aScale * (0.15 + 0.85 * t));
+  }
+  ctx.putImageData(img, 0, 0);
+  return canvas;
+}
+
+/** Load grayscale coverage PNG and colorize. */
+export async function loadManifoldHeatmapCanvas(
+  url: string,
+  opacity = 0.45,
+): Promise<HTMLCanvasElement> {
+  const { data, width, height } = await loadLabelPng(url);
+  return colorizeManifoldHeatmap(data, width, height, opacity);
+}
diff --git a/frontend/src/lib/ipredApi.ts b/frontend/src/lib/ipredApi.ts
new file mode 100644
index 0000000..240b99d
--- /dev/null
+++ b/frontend/src/lib/ipredApi.ts
@@ -0,0 +1,313 @@
+/**
+ * Thin client for Annotate → ipred proxy (`/api/ipred/*`).
+ * Frontend never talks to the ipred port directly.
+ */
+import { API_BASE } from '@/config';
+
+export interface IpredSession {
+  session_id: string;
+  project_id: string;
+  current_feature_id?: string | null;
+  current_model_id?: string | null;
+  current_run_id?: string | null;
+}
+
+export interface FeatureSetup {
+  id: string;
+  name: string;
+  kind: 'procedure' | 'weights' | string;
+  builtin?: boolean;
+  procedure_id?: string;
+  params?: Record<string, unknown>;
+  encoder_setup_id?: string;
+  weights_path?: string;
+  weights_format?: string;
+  inference?: Record<string, unknown>;
+  content_hash?: string;
+}
+
+export interface IpredPreprocessResult {
+  feature_id: string;
+  project_id: string;
+  setup_id: string;
+  slice_index: number;
+  n_channels: number;
+  height: number;
+  width: number;
+  labels: string[];
+  cache_hit: boolean;
+  blob_dir?: string;
+}
+
+async function parseError(res: Response): Promise<Error> {
+  const text = await res.text();
+  try {
+    const j = JSON.parse(text) as { detail?: unknown };
+    if (typeof j.detail === 'string') return new Error(j.detail);
+    return new Error(text || res.statusText);
+  } catch {
+    return new Error(text || res.statusText);
+  }
+}
+
+export async function ipredHealth(): Promise<{ status: string; service?: string }> {
+  const res = await fetch(`${API_BASE}/api/ipred/health`);
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<{ status: string; service?: string }>;
+}
+
+export async function openIpredSession(payload: {
+  kind: string;
+  source: string;
+  server_uri?: string | null;
+  root?: string | null;
+}): Promise<IpredSession> {
+  const res = await fetch(`${API_BASE}/api/ipred/sessions`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify(payload),
+  });
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<IpredSession>;
+}
+
+export async function listIpredSetups(): Promise<FeatureSetup[]> {
+  const res = await fetch(`${API_BASE}/api/ipred/setups`);
+  if (!res.ok) throw await parseError(res);
+  const body = (await res.json()) as { setups: FeatureSetup[] };
+  return body.setups ?? [];
+}
+
+export async function getIpredSetup(setupId: string): Promise<FeatureSetup> {
+  const res = await fetch(`${API_BASE}/api/ipred/setups/${encodeURIComponent(setupId)}`);
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<FeatureSetup>;
+}
+
+export async function upsertIpredSetup(payload: {
+  name: string;
+  kind: string;
+  procedure_id?: string;
+  params?: Record<string, unknown>;
+  encoder_setup_id?: string | null;
+  weights_path?: string;
+  weights_format?: string;
+  inference?: Record<string, unknown>;
+  setup_id?: string;
+}): Promise<FeatureSetup> {
+  const res = await fetch(`${API_BASE}/api/ipred/setups`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify(payload),
+  });
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<FeatureSetup>;
+}
+
+export async function listIpredTrainers(): Promise<string[]> {
+  const res = await fetch(`${API_BASE}/api/ipred/trainers`);
+  if (!res.ok) throw await parseError(res);
+  const body = (await res.json()) as { trainers: string[] };
+  return body.trainers ?? [];
+}
+
+export interface FeatureModuleInfo {
+  id: string;
+  name: string;
+  description: string;
+  runtime: string;
+  ready: boolean;
+  accepts_input_from: boolean;
+  produces_channels: boolean;
+  produces_embedding: boolean;
+  params_schema: Record<string, unknown>;
+}
+
+export interface CompositionNode {
+  id: string;
+  module: string;
+  params?: Record<string, unknown>;
+  input_from?: string;
+}
+
+export interface CompositionDoc {
+  id: string;
+  name: string;
+  kind?: string;
+  builtin?: boolean;
+  nodes: CompositionNode[];
+  outputs: string[];
+  content_hash?: string;
+  preview_labels?: string[];
+}
+
+export async function listIpredModules(): Promise<FeatureModuleInfo[]> {
+  const res = await fetch(`${API_BASE}/api/ipred/modules`);
+  if (!res.ok) throw await parseError(res);
+  const body = (await res.json()) as { modules: FeatureModuleInfo[] };
+  return body.modules ?? [];
+}
+
+export async function listIpredCompositions(): Promise<CompositionDoc[]> {
+  const res = await fetch(`${API_BASE}/api/ipred/compositions`);
+  if (!res.ok) throw await parseError(res);
+  const body = (await res.json()) as { compositions: CompositionDoc[] };
+  return body.compositions ?? [];
+}
+
+export async function getIpredComposition(id: string): Promise<CompositionDoc> {
+  const res = await fetch(
+    `${API_BASE}/api/ipred/compositions/${encodeURIComponent(id)}`,
+  );
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<CompositionDoc>;
+}
+
+export async function upsertIpredComposition(payload: {
+  name: string;
+  nodes: CompositionNode[];
+  outputs: string[];
+  composition_id?: string;
+  builtin?: boolean;
+}): Promise<CompositionDoc> {
+  const res = await fetch(`${API_BASE}/api/ipred/compositions`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify(payload),
+  });
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<CompositionDoc>;
+}
+
+export async function previewIpredComposition(payload: {
+  name?: string;
+  nodes: CompositionNode[];
+  outputs: string[];
+}): Promise<{ preview_labels: string[] }> {
+  const res = await fetch(`${API_BASE}/api/ipred/compositions/preview`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ name: payload.name ?? 'preview', ...payload }),
+  });
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<{ preview_labels: string[] }>;
+}
+
+export async function ipredPreprocess(payload: {
+  session_id: string;
+  feature_setup_id?: string;
+  composition_id?: string;
+  slice_index?: number;
+  array_ref?: string;
+}): Promise<IpredPreprocessResult> {
+  const res = await fetch(`${API_BASE}/api/ipred/preprocess`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify(payload),
+  });
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<IpredPreprocessResult>;
+}
+
+export function ipredChannelUrl(featureId: string, index: number): string {
+  return `${API_BASE}/api/ipred/features/${encodeURIComponent(featureId)}/channels/${index}`;
+}
+
+export interface IpredFeatureImportance {
+  label: string;
+  importance: number;
+}
+
+export interface IpredTrainResult {
+  model_id: string;
+  feature_id: string;
+  trainer_id: string;
+  class_ids: number[];
+  train_accuracy: number;
+  n_train: number;
+  n_cal: number;
+  n_samples: number;
+  params: Record<string, unknown>;
+  /** Trainer-specific (CatBoost); omit or empty when unavailable. */
+  feature_importances?: IpredFeatureImportance[];
+}
+
+export interface IpredInferResult {
+  run_id: string;
+  model_id: string;
+  feature_id: string;
+  alpha: number;
+  class_ids: number[];
+  counts: { singleton: number; multi: number; abstain: number };
+  q_by_class?: Record<string, number>;
+}
+
+export async function ipredTrain(payload: {
+  session_id: string;
+  shapes: unknown[];
+  feature_id?: string | null;
+  trainer_id?: string;
+  config?: Record<string, unknown>;
+}): Promise<IpredTrainResult> {
+  const res = await fetch(`${API_BASE}/api/ipred/train`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify(payload),
+  });
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<IpredTrainResult>;
+}
+
+export async function ipredInfer(payload: {
+  session_id: string;
+  model_id?: string | null;
+  feature_id?: string | null;
+  alpha?: number;
+}): Promise<IpredInferResult> {
+  const res = await fetch(`${API_BASE}/api/ipred/infer`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify(payload),
+  });
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<IpredInferResult>;
+}
+
+export function ipredRunCommitUrl(runId: string): string {
+  return `${API_BASE}/api/ipred/runs/${encodeURIComponent(runId)}/commit.png`;
+}
+
+export function ipredRunStatusUrl(runId: string): string {
+  return `${API_BASE}/api/ipred/runs/${encodeURIComponent(runId)}/status.png`;
+}
+
+export function ipredRunProbaUrl(runId: string, classIndex: number): string {
+  return `${API_BASE}/api/ipred/runs/${encodeURIComponent(runId)}/proba/${classIndex}.png`;
+}
+
+export interface IpredThresholdClassResult {
+  run_id: string;
+  class_id: number;
+  class_index: number;
+  threshold: number;
+  width: number;
+  height: number;
+  n_positive: number;
+  label_map_b64: string;
+}
+
+export async function ipredThresholdClass(
+  runId: string,
+  payload: { class_id: number; threshold: number },
+): Promise<IpredThresholdClassResult> {
+  const res = await fetch(
+    `${API_BASE}/api/ipred/runs/${encodeURIComponent(runId)}/threshold-class`,
+    {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify(payload),
+    },
+  );
+  if (!res.ok) throw await parseError(res);
+  return res.json() as Promise<IpredThresholdClassResult>;
+}
diff --git a/frontend/src/lib/pixelClf.overlay.test.ts b/frontend/src/lib/pixelClf.overlay.test.ts
new file mode 100644
index 0000000..cb55a76
--- /dev/null
+++ b/frontend/src/lib/pixelClf.overlay.test.ts
@@ -0,0 +1,23 @@
+import { describe, expect, it } from 'vitest';
+import { colorizeConformalOverlay } from './pixelClf';
+
+describe('colorizeConformalOverlay filters', () => {
+  it('builds a canvas with class / multi / abstain filters without throwing', () => {
+    const w = 2;
+    const h = 2;
+    const commit = new Uint8Array([1, 2, 0, 0]);
+    const status = new Uint8Array([1, 1, 2, 0]);
+    const colors = new Map([
+      [1, '#ff0000'],
+      [2, '#00ff00'],
+    ]);
+
+    const canvas = colorizeConformalOverlay(commit, status, w, h, colors, {
+      classVisible: (cid) => cid === 1,
+      showMulti: false,
+      showAbstain: false,
+    });
+    expect(canvas.width).toBe(2);
+    expect(canvas.height).toBe(2);
+  });
+});
diff --git a/frontend/src/lib/pixelClf.test.ts b/frontend/src/lib/pixelClf.test.ts
new file mode 100644
index 0000000..8275bd3
--- /dev/null
+++ b/frontend/src/lib/pixelClf.test.ts
@@ -0,0 +1,100 @@
+import { describe, expect, it } from 'vitest';
+import {
+  applyProbaThresholdRgba,
+  classProbaMaskSetName,
+  labelMapToPolygonShapes,
+  parseHexColor,
+} from './pixelClf';
+
+describe('parseHexColor', () => {
+  it('parses 6-digit hex', () => {
+    expect(parseHexColor('#ff0000')).toEqual([255, 0, 0]);
+  });
+});
+
+describe('classProbaMaskSetName', () => {
+  it('formats label and percent threshold', () => {
+    expect(classProbaMaskSetName(2, 0.65, 'membrane')).toBe('membrane p≥65%');
+  });
+
+  it('falls back to class id when label missing', () => {
+    expect(classProbaMaskSetName(3, 0.5, null)).toBe('class 3 p≥50%');
+  });
+});
+
+describe('applyProbaThresholdRgba', () => {
+  it('makes pixels below the cut transparent and colors the rest viridis', () => {
+    const rgba = new Uint8ClampedArray([
+      100, 100, 100, 255,
+      255, 255, 255, 255,
+    ]);
+    applyProbaThresholdRgba(rgba, 0.5); // cut = 0.5
+    expect(rgba[3]).toBe(0); // below: transparent
+    expect(rgba[7]).toBe(255); // above: opaque
+    // p=1 remaps to viridis end (yellowish)
+    expect(rgba[4]).toBeGreaterThan(200);
+    expect(rgba[5]).toBeGreaterThan(200);
+  });
+});
+
+describe('labelMapToPolygonShapes', () => {
+  it('emits a polygon for a solid class blob', () => {
+    const w = 16;
+    const h = 16;
+    const labels = new Uint8Array(w * h);
+    for (let y = 4; y < 12; y++) {
+      for (let x = 4; x < 12; x++) labels[y * w + x] = 1;
+    }
+    const shapes = labelMapToPolygonShapes(labels, w, h, [1], { minRegion: 4, smooth: 0 });
+    expect(shapes.length).toBeGreaterThanOrEqual(1);
+    expect(shapes[0].classId).toBe(1);
+    expect(shapes[0].kind).toBe('polygon');
+    expect(shapes[0].points.length).toBeGreaterThanOrEqual(6);
+  });
+
+  it('skips pixels covered by preserveShapes', () => {
+    const w = 16;
+    const h = 16;
+    const labels = new Uint8Array(w * h).fill(1);
+    const preserve = [
+      {
+        id: 'a',
+        classId: 1,
+        kind: 'rectangle' as const,
+        x: 0,
+        y: 0,
+        w: 16,
+        h: 16,
+      },
+    ];
+    const shapes = labelMapToPolygonShapes(labels, w, h, [1], {
+      minRegion: 4,
+      preserveShapes: preserve,
+      smooth: 0,
+    });
+    expect(shapes).toHaveLength(0);
+  });
+
+  it('carves holes so nested class regions do not fill through each other', () => {
+    const w = 32;
+    const h = 32;
+    const labels = new Uint8Array(w * h);
+    // Outer class 1 border; inner 10×10 is class 2
+    for (let y = 2; y < 30; y++) {
+      for (let x = 2; x < 30; x++) labels[y * w + x] = 1;
+    }
+    for (let y = 11; y < 21; y++) {
+      for (let x = 11; x < 21; x++) labels[y * w + x] = 2;
+    }
+    const shapes = labelMapToPolygonShapes(labels, w, h, [1, 2], {
+      minRegion: 4,
+      smooth: 0,
+    });
+    const c1 = shapes.filter((s) => s.classId === 1);
+    const c2 = shapes.filter((s) => s.classId === 2);
+    expect(c1.length).toBeGreaterThanOrEqual(1);
+    expect(c2.length).toBeGreaterThanOrEqual(1);
+    // Class 1 must have a hole for the class 2 island (pixel-accurate commit).
+    expect(c1.some((s) => (s.holes?.length ?? 0) > 0)).toBe(true);
+  });
+});
diff --git a/frontend/src/lib/pixelClf.ts b/frontend/src/lib/pixelClf.ts
new file mode 100644
index 0000000..9f89f2a
--- /dev/null
+++ b/frontend/src/lib/pixelClf.ts
@@ -0,0 +1,280 @@
+/**
+ * Pixel-classifier helpers: label PNG → colorized overlay / polygon shapes.
+ */
+import { v4 as uuidv4 } from 'uuid';
+import { maskToPolygonsWithHoles } from '@/lib/magicwand';
+import { rasterizeUnion } from '@/lib/rasterize';
+import type { PolygonShape, Shape } from '@/stores/annotationStore';
+import { viridisRgb } from '@/lib/viridis';
+
+/** Parse #rgb / #rrggbb into [r,g,b]. */
+export function parseHexColor(hex: string): [number, number, number] {
+  const h = hex.replace('#', '');
+  if (h.length === 3) {
+    return [
+      parseInt(h[0] + h[0], 16),
+      parseInt(h[1] + h[1], 16),
+      parseInt(h[2] + h[2], 16),
+    ];
+  }
+  return [
+    parseInt(h.slice(0, 2), 16) || 0,
+    parseInt(h.slice(2, 4), 16) || 0,
+    parseInt(h.slice(4, 6), 16) || 0,
+  ];
+}
+
+/** Mask-set name for a thresholded softmax class cache. */
+export function classProbaMaskSetName(
+  classId: number,
+  threshold: number,
+  classLabel?: string | null,
+): string {
+  const t = Math.round(Math.min(1, Math.max(0, threshold)) * 100);
+  const label = (classLabel ?? '').trim() || `class ${classId}`;
+  return `${label} p≥${t}%`;
+}
+
+/**
+ * Colorize grayscale probability RGBA with viridis; pixels below ``threshold``
+ * are transparent. Values at/above the cut are remapped to [0,1] so the full
+ * colormap spans the visible range.
+ */
+export function applyProbaThresholdRgba(
+  rgba: Uint8ClampedArray,
+  threshold: number,
+): void {
+  const t = Math.min(1, Math.max(0, threshold));
+  const span = Math.max(1e-6, 1 - t);
+  for (let i = 0; i < rgba.length; i += 4) {
+    const p = rgba[i] / 255;
+    if (p < t) {
+      rgba[i] = 0;
+      rgba[i + 1] = 0;
+      rgba[i + 2] = 0;
+      rgba[i + 3] = 0;
+      continue;
+    }
+    const [r, g, b] = viridisRgb((p - t) / span);
+    rgba[i] = r;
+    rgba[i + 1] = g;
+    rgba[i + 2] = b;
+    rgba[i + 3] = 255;
+  }
+}
+
+/** Render a softmax grayscale PNG as viridis, clipped below ``threshold``. */
+export async function thresholdProbaPngBlob(
+  sourceBlob: Blob,
+  threshold: number,
+): Promise<Blob> {
+  const url = URL.createObjectURL(sourceBlob);
+  try {
+    const img = await new Promise<HTMLImageElement>((resolve, reject) => {
+      const el = new Image();
+      el.onload = () => resolve(el);
+      el.onerror = () => reject(new Error('Failed to decode probability PNG'));
+      el.src = url;
+    });
+    const width = img.naturalWidth;
+    const height = img.naturalHeight;
+    const canvas = document.createElement('canvas');
+    canvas.width = width;
+    canvas.height = height;
+    const ctx = canvas.getContext('2d');
+    if (!ctx) throw new Error('2D context unavailable');
+    ctx.drawImage(img, 0, 0);
+    const imageData = ctx.getImageData(0, 0, width, height);
+    applyProbaThresholdRgba(imageData.data, threshold);
+    ctx.putImageData(imageData, 0, 0);
+    const out = await new Promise<Blob>((resolve, reject) => {
+      canvas.toBlob(
+        (b) => (b ? resolve(b) : reject(new Error('toBlob failed'))),
+        'image/png',
+      );
+    });
+    return out;
+  } finally {
+    URL.revokeObjectURL(url);
+  }
+}
+
+/** Load a grayscale classId PNG into Uint8Array + dimensions. */
+export async function loadLabelPng(
+  url: string,
+): Promise<{ data: Uint8Array; width: number; height: number }> {
+  const img = await new Promise<HTMLImageElement>((resolve, reject) => {
+    const el = new Image();
+    el.onload = () => resolve(el);
+    el.onerror = () => reject(new Error('Failed to load prediction PNG'));
+    el.src = url;
+  });
+  const width = img.naturalWidth;
+  const height = img.naturalHeight;
+  const canvas = document.createElement('canvas');
+  canvas.width = width;
+  canvas.height = height;
+  const ctx = canvas.getContext('2d');
+  if (!ctx) throw new Error('2D context unavailable');
+  ctx.drawImage(img, 0, 0);
+  const rgba = ctx.getImageData(0, 0, width, height).data;
+  const data = new Uint8Array(width * height);
+  for (let i = 0, p = 0; i < data.length; i++, p += 4) data[i] = rgba[p];
+  return { data, width, height };
+}
+
+/**
+ * Build a translucent RGBA canvas (one pixel per classId with known color).
+ * Unknown / 0 stays transparent.
+ */
+export function colorizeLabelMap(
+  labels: Uint8Array,
+  width: number,
+  height: number,
+  colorByClass: Map<number, string>,
+  alpha = 110,
+): HTMLCanvasElement {
+  const canvas = document.createElement('canvas');
+  canvas.width = width;
+  canvas.height = height;
+  const ctx = canvas.getContext('2d');
+  if (!ctx) return canvas;
+  const img = ctx.createImageData(width, height);
+  const out = img.data;
+  const rgbCache = new Map<number, [number, number, number]>();
+  for (const [cid, hex] of colorByClass) rgbCache.set(cid, parseHexColor(hex));
+
+  for (let i = 0; i < labels.length; i++) {
+    const cid = labels[i];
+    if (!cid) continue;
+    const rgb = rgbCache.get(cid);
+    if (!rgb) continue;
+    const p = i * 4;
+    out[p] = rgb[0];
+    out[p + 1] = rgb[1];
+    out[p + 2] = rgb[2];
+    out[p + 3] = alpha;
+  }
+  ctx.putImageData(img, 0, 0);
+  return canvas;
+}
+
+/**
+ * Build conformal overlay: singleton = class color, multi = amber hatch,
+ * abstain = dark translucent.
+ *
+ * Optional filters hide prediction classes / multi / abstain for the Layers panel.
+ */
+export function colorizeConformalOverlay(
+  commit: Uint8Array,
+  status: Uint8Array,
+  width: number,
+  height: number,
+  colorByClass: Map<number, string>,
+  opts?: {
+    classVisible?: (classId: number) => boolean;
+    showMulti?: boolean;
+    showAbstain?: boolean;
+  },
+): HTMLCanvasElement {
+  const canvas = document.createElement('canvas');
+  canvas.width = width;
+  canvas.height = height;
+  const ctx = canvas.getContext('2d');
+  if (!ctx) return canvas;
+  const img = ctx.createImageData(width, height);
+  const out = img.data;
+  const rgbCache = new Map<number, [number, number, number]>();
+  for (const [cid, hex] of colorByClass) rgbCache.set(cid, parseHexColor(hex));
+  const classVisible = opts?.classVisible ?? (() => true);
+  const showMulti = opts?.showMulti !== false;
+  const showAbstain = opts?.showAbstain !== false;
+
+  for (let i = 0; i < commit.length; i++) {
+    const st = status[i];
+    const p = i * 4;
+    const x = i % width;
+    const y = (i / width) | 0;
+    if (st === 1) {
+      // singleton
+      const cid = commit[i];
+      if (!classVisible(cid)) continue;
+      const rgb = rgbCache.get(cid);
+      if (!rgb) continue;
+      out[p] = rgb[0];
+      out[p + 1] = rgb[1];
+      out[p + 2] = rgb[2];
+      out[p + 3] = 120;
+    } else if (st === 2) {
+      if (!showMulti) continue;
+      // multi — diagonal hatch in amber
+      const on = ((x + y) & 3) < 2;
+      out[p] = on ? 245 : 180;
+      out[p + 1] = on ? 158 : 120;
+      out[p + 2] = on ? 11 : 40;
+      out[p + 3] = on ? 140 : 70;
+    } else if (showAbstain) {
+      // abstain
+      out[p] = 30;
+      out[p + 1] = 30;
+      out[p + 2] = 40;
+      out[p + 3] = 90;
+    }
+  }
+  ctx.putImageData(img, 0, 0);
+  return canvas;
+}
+
+/**
+ * Convert a classId label map into polygon shapes.
+ * Each pixel contributes to at most one class (labels are already argmax).
+ * Uses outer+hole rings so one class does not fill through another (or through
+ * preserved scribble gaps) — matching the pixel overlay.
+ * Pixels covered by *preserveShapes* are skipped so Commit keeps annotations.
+ */
+export function labelMapToPolygonShapes(
+  labels: Uint8Array,
+  width: number,
+  height: number,
+  classIds: number[],
+  {
+    minRegion = 64,
+    preserveShapes,
+    smooth = 1,
+  }: { minRegion?: number; preserveShapes?: Shape[]; smooth?: number } = {},
+): PolygonShape[] {
+  const preserve =
+    preserveShapes && preserveShapes.length > 0
+      ? rasterizeUnion(preserveShapes, width, height, 1)
+      : null;
+
+  const shapes: PolygonShape[] = [];
+  for (const classId of classIds) {
+    const mask = new Uint8Array(width * height);
+    let any = false;
+    for (let i = 0; i < labels.length; i++) {
+      if (preserve && preserve[i]) continue;
+      if (labels[i] === classId) {
+        mask[i] = 1;
+        any = true;
+      }
+    }
+    if (!any) continue;
+    const polys = maskToPolygonsWithHoles(mask, width, height, {
+      minRegion,
+      scale: 1,
+      smooth,
+    });
+    for (const { points, holes } of polys) {
+      if (points.length < 6) continue;
+      shapes.push({
+        id: uuidv4(),
+        classId,
+        kind: 'polygon',
+        points,
+        ...(holes.length ? { holes } : {}),
+      });
+    }
+  }
+  return shapes;
+}
diff --git a/frontend/src/lib/viridis.ts b/frontend/src/lib/viridis.ts
new file mode 100644
index 0000000..14ce9bd
--- /dev/null
+++ b/frontend/src/lib/viridis.ts
@@ -0,0 +1,269 @@
+/**
+ * Viridis (Matplotlib) 256-stop RGB LUT for probability overlays.
+ */
+export const VIRIDIS_RGB: ReadonlyArray<readonly [number, number, number]> = [
+  [68, 1, 84],
+  [68, 2, 86],
+  [69, 4, 87],
+  [69, 5, 89],
+  [70, 7, 90],
+  [70, 8, 92],
+  [70, 10, 93],
+  [70, 11, 94],
+  [71, 13, 96],
+  [71, 14, 97],
+  [71, 16, 99],
+  [71, 17, 100],
+  [71, 19, 101],
+  [72, 20, 103],
+  [72, 22, 104],
+  [72, 23, 105],
+  [72, 24, 106],
+  [72, 26, 108],
+  [72, 27, 109],
+  [72, 28, 110],
+  [72, 29, 111],
+  [72, 31, 112],
+  [72, 32, 113],
+  [72, 33, 115],
+  [72, 35, 116],
+  [72, 36, 117],
+  [72, 37, 118],
+  [72, 38, 119],
+  [72, 40, 120],
+  [72, 41, 121],
+  [71, 42, 122],
+  [71, 44, 122],
+  [71, 45, 123],
+  [71, 46, 124],
+  [71, 47, 125],
+  [70, 48, 126],
+  [70, 50, 126],
+  [70, 51, 127],
+  [70, 52, 128],
+  [69, 53, 129],
+  [69, 55, 129],
+  [69, 56, 130],
+  [68, 57, 131],
+  [68, 58, 131],
+  [68, 59, 132],
+  [67, 61, 132],
+  [67, 62, 133],
+  [66, 63, 133],
+  [66, 64, 134],
+  [66, 65, 134],
+  [65, 66, 135],
+  [65, 68, 135],
+  [64, 69, 136],
+  [64, 70, 136],
+  [63, 71, 136],
+  [63, 72, 137],
+  [62, 73, 137],
+  [62, 74, 137],
+  [62, 76, 138],
+  [61, 77, 138],
+  [61, 78, 138],
+  [60, 79, 138],
+  [60, 80, 139],
+  [59, 81, 139],
+  [59, 82, 139],
+  [58, 83, 139],
+  [58, 84, 140],
+  [57, 85, 140],
+  [57, 86, 140],
+  [56, 88, 140],
+  [56, 89, 140],
+  [55, 90, 140],
+  [55, 91, 141],
+  [54, 92, 141],
+  [54, 93, 141],
+  [53, 94, 141],
+  [53, 95, 141],
+  [52, 96, 141],
+  [52, 97, 141],
+  [51, 98, 141],
+  [51, 99, 141],
+  [50, 100, 142],
+  [50, 101, 142],
+  [49, 102, 142],
+  [49, 103, 142],
+  [49, 104, 142],
+  [48, 105, 142],
+  [48, 106, 142],
+  [47, 107, 142],
+  [47, 108, 142],
+  [46, 109, 142],
+  [46, 110, 142],
+  [46, 111, 142],
+  [45, 112, 142],
+  [45, 113, 142],
+  [44, 113, 142],
+  [44, 114, 142],
+  [44, 115, 142],
+  [43, 116, 142],
+  [43, 117, 142],
+  [42, 118, 142],
+  [42, 119, 142],
+  [42, 120, 142],
+  [41, 121, 142],
+  [41, 122, 142],
+  [41, 123, 142],
+  [40, 124, 142],
+  [40, 125, 142],
+  [39, 126, 142],
+  [39, 127, 142],
+  [39, 128, 142],
+  [38, 129, 142],
+  [38, 130, 142],
+  [38, 130, 142],
+  [37, 131, 142],
+  [37, 132, 142],
+  [37, 133, 142],
+  [36, 134, 142],
+  [36, 135, 142],
+  [35, 136, 142],
+  [35, 137, 142],
+  [35, 138, 141],
+  [34, 139, 141],
+  [34, 140, 141],
+  [34, 141, 141],
+  [33, 142, 141],
+  [33, 143, 141],
+  [33, 144, 141],
+  [33, 145, 140],
+  [32, 146, 140],
+  [32, 146, 140],
+  [32, 147, 140],
+  [31, 148, 140],
+  [31, 149, 139],
+  [31, 150, 139],
+  [31, 151, 139],
+  [31, 152, 139],
+  [31, 153, 138],
+  [31, 154, 138],
+  [30, 155, 138],
+  [30, 156, 137],
+  [30, 157, 137],
+  [31, 158, 137],
+  [31, 159, 136],
+  [31, 160, 136],
+  [31, 161, 136],
+  [31, 161, 135],
+  [31, 162, 135],
+  [32, 163, 134],
+  [32, 164, 134],
+  [33, 165, 133],
+  [33, 166, 133],
+  [34, 167, 133],
+  [34, 168, 132],
+  [35, 169, 131],
+  [36, 170, 131],
+  [37, 171, 130],
+  [37, 172, 130],
+  [38, 173, 129],
+  [39, 173, 129],
+  [40, 174, 128],
+  [41, 175, 127],
+  [42, 176, 127],
+  [44, 177, 126],
+  [45, 178, 125],
+  [46, 179, 124],
+  [47, 180, 124],
+  [49, 181, 123],
+  [50, 182, 122],
+  [52, 182, 121],
+  [53, 183, 121],
+  [55, 184, 120],
+  [56, 185, 119],
+  [58, 186, 118],
+  [59, 187, 117],
+  [61, 188, 116],
+  [63, 188, 115],
+  [64, 189, 114],
+  [66, 190, 113],
+  [68, 191, 112],
+  [70, 192, 111],
+  [72, 193, 110],
+  [74, 193, 109],
+  [76, 194, 108],
+  [78, 195, 107],
+  [80, 196, 106],
+  [82, 197, 105],
+  [84, 197, 104],
+  [86, 198, 103],
+  [88, 199, 101],
+  [90, 200, 100],
+  [92, 200, 99],
+  [94, 201, 98],
+  [96, 202, 96],
+  [99, 203, 95],
+  [101, 203, 94],
+  [103, 204, 92],
+  [105, 205, 91],
+  [108, 205, 90],
+  [110, 206, 88],
+  [112, 207, 87],
+  [115, 208, 86],
+  [117, 208, 84],
+  [119, 209, 83],
+  [122, 209, 81],
+  [124, 210, 80],
+  [127, 211, 78],
+  [129, 211, 77],
+  [132, 212, 75],
+  [134, 213, 73],
+  [137, 213, 72],
+  [139, 214, 70],
+  [142, 214, 69],
+  [144, 215, 67],
+  [147, 215, 65],
+  [149, 216, 64],
+  [152, 216, 62],
+  [155, 217, 60],
+  [157, 217, 59],
+  [160, 218, 57],
+  [162, 218, 55],
+  [165, 219, 54],
+  [168, 219, 52],
+  [170, 220, 50],
+  [173, 220, 48],
+  [176, 221, 47],
+  [178, 221, 45],
+  [181, 222, 43],
+  [184, 222, 41],
+  [186, 222, 40],
+  [189, 223, 38],
+  [192, 223, 37],
+  [194, 223, 35],
+  [197, 224, 33],
+  [200, 224, 32],
+  [202, 225, 31],
+  [205, 225, 29],
+  [208, 225, 28],
+  [210, 226, 27],
+  [213, 226, 26],
+  [216, 226, 25],
+  [218, 227, 25],
+  [221, 227, 24],
+  [223, 227, 24],
+  [226, 228, 24],
+  [229, 228, 25],
+  [231, 228, 25],
+  [234, 229, 26],
+  [236, 229, 27],
+  [239, 229, 28],
+  [241, 229, 29],
+  [244, 230, 30],
+  [246, 230, 32],
+  [248, 230, 33],
+  [251, 231, 35],
+  [253, 231, 37],
+];
+
+/** Sample viridis; ``t`` in [0, 1] → RGB. */
+export function viridisRgb(t: number): [number, number, number] {
+  const x = Math.min(1, Math.max(0, t));
+  const i = Math.min(255, Math.max(0, Math.round(x * 255)));
+  const rgb = VIRIDIS_RGB[i]!;
+  return [rgb[0], rgb[1], rgb[2]];
+}
diff --git a/frontend/src/stores/ipredStore.ts b/frontend/src/stores/ipredStore.ts
new file mode 100644
index 0000000..f03ac74
--- /dev/null
+++ b/frontend/src/stores/ipredStore.ts
@@ -0,0 +1,67 @@
+/**
+ * ipredStore — preferences + session state for the iPred (interactive
+ * segmentation) service. Separate from connectionStore (data-source
+ * connection) and datasetStore (active sample), per this codebase's existing
+ * store-separation convention.
+ */
+import { create } from 'zustand';
+
+export interface TrainerConfig {
+  iterations: number;
+  depth: number;
+  learning_rate: number;
+}
+
+export const DEFAULT_TRAINER_CONFIG: TrainerConfig = {
+  iterations: 200,
+  depth: 6,
+  learning_rate: 0.1,
+};
+
+export const DEFAULT_COMPOSITION_ID = 'comp-skimage-slimsam';
+export const DEFAULT_TRAINER_ID = 'catboost';
+
+export interface IpredState {
+  /** Preferred composition document id (modular feature graph). */
+  preferredCompositionId: string;
+  /** Preferred trainer plugin id. */
+  preferredTrainerId: string;
+  preferredTrainerConfig: TrainerConfig;
+  /** Active ipred session for the currently opened sample. */
+  ipredSessionId: string | null;
+  ipredProjectId: string | null;
+  setPreferredCompositionId: (id: string) => void;
+  setPreferredTrainer: (payload: { id?: string; config?: Partial<TrainerConfig> }) => void;
+  setIpredSession: (payload: { sessionId: string | null; projectId?: string | null }) => void;
+  reset: () => void;
+}
+
+export const useIpredStore = create<IpredState>((set) => ({
+  preferredCompositionId: DEFAULT_COMPOSITION_ID,
+  preferredTrainerId: DEFAULT_TRAINER_ID,
+  preferredTrainerConfig: { ...DEFAULT_TRAINER_CONFIG },
+  ipredSessionId: null,
+  ipredProjectId: null,
+
+  setPreferredCompositionId: (id) => set({ preferredCompositionId: id }),
+
+  setPreferredTrainer: ({ id, config }) =>
+    set((s) => ({
+      preferredTrainerId: id ?? s.preferredTrainerId,
+      preferredTrainerConfig: config
+        ? { ...s.preferredTrainerConfig, ...config }
+        : s.preferredTrainerConfig,
+    })),
+
+  setIpredSession: ({ sessionId, projectId = null }) =>
+    set({ ipredSessionId: sessionId, ipredProjectId: projectId }),
+
+  reset: () =>
+    set({
+      preferredCompositionId: DEFAULT_COMPOSITION_ID,
+      preferredTrainerId: DEFAULT_TRAINER_ID,
+      preferredTrainerConfig: { ...DEFAULT_TRAINER_CONFIG },
+      ipredSessionId: null,
+      ipredProjectId: null,
+    }),
+}));
diff --git a/graphify-out/.graphify_labels.json b/graphify-out/.graphify_labels.json
new file mode 100644
index 0000000..65f3efd
--- /dev/null
+++ b/graphify-out/.graphify_labels.json
@@ -0,0 +1,297 @@
+{
+  "0": "catalog.ts",
+  "1": "scrape-sciviscolor.py",
+  "2": "ManagedBuffer",
+  "3": "thresholdFit.test.ts",
+  "4": "ipred_routes.py",
+  "5": "annotation_thumbnails.py",
+  "6": "run",
+  "7": "manifold.py",
+  "8": "HTTPException",
+  "9": "math/index.ts",
+  "10": "TemporalAccumulator",
+  "11": "annotation_server.py",
+  "12": "ipred_client.py",
+  "13": "coco_export.py",
+  "14": "Vec3",
+  "15": "tomojepa_onnx.py",
+  "16": "ColumnBrowser.tsx",
+  "17": "Quat",
+  "18": "math/color.ts",
+  "19": "devDependencies",
+  "20": "render/index.ts",
+  "21": "accel.spec.ts",
+  "22": "compilerOptions",
+  "23": "compilerOptions",
+  "24": "dimension.ts",
+  "25": "test_ingest_conflict.py",
+  "26": "App.tsx",
+  "27": "src/index.ts",
+  "28": "catalog.py",
+  "29": "write_stack",
+  "30": "ingest.py",
+  "31": "test_zarr_source.py",
+  "32": "Catalog",
+  "33": "TiledNotifications/index.ts",
+  "34": "camera.ts",
+  "35": "preprocess.py",
+  "36": "compositions.py",
+  "37": "Vec4",
+  "38": "step_phantom",
+  "39": "tiff_stack_source.py",
+  "40": "ipred/__init__.py",
+  "41": "train_infer.py",
+  "42": "volume/colormaps.ts",
+  "43": "Node",
+  "44": "Quantity",
+  "45": "Matrix",
+  "46": "ipred — architecture and operations guide",
+  "47": "AnnotationCanvas/index.tsx",
+  "48": "io/index.ts",
+  "49": "ipredApi.ts",
+  "50": "OrbitControls",
+  "51": "Vec2",
+  "52": "cli.py",
+  "53": "array.ts",
+  "54": "packing.ts",
+  "55": "scalar.ts",
+  "56": "feature_setups.py",
+  "57": "arrays.py",
+  "58": "IngestDropzone.tsx",
+  "59": "VectorGrid3",
+  "60": "core/index.ts",
+  "61": "getTiledBaseUrl",
+  "62": "constants.ts",
+  "63": "compilerOptions",
+  "64": "browse_helpers.py",
+  "65": "provenance.ts",
+  "66": "schemas.py",
+  "67": "FakeNode",
+  "68": "zarr_source.py",
+  "69": "dependencies",
+  "70": "ome-zarr.ts",
+  "71": "start_all.sh",
+  "72": "drafts.py",
+  "73": "tiled_clients.py",
+  "74": "App.jsx",
+  "75": "viewerState.ts",
+  "76": "devDependencies",
+  "77": "get_tiled_client",
+  "78": "useTabsStore.ts",
+  "79": "zarr-viewer/package.json",
+  "80": "volume-renderer.ts",
+  "81": "features.py",
+  "82": "TrainerPlugin",
+  "83": "sam_embed.py",
+  "84": "start_all.ps1",
+  "85": "Installation via Docker (Recommended)",
+  "86": "parse.ts",
+  "87": "TTLCache",
+  "88": "magicwand.ts",
+  "89": "Segmentation Annotation Studio",
+  "90": "tiled_mask_sync.py",
+  "91": "denoise.py",
+  "92": "Software architecture",
+  "93": "annotationStore.ts",
+  "94": "_fake_pyramid",
+  "95": "ManagedTexture",
+  "96": "FastAPI",
+  "97": "fake_stack",
+  "98": "geometry.ts",
+  "99": "length-units.ts",
+  "100": "codecs.ts",
+  "101": ".alignTo",
+  "102": "Vec3Like",
+  "103": "denoise_bake.py",
+  "104": "3. Annotate",
+  "105": "datasetStats.ts",
+  "106": "SamClient",
+  "107": "samWorker.ts",
+  "108": "TomoJepaModule",
+  "109": "fx/package.json",
+  "110": "predicates.ts",
+  "111": "test_tomojepa_embed.py",
+  "112": "export_jobs.py",
+  "113": "polybool.ts",
+  "114": "perf.ts",
+  "115": "compilerOptions",
+  "116": "list_module_catalog",
+  "117": ".multiplyScalar",
+  "118": "mergeSameClass.ts",
+  "119": "rasterizeShapes",
+  "120": "morphology.ts",
+  "121": "livewire.ts",
+  "122": "run_composition",
+  "123": "5. Export & download",
+  "124": "Mat3",
+  "125": "test_tiled_config.py",
+  "126": "frontend/package.json",
+  "127": "TestCascade",
+  "128": "systems.ts",
+  "129": "OmeZarrVolumeSource",
+  "130": "ViewportOverlay",
+  "131": "TestBuildLevel",
+  "132": "test_compositions.py",
+  "133": "test_local_fs.py",
+  "134": "editHistory.ts",
+  "135": "pixelClf.ts",
+  "136": "test_feature_setups.py",
+  "137": "test_tiff_stack_source.py",
+  "138": "Random",
+  "139": "array_source.py",
+  "140": "AGENTS.md — Segmentation Annotation Studio",
+  "141": "guides.py",
+  "142": "test_drafts.py",
+  "143": "quick-start.md",
+  "144": "Installation",
+  "145": "Quick start",
+  "146": "Emitter",
+  "147": "test_catalog_sessions.py",
+  "148": "import_dataset",
+  "149": "describe_methods",
+  "150": "clahe.ts",
+  "151": "TestBlockMean",
+  "152": "bboxPrefilter.equivalence.test.ts",
+  "153": "Pool",
+  "154": "Dual",
+  "155": "test_array_blobs.py",
+  "156": "test_preprocess_cache.py",
+  "157": "TestMultiscalesMetadata",
+  "158": "TestPyramidPlan",
+  "159": "usePixelClassifier.ts",
+  "160": "datasetStore.ts",
+  "161": "toolStore.ts",
+  "162": "vite-zarr-static.ts",
+  "163": "test_api_health.py",
+  "164": "main",
+  "165": "test_health.py",
+  "166": "TestPyramidStore",
+  "167": "Segmentation Annotation Studio",
+  "168": "Troubleshooting",
+  "169": "ClassManager/index.tsx",
+  "170": "Toolbar/index.tsx",
+  "171": "ZarrLoader.tsx",
+  "172": "BuildVolumePanel.tsx",
+  "173": "config.ts",
+  "174": "useGuideSync.ts",
+  "175": "useSave.ts",
+  "176": "regionOps.ts",
+  "177": "zarrUrl.ts",
+  "178": "ipredStore.ts",
+  "179": "layerVisibilityStore.ts",
+  "180": "OME-Zarr Viewer",
+  "181": ".preview_labels",
+  "182": "include",
+  "183": "Pool",
+  "184": "Soa",
+  "185": "_denoised_slice",
+  "186": "2. Browse & select",
+  "187": "1. Connect to data",
+  "188": "fetch-sam-model.mjs",
+  "189": "DenoiseBakeModal.tsx",
+  "190": "DownloadModal.tsx",
+  "191": "InsightsModal.tsx",
+  "192": "MeasurementPanel/index.tsx",
+  "193": "VersionHistoryModal.tsx",
+  "194": "useFeatureManifold.ts",
+  "195": "classColors.ts",
+  "196": "lib/colormaps.ts",
+  "197": "featureManifold.ts",
+  "198": "stretch.test.ts",
+  "199": ".preview_labels",
+  "200": ".preview_labels",
+  "201": "test_manifold.py",
+  "202": "test_train_rethreshold.py",
+  "203": ".preview_labels",
+  "204": "Keyboard shortcuts",
+  "205": "LayersPanel/index.tsx",
+  "206": "PerfOverlay/index.tsx",
+  "207": "PixelClassifierPanel/index.tsx",
+  "208": "SaveModal.tsx",
+  "209": "CompositionPanel/index.tsx",
+  "210": "useFeatureChannels.ts",
+  "211": "measure.ts",
+  "212": "buildSourceKey",
+  "214": "Mat4",
+  "215": "start.sh",
+  "216": "reingest_tiffs_as_individual.py",
+  "217": "DenoisePanel/index.tsx",
+  "218": "DisplayControls/index.tsx",
+  "219": "FeatureChannelsPanel/index.tsx",
+  "220": "HistogramControl/index.tsx",
+  "221": "VersionPreviewBar.tsx",
+  "222": "HubMainContent.tsx",
+  "223": "VolumeViewer.tsx",
+  "224": "useDraftSync.ts",
+  "225": "useExportJob.ts",
+  "226": "useMaskOps.ts",
+  "227": "useOpenInAnnotate.ts",
+  "228": "feedbackContext.ts",
+  "229": "adjust.ts",
+  "230": "sharpen.test.ts",
+  "231": "classStore.ts",
+  "232": "ratingStore.ts",
+  "233": "referenceGuideStore.ts",
+  "234": "settingsStore.ts",
+  "235": "fx.spec.ts",
+  "236": "frontend/vite.config.ts",
+  "237": "ipred/models",
+  "238": "test_emb_pca_channels.py",
+  "239": "test_tomojepa_onnx.py",
+  "240": "ExportConflict",
+  "241": "add_sample_metadata.py",
+  "242": "repair_catalog_paths.py",
+  "243": "Using the tool",
+  "244": "4. Annotation guide",
+  "245": "ExportPage.tsx",
+  "246": "MaskToolsPanel/index.tsx",
+  "247": "DebouncedSlider.tsx",
+  "248": "CustomizePages.tsx",
+  "249": "HubAppLayout.tsx",
+  "250": "HubHeader.tsx",
+  "251": "HubSidebar.tsx",
+  "252": "IframeModal.tsx",
+  "253": "useAnnotatedSourceKeys.ts",
+  "254": "useKeybinds.ts",
+  "255": "viridis.ts",
+  "256": "clipboardStore.ts",
+  "257": "connectionStore.ts",
+  "258": "annotationFilter.ts",
+  "259": "vite-env.d.ts",
+  "260": "frontend/tsconfig.json",
+  "261": "patches/README.md",
+  "262": "Button.tsx",
+  "263": "ItkVtkFrame.tsx",
+  "264": "Bundled Tiled catalog (committed to git)",
+  "265": "@phosphor-icons/react",
+  "266": "zundo",
+  "267": "@testing-library/react",
+  "268": "@types/node",
+  "269": "@types/react",
+  "270": "vite",
+  "271": "@vitejs/plugin-react",
+  "272": "vitest",
+  "273": "SliceNavigator/index.tsx",
+  "274": "useHubSelectedTabs.ts",
+  "275": "useSam.ts",
+  "276": "lib/utils.ts",
+  "277": "navigationRouterTypes.ts",
+  "278": "server.ts",
+  "279": "ItkVtkViewerComponent.jsx",
+  "280": "vitest.config.ts",
+  "281": "vite.config.js",
+  "285": "scripts/__init__.py",
+  "286": "tests/__init__.py",
+  "287": "postcss.config.js",
+  "288": "layerVisibilityStore.test.ts",
+  "289": "setup.ts",
+  "290": "webgpu.d.ts",
+  "291": "tailwind.config.js",
+  "292": "eslint.config.js",
+  "293": "tiled-auth-interceptor.js",
+  "294": ".open",
+  "295": ".send",
+  "296": "ipred",
+  "297": "sam3-annotation-studio-backend",
+  "298": "tailwindcss"
+}
diff --git a/graphify-out/.graphify_labels.json.sig b/graphify-out/.graphify_labels.json.sig
new file mode 100644
index 0000000..19d78ec
--- /dev/null
+++ b/graphify-out/.graphify_labels.json.sig
@@ -0,0 +1 @@
+{"0": "9d217d5b093f9b00", "1": "92cdfbb2d1ecb1a2", "2": "9a616e9ea1361378", "3": "a6c97f0cc7337225", "4": "98bcfd1a5bd7adcb", "5": "d0da13ba92ca6801", "6": "f21e3da03846ff67", "7": "91aaebacccdf767c", "8": "ff027da653010318", "9": "d1c220058fc92278", "10": "f0e70663e44e1a05", "11": "101eaf2d2e752985", "12": "516319be7679c476", "13": "6853106ceb4b03ac", "14": "5f69e94edbb58531", "15": "8bfe51bbcb111d30", "16": "97b4e312677b365f", "17": "2ff22e41ce6fed74", "18": "f407c526a350ca4d", "19": "3148edf587b6c890", "20": "bdf0e54f0966e28b", "21": "0ba0e83e5b5d7c75", "22": "389c7fd0ee1e4058", "23": "ebe797ee63178af6", "24": "5bfddf82440253e6", "25": "0b5e6f8403820f57", "26": "1675fd672f55c2d4", "27": "52fe7314cccfb9ea", "28": "4433076bb364609c", "29": "40bcc6f59b0ec048", "30": "a4a6735ae428f40d", "31": "f03f0da73dabcad0", "32": "36f741667d99e55f", "33": "5a876722406c90d2", "34": "82dac5af06ecd7d0", "35": "0279f139dd1f85c8", "36": "153107dc7a9f481c", "37": "d5ad367703d7183a", "38": 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"188": "936ff6eee9b40eee", "189": "474d5257e504d1e9", "190": "c0bb7c436aa209aa", "191": "bd7bf48a954afc2f", "192": "edf684a877a782af", "193": "cf047d97bcbe81fb", "194": "2fc2de6994f61a7d", "195": "3254a234d2c307f2", "196": "db55bac01d8f92f6", "197": "f71df20811f20e4e", "198": "c1a73bff64c481b7", "199": "0e68c4725a63bf66", "200": "ed8c12691837f8f7", "201": "fea4b6c7c8f759a6", "202": "fc150808baf87320", "203": "1bdda4d840e41fcb", "204": "acb857c221da4d58", "205": "bf0306f67f96ae01", "206": "237c9e1d4725c8aa", "207": "4ff50fea66f9d126", "208": "b9bcd4250a7ccb21", "209": "655f8100b2b21cae", "210": "dd5cc6fec20398a4", "211": "7b7dbfb4885293c8", "212": "47dc474c19917911", "214": "711d49738d580ea5", "215": "9197bf7a2d3d03f7", "216": "0b086af62a86e704", "217": "cdb6f75b5aa3deb5", "218": "031cc455f7cb660f", "219": "f8fa600c062b6931", "220": "8737765171cf8daa", "221": "e4800f1cfece4f3a", "222": "3467057adcb5a47d", "223": "7239632b13099e96", "224": "097080d71114d141", "225": "1a77aa9294813541", "226": "74ffcfc8898ff51d", "227": "5efb5617da941849", "228": "b822407890d2217e", "229": "fe4fa1a7b754a3e0", "230": "6adb5e6dd5c68508", "231": "debe5675379af729", "232": "52c1a9639abb9feb", "233": "f32214d70e4547e3", "234": "478c1a5ea7a7caec", "235": "e95ffd84953949e9", "236": "9523ce7f8596e683", "237": "a131aead127dce73", "238": "0e0cbe99478c1405", "239": "27ad6b6268e7f4bc", "240": "deb0010d484d18d3", "241": "2e275785b6e7cf7a", "242": "424f425bc17f73c5", "243": "05ae6eab6488bdb6", "244": "e0690b39dea5820f", "245": "46fc649b44772200", "246": "bb9ffc1d9b0368d5", "247": "d313af354155092b", "248": "0ff3684855fd010c", "249": "aac3212334fa899a", "250": "986bb318d0e8b278", "251": "5b98351ce17bddb5", "252": "fb3bea3f5cf2cfb2", "253": "360407397eaa9f8a", "254": "b4ac16c3ab0d31ff", "255": "2a1ac266844119d4", "256": "c6fd08d60215bee6", "257": "6d7e8b2f62af739d", "258": "006c5062ca738d84", "259": "8ad742a8b1f8d9d1", "260": "fa34349dea4896a5", "261": "890965940be37367", "262": "d17e5ce7908c5855", "263": "64d0bc0b4ac976fe", "264": "c416733e6365e588", "265": "c90173a9552046e0", "266": "80e2a3703868b1e0", "267": "2735bfc7a6304c2e", "268": "497f54d7a9d09619", "269": "51bc88f1c437863e", "270": "8a4119554c8e7b37", "271": "51ac4e0432215980", "272": "1d909e01dfb86af5", "273": "877b9ba68f57a851", "274": "f44e22801fb6cf45", "275": "b30122997068b1c2", "276": "f17c3963b45b3a48", "277": "6d3f82a82ee191c1", "278": "2b6a34d3b33e21ab", "279": "7faa051e7be3a1aa", "280": "7c21a2f83cf08ee6", "281": "80ca20158ec79144", "285": "03a189e5330ca906", "286": "7f606a615a0aa16a", "287": "00a416b22ad70b42", "288": "69463005feae5109", "289": "1537117b85b1bcc8", "290": "cf92e0d376ecf804", "291": "ff46d704a44a33a3", "292": "54e0ca4a179bb917", "293": "d7f9e0d5c9117322", "294": "49161c659b4956aa", "295": "5e1a6ce3bc340b39", "296": "d3a75079dea7ccef", "297": "2451684886c7721e", "298": "321732baccae846b"}
\ No newline at end of file
diff --git a/graphify-out/.graphify_root b/graphify-out/.graphify_root
new file mode 100644
index 0000000..945c9b4
--- /dev/null
+++ b/graphify-out/.graphify_root
@@ -0,0 +1 @@
+.
\ No newline at end of file
diff --git a/graphify-out/2026-08-26/.graphify_labels.json b/graphify-out/2026-08-26/.graphify_labels.json
new file mode 100644
index 0000000..56e4ee7
--- /dev/null
+++ b/graphify-out/2026-08-26/.graphify_labels.json
@@ -0,0 +1,289 @@
+{
+  "0": "catalog.ts",
+  "1": "get",
+  "2": "occupancy.ts",
+  "3": "thresholdFit.test.ts",
+  "4": "ipred_routes.py",
+  "5": "annotation_thumbnails.py",
+  "6": "run",
+  "7": "manifold.py",
+  "8": "api.py",
+  "9": "math/index.ts",
+  "10": "TemporalAccumulator",
+  "11": "annotation_server.py",
+  "12": "ipred_client.py",
+  "13": "coco_export.py",
+  "14": "Vec3",
+  "15": "tomojepa_onnx.py",
+  "16": "ColumnBrowser.tsx",
+  "17": "Quat",
+  "18": "math/color.ts",
+  "19": "devDependencies",
+  "20": "render/index.ts",
+  "21": "flip.ts",
+  "22": "compilerOptions",
+  "23": "compilerOptions",
+  "24": "dimension.ts",
+  "25": "test_ingest_conflict.py",
+  "26": "App.tsx",
+  "27": "src/index.ts",
+  "28": "catalog.py",
+  "29": "test_tiff_stack_source.py",
+  "30": "ingest.py",
+  "31": "test_zarr_source.py",
+  "32": "Catalog",
+  "33": "TiledNotifications/index.ts",
+  "34": "camera.ts",
+  "35": "ome-zarr-viewer.ts",
+  "36": "compositions.py",
+  "37": "Vec4",
+  "38": "step_phantom",
+  "39": "tiff_stack_source.py",
+  "40": "ipred/__init__.py",
+  "41": "train_infer.py",
+  "42": "volume/colormaps.ts",
+  "43": "Node",
+  "44": "Quantity",
+  "45": "Matrix",
+  "46": "ipred — architecture and operations guide",
+  "47": "AnnotationCanvas/index.tsx",
+  "48": "io/index.ts",
+  "49": "ipredApi.ts",
+  "50": "OrbitControls",
+  "51": "Vec2",
+  "52": "TrainerPlugin",
+  "53": "array.ts",
+  "54": "packing.ts",
+  "55": "scalar.ts",
+  "56": "feature_setups.py",
+  "57": "arrays.py",
+  "58": "IngestDropzone.tsx",
+  "59": "VectorGrid3",
+  "60": "core/index.ts",
+  "61": "getTiledBaseUrl",
+  "62": "constants.ts",
+  "63": "compilerOptions",
+  "64": "browse_helpers.py",
+  "66": "schemas.py",
+  "67": "FakeNode",
+  "68": "zarr_source.py",
+  "69": "dependencies",
+  "70": "ome-zarr.ts",
+  "71": "start_all.sh",
+  "72": "drafts.py",
+  "73": "tiled_clients.py",
+  "74": "App.jsx",
+  "75": "viewerState.ts",
+  "76": "devDependencies",
+  "77": "get_tiled_client",
+  "78": "useTabsStore.ts",
+  "79": "zarr-viewer/package.json",
+  "80": "volume-renderer.ts",
+  "81": "features.py",
+  "82": "Vec3Like",
+  "83": "sam_embed.py",
+  "84": "start_all.ps1",
+  "85": "Installation via Docker (Recommended)",
+  "86": "parse.ts",
+  "87": "TTLCache",
+  "88": "magicwand.ts",
+  "89": "Segmentation Annotation Studio",
+  "90": "tiled_mask_sync.py",
+  "91": "denoise.py",
+  "92": "Software architecture",
+  "93": "annotationStore.ts",
+  "94": "VolumeLoader",
+  "95": "volume-texture.ts",
+  "96": "HTTPException",
+  "97": "fake_stack",
+  "98": "geometry.ts",
+  "99": "length-units.ts",
+  "100": "codecs.ts",
+  "101": ".alignTo",
+  "102": "Aabb",
+  "103": "denoise_bake.py",
+  "104": "3. Annotate",
+  "105": "datasetStats.ts",
+  "106": "SamClient",
+  "107": "samWorker.ts",
+  "108": "periodic-table.ts",
+  "109": "fx/package.json",
+  "110": "predicates.ts",
+  "111": "test_tomojepa_embed.py",
+  "112": "export_jobs.py",
+  "113": "polybool.ts",
+  "114": "perf.ts",
+  "115": "compilerOptions",
+  "116": ".preview_labels",
+  "118": "mergeSameClass.ts",
+  "119": "rasterizeShapes",
+  "120": "morphology.ts",
+  "121": "livewire.ts",
+  "122": "log.ts",
+  "123": "5. Export & download",
+  "124": "Mat3",
+  "125": "test_tiled_config.py",
+  "126": "frontend/package.json",
+  "127": "api_feature_channel",
+  "128": "systems.ts",
+  "129": "OmeZarrVolumeSource",
+  "130": "ViewportOverlay",
+  "131": "id.ts",
+  "132": "test_compositions.py",
+  "133": "FastAPI",
+  "134": "editHistory.ts",
+  "135": "pixelClf.ts",
+  "136": "Clock",
+  "137": "transfer-function.ts",
+  "138": "Random",
+  "139": "array_source.py",
+  "140": "AGENTS.md — Segmentation Annotation Studio",
+  "141": "guides.py",
+  "142": "test_drafts.py",
+  "143": "quick-start.md",
+  "144": "Installation",
+  "145": "Quick start",
+  "146": "Emitter",
+  "147": "test_catalog_sessions.py",
+  "148": "import_dataset",
+  "149": "describe_methods",
+  "150": "clahe.ts",
+  "152": "bboxPrefilter.equivalence.test.ts",
+  "153": "Pool",
+  "154": "Dual",
+  "155": "test_array_blobs.py",
+  "156": "test_preprocess_cache.py",
+  "158": "TestPyramidPlan",
+  "159": "usePixelClassifier.ts",
+  "160": "datasetStore.ts",
+  "161": "toolStore.ts",
+  "162": "vite-zarr-static.ts",
+  "163": "test_api_health.py",
+  "164": "main",
+  "165": "test_health.py",
+  "166": "TestPyramidStore",
+  "167": "Segmentation Annotation Studio",
+  "168": "Troubleshooting",
+  "169": "ClassManager/index.tsx",
+  "170": "Toolbar/index.tsx",
+  "171": "ZarrLoader.tsx",
+  "172": "BuildVolumePanel.tsx",
+  "173": "config.ts",
+  "174": "useGuideSync.ts",
+  "175": "useSave.ts",
+  "176": "regionOps.ts",
+  "177": "zarrUrl.ts",
+  "178": "ipredStore.ts",
+  "179": "layerVisibilityStore.ts",
+  "180": "OME-Zarr Viewer",
+  "182": "include",
+  "183": "Pool",
+  "184": "Soa",
+  "185": "_denoised_slice",
+  "186": "2. Browse & select",
+  "187": "1. Connect to data",
+  "188": "fetch-sam-model.mjs",
+  "189": "DenoiseBakeModal.tsx",
+  "190": "DownloadModal.tsx",
+  "191": "InsightsModal.tsx",
+  "192": "MeasurementPanel/index.tsx",
+  "193": "VersionHistoryModal.tsx",
+  "194": "useFeatureManifold.ts",
+  "195": "classColors.ts",
+  "196": "lib/colormaps.ts",
+  "197": "featureManifold.ts",
+  "198": "stretch.test.ts",
+  "201": "test_manifold.py",
+  "202": "test_train_rethreshold.py",
+  "204": "Keyboard shortcuts",
+  "205": "LayersPanel/index.tsx",
+  "206": "PerfOverlay/index.tsx",
+  "207": "PixelClassifierPanel/index.tsx",
+  "208": "SaveModal.tsx",
+  "209": "CompositionPanel/index.tsx",
+  "210": "useFeatureChannels.ts",
+  "211": "measure.ts",
+  "212": "buildSourceKey",
+  "214": "Mat4",
+  "215": "start.sh",
+  "216": "reingest_tiffs_as_individual.py",
+  "217": "DenoisePanel/index.tsx",
+  "218": "DisplayControls/index.tsx",
+  "219": "FeatureChannelsPanel/index.tsx",
+  "220": "HistogramControl/index.tsx",
+  "221": "VersionPreviewBar.tsx",
+  "222": "HubMainContent.tsx",
+  "223": "VolumeViewer.tsx",
+  "224": "useDraftSync.ts",
+  "225": "useExportJob.ts",
+  "226": "useMaskOps.ts",
+  "227": "useOpenInAnnotate.ts",
+  "228": "feedbackContext.ts",
+  "229": "adjust.ts",
+  "230": "sharpen.test.ts",
+  "231": "classStore.ts",
+  "232": "ratingStore.ts",
+  "233": "referenceGuideStore.ts",
+  "234": "settingsStore.ts",
+  "235": "fx.spec.ts",
+  "236": "frontend/vite.config.ts",
+  "237": "ipred/models",
+  "238": "test_emb_pca_channels.py",
+  "239": "test_tomojepa_onnx.py",
+  "240": "ExportConflict",
+  "241": "add_sample_metadata.py",
+  "242": "repair_catalog_paths.py",
+  "243": "Using the tool",
+  "244": "4. Annotation guide",
+  "245": "ExportPage.tsx",
+  "246": "MaskToolsPanel/index.tsx",
+  "247": "DebouncedSlider.tsx",
+  "248": "CustomizePages.tsx",
+  "249": "HubAppLayout.tsx",
+  "250": "HubHeader.tsx",
+  "251": "HubSidebar.tsx",
+  "252": "IframeModal.tsx",
+  "253": "useAnnotatedSourceKeys.ts",
+  "254": "useKeybinds.ts",
+  "255": "viridis.ts",
+  "256": "clipboardStore.ts",
+  "257": "connectionStore.ts",
+  "258": "annotationFilter.ts",
+  "259": "vite-env.d.ts",
+  "260": "frontend/tsconfig.json",
+  "261": "patches/README.md",
+  "262": "Button.tsx",
+  "263": "ItkVtkFrame.tsx",
+  "264": "Bundled Tiled catalog (committed to git)",
+  "265": "@phosphor-icons/react",
+  "266": "zundo",
+  "267": "@testing-library/react",
+  "268": "@types/node",
+  "269": "@types/react",
+  "270": "vite",
+  "271": "@vitejs/plugin-react",
+  "272": "vitest",
+  "273": "SliceNavigator/index.tsx",
+  "274": "useHubSelectedTabs.ts",
+  "275": "useSam.ts",
+  "276": "lib/utils.ts",
+  "277": "navigationRouterTypes.ts",
+  "278": "server.ts",
+  "279": "ItkVtkViewerComponent.jsx",
+  "280": "vitest.config.ts",
+  "281": "vite.config.js",
+  "285": "scripts/__init__.py",
+  "286": "tests/__init__.py",
+  "287": "postcss.config.js",
+  "288": "layerVisibilityStore.test.ts",
+  "289": "setup.ts",
+  "290": "webgpu.d.ts",
+  "291": "tailwind.config.js",
+  "292": "eslint.config.js",
+  "293": "tiled-auth-interceptor.js",
+  "294": ".open",
+  "295": ".send",
+  "296": "ipred",
+  "297": "sam3-annotation-studio-backend",
+  "298": "tailwindcss"
+}
diff --git a/graphify-out/2026-08-26/GRAPH_REPORT.md b/graphify-out/2026-08-26/GRAPH_REPORT.md
new file mode 100644
index 0000000..d0fb9f6
--- /dev/null
+++ b/graphify-out/2026-08-26/GRAPH_REPORT.md
@@ -0,0 +1,1159 @@
+# Graph Report - forge_Segementation_Finch  (2026-08-26)
+
+## Corpus Check
+- 437 files · ~371,392 words
+- Verdict: corpus is large enough that graph structure adds value.
+
+## Summary
+- 4927 nodes · 8836 edges · 287 communities (223 shown, 64 thin omitted)
+- Extraction: 97% EXTRACTED · 3% INFERRED · 0% AMBIGUOUS · INFERRED: 294 edges (avg confidence: 0.85)
+- Token cost: 0 input · 0 output
+
+## Graph Freshness
+- Built from commit: `e8600297`
+- Run `git rev-parse HEAD` and compare to check if the graph is stale.
+- Run `graphify update .` after code changes (no API cost).
+
+## Community Hubs (Navigation)
+- catalog.ts
+- get
+- occupancy.ts
+- thresholdFit.test.ts
+- ipred_routes.py
+- annotation_thumbnails.py
+- run
+- manifold.py
+- api.py
+- math/index.ts
+- TemporalAccumulator
+- annotation_server.py
+- ipred_client.py
+- coco_export.py
+- Vec3
+- tomojepa_onnx.py
+- ColumnBrowser.tsx
+- Quat
+- math/color.ts
+- devDependencies
+- render/index.ts
+- flip.ts
+- compilerOptions
+- compilerOptions
+- dimension.ts
+- test_ingest_conflict.py
+- App.tsx
+- src/index.ts
+- catalog.py
+- test_tiff_stack_source.py
+- ingest.py
+- test_zarr_source.py
+- Catalog
+- TiledNotifications/index.ts
+- camera.ts
+- ome-zarr-viewer.ts
+- compositions.py
+- Vec4
+- step_phantom
+- tiff_stack_source.py
+- ipred/__init__.py
+- train_infer.py
+- volume/colormaps.ts
+- Node
+- Quantity
+- Matrix
+- ipred — architecture and operations guide
+- AnnotationCanvas/index.tsx
+- io/index.ts
+- ipredApi.ts
+- OrbitControls
+- Vec2
+- TrainerPlugin
+- array.ts
+- packing.ts
+- scalar.ts
+- feature_setups.py
+- arrays.py
+- IngestDropzone.tsx
+- VectorGrid3
+- core/index.ts
+- getTiledBaseUrl
+- constants.ts
+- compilerOptions
+- browse_helpers.py
+- schemas.py
+- FakeNode
+- zarr_source.py
+- dependencies
+- ome-zarr.ts
+- start_all.sh
+- drafts.py
+- tiled_clients.py
+- App.jsx
+- viewerState.ts
+- devDependencies
+- get_tiled_client
+- useTabsStore.ts
+- zarr-viewer/package.json
+- volume-renderer.ts
+- features.py
+- Vec3Like
+- sam_embed.py
+- start_all.ps1
+- Installation via Docker (Recommended)
+- parse.ts
+- TTLCache
+- magicwand.ts
+- Segmentation Annotation Studio
+- tiled_mask_sync.py
+- denoise.py
+- Software architecture
+- annotationStore.ts
+- VolumeLoader
+- volume-texture.ts
+- HTTPException
+- fake_stack
+- geometry.ts
+- length-units.ts
+- codecs.ts
+- .alignTo
+- Aabb
+- denoise_bake.py
+- 3. Annotate
+- datasetStats.ts
+- SamClient
+- samWorker.ts
+- periodic-table.ts
+- fx/package.json
+- predicates.ts
+- test_tomojepa_embed.py
+- export_jobs.py
+- polybool.ts
+- perf.ts
+- compilerOptions
+- .preview_labels
+- mergeSameClass.ts
+- rasterizeShapes
+- morphology.ts
+- livewire.ts
+- log.ts
+- 5. Export & download
+- Mat3
+- test_tiled_config.py
+- frontend/package.json
+- api_feature_channel
+- systems.ts
+- OmeZarrVolumeSource
+- ViewportOverlay
+- id.ts
+- test_compositions.py
+- FastAPI
+- editHistory.ts
+- pixelClf.ts
+- Clock
+- transfer-function.ts
+- Random
+- array_source.py
+- AGENTS.md — Segmentation Annotation Studio
+- guides.py
+- test_drafts.py
+- Installation
+- Quick start
+- Emitter
+- test_catalog_sessions.py
+- import_dataset
+- describe_methods
+- clahe.ts
+- bboxPrefilter.equivalence.test.ts
+- Pool
+- Dual
+- test_array_blobs.py
+- test_preprocess_cache.py
+- TestPyramidPlan
+- usePixelClassifier.ts
+- datasetStore.ts
+- toolStore.ts
+- vite-zarr-static.ts
+- test_api_health.py
+- main
+- test_health.py
+- TestPyramidStore
+- Segmentation Annotation Studio
+- Troubleshooting
+- ClassManager/index.tsx
+- Toolbar/index.tsx
+- ZarrLoader.tsx
+- BuildVolumePanel.tsx
+- config.ts
+- useGuideSync.ts
+- useSave.ts
+- regionOps.ts
+- zarrUrl.ts
+- ipredStore.ts
+- layerVisibilityStore.ts
+- OME-Zarr Viewer
+- include
+- Pool
+- Soa
+- _denoised_slice
+- 2. Browse & select
+- 1. Connect to data
+- fetch-sam-model.mjs
+- DenoiseBakeModal.tsx
+- DownloadModal.tsx
+- InsightsModal.tsx
+- MeasurementPanel/index.tsx
+- VersionHistoryModal.tsx
+- useFeatureManifold.ts
+- classColors.ts
+- lib/colormaps.ts
+- featureManifold.ts
+- stretch.test.ts
+- test_manifold.py
+- test_train_rethreshold.py
+- Keyboard shortcuts
+- LayersPanel/index.tsx
+- PerfOverlay/index.tsx
+- PixelClassifierPanel/index.tsx
+- SaveModal.tsx
+- CompositionPanel/index.tsx
+- useFeatureChannels.ts
+- measure.ts
+- buildSourceKey
+- Mat4
+- start.sh
+- reingest_tiffs_as_individual.py
+- DenoisePanel/index.tsx
+- DisplayControls/index.tsx
+- FeatureChannelsPanel/index.tsx
+- HistogramControl/index.tsx
+- VersionPreviewBar.tsx
+- HubMainContent.tsx
+- VolumeViewer.tsx
+- useDraftSync.ts
+- useExportJob.ts
+- useMaskOps.ts
+- useOpenInAnnotate.ts
+- feedbackContext.ts
+- adjust.ts
+- sharpen.test.ts
+- classStore.ts
+- ratingStore.ts
+- referenceGuideStore.ts
+- settingsStore.ts
+- fx.spec.ts
+- ipred/models
+- test_emb_pca_channels.py
+- test_tomojepa_onnx.py
+- ExportConflict
+- add_sample_metadata.py
+- repair_catalog_paths.py
+- Using the tool
+- 4. Annotation guide
+- ExportPage.tsx
+- MaskToolsPanel/index.tsx
+- DebouncedSlider.tsx
+- CustomizePages.tsx
+- HubAppLayout.tsx
+- HubHeader.tsx
+- HubSidebar.tsx
+- IframeModal.tsx
+- useAnnotatedSourceKeys.ts
+- useKeybinds.ts
+- viridis.ts
+- clipboardStore.ts
+- connectionStore.ts
+- annotationFilter.ts
+- vite-env.d.ts
+- frontend/tsconfig.json
+- patches/README.md
+- Button.tsx
+- ItkVtkFrame.tsx
+- Bundled Tiled catalog (committed to git)
+- @phosphor-icons/react
+- zundo
+- @testing-library/react
+- @types/node
+- @types/react
+- vite
+- @vitejs/plugin-react
+- vitest
+- navigationRouterTypes.ts
+- server.ts
+- vitest.config.ts
+- vite.config.js
+- scripts/__init__.py
+- ipred
+- sam3-annotation-studio-backend
+
+## God Nodes (most connected - your core abstractions)
+1. `Vec3` - 99 edges
+2. `run()` - 98 edges
+3. `Catalog` - 69 edges
+4. `get()` - 58 edges
+5. `Mat4` - 54 edges
+6. `VolumeRenderer` - 51 edges
+7. `Quat` - 46 edges
+8. `Vec2` - 35 edges
+9. `OrbitControls` - 33 edges
+10. `get_tiled_client()` - 29 edges
+
+## Surprising Connections (you probably didn't know these)
+- `health()` --references--> `get()`  [EXTRACTED]
+  ipred/src/ipred/api.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
+- `get_servers()` --references--> `get()`  [EXTRACTED]
+  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
+- `browse_column()` --references--> `get()`  [EXTRACTED]
+  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
+- `browse_items()` --references--> `get()`  [EXTRACTED]
+  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
+- `browse_thumbnail()` --references--> `get()`  [EXTRACTED]
+  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
+
+## Import Cycles
+- 3-file cycle: `frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat4.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts`
+- 4-file cycle: `frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat4.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/quat.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts`
+
+## Communities (287 total, 64 thin omitted)
+
+### Community 0 - "catalog.ts"
+Cohesion: 0.02
+Nodes (116): acre, ALL_UNITS, ampere, arcminute, arcsecond, astronomicalUnit, atmosphere, atomicMassUnit (+108 more)
+
+### Community 1 - "get"
+Cohesion: 0.05
+Nodes (40): browse_facets(), browse_slices(), connect_summary(), denoise_auto(), denoise_methods(), get_draft(), get_guide(), get_version_route() (+32 more)
+
+### Community 2 - "occupancy.ts"
+Cohesion: 0.04
+Nodes (32): Disposable, DisposableScope, DisposeFn, toDisposeFn(), CELL_STRIDE_FLOATS, CHEBYSHEV_INF, chebyshevDistanceField(), densityToLutRange() (+24 more)
+
+### Community 3 - "thresholdFit.test.ts"
+Cohesion: 0.07
+Nodes (35): applyGaussianBlurGray(), applyGaussianBlurRgba(), boxBlurH(), boxBlurV(), boxRadiiForGaussian(), baseToDisplay(), DisplayAffine, displayAffineFor() (+27 more)
+
+### Community 4 - "ipred_routes.py"
+Cohesion: 0.05
+Nodes (69): ipred_feature_channel(), ipred_get_composition(), ipred_get_setup(), ipred_health(), _ipred_http_error(), ipred_infer(), ipred_list_compositions(), ipred_list_modules() (+61 more)
+
+### Community 5 - "annotation_thumbnails.py"
+Cohesion: 0.05
+Nodes (58): decode_thumbnail_base64(), _draw_shapes(), _hex_to_rgba(), _parse_source_key(), Any, ndarray, Annotated thumbnail generation for version history previews. Renders the most-…, Render a PNG thumbnail of the most-annotated slice with shapes overlaid. Args:… (+50 more)
+
+### Community 6 - "run"
+Cohesion: 0.07
+Nodes (6): run(), VisibilityFeedback, volumeRaymarchWgsl(), BrickLoader, VolumeRenderer, chooseVolumeFormat()
+
+### Community 7 - "manifold.py"
+Cohesion: 0.06
+Nodes (51): Tiny TTL cache for short-lived ipred artifacts (manifold heatmaps)., Thread-safe TTL + max-entries cache., TTLCache, _brush_mask(), build_label_map(), _ellipse_mask(), _polygon_mask(), Any (+43 more)
+
+### Community 8 - "api.py"
+Cohesion: 0.07
+Nodes (60): api_get_composition(), api_get_run(), api_infer(), api_list_compositions(), api_list_modules(), api_list_setups(), api_list_trainers(), api_manifold_sample() (+52 more)
+
+### Community 9 - "math/index.ts"
+Cohesion: 0.06
+Nodes (60): derivative(), dualCos(), dualExp(), dualLog(), dualPow(), dualSin(), dualSqrt(), dualTan() (+52 more)
+
+### Community 10 - "TemporalAccumulator"
+Cohesion: 0.07
+Nodes (12): halton(), TemporalAccumulator, CompiledGraph, descSignature(), PassContext, PassDesc, push(), RenderGraph (+4 more)
+
+### Community 11 - "annotation_server.py"
+Cohesion: 0.06
+Nodes (51): denoise_bake(), export_cancel(), export_coco(), generate_guide_route(), get_servers(), import_coco(), ingest_preflight(), ingest_upload() (+43 more)
+
+### Community 12 - "ipred_client.py"
+Cohesion: 0.07
+Nodes (53): _client(), feature_channel_bytes(), get_composition(), get_setup(), health(), infer(), ipred_url(), list_compositions() (+45 more)
+
+### Community 13 - "coco_export.py"
+Cohesion: 0.05
+Nodes (77): Background worker: render+rasterize all sources, write the dataset tree (images…, _run_export_job(), _apply_erased(), _brush_mask(), build_export_plan(), _ellipse_mask(), _encode_png(), fold_lightly_splits() (+69 more)
+
+### Community 14 - "Vec3"
+Cohesion: 0.05
+Nodes (4): Plane, Ray, Sphere, Vec3
+
+### Community 15 - "tomojepa_onnx.py"
+Cohesion: 0.06
+Nodes (48): main(), build_encoder(), _cached_model(), encode_dense_embeddings(), encoder_available(), _gray_unit_interval(), load_checkpoint_state(), _pad_to_multiple() (+40 more)
+
+### Community 16 - "ColumnBrowser.tsx"
+Cohesion: 0.07
+Nodes (37): BrowseColumn(), BrowseColumnProps, BrowseDetailPanel(), BrowseDetailPanelProps, displayKey(), formatValue(), SECTION_ORDER, ColumnBrowser() (+29 more)
+
+### Community 17 - "Quat"
+Cohesion: 0.08
+Nodes (3): q(), DualQuat, Quat
+
+### Community 18 - "math/color.ts"
+Cohesion: 0.11
+Nodes (22): clamp01(), clampColor3(), clampColor4(), color3ToVec3(), color4ToVec4(), colorToCss(), isColor3Tuple(), isColor4Tuple() (+14 more)
+
+### Community 19 - "devDependencies"
+Cohesion: 0.04
+Nodes (48): @blueskyproject/tiled, eslint, @eslint/js, eslint-plugin-react, eslint-plugin-react-hooks, eslint-plugin-react-refresh, dependencies, @blueskyproject/tiled (+40 more)
+
+### Community 20 - "render/index.ts"
+Cohesion: 0.10
+Nodes (29): Color3, createContext(), DeviceOptions, keyDirectionalFromLights(), lightBufferByteSize(), LightingEnvironment, packLightsStd430(), DEFAULT_POINT_RANGE (+21 more)
+
+### Community 21 - "flip.ts"
+Cohesion: 0.27
+Nodes (11): clamp01(), convSep(), FLIP_PPD, FlipResult, gaussianKernel(), grayConv(), linearToYCxCz(), meanFlip() (+3 more)
+
+### Community 22 - "compilerOptions"
+Cohesion: 0.05
+Nodes (45): compilerOptions, allowImportingTsExtensions, isolatedModules, jsx, lib, module, moduleDetection, moduleResolution (+37 more)
+
+### Community 23 - "compilerOptions"
+Cohesion: 0.05
+Nodes (45): compilerOptions, baseUrl, isolatedModules, lib, module, moduleResolution, noEmit, noUnusedLocals (+37 more)
+
+### Community 24 - "dimension.ts"
+Cohesion: 0.04
+Nodes (44): ABSORBED_DOSE, ACCELERATION, ACTION, AMOUNT, ANGLE, ANGULAR_MOMENTUM, ANGULAR_VELOCITY, AREA (+36 more)
+
+### Community 25 - "test_ingest_conflict.py"
+Cohesion: 0.06
+Nodes (31): image_meta(), Return shape / dtype metadata for an image source., fake_client(), FakeArray, FakeNode, _npy(), Exception, fixture (+23 more)
+
+### Community 26 - "App.tsx"
+Cohesion: 0.06
+Nodes (23): allRoutes, AnnotatePage, App(), BrowsePage, ConnectPage, DEFAULT_PATHS, ReferencePage, VolumePage (+15 more)
+
+### Community 27 - "src/index.ts"
+Cohesion: 0.06
+Nodes (29): bloom(), depthOfField(), fxaa(), OP_INDEX, outline(), sharpen(), ssao(), taa() (+21 more)
+
+### Community 28 - "catalog.py"
+Cohesion: 0.10
+Nodes (31): arrays_dir(), load_array_blob(), Any, ndarray, Path, Content-addressed array blobs for remote compute (data ≠ compute host)., Blob directory for uploaded arrays., Persist ``arr`` and return ``{array_ref, shape, dtype, ...}``. (+23 more)
+
+### Community 29 - "test_tiff_stack_source.py"
+Cohesion: 0.06
+Nodes (11): Path, Tests for exposing a TIFF directory as a streamable 3-D Zarr volume. The…, A directory of zero-padded 2-D TIFFs whose value encodes the slice index., Coarse levels are built from the level above, not from the source. That is what…, TestBlockMean, TestBuildLevel, TestCascade, TestInspect (+3 more)
+
+### Community 30 - "ingest.py"
+Cohesion: 0.09
+Nodes (36): _bump(), _child_keys(), _classify_error(), _ensure_container(), get_job(), _image_number(), new_job(), _pad_width() (+28 more)
+
+### Community 31 - "test_zarr_source.py"
+Cohesion: 0.09
+Nodes (16): _fake_pyramid(), _FakeArray, _FakeContainer, _make_multiscale(), fixture, ndarray, Path, pyramid() (+8 more)
+
+### Community 32 - "Catalog"
+Cohesion: 0.09
+Nodes (23): Connection, Catalog, ProjectRow, Any, Path, Create or return the project for this source identity., Ensure project exists and open a new session on it., Update session current_* pointers (only provided fields). (+15 more)
+
+### Community 33 - "TiledNotifications/index.ts"
+Cohesion: 0.14
+Nodes (30): buildTiledStreamUrl(), buildZarrUrlForChild(), ensureNotificationPermission(), isSupported(), OsNotificationOptions, showOsNotification(), EMPTY_PATHS, NodeStream() (+22 more)
+
+### Community 34 - "camera.ts"
+Cohesion: 0.09
+Nodes (9): Controller, FirstPersonControls, FlyControls, OrbitMode, OrbitState, IMPORTANT: `eye` may be `this._eye` — never overwrite it before reading world…, AxisBinding, Binding (+1 more)
+
+### Community 35 - "ome-zarr-viewer.ts"
+Cohesion: 0.10
+Nodes (29): createDemoHud(), createDemoSession(), DemoHandle, resetDemoStage(), resizeDemoCanvas(), ensureHudStyles(), boot(), stageEl (+21 more)
+
+### Community 36 - "compositions.py"
+Cohesion: 0.07
+Nodes (48): Any, ndarray, Run composition nodes; concatenate ``outputs`` channel blocks. Returns:…, run_composition(), compositions_root(), content_hash(), _dir(), ensure_default_compositions() (+40 more)
+
+### Community 38 - "step_phantom"
+Cohesion: 0.08
+Nodes (11): ndarray, parametrize, Tests for the classical denoise filters. Ported alongside :mod:`denoise`…, A hard vertical edge plus additive Gaussian noise. The standard shape for…, snr_db(), step_phantom(), TestAutoStrength, TestCapabilities (+3 more)
+
+### Community 39 - "tiff_stack_source.py"
+Cohesion: 0.11
+Nodes (35): block_mean(), _build_level(), _cast_like(), downsample_array(), inspect_tiff_stack(), multiscales_metadata(), preflight_tiff_stack(), Any (+27 more)
+
+### Community 40 - "ipred/__init__.py"
+Cohesion: 0.06
+Nodes (48): Execute a composition document on a raw image array., ipred — generic iterative prediction backend., ChannelBlock, FeatureModule, ModuleContext, ModuleMeta, Protocol, Feature module protocol — typed channel producers for compositions. (+40 more)
+
+### Community 41 - "train_infer.py"
+Cohesion: 0.10
+Nodes (37): cal_scores_from_json(), conformal_quantile(), counts_from_status(), maps_from_proba(), mondrian_thresholds(), Any, ndarray, Mondrian split-conformal thresholds and maps from cached proba. (+29 more)
+
+### Community 42 - "volume/colormaps.ts"
+Cohesion: 0.12
+Nodes (22): BuiltinColorMapName, ColorMapFn, ColorMapName, colorMapNames(), lerp3(), MAPS, Rgb, sampleColorMap() (+14 more)
+
+### Community 43 - "Node"
+Cohesion: 0.16
+Nodes (11): CameraComponent, Component, ComponentBase, ComponentKind, LightComponent, MeshComponent, PhysicsBodyComponent, VolumeComponent (+3 more)
+
+### Community 44 - "Quantity"
+Cohesion: 0.10
+Nodes (3): Dimension, Quantity, Unit
+
+### Community 46 - "ipred — architecture and operations guide"
+Cohesion: 0.06
+Nodes (32): 10. Manifold / Suggest Labels, 11. Frontend map, 12. Package map (`ipred/src/ipred/`), 13. Mental model (one paragraph), 14. Quick ops, 1. Big picture, 2. Where compute lives, 3. How the UI reaches compute (proxy boundary) (+24 more)
+
+### Community 47 - "AnnotationCanvas/index.tsx"
+Cohesion: 0.11
+Nodes (29): AnnotationCanvas(), AnnotationCanvasProps, buildRingsPath(), distToSegment(), EMPTY_CLASS_COLOR_BY_ID, EMPTY_MANIFOLD_MARKERS, EMPTY_SHAPES, hexToRgb() (+21 more)
+
+### Community 48 - "io/index.ts"
+Cohesion: 0.11
+Nodes (25): aabbRaySlab(), DensityField, extractIsosurface(), ExtractIsosurfaceOptions, inside(), loadDensityField(), LoadDensityFieldOptions, MeasureThresholdOptions (+17 more)
+
+### Community 49 - "ipredApi.ts"
+Cohesion: 0.10
+Nodes (26): CompositionDoc, CompositionNode, FeatureModuleInfo, FeatureSetup, getIpredComposition(), getIpredSetup(), IpredFeatureImportance, ipredHealth() (+18 more)
+
+### Community 52 - "TrainerPlugin"
+Cohesion: 0.09
+Nodes (21): ModelArtifacts, Any, ndarray, Path, Protocol, Trainer plugin interface., Artifacts produced by ``train``., Train / predict_proba contract for pixel classifiers. (+13 more)
+
+### Community 53 - "array.ts"
+Cohesion: 0.13
+Nodes (13): chunkBytes(), DecodedChunkCache, dtypeByteSize(), VolumeChunk, chunkKey(), cropCOrderChunk(), fillBuffer(), OpenZarrArrayOptions (+5 more)
+
+### Community 54 - "packing.ts"
+Cohesion: 0.10
+Nodes (23): Euler, scratchMat, compact1By2(), decodeOctNormal(), demorton3D(), encodeOctNormal(), _f32, floatToHalf() (+15 more)
+
+### Community 55 - "scalar.ts"
+Cohesion: 0.10
+Nodes (19): bitReverse(), fft(), fftReal(), ifft(), DEG2RAD, deltaAngle(), HALF_PI, inverseLerp() (+11 more)
+
+### Community 56 - "feature_setups.py"
+Cohesion: 0.11
+Nodes (36): ArgumentParser, _add_project_sugar(), _catalog(), main(), _print(), Any, CLI entrypoint ``ipred`` with --project session sugar., Return session_id from --session or --project sugar. (+28 more)
+
+### Community 57 - "arrays.py"
+Cohesion: 0.14
+Nodes (27): array_shape_meta(), _descend_to_stack(), _is_container_node(), _level_array(), multiscale_levels(), node_keywords(), Any, ndarray (+19 more)
+
+### Community 58 - "IngestDropzone.tsx"
+Cohesion: 0.12
+Nodes (23): ConflictDialog(), ConflictDialogProps, IngestConflict, collectFromDrop(), fileStem(), IngestDropzone(), IngestDropzoneProps, isSupported() (+15 more)
+
+### Community 59 - "VectorGrid3"
+Cohesion: 0.12
+Nodes (5): Grid3Base, Grid3Spec, resolveSpacing(), ScalarGrid3, VectorGrid3
+
+### Community 60 - "core/index.ts"
+Cohesion: 0.18
+Nodes (14): assert(), assertDefined(), AssertionError, assertNever(), chemistry, err, isErr(), isOk() (+6 more)
+
+### Community 61 - "getTiledBaseUrl"
+Cohesion: 0.18
+Nodes (21): Header(), getValidAccessToken(), installTiledFetchInterceptor(), nativeFetch, refreshAccessToken(), IMPORTANT: this must be imported before `<App>` (and therefore before the…, Window, getActiveServer() (+13 more)
+
+### Community 62 - "constants.ts"
+Cohesion: 0.07
+Nodes (26): ATOMIC_MASS_UNIT, AVOGADRO, BOHR_RADIUS, BOLTZMANN, COULOMB_CONSTANT, EARTH_MASS, EARTH_RADIUS, ELECTRON_MASS (+18 more)
+
+### Community 63 - "compilerOptions"
+Cohesion: 0.07
+Nodes (26): compilerOptions, allowUnreachableCode, declaration, declarationMap, forceConsistentCasingInFileNames, isolatedModules, lib, module (+18 more)
+
+### Community 64 - "browse_helpers.py"
+Cohesion: 0.10
+Nodes (37): Return a cached :class:`FieldMapping` for the given container., _resolve_field_mapping(), _apply_filters(), build_field_mapping(), _combined_metadata_keys(), _display_name(), distinct_from_rows(), FieldMapping (+29 more)
+
+### Community 66 - "schemas.py"
+Cohesion: 0.07
+Nodes (45): BrushShape, BrushStroke, DenoiseBakeRequest, EllipseShape, GuideClass, GuidePayload, ImageMeta, IngestPreflightRequest (+37 more)
+
+### Community 67 - "FakeNode"
+Cohesion: 0.09
+Nodes (9): catalog(), FakeNode, fixture, Tests for locating a dataset's renderable 3-D volume. This is the module that…, A catalog node: metadata plus children, indexable like a Tiled container., A catalog with one of each shape the resolver has to tell apart., TestHasMultiscales, TestResolveVolume (+1 more)
+
+### Community 68 - "zarr_source.py"
+Cohesion: 0.15
+Nodes (25): _existing_node_info(), inspect_zarr(), _is_zarr_dir(), _multiscale_datasets(), preflight_zarr(), Any, Path, Register on-disk Zarr volumes with Tiled, without copying any data. The drag-… (+17 more)
+
+### Community 69 - "dependencies"
+Cohesion: 0.08
+Nodes (26): @blueskyproject/finch, clsx, dependencies, @blueskyproject/finch, clsx, @huggingface/transformers, konva, polygon-clipping (+18 more)
+
+### Community 70 - "ome-zarr.ts"
+Cohesion: 0.13
+Nodes (23): units, axisToXyz(), LevelInfo, openOmeZarr(), OpenOmeZarrOptions, scaleTranslation(), estimateValueRange(), openZarrArray() (+15 more)
+
+### Community 71 - "start_all.sh"
+Cohesion: 0.14
+Nodes (25): API_PROXY_TARGET, can_run_npm(), cleanup(), cleanup_managed_processes(), cleanup_pid_file(), ensure_backend_env(), ensure_docs_env(), ensure_frontend_runtime() (+17 more)
+
+### Community 72 - "drafts.py"
+Cohesion: 0.13
+Nodes (23): _draft_path(), get_version(), get_version_thumbnail(), list_drafts(), list_versions(), load_draft(), Any, Path (+15 more)
+
+### Community 73 - "tiled_clients.py"
+Cohesion: 0.13
+Nodes (22): get_browse_container(), get_browse_container_for(), Any, Tiled client factory + browse-container discovery. Clients are cached per…, Return ``(container_node, path_prefix)`` for the Metadata Browser root. Checks…, Return ``(container_node, path_prefix)`` for a specific browse target. When…, clear_empty_tiled_api_key_env(), get_tiled_api_key() (+14 more)
+
+### Community 74 - "App.jsx"
+Cohesion: 0.14
+Nodes (22): RFC-4648, App(), LinkOptions, useLinkedWebGpuViewers(), queryClient, buildShareUrl(), copyToClipboard(), decodeState() (+14 more)
+
+### Community 75 - "viewerState.ts"
+Cohesion: 0.13
+Nodes (22): LinkOptions, Subscription, useLinkedViewers(), ItkVktNative(), ItkVktProps, ItkVtkCamera, ItkVtkViewerApi, ItkVtkViewerInstance (+14 more)
+
+### Community 76 - "devDependencies"
+Cohesion: 0.09
+Nodes (23): autoprefixer, devDependencies, autoprefixer, jsdom, postcss, tailwindcss, @testing-library/jest-dom, @testing-library/user-event (+15 more)
+
+### Community 77 - "get_tiled_client"
+Cohesion: 0.15
+Nodes (21): api_key_for_uri(), get_tiled_client(), Look up a configured API key for *uri*, falling back to None., Return a (cached) ``tiled.client`` connected to the requested server., build_volume(), inspect_volume_build(), Any, Build a renderable 3-D volume from a slice stack already in Tiled.… (+13 more)
+
+### Community 78 - "useTabsStore.ts"
+Cohesion: 0.14
+Nodes (20): HeaderProps, graceTimers, makeId(), nameFromUrl(), PaneSide, ReconTab, RendererKind, reposition() (+12 more)
+
+### Community 79 - "zarr-viewer/package.json"
+Cohesion: 0.09
+Nodes (22): description, devDependencies, @types/node, typescript, vite, vitest, @webgpu/types, @types/node (+14 more)
+
+### Community 80 - "volume-renderer.ts"
+Cohesion: 0.06
+Nodes (55): asColor3(), asColor4(), Color3Like, Color4Like, FLIP_CONFIGS, FlipConfig, flipConfigToLut(), concat() (+47 more)
+
+### Community 81 - "features.py"
+Cohesion: 0.13
+Nodes (22): apply_clahe(), compute_clahe_stack(), compute_feature_stacks(), encode_channel_png(), feature_channel_labels(), _float_feats_to_uint8(), _fmt_sigma(), list_sigmas() (+14 more)
+
+### Community 82 - "Vec3Like"
+Cohesion: 0.08
+Nodes (24): _q0, _q1, _q2, _tv, _decomposeRot, QuatLike, _sqA, _sqB (+16 more)
+
+### Community 83 - "sam_embed.py"
+Cohesion: 0.14
+Nodes (21): bilinear_sample_emb(), emb_grid_to_pca_channels(), encode_image_embeddings(), encoder_available(), fit_pca(), Any, ndarray, Path (+13 more)
+
+### Community 84 - "start_all.ps1"
+Cohesion: 0.19
+Nodes (19): Ensure-BackendEnv(), Ensure-DocsEnv(), Ensure-FrontendRuntime(), Ensure-TiledKey(), Ensure-Uv(), Err(), Get-FreePort(), Get-ListenerPids() (+11 more)
+
+### Community 85 - "Installation via Docker (Recommended)"
+Cohesion: 0.10
+Nodes (20): Add Tiled, Authenticate, `Bluesky Tiled`, Build and start the application, Clone and install repository, Clone repository, Debugging, Install (+12 more)
+
+### Community 86 - "parse.ts"
+Cohesion: 0.17
+Nodes (15): DIMENSIONLESS, constants, ALL_PREFIXES, parseQuantity(), parseUnit(), PREFIX_KEYS, resolveSymbol(), Term (+7 more)
+
+### Community 87 - "TTLCache"
+Cohesion: 0.12
+Nodes (14): Any, Small TTL + LRU cache used by the Browse API. Kept intentionally tiny: the…, Thread-safe cache with per-entry expiry and an insertion-order size cap., TTLCache, encode_png(), Any, ndarray, Slice-to-PNG rendering with explicit normalisation modes. Two normalisation… (+6 more)
+
+### Community 88 - "magicwand.ts"
+Cohesion: 0.14
+Nodes (18): bboxContainsRing(), boxBlur(), buildField(), chaikin(), gradientField(), GrayField, magicSelect(), MaskPolyOpts (+10 more)
+
+### Community 89 - "Segmentation Annotation Studio"
+Cohesion: 0.10
+Nodes (18): Annotation tools, Development, Export formats, Mac/Linux, Node.js, Production build (single origin), Project layout, Quick start (+10 more)
+
+### Community 90 - "tiled_mask_sync.py"
+Cohesion: 0.13
+Nodes (30): Filesystem-safe folder name for a class label., _safe_name(), ExportSourceItem, A single annotated sample within a multi-source export. Attributes: kind:…, _classes(), Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization →…, test_build_mask_volumes_stacks_and_labels(), test_merge_fresh_when_no_existing() (+22 more)
+
+### Community 91 - "denoise.py"
+Cohesion: 0.20
+Nodes (18): auto_strength(), denoise_slice(), denoise_stack(), estimate_noise_sigma(), _from_unit(), _median3d(), _odd(), dtype (+10 more)
+
+### Community 92 - "Software architecture"
+Cohesion: 0.11
+Nodes (18): Backend architecture, Canvas rendering, Client-side geometry & tools (`src/lib/`), Component shell, Data model, Deployment topology, Draw, autosave, and save a version, Export a dataset (+10 more)
+
+### Community 93 - "annotationStore.ts"
+Cohesion: 0.15
+Nodes (15): AnnotationState, BaseShape, BrushShape, BrushStroke, cloneShapeWithNewId(), DraftState, EllipseShape, EMPTY_DRAFT (+7 more)
+
+### Community 95 - "volume-texture.ts"
+Cohesion: 0.18
+Nodes (13): asFloatSamples(), clamp01(), encodeVoxel(), f32Scratch, floatToHalf(), HISTOGRAM_BINS, i32Scratch, packChunkInto() (+5 more)
+
+### Community 96 - "HTTPException"
+Cohesion: 0.08
+Nodes (38): browse_column(), browse_items(), browse_thumbnail(), export_download(), export_status(), get_version_thumbnail(), image_slice(), _parse_json_filters() (+30 more)
+
+### Community 97 - "fake_stack"
+Cohesion: 0.16
+Nodes (7): fake_stack(), fixture, Tests for building a 3-D volume from a slice stack already in Tiled. The…, Stand in for a Tiled stack: `resolve_array` + `array_shape_meta` + `read_slice`., TestBuildGuards, TestInspect, TestMetadataShape
+
+### Community 98 - "geometry.ts"
+Cohesion: 0.18
+Nodes (15): BBox, bboxCoversRaster(), bboxIntersects(), bboxNear(), EMPTY_BBOX, growByFlat(), normalizeEllipse(), normalizeRect() (+7 more)
+
+### Community 99 - "length-units.ts"
+Cohesion: 0.12
+Nodes (16): angstrom, centimeter, femtometer, foot, inch, kilometer, meter, micrometer (+8 more)
+
+### Community 100 - "codecs.ts"
+Cohesion: 0.20
+Nodes (15): bloscCodec(), BloscCompressorConfig, bloscCompressorName(), bloscDecodeSync(), bloscUnshuffle(), Codec, codecFromCompressor(), decompressStream() (+7 more)
+
+### Community 101 - ".alignTo"
+Cohesion: 0.15
+Nodes (6): LayoutBuilder, std140Alignment(), Std140Builder, std430Alignment(), Std430Builder, WgslBaseType
+
+### Community 102 - "Aabb"
+Cohesion: 0.11
+Nodes (9): Aabb, Frustum, computeWorldBounds(), _corner, cullFrustum(), expandByTransformedBox(), _localMax, _localMin (+1 more)
+
+### Community 103 - "denoise_bake.py"
+Cohesion: 0.18
+Nodes (13): default_target_path(), _denoise_one(), _ModelDenoiser, Any, ndarray, Apply a denoise filter to a whole volume and save the result as a NEW Tiled…, Applies a trained Noise2Noise/Noise2Void run across a whole volume. Loads the…, Release ``ML_LOCK``. Idempotent — callers release on both the happy path and in… (+5 more)
+
+### Community 104 - "3. Annotate"
+Cohesion: 0.12
+Nodes (17): 3. Annotate, Classes, Colorblind-safe colors, Cross-slice, Display (view-only), Export, How each tool works, Insights (quality checks) (+9 more)
+
+### Community 105 - "datasetStats.ts"
+Cohesion: 0.18
+Nodes (16): analyticArea(), ClassStat, computeSampleStats(), Coverage, isSelfIntersecting(), labelFor(), MaskInfo, overlapCount() (+8 more)
+
+### Community 106 - "SamClient"
+Cohesion: 0.14
+Nodes (7): Granularity, Pending, PromptBox, PromptPoint, SamClient, SamMask, SamStatus
+
+### Community 107 - "samWorker.ts"
+Cohesion: 0.18
+Nodes (16): Box, decode(), Device, encode(), fallbackToWasm(), freshRaw(), Granularity, InMsg (+8 more)
+
+### Community 108 - "periodic-table.ts"
+Cohesion: 0.24
+Nodes (13): atomicMass(), atomicNumber(), BY_SYMBOL, BY_Z, covalentRadius(), cpkColor(), elementBySymbol(), elementByZ() (+5 more)
+
+### Community 109 - "fx/package.json"
+Cohesion: 0.12
+Nodes (16): dependencies, @prism/core, @prism/math, @prism/render, description, exports, main, module (+8 more)
+
+### Community 110 - "predicates.ts"
+Cohesion: 0.20
+Nodes (12): det2(), orient2d(), orient3d(), Point2Like, compensatedDot(), CompensatedSum, kahanSum(), neumaierSum() (+4 more)
+
+### Community 111 - "test_tomojepa_embed.py"
+Cohesion: 0.16
+Nodes (15): MonkeyPatch, Path, Mark25 / TomoJEPA dense embedding encode., Mark11 dense projector is 256-D (Mark25 is 64-D)., All Mark25 paths must pass intensities in [-1, 1] into the network., test_encode_dense_honors_input_size(), test_encode_dense_shape_512(), test_encode_feeds_model_in_minus_one_one() (+7 more)
+
+### Community 112 - "export_jobs.py"
+Cohesion: 0.12
+Nodes (11): cancel_requested(), get_job(), new_job(), Any, In-memory job registry for COCO/mask exports. Mirrors the ingest job pattern…, Register a new export job and return its id., Ask a running job to stop; returns False if the job is unknown. Cooperative:…, True if cancellation was requested for *jid*. Reads the flag directly under the… (+3 more)
+
+### Community 113 - "polybool.ts"
+Cohesion: 0.28
+Nodes (12): clipShapesToOthers_naive(), eraseStampToMultiPolygon(), flatToRing(), multiPolygonToShapes(), regionsToMultiPolygon(), ringArea(), ringToFlat(), shapeToMultiPolygon() (+4 more)
+
+### Community 114 - "perf.ts"
+Cohesion: 0.20
+Nodes (12): initPerf(), listeners, mark(), perfEnabled(), PerfLabel, PerfStat, resetPerf(), scheduleNotify() (+4 more)
+
+### Community 115 - "compilerOptions"
+Cohesion: 0.12
+Nodes (15): compilerOptions, allowImportingTsExtensions, isolatedModules, lib, module, moduleDetection, moduleResolution, noEmit (+7 more)
+
+### Community 118 - "mergeSameClass.ts"
+Cohesion: 0.39
+Nodes (7): bboxOverlap(), expandSameClassOverlap(), inflate(), masksIntersect(), masksIntersectIn(), mergeNewWithSameClass(), MergeResult
+
+### Community 119 - "rasterizeShapes"
+Cohesion: 0.19
+Nodes (16): expandSameClassOverlap_naive(), mergeNewWithSameClass_naive(), incoming, otherClass, realOverlap(), thresholdShapes(), area(), fillEllipse() (+8 more)
+
+### Community 120 - "morphology.ts"
+Cohesion: 0.19
+Nodes (11): dilate(), dilateOnce(), erode(), erodeOnce(), fillHoles(), keepComponentsAtPoints(), removeSmallComponents(), smooth() (+3 more)
+
+### Community 121 - "livewire.ts"
+Cohesion: 0.26
+Nodes (6): CostMap, dijkstra(), imageToGrid(), MinHeap, simplifyPath(), tracePath()
+
+### Community 122 - "log.ts"
+Cohesion: 0.14
+Nodes (7): createLogger(), Logger, LogLevel, LogSink, ORDER, setLogLevel(), setLogSink()
+
+### Community 123 - "5. Export & download"
+Cohesion: 0.15
+Nodes (13): 5. Export & download, Alternative: Push masks to Tiled, DINOv3 / Lightly format, Downloading a COCO dataset, Importing a dataset back, Step 1 — Prepare (optional but recommended), Step 2 — Open the export dialog, Step 3 — Choose a scope (+5 more)
+
+### Community 125 - "test_tiled_config.py"
+Cohesion: 0.17
+Nodes (11): Tests for Tiled key hardening (empty-key clearing + config-key fallback)., Blank/whitespace key env vars are deleted; real ones are kept., Reads authentication.single_user_api_key from a config.yml., A ${TILED_API_KEY} placeholder in config.yml expands from the env; unset → None., _local_api_key prefers env, else falls back to the config single-user key., Importing the module clears a blank TILED_API_KEY from the env., test_clear_empty_tiled_api_key_env_removes_blank_vars(), test_config_key_env_interpolation() (+3 more)
+
+### Community 126 - "frontend/package.json"
+Cohesion: 0.17
+Nodes (11): name, private, scripts, build, dev, preview, test, test:watch (+3 more)
+
+### Community 127 - "api_feature_channel"
+Cohesion: 0.20
+Nodes (12): Serve a real static file when it exists, else index.html (SPA routing)., spa_fallback(), FileResponse, api_feature_channel(), api_manifold_heatmap(), api_run_commit(), api_run_proba_channel(), api_run_status() (+4 more)
+
+### Community 128 - "systems.ts"
+Cohesion: 0.21
+Nodes (6): FloatingOrigin, fromSim(), scaleFactor(), toSim(), UNIT_PRESETS, UnitSystem
+
+### Community 129 - "OmeZarrVolumeSource"
+Cohesion: 0.31
+Nodes (3): OmeZarrVolumeSource, VolumeDType, ParsedDType
+
+### Community 130 - "ViewportOverlay"
+Cohesion: 0.24
+Nodes (4): AXES, OverlayDrawParams, OverlayRuler, ViewportOverlay
+
+### Community 131 - "id.ts"
+Cohesion: 0.40
+Nodes (4): RFC-4122, createHandleAllocator(), Handle, uuid()
+
+### Community 132 - "test_compositions.py"
+Cohesion: 0.24
+Nodes (10): catalog(), fixture, MonkeyPatch, Composition validation, builtins, and legacy setup mapping., test_ensure_default_compositions(), test_feature_setups_still_seed(), test_legacy_setup_maps_to_composition(), test_module_catalog_lists_core_modules() (+2 more)
+
+### Community 133 - "FastAPI"
+Cohesion: 0.18
+Nodes (10): Tests for local filesystem sandboxing., Paths escaping LOCAL_DATA_ROOT must raise HTTP 403., list_dir should return file entries for an existing directory., list_dir on a non-existent path should raise HTTP 404., open_array on an unsupported extension should raise HTTP 422., test_list_dir_missing_raises_404(), test_list_dir_returns_entries(), test_open_array_unsupported_type_raises_422() (+2 more)
+
+### Community 134 - "editHistory.ts"
+Cohesion: 0.22
+Nodes (7): ClassDeleteEntry, currentSourceKey(), Entry, redo(), redoOps, undo(), undoOps
+
+### Community 135 - "pixelClf.ts"
+Cohesion: 0.33
+Nodes (7): applyProbaThresholdRgba(), classProbaMaskSetName(), colorizeConformalOverlay(), colorizeLabelMap(), labelMapToPolygonShapes(), parseHexColor(), thresholdProbaPngBlob()
+
+### Community 136 - "Clock"
+Cohesion: 0.24
+Nodes (3): Clock, FixedTimestep, now()
+
+### Community 137 - "transfer-function.ts"
+Cohesion: 0.26
+Nodes (8): Color4, clamp01(), normalizeWindowLevel(), sampleStops(), TransferFunction, TransferStop, WindowLevelOptions, windowLevelTransferFunction()
+
+### Community 139 - "array_source.py"
+Cohesion: 0.33
+Nodes (10): _index_slice(), _local_root(), ndarray, Path, Minimal local / Tiled slice reader (no annotate-backend imports)., Load one 2D/ HxWxC slice array., Pick HW or HWC frame from NHW / NHWC / HW / HWC., _read_local() (+2 more)
+
+### Community 140 - "AGENTS.md — Segmentation Annotation Studio"
+Cohesion: 0.20
+Nodes (9): AGENTS.md — Segmentation Annotation Studio, ALS Standards, Annotation data model (do NOT change without updating both backend/schemas.py and frontend stores), Git, Secrets, Security rules, Stack, Testing rules (+1 more)
+
+### Community 141 - "guides.py"
+Cohesion: 0.27
+Nodes (9): _guide_path(), load_guide(), Any, Path, Annotation-guide persistence for the Segmentation Annotation Studio. A *guide*…, Return the JSON file path for *source_key*'s guide., Persist the annotation guide for *source_key*. Args: source_key: Arbitrary…, Return the saved guide document for *source_key*, or ``None``. (+1 more)
+
+### Community 142 - "test_drafts.py"
+Cohesion: 0.20
+Nodes (9): Tests for session-draft persistence., A saved draft should be loadable with the correct source_key., Loading a draft that was never saved should return None., list_drafts should include a previously saved draft., list_drafts should return an empty list when no drafts exist., test_list_drafts_empty_when_none(), test_list_drafts_returns_saved(), test_load_missing_draft() (+1 more)
+
+### Community 144 - "Installation"
+Cohesion: 0.20
+Nodes (10): Backend, Changing the ports, Environment variables, Frontend, Installation, Manual setup, Option 1 — One-command local start (recommended), Option 2 — Docker (production) (+2 more)
+
+### Community 145 - "Quick start"
+Cohesion: 0.20
+Nodes (10): 1. Connect to your data, 2. Open an image to annotate, 3. Add a class, 4. Draw a mask, 5. Save your work, 6. Export a COCO dataset, :material-draw: Master annotation, :material-keyboard: Keyboard shortcuts (+2 more)
+
+### Community 146 - "Emitter"
+Cohesion: 0.27
+Nodes (3): Emitter, Listener, Unsubscribe
+
+### Community 147 - "test_catalog_sessions.py"
+Cohesion: 0.24
+Nodes (9): project_id_for(), Stable project id from source identity., catalog(), fixture, MonkeyPatch, Catalog project/session persistence., test_open_session_creates_project(), test_project_id_stable() (+1 more)
+
+### Community 148 - "import_dataset"
+Cohesion: 0.33
+Nodes (8): import_dataset(), _import_from_coco(), _import_with_sidecar(), Any, COCO + sidecar import: round-trip back to annotation payload. Prefers the…, Load a COCO dataset directory back into editor payload format. Args:…, Lossless import using _studio_shapes.json., Best-effort import from COCO only (no sidecar). Brush shapes become polygons…
+
+### Community 149 - "describe_methods"
+Cohesion: 0.22
+Nodes (9): available_methods(), describe_methods(), Any, Method metadata for the capability endpoint / UI menu. ``cost`` drives whether…, True if wavelet denoising can actually run.…, Methods that can actually run in this environment., Z-neighbours needed on each side of the target slice (0 for 2-D methods)., wavelet_available() (+1 more)
+
+### Community 150 - "clahe.ts"
+Cohesion: 0.36
+Nodes (5): applyClaheGray(), buildTileLut(), ClaheOptions, claheRgba(), identityLut()
+
+### Community 152 - "bboxPrefilter.equivalence.test.ts"
+Cohesion: 0.24
+Nodes (11): expectSameRegions(), iou(), makeShape(), mulberry32(), randomSlice(), regionOf(), perShape(), clipBatchBoolean() (+3 more)
+
+### Community 155 - "test_array_blobs.py"
+Cohesion: 0.31
+Nodes (8): client(), fixture, MonkeyPatch, TestClient, Array blob data plane for remote preprocess., test_modules_and_compositions_api(), test_save_load_array_blob(), test_upload_array_and_preprocess()
+
+### Community 156 - "test_preprocess_cache.py"
+Cohesion: 0.33
+Nodes (8): catalog(), fixture, MonkeyPatch, Cache-aware preprocess., test_preprocess_cache_hit(), test_preprocess_mark25_clahe_no_skimage(), test_preprocess_mark25_writes_dense_emb(), skipif
+
+### Community 159 - "usePixelClassifier.ts"
+Cohesion: 0.25
+Nodes (6): ClfFeatureImportance, ClfParams, ClfPredictCounts, ClfTrainResult, DEFAULT_CLF_PARAMS, UsePixelClassifierArgs
+
+### Community 160 - "datasetStore.ts"
+Cohesion: 0.25
+Nodes (7): DatasetState, DEFAULT_DENOISE, DEFAULT_RENDER, DenoiseOpts, ImageMeta, RenderOpts, useDatasetStore
+
+### Community 161 - "toolStore.ts"
+Cohesion: 0.25
+Nodes (7): MagicEngine, MagicMode, SamDetail, SelectScope, Tool, ToolState, useToolStore
+
+### Community 162 - "vite-zarr-static.ts"
+Cohesion: 0.32
+Nodes (4): src, PETIOLE_ZARR_MOUNT, ZarrStaticMount, zarrStaticPlugin()
+
+### Community 163 - "test_api_health.py"
+Cohesion: 0.32
+Nodes (7): client(), fixture, MonkeyPatch, TestClient, API health + session open., test_health(), test_open_session_and_list_setups()
+
+### Community 164 - "main"
+Cohesion: 0.48
+Nodes (6): _ensure_container(), _json_safe(), _load_pairs(), main(), Path, Return (png_path, txt_path) for stems that have both files.
+
+### Community 165 - "test_health.py"
+Cohesion: 0.33
+Nodes (6): asyncio, Tests for the health endpoint., Health endpoint must return status ok., Servers endpoint must not expose api_key in response., test_health_returns_ok(), test_servers_endpoint_no_api_key()
+
+### Community 167 - "Segmentation Annotation Studio"
+Cohesion: 0.29
+Nodes (7): :material-download-box: Install, :material-draw: Annotate, :material-export: Export, :material-rocket-launch: Quick start, Segmentation Annotation Studio, The workflow at a glance, What is this tool?
+
+### Community 168 - "Troubleshooting"
+Cohesion: 0.29
+Nodes (6): Connecting to data, Exporting, Saving, The app won't start, The Smart (AI) tool, Troubleshooting
+
+### Community 169 - "ClassManager/index.tsx"
+Cohesion: 0.33
+Nodes (5): ClassManager(), ClassManagerProps, ClassRowProps, countShapesForClass(), DEFAULT_SUGGESTED_CLASSES
+
+### Community 170 - "Toolbar/index.tsx"
+Cohesion: 0.29
+Nodes (3): CHANNEL_LABELS, ToolbarProps, ToolButtonProps
+
+### Community 171 - "ZarrLoader.tsx"
+Cohesion: 0.33
+Nodes (6): describeLevel(), ExistingInfo, ZarrInfo, ZarrLevel, ZarrLoader(), ZarrLoaderProps
+
+### Community 172 - "BuildVolumePanel.tsx"
+Cohesion: 0.33
+Nodes (6): BuildInfo, BuildVolumePanel(), BuildVolumePanelProps, JobState, PyramidLevel, readError()
+
+### Community 173 - "config.ts"
+Cohesion: 0.29
+Nodes (6): API_BASE, APP_VERSION, DOCS_URL, FEEDBACK_ENTRY_ID, FEEDBACK_FORM_URL, GIT_COMMIT
+
+### Community 174 - "useGuideSync.ts"
+Cohesion: 0.43
+Nodes (5): GuideGenPayload, loadGuide(), saveGuide(), useGuideLoad(), useGuideSync()
+
+### Community 175 - "useSave.ts"
+Cohesion: 0.33
+Nodes (5): SaveDraftPayload, SaveOptions, UseSaveReturn, VersionMeta, VersionPayload
+
+### Community 176 - "regionOps.ts"
+Cohesion: 0.33
+Nodes (3): computeRegionOps(), RegionOp, RegionResult
+
+### Community 177 - "zarrUrl.ts"
+Cohesion: 0.48
+Nodes (5): buildZarrUrl(), describeUnavailable(), zarrRootFor(), ZarrUnavailable, ZarrUrlResult
+
+### Community 178 - "ipredStore.ts"
+Cohesion: 0.29
+Nodes (6): DEFAULT_COMPOSITION_ID, DEFAULT_TRAINER_CONFIG, DEFAULT_TRAINER_ID, IpredState, TrainerConfig, useIpredStore
+
+### Community 179 - "layerVisibilityStore.ts"
+Cohesion: 0.29
+Nodes (5): DEFAULT_GROUPS, LAYER_GROUP_META, LayerGroupId, LayerVisibilityState, useLayerVisibilityStore
+
+### Community 180 - "OME-Zarr Viewer"
+Cohesion: 0.29
+Nodes (6): Notes, OME-Zarr Viewer, Point at a local dataset, Quick start, Requirements, Use in another project
+
+### Community 182 - "include"
+Cohesion: 0.29
+Nodes (6): extends, include, src, bench, test, ../../tsconfig.base.json
+
+### Community 185 - "_denoised_slice"
+Cohesion: 0.40
+Nodes (6): _centre_crop(), _denoised_slice(), Any, ndarray, Centred square crop of *size*, or *arr* unchanged if it already fits., Read *slice_index* and denoise it, pulling z-neighbours for 3-D methods. The…
+
+### Community 186 - "2. Browse & select"
+Cohesion: 0.33
+Nodes (6): 2. Browse & select, Local: the flat file list, Opening a sample, The detail panel, The Samples column, Tiled: the metadata browser
+
+### Community 187 - "1. Connect to data"
+Cohesion: 0.33
+Nodes (6): 1. Connect to data, Connect to Tiled, Ingesting data (Tiled only), Load / Ingest Datasets, Local Folder mode, Tiled Server mode
+
+### Community 188 - "fetch-sam-model.mjs"
+Cohesion: 0.33
+Nodes (4): HERE, OUT, python, res
+
+### Community 189 - "DenoiseBakeModal.tsx"
+Cohesion: 0.47
+Nodes (5): defaultTarget(), DenoiseBakeModal(), DenoiseBakeModalProps, JobState, readError()
+
+### Community 190 - "DownloadModal.tsx"
+Cohesion: 0.40
+Nodes (5): DownloadModal(), DownloadModalProps, parseSourceKey(), Scope, SCOPE_OPTIONS
+
+### Community 191 - "InsightsModal.tsx"
+Cohesion: 0.40
+Nodes (4): FLAG_LABEL, formatArea(), InsightsModal(), InsightsModalProps
+
+### Community 192 - "MeasurementPanel/index.tsx"
+Cohesion: 0.40
+Nodes (4): fmt(), IntensityStats, MeasurementPanel(), MeasurementPanelProps
+
+### Community 193 - "VersionHistoryModal.tsx"
+Cohesion: 0.47
+Nodes (4): formatDate(), Props, thumbnailUrl(), VersionHistoryModal()
+
+### Community 194 - "useFeatureManifold.ts"
+Cohesion: 0.40
+Nodes (5): DEFAULT_MANIFOLD_PARAMS, ManifoldParams, readErrorDetail(), useFeatureManifold(), UseFeatureManifoldArgs
+
+### Community 195 - "classColors.ts"
+Cohesion: 0.67
+Nodes (4): COLORBLIND_COLORS, DEFAULT_COLORS, getClassPalette(), pickNextColor()
+
+### Community 196 - "lib/colormaps.ts"
+Cohesion: 0.33
+Nodes (3): COLORMAP_NAMES, ColormapName, STOPS
+
+### Community 197 - "featureManifold.ts"
+Cohesion: 0.53
+Nodes (4): colorizeManifoldHeatmap(), loadManifoldHeatmapCanvas(), manifoldMarkerRect(), ManifoldPoint
+
+### Community 201 - "test_manifold.py"
+Cohesion: 0.40
+Nodes (5): catalog(), fixture, MonkeyPatch, Manifold Suggest Labels on persisted ipred feature banks., test_manifold_sample_on_feature_bank()
+
+### Community 202 - "test_train_rethreshold.py"
+Cohesion: 0.40
+Nodes (5): catalog(), fixture, MonkeyPatch, Train → infer → rethreshold without recompute., test_train_infer_rethreshold()
+
+### Community 204 - "Keyboard shortcuts"
+Cohesion: 0.40
+Nodes (5): Canvas & navigation, Editing, Keyboard shortcuts, Mouse modifiers (Magic / Fill / SAM), Tools
+
+### Community 206 - "PerfOverlay/index.tsx"
+Cohesion: 0.50
+Nodes (3): PerfOverlayProps, Row(), severity()
+
+### Community 207 - "PixelClassifierPanel/index.tsx"
+Cohesion: 0.50
+Nodes (3): coverageHeadline(), PixelClassifierPanel(), PixelClassifierPanelProps
+
+### Community 208 - "SaveModal.tsx"
+Cohesion: 0.60
+Nodes (4): blobToBase64(), fetchPreviewBlob(), SaveModal(), SaveModalProps
+
+### Community 209 - "CompositionPanel/index.tsx"
+Cohesion: 0.70
+Nodes (3): CompositionPanel(), defaultParams(), newNodeId()
+
+### Community 210 - "useFeatureChannels.ts"
+Cohesion: 0.40
+Nodes (3): FeatureChannelInfo, FeatureJobInfo, UseFeatureChannelsArgs
+
+### Community 211 - "measure.ts"
+Cohesion: 0.60
+Nodes (4): measureRegion(), RegionMeasurement, ringPerimeter(), shapePerimeter()
+
+### Community 212 - "buildSourceKey"
+Cohesion: 0.80
+Nodes (3): buildSourceKey(), isAnnotatedPath(), key()
+
+### Community 215 - "start.sh"
+Cohesion: 0.40
+Nodes (3): HOST, start.sh script, TILED_ALLOW_ORIGINS
+
+### Community 216 - "reingest_tiffs_as_individual.py"
+Cohesion: 0.67
+Nodes (3): compute_structure_id(), main(), Re-ingest stacked TIFF array as 690 individual sample nodes. The 690 TIFFs were…
+
+### Community 219 - "FeatureChannelsPanel/index.tsx"
+Cohesion: 0.40
+Nodes (3): FeatureChannelsPanelProps, PRESET_HINTS, PRESET_LABELS
+
+### Community 221 - "VersionPreviewBar.tsx"
+Cohesion: 0.67
+Nodes (3): formatDate(), Props, VersionPreviewBar()
+
+### Community 223 - "VolumeViewer.tsx"
+Cohesion: 0.67
+Nodes (3): VolumeViewer(), VolumeViewerProps, webGpuAvailability()
+
+### Community 226 - "useMaskOps.ts"
+Cohesion: 0.67
+Nodes (3): CleanupOp, polysToShapes(), useMaskOps()
+
+### Community 227 - "useOpenInAnnotate.ts"
+Cohesion: 0.83
+Nodes (3): applyDraft(), classesFromKeywords(), useOpenInAnnotate()
+
+### Community 229 - "adjust.ts"
+Cohesion: 0.67
+Nodes (3): PreprocessOpts, renderAdjusted(), renderPreprocessOnly()
+
+### Community 231 - "classStore.ts"
+Cohesion: 0.50
+Nodes (3): AnnotationClass, ClassStore, useClassStore
+
+### Community 232 - "ratingStore.ts"
+Cohesion: 0.50
+Nodes (3): RatingState, StarRating, useRatingStore
+
+### Community 233 - "referenceGuideStore.ts"
+Cohesion: 0.50
+Nodes (3): GuideClass, ReferenceGuideStore, useReferenceGuideStore
+
+### Community 234 - "settingsStore.ts"
+Cohesion: 0.67
+Nodes (3): genId(), SettingsStore, useSettingsStore
+
+### Community 237 - "ipred/models"
+Cohesion: 0.50
+Nodes (3): ipred/models, SlimSAM (ONNX), TomoJEPA (Mark25 / Mark11)
+
+### Community 239 - "test_tomojepa_onnx.py"
+Cohesion: 0.50
+Nodes (3): TomoJEPA ONNX encode smoke (skips if onnx file missing)., test_onnx_encode_dense_shape(), needs_onnx
+
+### Community 240 - "ExportConflict"
+Cohesion: 0.67
+Nodes (3): ExportConflict, Exception, Raised when a merge would corrupt category ids or names.
+
+### Community 243 - "Using the tool"
+Cohesion: 0.67
+Nodes (3): Recommended reading order, The Finch shell, Using the tool
+
+### Community 244 - "4. Annotation guide"
+Cohesion: 0.67
+Nodes (3): 4. Annotation guide, How it connects to Annotate, What you can do here
+
+## Knowledge Gaps
+- **875 isolated node(s):** `sam3-annotation-studio-backend`, `name`, `private`, `version`, `type` (+870 more)
+  These have ≤1 connection - possible missing edges or undocumented components.
+- **64 thin communities (<3 nodes) omitted from report** — run `graphify query` to explore isolated nodes.
+
+## Suggested Questions
+_Questions this graph is uniquely positioned to answer:_
+
+- **Why does `uuid` connect `catalog.py` to `compositions.py`, `dependencies`, `manifold.py`, `train_infer.py`, `export_jobs.py`, `feature_setups.py`, `ingest.py`?**
+  _High betweenness centrality (0.025) - this node is a cross-community bridge._
+- **Why does `Vec3` connect `Vec3` to `camera.ts`, `ome-zarr-viewer.ts`, `Aabb`, `math/index.ts`, `Random`, `io/index.ts`, `Quat`, `math/color.ts`, `OrbitControls`, `Vec3Like`, `Mat4`, `packing.ts`, `VectorGrid3`, `Mat3`?**
+  _High betweenness centrality (0.017) - this node is a cross-community bridge._
+- **Why does `Catalog` connect `Catalog` to `compositions.py`, `test_compositions.py`, `manifold.py`, `api.py`, `train_infer.py`, `test_manifold.py`, `test_train_rethreshold.py`, `test_preprocess_cache.py`, `test_catalog_sessions.py`, `feature_setups.py`, `catalog.py`?**
+  _High betweenness centrality (0.016) - this node is a cross-community bridge._
+- **Are the 57 inferred relationships involving `run()` (e.g. with `.dispose()` and `.getState()`) actually correct?**
+  _`run()` has 57 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 39 inferred relationships involving `Catalog` (e.g. with `_resolve_session()` and `ensure_default_compositions()`) actually correct?**
+  _`Catalog` has 39 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 61 inferred relationships involving `HTTPException` (e.g. with `browse_column()` and `browse_items()`) actually correct?**
+  _`HTTPException` has 61 INFERRED edges - model-reasoned connections that need verification._
+- **What connects `sam3-annotation-studio-backend`, `name`, `private` to the rest of the system?**
+  _875 weakly-connected nodes found - possible documentation gaps or missing edges._
\ No newline at end of file
diff --git a/graphify-out/2026-08-26/graph.json b/graphify-out/2026-08-26/graph.json
new file mode 100644
index 0000000..3471dd1
--- /dev/null
+++ b/graphify-out/2026-08-26/graph.json
@@ -0,0 +1,159538 @@
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+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "rendergraph",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_rendergraphtexturedesc",
+      "label": "RenderGraphTextureDesc",
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+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "rendergraphtexturedesc",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_resourcelifetime",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_zarr_codecs_codec",
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+      "_origin": "ast",
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+      "file_type": "code",
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+    },
+    {
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+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 102,
+      "community_name": "Aabb",
+      "file_type": "code",
+      "norm_label": "aabb",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_shapes_frustum",
+      "label": "Frustum",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 102,
+      "community_name": "Aabb",
+      "file_type": "code",
+      "norm_label": "frustum",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/shapes.ts",
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+    },
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+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 103,
+      "community_name": "denoise_bake.py",
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+    },
+    {
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+      "label": "ClassStat",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "classstat",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L16"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_coverage",
+      "label": "Coverage",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "coverage",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L25"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_maskinfo",
+      "label": "MaskInfo",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "maskinfo",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L182"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_qaflag",
+      "label": "QAFlag",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "qaflag",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L35"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_qaflagkind",
+      "label": "QAFlagKind",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "qaflagkind",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L33"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_samplestats",
+      "label": "SampleStats",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "samplestats",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L45"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_slicesmap",
+      "label": "SlicesMap",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "slicesmap",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L56"
+    },
+    {
+      "id": "frontend_src_lib_sam_samclient_granularity",
+      "label": "Granularity",
+      "_callable": true,
+      "_callable_class": true,
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_rendergraph_markoutput",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_zarr_lz4_lz4blockdecompress",
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+    },
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+      "source_location": "L35"
+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_layoutbuilder_mat3",
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+      "id": "frontend_src_hooks_useguidesync_generateguide",
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+      "id": "frontend_src_hooks_useguidesync_useguideload",
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+      "id": "frontend_src_hooks_useguidesync_useguidesync",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_shadow_map_shadowmap_constructor",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_tiles_tilecompactor_dummytiles",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_resources_pipeline_pipelinecache_getcomputepipeline",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_resources_texture_managedtexture_createview",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_resources_texture_managedtexture_depthtarget",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_resources_texture_managedtexture_msaacolortarget",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_constructor",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setliquidshading",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_device_context_createcontext",
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+      "source_location": "L49"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_extract_keydirectionalfromlights",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightbufferbytesize",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_ambient",
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+      "_origin": "ast",
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+      "source_location": "L99"
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_constructor",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_dispose",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_opacity_curve_editor_opacitycurveeditor_sethistogram",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_opacity_curve_editor_opacitycurveeditor_setpoints",
+      "label": ".setPoints()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_opacity_curve_samplelut",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_opacity_curve_sampleopacity",
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+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_opacity_curve_setopacitypoint",
+      "label": "setOpacityPoint()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_firstpersoncontrols_constructor",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_firstpersoncontrols_syncfromnode",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_flycontrols_constructor",
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+      "source_location": "L763"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_flycontrols_syncfromnode",
+      "label": ".syncFromNode()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_constructor",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_add",
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+      "_origin": "ast",
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+      "file_type": "code",
+      "norm_label": ".add()",
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+      "source_location": "L45"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_constructor",
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+      "_origin": "ast",
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+      "source_location": "L42"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_localmatrix",
+      "label": ".localMatrix()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 43,
+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".localmatrix()",
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+      "source_location": "L105"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_remove",
+      "label": ".remove()",
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+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".remove()",
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+      "source_location": "L54"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_transformchanged",
+      "label": ".transformChanged()",
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+      "_origin": "ast",
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+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".transformchanged()",
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+      "source_location": "L72"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_worldmatrix",
+      "label": ".worldMatrix()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 43,
+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".worldmatrix()",
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+      "source_location": "L113"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_scene_scene_attach",
+      "label": ".attach()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 43,
+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".attach()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/scene/scene.ts",
+      "source_location": "L36"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_scene_scene_detach",
+      "label": ".detach()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 43,
+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".detach()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/scene/scene.ts",
+      "source_location": "L49"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_scene_scene_query",
+      "label": ".query()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 43,
+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".query()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/scene/scene.ts",
+      "source_location": "L68"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_constructor",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".constructor()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L21"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_div",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".div()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L52"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_equals",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".equals()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L78"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_isdimensionless",
+      "label": ".isDimensionless()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".isdimensionless()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L91"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_mul",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".mul()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L39"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_pow",
+      "label": ".pow()",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".pow()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L65"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_tostring",
+      "label": ".toString()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".tostring()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L96"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_abs",
+      "label": ".abs()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".abs()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L74"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_add",
+      "label": ".add()",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".add()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L41"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_approxequals",
+      "label": ".approxEquals()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".approxequals()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L88"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_assertsamedimension",
+      "label": ".assertSameDimension()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".assertsamedimension()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L99"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_compare",
+      "label": ".compare()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".compare()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L79"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_constructor",
+      "label": ".constructor()",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".constructor()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L15"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_div",
+      "label": ".div()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".div()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L59"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_equals",
+      "label": ".equals()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".equals()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L84"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_mul",
+      "label": ".mul()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".mul()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L52"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_neg",
+      "label": ".neg()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".neg()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L70"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_pow",
+      "label": ".pow()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".pow()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L66"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_scalar",
+      "label": ".scalar()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".scalar()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L27"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_sub",
+      "label": ".sub()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".sub()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L46"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_to",
+      "label": ".to()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".to()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L32"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_tostring",
+      "label": ".toString()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".tostring()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L93"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_assertratio",
+      "label": ".assertRatio()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".assertratio()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/unit.ts",
+      "source_location": "L67"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_constructor",
+      "label": ".constructor()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_applyeyelookat",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_basisfromgaze",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_gazeup",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_isanimating",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_isdragging",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_orbittrackball",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_orbitturntable",
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+      "_origin": "ast",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_orthonormalizegaze",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_syncfromnode",
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+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_syncsphericalfromoffset",
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+      "_origin": "ast",
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+      "community_name": "OrbitControls",
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+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_update",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_writeoffsetfromspherical",
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+      "_origin": "ast",
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+      "file_type": "code",
+      "norm_label": ".writeoffsetfromspherical()",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_shapes_aabb_getcenter",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_clamplength",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_dividescalar",
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+      "_origin": "ast",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_length",
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+      "_origin": "ast",
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+      "file_type": "code",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_multiplyscalar",
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+      "_origin": "ast",
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+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".multiplyscalar()",
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+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_normalize",
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+      "_origin": "ast",
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+      "file_type": "code",
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+      "source_location": "L228"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_setlength",
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+      "_origin": "ast",
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+      "file_type": "code",
+      "norm_label": ".setlength()",
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+      "source_location": "L234"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_transformdirection",
+      "label": ".transformDirection()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".transformdirection()",
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+      "source_location": "L323"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_markdirty",
+      "label": ".markDirty()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".markdirty()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/scene/node.ts",
+      "source_location": "L67"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_add",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_brick_loader_brickloader_onbrick",
+      "label": ".onBrick()",
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+      "_origin": "ast",
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+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".onbrick()",
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+      "source_location": "L64"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_brick_loader_brickloader_onclear",
+      "label": ".onClear()",
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+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".onclear()",
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+      "source_location": "L67"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_brick_loader_brickloader_onprogress",
+      "label": ".onProgress()",
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+      "_origin": "ast",
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+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".onprogress()",
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+      "source_location": "L71"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_brick_loader_brickloader_request",
+      "label": ".request()",
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+      "_origin": "ast",
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+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".request()",
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+      "source_location": "L83"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_brick_loader_brickloader_stream",
+      "label": ".stream()",
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+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".stream()",
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+      "source_location": "L114"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_aabbscreenbbox",
+      "label": ".aabbScreenBbox()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".aabbscreenbbox()",
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+      "source_location": "L559"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_aftersubmit",
+      "label": ".afterSubmit()",
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+      "norm_label": ".aftersubmit()",
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+      "source_location": "L597"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_approximateshadingbanner",
+      "label": ".approximateShadingBanner()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".approximateshadingbanner()",
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+      "source_location": "L616"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_dispose",
+      "label": ".dispose()",
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+      "file_type": "code",
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+      "source_location": "L909"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_ensurebindgroup",
+      "label": ".ensureBindGroup()",
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+      "norm_label": ".ensurebindgroup()",
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+      "source_location": "L1056"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_ensurepipeline",
+      "label": ".ensurePipeline()",
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+      "source_location": "L931"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_recordinto",
+      "label": ".recordInto()",
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+      "_origin": "ast",
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+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".recordinto()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_recordprepasses",
+      "label": ".recordPrePasses()",
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+      "_origin": "ast",
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+      "norm_label": ".recordprepasses()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_render",
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+      "file_type": "code",
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+      "source_location": "L714"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_resetcrop",
+      "label": ".resetCrop()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setblendmode",
+      "label": ".setBlendMode()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setblendmode()",
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+      "source_location": "L449"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setboxhalfsize",
+      "label": ".setBoxHalfSize()",
+      "_callable": true,
+      "_origin": "ast",
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+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setboxhalfsize()",
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+      "source_location": "L331"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setbrick",
+      "label": ".setBrick()",
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+      "_origin": "ast",
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+      "file_type": "code",
+      "norm_label": ".setbrick()",
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+      "source_location": "L312"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setbrickblend",
+      "label": ".setBrickBlend()",
+      "_callable": true,
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+      "file_type": "code",
+      "norm_label": ".setbrickblend()",
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+      "source_location": "L327"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setcrop",
+      "label": ".setCrop()",
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+      "_origin": "ast",
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+      "file_type": "code",
+      "norm_label": ".setcrop()",
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+      "source_location": "L625"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setearlyraytermination",
+      "label": ".setEarlyRayTermination()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setearlyraytermination()",
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+      "source_location": "L477"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setinternalsize",
+      "label": ".setInternalSize()",
+      "_callable": true,
+      "_origin": "ast",
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+      "file_type": "code",
+      "norm_label": ".setinternalsize()",
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+      "source_location": "L482"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setlightingparams",
+      "label": ".setLightingParams()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setlightingparams()",
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+      "source_location": "L423"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setlights",
+      "label": ".setLights()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setlights()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L398"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setmeasureplane",
+      "label": ".setMeasurePlane()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setmeasureplane()",
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+      "source_location": "L408"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setparams",
+      "label": ".setParams()",
+      "_callable": true,
+      "_origin": "ast",
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+      "file_type": "code",
+      "norm_label": ".setparams()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L669"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setreprojectfar",
+      "label": ".setReprojectFar()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setreprojectfar()",
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+      "source_location": "L488"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setshaderconfig",
+      "label": ".setShaderConfig()",
+      "_callable": true,
+      "_origin": "ast",
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+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setshaderconfig()",
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+      "source_location": "L454"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setshadowmap",
+      "label": ".setShadowMap()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setshadowmap()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L297"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setsliceenabled",
+      "label": ".setSliceEnabled()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setsliceenabled()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L658"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setsliceplanesvisible",
+      "label": ".setSlicePlanesVisible()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setsliceplanesvisible()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L665"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setslices",
+      "label": ".setSlices()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setslices()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L652"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setviewmode",
+      "label": ".setViewMode()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setviewmode()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L620"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setvisibilityfeedback",
+      "label": ".setVisibilityFeedback()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setvisibilityfeedback()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L466"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setvolume",
+      "label": ".setVolume()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".setvolume()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L274"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_visibility",
+      "label": ".visibility()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".visibility()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L472"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_writeuniforms",
+      "label": ".writeUniforms()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": ".writeuniforms()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-renderer.ts",
+      "source_location": "L772"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_texture_choosevolumeformat",
+      "label": "chooseVolumeFormat()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 6,
+      "community_name": "run",
+      "file_type": "code",
+      "norm_label": "choosevolumeformat()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/volume/volume-texture.ts",
+      "source_location": "L271"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_assert_assert",
+      "label": "assert()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 60,
+      "community_name": "core/index.ts",
+      "file_type": "code",
+      "norm_label": "assert()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/assert.ts",
+      "source_location": "L34"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_assert_assertdefined",
+      "label": "assertDefined()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 60,
+      "community_name": "core/index.ts",
+      "file_type": "code",
+      "norm_label": "assertdefined()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/assert.ts",
+      "source_location": "L49"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_assert_assertnever",
+      "label": "assertNever()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 60,
+      "community_name": "core/index.ts",
+      "file_type": "code",
+      "norm_label": "assertnever()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/assert.ts",
+      "source_location": "L69"
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+      "source_location": "L62"
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+      "label": "OverlaysLayer.tsx",
+      "_origin": "ast",
+      "community": 47,
+      "community_name": "AnnotationCanvas/index.tsx",
+      "file_type": "code",
+      "norm_label": "overlayslayer.tsx",
+      "source_file": "frontend/src/components/annotate/AnnotationCanvas/OverlaysLayer.tsx",
+      "source_location": "L1"
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+    {
+      "id": "frontend_src_components_annotate_annotationcanvas_shapeslayer",
+      "label": "ShapesLayer.tsx",
+      "_origin": "ast",
+      "community": 47,
+      "community_name": "AnnotationCanvas/index.tsx",
+      "file_type": "code",
+      "norm_label": "shapeslayer.tsx",
+      "source_file": "frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.tsx",
+      "source_location": "L1"
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+    {
+      "id": "frontend_src_components_annotate_annotationcanvas_index_rationale_608",
+      "label": "NOTE: thresholdLo/thresholdHi are deliberately NOT subscribed \u2014 dragging the",
+      "_origin": "ast",
+      "community": 47,
+      "community_name": "AnnotationCanvas/index.tsx",
+      "file_type": "rationale",
+      "norm_label": "note: thresholdlo/thresholdhi are deliberately not subscribed \u2014 dragging the",
+      "source_file": "frontend/src/components/annotate/AnnotationCanvas/index.tsx",
+      "source_location": "L608"
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_index",
+      "label": "io/index.ts",
+      "_origin": "ast",
+      "community": 48,
+      "community_name": "io/index.ts",
+      "file_type": "code",
+      "norm_label": "io/index.ts",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/io/index.ts",
+      "source_location": "L1"
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+    {
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+      "_origin": "ast",
+      "community": 48,
+      "community_name": "io/index.ts",
+      "file_type": "code",
+      "norm_label": "analysis.ts",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/io/volume/analysis.ts",
+      "source_location": "L1"
+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_geometry_types",
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+      "_origin": "ast",
+      "community": 48,
+      "community_name": "io/index.ts",
+      "file_type": "code",
+      "norm_label": "geometry-types.ts",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/io/volume/geometry-types.ts",
+      "source_location": "L1"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_volume_geom",
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+      "_origin": "ast",
+      "community": 48,
+      "community_name": "io/index.ts",
+      "file_type": "code",
+      "norm_label": "volume-geom.ts",
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+      "source_location": "L1"
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+      "id": "frontend_src_lib_ipredapi",
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+      "_origin": "ast",
+      "community": 49,
+      "community_name": "ipredApi.ts",
+      "file_type": "code",
+      "norm_label": "ipredapi.ts",
+      "source_file": "frontend/src/lib/ipredApi.ts",
+      "source_location": "L1"
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+    {
+      "id": "backend_annotation_thumbnails",
+      "label": "annotation_thumbnails.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "annotation_thumbnails.py",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_annotation_thumbnails_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_annotation_thumbnails_py_ndarray",
+      "label": "ndarray",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "ndarray",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_guide_gen",
+      "label": "guide_gen.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "guide_gen.py",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_guide_gen_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_guide_gen_py_ndarray",
+      "label": "ndarray",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "ndarray",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_source_keys",
+      "label": "source_keys.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "source_keys.py",
+      "source_file": "backend/source_keys.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tests_test_tiled_annotation_sync",
+      "label": "test_tiled_annotation_sync.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "test_tiled_annotation_sync.py",
+      "source_file": "backend/tests/test_tiled_annotation_sync.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_thumbnails",
+      "label": "thumbnails.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "thumbnails.py",
+      "source_file": "backend/thumbnails.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_thumbnails_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_thumbnails_py_ndarray",
+      "label": "ndarray",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "ndarray",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_tiled_annotation_sync",
+      "label": "tiled_annotation_sync.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "tiled_annotation_sync.py",
+      "source_file": "backend/tiled_annotation_sync.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tiled_annotation_sync_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "imagedraw",
+      "label": "ImageDraw",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "imagedraw",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_1",
+      "label": "Annotated thumbnail generation for version history previews. Renders the most-\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "annotated thumbnail generation for version history previews. renders the most-...",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_130",
+      "label": "Render a PNG thumbnail of the most-annotated slice with shapes overlaid. Args:\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "render a png thumbnail of the most-annotated slice with shapes overlaid. args:...",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L130"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_214",
+      "label": "Write the annotated thumbnail to a Tiled sibling node (best-effort). Creates\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "write the annotated thumbnail to a tiled sibling node (best-effort). creates...",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L214"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_27",
+      "label": "Return (kind, source, server_uri) from a canonical source key.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "return (kind, source, server_uri) from a canonical source key.",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L27"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_35",
+      "label": "Decode a base64 PNG from the save modal preview, or return None.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "decode a base64 png from the save modal preview, or return none.",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L35"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_49",
+      "label": "Fast nearest-neighbour downsample so huge volumes don't stall thumbnails.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "fast nearest-neighbour downsample so huge volumes don't stall thumbnails.",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L49"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_74",
+      "label": "Draw annotation shapes onto *draw* at thumbnail scale.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "draw annotation shapes onto *draw* at thumbnail scale.",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L74"
+    },
+    {
+      "id": "backend_guide_gen_rationale_1",
+      "label": "Generate an annotation guide from an existing annotation. Given a set of\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "generate an annotation guide from an existing annotation. given a set of...",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_guide_gen_rationale_120",
+      "label": "Return a copy of *shape* with all coordinates scaled by *f* (for rasterizing at\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "return a copy of *shape* with all coordinates scaled by *f* (for rasterizing at...",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L120"
+    },
+    {
+      "id": "backend_guide_gen_rationale_148",
+      "label": "Boolean boundary ring of *mask* (mask minus its 4-neighbour erosion), dilated\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "boolean boundary ring of *mask* (mask minus its 4-neighbour erosion), dilated...",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L148"
+    },
+    {
+      "id": "backend_guide_gen_rationale_173",
+      "label": "Crop *rgb* to the mask's bbox (+margin), tint the masked region with *color*,\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "crop *rgb* to the mask's bbox (+margin), tint the masked region with *color*,...",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L173"
+    },
+    {
+      "id": "backend_guide_gen_rationale_216",
+      "label": "Build a guide skeleton (per-class label/color + color-coded example crops).\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "build a guide skeleton (per-class label/color + color-coded example crops)....",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L216"
+    },
+    {
+      "id": "backend_guide_gen_rationale_91",
+      "label": "(x0, y0, x1, y1) in image px, or None.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "(x0, y0, x1, y1) in image px, or none.",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L91"
+    },
+    {
+      "id": "backend_source_keys_rationale_1",
+      "label": "Canonical source-key parsing (mirrors frontend ``buildSourceKey``).",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "canonical source-key parsing (mirrors frontend ``buildsourcekey``).",
+      "source_file": "backend/source_keys.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_source_keys_rationale_9",
+      "label": "Parse a draft/source key into kind, server URI, and path. Formats:\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "parse a draft/source key into kind, server uri, and path. formats:...",
+      "source_file": "backend/source_keys.py",
+      "source_location": "L9"
+    },
+    {
+      "id": "backend_tests_test_tiled_annotation_sync_rationale_1",
+      "label": "Tests for source key parsing and annotation metadata helpers.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "tests for source key parsing and annotation metadata helpers.",
+      "source_file": "backend/tests/test_tiled_annotation_sync.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_thumbnails_rationale_1",
+      "label": "PNG thumbnail rendering for Tiled array nodes. Handles two common cases: * 3D\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "png thumbnail rendering for tiled array nodes. handles two common cases: * 3d...",
+      "source_file": "backend/thumbnails.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_thumbnails_rationale_32",
+      "label": "Render a PNG thumbnail for *node* (array or container-of-arrays). Returns\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "render a png thumbnail for *node* (array or container-of-arrays). returns...",
+      "source_file": "backend/thumbnails.py",
+      "source_location": "L32"
+    },
+    {
+      "id": "backend_thumbnails_rationale_54",
+      "label": "Return *node* if it's already an array, else its first array child.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "return *node* if it's already an array, else its first array child.",
+      "source_file": "backend/thumbnails.py",
+      "source_location": "L54"
+    },
+    {
+      "id": "backend_thumbnails_rationale_85",
+      "label": "Log-scale, normalise, and colour-map a 2D intensity array to uint8 RGB.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "log-scale, normalise, and colour-map a 2d intensity array to uint8 rgb.",
+      "source_file": "backend/thumbnails.py",
+      "source_location": "L85"
+    },
+    {
+      "id": "backend_tiled_annotation_sync_rationale_1",
+      "label": "Sync annotation session state into Tiled node metadata for Browse discovery.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "sync annotation session state into tiled node metadata for browse discovery.",
+      "source_file": "backend/tiled_annotation_sync.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tiled_annotation_sync_rationale_22",
+      "label": "Build Tiled-safe metadata fields from a draft/export payload.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "build tiled-safe metadata fields from a draft/export payload.",
+      "source_file": "backend/tiled_annotation_sync.py",
+      "source_location": "L22"
+    },
+    {
+      "id": "backend_tiled_annotation_sync_rationale_37",
+      "label": "Merge annotation summary metadata onto a Tiled node. No-op for local sources.\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "merge annotation summary metadata onto a tiled node. no-op for local sources....",
+      "source_file": "backend/tiled_annotation_sync.py",
+      "source_location": "L37"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_firstpersoncontrols_onpointermove",
+      "label": ".onPointerMove()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onpointermove()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L905"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_flycontrols_onpointermove",
+      "label": ".onPointerMove()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onpointermove()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L776"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_oncontextmenu",
+      "label": ".onContextMenu()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".oncontextmenu()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L228"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onkeydown",
+      "label": ".onKeyDown()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onkeydown()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L229"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onkeyup",
+      "label": ".onKeyUp()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onkeyup()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L242"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onpointerdown",
+      "label": ".onPointerDown()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
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+      "norm_label": "@webgpu/types",
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+      "label": "Any",
+      "_origin": "ast",
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+      "community_name": "browse_helpers.py",
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+      "source_location": ""
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+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
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+    },
+    {
+      "id": "backend_browse_helpers_rationale_1",
+      "label": "Browse helper functions used by the Browse API routes. Exports ------- *\u2026",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
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+      "source_file": "backend/browse_helpers.py",
+      "source_location": "L1"
+    },
+    {
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+      "label": "Ensure studio annotation + ingest description fields are always in Browse.",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "ensure studio annotation + ingest description fields are always in browse.",
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+      "source_location": "L148"
+    },
+    {
+      "id": "backend_browse_helpers_rationale_156",
+      "label": "Return metadata keys present on the sample/container node. We intentionally do\u2026",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "return metadata keys present on the sample/container node. we intentionally do...",
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+      "source_location": "L156"
+    },
+    {
+      "id": "backend_browse_helpers_rationale_186",
+      "label": "Read each child's metadata once for a *specific* container. Tiled's\u2026",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "read each child's metadata once for a *specific* container. tiled's...",
+      "source_file": "backend/browse_helpers.py",
+      "source_location": "L186"
+    },
+    {
+      "id": "backend_browse_helpers_rationale_207",
+      "label": "Tally distinct values of *raw_key* across pre-read metadata *rows*. List-valued\u2026",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "tally distinct values of *raw_key* across pre-read metadata *rows*. list-valued...",
+      "source_file": "backend/browse_helpers.py",
+      "source_location": "L207"
+    },
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+      "id": "backend_browse_helpers_rationale_233",
+      "label": "Return distinct values (+ counts) for *raw_key* under optional filters. When\u2026",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "return distinct values (+ counts) for *raw_key* under optional filters. when...",
+      "source_file": "backend/browse_helpers.py",
+      "source_location": "L233"
+    },
+    {
+      "id": "backend_browse_helpers_rationale_298",
+      "label": "Apply *filters* via ``search(Key())`` and return matching container entries.\u2026",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "apply *filters* via ``search(key())`` and return matching container entries....",
+      "source_file": "backend/browse_helpers.py",
+      "source_location": "L298"
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+      "id": "backend_browse_helpers_rationale_368",
+      "label": "True if *entry* is a Tiled container (vs. an array/leaf) node.",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "true if *entry* is a tiled container (vs. an array/leaf) node.",
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+      "id": "backend_browse_helpers_rationale_454",
+      "label": "Convert a display-key filter dict to (raw_key, string-value) pairs.",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "convert a display-key filter dict to (raw_key, string-value) pairs.",
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+      "label": "Apply each filter to *node*, skipping failures. List-valued keys (e.g.\u2026",
+      "_origin": "ast",
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+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "apply each filter to *node*, skipping failures. list-valued keys (e.g....",
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+      "label": "Case-insensitive match over container-level metadata. Scalar values match by\u2026",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "case-insensitive match over container-level metadata. scalar values match by...",
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+      "label": "Coerce a filter string to int / float when it round-trips exactly. Tiled stores\u2026",
+      "_origin": "ast",
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+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "coerce a filter string to int / float when it round-trips exactly. tiled stores...",
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+      "_origin": "ast",
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+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "mapping between raw tiled metadata keys and friendly display names.",
+      "source_file": "backend/browse_helpers.py",
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+    },
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+      "id": "backend_browse_helpers_rationale_91",
+      "label": "Introspect *container_node* to discover available metadata keys. Walks up to\u2026",
+      "_origin": "ast",
+      "community": 64,
+      "community_name": "browse_helpers.py",
+      "file_type": "rationale",
+      "norm_label": "introspect *container_node* to discover available metadata keys. walks up to...",
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+      "norm_label": "schemas.py",
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+      "source_location": "L1"
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+      "id": "backend_schemas_py_basemodel",
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+      "norm_label": "test_schemas.py",
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+      "source_location": "L1"
+    },
+    {
+      "id": "backend_schemas_rationale_1",
+      "label": "Pydantic models for the Segmentation Annotation Studio API. Shapes ------ *\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "pydantic models for the segmentation annotation studio api. shapes ------ *...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_schemas_rationale_101",
+      "label": "A collection of freehand brush strokes forming one annotation region.\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "a collection of freehand brush strokes forming one annotation region....",
+      "source_file": "backend/schemas.py",
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+      "id": "backend_schemas_rationale_238",
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+      "_origin": "ast",
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+      "community_name": "schemas.py",
+      "file_type": "rationale",
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+      "label": "Request body for per-region intensity measurement. Attributes: slice_index:\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "request body for per-region intensity measurement. attributes: slice_index:...",
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+      "id": "backend_schemas_rationale_266",
+      "label": "Request body for the pre-upload duplicate check. Sent as a POST body rather\u2026",
+      "_origin": "ast",
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+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "request body for the pre-upload duplicate check. sent as a post body rather...",
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+      "community_name": "schemas.py",
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+      "norm_label": "a single continuous stroke made by the brush tool. attributes: points: flat...",
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+    },
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+      "id": "backend_schemas_rationale_320",
+      "label": "Request body to denoise a whole volume into a new Tiled dataset. The Annotate\u2026",
+      "_origin": "ast",
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+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "request body to denoise a whole volume into a new tiled dataset. the annotate...",
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+      "id": "backend_schemas_rationale_350",
+      "label": "Request body for building a 3-D volume from a stack already in Tiled.\u2026",
+      "_origin": "ast",
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+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "request body for building a 3-d volume from a stack already in tiled....",
+      "source_file": "backend/schemas.py",
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+    },
+    {
+      "id": "backend_schemas_rationale_371",
+      "label": "One class entry in an annotation guide. Attributes: label: Human-readable class\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "one class entry in an annotation guide. attributes: label: human-readable class...",
+      "source_file": "backend/schemas.py",
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+    },
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+      "id": "backend_schemas_rationale_387",
+      "label": "A project lead's annotation guide for a dataset. Attributes: classes: Ordered\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "a project lead's annotation guide for a dataset. attributes: classes: ordered...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L387"
+    },
+    {
+      "id": "backend_schemas_rationale_399",
+      "label": "Shape and dtype metadata for an opened image source. Attributes: n_slices:\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "shape and dtype metadata for an opened image source. attributes: n_slices:...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L399"
+    },
+    {
+      "id": "backend_schemas_rationale_41",
+      "label": "A closed polygon annotation. Attributes: id: Unique shape identifier. classId:\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "a closed polygon annotation. attributes: id: unique shape identifier. classid:...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L41"
+    },
+    {
+      "id": "backend_schemas_rationale_57",
+      "label": "An axis-aligned bounding-box annotation. Attributes: id: Unique shape\u2026",
+      "_origin": "ast",
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+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "an axis-aligned bounding-box annotation. attributes: id: unique shape...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L57"
+    },
+    {
+      "id": "backend_schemas_rationale_79",
+      "label": "An ellipse annotation. Attributes: id: Unique shape identifier. classId:\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "an ellipse annotation. attributes: id: unique shape identifier. classid:...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L79"
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+      "id": "backend_tests_test_schemas_rationale_1",
+      "label": "Tests for Pydantic schema models.",
+      "_origin": "ast",
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+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "tests for pydantic schema models.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L1"
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+      "id": "backend_tests_test_schemas_rationale_17",
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+      "id": "backend_tests_test_schemas_rationale_30",
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+      "_origin": "ast",
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+      "file_type": "rationale",
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+      "source_file": "backend/tests/test_schemas.py",
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+      "id": "backend_tests_test_schemas_rationale_38",
+      "label": "RenderOpts should have sensible defaults without explicit values.",
+      "_origin": "ast",
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+      "source_file": "backend/tests/test_schemas.py",
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+      "id": "backend_tests_test_schemas_rationale_46",
+      "label": "RectShape should expose geometry fields correctly.",
+      "_origin": "ast",
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+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "rectshape should expose geometry fields correctly.",
+      "source_file": "backend/tests/test_schemas.py",
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+      "id": "backend_tests_test_schemas_rationale_53",
+      "label": "EllipseShape should expose centre and radii correctly.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "ellipseshape should expose centre and radii correctly.",
+      "source_file": "backend/tests/test_schemas.py",
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+      "id": "backend_tests_test_schemas_rationale_60",
+      "label": "BrushStroke should accept 'erase' mode.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "brushstroke should accept 'erase' mode.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L60"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_66",
+      "label": "A folder's worth of filenames must fit \u2014 as query params it 431'd.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "a folder's worth of filenames must fit \u2014 as query params it 431'd.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L66"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_74",
+      "label": "Omitting names is allowed \u2014 used to only ask for a free container name.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "omitting names is allowed \u2014 used to only ask for a free container name.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L74"
+    },
+    {
+      "id": "backend_tests_test_volume_nodes",
+      "label": "test_volume_nodes.py",
+      "_origin": "ast",
+      "community": 67,
+      "community_name": "FakeNode",
+      "file_type": "code",
+      "norm_label": "test_volume_nodes.py",
+      "source_file": "backend/tests/test_volume_nodes.py",
+      "source_location": "L1"
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+      "id": "backend_tests_test_volume_nodes_py_fixture",
+      "label": "fixture",
+      "_origin": "ast",
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+      "community_name": "FakeNode",
+      "file_type": "code",
+      "norm_label": "fixture",
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+      "source_location": ""
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+      "id": "backend_tests_test_volume_nodes_rationale_1",
+      "label": "Tests for locating a dataset's renderable 3-D volume. This is the module that\u2026",
+      "_origin": "ast",
+      "community": 67,
+      "community_name": "FakeNode",
+      "file_type": "rationale",
+      "norm_label": "tests for locating a dataset's renderable 3-d volume. this is the module that...",
+      "source_file": "backend/tests/test_volume_nodes.py",
+      "source_location": "L1"
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+      "community_name": "drafts.py",
+      "file_type": "rationale",
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+}
\ No newline at end of file
diff --git a/graphify-out/GRAPH_REPORT.md b/graphify-out/GRAPH_REPORT.md
new file mode 100644
index 0000000..4262ba6
--- /dev/null
+++ b/graphify-out/GRAPH_REPORT.md
@@ -0,0 +1,1166 @@
+# Graph Report - forge_Segementation_Finch  (2026-08-26)
+
+## Corpus Check
+- 438 files · ~371,790 words
+- Verdict: corpus is large enough that graph structure adds value.
+
+## Summary
+- 4944 nodes · 8864 edges · 295 communities (224 shown, 71 thin omitted)
+- Extraction: 97% EXTRACTED · 3% INFERRED · 0% AMBIGUOUS · INFERRED: 294 edges (avg confidence: 0.85)
+- Token cost: 0 input · 0 output
+
+## Graph Freshness
+- Built from commit: `e8600297`
+- Run `git rev-parse HEAD` and compare to check if the graph is stale.
+- Run `graphify update .` after code changes (no API cost).
+
+## Community Hubs (Navigation)
+- catalog.ts
+- scrape-sciviscolor.py
+- ManagedBuffer
+- thresholdFit.test.ts
+- ipred_routes.py
+- annotation_thumbnails.py
+- run
+- manifold.py
+- HTTPException
+- math/index.ts
+- TemporalAccumulator
+- annotation_server.py
+- ipred_client.py
+- coco_export.py
+- Vec3
+- tomojepa_onnx.py
+- ColumnBrowser.tsx
+- Quat
+- math/color.ts
+- devDependencies
+- render/index.ts
+- accel.spec.ts
+- compilerOptions
+- compilerOptions
+- dimension.ts
+- test_ingest_conflict.py
+- App.tsx
+- src/index.ts
+- catalog.py
+- write_stack
+- ingest.py
+- test_zarr_source.py
+- Catalog
+- TiledNotifications/index.ts
+- camera.ts
+- preprocess.py
+- compositions.py
+- Vec4
+- step_phantom
+- tiff_stack_source.py
+- ipred/__init__.py
+- train_infer.py
+- volume/colormaps.ts
+- Node
+- Quantity
+- Matrix
+- ipred — architecture and operations guide
+- AnnotationCanvas/index.tsx
+- io/index.ts
+- ipredApi.ts
+- OrbitControls
+- Vec2
+- cli.py
+- array.ts
+- packing.ts
+- scalar.ts
+- feature_setups.py
+- arrays.py
+- IngestDropzone.tsx
+- VectorGrid3
+- core/index.ts
+- getTiledBaseUrl
+- constants.ts
+- compilerOptions
+- browse_helpers.py
+- provenance.ts
+- schemas.py
+- FakeNode
+- zarr_source.py
+- dependencies
+- ome-zarr.ts
+- start_all.sh
+- drafts.py
+- tiled_clients.py
+- App.jsx
+- viewerState.ts
+- devDependencies
+- get_tiled_client
+- useTabsStore.ts
+- zarr-viewer/package.json
+- volume-renderer.ts
+- features.py
+- TrainerPlugin
+- sam_embed.py
+- start_all.ps1
+- Installation via Docker (Recommended)
+- parse.ts
+- TTLCache
+- magicwand.ts
+- Segmentation Annotation Studio
+- tiled_mask_sync.py
+- denoise.py
+- Software architecture
+- annotationStore.ts
+- _fake_pyramid
+- ManagedTexture
+- FastAPI
+- fake_stack
+- geometry.ts
+- length-units.ts
+- codecs.ts
+- .alignTo
+- Vec3Like
+- denoise_bake.py
+- 3. Annotate
+- datasetStats.ts
+- SamClient
+- samWorker.ts
+- TomoJepaModule
+- fx/package.json
+- predicates.ts
+- test_tomojepa_embed.py
+- export_jobs.py
+- polybool.ts
+- perf.ts
+- compilerOptions
+- list_module_catalog
+- mergeSameClass.ts
+- rasterizeShapes
+- morphology.ts
+- livewire.ts
+- run_composition
+- 5. Export & download
+- Mat3
+- test_tiled_config.py
+- frontend/package.json
+- TestCascade
+- systems.ts
+- OmeZarrVolumeSource
+- ViewportOverlay
+- TestBuildLevel
+- test_compositions.py
+- test_local_fs.py
+- editHistory.ts
+- pixelClf.ts
+- test_feature_setups.py
+- test_tiff_stack_source.py
+- Random
+- array_source.py
+- AGENTS.md — Segmentation Annotation Studio
+- guides.py
+- test_drafts.py
+- Installation
+- Quick start
+- Emitter
+- test_catalog_sessions.py
+- import_dataset
+- describe_methods
+- clahe.ts
+- TestBlockMean
+- bboxPrefilter.equivalence.test.ts
+- Pool
+- Dual
+- test_array_blobs.py
+- test_preprocess_cache.py
+- TestMultiscalesMetadata
+- TestPyramidPlan
+- usePixelClassifier.ts
+- datasetStore.ts
+- toolStore.ts
+- vite-zarr-static.ts
+- test_api_health.py
+- main
+- test_health.py
+- TestPyramidStore
+- Segmentation Annotation Studio
+- Troubleshooting
+- ClassManager/index.tsx
+- Toolbar/index.tsx
+- ZarrLoader.tsx
+- BuildVolumePanel.tsx
+- config.ts
+- useGuideSync.ts
+- useSave.ts
+- regionOps.ts
+- zarrUrl.ts
+- ipredStore.ts
+- layerVisibilityStore.ts
+- OME-Zarr Viewer
+- .preview_labels
+- include
+- Pool
+- Soa
+- _denoised_slice
+- 2. Browse & select
+- 1. Connect to data
+- fetch-sam-model.mjs
+- DenoiseBakeModal.tsx
+- DownloadModal.tsx
+- InsightsModal.tsx
+- MeasurementPanel/index.tsx
+- VersionHistoryModal.tsx
+- useFeatureManifold.ts
+- classColors.ts
+- lib/colormaps.ts
+- featureManifold.ts
+- stretch.test.ts
+- .preview_labels
+- .preview_labels
+- test_manifold.py
+- test_train_rethreshold.py
+- .preview_labels
+- Keyboard shortcuts
+- LayersPanel/index.tsx
+- PerfOverlay/index.tsx
+- PixelClassifierPanel/index.tsx
+- SaveModal.tsx
+- CompositionPanel/index.tsx
+- useFeatureChannels.ts
+- measure.ts
+- buildSourceKey
+- Mat4
+- start.sh
+- reingest_tiffs_as_individual.py
+- DenoisePanel/index.tsx
+- DisplayControls/index.tsx
+- FeatureChannelsPanel/index.tsx
+- HistogramControl/index.tsx
+- VersionPreviewBar.tsx
+- HubMainContent.tsx
+- VolumeViewer.tsx
+- useDraftSync.ts
+- useExportJob.ts
+- useMaskOps.ts
+- useOpenInAnnotate.ts
+- feedbackContext.ts
+- adjust.ts
+- sharpen.test.ts
+- classStore.ts
+- ratingStore.ts
+- referenceGuideStore.ts
+- settingsStore.ts
+- fx.spec.ts
+- ipred/models
+- test_emb_pca_channels.py
+- test_tomojepa_onnx.py
+- ExportConflict
+- add_sample_metadata.py
+- repair_catalog_paths.py
+- Using the tool
+- 4. Annotation guide
+- ExportPage.tsx
+- MaskToolsPanel/index.tsx
+- DebouncedSlider.tsx
+- CustomizePages.tsx
+- HubAppLayout.tsx
+- HubHeader.tsx
+- HubSidebar.tsx
+- IframeModal.tsx
+- useAnnotatedSourceKeys.ts
+- useKeybinds.ts
+- viridis.ts
+- clipboardStore.ts
+- connectionStore.ts
+- annotationFilter.ts
+- vite-env.d.ts
+- frontend/tsconfig.json
+- patches/README.md
+- Button.tsx
+- ItkVtkFrame.tsx
+- Bundled Tiled catalog (committed to git)
+- @phosphor-icons/react
+- zundo
+- @testing-library/react
+- @types/node
+- @types/react
+- vite
+- @vitejs/plugin-react
+- vitest
+- navigationRouterTypes.ts
+- server.ts
+- vitest.config.ts
+- vite.config.js
+- scripts/__init__.py
+- ipred
+- sam3-annotation-studio-backend
+
+## God Nodes (most connected - your core abstractions)
+1. `Vec3` - 99 edges
+2. `run()` - 98 edges
+3. `Catalog` - 69 edges
+4. `get()` - 58 edges
+5. `Mat4` - 54 edges
+6. `VolumeRenderer` - 51 edges
+7. `Quat` - 46 edges
+8. `Vec2` - 35 edges
+9. `OrbitControls` - 33 edges
+10. `get_tiled_client()` - 29 edges
+
+## Surprising Connections (you probably didn't know these)
+- `health()` --references--> `get()`  [EXTRACTED]
+  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
+- `get_servers()` --references--> `get()`  [EXTRACTED]
+  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
+- `browse_facets()` --references--> `get()`  [EXTRACTED]
+  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
+- `browse_column()` --references--> `get()`  [EXTRACTED]
+  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
+- `browse_items()` --references--> `get()`  [EXTRACTED]
+  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
+
+## Import Cycles
+- 3-file cycle: `frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat4.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts`
+- 4-file cycle: `frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat4.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/quat.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts`
+
+## Communities (295 total, 71 thin omitted)
+
+### Community 0 - "catalog.ts"
+Cohesion: 0.02
+Nodes (116): acre, ALL_UNITS, ampere, arcminute, arcsecond, astronomicalUnit, atmosphere, atomicMassUnit (+108 more)
+
+### Community 1 - "scrape-sciviscolor.py"
+Cohesion: 0.60
+Nodes (4): build_catalog(), main(), parse_points(), basename -> /media/... path, scraped from the category + linear-hue pages.
+
+### Community 2 - "ManagedBuffer"
+Cohesion: 0.10
+Nodes (4): OccupancyGrid, u32params(), TileCompactor, ManagedBuffer
+
+### Community 3 - "thresholdFit.test.ts"
+Cohesion: 0.07
+Nodes (35): applyGaussianBlurGray(), applyGaussianBlurRgba(), boxBlurH(), boxBlurV(), boxRadiiForGaussian(), baseToDisplay(), DisplayAffine, displayAffineFor() (+27 more)
+
+### Community 4 - "ipred_routes.py"
+Cohesion: 0.05
+Nodes (69): ipred_feature_channel(), ipred_get_composition(), ipred_get_setup(), ipred_health(), _ipred_http_error(), ipred_infer(), ipred_list_compositions(), ipred_list_modules() (+61 more)
+
+### Community 5 - "annotation_thumbnails.py"
+Cohesion: 0.05
+Nodes (58): decode_thumbnail_base64(), _draw_shapes(), _hex_to_rgba(), _parse_source_key(), Any, ndarray, Annotated thumbnail generation for version history previews. Renders the most-…, Render a PNG thumbnail of the most-annotated slice with shapes overlaid. Args:… (+50 more)
+
+### Community 6 - "run"
+Cohesion: 0.05
+Nodes (31): createDemoHud(), createDemoSession(), DemoHandle, resetDemoStage(), resizeDemoCanvas(), ensureHudStyles(), boot(), stageEl (+23 more)
+
+### Community 7 - "manifold.py"
+Cohesion: 0.05
+Nodes (52): Tiny TTL cache for short-lived ipred artifacts (manifold heatmaps)., Thread-safe TTL + max-entries cache., TTLCache, _brush_mask(), build_label_map(), _ellipse_mask(), _polygon_mask(), Any (+44 more)
+
+### Community 8 - "HTTPException"
+Cohesion: 0.04
+Nodes (99): browse_slices(), browse_thumbnail(), connect_summary(), denoise_auto(), export_download(), export_status(), get_draft(), get_guide() (+91 more)
+
+### Community 9 - "math/index.ts"
+Cohesion: 0.05
+Nodes (66): derivative(), dualCos(), dualExp(), dualLog(), dualPow(), dualSin(), dualSqrt(), dualTan() (+58 more)
+
+### Community 10 - "TemporalAccumulator"
+Cohesion: 0.05
+Nodes (17): GpuTimer, halton(), TemporalAccumulator, GpuContext, CompiledGraph, descSignature(), PassContext, PassDesc (+9 more)
+
+### Community 11 - "annotation_server.py"
+Cohesion: 0.04
+Nodes (74): browse_column(), browse_facets(), browse_items(), denoise_bake(), denoise_methods(), export_cancel(), generate_guide_route(), get_servers() (+66 more)
+
+### Community 12 - "ipred_client.py"
+Cohesion: 0.07
+Nodes (53): _client(), feature_channel_bytes(), get_composition(), get_setup(), health(), infer(), ipred_url(), list_compositions() (+45 more)
+
+### Community 13 - "coco_export.py"
+Cohesion: 0.05
+Nodes (78): export_coco(), Path, Background worker: render+rasterize all sources, write the dataset tree (images…, Write the COCO dataset for one or more annotated samples. Supports two modes: -…, _run_export_job(), _apply_erased(), _brush_mask(), build_export_plan() (+70 more)
+
+### Community 14 - "Vec3"
+Cohesion: 0.05
+Nodes (4): Plane, Ray, Sphere, Vec3
+
+### Community 15 - "tomojepa_onnx.py"
+Cohesion: 0.06
+Nodes (48): main(), build_encoder(), _cached_model(), encode_dense_embeddings(), encoder_available(), _gray_unit_interval(), load_checkpoint_state(), _pad_to_multiple() (+40 more)
+
+### Community 16 - "ColumnBrowser.tsx"
+Cohesion: 0.07
+Nodes (37): BrowseColumn(), BrowseColumnProps, BrowseDetailPanel(), BrowseDetailPanelProps, displayKey(), formatValue(), SECTION_ORDER, ColumnBrowser() (+29 more)
+
+### Community 17 - "Quat"
+Cohesion: 0.09
+Nodes (3): q(), DualQuat, Quat
+
+### Community 18 - "math/color.ts"
+Cohesion: 0.11
+Nodes (22): clamp01(), clampColor3(), clampColor4(), color3ToVec3(), color4ToVec4(), colorToCss(), isColor3Tuple(), isColor4Tuple() (+14 more)
+
+### Community 19 - "devDependencies"
+Cohesion: 0.04
+Nodes (48): @blueskyproject/tiled, eslint, @eslint/js, eslint-plugin-react, eslint-plugin-react-hooks, eslint-plugin-react-refresh, dependencies, @blueskyproject/tiled (+40 more)
+
+### Community 20 - "render/index.ts"
+Cohesion: 0.12
+Nodes (25): Color3, createContext(), DeviceOptions, keyDirectionalFromLights(), lightBufferByteSize(), LightingEnvironment, packLightsStd430(), DEFAULT_POINT_RANGE (+17 more)
+
+### Community 21 - "accel.spec.ts"
+Cohesion: 0.06
+Nodes (43): clamp01(), FLIP_CONFIGS, FlipConfig, flipConfigToLut(), convSep(), FLIP_PPD, FlipResult, gaussianKernel() (+35 more)
+
+### Community 22 - "compilerOptions"
+Cohesion: 0.05
+Nodes (45): compilerOptions, allowImportingTsExtensions, isolatedModules, jsx, lib, module, moduleDetection, moduleResolution (+37 more)
+
+### Community 23 - "compilerOptions"
+Cohesion: 0.05
+Nodes (45): compilerOptions, baseUrl, isolatedModules, lib, module, moduleResolution, noEmit, noUnusedLocals (+37 more)
+
+### Community 24 - "dimension.ts"
+Cohesion: 0.04
+Nodes (44): ABSORBED_DOSE, ACCELERATION, ACTION, AMOUNT, ANGLE, ANGULAR_MOMENTUM, ANGULAR_VELOCITY, AREA (+36 more)
+
+### Community 25 - "test_ingest_conflict.py"
+Cohesion: 0.06
+Nodes (28): fake_client(), FakeArray, FakeNode, _npy(), Exception, fixture, parametrize, Path (+20 more)
+
+### Community 26 - "App.tsx"
+Cohesion: 0.06
+Nodes (23): allRoutes, AnnotatePage, App(), BrowsePage, ConnectPage, DEFAULT_PATHS, ReferencePage, VolumePage (+15 more)
+
+### Community 27 - "src/index.ts"
+Cohesion: 0.09
+Nodes (24): bloom(), depthOfField(), fxaa(), OP_INDEX, outline(), sharpen(), ssao(), taa() (+16 more)
+
+### Community 28 - "catalog.py"
+Cohesion: 0.17
+Nodes (18): SQLite catalog for projects, sessions, feature banks, models, and runs., catalog_db_path(), engine_root(), local_data_root(), project_blob_dir(), Path, Filesystem roots for ipred under LOCAL_DATA_ROOT., Return resolved ``LOCAL_DATA_ROOT`` (default ``~/data``). (+10 more)
+
+### Community 29 - "write_stack"
+Cohesion: 0.24
+Nodes (4): Path, A directory of zero-padded 2-D TIFFs whose value encodes the slice index., TestInspect, write_stack()
+
+### Community 30 - "ingest.py"
+Cohesion: 0.09
+Nodes (36): _bump(), _child_keys(), _classify_error(), _ensure_container(), get_job(), _image_number(), new_job(), _pad_width() (+28 more)
+
+### Community 31 - "test_zarr_source.py"
+Cohesion: 0.09
+Nodes (16): _fake_pyramid(), _FakeArray, _FakeContainer, _make_multiscale(), dict, fixture, ndarray, Path (+8 more)
+
+### Community 32 - "Catalog"
+Cohesion: 0.11
+Nodes (18): Connection, Catalog, ProjectRow, Any, Path, Create or return the project for this source identity., Ensure project exists and open a new session on it., Update session current_* pointers (only provided fields). (+10 more)
+
+### Community 33 - "TiledNotifications/index.ts"
+Cohesion: 0.14
+Nodes (30): buildTiledStreamUrl(), buildZarrUrlForChild(), ensureNotificationPermission(), isSupported(), OsNotificationOptions, showOsNotification(), EMPTY_PATHS, NodeStream() (+22 more)
+
+### Community 34 - "camera.ts"
+Cohesion: 0.09
+Nodes (9): Controller, FirstPersonControls, FlyControls, OrbitMode, OrbitState, IMPORTANT: `eye` may be `this._eye` — never overwrite it before reading world…, AxisBinding, Binding (+1 more)
+
+### Community 35 - "preprocess.py"
+Cohesion: 0.11
+Nodes (25): arrays_dir(), load_array_blob(), Any, ndarray, Path, Content-addressed array blobs for remote compute (data ≠ compute host)., Blob directory for uploaded arrays., Persist ``arr`` and return ``{array_ref, shape, dtype, ...}``. (+17 more)
+
+### Community 36 - "compositions.py"
+Cohesion: 0.13
+Nodes (29): compositions_root(), content_hash(), _dir(), ensure_default_compositions(), list_compositions(), load_composition(), preview_concat_labels(), Any (+21 more)
+
+### Community 38 - "step_phantom"
+Cohesion: 0.08
+Nodes (11): ndarray, parametrize, Tests for the classical denoise filters. Ported alongside :mod:`denoise`…, A hard vertical edge plus additive Gaussian noise. The standard shape for…, snr_db(), step_phantom(), TestAutoStrength, TestCapabilities (+3 more)
+
+### Community 39 - "tiff_stack_source.py"
+Cohesion: 0.11
+Nodes (35): block_mean(), _build_level(), _cast_like(), downsample_array(), inspect_tiff_stack(), multiscales_metadata(), preflight_tiff_stack(), Any (+27 more)
+
+### Community 40 - "ipred/__init__.py"
+Cohesion: 0.11
+Nodes (25): Execute a composition document on a raw image array., ipred — generic iterative prediction backend., ChannelBlock, ModuleContext, ModuleMeta, Feature module protocol — typed channel producers for compositions., Catalog entry for a feature module., One module's contribution to the feature bank. (+17 more)
+
+### Community 41 - "train_infer.py"
+Cohesion: 0.10
+Nodes (36): cal_scores_from_json(), conformal_quantile(), counts_from_status(), maps_from_proba(), mondrian_thresholds(), Any, ndarray, Mondrian split-conformal thresholds and maps from cached proba. (+28 more)
+
+### Community 42 - "volume/colormaps.ts"
+Cohesion: 0.11
+Nodes (24): WebGpuRenderingState, BuiltinColorMapName, ColorMapFn, ColorMapName, colorMapNames(), lerp3(), MAPS, Rgb (+16 more)
+
+### Community 43 - "Node"
+Cohesion: 0.11
+Nodes (18): CameraComponent, Component, ComponentBase, ComponentKind, LightComponent, MeshComponent, PhysicsBodyComponent, VolumeComponent (+10 more)
+
+### Community 44 - "Quantity"
+Cohesion: 0.10
+Nodes (3): Dimension, Quantity, Unit
+
+### Community 46 - "ipred — architecture and operations guide"
+Cohesion: 0.06
+Nodes (32): 10. Manifold / Suggest Labels, 11. Frontend map, 12. Package map (`ipred/src/ipred/`), 13. Mental model (one paragraph), 14. Quick ops, 1. Big picture, 2. Where compute lives, 3. How the UI reaches compute (proxy boundary) (+24 more)
+
+### Community 47 - "AnnotationCanvas/index.tsx"
+Cohesion: 0.11
+Nodes (29): AnnotationCanvas(), AnnotationCanvasProps, buildRingsPath(), distToSegment(), EMPTY_CLASS_COLOR_BY_ID, EMPTY_MANIFOLD_MARKERS, EMPTY_SHAPES, hexToRgb() (+21 more)
+
+### Community 48 - "io/index.ts"
+Cohesion: 0.10
+Nodes (28): units, aabbRaySlab(), DensityField, extractIsosurface(), ExtractIsosurfaceOptions, inside(), loadDensityField(), LoadDensityFieldOptions (+20 more)
+
+### Community 49 - "ipredApi.ts"
+Cohesion: 0.10
+Nodes (26): CompositionDoc, CompositionNode, FeatureModuleInfo, FeatureSetup, getIpredComposition(), getIpredSetup(), IpredFeatureImportance, ipredHealth() (+18 more)
+
+### Community 52 - "cli.py"
+Cohesion: 0.10
+Nodes (24): ArgumentParser, _add_project_sugar(), _catalog(), main(), _print(), Any, CLI entrypoint ``ipred`` with --project session sugar., Return session_id from --session or --project sugar. (+16 more)
+
+### Community 53 - "array.ts"
+Cohesion: 0.13
+Nodes (13): chunkBytes(), DecodedChunkCache, dtypeByteSize(), VolumeChunk, chunkKey(), cropCOrderChunk(), fillBuffer(), OpenZarrArrayOptions (+5 more)
+
+### Community 54 - "packing.ts"
+Cohesion: 0.10
+Nodes (23): Euler, scratchMat, compact1By2(), decodeOctNormal(), demorton3D(), encodeOctNormal(), _f32, floatToHalf() (+15 more)
+
+### Community 55 - "scalar.ts"
+Cohesion: 0.10
+Nodes (19): bitReverse(), fft(), fftReal(), ifft(), DEG2RAD, deltaAngle(), HALF_PI, inverseLerp() (+11 more)
+
+### Community 56 - "feature_setups.py"
+Cohesion: 0.16
+Nodes (28): content_hash(), _dir(), ensure_default_setups(), _ensure_procedure_with_param_backfill(), _ensure_tomojepa_variant(), list_setups(), load_setup(), Any (+20 more)
+
+### Community 57 - "arrays.py"
+Cohesion: 0.14
+Nodes (27): array_shape_meta(), _descend_to_stack(), _is_container_node(), _level_array(), multiscale_levels(), node_keywords(), Any, ndarray (+19 more)
+
+### Community 58 - "IngestDropzone.tsx"
+Cohesion: 0.12
+Nodes (23): ConflictDialog(), ConflictDialogProps, IngestConflict, collectFromDrop(), fileStem(), IngestDropzone(), IngestDropzoneProps, isSupported() (+15 more)
+
+### Community 59 - "VectorGrid3"
+Cohesion: 0.12
+Nodes (5): Grid3Base, Grid3Spec, resolveSpacing(), ScalarGrid3, VectorGrid3
+
+### Community 60 - "core/index.ts"
+Cohesion: 0.04
+Nodes (51): RFC-4122, assert(), assertDefined(), AssertionError, assertNever(), atomicMass(), atomicNumber(), BY_SYMBOL (+43 more)
+
+### Community 61 - "getTiledBaseUrl"
+Cohesion: 0.18
+Nodes (21): Header(), getValidAccessToken(), installTiledFetchInterceptor(), nativeFetch, refreshAccessToken(), IMPORTANT: this must be imported before `<App>` (and therefore before the…, Window, getActiveServer() (+13 more)
+
+### Community 62 - "constants.ts"
+Cohesion: 0.07
+Nodes (26): ATOMIC_MASS_UNIT, AVOGADRO, BOHR_RADIUS, BOLTZMANN, COULOMB_CONSTANT, EARTH_MASS, EARTH_RADIUS, ELECTRON_MASS (+18 more)
+
+### Community 63 - "compilerOptions"
+Cohesion: 0.07
+Nodes (26): compilerOptions, allowUnreachableCode, declaration, declarationMap, forceConsistentCasingInFileNames, isolatedModules, lib, module (+18 more)
+
+### Community 64 - "browse_helpers.py"
+Cohesion: 0.10
+Nodes (37): Return a cached :class:`FieldMapping` for the given container., _resolve_field_mapping(), _apply_filters(), build_field_mapping(), _combined_metadata_keys(), _display_name(), distinct_from_rows(), FieldMapping (+29 more)
+
+### Community 65 - "provenance.ts"
+Cohesion: 0.22
+Nodes (17): concat(), crc32(), CRC_TABLE, fnv1aHex(), formatProvenance(), hashTransferFunction(), latin1(), PNG_SIG (+9 more)
+
+### Community 66 - "schemas.py"
+Cohesion: 0.05
+Nodes (62): image_meta(), Return shape / dtype metadata for an image source., AnnotationClass, BrushShape, BrushStroke, DenoiseBakeRequest, EllipseShape, ExportSourceItem (+54 more)
+
+### Community 67 - "FakeNode"
+Cohesion: 0.09
+Nodes (9): catalog(), FakeNode, fixture, Tests for locating a dataset's renderable 3-D volume. This is the module that…, A catalog node: metadata plus children, indexable like a Tiled container., A catalog with one of each shape the resolver has to tell apart., TestHasMultiscales, TestResolveVolume (+1 more)
+
+### Community 68 - "zarr_source.py"
+Cohesion: 0.15
+Nodes (25): _existing_node_info(), inspect_zarr(), _is_zarr_dir(), _multiscale_datasets(), preflight_zarr(), Any, Path, Register on-disk Zarr volumes with Tiled, without copying any data. The drag-… (+17 more)
+
+### Community 69 - "dependencies"
+Cohesion: 0.08
+Nodes (26): @blueskyproject/finch, clsx, dependencies, @blueskyproject/finch, clsx, @huggingface/transformers, konva, polygon-clipping (+18 more)
+
+### Community 70 - "ome-zarr.ts"
+Cohesion: 0.15
+Nodes (21): axisToXyz(), openOmeZarr(), OpenOmeZarrOptions, scaleTranslation(), estimateValueRange(), openZarrArray(), joinPath(), parseNumpyDtype() (+13 more)
+
+### Community 71 - "start_all.sh"
+Cohesion: 0.14
+Nodes (25): API_PROXY_TARGET, can_run_npm(), cleanup(), cleanup_managed_processes(), cleanup_pid_file(), ensure_backend_env(), ensure_docs_env(), ensure_frontend_runtime() (+17 more)
+
+### Community 72 - "drafts.py"
+Cohesion: 0.13
+Nodes (23): _draft_path(), get_version(), get_version_thumbnail(), list_drafts(), list_versions(), load_draft(), Any, Path (+15 more)
+
+### Community 73 - "tiled_clients.py"
+Cohesion: 0.13
+Nodes (22): get_browse_container(), get_browse_container_for(), Any, Tiled client factory + browse-container discovery. Clients are cached per…, Return ``(container_node, path_prefix)`` for the Metadata Browser root. Checks…, Return ``(container_node, path_prefix)`` for a specific browse target. When…, clear_empty_tiled_api_key_env(), get_tiled_api_key() (+14 more)
+
+### Community 74 - "App.jsx"
+Cohesion: 0.14
+Nodes (22): RFC-4648, App(), LinkOptions, useLinkedWebGpuViewers(), queryClient, buildShareUrl(), copyToClipboard(), decodeState() (+14 more)
+
+### Community 75 - "viewerState.ts"
+Cohesion: 0.13
+Nodes (22): LinkOptions, Subscription, useLinkedViewers(), ItkVktNative(), ItkVktProps, ItkVtkCamera, ItkVtkViewerApi, ItkVtkViewerInstance (+14 more)
+
+### Community 76 - "devDependencies"
+Cohesion: 0.09
+Nodes (23): autoprefixer, devDependencies, autoprefixer, jsdom, postcss, tailwindcss, @testing-library/jest-dom, @testing-library/user-event (+15 more)
+
+### Community 77 - "get_tiled_client"
+Cohesion: 0.15
+Nodes (21): api_key_for_uri(), get_tiled_client(), Look up a configured API key for *uri*, falling back to None., Return a (cached) ``tiled.client`` connected to the requested server., build_volume(), inspect_volume_build(), Any, Build a renderable 3-D volume from a slice stack already in Tiled.… (+13 more)
+
+### Community 78 - "useTabsStore.ts"
+Cohesion: 0.14
+Nodes (20): HeaderProps, graceTimers, makeId(), nameFromUrl(), PaneSide, ReconTab, RendererKind, reposition() (+12 more)
+
+### Community 79 - "zarr-viewer/package.json"
+Cohesion: 0.09
+Nodes (22): description, devDependencies, @types/node, typescript, vite, vitest, @webgpu/types, @types/node (+14 more)
+
+### Community 80 - "volume-renderer.ts"
+Cohesion: 0.08
+Nodes (31): asColor3(), asColor4(), Color3Like, Color4, Color4Like, RenderProvenance, approximateShadingActive(), approximateShadingLabel() (+23 more)
+
+### Community 81 - "features.py"
+Cohesion: 0.16
+Nodes (19): apply_clahe(), compute_clahe_stack(), compute_feature_stacks(), encode_channel_png(), feature_channel_labels(), _float_feats_to_uint8(), _fmt_sigma(), list_sigmas() (+11 more)
+
+### Community 82 - "TrainerPlugin"
+Cohesion: 0.20
+Nodes (10): Any, ndarray, Path, Protocol, Train / predict_proba contract for pixel classifiers., Fit model and compute Mondrian calibration scores., Return NxK probabilities aligned with training class order., Persist model weights into *dest_dir*. (+2 more)
+
+### Community 83 - "sam_embed.py"
+Cohesion: 0.14
+Nodes (21): bilinear_sample_emb(), emb_grid_to_pca_channels(), encode_image_embeddings(), encoder_available(), fit_pca(), Any, ndarray, Path (+13 more)
+
+### Community 84 - "start_all.ps1"
+Cohesion: 0.19
+Nodes (19): Ensure-BackendEnv(), Ensure-DocsEnv(), Ensure-FrontendRuntime(), Ensure-TiledKey(), Ensure-Uv(), Err(), Get-FreePort(), Get-ListenerPids() (+11 more)
+
+### Community 85 - "Installation via Docker (Recommended)"
+Cohesion: 0.10
+Nodes (20): Add Tiled, Authenticate, `Bluesky Tiled`, Build and start the application, Clone and install repository, Clone repository, Debugging, Install (+12 more)
+
+### Community 86 - "parse.ts"
+Cohesion: 0.17
+Nodes (15): DIMENSIONLESS, constants, ALL_PREFIXES, parseQuantity(), parseUnit(), PREFIX_KEYS, resolveSymbol(), Term (+7 more)
+
+### Community 87 - "TTLCache"
+Cohesion: 0.12
+Nodes (14): Any, Small TTL + LRU cache used by the Browse API. Kept intentionally tiny: the…, Thread-safe cache with per-entry expiry and an insertion-order size cap., TTLCache, encode_png(), Any, ndarray, Slice-to-PNG rendering with explicit normalisation modes. Two normalisation… (+6 more)
+
+### Community 88 - "magicwand.ts"
+Cohesion: 0.14
+Nodes (18): bboxContainsRing(), boxBlur(), buildField(), chaikin(), gradientField(), GrayField, magicSelect(), MaskPolyOpts (+10 more)
+
+### Community 89 - "Segmentation Annotation Studio"
+Cohesion: 0.10
+Nodes (18): Annotation tools, Development, Export formats, Mac/Linux, Node.js, Production build (single origin), Project layout, Quick start (+10 more)
+
+### Community 90 - "tiled_mask_sync.py"
+Cohesion: 0.18
+Nodes (18): Filesystem-safe folder name for a class label., _safe_name(), _category_maps(), _legend_id_to_name(), merge_mask_volumes(), Any, ndarray, Write rasterized annotation masks into Tiled as stacked volumes. Standalone… (+10 more)
+
+### Community 91 - "denoise.py"
+Cohesion: 0.20
+Nodes (18): auto_strength(), denoise_slice(), denoise_stack(), estimate_noise_sigma(), _from_unit(), _median3d(), _odd(), dtype (+10 more)
+
+### Community 92 - "Software architecture"
+Cohesion: 0.11
+Nodes (18): Backend architecture, Canvas rendering, Client-side geometry & tools (`src/lib/`), Component shell, Data model, Deployment topology, Draw, autosave, and save a version, Export a dataset (+10 more)
+
+### Community 93 - "annotationStore.ts"
+Cohesion: 0.15
+Nodes (15): AnnotationState, BaseShape, BrushShape, BrushStroke, cloneShapeWithNewId(), DraftState, EllipseShape, EMPTY_DRAFT (+7 more)
+
+### Community 94 - "_fake_pyramid"
+Cohesion: 0.24
+Nodes (6): _fake_pyramid(), _FakeArray, _FakeContainer, dict, Container mimicking a registered multiscale volume: scaleN -> {image: array}., TestDescendToArray
+
+### Community 95 - "ManagedTexture"
+Cohesion: 0.07
+Nodes (23): ShadowMap, ManagedTexture, BrickLoaderOptions, BrickRequest, BrickResult, VolumeLevelResult, VolumeLoader, VolumeLoaderOptions (+15 more)
+
+### Community 96 - "FastAPI"
+Cohesion: 0.15
+Nodes (20): count_image_files(), list_dir(), list_image_files(), open_array(), Any, Path, Sandboxed local filesystem access for the Segmentation Annotation Studio. All…, Return a recursive count of image files under ``<root>/rel``. Args: rel:… (+12 more)
+
+### Community 97 - "fake_stack"
+Cohesion: 0.16
+Nodes (7): fake_stack(), fixture, Tests for building a 3-D volume from a slice stack already in Tiled. The…, Stand in for a Tiled stack: `resolve_array` + `array_shape_meta` + `read_slice`., TestBuildGuards, TestInspect, TestMetadataShape
+
+### Community 98 - "geometry.ts"
+Cohesion: 0.18
+Nodes (15): BBox, bboxCoversRaster(), bboxIntersects(), bboxNear(), EMPTY_BBOX, growByFlat(), normalizeEllipse(), normalizeRect() (+7 more)
+
+### Community 99 - "length-units.ts"
+Cohesion: 0.12
+Nodes (16): angstrom, centimeter, femtometer, foot, inch, kilometer, meter, micrometer (+8 more)
+
+### Community 100 - "codecs.ts"
+Cohesion: 0.20
+Nodes (15): bloscCodec(), BloscCompressorConfig, bloscCompressorName(), bloscDecodeSync(), bloscUnshuffle(), Codec, codecFromCompressor(), decompressStream() (+7 more)
+
+### Community 101 - ".alignTo"
+Cohesion: 0.18
+Nodes (3): LayoutBuilder, Std140Builder, Std430Builder
+
+### Community 102 - "Vec3Like"
+Cohesion: 0.11
+Nodes (9): Aabb, barycentric(), clampUnit(), closestPointOnAabb(), closestPointOnSegment(), closestPointOnTriangle(), closestPointsBetweenSegments(), Frustum (+1 more)
+
+### Community 103 - "denoise_bake.py"
+Cohesion: 0.18
+Nodes (13): default_target_path(), _denoise_one(), _ModelDenoiser, Any, ndarray, Apply a denoise filter to a whole volume and save the result as a NEW Tiled…, Applies a trained Noise2Noise/Noise2Void run across a whole volume. Loads the…, Release ``ML_LOCK``. Idempotent — callers release on both the happy path and in… (+5 more)
+
+### Community 104 - "3. Annotate"
+Cohesion: 0.12
+Nodes (17): 3. Annotate, Classes, Colorblind-safe colors, Cross-slice, Display (view-only), Export, How each tool works, Insights (quality checks) (+9 more)
+
+### Community 105 - "datasetStats.ts"
+Cohesion: 0.18
+Nodes (16): analyticArea(), ClassStat, computeSampleStats(), Coverage, isSelfIntersecting(), labelFor(), MaskInfo, overlapCount() (+8 more)
+
+### Community 106 - "SamClient"
+Cohesion: 0.14
+Nodes (7): Granularity, Pending, PromptBox, PromptPoint, SamClient, SamMask, SamStatus
+
+### Community 107 - "samWorker.ts"
+Cohesion: 0.18
+Nodes (16): Box, decode(), Device, encode(), fallbackToWasm(), freshRaw(), Granularity, InMsg (+8 more)
+
+### Community 108 - "TomoJepaModule"
+Cohesion: 0.27
+Nodes (8): _encoder_name(), Any, Path, Resolve Mark25/11 weights from params., Dense TomoJEPA embeddings (torch; prefers ONNX when present)., _resolve_weights(), TomoJepaModule, _weights_format()
+
+### Community 109 - "fx/package.json"
+Cohesion: 0.12
+Nodes (16): dependencies, @prism/core, @prism/math, @prism/render, description, exports, main, module (+8 more)
+
+### Community 110 - "predicates.ts"
+Cohesion: 0.20
+Nodes (12): det2(), orient2d(), orient3d(), Point2Like, compensatedDot(), CompensatedSum, kahanSum(), neumaierSum() (+4 more)
+
+### Community 111 - "test_tomojepa_embed.py"
+Cohesion: 0.16
+Nodes (15): MonkeyPatch, Path, Mark25 / TomoJEPA dense embedding encode., Mark11 dense projector is 256-D (Mark25 is 64-D)., All Mark25 paths must pass intensities in [-1, 1] into the network., test_encode_dense_honors_input_size(), test_encode_dense_shape_512(), test_encode_feeds_model_in_minus_one_one() (+7 more)
+
+### Community 112 - "export_jobs.py"
+Cohesion: 0.12
+Nodes (11): cancel_requested(), get_job(), new_job(), Any, In-memory job registry for COCO/mask exports. Mirrors the ingest job pattern…, Register a new export job and return its id., Ask a running job to stop; returns False if the job is unknown. Cooperative:…, True if cancellation was requested for *jid*. Reads the flag directly under the… (+3 more)
+
+### Community 113 - "polybool.ts"
+Cohesion: 0.28
+Nodes (12): clipShapesToOthers_naive(), eraseStampToMultiPolygon(), flatToRing(), multiPolygonToShapes(), regionsToMultiPolygon(), ringArea(), ringToFlat(), shapeToMultiPolygon() (+4 more)
+
+### Community 114 - "perf.ts"
+Cohesion: 0.20
+Nodes (12): initPerf(), listeners, mark(), perfEnabled(), PerfLabel, PerfStat, resetPerf(), scheduleNotify() (+4 more)
+
+### Community 115 - "compilerOptions"
+Cohesion: 0.12
+Nodes (15): compilerOptions, allowImportingTsExtensions, isolatedModules, lib, module, moduleDetection, moduleResolution, noEmit (+7 more)
+
+### Community 116 - "list_module_catalog"
+Cohesion: 0.14
+Nodes (13): FeatureModule, Any, Protocol, Runnable feature module., True when dependencies/weights are available., Labels that would appear in the bank for these params (no compute)., get_module(), get_registry() (+5 more)
+
+### Community 118 - "mergeSameClass.ts"
+Cohesion: 0.39
+Nodes (7): bboxOverlap(), expandSameClassOverlap(), inflate(), masksIntersect(), masksIntersectIn(), mergeNewWithSameClass(), MergeResult
+
+### Community 119 - "rasterizeShapes"
+Cohesion: 0.19
+Nodes (16): expandSameClassOverlap_naive(), mergeNewWithSameClass_naive(), incoming, otherClass, realOverlap(), thresholdShapes(), area(), fillEllipse() (+8 more)
+
+### Community 120 - "morphology.ts"
+Cohesion: 0.19
+Nodes (11): dilate(), dilateOnce(), erode(), erodeOnce(), fillHoles(), keepComponentsAtPoints(), removeSmallComponents(), smooth() (+3 more)
+
+### Community 121 - "livewire.ts"
+Cohesion: 0.26
+Nodes (6): CostMap, dijkstra(), imageToGrid(), MinHeap, simplifyPath(), tracePath()
+
+### Community 122 - "run_composition"
+Cohesion: 0.29
+Nodes (7): Any, ndarray, Run composition nodes; concatenate ``outputs`` channel blocks. Returns:…, run_composition(), ndarray, Display uint8 for a float HxWxC bank., uint8_from_float()
+
+### Community 123 - "5. Export & download"
+Cohesion: 0.15
+Nodes (13): 5. Export & download, Alternative: Push masks to Tiled, DINOv3 / Lightly format, Downloading a COCO dataset, Importing a dataset back, Step 1 — Prepare (optional but recommended), Step 2 — Open the export dialog, Step 3 — Choose a scope (+5 more)
+
+### Community 125 - "test_tiled_config.py"
+Cohesion: 0.17
+Nodes (11): Tests for Tiled key hardening (empty-key clearing + config-key fallback)., Blank/whitespace key env vars are deleted; real ones are kept., Reads authentication.single_user_api_key from a config.yml., A ${TILED_API_KEY} placeholder in config.yml expands from the env; unset → None., _local_api_key prefers env, else falls back to the config single-user key., Importing the module clears a blank TILED_API_KEY from the env., test_clear_empty_tiled_api_key_env_removes_blank_vars(), test_config_key_env_interpolation() (+3 more)
+
+### Community 126 - "frontend/package.json"
+Cohesion: 0.17
+Nodes (11): name, private, scripts, build, dev, preview, test, test:watch (+3 more)
+
+### Community 128 - "systems.ts"
+Cohesion: 0.21
+Nodes (6): FloatingOrigin, fromSim(), scaleFactor(), toSim(), UNIT_PRESETS, UnitSystem
+
+### Community 129 - "OmeZarrVolumeSource"
+Cohesion: 0.31
+Nodes (3): OmeZarrVolumeSource, VolumeDType, ParsedDType
+
+### Community 130 - "ViewportOverlay"
+Cohesion: 0.24
+Nodes (4): AXES, OverlayDrawParams, OverlayRuler, ViewportOverlay
+
+### Community 132 - "test_compositions.py"
+Cohesion: 0.24
+Nodes (10): catalog(), fixture, MonkeyPatch, Composition validation, builtins, and legacy setup mapping., test_ensure_default_compositions(), test_feature_setups_still_seed(), test_legacy_setup_maps_to_composition(), test_module_catalog_lists_core_modules() (+2 more)
+
+### Community 133 - "test_local_fs.py"
+Cohesion: 0.20
+Nodes (9): Tests for local filesystem sandboxing., Paths escaping LOCAL_DATA_ROOT must raise HTTP 403., list_dir should return file entries for an existing directory., list_dir on a non-existent path should raise HTTP 404., open_array on an unsupported extension should raise HTTP 422., test_list_dir_missing_raises_404(), test_list_dir_returns_entries(), test_open_array_unsupported_type_raises_422() (+1 more)
+
+### Community 134 - "editHistory.ts"
+Cohesion: 0.22
+Nodes (7): ClassDeleteEntry, currentSourceKey(), Entry, redo(), redoOps, undo(), undoOps
+
+### Community 135 - "pixelClf.ts"
+Cohesion: 0.33
+Nodes (7): applyProbaThresholdRgba(), classProbaMaskSetName(), colorizeConformalOverlay(), colorizeLabelMap(), labelMapToPolygonShapes(), parseHexColor(), thresholdProbaPngBlob()
+
+### Community 136 - "test_feature_setups.py"
+Cohesion: 0.40
+Nodes (5): catalog(), fixture, MonkeyPatch, Feature Setup shelf defaults., test_ensure_defaults()
+
+### Community 139 - "array_source.py"
+Cohesion: 0.25
+Nodes (14): _descend_to_array(), _index_slice(), _is_container_node(), _local_root(), Any, ndarray, Path, Minimal local / Tiled slice reader (no annotate-backend imports). (+6 more)
+
+### Community 140 - "AGENTS.md — Segmentation Annotation Studio"
+Cohesion: 0.20
+Nodes (9): AGENTS.md — Segmentation Annotation Studio, ALS Standards, Annotation data model (do NOT change without updating both backend/schemas.py and frontend stores), Git, Secrets, Security rules, Stack, Testing rules (+1 more)
+
+### Community 141 - "guides.py"
+Cohesion: 0.27
+Nodes (9): _guide_path(), load_guide(), Any, Path, Annotation-guide persistence for the Segmentation Annotation Studio. A *guide*…, Return the JSON file path for *source_key*'s guide., Persist the annotation guide for *source_key*. Args: source_key: Arbitrary…, Return the saved guide document for *source_key*, or ``None``. (+1 more)
+
+### Community 142 - "test_drafts.py"
+Cohesion: 0.20
+Nodes (9): Tests for session-draft persistence., A saved draft should be loadable with the correct source_key., Loading a draft that was never saved should return None., list_drafts should include a previously saved draft., list_drafts should return an empty list when no drafts exist., test_list_drafts_empty_when_none(), test_list_drafts_returns_saved(), test_load_missing_draft() (+1 more)
+
+### Community 144 - "Installation"
+Cohesion: 0.20
+Nodes (10): Backend, Changing the ports, Environment variables, Frontend, Installation, Manual setup, Option 1 — One-command local start (recommended), Option 2 — Docker (production) (+2 more)
+
+### Community 145 - "Quick start"
+Cohesion: 0.20
+Nodes (10): 1. Connect to your data, 2. Open an image to annotate, 3. Add a class, 4. Draw a mask, 5. Save your work, 6. Export a COCO dataset, :material-draw: Master annotation, :material-keyboard: Keyboard shortcuts (+2 more)
+
+### Community 147 - "test_catalog_sessions.py"
+Cohesion: 0.24
+Nodes (9): project_id_for(), Stable project id from source identity., catalog(), fixture, MonkeyPatch, Catalog project/session persistence., test_open_session_creates_project(), test_project_id_stable() (+1 more)
+
+### Community 148 - "import_dataset"
+Cohesion: 0.33
+Nodes (8): import_dataset(), _import_from_coco(), _import_with_sidecar(), Any, COCO + sidecar import: round-trip back to annotation payload. Prefers the…, Load a COCO dataset directory back into editor payload format. Args:…, Lossless import using _studio_shapes.json., Best-effort import from COCO only (no sidecar). Brush shapes become polygons…
+
+### Community 149 - "describe_methods"
+Cohesion: 0.22
+Nodes (9): available_methods(), describe_methods(), Any, Method metadata for the capability endpoint / UI menu. ``cost`` drives whether…, True if wavelet denoising can actually run.…, Methods that can actually run in this environment., Z-neighbours needed on each side of the target slice (0 for 2-D methods)., wavelet_available() (+1 more)
+
+### Community 150 - "clahe.ts"
+Cohesion: 0.36
+Nodes (5): applyClaheGray(), buildTileLut(), ClaheOptions, claheRgba(), identityLut()
+
+### Community 152 - "bboxPrefilter.equivalence.test.ts"
+Cohesion: 0.24
+Nodes (11): expectSameRegions(), iou(), makeShape(), mulberry32(), randomSlice(), regionOf(), perShape(), clipBatchBoolean() (+3 more)
+
+### Community 155 - "test_array_blobs.py"
+Cohesion: 0.31
+Nodes (8): client(), fixture, MonkeyPatch, TestClient, Array blob data plane for remote preprocess., test_modules_and_compositions_api(), test_save_load_array_blob(), test_upload_array_and_preprocess()
+
+### Community 156 - "test_preprocess_cache.py"
+Cohesion: 0.33
+Nodes (8): catalog(), fixture, MonkeyPatch, Cache-aware preprocess., test_preprocess_cache_hit(), test_preprocess_mark25_clahe_no_skimage(), test_preprocess_mark25_writes_dense_emb(), skipif
+
+### Community 159 - "usePixelClassifier.ts"
+Cohesion: 0.25
+Nodes (6): ClfFeatureImportance, ClfParams, ClfPredictCounts, ClfTrainResult, DEFAULT_CLF_PARAMS, UsePixelClassifierArgs
+
+### Community 160 - "datasetStore.ts"
+Cohesion: 0.25
+Nodes (7): DatasetState, DEFAULT_DENOISE, DEFAULT_RENDER, DenoiseOpts, ImageMeta, RenderOpts, useDatasetStore
+
+### Community 161 - "toolStore.ts"
+Cohesion: 0.25
+Nodes (7): MagicEngine, MagicMode, SamDetail, SelectScope, Tool, ToolState, useToolStore
+
+### Community 162 - "vite-zarr-static.ts"
+Cohesion: 0.32
+Nodes (4): src, PETIOLE_ZARR_MOUNT, ZarrStaticMount, zarrStaticPlugin()
+
+### Community 163 - "test_api_health.py"
+Cohesion: 0.32
+Nodes (7): client(), fixture, MonkeyPatch, TestClient, API health + session open., test_health(), test_open_session_and_list_setups()
+
+### Community 164 - "main"
+Cohesion: 0.48
+Nodes (6): _ensure_container(), _json_safe(), _load_pairs(), main(), Path, Return (png_path, txt_path) for stems that have both files.
+
+### Community 165 - "test_health.py"
+Cohesion: 0.33
+Nodes (6): asyncio, Tests for the health endpoint., Health endpoint must return status ok., Servers endpoint must not expose api_key in response., test_health_returns_ok(), test_servers_endpoint_no_api_key()
+
+### Community 167 - "Segmentation Annotation Studio"
+Cohesion: 0.29
+Nodes (7): :material-download-box: Install, :material-draw: Annotate, :material-export: Export, :material-rocket-launch: Quick start, Segmentation Annotation Studio, The workflow at a glance, What is this tool?
+
+### Community 168 - "Troubleshooting"
+Cohesion: 0.29
+Nodes (6): Connecting to data, Exporting, Saving, The app won't start, The Smart (AI) tool, Troubleshooting
+
+### Community 169 - "ClassManager/index.tsx"
+Cohesion: 0.33
+Nodes (5): ClassManager(), ClassManagerProps, ClassRowProps, countShapesForClass(), DEFAULT_SUGGESTED_CLASSES
+
+### Community 170 - "Toolbar/index.tsx"
+Cohesion: 0.29
+Nodes (3): CHANNEL_LABELS, ToolbarProps, ToolButtonProps
+
+### Community 171 - "ZarrLoader.tsx"
+Cohesion: 0.33
+Nodes (6): describeLevel(), ExistingInfo, ZarrInfo, ZarrLevel, ZarrLoader(), ZarrLoaderProps
+
+### Community 172 - "BuildVolumePanel.tsx"
+Cohesion: 0.33
+Nodes (6): BuildInfo, BuildVolumePanel(), BuildVolumePanelProps, JobState, PyramidLevel, readError()
+
+### Community 173 - "config.ts"
+Cohesion: 0.29
+Nodes (6): API_BASE, APP_VERSION, DOCS_URL, FEEDBACK_ENTRY_ID, FEEDBACK_FORM_URL, GIT_COMMIT
+
+### Community 174 - "useGuideSync.ts"
+Cohesion: 0.43
+Nodes (5): GuideGenPayload, loadGuide(), saveGuide(), useGuideLoad(), useGuideSync()
+
+### Community 175 - "useSave.ts"
+Cohesion: 0.33
+Nodes (5): SaveDraftPayload, SaveOptions, UseSaveReturn, VersionMeta, VersionPayload
+
+### Community 176 - "regionOps.ts"
+Cohesion: 0.33
+Nodes (3): computeRegionOps(), RegionOp, RegionResult
+
+### Community 177 - "zarrUrl.ts"
+Cohesion: 0.48
+Nodes (5): buildZarrUrl(), describeUnavailable(), zarrRootFor(), ZarrUnavailable, ZarrUrlResult
+
+### Community 178 - "ipredStore.ts"
+Cohesion: 0.29
+Nodes (6): DEFAULT_COMPOSITION_ID, DEFAULT_TRAINER_CONFIG, DEFAULT_TRAINER_ID, IpredState, TrainerConfig, useIpredStore
+
+### Community 179 - "layerVisibilityStore.ts"
+Cohesion: 0.29
+Nodes (5): DEFAULT_GROUPS, LAYER_GROUP_META, LayerGroupId, LayerVisibilityState, useLayerVisibilityStore
+
+### Community 180 - "OME-Zarr Viewer"
+Cohesion: 0.29
+Nodes (6): Notes, OME-Zarr Viewer, Point at a local dataset, Quick start, Requirements, Use in another project
+
+### Community 182 - "include"
+Cohesion: 0.29
+Nodes (6): extends, include, src, bench, test, ../../tsconfig.base.json
+
+### Community 185 - "_denoised_slice"
+Cohesion: 0.40
+Nodes (6): _centre_crop(), _denoised_slice(), Any, ndarray, Centred square crop of *size*, or *arr* unchanged if it already fits., Read *slice_index* and denoise it, pulling z-neighbours for 3-D methods. The…
+
+### Community 186 - "2. Browse & select"
+Cohesion: 0.33
+Nodes (6): 2. Browse & select, Local: the flat file list, Opening a sample, The detail panel, The Samples column, Tiled: the metadata browser
+
+### Community 187 - "1. Connect to data"
+Cohesion: 0.33
+Nodes (6): 1. Connect to data, Connect to Tiled, Ingesting data (Tiled only), Load / Ingest Datasets, Local Folder mode, Tiled Server mode
+
+### Community 188 - "fetch-sam-model.mjs"
+Cohesion: 0.33
+Nodes (4): HERE, OUT, python, res
+
+### Community 189 - "DenoiseBakeModal.tsx"
+Cohesion: 0.47
+Nodes (5): defaultTarget(), DenoiseBakeModal(), DenoiseBakeModalProps, JobState, readError()
+
+### Community 190 - "DownloadModal.tsx"
+Cohesion: 0.40
+Nodes (5): DownloadModal(), DownloadModalProps, parseSourceKey(), Scope, SCOPE_OPTIONS
+
+### Community 191 - "InsightsModal.tsx"
+Cohesion: 0.40
+Nodes (4): FLAG_LABEL, formatArea(), InsightsModal(), InsightsModalProps
+
+### Community 192 - "MeasurementPanel/index.tsx"
+Cohesion: 0.40
+Nodes (4): fmt(), IntensityStats, MeasurementPanel(), MeasurementPanelProps
+
+### Community 193 - "VersionHistoryModal.tsx"
+Cohesion: 0.47
+Nodes (4): formatDate(), Props, thumbnailUrl(), VersionHistoryModal()
+
+### Community 194 - "useFeatureManifold.ts"
+Cohesion: 0.40
+Nodes (5): DEFAULT_MANIFOLD_PARAMS, ManifoldParams, readErrorDetail(), useFeatureManifold(), UseFeatureManifoldArgs
+
+### Community 195 - "classColors.ts"
+Cohesion: 0.67
+Nodes (4): COLORBLIND_COLORS, DEFAULT_COLORS, getClassPalette(), pickNextColor()
+
+### Community 196 - "lib/colormaps.ts"
+Cohesion: 0.33
+Nodes (3): COLORMAP_NAMES, ColormapName, STOPS
+
+### Community 197 - "featureManifold.ts"
+Cohesion: 0.53
+Nodes (4): colorizeManifoldHeatmap(), loadManifoldHeatmapCanvas(), manifoldMarkerRect(), ManifoldPoint
+
+### Community 201 - "test_manifold.py"
+Cohesion: 0.40
+Nodes (5): catalog(), fixture, MonkeyPatch, Manifold Suggest Labels on persisted ipred feature banks., test_manifold_sample_on_feature_bank()
+
+### Community 202 - "test_train_rethreshold.py"
+Cohesion: 0.40
+Nodes (5): catalog(), fixture, MonkeyPatch, Train → infer → rethreshold without recompute., test_train_infer_rethreshold()
+
+### Community 204 - "Keyboard shortcuts"
+Cohesion: 0.40
+Nodes (5): Canvas & navigation, Editing, Keyboard shortcuts, Mouse modifiers (Magic / Fill / SAM), Tools
+
+### Community 206 - "PerfOverlay/index.tsx"
+Cohesion: 0.50
+Nodes (3): PerfOverlayProps, Row(), severity()
+
+### Community 207 - "PixelClassifierPanel/index.tsx"
+Cohesion: 0.50
+Nodes (3): coverageHeadline(), PixelClassifierPanel(), PixelClassifierPanelProps
+
+### Community 208 - "SaveModal.tsx"
+Cohesion: 0.60
+Nodes (4): blobToBase64(), fetchPreviewBlob(), SaveModal(), SaveModalProps
+
+### Community 209 - "CompositionPanel/index.tsx"
+Cohesion: 0.70
+Nodes (3): CompositionPanel(), defaultParams(), newNodeId()
+
+### Community 210 - "useFeatureChannels.ts"
+Cohesion: 0.40
+Nodes (3): FeatureChannelInfo, FeatureJobInfo, UseFeatureChannelsArgs
+
+### Community 211 - "measure.ts"
+Cohesion: 0.60
+Nodes (4): measureRegion(), RegionMeasurement, ringPerimeter(), shapePerimeter()
+
+### Community 212 - "buildSourceKey"
+Cohesion: 0.80
+Nodes (3): buildSourceKey(), isAnnotatedPath(), key()
+
+### Community 214 - "Mat4"
+Cohesion: 0.06
+Nodes (16): _q0, _q1, _q2, _tv, std140Alignment(), std430Alignment(), WgslBaseType, _decomposeRot (+8 more)
+
+### Community 215 - "start.sh"
+Cohesion: 0.40
+Nodes (3): HOST, start.sh script, TILED_ALLOW_ORIGINS
+
+### Community 216 - "reingest_tiffs_as_individual.py"
+Cohesion: 0.67
+Nodes (3): compute_structure_id(), main(), Re-ingest stacked TIFF array as 690 individual sample nodes. The 690 TIFFs were…
+
+### Community 219 - "FeatureChannelsPanel/index.tsx"
+Cohesion: 0.40
+Nodes (3): FeatureChannelsPanelProps, PRESET_HINTS, PRESET_LABELS
+
+### Community 221 - "VersionPreviewBar.tsx"
+Cohesion: 0.67
+Nodes (3): formatDate(), Props, VersionPreviewBar()
+
+### Community 223 - "VolumeViewer.tsx"
+Cohesion: 0.67
+Nodes (3): VolumeViewer(), VolumeViewerProps, webGpuAvailability()
+
+### Community 226 - "useMaskOps.ts"
+Cohesion: 0.67
+Nodes (3): CleanupOp, polysToShapes(), useMaskOps()
+
+### Community 227 - "useOpenInAnnotate.ts"
+Cohesion: 0.83
+Nodes (3): applyDraft(), classesFromKeywords(), useOpenInAnnotate()
+
+### Community 229 - "adjust.ts"
+Cohesion: 0.67
+Nodes (3): PreprocessOpts, renderAdjusted(), renderPreprocessOnly()
+
+### Community 231 - "classStore.ts"
+Cohesion: 0.50
+Nodes (3): AnnotationClass, ClassStore, useClassStore
+
+### Community 232 - "ratingStore.ts"
+Cohesion: 0.50
+Nodes (3): RatingState, StarRating, useRatingStore
+
+### Community 233 - "referenceGuideStore.ts"
+Cohesion: 0.50
+Nodes (3): GuideClass, ReferenceGuideStore, useReferenceGuideStore
+
+### Community 234 - "settingsStore.ts"
+Cohesion: 0.67
+Nodes (3): genId(), SettingsStore, useSettingsStore
+
+### Community 237 - "ipred/models"
+Cohesion: 0.50
+Nodes (3): ipred/models, SlimSAM (ONNX), TomoJEPA (Mark25 / Mark11)
+
+### Community 239 - "test_tomojepa_onnx.py"
+Cohesion: 0.50
+Nodes (3): TomoJEPA ONNX encode smoke (skips if onnx file missing)., test_onnx_encode_dense_shape(), needs_onnx
+
+### Community 240 - "ExportConflict"
+Cohesion: 0.67
+Nodes (3): ExportConflict, Exception, Raised when a merge would corrupt category ids or names.
+
+### Community 243 - "Using the tool"
+Cohesion: 0.67
+Nodes (3): Recommended reading order, The Finch shell, Using the tool
+
+### Community 244 - "4. Annotation guide"
+Cohesion: 0.67
+Nodes (3): 4. Annotation guide, How it connects to Annotate, What you can do here
+
+## Knowledge Gaps
+- **875 isolated node(s):** `sam3-annotation-studio-backend`, `name`, `private`, `version`, `type` (+870 more)
+  These have ≤1 connection - possible missing edges or undocumented components.
+- **71 thin communities (<3 nodes) omitted from report** — run `graphify query` to explore isolated nodes.
+
+## Suggested Questions
+_Questions this graph is uniquely positioned to answer:_
+
+- **Why does `uuid` connect `preprocess.py` to `compositions.py`, `dependencies`, `manifold.py`, `train_infer.py`, `export_jobs.py`, `feature_setups.py`, `catalog.py`, `ingest.py`?**
+  _High betweenness centrality (0.026) - this node is a cross-community bridge._
+- **Why does `Catalog` connect `Catalog` to `preprocess.py`, `compositions.py`, `test_compositions.py`, `manifold.py`, `HTTPException`, `train_infer.py`, `test_feature_setups.py`, `test_manifold.py`, `test_train_rethreshold.py`, `test_preprocess_cache.py`, `test_catalog_sessions.py`, `cli.py`, `feature_setups.py`, `catalog.py`?**
+  _High betweenness centrality (0.019) - this node is a cross-community bridge._
+- **Why does `Vec3` connect `Vec3` to `camera.ts`, `Vec3Like`, `run`, `math/index.ts`, `Random`, `Node`, `io/index.ts`, `math/color.ts`, `OrbitControls`, `.multiplyScalar`, `Mat4`, `packing.ts`, `VectorGrid3`, `Mat3`?**
+  _High betweenness centrality (0.017) - this node is a cross-community bridge._
+- **Are the 57 inferred relationships involving `run()` (e.g. with `.dispose()` and `.getState()`) actually correct?**
+  _`run()` has 57 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 39 inferred relationships involving `Catalog` (e.g. with `_resolve_session()` and `ensure_default_compositions()`) actually correct?**
+  _`Catalog` has 39 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 61 inferred relationships involving `HTTPException` (e.g. with `browse_column()` and `browse_items()`) actually correct?**
+  _`HTTPException` has 61 INFERRED edges - model-reasoned connections that need verification._
+- **What connects `sam3-annotation-studio-backend`, `name`, `private` to the rest of the system?**
+  _875 weakly-connected nodes found - possible documentation gaps or missing edges._
\ No newline at end of file
diff --git a/graphify-out/cache/ast/v0.9.50-s2/03f3223d98994af811d748d9f5feda9d72ac7e3b8a81aca145221cddb1cc47ed.json b/graphify-out/cache/ast/v0.9.50-s2/03f3223d98994af811d748d9f5feda9d72ac7e3b8a81aca145221cddb1cc47ed.json
new file mode 100644
index 0000000..dc3d6fe
--- /dev/null
+++ b/graphify-out/cache/ast/v0.9.50-s2/03f3223d98994af811d748d9f5feda9d72ac7e3b8a81aca145221cddb1cc47ed.json
@@ -0,0 +1 @@
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new file mode 100644
index 0000000..8dd6202
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new file mode 100644
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new file mode 100644
index 0000000..da518dc
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index 0000000..120793d
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new file mode 100644
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/44582ae2d5b190f52625e71ce1b3b45b5089ebff7c3e7c4a89613923a9d98a1e.json b/graphify-out/cache/ast/v0.9.50-s2/44582ae2d5b190f52625e71ce1b3b45b5089ebff7c3e7c4a89613923a9d98a1e.json
new file mode 100644
index 0000000..3d6fbda
--- /dev/null
+++ b/graphify-out/cache/ast/v0.9.50-s2/44582ae2d5b190f52625e71ce1b3b45b5089ebff7c3e7c4a89613923a9d98a1e.json
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new file mode 100644
index 0000000..5501a8b
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new file mode 100644
index 0000000..08f15ba
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/6417be9f126b1fcac9cef4c8db8a9fdb2d1fa59c24cb270ca50e568dffea9c88.json b/graphify-out/cache/ast/v0.9.50-s2/6417be9f126b1fcac9cef4c8db8a9fdb2d1fa59c24cb270ca50e568dffea9c88.json
new file mode 100644
index 0000000..656d478
--- /dev/null
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/68f51f7114b4c8d41ea25587e1f7dccb03026a32a691afd621d2ef1483b4a3f7.json b/graphify-out/cache/ast/v0.9.50-s2/68f51f7114b4c8d41ea25587e1f7dccb03026a32a691afd621d2ef1483b4a3f7.json
new file mode 100644
index 0000000..8f805f9
--- /dev/null
+++ b/graphify-out/cache/ast/v0.9.50-s2/68f51f7114b4c8d41ea25587e1f7dccb03026a32a691afd621d2ef1483b4a3f7.json
@@ -0,0 +1 @@
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new file mode 100644
index 0000000..8c6f84b
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/6b7db4d94e5c2f120023c2d6162c148f18d39e533dad4bfeb47a5658fa6f2a43.json b/graphify-out/cache/ast/v0.9.50-s2/6b7db4d94e5c2f120023c2d6162c148f18d39e533dad4bfeb47a5658fa6f2a43.json
new file mode 100644
index 0000000..edcf040
--- /dev/null
+++ b/graphify-out/cache/ast/v0.9.50-s2/6b7db4d94e5c2f120023c2d6162c148f18d39e533dad4bfeb47a5658fa6f2a43.json
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/74767bac2e9869ce5ced55d9834b10d190662c9a53a73a1090611b17e8e730bc.json b/graphify-out/cache/ast/v0.9.50-s2/74767bac2e9869ce5ced55d9834b10d190662c9a53a73a1090611b17e8e730bc.json
new file mode 100644
index 0000000..f785086
--- /dev/null
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new file mode 100644
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new file mode 100644
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new file mode 100644
index 0000000..efa8942
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+++ b/graphify-out/cache/ast/v0.9.50-s2/814e1bea2558315b4fff0f789a6a8aebb481d6737f42fac8f7e7ab5a7b986bf7.json
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/887ec22cc7abafc77862dc79f977919f2297f8fddad99cfbdb802fe1937c3b35.json b/graphify-out/cache/ast/v0.9.50-s2/887ec22cc7abafc77862dc79f977919f2297f8fddad99cfbdb802fe1937c3b35.json
new file mode 100644
index 0000000..6fc48ce
--- /dev/null
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new file mode 100644
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\ No newline at end of file
diff --git a/graphify-out/cache/ast/v0.9.50-s2/b9aa0da6b09351a314c97bbdf3f6366970542a7fbe7e8f78abb92c9028d1c34c.json b/graphify-out/cache/ast/v0.9.50-s2/b9aa0da6b09351a314c97bbdf3f6366970542a7fbe7e8f78abb92c9028d1c34c.json
new file mode 100644
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new file mode 100644
index 0000000..ceb4b6e
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new file mode 100644
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/d18346c2412f640c2f7e1578a6a3dde4d324d2b4990a099d55206860474434df.json b/graphify-out/cache/ast/v0.9.50-s2/d18346c2412f640c2f7e1578a6a3dde4d324d2b4990a099d55206860474434df.json
new file mode 100644
index 0000000..bbca017
--- /dev/null
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new file mode 100644
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new file mode 100644
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new file mode 100644
index 0000000..1e04099
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new file mode 100644
index 0000000..b07ed8c
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new file mode 100644
index 0000000..d3b9287
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+<script>
+const RAW_NODES = [{"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_gpu_timer_gputimer", "label": "GpuTimer", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.1, "font": {"size": 0, "color": "#ffffff"}, "title": "GpuTimer", "community": 10, "community_name": "TemporalAccumulator", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/accel/gpu-timer.ts", "file_type": "code", "degree": 11}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_taau_temporalaccumulator", "label": "TemporalAccumulator", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "TemporalAccumulator", "community": 10, "community_name": "TemporalAccumulator", "source_file": 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"backend_tiled_annotation_sync", "label": "tiled_annotation_sync.py", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 11.3, "font": {"size": 0, "color": "#ffffff"}, "title": "tiled_annotation_sync.py", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/tiled_annotation_sync.py", "file_type": "code", "degree": 7}, {"id": "backend_tiled_annotation_sync_py_any", "label": "Any", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "Any", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "", "file_type": "code", "degree": 2}, {"id": "imagedraw", "label": "ImageDraw", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": 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Formats:\u2026", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/source_keys.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_tiled_annotation_sync_rationale_1", "label": "Tests for source key parsing and annotation metadata helpers.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tests for source key parsing and annotation metadata helpers.", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/tests/test_tiled_annotation_sync.py", "file_type": "rationale", "degree": 1}, {"id": "backend_thumbnails_rationale_1", "label": "PNG thumbnail rendering for Tiled array nodes. 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Returns\u2026", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/thumbnails.py", "file_type": "rationale", "degree": 1}, {"id": "backend_thumbnails_rationale_54", "label": "Return *node* if it's already an array, else its first array child.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return *node* if it&#x27;s already an array, else its first array child.", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/thumbnails.py", "file_type": "rationale", "degree": 1}, {"id": "backend_thumbnails_rationale_85", "label": "Log-scale, normalise, and colour-map a 2D intensity array to uint8 RGB.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Log-scale, normalise, and colour-map a 2D intensity array to uint8 RGB.", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/thumbnails.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_annotation_sync_rationale_1", "label": "Sync annotation session state into Tiled node metadata for Browse discovery.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Sync annotation session state into Tiled node metadata for Browse discovery.", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/tiled_annotation_sync.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_annotation_sync_rationale_22", "label": "Build Tiled-safe metadata fields from a draft/export payload.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Build Tiled-safe metadata fields from a draft/export payload.", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/tiled_annotation_sync.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_annotation_sync_rationale_37", "label": "Merge annotation summary metadata onto a Tiled node. 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"font": {"size": 0, "color": "#ffffff"}, "title": ".onPointerMove()", "community": 50, "community_name": "OrbitControls", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts", "file_type": "code", "degree": 2}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_oncontextmenu", "label": ".onContextMenu()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": ".onContextMenu()", "community": 50, "community_name": "OrbitControls", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts", "file_type": "code", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onkeydown", "label": ".onKeyDown()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": ".onKeyDown()", "community": 50, "community_name": "OrbitControls", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts", "file_type": "code", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onkeyup", "label": ".onKeyUp()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": ".onKeyUp()", "community": 50, "community_name": "OrbitControls", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts", "file_type": "code", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onpointerdown", "label": ".onPointerDown()", "color": {"background": 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Tiled&#x27;s\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_207", "label": "Tally distinct values of *raw_key* across pre-read metadata *rows*. List-valued\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tally distinct values of *raw_key* across pre-read metadata *rows*. List-valued\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_233", "label": "Return distinct values (+ counts) for *raw_key* under optional filters. When\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return distinct values (+ counts) for *raw_key* under optional filters. When\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_298", "label": "Apply *filters* via ``search(Key())`` and return matching container entries.\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Apply *filters* via ``search(Key())`` and return matching container entries.\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_368", "label": "True if *entry* is a Tiled container (vs. an array/leaf) node.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "True if *entry* is a Tiled container (vs. an array/leaf) node.", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_454", "label": "Convert a display-key filter dict to (raw_key, string-value) pairs.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Convert a display-key filter dict to (raw_key, string-value) pairs.", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_464", "label": "Apply each filter to *node*, skipping failures. List-valued keys (e.g.\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Apply each filter to *node*, skipping failures. List-valued keys (e.g.\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_488", "label": "Case-insensitive match over container-level metadata. Scalar values match by\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Case-insensitive match over container-level metadata. Scalar values match by\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_507", "label": "Coerce a filter string to int / float when it round-trips exactly. Tiled stores\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Coerce a filter string to int / float when it round-trips exactly. Tiled stores\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_79", "label": "Mapping between raw Tiled metadata keys and friendly display names.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Mapping between raw Tiled metadata keys and friendly display names.", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_91", "label": "Introspect *container_node* to discover available metadata keys. Walks up to\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Introspect *container_node* to discover available metadata keys. Walks up to\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_provenance", "label": "provenance.ts", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 14.3, "font": {"size": 0, "color": "#ffffff"}, "title": "provenance.ts", "community": 65, "community_name": "provenance.ts", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/accel/provenance.ts", "file_type": "code", "degree": 23}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_provenance_crc_table", "label": "CRC_TABLE", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "CRC_TABLE", "community": 65, "community_name": "provenance.ts", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/accel/provenance.ts", "file_type": "code", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_provenance_png_sig", "label": "PNG_SIG", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "PNG_SIG", "community": 65, "community_name": "provenance.ts", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/accel/provenance.ts", "file_type": "code", "degree": 1}, {"id": "backend_schemas", "label": "schemas.py", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 14.5, "font": {"size": 12, "color": "#ffffff"}, "title": "schemas.py", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "code", "degree": 24}, {"id": "backend_schemas_py_basemodel", "label": "BaseModel", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 13.6, "font": {"size": 0, "color": "#ffffff"}, "title": "BaseModel", "community": 66, "community_name": "schemas.py", "source_file": "", "file_type": "code", "degree": 19}, {"id": "backend_tests_test_schemas", "label": "test_schemas.py", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "test_schemas.py", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "code", "degree": 17}, {"id": "backend_tests_test_tiled_mask_sync", "label": "test_tiled_mask_sync.py", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "test_tiled_mask_sync.py", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_tiled_mask_sync.py", "file_type": "code", "degree": 14}, {"id": "imagemeta", "label": "ImageMeta", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "ImageMeta", "community": 66, "community_name": "schemas.py", "source_file": "", "file_type": "code", "degree": 1}, {"id": "backend_annotation_server_rationale_577", "label": "Return shape / dtype metadata for an image source.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return shape / dtype metadata for an image source.", "community": 66, "community_name": "schemas.py", "source_file": "backend/annotation_server.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_1", "label": "Pydantic models for the Segmentation Annotation Studio API. Shapes ------ *\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Pydantic models for the Segmentation Annotation Studio API. Shapes ------ *\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_101", "label": "A collection of freehand brush strokes forming one annotation region.\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A collection of freehand brush strokes forming one annotation region.\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_120", "label": "A labelled class used for annotating images. Attributes: classId: Integer class\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A labelled class used for annotating images. Attributes: classId: Integer class\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_155", "label": "A single annotated sample within a multi-source export. Attributes: kind:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A single annotated sample within a multi-source export. Attributes: kind:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_238", "label": "Explicit save request with optional version metadata. Attributes: payload:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Explicit save request with optional version metadata. Attributes: payload:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_254", "label": "Request body for per-region intensity measurement. Attributes: slice_index:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Request body for per-region intensity measurement. Attributes: slice_index:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_266", "label": "Request body for the pre-upload duplicate check. Sent as a POST body rather\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Request body for the pre-upload duplicate check. Sent as a POST body rather\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_27", "label": "A single continuous stroke made by the brush tool. Attributes: points: Flat\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A single continuous stroke made by the brush tool. Attributes: points: Flat\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_320", "label": "Request body to denoise a whole volume into a new Tiled dataset. The Annotate\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Request body to denoise a whole volume into a new Tiled dataset. The Annotate\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_350", "label": "Request body for building a 3-D volume from a stack already in Tiled.\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Request body for building a 3-D volume from a stack already in Tiled.\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_371", "label": "One class entry in an annotation guide. Attributes: label: Human-readable class\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "One class entry in an annotation guide. Attributes: label: Human-readable class\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_387", "label": "A project lead's annotation guide for a dataset. Attributes: classes: Ordered\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A project lead&#x27;s annotation guide for a dataset. Attributes: classes: Ordered\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_399", "label": "Shape and dtype metadata for an opened image source. Attributes: n_slices:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Shape and dtype metadata for an opened image source. Attributes: n_slices:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_41", "label": "A closed polygon annotation. Attributes: id: Unique shape identifier. classId:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A closed polygon annotation. Attributes: id: Unique shape identifier. classId:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_57", "label": "An axis-aligned bounding-box annotation. Attributes: id: Unique shape\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "An axis-aligned bounding-box annotation. Attributes: id: Unique shape\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_79", "label": "An ellipse annotation. Attributes: id: Unique shape identifier. classId:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "An ellipse annotation. Attributes: id: Unique shape identifier. classId:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_1", "label": "Tests for Pydantic schema models.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tests for Pydantic schema models.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_17", "label": "PolygonShape should serialise and deserialise cleanly.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "PolygonShape should serialise and deserialise cleanly.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_30", "label": "BrushShape should store stroke list with correct attributes.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "BrushShape should store stroke list with correct attributes.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_38", "label": "RenderOpts should have sensible defaults without explicit values.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "RenderOpts should have sensible defaults without explicit values.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_46", "label": "RectShape should expose geometry fields correctly.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "RectShape should expose geometry fields correctly.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_53", "label": "EllipseShape should expose centre and radii correctly.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "EllipseShape should expose centre and radii correctly.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_60", "label": "BrushStroke should accept 'erase' mode.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "BrushStroke should accept &#x27;erase&#x27; mode.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_66", "label": "A folder's worth of filenames must fit \u2014 as query params it 431'd.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A folder&#x27;s worth of filenames must fit \u2014 as query params it 431&#x27;d.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_74", "label": "Omitting names is allowed \u2014 used to only ask for a free container name.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Omitting names is allowed \u2014 used to only ask for a free container name.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_tiled_mask_sync_rationale_1", "label": "Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization \u2192\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization \u2192\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_tiled_mask_sync.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_mask_sync_rationale_53", "label": "Rasterize an item's shapes into stacked mask volumes. Returns ``{semantic,\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Rasterize an item&#x27;s shapes into stacked mask volumes. Returns ``{semantic,\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/tiled_mask_sync.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_volume_nodes", "label": "test_volume_nodes.py", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.5, "font": {"size": 0, "color": "#ffffff"}, "title": "test_volume_nodes.py", "community": 67, "community_name": "FakeNode", "source_file": "backend/tests/test_volume_nodes.py", "file_type": "code", "degree": 8}, {"id": "backend_tests_test_volume_nodes_py_fixture", "label": "fixture", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "fixture", "community": 67, "community_name": "FakeNode", "source_file": "", "file_type": "code", "degree": 1}, {"id": "backend_tests_test_volume_nodes_rationale_1", "label": "Tests for locating a dataset's renderable 3-D volume. This is the module that\u2026", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tests for locating a dataset&#x27;s renderable 3-D volume. This is the module that\u2026", "community": 67, "community_name": "FakeNode", "source_file": "backend/tests/test_volume_nodes.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_volume_nodes_rationale_28", "label": "A catalog node: metadata plus children, indexable like a Tiled container.", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A catalog node: metadata plus children, indexable like a Tiled container.", "community": 67, "community_name": "FakeNode", "source_file": "backend/tests/test_volume_nodes.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_volume_nodes_rationale_40", "label": "A catalog with one of each shape the resolver has to tell apart.", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A catalog with one of each shape the resolver has to tell apart.", "community": 67, "community_name": "FakeNode", "source_file": "backend/tests/test_volume_nodes.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source", "label": "zarr_source.py", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 13.6, "font": {"size": 0, "color": "#ffffff"}, "title": "zarr_source.py", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "code", "degree": 19}, {"id": "backend_zarr_source_py_any", "label": "Any", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.3, "font": {"size": 0, "color": "#ffffff"}, "title": "Any", "community": 68, "community_name": "zarr_source.py", "source_file": "", "file_type": "code", "degree": 7}, {"id": "backend_zarr_source_py_path", "label": "Path", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Path", "community": 68, "community_name": "zarr_source.py", "source_file": "", "file_type": "code", "degree": 6}, {"id": "backend_zarr_source_rationale_1", "label": "Register on-disk Zarr volumes with Tiled, without copying any data. The drag-\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Register on-disk Zarr volumes with Tiled, without copying any data. The drag-\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_107", "label": "Pull the ``scale`` transform (voxel size per axis) out of a dataset entry.", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Pull the ``scale`` transform (voxel size per axis) out of a dataset entry.", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_117", "label": "Depth-first list of ``(path, array)`` for every 3-D array in *group*.", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Depth-first list of ``(path, array)`` for every 3-D array in *group*.", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_138", "label": "Describe a Zarr store's resolution pyramid without registering anything. Args:\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Describe a Zarr store&#x27;s resolution pyramid without registering anything. Args:\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_223", "label": "Axis unit declared in the store's ``multiscales`` axes (e.g. micrometer).", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Axis unit declared in the store&#x27;s ``multiscales`` axes (e.g. micrometer).", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_239", "label": "The node key Tiled will use for *path*. Tiled strips the extension, so\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "The node key Tiled will use for *path*. Tiled strips the extension, so\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_253", "label": "Describe what already occupies a key, for the conflict prompt. ``external``\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Describe what already occupies a key, for the conflict prompt. ``external``\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_280", "label": "Report whether registering *raw_path* would collide, without changing anything.\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Report whether registering *raw_path* would collide, without changing anything.\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_303", "label": "Register a Zarr store into a Tiled container, copying no data. Args:\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Register a Zarr store into a Tiled container, copying no data. Args:\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_49", "label": "True if *path* looks like the root of a Zarr store.", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "True if *path* looks like the root of a Zarr store.", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_54", "label": "Expand and validate a user-supplied absolute path to a Zarr store. Raises:\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Expand and validate a user-supplied absolute path to a Zarr store. Raises:\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_87", "label": "Read OME-NGFF ``multiscales`` from ``.zattrs``, if present. Returns the\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Read OME-NGFF ``multiscales`` from ``.zattrs``, if present. Returns the\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "frontend_package_dependencies", "label": "dependencies", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 13.0, "font": {"size": 0, "color": "#ffffff"}, "title": "dependencies", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 16}, {"id": "frontend_package_dependencies_blueskyproject_finch", "label": "@blueskyproject/finch", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "@blueskyproject/finch", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_clsx", "label": "clsx", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "clsx", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_huggingface_transformers", "label": "@huggingface/transformers", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "@huggingface/transformers", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_konva", "label": "konva", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "konva", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_polygon_clipping", "label": "polygon-clipping", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "polygon-clipping", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_react", "label": "react", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "react", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_react_dom", "label": "react-dom", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "react-dom", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_react_konva", "label": "react-konva", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "react-konva", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_react_router", "label": "react-router", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "react-router", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_tailwind_merge", "label": "tailwind-merge", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "tailwind-merge", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_tanstack_react_query", "label": "@tanstack/react-query", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "@tanstack/react-query", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_uuid", "label": "uuid", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "uuid", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_zustand", "label": "zustand", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "zustand", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "blueskyproject_finch", "label": "@blueskyproject/finch", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "@blueskyproject/finch", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "clsx", "label": "clsx", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "clsx", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "frontend_package_json_react", "label": "react", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "react", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "frontend_package_json_react_dom", "label": "react-dom", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "react-dom", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "frontend_package_json_tanstack_react_query", "label": "@tanstack/react-query", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "@tanstack/react-query", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "frontend_package_json_zustand", "label": "zustand", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "zustand", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "huggingface_transformers", "label": "@huggingface/transformers", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "@huggingface/transformers", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "konva", "label": "konva", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "konva", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "polygon_clipping", "label": "polygon-clipping", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "polygon-clipping", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "react_konva", "label": "react-konva", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "react-konva", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "react_router", "label": "react-router", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "react-router", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", 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"start_all.sh", "file_type": "code", "degree": 2}, {"id": "start_all_ipred_url", "label": "IPRED_URL", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "IPRED_URL", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 1}, {"id": "start_all_pick_free_port", "label": "pick_free_port()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.6, "font": {"size": 0, "color": "#ffffff"}, "title": "pick_free_port()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 3}, {"id": "start_all_port_is_listening", "label": "port_is_listening()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "port_is_listening()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 4}, {"id": "start_all_reclaim_orphaned_repo_ports", "label": "reclaim_orphaned_repo_ports()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.6, "font": {"size": 0, "color": "#ffffff"}, "title": "reclaim_orphaned_repo_ports()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 3}, {"id": "start_all_require_free_port", "label": "require_free_port()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "require_free_port()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 2}, {"id": "start_all_sh__entry", "label": "start_all.sh script", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 11.9, "font": {"size": 0, "color": "#ffffff"}, "title": "start_all.sh script", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 10}, {"id": "start_all_stop_managed_process", "label": "stop_managed_process()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.6, "font": {"size": 0, "color": "#ffffff"}, "title": "stop_managed_process()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 3}, {"id": "start_all_stop_repo_listener_on_port", "label": "stop_repo_listener_on_port()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "stop_repo_listener_on_port()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 4}, {"id": "start_all_tiled_cmd", "label": "tiled_cmd()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "tiled_cmd()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 2}, {"id": "start_all_tiled_uri", "label": "TILED_URI", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "TILED_URI", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 1}, {"id": "start_all_vite_docs_url", "label": "VITE_DOCS_URL", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "VITE_DOCS_URL", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 1}, {"id": "backend_drafts", "label": "drafts.py", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 12.3, "font": {"size": 0, "color": "#ffffff"}, "title": "drafts.py", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "code", "degree": 12}, {"id": "backend_drafts_py_any", "label": "Any", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Any", "community": 72, "community_name": "drafts.py", "source_file": "", "file_type": "code", "degree": 6}, {"id": "backend_drafts_py_path", "label": "Path", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "Path", "community": 72, "community_name": "drafts.py", "source_file": "", "file_type": "code", "degree": 2}, {"id": "backend_drafts_rationale_1", "label": "Session-draft persistence and version history for the Segmentation Annotation\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Session-draft persistence and version history for the Segmentation Annotation\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_139", "label": "Create a new immutable version snapshot for *source_key*. Also updates the\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Create a new immutable version snapshot for *source_key*. Also updates the\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_189", "label": "List version metadata (without payload) sorted oldest-first. Args: source_key:\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "List version metadata (without payload) sorted oldest-first. Args: source_key:\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_225", "label": "Persist an annotated thumbnail PNG for *version*. Stored alongside the version\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Persist an annotated thumbnail PNG for *version*. Stored alongside the version\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_243", "label": "Return the thumbnail PNG bytes for *version*, or ``None``. Args: source_key:\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the thumbnail PNG bytes for *version*, or ``None``. Args: source_key:\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_259", "label": "Return the full document (including payload) for a specific version. Args:\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the full document (including payload) for a specific version. Args:\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_33", "label": "Return the JSON file path for *source_key*. Args: source_key: Arbitrary string\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the JSON file path for *source_key*. Args: source_key: Arbitrary string\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_46", "label": "Return the versions directory path for *source_key*.", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the versions directory path for *source_key*.", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_52", "label": "Persist a session draft for *source_key* (crash-recovery autosave). Does NOT\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Persist a session draft for *source_key* (crash-recovery autosave). Does NOT\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_78", "label": "Return the saved draft for *source_key*, or ``None``. Args: source_key:\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the saved draft for *source_key*, or ``None``. Args: source_key:\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_97", "label": "List all saved drafts with summary metadata. Returns: List of dicts with\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "List all saved drafts with summary metadata. Returns: List of dicts with\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_clients", "label": "tiled_clients.py", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.4, "font": {"size": 0, "color": "#ffffff"}, "title": "tiled_clients.py", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_clients.py", "file_type": "code", "degree": 18}, {"id": "backend_tiled_clients_py_any", "label": "Any", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Any", "community": 73, "community_name": "tiled_clients.py", "source_file": "", "file_type": "code", "degree": 3}, {"id": "backend_tiled_config", "label": "tiled_config.py", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 12.5, "font": {"size": 0, "color": "#ffffff"}, "title": "tiled_config.py", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "code", "degree": 13}, {"id": "backend_tiled_config_py_path", "label": "Path", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Path", "community": 73, "community_name": "tiled_clients.py", "source_file": "", "file_type": "code", "degree": 1}, {"id": "backend_tiled_clients_rationale_1", "label": "Tiled client factory + browse-container discovery. Clients are cached per\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tiled client factory + browse-container discovery. Clients are cached per\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_clients.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_clients_rationale_65", "label": "Return ``(container_node, path_prefix)`` for the Metadata Browser root. Checks\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return ``(container_node, path_prefix)`` for the Metadata Browser root. Checks\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_clients.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_clients_rationale_95", "label": "Return ``(container_node, path_prefix)`` for a specific browse target. When\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return ``(container_node, path_prefix)`` for a specific browse target. When\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_clients.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_1", "label": "Tiled server configuration loaded from environment variables. Reads\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tiled server configuration loaded from environment variables. Reads\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_101", "label": "Return mapping of ``{name: {\"uri\": str, \"api_key\": str | None}}``. Discovery\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return mapping of ``{name: {&quot;uri&quot;: str, &quot;api_key&quot;: str | None}}``. Discovery\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_132", "label": "Return the default Tiled base URI.", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the default Tiled base URI.", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_137", "label": "Return the default API key (local override takes precedence).", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the default API key (local override takes precedence).", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_33", "label": "Delete blank ``TILED_API_KEY`` / ``TILED_LOCAL_API_KEY`` from the process env.\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Delete blank ``TILED_API_KEY`` / ``TILED_LOCAL_API_KEY`` from the process env.\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_55", "label": "Return ``authentication.single_user_api_key`` from ``tiled/config.yml``, or\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return ``authentication.single_user_api_key`` from ``tiled/config.yml``, or\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_82", "label": "Local Tiled base URI. ``TILED_URI`` (set by ``start_all.sh`` to whatever port\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Local Tiled base URI. ``TILED_URI`` (set by ``start_all.sh`` to whatever port\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_app", "label": "App.jsx", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 17.7, "font": {"size": 12, "color": "#ffffff"}, "title": "App.jsx", "community": 74, "community_name": "App.jsx", "source_file": "frontend/vendor/view_tomography_recon_app/src/App.jsx", "file_type": "code", "degree": 41}, {"id": "frontend_vendor_view_tomography_recon_app_src_hooks_uselinkedwebgpuviewers", "label": "useLinkedWebGpuViewers.ts", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "useLinkedWebGpuViewers.ts", "community": 74, "community_name": "App.jsx", "source_file": "frontend/vendor/view_tomography_recon_app/src/hooks/useLinkedWebGpuViewers.ts", "file_type": "code", "degree": 6}, {"id": "frontend_vendor_view_tomography_recon_app_src_main", "label": "main.jsx", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "main.jsx", "community": 74, 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"ManagedTexture", "count": 51}, {"cid": 96, "color": "#B07AA1", "label": "FastAPI", "count": 21}, {"cid": 97, "color": "#FF9DA7", "label": "fake_stack", "count": 18}, {"cid": 98, "color": "#9C755F", "label": "geometry.ts", "count": 19}, {"cid": 99, "color": "#BAB0AC", "label": "length-units.ts", "count": 18}, {"cid": 100, "color": "#4E79A7", "label": "codecs.ts", "count": 18}, {"cid": 101, "color": "#F28E2B", "label": ".alignTo", "count": 18}, {"cid": 102, "color": "#E15759", "label": "Vec3Like", "count": 25}, {"cid": 103, "color": "#76B7B2", "label": "denoise_bake.py", "count": 17}, {"cid": 104, "color": "#59A14F", "label": "3. 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Annotation guide", "count": 3}, {"cid": 245, "color": "#EDC948", "label": "ExportPage.tsx", "count": 3}, {"cid": 246, "color": "#B07AA1", "label": "MaskToolsPanel/index.tsx", "count": 3}, {"cid": 247, "color": "#FF9DA7", "label": "DebouncedSlider.tsx", "count": 3}, {"cid": 248, "color": "#9C755F", "label": "CustomizePages.tsx", "count": 3}, {"cid": 249, "color": "#BAB0AC", "label": "HubAppLayout.tsx", "count": 3}, {"cid": 250, "color": "#4E79A7", "label": "HubHeader.tsx", "count": 3}, {"cid": 251, "color": "#F28E2B", "label": "HubSidebar.tsx", "count": 3}, {"cid": 252, "color": "#E15759", "label": "IframeModal.tsx", "count": 3}, {"cid": 253, "color": "#76B7B2", "label": "useAnnotatedSourceKeys.ts", "count": 3}, {"cid": 254, "color": "#59A14F", "label": "useKeybinds.ts", "count": 3}, {"cid": 255, "color": "#EDC948", "label": "viridis.ts", "count": 3}, {"cid": 256, "color": "#B07AA1", "label": "clipboardStore.ts", "count": 3}, {"cid": 257, "color": "#FF9DA7", "label": "connectionStore.ts", "count": 3}, {"cid": 258, "color": "#9C755F", "label": "annotationFilter.ts", "count": 3}, {"cid": 259, "color": "#BAB0AC", "label": "vite-env.d.ts", "count": 3}, {"cid": 260, "color": "#4E79A7", "label": "frontend/tsconfig.json", "count": 3}, {"cid": 261, "color": "#F28E2B", "label": "patches/README.md", "count": 3}, {"cid": 262, "color": "#E15759", "label": "Button.tsx", "count": 3}, {"cid": 263, "color": "#76B7B2", "label": "ItkVtkFrame.tsx", "count": 3}, {"cid": 264, "color": "#59A14F", "label": "Bundled Tiled catalog (committed to git)", "count": 3}, {"cid": 265, "color": "#EDC948", "label": "@phosphor-icons/react", "count": 2}, {"cid": 266, "color": "#B07AA1", "label": "zundo", "count": 2}, {"cid": 267, "color": "#FF9DA7", "label": "@testing-library/react", "count": 2}, {"cid": 268, "color": "#9C755F", "label": "@types/node", "count": 2}, {"cid": 269, "color": "#BAB0AC", "label": "@types/react", "count": 2}, {"cid": 270, "color": "#4E79A7", "label": "vite", "count": 2}, {"cid": 271, "color": "#F28E2B", "label": "@vitejs/plugin-react", "count": 2}, {"cid": 272, "color": "#E15759", "label": "vitest", "count": 2}, {"cid": 273, "color": "#76B7B2", "label": "SliceNavigator/index.tsx", "count": 2}, {"cid": 274, "color": "#59A14F", "label": "useHubSelectedTabs.ts", "count": 2}, {"cid": 275, "color": "#EDC948", "label": "useSam.ts", "count": 2}, {"cid": 276, "color": "#B07AA1", "label": "lib/utils.ts", "count": 2}, {"cid": 277, "color": "#FF9DA7", "label": "navigationRouterTypes.ts", "count": 2}, {"cid": 278, "color": "#9C755F", "label": "server.ts", "count": 2}, {"cid": 279, "color": "#BAB0AC", "label": "ItkVtkViewerComponent.jsx", "count": 2}, {"cid": 280, "color": "#4E79A7", "label": "vitest.config.ts", "count": 2}, {"cid": 281, "color": "#F28E2B", "label": "vite.config.js", "count": 2}, {"cid": 285, "color": "#EDC948", "label": "scripts/__init__.py", "count": 2}, {"cid": 286, "color": "#B07AA1", "label": "tests/__init__.py", "count": 1}, {"cid": 287, "color": "#FF9DA7", "label": "postcss.config.js", "count": 1}, {"cid": 288, "color": "#9C755F", "label": "layerVisibilityStore.test.ts", "count": 1}, {"cid": 289, "color": "#BAB0AC", "label": "setup.ts", "count": 1}, {"cid": 290, "color": "#4E79A7", "label": "webgpu.d.ts", "count": 1}, {"cid": 291, "color": "#F28E2B", "label": "tailwind.config.js", "count": 1}, {"cid": 292, "color": "#E15759", "label": "eslint.config.js", "count": 1}, {"cid": 293, "color": "#76B7B2", "label": "tiled-auth-interceptor.js", "count": 1}, {"cid": 294, "color": "#59A14F", "label": ".open", "count": 1}, {"cid": 295, "color": "#EDC948", "label": ".send", "count": 1}, {"cid": 296, "color": "#B07AA1", "label": "ipred", "count": 1}, {"cid": 297, "color": "#FF9DA7", "label": "sam3-annotation-studio-backend", "count": 1}, {"cid": 298, "color": "#9C755F", "label": "tailwindcss", "count": 1}];
+
+// HTML-escape helper — prevents XSS when injecting graph data into innerHTML
+function esc(s) {
+  return String(s).replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;').replace(/"/g,'&quot;').replace(/'/g,'&#39;');
+}
+
+// Build vis datasets
+const nodesDS = new vis.DataSet(RAW_NODES.map(n => ({
+  id: n.id, label: n.label, color: n.color, size: n.size,
+  font: n.font, title: n.title,
+  _community: n.community, _community_name: n.community_name,
+  _source_file: n.source_file, _file_type: n.file_type, _degree: n.degree,
+})));
+
+const edgesDS = new vis.DataSet(RAW_EDGES.map((e, i) => ({
+  id: i, from: e.from, to: e.to,
+  label: '',
+  title: e.title,
+  dashes: e.dashes,
+  width: e.width,
+  color: e.color,
+  arrows: { to: { enabled: true, scaleFactor: 0.5 } },
+})));
+
+const container = document.getElementById('graph');
+const network = new vis.Network(container, { nodes: nodesDS, edges: edgesDS }, {
+  physics: {
+    enabled: true,
+    solver: 'forceAtlas2Based',
+    forceAtlas2Based: {
+      gravitationalConstant: -60,
+      centralGravity: 0.005,
+      springLength: 120,
+      springConstant: 0.08,
+      damping: 0.4,
+      avoidOverlap: 0.8,
+    },
+    stabilization: { iterations: 200, fit: true },
+  },
+  interaction: {
+    hover: true,
+    tooltipDelay: 100,
+    hideEdgesOnDrag: true,
+    navigationButtons: false,
+    keyboard: false,
+  },
+  nodes: { shape: 'dot', borderWidth: 1.5 },
+  edges: { smooth: { type: 'continuous', roundness: 0.2 }, selectionWidth: 3 },
+});
+
+network.once('stabilizationIterationsDone', () => {
+  network.setOptions({ physics: { enabled: false } });
+});
+
+function showInfo(nodeId) {
+  const n = nodesDS.get(nodeId);
+  if (!n) return;
+  const neighborIds = network.getConnectedNodes(nodeId);
+  const neighborItems = neighborIds.map(nid => {
+    const nb = nodesDS.get(nid);
+    const color = nb ? nb.color.background : '#555';
+    return `<span class="neighbor-link" style="border-left-color:${esc(color)}" data-nid="${esc(nid)}">${esc(nb ? nb.label : nid)}</span>`;
+  }).join('');
+  document.getElementById('info-content').innerHTML = `
+    <div class="field"><b>${esc(n.label)}</b></div>
+    <div class="field">Type: ${esc(n._file_type || 'unknown')}</div>
+    <div class="field">Community: ${esc(n._community_name)}</div>
+    <div class="field">Source: ${esc(n._source_file || '-')}</div>
+    <div class="field">Degree: ${n._degree}</div>
+    ${neighborIds.length ? `<div class="field" style="margin-top:8px;color:#aaa;font-size:11px">Neighbors (${neighborIds.length})</div><div id="neighbors-list">${neighborItems}</div>` : ''}
+  `;
+}
+
+function focusNode(nodeId) {
+  network.focus(nodeId, { scale: 1.4, animation: true });
+  network.selectNodes([nodeId]);
+  showInfo(nodeId);
+}
+
+// Neighbor links use a data attribute + one delegated listener rather than an
+// inline onclick. A node id/label sourced from a document or a scraped URL
+// (graphify add) can contain a double-quote; dropping the stringified id
+// unescaped into a quoted onclick both broke every link and allowed a hostile
+// source to inject an event handler into the local report (stored XSS, #1838).
+// esc() on data-nid keeps the value inside the attribute; the listener reads it
+// back verbatim. Bound to document so it survives the innerHTML rebuild that
+// recreates #neighbors-list on each showInfo().
+document.addEventListener('click', e => {
+  const el = e.target.closest('.neighbor-link');
+  if (el && el.dataset.nid !== undefined) focusNode(el.dataset.nid);
+});
+
+// Track hovered node — hover detection is more reliable than click params
+let hoveredNodeId = null;
+network.on('hoverNode', params => {
+  hoveredNodeId = params.node;
+  container.style.cursor = 'pointer';
+});
+network.on('blurNode', () => {
+  hoveredNodeId = null;
+  container.style.cursor = 'default';
+});
+container.addEventListener('click', () => {
+  if (hoveredNodeId !== null) {
+    showInfo(hoveredNodeId);
+    network.selectNodes([hoveredNodeId]);
+  }
+});
+network.on('click', params => {
+  if (params.nodes.length > 0) {
+    showInfo(params.nodes[0]);
+  } else if (hoveredNodeId === null) {
+    document.getElementById('info-content').innerHTML = '<span class="empty">Click a node to inspect it</span>';
+  }
+});
+
+const searchInput = document.getElementById('search');
+const searchResults = document.getElementById('search-results');
+searchInput.addEventListener('input', () => {
+  const q = searchInput.value.toLowerCase().trim();
+  searchResults.innerHTML = '';
+  if (!q) { searchResults.style.display = 'none'; return; }
+  const matches = RAW_NODES.filter(n => n.label.toLowerCase().includes(q)).slice(0, 20);
+  if (!matches.length) { searchResults.style.display = 'none'; return; }
+  searchResults.style.display = 'block';
+  matches.forEach(n => {
+    const el = document.createElement('div');
+    el.className = 'search-item';
+    el.textContent = n.label;
+    el.style.borderLeft = `3px solid ${n.color.background}`;
+    el.style.paddingLeft = '8px';
+    el.onclick = () => {
+      network.focus(n.id, { scale: 1.5, animation: true });
+      network.selectNodes([n.id]);
+      showInfo(n.id);
+      searchResults.style.display = 'none';
+      searchInput.value = '';
+    };
+    searchResults.appendChild(el);
+  });
+});
+document.addEventListener('click', e => {
+  if (!searchResults.contains(e.target) && e.target !== searchInput)
+    searchResults.style.display = 'none';
+});
+
+const hiddenCommunities = new Set();
+
+const selectAllCb = document.getElementById('select-all-cb');
+
+function updateSelectAllState() {
+  const total = LEGEND.length;
+  const hidden = hiddenCommunities.size;
+  selectAllCb.checked = hidden === 0;
+  selectAllCb.indeterminate = hidden > 0 && hidden < total;
+}
+
+function toggleAllCommunities(hide) {
+  document.querySelectorAll('.legend-item').forEach(item => {
+    hide ? item.classList.add('dimmed') : item.classList.remove('dimmed');
+  });
+  document.querySelectorAll('.legend-cb').forEach(cb => {
+    cb.checked = !hide;
+  });
+  LEGEND.forEach(c => {
+    if (hide) hiddenCommunities.add(c.cid); else hiddenCommunities.delete(c.cid);
+  });
+  const updates = RAW_NODES.map(n => ({ id: n.id, hidden: hide }));
+  nodesDS.update(updates);
+  updateSelectAllState();
+}
+
+const legendEl = document.getElementById('legend');
+LEGEND.forEach(c => {
+  const item = document.createElement('div');
+  item.className = 'legend-item';
+  const cb = document.createElement('input');
+  cb.type = 'checkbox';
+  cb.className = 'legend-cb';
+  cb.checked = true;
+  cb.addEventListener('change', (e) => {
+    e.stopPropagation();
+    if (cb.checked) {
+      hiddenCommunities.delete(c.cid);
+      item.classList.remove('dimmed');
+    } else {
+      hiddenCommunities.add(c.cid);
+      item.classList.add('dimmed');
+    }
+    const updates = RAW_NODES
+      .filter(n => n.community === c.cid)
+      .map(n => ({ id: n.id, hidden: !cb.checked }));
+    nodesDS.update(updates);
+    updateSelectAllState();
+  });
+  item.innerHTML = `<div class="legend-dot" style="background:${c.color}"></div>
+    <span class="legend-label">${c.label}</span>
+    <span class="legend-count">${c.count}</span>`;
+  item.prepend(cb);
+  item.onclick = (e) => {
+    if (e.target === cb) return;
+    cb.checked = !cb.checked;
+    cb.dispatchEvent(new Event('change'));
+  };
+  legendEl.appendChild(item);
+});
+</script>
+<script>
+// Render hyperedges as shaded regions
+const hyperedges = [];
+// afterDrawing passes ctx already transformed to network coordinate space.
+// Draw node positions raw — no manual pan/zoom/DPR math needed.
+
+// Andrew's monotone chain. Returns the hull in counter-clockwise order, which
+// is what the perimeter must be traced in. Collinear and duplicate points
+// collapse to the extremes, so degenerate member sets render as a segment
+// rather than a zero-area crossed path.
+function convexHull(pts) {
+    const p = pts.slice().sort((a, b) => (a.x - b.x) || (a.y - b.y));
+    if (p.length < 3) return p;
+    const cross = (o, a, b) => (a.x - o.x) * (b.y - o.y) - (a.y - o.y) * (b.x - o.x);
+    const build = seq => {
+        const out = [];
+        for (const q of seq) {
+            while (out.length >= 2 && cross(out[out.length - 2], out[out.length - 1], q) <= 0) out.pop();
+            out.push(q);
+        }
+        out.pop();
+        return out;
+    };
+    const hull = build(p).concat(build(p.slice().reverse()));
+    return hull.length >= 3 ? hull : p;
+}
+network.on('afterDrawing', function(ctx) {
+    hyperedges.forEach(h => {
+        const positions = h.nodes
+            .map(nid => network.getPositions([nid])[nid])
+            .filter(p => p !== undefined);
+        if (positions.length < 2) return;
+        ctx.save();
+        ctx.globalAlpha = 0.12;
+        ctx.fillStyle = '#6366f1';
+        ctx.strokeStyle = '#6366f1';
+        ctx.lineWidth = 2;
+        ctx.beginPath();
+        // Centroid and expanded hull in network coordinates.
+        // The perimeter must follow hull order, not h.nodes order: tracing the
+        // raw member order self-intersects whenever the layout does not happen
+        // to place members in angular order, filling as crossed wedges.
+        const cx = positions.reduce((s, p) => s + p.x, 0) / positions.length;
+        const cy = positions.reduce((s, p) => s + p.y, 0) / positions.length;
+        const hull = convexHull(positions);
+        const expanded = hull.map(p => ({
+            x: cx + (p.x - cx) * 1.15,
+            y: cy + (p.y - cy) * 1.15
+        }));
+        ctx.moveTo(expanded[0].x, expanded[0].y);
+        expanded.slice(1).forEach(p => ctx.lineTo(p.x, p.y));
+        ctx.closePath();
+        ctx.fill();
+        ctx.globalAlpha = 0.4;
+        ctx.stroke();
+        // Label
+        ctx.globalAlpha = 0.8;
+        ctx.fillStyle = '#4f46e5';
+        ctx.font = 'bold 11px sans-serif';
+        ctx.textAlign = 'center';
+        ctx.fillText(h.label, cx, cy - 5);
+        ctx.restore();
+    });
+});
+</script>
+</body>
+</html>
\ No newline at end of file
diff --git a/graphify-out/graph.json b/graphify-out/graph.json
new file mode 100644
index 0000000..de7e6f8
--- /dev/null
+++ b/graphify-out/graph.json
@@ -0,0 +1,160061 @@
+{
+  "directed": false,
+  "multigraph": false,
+  "graph": {},
+  "nodes": [
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+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
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+      "source_location": "L25"
+    },
+    {
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+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "temporalaccumulator",
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+      "source_location": "L111"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_device_context_gpucontext",
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+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "gpucontext",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/device/context.ts",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_compiledgraph",
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+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "compiledgraph",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
+      "source_location": "L58"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_passcontext",
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+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "passcontext",
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+      "source_location": "L33"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_passdesc",
+      "label": "PassDesc",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "passdesc",
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+    },
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+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "rendergraph",
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+      "source_location": "L93"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_rendergraphtexturedesc",
+      "label": "RenderGraphTextureDesc",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "rendergraphtexturedesc",
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+      "source_location": "L23"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_resourcehandle",
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+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "resourcehandle",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
+      "source_location": "L20"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_resourcelifetime",
+      "label": "ResourceLifetime",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "resourcelifetime",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
+      "source_location": "L50"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_resourcerecord",
+      "label": "ResourceRecord",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "resourcerecord",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
+      "source_location": "L71"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_post_fx_pipeline_fxpipeline",
+      "label": "FxPipeline",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "fxpipeline",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/post/fx-pipeline.ts",
+      "source_location": "L26"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_resources_pipeline_pipelinecache",
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+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 10,
+      "community_name": "TemporalAccumulator",
+      "file_type": "code",
+      "norm_label": "pipelinecache",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/resources/pipeline.ts",
+      "source_location": "L13"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_zarr_codecs_blosccompressorconfig",
+      "label": "BloscCompressorConfig",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 100,
+      "community_name": "codecs.ts",
+      "file_type": "code",
+      "norm_label": "blosccompressorconfig",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/io/volume/zarr/codecs.ts",
+      "source_location": "L18"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_zarr_codecs_codec",
+      "label": "Codec",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 100,
+      "community_name": "codecs.ts",
+      "file_type": "code",
+      "norm_label": "codec",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/io/volume/zarr/codecs.ts",
+      "source_location": "L12"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_layoutbuilder",
+      "label": "LayoutBuilder",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 101,
+      "community_name": ".alignTo",
+      "file_type": "code",
+      "norm_label": "layoutbuilder",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/gpu-layout.ts",
+      "source_location": "L25"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_std140builder",
+      "label": "Std140Builder",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 101,
+      "community_name": ".alignTo",
+      "file_type": "code",
+      "norm_label": "std140builder",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/gpu-layout.ts",
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+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_std430builder",
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+      "_callable_class": true,
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+      "community_name": ".alignTo",
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+      "source_location": "L138"
+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_shapes_aabb",
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+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 102,
+      "community_name": "Vec3Like",
+      "file_type": "code",
+      "norm_label": "aabb",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/shapes.ts",
+      "source_location": "L13"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_shapes_frustum",
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+      "_callable_class": true,
+      "_origin": "ast",
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+      "community_name": "Vec3Like",
+      "file_type": "code",
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+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/shapes.ts",
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+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3like",
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+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 102,
+      "community_name": "Vec3Like",
+      "file_type": "code",
+      "norm_label": "vec3like",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec3.ts",
+      "source_location": "L17"
+    },
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+      "id": "backend_denoise_bake_modeldenoiser",
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+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 103,
+      "community_name": "denoise_bake.py",
+      "file_type": "code",
+      "norm_label": "_modeldenoiser",
+      "source_file": "backend/denoise_bake.py",
+      "source_location": "L242"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_classstat",
+      "label": "ClassStat",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "classstat",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L16"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_coverage",
+      "label": "Coverage",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "coverage",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L25"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_maskinfo",
+      "label": "MaskInfo",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "maskinfo",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L182"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_qaflag",
+      "label": "QAFlag",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "qaflag",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L35"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_qaflagkind",
+      "label": "QAFlagKind",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "qaflagkind",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L33"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_samplestats",
+      "label": "SampleStats",
+      "_callable": true,
+      "_callable_class": true,
+      "_origin": "ast",
+      "community": 105,
+      "community_name": "datasetStats.ts",
+      "file_type": "code",
+      "norm_label": "samplestats",
+      "source_file": "frontend/src/lib/datasetStats.ts",
+      "source_location": "L45"
+    },
+    {
+      "id": "frontend_src_lib_datasetstats_slicesmap",
+      "label": "SlicesMap",
+      "_callable": true,
+      "_callable_class": true,
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_zarr_lz4_lz4blockdecompress",
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+      "file_type": "code",
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+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_layoutbuilder_alignto",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_layoutbuilder_bytelength",
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+      "_origin": "ast",
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+      "community_name": ".alignTo",
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+      "norm_label": ".bytelength()",
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+      "source_location": "L80"
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_layoutbuilder_f32",
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+      "source_location": "L35"
+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_layoutbuilder_mat3",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_crossvectors",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_isnan",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_negate",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_projectonto",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_set",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_setscalar",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_volume_volume_renderer_volumerenderer_setliquidshading",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_ambient",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_keylightdirection",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_keylightradiance",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_lightcount",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_lights",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_setibl",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_setlights",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_lightingenvironment_upload",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_lighting_light_buffer_packlightsstd430",
+      "label": "packLightsStd430()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_culling_cullfrustum",
+      "label": "cullFrustum()",
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+      "norm_label": "cullfrustum()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_culling_expandbytransformedbox",
+      "label": "expandByTransformedBox()",
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+      "source_location": "L23"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_add",
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+      "norm_label": ".add()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_constructor",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_localmatrix",
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+      "_origin": "ast",
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+      "file_type": "code",
+      "norm_label": ".localmatrix()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_markdirty",
+      "label": ".markDirty()",
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+      "_origin": "ast",
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+      "norm_label": ".markdirty()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_remove",
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+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".remove()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_transformchanged",
+      "label": ".transformChanged()",
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+      "_origin": "ast",
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+      "norm_label": ".transformchanged()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_traverse",
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+      "_origin": "ast",
+      "community": 43,
+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".traverse()",
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+      "source_location": "L124"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_node_node_worldmatrix",
+      "label": ".worldMatrix()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 43,
+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".worldmatrix()",
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+      "source_location": "L113"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_scene_scene_attach",
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+      "_origin": "ast",
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+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".attach()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/scene/scene.ts",
+      "source_location": "L36"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_scene_scene_detach",
+      "label": ".detach()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 43,
+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".detach()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/scene/scene.ts",
+      "source_location": "L49"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_scene_scene_query",
+      "label": ".query()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 43,
+      "community_name": "Node",
+      "file_type": "code",
+      "norm_label": ".query()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/scene/scene.ts",
+      "source_location": "L68"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_constructor",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".constructor()",
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+      "source_location": "L21"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_div",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".div()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L52"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_equals",
+      "label": ".equals()",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".equals()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L78"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_isdimensionless",
+      "label": ".isDimensionless()",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".isdimensionless()",
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+      "source_location": "L91"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_mul",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".mul()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L39"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_pow",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".pow()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L65"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_dimension_dimension_tostring",
+      "label": ".toString()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".tostring()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/dimension.ts",
+      "source_location": "L96"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_abs",
+      "label": ".abs()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".abs()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L74"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_add",
+      "label": ".add()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".add()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L41"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_approxequals",
+      "label": ".approxEquals()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".approxequals()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L88"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_assertsamedimension",
+      "label": ".assertSameDimension()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".assertsamedimension()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L99"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_compare",
+      "label": ".compare()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".compare()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L79"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_constructor",
+      "label": ".constructor()",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".constructor()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L15"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_div",
+      "label": ".div()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".div()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L59"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_equals",
+      "label": ".equals()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".equals()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L84"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_mul",
+      "label": ".mul()",
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+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".mul()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L52"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_neg",
+      "label": ".neg()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".neg()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L70"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_pow",
+      "label": ".pow()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".pow()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L66"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_scalar",
+      "label": ".scalar()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".scalar()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L27"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_sub",
+      "label": ".sub()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".sub()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L46"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_to",
+      "label": ".to()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".to()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L32"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_tostring",
+      "label": ".toString()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".tostring()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/quantity.ts",
+      "source_location": "L93"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_assertratio",
+      "label": ".assertRatio()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".assertratio()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/unit.ts",
+      "source_location": "L67"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_constructor",
+      "label": ".constructor()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".constructor()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/unit.ts",
+      "source_location": "L15"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_div",
+      "label": ".div()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".div()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/unit.ts",
+      "source_location": "L47"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_fromsi",
+      "label": ".fromSI()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".fromsi()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/unit.ts",
+      "source_location": "L31"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_isratio",
+      "label": ".isRatio()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".isratio()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/unit.ts",
+      "source_location": "L36"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_labeled",
+      "label": ".labeled()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".labeled()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/unit.ts",
+      "source_location": "L59"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_mul",
+      "label": ".mul()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".mul()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/unit.ts",
+      "source_location": "L41"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_pow",
+      "label": ".pow()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 44,
+      "community_name": "Quantity",
+      "file_type": "code",
+      "norm_label": ".pow()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/core/units/unit.ts",
+      "source_location": "L53"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_tostring",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_applyeyelookat",
+      "label": ".applyEyeLookAt()",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_dispose",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_dollyby",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_eye",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_flush",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_gazeup",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_getstate",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_isanimating",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_isdragging",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_orbitturntable",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_panby",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_setstate",
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+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_syncfromnode",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_syncsphericalfromoffset",
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+      "source_location": "L505"
+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_update",
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+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_writeoffsetfromspherical",
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+      "_origin": "ast",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_shapes_aabb_getcenter",
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+      "norm_label": ".getcenter()",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3_add",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_add",
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+    },
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_addscaled",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_addscaledvector",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_angle",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_approxequals",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_clamp",
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_clone",
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+      "source_location": "L65"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_constructor",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_copy",
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+      "source_location": "L58"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_cross",
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+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_distance",
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+      "source_location": "L261"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_distanceto",
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+      "_origin": "ast",
+      "community": 51,
+      "community_name": "Vec2",
+      "file_type": "code",
+      "norm_label": ".distanceto()",
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+      "source_location": "L130"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_dividescalar",
+      "label": ".divideScalar()",
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+      "_origin": "ast",
+      "community": 51,
+      "community_name": "Vec2",
+      "file_type": "code",
+      "norm_label": ".dividescalar()",
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+      "source_location": "L98"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_dot",
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+      "_origin": "ast",
+      "community": 51,
+      "community_name": "Vec2",
+      "file_type": "code",
+      "norm_label": ".dot()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec2.ts",
+      "source_location": "L110"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_equals",
+      "label": ".equals()",
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+      "_origin": "ast",
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+      "community_name": "Vec2",
+      "file_type": "code",
+      "norm_label": ".equals()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec2.ts",
+      "source_location": "L178"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_fromarray",
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+      "_origin": "ast",
+      "community": 51,
+      "community_name": "Vec2",
+      "file_type": "code",
+      "norm_label": ".fromarray()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec2.ts",
+      "source_location": "L46"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_length",
+      "label": ".length()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 51,
+      "community_name": "Vec2",
+      "file_type": "code",
+      "norm_label": ".length()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec2.ts",
+      "source_location": "L120"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec2_vec2_lengthsq",
+      "label": ".lengthSq()",
+      "_callable": true,
+      "_origin": "ast",
+      "community": 51,
+      "community_name": "Vec2",
+      "file_type": "code",
+      "norm_label": ".lengthsq()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec2.ts",
+      "source_location": "L125"
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+      "community_name": "coco_export.py",
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+      "community_name": "coco_export.py",
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+      "community_name": "coco_export.py",
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+    },
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+      "community_name": "coco_export.py",
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+      "community_name": "coco_export.py",
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+      "community": 13,
+      "community_name": "coco_export.py",
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+    },
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+      "community_name": "coco_export.py",
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+    },
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+      "community": 13,
+      "community_name": "coco_export.py",
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+      "community": 13,
+      "community_name": "coco_export.py",
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+      "community_name": "coco_export.py",
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+      "community_name": "coco_export.py",
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+    },
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+      "_origin": "ast",
+      "community": 13,
+      "community_name": "coco_export.py",
+      "file_type": "rationale",
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+    },
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+      "community": 13,
+      "community_name": "coco_export.py",
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+      "_origin": "ast",
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+      "community_name": "coco_export.py",
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+    },
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+      "_origin": "ast",
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+      "community_name": "coco_export.py",
+      "file_type": "rationale",
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+      "community_name": "ViewportOverlay",
+      "file_type": "code",
+      "norm_label": "viewport-overlay.ts",
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+    },
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+      "_origin": "ast",
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+      "community_name": "ViewportOverlay",
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+      "norm_label": "axes",
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+      "_origin": "ast",
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+      "community_name": "test_compositions.py",
+      "file_type": "code",
+      "norm_label": "test_compositions.py",
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+      "_origin": "ast",
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+      "community_name": "test_compositions.py",
+      "file_type": "code",
+      "norm_label": "fixture",
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+      "source_location": ""
+    },
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+      "_origin": "ast",
+      "community": 132,
+      "community_name": "test_compositions.py",
+      "file_type": "code",
+      "norm_label": "monkeypatch",
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+      "source_location": ""
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+      "_origin": "ast",
+      "community": 132,
+      "community_name": "test_compositions.py",
+      "file_type": "rationale",
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+      "source_file": "ipred/tests/test_compositions.py",
+      "source_location": "L1"
+    },
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+      "label": "test_local_fs.py",
+      "_origin": "ast",
+      "community": 133,
+      "community_name": "test_local_fs.py",
+      "file_type": "code",
+      "norm_label": "test_local_fs.py",
+      "source_file": "backend/tests/test_local_fs.py",
+      "source_location": "L1"
+    },
+    {
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+      "_origin": "ast",
+      "community": 133,
+      "community_name": "test_local_fs.py",
+      "file_type": "rationale",
+      "norm_label": "tests for local filesystem sandboxing.",
+      "source_file": "backend/tests/test_local_fs.py",
+      "source_location": "L1"
+    },
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+      "community": 133,
+      "community_name": "test_local_fs.py",
+      "file_type": "rationale",
+      "norm_label": "paths escaping local_data_root must raise http 403.",
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+      "source_location": "L13"
+    },
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+      "_origin": "ast",
+      "community": 133,
+      "community_name": "test_local_fs.py",
+      "file_type": "rationale",
+      "norm_label": "list_dir should return file entries for an existing directory.",
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+      "source_location": "L26"
+    },
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+      "label": "list_dir on a non-existent path should raise HTTP 404.",
+      "_origin": "ast",
+      "community": 133,
+      "community_name": "test_local_fs.py",
+      "file_type": "rationale",
+      "norm_label": "list_dir on a non-existent path should raise http 404.",
+      "source_file": "backend/tests/test_local_fs.py",
+      "source_location": "L40"
+    },
+    {
+      "id": "backend_tests_test_local_fs_rationale_53",
+      "label": "open_array on an unsupported extension should raise HTTP 422.",
+      "_origin": "ast",
+      "community": 133,
+      "community_name": "test_local_fs.py",
+      "file_type": "rationale",
+      "norm_label": "open_array on an unsupported extension should raise http 422.",
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+      "source_location": "L53"
+    },
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+      "id": "frontend_src_hooks_edithistory",
+      "label": "editHistory.ts",
+      "_origin": "ast",
+      "community": 134,
+      "community_name": "editHistory.ts",
+      "file_type": "code",
+      "norm_label": "edithistory.ts",
+      "source_file": "frontend/src/hooks/editHistory.ts",
+      "source_location": "L1"
+    },
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+      "id": "frontend_src_hooks_edithistory_redoops",
+      "label": "redoOps",
+      "_origin": "ast",
+      "community": 134,
+      "community_name": "editHistory.ts",
+      "file_type": "code",
+      "norm_label": "redoops",
+      "source_file": "frontend/src/hooks/editHistory.ts",
+      "source_location": "L30"
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+      "id": "frontend_src_hooks_edithistory_undoops",
+      "label": "undoOps",
+      "_origin": "ast",
+      "community": 134,
+      "community_name": "editHistory.ts",
+      "file_type": "code",
+      "norm_label": "undoops",
+      "source_file": "frontend/src/hooks/editHistory.ts",
+      "source_location": "L29"
+    },
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+      "id": "frontend_src_lib_pixelclf",
+      "label": "pixelClf.ts",
+      "_origin": "ast",
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+      "community_name": "pixelClf.ts",
+      "file_type": "code",
+      "norm_label": "pixelclf.ts",
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+      "source_location": "L1"
+    },
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+      "id": "frontend_src_lib_pixelclf_overlay_test",
+      "label": "pixelClf.overlay.test.ts",
+      "_origin": "ast",
+      "community": 135,
+      "community_name": "pixelClf.ts",
+      "file_type": "code",
+      "norm_label": "pixelclf.overlay.test.ts",
+      "source_file": "frontend/src/lib/pixelClf.overlay.test.ts",
+      "source_location": "L1"
+    },
+    {
+      "id": "frontend_src_lib_pixelclf_test",
+      "label": "pixelClf.test.ts",
+      "_origin": "ast",
+      "community": 135,
+      "community_name": "pixelClf.ts",
+      "file_type": "code",
+      "norm_label": "pixelclf.test.ts",
+      "source_file": "frontend/src/lib/pixelClf.test.ts",
+      "source_location": "L1"
+    },
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+      "label": "test_feature_setups.py",
+      "_origin": "ast",
+      "community": 136,
+      "community_name": "test_feature_setups.py",
+      "file_type": "code",
+      "norm_label": "test_feature_setups.py",
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+      "source_location": "L1"
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+      "label": "fixture",
+      "_origin": "ast",
+      "community": 136,
+      "community_name": "test_feature_setups.py",
+      "file_type": "code",
+      "norm_label": "fixture",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "ipred_tests_test_feature_setups_py_monkeypatch",
+      "label": "MonkeyPatch",
+      "_origin": "ast",
+      "community": 136,
+      "community_name": "test_feature_setups.py",
+      "file_type": "code",
+      "norm_label": "monkeypatch",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "ipred_tests_test_feature_setups_rationale_1",
+      "label": "Feature Setup shelf defaults.",
+      "_origin": "ast",
+      "community": 136,
+      "community_name": "test_feature_setups.py",
+      "file_type": "rationale",
+      "norm_label": "feature setup shelf defaults.",
+      "source_file": "ipred/tests/test_feature_setups.py",
+      "source_location": "L1"
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+      "_origin": "ast",
+      "community": 137,
+      "community_name": "test_tiff_stack_source.py",
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+      "source_location": "L1"
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+      "label": "Tests for exposing a TIFF directory as a streamable 3-D Zarr volume. The\u2026",
+      "_origin": "ast",
+      "community": 137,
+      "community_name": "test_tiff_stack_source.py",
+      "file_type": "rationale",
+      "norm_label": "tests for exposing a tiff directory as a streamable 3-d zarr volume. the...",
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+      "source_location": "L1"
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+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_random",
+      "label": "random.ts",
+      "_origin": "ast",
+      "community": 138,
+      "community_name": "Random",
+      "file_type": "code",
+      "norm_label": "random.ts",
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+      "label": "array_source.py",
+      "_origin": "ast",
+      "community": 139,
+      "community_name": "array_source.py",
+      "file_type": "code",
+      "norm_label": "array_source.py",
+      "source_file": "ipred/src/ipred/array_source.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "ipred_src_ipred_array_source_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 139,
+      "community_name": "array_source.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "ipred_src_ipred_array_source_py_ndarray",
+      "label": "ndarray",
+      "_origin": "ast",
+      "community": 139,
+      "community_name": "array_source.py",
+      "file_type": "code",
+      "norm_label": "ndarray",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "ipred_src_ipred_array_source_py_path",
+      "label": "Path",
+      "_origin": "ast",
+      "community": 139,
+      "community_name": "array_source.py",
+      "file_type": "code",
+      "norm_label": "path",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "ipred_src_ipred_array_source_rationale_1",
+      "label": "Minimal local / Tiled slice reader (no annotate-backend imports).",
+      "_origin": "ast",
+      "community": 139,
+      "community_name": "array_source.py",
+      "file_type": "rationale",
+      "norm_label": "minimal local / tiled slice reader (no annotate-backend imports).",
+      "source_file": "ipred/src/ipred/array_source.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "ipred_src_ipred_array_source_rationale_141",
+      "label": "Pick HW or HWC frame from NHW / NHWC / HW / HWC.",
+      "_origin": "ast",
+      "community": 139,
+      "community_name": "array_source.py",
+      "file_type": "rationale",
+      "norm_label": "pick hw or hwc frame from nhw / nhwc / hw / hwc.",
+      "source_file": "ipred/src/ipred/array_source.py",
+      "source_location": "L141"
+    },
+    {
+      "id": "ipred_src_ipred_array_source_rationale_26",
+      "label": "Load one 2D/ HxWxC slice array.",
+      "_origin": "ast",
+      "community": 139,
+      "community_name": "array_source.py",
+      "file_type": "rationale",
+      "norm_label": "load one 2d/ hxwxc slice array.",
+      "source_file": "ipred/src/ipred/array_source.py",
+      "source_location": "L26"
+    },
+    {
+      "id": "ipred_src_ipred_array_source_rationale_66",
+      "label": "Resolve a Tiled container to the single array/stack it represents. Handles two\u2026",
+      "_origin": "ast",
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+      "source_location": "L1"
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+      "community_name": "ipred_routes.py",
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+      "community_name": "ipred_routes.py",
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+      "label": "Open/create an engine session for a project.",
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+      "label": "Proxy manifold residual heatmap PNG.",
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+      "community_name": "ipred_routes.py",
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+      "label": "Cache-aware featurize via ipred.",
+      "_origin": "ast",
+      "community": 4,
+      "community_name": "ipred_routes.py",
+      "file_type": "rationale",
+      "norm_label": "cache-aware featurize via ipred.",
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+      "community_name": "ipred_routes.py",
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+      "norm_label": "proxy a feature-channel png from the ipred.",
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+      "label": "Train via ipred trainer plugin.",
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+      "norm_label": "train via ipred trainer plugin.",
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+      "norm_label": "rethreshold from cached proba via ipred.",
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+      "community_name": "io/index.ts",
+      "file_type": "code",
+      "norm_label": "geometry-types.ts",
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+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_volume_geom",
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+      "_origin": "ast",
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+      "community_name": "io/index.ts",
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+      "norm_label": "volume-geom.ts",
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+      "source_location": "L1"
+    },
+    {
+      "id": "frontend_src_lib_ipredapi",
+      "label": "ipredApi.ts",
+      "_origin": "ast",
+      "community": 49,
+      "community_name": "ipredApi.ts",
+      "file_type": "code",
+      "norm_label": "ipredapi.ts",
+      "source_file": "frontend/src/lib/ipredApi.ts",
+      "source_location": "L1"
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+    {
+      "id": "backend_annotation_thumbnails",
+      "label": "annotation_thumbnails.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "annotation_thumbnails.py",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_annotation_thumbnails_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_annotation_thumbnails_py_ndarray",
+      "label": "ndarray",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "ndarray",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_guide_gen",
+      "label": "guide_gen.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "guide_gen.py",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_guide_gen_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_guide_gen_py_ndarray",
+      "label": "ndarray",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "ndarray",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_source_keys",
+      "label": "source_keys.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "source_keys.py",
+      "source_file": "backend/source_keys.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tests_test_tiled_annotation_sync",
+      "label": "test_tiled_annotation_sync.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "test_tiled_annotation_sync.py",
+      "source_file": "backend/tests/test_tiled_annotation_sync.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_thumbnails",
+      "label": "thumbnails.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "thumbnails.py",
+      "source_file": "backend/thumbnails.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_thumbnails_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_thumbnails_py_ndarray",
+      "label": "ndarray",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "ndarray",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_tiled_annotation_sync",
+      "label": "tiled_annotation_sync.py",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "tiled_annotation_sync.py",
+      "source_file": "backend/tiled_annotation_sync.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tiled_annotation_sync_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "imagedraw",
+      "label": "ImageDraw",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "code",
+      "norm_label": "imagedraw",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_1",
+      "label": "Annotated thumbnail generation for version history previews. Renders the most-\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "annotated thumbnail generation for version history previews. renders the most-...",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_130",
+      "label": "Render a PNG thumbnail of the most-annotated slice with shapes overlaid. Args:\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "render a png thumbnail of the most-annotated slice with shapes overlaid. args:...",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L130"
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+    {
+      "id": "backend_annotation_thumbnails_rationale_214",
+      "label": "Write the annotated thumbnail to a Tiled sibling node (best-effort). Creates\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "write the annotated thumbnail to a tiled sibling node (best-effort). creates...",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L214"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_27",
+      "label": "Return (kind, source, server_uri) from a canonical source key.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "return (kind, source, server_uri) from a canonical source key.",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L27"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_35",
+      "label": "Decode a base64 PNG from the save modal preview, or return None.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "decode a base64 png from the save modal preview, or return none.",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L35"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_49",
+      "label": "Fast nearest-neighbour downsample so huge volumes don't stall thumbnails.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "fast nearest-neighbour downsample so huge volumes don't stall thumbnails.",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L49"
+    },
+    {
+      "id": "backend_annotation_thumbnails_rationale_74",
+      "label": "Draw annotation shapes onto *draw* at thumbnail scale.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "draw annotation shapes onto *draw* at thumbnail scale.",
+      "source_file": "backend/annotation_thumbnails.py",
+      "source_location": "L74"
+    },
+    {
+      "id": "backend_guide_gen_rationale_1",
+      "label": "Generate an annotation guide from an existing annotation. Given a set of\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "generate an annotation guide from an existing annotation. given a set of...",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_guide_gen_rationale_120",
+      "label": "Return a copy of *shape* with all coordinates scaled by *f* (for rasterizing at\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "return a copy of *shape* with all coordinates scaled by *f* (for rasterizing at...",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L120"
+    },
+    {
+      "id": "backend_guide_gen_rationale_148",
+      "label": "Boolean boundary ring of *mask* (mask minus its 4-neighbour erosion), dilated\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "boolean boundary ring of *mask* (mask minus its 4-neighbour erosion), dilated...",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L148"
+    },
+    {
+      "id": "backend_guide_gen_rationale_173",
+      "label": "Crop *rgb* to the mask's bbox (+margin), tint the masked region with *color*,\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "crop *rgb* to the mask's bbox (+margin), tint the masked region with *color*,...",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L173"
+    },
+    {
+      "id": "backend_guide_gen_rationale_216",
+      "label": "Build a guide skeleton (per-class label/color + color-coded example crops).\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "build a guide skeleton (per-class label/color + color-coded example crops)....",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L216"
+    },
+    {
+      "id": "backend_guide_gen_rationale_91",
+      "label": "(x0, y0, x1, y1) in image px, or None.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "(x0, y0, x1, y1) in image px, or none.",
+      "source_file": "backend/guide_gen.py",
+      "source_location": "L91"
+    },
+    {
+      "id": "backend_source_keys_rationale_1",
+      "label": "Canonical source-key parsing (mirrors frontend ``buildSourceKey``).",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "canonical source-key parsing (mirrors frontend ``buildsourcekey``).",
+      "source_file": "backend/source_keys.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_source_keys_rationale_9",
+      "label": "Parse a draft/source key into kind, server URI, and path. Formats:\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "parse a draft/source key into kind, server uri, and path. formats:...",
+      "source_file": "backend/source_keys.py",
+      "source_location": "L9"
+    },
+    {
+      "id": "backend_tests_test_tiled_annotation_sync_rationale_1",
+      "label": "Tests for source key parsing and annotation metadata helpers.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "tests for source key parsing and annotation metadata helpers.",
+      "source_file": "backend/tests/test_tiled_annotation_sync.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_thumbnails_rationale_1",
+      "label": "PNG thumbnail rendering for Tiled array nodes. Handles two common cases: * 3D\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "png thumbnail rendering for tiled array nodes. handles two common cases: * 3d...",
+      "source_file": "backend/thumbnails.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_thumbnails_rationale_32",
+      "label": "Render a PNG thumbnail for *node* (array or container-of-arrays). Returns\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "render a png thumbnail for *node* (array or container-of-arrays). returns...",
+      "source_file": "backend/thumbnails.py",
+      "source_location": "L32"
+    },
+    {
+      "id": "backend_thumbnails_rationale_54",
+      "label": "Return *node* if it's already an array, else its first array child.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "return *node* if it's already an array, else its first array child.",
+      "source_file": "backend/thumbnails.py",
+      "source_location": "L54"
+    },
+    {
+      "id": "backend_thumbnails_rationale_85",
+      "label": "Log-scale, normalise, and colour-map a 2D intensity array to uint8 RGB.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "log-scale, normalise, and colour-map a 2d intensity array to uint8 rgb.",
+      "source_file": "backend/thumbnails.py",
+      "source_location": "L85"
+    },
+    {
+      "id": "backend_tiled_annotation_sync_rationale_1",
+      "label": "Sync annotation session state into Tiled node metadata for Browse discovery.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "sync annotation session state into tiled node metadata for browse discovery.",
+      "source_file": "backend/tiled_annotation_sync.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tiled_annotation_sync_rationale_22",
+      "label": "Build Tiled-safe metadata fields from a draft/export payload.",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "build tiled-safe metadata fields from a draft/export payload.",
+      "source_file": "backend/tiled_annotation_sync.py",
+      "source_location": "L22"
+    },
+    {
+      "id": "backend_tiled_annotation_sync_rationale_37",
+      "label": "Merge annotation summary metadata onto a Tiled node. No-op for local sources.\u2026",
+      "_origin": "ast",
+      "community": 5,
+      "community_name": "annotation_thumbnails.py",
+      "file_type": "rationale",
+      "norm_label": "merge annotation summary metadata onto a tiled node. no-op for local sources....",
+      "source_file": "backend/tiled_annotation_sync.py",
+      "source_location": "L37"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_firstpersoncontrols_onpointermove",
+      "label": ".onPointerMove()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onpointermove()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L905"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_flycontrols_onpointermove",
+      "label": ".onPointerMove()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onpointermove()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L776"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_oncontextmenu",
+      "label": ".onContextMenu()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".oncontextmenu()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L228"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onkeydown",
+      "label": ".onKeyDown()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onkeydown()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L229"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onkeyup",
+      "label": ".onKeyUp()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onkeyup()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L242"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onpointerdown",
+      "label": ".onPointerDown()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onpointerdown()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L157"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onpointermove",
+      "label": ".onPointerMove()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onpointermove()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L173"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onpointerup",
+      "label": ".onPointerUp()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onpointerup()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L191"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onwheel",
+      "label": ".onWheel()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onwheel()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L199"
+    },
+    {
+      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onwindowblur",
+      "label": ".onWindowBlur()",
+      "_origin": "ast",
+      "community": 50,
+      "community_name": "OrbitControls",
+      "file_type": "code",
+      "norm_label": ".onwindowblur()",
+      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts",
+      "source_location": "L245"
+    },
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+      "id": "argumentparser",
+      "label": "ArgumentParser",
+      "_origin": "ast",
+      "community": 52,
+      "community_name": "cli.py",
+      "file_type": "code",
+      "norm_label": "argumentparser",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "ipred_src_ipred_cli",
+      "label": "cli.py",
+      "_origin": "ast",
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+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "explicit save request with optional version metadata. attributes: payload:...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L238"
+    },
+    {
+      "id": "backend_schemas_rationale_254",
+      "label": "Request body for per-region intensity measurement. Attributes: slice_index:\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "request body for per-region intensity measurement. attributes: slice_index:...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L254"
+    },
+    {
+      "id": "backend_schemas_rationale_266",
+      "label": "Request body for the pre-upload duplicate check. Sent as a POST body rather\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "request body for the pre-upload duplicate check. sent as a post body rather...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L266"
+    },
+    {
+      "id": "backend_schemas_rationale_27",
+      "label": "A single continuous stroke made by the brush tool. Attributes: points: Flat\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "a single continuous stroke made by the brush tool. attributes: points: flat...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L27"
+    },
+    {
+      "id": "backend_schemas_rationale_320",
+      "label": "Request body to denoise a whole volume into a new Tiled dataset. The Annotate\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "request body to denoise a whole volume into a new tiled dataset. the annotate...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L320"
+    },
+    {
+      "id": "backend_schemas_rationale_350",
+      "label": "Request body for building a 3-D volume from a stack already in Tiled.\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "request body for building a 3-d volume from a stack already in tiled....",
+      "source_file": "backend/schemas.py",
+      "source_location": "L350"
+    },
+    {
+      "id": "backend_schemas_rationale_371",
+      "label": "One class entry in an annotation guide. Attributes: label: Human-readable class\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "one class entry in an annotation guide. attributes: label: human-readable class...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L371"
+    },
+    {
+      "id": "backend_schemas_rationale_387",
+      "label": "A project lead's annotation guide for a dataset. Attributes: classes: Ordered\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "a project lead's annotation guide for a dataset. attributes: classes: ordered...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L387"
+    },
+    {
+      "id": "backend_schemas_rationale_399",
+      "label": "Shape and dtype metadata for an opened image source. Attributes: n_slices:\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "shape and dtype metadata for an opened image source. attributes: n_slices:...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L399"
+    },
+    {
+      "id": "backend_schemas_rationale_41",
+      "label": "A closed polygon annotation. Attributes: id: Unique shape identifier. classId:\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "a closed polygon annotation. attributes: id: unique shape identifier. classid:...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L41"
+    },
+    {
+      "id": "backend_schemas_rationale_57",
+      "label": "An axis-aligned bounding-box annotation. Attributes: id: Unique shape\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "an axis-aligned bounding-box annotation. attributes: id: unique shape...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L57"
+    },
+    {
+      "id": "backend_schemas_rationale_79",
+      "label": "An ellipse annotation. Attributes: id: Unique shape identifier. classId:\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "an ellipse annotation. attributes: id: unique shape identifier. classid:...",
+      "source_file": "backend/schemas.py",
+      "source_location": "L79"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_1",
+      "label": "Tests for Pydantic schema models.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "tests for pydantic schema models.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_17",
+      "label": "PolygonShape should serialise and deserialise cleanly.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "polygonshape should serialise and deserialise cleanly.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L17"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_30",
+      "label": "BrushShape should store stroke list with correct attributes.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "brushshape should store stroke list with correct attributes.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L30"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_38",
+      "label": "RenderOpts should have sensible defaults without explicit values.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "renderopts should have sensible defaults without explicit values.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L38"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_46",
+      "label": "RectShape should expose geometry fields correctly.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "rectshape should expose geometry fields correctly.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L46"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_53",
+      "label": "EllipseShape should expose centre and radii correctly.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "ellipseshape should expose centre and radii correctly.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L53"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_60",
+      "label": "BrushStroke should accept 'erase' mode.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "brushstroke should accept 'erase' mode.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L60"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_66",
+      "label": "A folder's worth of filenames must fit \u2014 as query params it 431'd.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "a folder's worth of filenames must fit \u2014 as query params it 431'd.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L66"
+    },
+    {
+      "id": "backend_tests_test_schemas_rationale_74",
+      "label": "Omitting names is allowed \u2014 used to only ask for a free container name.",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "omitting names is allowed \u2014 used to only ask for a free container name.",
+      "source_file": "backend/tests/test_schemas.py",
+      "source_location": "L74"
+    },
+    {
+      "id": "backend_tests_test_tiled_mask_sync_rationale_1",
+      "label": "Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization \u2192\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization \u2192...",
+      "source_file": "backend/tests/test_tiled_mask_sync.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tiled_mask_sync_rationale_53",
+      "label": "Rasterize an item's shapes into stacked mask volumes. Returns ``{semantic,\u2026",
+      "_origin": "ast",
+      "community": 66,
+      "community_name": "schemas.py",
+      "file_type": "rationale",
+      "norm_label": "rasterize an item's shapes into stacked mask volumes. returns ``{semantic,...",
+      "source_file": "backend/tiled_mask_sync.py",
+      "source_location": "L53"
+    },
+    {
+      "id": "backend_tests_test_volume_nodes",
+      "label": "test_volume_nodes.py",
+      "_origin": "ast",
+      "community": 67,
+      "community_name": "FakeNode",
+      "file_type": "code",
+      "norm_label": "test_volume_nodes.py",
+      "source_file": "backend/tests/test_volume_nodes.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tests_test_volume_nodes_py_fixture",
+      "label": "fixture",
+      "_origin": "ast",
+      "community": 67,
+      "community_name": "FakeNode",
+      "file_type": "code",
+      "norm_label": "fixture",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_tests_test_volume_nodes_rationale_1",
+      "label": "Tests for locating a dataset's renderable 3-D volume. This is the module that\u2026",
+      "_origin": "ast",
+      "community": 67,
+      "community_name": "FakeNode",
+      "file_type": "rationale",
+      "norm_label": "tests for locating a dataset's renderable 3-d volume. this is the module that...",
+      "source_file": "backend/tests/test_volume_nodes.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tests_test_volume_nodes_rationale_28",
+      "label": "A catalog node: metadata plus children, indexable like a Tiled container.",
+      "_origin": "ast",
+      "community": 67,
+      "community_name": "FakeNode",
+      "file_type": "rationale",
+      "norm_label": "a catalog node: metadata plus children, indexable like a tiled container.",
+      "source_file": "backend/tests/test_volume_nodes.py",
+      "source_location": "L28"
+    },
+    {
+      "id": "backend_tests_test_volume_nodes_rationale_40",
+      "label": "A catalog with one of each shape the resolver has to tell apart.",
+      "_origin": "ast",
+      "community": 67,
+      "community_name": "FakeNode",
+      "file_type": "rationale",
+      "norm_label": "a catalog with one of each shape the resolver has to tell apart.",
+      "source_file": "backend/tests/test_volume_nodes.py",
+      "source_location": "L40"
+    },
+    {
+      "id": "backend_zarr_source",
+      "label": "zarr_source.py",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "code",
+      "norm_label": "zarr_source.py",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_zarr_source_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_zarr_source_py_path",
+      "label": "Path",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "code",
+      "norm_label": "path",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_zarr_source_rationale_1",
+      "label": "Register on-disk Zarr volumes with Tiled, without copying any data. The drag-\u2026",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "register on-disk zarr volumes with tiled, without copying any data. the drag-...",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_zarr_source_rationale_107",
+      "label": "Pull the ``scale`` transform (voxel size per axis) out of a dataset entry.",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "pull the ``scale`` transform (voxel size per axis) out of a dataset entry.",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L107"
+    },
+    {
+      "id": "backend_zarr_source_rationale_117",
+      "label": "Depth-first list of ``(path, array)`` for every 3-D array in *group*.",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "depth-first list of ``(path, array)`` for every 3-d array in *group*.",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L117"
+    },
+    {
+      "id": "backend_zarr_source_rationale_138",
+      "label": "Describe a Zarr store's resolution pyramid without registering anything. Args:\u2026",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "describe a zarr store's resolution pyramid without registering anything. args:...",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L138"
+    },
+    {
+      "id": "backend_zarr_source_rationale_223",
+      "label": "Axis unit declared in the store's ``multiscales`` axes (e.g. micrometer).",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "axis unit declared in the store's ``multiscales`` axes (e.g. micrometer).",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L223"
+    },
+    {
+      "id": "backend_zarr_source_rationale_239",
+      "label": "The node key Tiled will use for *path*. Tiled strips the extension, so\u2026",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "the node key tiled will use for *path*. tiled strips the extension, so...",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L239"
+    },
+    {
+      "id": "backend_zarr_source_rationale_253",
+      "label": "Describe what already occupies a key, for the conflict prompt. ``external``\u2026",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "describe what already occupies a key, for the conflict prompt. ``external``...",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L253"
+    },
+    {
+      "id": "backend_zarr_source_rationale_280",
+      "label": "Report whether registering *raw_path* would collide, without changing anything.\u2026",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "report whether registering *raw_path* would collide, without changing anything....",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L280"
+    },
+    {
+      "id": "backend_zarr_source_rationale_303",
+      "label": "Register a Zarr store into a Tiled container, copying no data. Args:\u2026",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "register a zarr store into a tiled container, copying no data. args:...",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L303"
+    },
+    {
+      "id": "backend_zarr_source_rationale_49",
+      "label": "True if *path* looks like the root of a Zarr store.",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "true if *path* looks like the root of a zarr store.",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L49"
+    },
+    {
+      "id": "backend_zarr_source_rationale_54",
+      "label": "Expand and validate a user-supplied absolute path to a Zarr store. Raises:\u2026",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "expand and validate a user-supplied absolute path to a zarr store. raises:...",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L54"
+    },
+    {
+      "id": "backend_zarr_source_rationale_87",
+      "label": "Read OME-NGFF ``multiscales`` from ``.zattrs``, if present. Returns the\u2026",
+      "_origin": "ast",
+      "community": 68,
+      "community_name": "zarr_source.py",
+      "file_type": "rationale",
+      "norm_label": "read ome-ngff ``multiscales`` from ``.zattrs``, if present. returns the...",
+      "source_file": "backend/zarr_source.py",
+      "source_location": "L87"
+    },
+    {
+      "id": "frontend_package_dependencies",
+      "label": "dependencies",
+      "_origin": "ast",
+      "community": 69,
+      "community_name": "dependencies",
+      "file_type": "code",
+      "norm_label": "dependencies",
+      "source_file": "frontend/package.json",
+      "source_location": "L14"
+    },
+    {
+      "id": "frontend_package_dependencies_blueskyproject_finch",
+      "label": "@blueskyproject/finch",
+      "_origin": "ast",
+      "community": 69,
+      "community_name": "dependencies",
+      "file_type": "code",
+      "norm_label": "@blueskyproject/finch",
+      "source_file": "frontend/package.json",
+      "source_location": "L15"
+    },
+    {
+      "id": "frontend_package_dependencies_clsx",
+      "label": "clsx",
+      "_origin": "ast",
+      "community": 69,
+      "community_name": "dependencies",
+      "file_type": "code",
+      "norm_label": "clsx",
+      "source_file": "frontend/package.json",
+      "source_location": "L19"
+    },
+    {
+      "id": "frontend_package_dependencies_huggingface_transformers",
+      "label": "@huggingface/transformers",
+      "_origin": "ast",
+      "community": 69,
+      "community_name": "dependencies",
+      "file_type": "code",
+      "norm_label": "@huggingface/transformers",
+      "source_file": "frontend/package.json",
+      "source_location": "L16"
+    },
+    {
+      "id": "frontend_package_dependencies_konva",
+      "label": "konva",
+      "_origin": "ast",
+      "community": 69,
+      "community_name": "dependencies",
+      "file_type": "code",
+      "norm_label": "konva",
+      "source_file": "frontend/package.json",
+      "source_location": "L20"
+    },
+    {
+      "id": "frontend_package_dependencies_polygon_clipping",
+      "label": "polygon-clipping",
+      "_origin": "ast",
+      "community": 69,
+      "community_name": "dependencies",
+      "file_type": "code",
+      "norm_label": "polygon-clipping",
+      "source_file": "frontend/package.json",
+      "source_location": "L21"
+    },
+    {
+      "id": "frontend_package_dependencies_react",
+      "label": "react",
+      "_origin": "ast",
+      "community": 69,
+      "community_name": "dependencies",
+      "file_type": "code",
+      "norm_label": "react",
+      "source_file": "frontend/package.json",
+      "source_location": "L22"
+    },
+    {
+      "id": "frontend_package_dependencies_react_dom",
+      "label": "react-dom",
+      "_origin": "ast",
+      "community": 69,
+      "community_name": "dependencies",
+      "file_type": "code",
+      "norm_label": "react-dom",
+      "source_file": "frontend/package.json",
+      "source_location": "L23"
+    },
+    {
+      "id": "frontend_package_dependencies_react_konva",
+      "label": "react-konva",
+      "_origin": "ast",
+      "community": 69,
+      "community_name": "dependencies",
+      "file_type": "code",
+      "norm_label": "react-konva",
+      "source_file": "frontend/package.json",
+      "source_location": "L24"
+    },
+    {
+      "id": "frontend_package_dependencies_react_router",
+      "label": "react-router",
+      "_origin": "ast",
+      "community": 69,
+      "community_name": "dependencies",
+      "file_type": "code",
+      "norm_label": "react-router",
+      "source_file": "frontend/package.json",
+      "source_location": "L25"
+    },
+    {
+      "id": "frontend_package_dependencies_tailwind_merge",
+      "label": "tailwind-merge",
+      "_origin": "ast",
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+      "label": "cleanup()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "source_file": "start_all.sh",
+      "source_location": "L341"
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+      "id": "start_all_cleanup_managed_processes",
+      "label": "cleanup_managed_processes()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "cleanup_managed_processes()",
+      "source_file": "start_all.sh",
+      "source_location": "L126"
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+      "label": "cleanup_pid_file()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "cleanup_pid_file()",
+      "source_file": "start_all.sh",
+      "source_location": "L89"
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+      "label": "ensure_backend_env()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "ensure_backend_env()",
+      "source_file": "start_all.sh",
+      "source_location": "L267"
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+      "label": "ensure_docs_env()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "ensure_docs_env()",
+      "source_file": "start_all.sh",
+      "source_location": "L316"
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+      "label": "ensure_frontend_runtime()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "ensure_ipred_env()",
+      "source_file": "start_all.sh",
+      "source_location": "L290"
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+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "ensure_sam_model()",
+      "source_file": "start_all.sh",
+      "source_location": "L459"
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+      "label": "ensure_uv()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "ensure_uv()",
+      "source_file": "start_all.sh",
+      "source_location": "L241"
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+      "label": "find_listener_processes()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "find_listener_processes()",
+      "source_file": "start_all.sh",
+      "source_location": "L155"
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+      "label": "get_process_command()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "get_process_command()",
+      "source_file": "start_all.sh",
+      "source_location": "L135"
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+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "label": "IPRED_URL",
+      "_origin": "ast",
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+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "code"
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+      "norm_label": "ipred_url",
+      "source_file": "start_all.sh",
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+      "label": "pick_free_port()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "source_file": "start_all.sh",
+      "source_location": "L76"
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+      "label": "port_is_listening()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "port_is_listening()",
+      "source_file": "start_all.sh",
+      "source_location": "L51"
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+    {
+      "id": "start_all_reclaim_orphaned_repo_ports",
+      "label": "reclaim_orphaned_repo_ports()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "reclaim_orphaned_repo_ports()",
+      "source_file": "start_all.sh",
+      "source_location": "L222"
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+    {
+      "id": "start_all_require_free_port",
+      "label": "require_free_port()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
+      },
+      "norm_label": "require_free_port()",
+      "source_file": "start_all.sh",
+      "source_location": "L64"
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+    {
+      "id": "start_all_sh__entry",
+      "label": "start_all.sh script",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_entrypoint"
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+      "norm_label": "start_all.sh script",
+      "source_file": "start_all.sh",
+      "source_location": "L1"
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+    {
+      "id": "start_all_stop_managed_process",
+      "label": "stop_managed_process()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "stop_managed_process()",
+      "source_file": "start_all.sh",
+      "source_location": "L94"
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+      "label": "stop_repo_listener_on_port()",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "stop_repo_listener_on_port()",
+      "source_file": "start_all.sh",
+      "source_location": "L173"
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+      "id": "start_all_tiled_cmd",
+      "label": "tiled_cmd()",
+      "_origin": "ast",
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+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "bash_function"
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+      "norm_label": "tiled_cmd()",
+      "source_file": "start_all.sh",
+      "source_location": "L329"
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+    {
+      "id": "start_all_tiled_uri",
+      "label": "TILED_URI",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "code"
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+      "norm_label": "tiled_uri",
+      "source_file": "start_all.sh",
+      "source_location": "L421"
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+    {
+      "id": "start_all_vite_docs_url",
+      "label": "VITE_DOCS_URL",
+      "_origin": "ast",
+      "community": 71,
+      "community_name": "start_all.sh",
+      "file_type": "code",
+      "metadata": {
+        "language": "bash",
+        "kind": "code"
+      },
+      "norm_label": "vite_docs_url",
+      "source_file": "start_all.sh",
+      "source_location": "L402"
+    },
+    {
+      "id": "backend_drafts",
+      "label": "drafts.py",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "code",
+      "norm_label": "drafts.py",
+      "source_file": "backend/drafts.py",
+      "source_location": "L1"
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+    {
+      "id": "backend_drafts_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_drafts_py_path",
+      "label": "Path",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "code",
+      "norm_label": "path",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_drafts_rationale_1",
+      "label": "Session-draft persistence and version history for the Segmentation Annotation\u2026",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "session-draft persistence and version history for the segmentation annotation...",
+      "source_file": "backend/drafts.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_drafts_rationale_139",
+      "label": "Create a new immutable version snapshot for *source_key*. Also updates the\u2026",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "create a new immutable version snapshot for *source_key*. also updates the...",
+      "source_file": "backend/drafts.py",
+      "source_location": "L139"
+    },
+    {
+      "id": "backend_drafts_rationale_189",
+      "label": "List version metadata (without payload) sorted oldest-first. Args: source_key:\u2026",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "list version metadata (without payload) sorted oldest-first. args: source_key:...",
+      "source_file": "backend/drafts.py",
+      "source_location": "L189"
+    },
+    {
+      "id": "backend_drafts_rationale_225",
+      "label": "Persist an annotated thumbnail PNG for *version*. Stored alongside the version\u2026",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "persist an annotated thumbnail png for *version*. stored alongside the version...",
+      "source_file": "backend/drafts.py",
+      "source_location": "L225"
+    },
+    {
+      "id": "backend_drafts_rationale_243",
+      "label": "Return the thumbnail PNG bytes for *version*, or ``None``. Args: source_key:\u2026",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "return the thumbnail png bytes for *version*, or ``none``. args: source_key:...",
+      "source_file": "backend/drafts.py",
+      "source_location": "L243"
+    },
+    {
+      "id": "backend_drafts_rationale_259",
+      "label": "Return the full document (including payload) for a specific version. Args:\u2026",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "return the full document (including payload) for a specific version. args:...",
+      "source_file": "backend/drafts.py",
+      "source_location": "L259"
+    },
+    {
+      "id": "backend_drafts_rationale_33",
+      "label": "Return the JSON file path for *source_key*. Args: source_key: Arbitrary string\u2026",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "return the json file path for *source_key*. args: source_key: arbitrary string...",
+      "source_file": "backend/drafts.py",
+      "source_location": "L33"
+    },
+    {
+      "id": "backend_drafts_rationale_46",
+      "label": "Return the versions directory path for *source_key*.",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "return the versions directory path for *source_key*.",
+      "source_file": "backend/drafts.py",
+      "source_location": "L46"
+    },
+    {
+      "id": "backend_drafts_rationale_52",
+      "label": "Persist a session draft for *source_key* (crash-recovery autosave). Does NOT\u2026",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "persist a session draft for *source_key* (crash-recovery autosave). does not...",
+      "source_file": "backend/drafts.py",
+      "source_location": "L52"
+    },
+    {
+      "id": "backend_drafts_rationale_78",
+      "label": "Return the saved draft for *source_key*, or ``None``. Args: source_key:\u2026",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "return the saved draft for *source_key*, or ``none``. args: source_key:...",
+      "source_file": "backend/drafts.py",
+      "source_location": "L78"
+    },
+    {
+      "id": "backend_drafts_rationale_97",
+      "label": "List all saved drafts with summary metadata. Returns: List of dicts with\u2026",
+      "_origin": "ast",
+      "community": 72,
+      "community_name": "drafts.py",
+      "file_type": "rationale",
+      "norm_label": "list all saved drafts with summary metadata. returns: list of dicts with...",
+      "source_file": "backend/drafts.py",
+      "source_location": "L97"
+    },
+    {
+      "id": "backend_tiled_clients",
+      "label": "tiled_clients.py",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "code",
+      "norm_label": "tiled_clients.py",
+      "source_file": "backend/tiled_clients.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tiled_clients_py_any",
+      "label": "Any",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "code",
+      "norm_label": "any",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_tiled_config",
+      "label": "tiled_config.py",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "code",
+      "norm_label": "tiled_config.py",
+      "source_file": "backend/tiled_config.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tiled_config_py_path",
+      "label": "Path",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "code",
+      "norm_label": "path",
+      "source_file": "",
+      "source_location": ""
+    },
+    {
+      "id": "backend_tiled_clients_rationale_1",
+      "label": "Tiled client factory + browse-container discovery. Clients are cached per\u2026",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "rationale",
+      "norm_label": "tiled client factory + browse-container discovery. clients are cached per...",
+      "source_file": "backend/tiled_clients.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tiled_clients_rationale_65",
+      "label": "Return ``(container_node, path_prefix)`` for the Metadata Browser root. Checks\u2026",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "rationale",
+      "norm_label": "return ``(container_node, path_prefix)`` for the metadata browser root. checks...",
+      "source_file": "backend/tiled_clients.py",
+      "source_location": "L65"
+    },
+    {
+      "id": "backend_tiled_clients_rationale_95",
+      "label": "Return ``(container_node, path_prefix)`` for a specific browse target. When\u2026",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "rationale",
+      "norm_label": "return ``(container_node, path_prefix)`` for a specific browse target. when...",
+      "source_file": "backend/tiled_clients.py",
+      "source_location": "L95"
+    },
+    {
+      "id": "backend_tiled_config_rationale_1",
+      "label": "Tiled server configuration loaded from environment variables. Reads\u2026",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "rationale",
+      "norm_label": "tiled server configuration loaded from environment variables. reads...",
+      "source_file": "backend/tiled_config.py",
+      "source_location": "L1"
+    },
+    {
+      "id": "backend_tiled_config_rationale_101",
+      "label": "Return mapping of ``{name: {\"uri\": str, \"api_key\": str | None}}``. Discovery\u2026",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "rationale",
+      "norm_label": "return mapping of ``{name: {\"uri\": str, \"api_key\": str | none}}``. discovery...",
+      "source_file": "backend/tiled_config.py",
+      "source_location": "L101"
+    },
+    {
+      "id": "backend_tiled_config_rationale_132",
+      "label": "Return the default Tiled base URI.",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "rationale",
+      "norm_label": "return the default tiled base uri.",
+      "source_file": "backend/tiled_config.py",
+      "source_location": "L132"
+    },
+    {
+      "id": "backend_tiled_config_rationale_137",
+      "label": "Return the default API key (local override takes precedence).",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "rationale",
+      "norm_label": "return the default api key (local override takes precedence).",
+      "source_file": "backend/tiled_config.py",
+      "source_location": "L137"
+    },
+    {
+      "id": "backend_tiled_config_rationale_33",
+      "label": "Delete blank ``TILED_API_KEY`` / ``TILED_LOCAL_API_KEY`` from the process env.\u2026",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "rationale",
+      "norm_label": "delete blank ``tiled_api_key`` / ``tiled_local_api_key`` from the process env....",
+      "source_file": "backend/tiled_config.py",
+      "source_location": "L33"
+    },
+    {
+      "id": "backend_tiled_config_rationale_55",
+      "label": "Return ``authentication.single_user_api_key`` from ``tiled/config.yml``, or\u2026",
+      "_origin": "ast",
+      "community": 73,
+      "community_name": "tiled_clients.py",
+      "file_type": "rationale",
+      "norm_label": "return ``authentication.single_user_api_key`` from ``tiled/config.yml``, or...",
+      "source_file": "backend/tiled_config.py",
+      "source_location": "L55"
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\ No newline at end of file
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+  "frontend/src/assets/alsLogo.png": {
+    "mtime": 1784234153.1897612,
+    "seen": 1787786559.290293,
+    "ast_hash": "2f377201a9c36912806d89d58dc3426a",
+    "semantic_hash": ""
+  },
+  "frontend/vendor/view_tomography_recon_app/public/images/als_logo_wheel.png": {
+    "mtime": 1787697136.421411,
+    "seen": 1787786559.290293,
+    "ast_hash": "9daebd22309fde01dbab662c9ea1af86",
+    "semantic_hash": ""
+  },
+  "frontend/vendor/view_tomography_recon_app/public/images/favicon.png": {
+    "mtime": 1787697136.421558,
+    "seen": 1787786559.290294,
+    "ast_hash": "54e831703e88688e4a28a3e19b6911bf",
+    "semantic_hash": ""
+  },
+  "frontend/vendor/view_tomography_recon_app/src/assets/react.svg": {
+    "mtime": 1787697136.4248815,
+    "seen": 1787786559.290294,
+    "ast_hash": "f0402b67b6ce880f65666bb49e841696",
+    "semantic_hash": ""
+  },
+  "ipred/tests/test_array_source.py": {
+    "mtime": 1787788838.1938908,
+    "seen": 1787788970.829031,
+    "ast_hash": "2235c099dc2346b7a5a910325a9c75b0",
+    "semantic_hash": ""
+  }
+}
\ No newline at end of file
diff --git a/ipred/.gitignore b/ipred/.gitignore
new file mode 100644
index 0000000..b900aeb
--- /dev/null
+++ b/ipred/.gitignore
@@ -0,0 +1,2 @@
+models/*.pth
+models/*.onnx
diff --git a/ipred/README.md b/ipred/README.md
new file mode 100644
index 0000000..fe0b22d
--- /dev/null
+++ b/ipred/README.md
@@ -0,0 +1,436 @@
+# ipred — architecture and operations guide
+
+`ipred` is the **iterative prediction** compute service for SAM3 Annotation Studio. It owns feature compositions, preprocessing / feature banks, CatBoost training, Mondrian conformal prediction, and Suggest Labels (manifold). The Vite UI never talks to it directly: the Annotate FastAPI backend (`:8002`) proxies all GUI calls.
+
+This document describes how the service is laid out, where compute runs, how data reaches it, and how compositions / models are discovered.
+
+---
+
+## 1. Big picture
+
+```text
+┌──────────────┐   HTTP    ┌────────────────────┐   HTTP    ┌─────────────────┐
+│  Vite UI     │ ───────▶  │ Annotate backend   │ ───────▶  │ ipred FastAPI   │
+│  :5173       │ /api/ipred│ 127.0.0.1:8002     │ IPRED_URL │ 127.0.0.1:8003  │
+└──────────────┘           └────────────────────┘           └────────┬────────┘
+                                                                     │
+                    ┌────────────────────────────────────────────────┤
+                    │                                                │
+                    ▼                                                ▼
+         LOCAL_DATA_ROOT/ipred/                          Data plane:
+         catalog.db, projects/*/                         • local files under LOCAL_DATA_ROOT
+                                                         • Tiled over HTTP (:8010)
+                                                         • optional uploaded array blobs
+```
+
+| Role | Process | Bind | Responsibility |
+|------|---------|------|----------------|
+| UI | Vite | `127.0.0.1:5173` | Composition editor, Draw / Train, Layers |
+| Annotate | FastAPI | `127.0.0.1:8002` | Tiled/browse/auth; **proxies** `/api/ipred/*` |
+| **ipred** | FastAPI (`ipred.api:app`) | `127.0.0.1:8003` | Features, train, infer, manifold |
+| Tiled | catalog + API | `127.0.0.1:8010` | Array storage / browse |
+
+All local binds stay on **loopback** (`127.0.0.1`), never `0.0.0.0`.
+
+---
+
+## 2. Where compute lives
+
+### Process
+
+- Package: `ipred/` (editable install into the shared repo `.venv`).
+- Entry module: `ipred.api:app` in `ipred/src/ipred/api.py`.
+- Launch (via `start_all.sh`):
+
+  ```bash
+  cd ipred
+  PYTHONPATH=src uvicorn ipred.api:app --host 127.0.0.1 --port "${IPRED_PORT:-8003}"
+  ```
+
+- The `ipred` console script (`ipred.cli:main`) is an **offline CLI** against the same SQLite/FS layout; it does **not** start uvicorn.
+
+### Engine filesystem root
+
+Everything durable for a running engine lives under:
+
+```text
+$LOCAL_DATA_ROOT/ipred/          # default LOCAL_DATA_ROOT=~/data
+  catalog.db                     # projects, sessions, banks, models, runs
+  projects/<project_id>/
+    features/<feature_id>/       # FeatureBank blobs
+    models/<model_id>/           # CatBoost + conformal calibration
+    runs/<run_id>/               # proba + conformal maps
+    arrays/<array_ref>/          # optional uploaded tensors
+```
+
+Implemented in `ipred/src/ipred/paths.py`:
+
+| Helper | Path |
+|--------|------|
+| `local_data_root()` | `$LOCAL_DATA_ROOT` (default `~/data`) |
+| `engine_root()` | `$LOCAL_DATA_ROOT/ipred` |
+| `catalog_db_path()` | `…/ipred/catalog.db` |
+| `project_blob_dir(id)` | `…/ipred/projects/<id>/` |
+| `feature_models_root()` | `$LOCAL_DATA_ROOT/.feature_models` (compositions + legacy setups) |
+
+**Compute affinity:** all CPU/GPU work for encode / CatBoost / PCA / manifold runs **inside the ipred process**. The Annotate backend only forwards HTTP.
+
+### Startup order (`start_all.sh`)
+
+1. Shared `.venv` (+ `uv pip install -e ipred`)
+2. Load `backend/.env`
+3. **Tiled** → wait ready  
+4. **ipred** → wait `GET http://127.0.0.1:8003/health`  
+5. **Annotate backend** (receives `IPRED_URL`) → wait `/health`  
+6. **Frontend**
+
+On API lifespan, ipred opens the catalog and seeds default feature setups + compositions.
+
+---
+
+## 3. How the UI reaches compute (proxy boundary)
+
+### Rule
+
+The browser **never** calls `:8003`. It calls Annotate:
+
+```text
+frontend  →  ${API_BASE}/api/ipred/...  →  backend/ipred_client.py  →  ${IPRED_URL}/...
+```
+
+- Client library: `frontend/src/lib/ipredApi.ts`
+- Server client: `backend/ipred_client.py` (httpx; **no** Python import of the `ipred` package)
+- Env: `IPRED_URL` (default `http://127.0.0.1:8003`; legacy alias `CLF_ENGINE_URL`)
+
+If ipred is down, Annotate returns **503** (`ipred unreachable at …`).
+
+### Representative route mirror
+
+| UI / Annotate | ipred |
+|---------------|-------|
+| `GET /api/ipred/health` | `GET /health` |
+| `POST /api/ipred/sessions` | `POST /sessions` |
+| `GET /api/ipred/modules` | `GET /modules` |
+| `GET/POST /api/ipred/compositions` | `/compositions` |
+| `POST /api/ipred/preprocess` | `POST /preprocess` |
+| `POST /api/ipred/train` / `infer` / `rethreshold` | same |
+| `GET /api/ipred/runs/{id}/proba/{i}.png` | same |
+| `POST /api/ipred/runs/{id}/threshold-class` | same |
+| `POST /api/ipred/manifold/sample` | same |
+
+---
+
+## 4. Sessions, projects, and identity
+
+A **project** is a content-addressed identity of *which array source* you are working on. A **session** is a UUID workspace pointer into that project (current feature / model / run ids).
+
+`POST /sessions` body:
+
+```json
+{
+  "kind": "local" | "tiled",
+  "source": "<path or tiled container path>",
+  "server_uri": "<optional tiled URI>",
+  "root": "<optional local root override>"
+}
+```
+
+- `project_id` = first 16 hex of SHA1 over `{kind, source, server_uri, root}`
+- `session_id` = fresh `uuid4` each open (many sessions can share one project)
+
+Stored in SQLite `catalog.db`. Blob trees hang off `projects/<project_id>/`.
+
+Typical open path: Browse / Connect → `useOpenInAnnotate` → `openIpredSession` → `connectionStore.ipredSessionId` + `ipredProjectId`.
+
+---
+
+## 5. Data plane — how pixels reach the engine
+
+Three mutually exclusive ways to get a 2‑D slice into preprocess:
+
+### A. Local files (default for local Browse)
+
+- Read: `array_source.read_slice(kind="local", …)`
+- Path: `(root or LOCAL_DATA_ROOT) / source`, with a containment check
+- Formats: TIFF (tifffile) or PIL-readable; then z/slice index
+
+**Requirement:** the ipred host must see that filesystem (same machine, NFS, etc.).
+
+### B. Tiled over HTTP
+
+- Read: `tiled.client.from_uri(server_uri or TILED_URI)` + optional `TILED_API_KEY`
+- Walk `source` path; pull the slice array over the network
+
+**Requirement:** network reachability to Tiled. Compute does **not** need the Tiled data directory on disk.
+
+### C. Uploaded array blobs (compute ≠ data host)
+
+For when Annotate and ipred do not share `LOCAL_DATA_ROOT`:
+
+1. `POST /sessions/{id}/arrays` with base64 float32 payload → content-addressed  
+   `$LOCAL_DATA_ROOT/ipred/projects/<pid>/arrays/<array_ref>/{array.npy, meta.json}`
+2. `POST /preprocess` with `array_ref` loads that blob
+
+When `array_ref` is set, **preprocess cache hits are skipped** (identity is upload content + composition, not just slice index on a stable URI).
+
+### Environment checklist
+
+| Variable | Purpose |
+|----------|---------|
+| `LOCAL_DATA_ROOT` | Engine + local file root |
+| `TILED_URI` | Default tiled server (`http://127.0.0.1:8010`) |
+| `TILED_API_KEY` | Optional tiled auth **only on server side** |
+| `IPRED_URL` | Annotate → ipred base URL |
+
+---
+
+## 6. Discovering modules and compositions
+
+### Module catalog (runtime capability discovery)
+
+`GET /modules` → `ipred.modules.list_module_catalog()`.
+
+Registered producers (`ipred/src/ipred/modules/`):
+
+| Module id | Typical runtime | Output |
+|-----------|-----------------|--------|
+| `skimage_multiscale` | numpy / skimage | Multi-channel float stack |
+| `clahe` | numpy | Optional CLAHE grayscale channel |
+| `slimsam` | **onnx** | Dense embedding |
+| `tomojepa` | **onnx** if matching `.onnx` present, else **torch** | Dense embedding |
+| `pca` | numpy / sklearn | Embedding → PCA channels |
+
+Each entry reports `id`, `params_schema`, `runtime`, `ready`, and whether it accepts `input_from` / produces channels vs embeddings.
+
+UI: left column of **CompositionPanel** on the Ipred page.
+
+### Composition documents (feature graphs)
+
+A composition is an ordered graph of module instances. The feature bank is the **channel-wise concatenation** of nodes listed in `outputs`.
+
+Stored under:
+
+```text
+$LOCAL_DATA_ROOT/.feature_models/_compositions/<id>/meta.json
+```
+
+Schema (v1):
+
+```json
+{
+  "id": "comp-skimage-mark11",
+  "kind": "composition",
+  "name": "…",
+  "nodes": [
+    {"id": "n1", "module": "skimage_multiscale", "params": {…}},
+    {"id": "n2", "module": "clahe", "params": {…}},
+    {"id": "n3", "module": "tomojepa", "params": {"weights_id": "mark11", "input_size": 512}, "input_from": "n2"},
+    {"id": "n4", "module": "pca", "params": {"dims": 64}, "input_from": "n3"}
+  ],
+  "outputs": ["n1", "n4"]
+}
+```
+
+- `input_from` wires grayscale (or prior emb) into the next module — this is how “CLAHE → Mark11” is made explicit.
+- `content_hash` = short SHA1 of `{kind, nodes, outputs}` — part of the preprocess cache key.
+- API: `GET/POST /compositions`, `GET /compositions/{id}`, `POST /compositions/preview` (concat channel labels without running encode).
+
+**Builtins** (auto-seeded):  
+`comp-skimage`, `comp-skimage-slimsam`, `comp-slimsam-clahe`, `comp-skimage-mark25`, `comp-mark25-clahe`, `comp-skimage-mark11`, `comp-mark11-clahe`.
+
+Default preference in the UI (`connectionStore`):
+
+- `preferredCompositionId = "comp-skimage-slimsam"`
+- also mirrored to legacy `preferredFeatureSetupId` for one release
+
+### Legacy Feature Setups (migration)
+
+Older “procedure × encoder” combos still exist as disk shelves under `$LOCAL_DATA_ROOT/.feature_models/<setup_id>/` (`feature_setups.py`).  
+`preprocess` accepts either `composition_id` or `feature_setup_id`; legacy ids map via `_LEGACY_SETUP_MAP` (e.g. `default-skimage-slimsam` → `comp-skimage-slimsam`).
+
+Prefer compositions for new work; setups are a compatibility layer.
+
+---
+
+## 7. Preprocess → FeatureBank
+
+`POST /preprocess`:
+
+```json
+{
+  "session_id": "…",
+  "composition_id": "comp-skimage-slimsam",
+  "slice_index": 0,
+  "array_ref": null
+}
+```
+
+Pipeline:
+
+1. Resolve composition (or legacy setup → composition).
+2. Load slice (local / tiled / array_ref).
+3. Cache lookup on `(project_id, setup_id, content_hash, slice_index)` when `array_ref` is null.
+4. Else `compose_run.run_composition(doc)` → concat outputs.
+5. Write FeatureBank under `projects/<pid>/features/<feature_id>/`:
+
+| Artifact | Meaning |
+|----------|---------|
+| `float_stack.npy` | Training features (float16 HxWxC) |
+| `uint8_stack.npy` | Display-oriented stack |
+| `labels.json` | Channel names (concat order) |
+| `channels/NNNN.png` | Browseable per-channel previews |
+| `sam_emb.npy` + `sam_meta.json` | Dense emb when an encoder ran (unless fully baked via PCA) |
+
+UI hook: `useFeatureChannels` (Preprocess stage) calls proxy preprocess and exposes channel navigation.
+
+---
+
+## 8. Train, predict, probability maps
+
+### Trainer discovery
+
+`GET /trainers` → plugin list. Today only **`catboost`** (`ipred.trainers.catboost_trainer.CatBoostTrainer`).  
+UI preference: `connectionStore.preferredTrainerId` (+ depth / trees / LR).
+
+### Train (`POST /train`)
+
+- Rasterize sparse Draw shapes → label map.
+- Stratified train / calibration split.
+- Fit CatBoost → write:
+
+  ```text
+  projects/<pid>/models/<model_id>/
+    model.cbm
+    meta.json
+    cal_scores.json
+    (optional sam_pca.npz)
+  ```
+
+- Register row in catalog `models`; set session `current_model_id`.
+
+### Infer (`POST /infer`)
+
+- Full-image `predict_proba` → `proba.npy`.
+- Mondrian split-conformal (`conformal.py`) using calibration scores + α → membership / commit / status.
+- Run directory:
+
+  ```text
+  projects/<pid>/runs/<run_id>/
+    proba.npy
+    commit.npy / status.npy / membership.npy
+    commit.png / status.png
+    meta.json
+  ```
+
+Status codes: abstain `0`, singleton `1`, multi `2`.
+
+### Probability overlays
+
+- `GET /runs/{run_id}/proba/{class_index}.png` — grayscale softmax channel.
+- Frontend colorizes with **viridis**, clipping below the per-class threshold (transparent).
+- `POST /runs/{run_id}/threshold-class` `{class_id, threshold}` → dense label map for mask-set cache.
+
+Layers panel keeps Image / Features / Probability / Predictions / Annotations independently toggleable.
+
+### Two “model” concepts (do not confuse)
+
+| Store | Path | Owner | Purpose |
+|-------|------|-------|---------|
+| **ipred project models** | `$LOCAL_DATA_ROOT/ipred/projects/<pid>/models/` | ipred | Session train artifacts |
+| **Annotate clf shelf** | `$LOCAL_DATA_ROOT/.clf_models/` | Annotate (`backend/clf_shelf.py`) | Named reusable classifiers from Connect/Browse scaffold UI |
+
+The shelf is separately flagged `# REMOVE THIS AND USE YOUR OWN STUFF` in the UI.
+
+---
+
+## 9. Encoder backends (ONNX vs torch)
+
+| Encoder | Preferred | Fallback / notes |
+|---------|-----------|------------------|
+| **SlimSAM** | ONNX (`FEATURE_ENCODER_ONNX` or repo SlimSAM `vision_encoder.onnx`) | No torch path |
+| **TomoJEPA Mark25/11** | ONNX if `TOMOJEPA[_11]_ONNX` or `ipred/models/tomojepa{25,11}.onnx` **and** spatial size matches requested `input_size` | Else torch `.pth` (`TOMOJEPA[_11]_WEIGHTS` or `ipred/models/*.pth`; needs `ipred[torch]`) |
+
+Weights / ONNX are **gitignored**. Export:
+
+```bash
+python -m ipred.scripts.export_tomojepa_onnx --input-size 512
+```
+
+`GET /modules` reports `runtime` and `ready` so the Composition UI can grey out broken modules.
+
+---
+
+## 10. Manifold / Suggest Labels
+
+Lightweight placement suggestions on an existing FeatureBank:
+
+1. `POST /manifold/sample` — PCA-whitened variance windows + exclusion; optional ROI from selected shapes.
+2. `GET /manifold/{sample_id}/heatmap.png` — residual interestingness.
+
+Implementation: `manifold.py` + `manifold_jobs.py`; results TTL-cached in process. Wired from Preprocess via `ManifoldSuggestPanel` / `useFeatureManifold` (still through `/api/ipred/…`).
+
+---
+
+## 11. Frontend map
+
+| Surface | Path | Talks to |
+|---------|------|----------|
+| Ipred hub page | `frontend/src/app/pages/IpredPage.tsx` | Composition + trainer prefs |
+| Composition window | `frontend/src/components/CompositionPanel/` | `/modules`, `/compositions` |
+| Prefer composition | `connectionStore.preferredCompositionId` | Used by preprocess / train hooks |
+| Preprocess / Draw / Train | `AnnotateWorkspace` stages | features, classifier, layers |
+| Layers | `LayersPanel` + `layerVisibilityStore` | Canvas only (no ipred) |
+| API wrapper | `frontend/src/lib/ipredApi.ts` | Annotate proxy only |
+
+---
+
+## 12. Package map (`ipred/src/ipred/`)
+
+| Module | Responsibility |
+|--------|----------------|
+| `api.py` | FastAPI routes |
+| `cli.py` | Offline JSON CLI |
+| `catalog.py` | SQLite schema + CRUD |
+| `paths.py` | Roots under `LOCAL_DATA_ROOT` |
+| `array_source.py` | local / tiled slice load |
+| `array_blobs.py` | uploaded content-addressed arrays |
+| `compositions.py` | Composition docs + migration |
+| `compose_run.py` | Execute module graph |
+| `modules/*` | Feature module registry |
+| `preprocess.py` | Cache + FeatureBank write |
+| `feature_setups.py` | Legacy setup shelf |
+| `features.py` | Skimage / PNG helpers |
+| `sam_embed.py` / `tomojepa_*.py` | Encoders |
+| `train_infer.py` | Train / infer / rethreshold / proba |
+| `trainers/*` | Trainer plugins |
+| `conformal.py` | Mondrian conformal maps |
+| `labels.py` | Shapes → label raster |
+| `manifold*.py` | Suggest Labels |
+| `cache.py` | In-memory TTL (manifold) |
+
+Tests live in `ipred/tests/` (pytest). Optional deps: base wheel includes **onnxruntime**; torch/timm via `ipred[torch]`.
+
+---
+
+## 13. Mental model (one paragraph)
+
+**ipred is a loopback compute worker** whose durable state sits under `$LOCAL_DATA_ROOT/ipred`. The UI discovers **modules** (`GET /modules`) and builds **compositions** (graphs). Preprocess runs that graph against a session’s data identity (local path, Tiled URI, or uploaded blob) and caches a **FeatureBank**. Train writes a **project model**; infer writes a **run** with softmax + conformal maps. Annotate is only a security/data façade: it never exposes Tiled keys or `:8003` to the browser, and optional array upload lets compute run without sharing the data filesystem.
+
+---
+
+## 14. Quick ops
+
+```bash
+# health
+curl -s http://127.0.0.1:8003/health
+
+# modules
+curl -s http://127.0.0.1:8003/modules | python -m json.tool
+
+# full stack
+./start_all.sh
+# ipred:    http://127.0.0.1:8003
+# annotate: http://127.0.0.1:8002  (proxies /api/ipred)
+```
+
+Set secrets and URLs in `backend/.env` (see `backend/.env.example`): `LOCAL_DATA_ROOT`, `IPRED_URL`, `TILED_*`, `TOMOJEPA_*`, `TOMOJEPA_ONNX`, etc.
diff --git a/ipred/models/README.md b/ipred/models/README.md
new file mode 100644
index 0000000..8b81c25
--- /dev/null
+++ b/ipred/models/README.md
@@ -0,0 +1,35 @@
+# ipred/models
+
+Local model weights for iPred's feature modules. Gitignored — nothing here ships
+with the repo.
+
+## SlimSAM (ONNX)
+
+Auto-vendored by the Magic tool's fetch script — no separate step needed here.
+`slimsam_mod.py` resolves it from `frontend/public/models/slimsam-77-uniform/onnx/vision_encoder.onnx`:
+
+```
+node frontend/scripts/fetch-sam-model.mjs
+```
+
+## TomoJEPA (Mark25 / Mark11)
+
+**Not fetched automatically — no public source exists.** These are private
+trained checkpoints (~365 MB each), not published on HuggingFace or anywhere
+else; `github.com/phzwart/tomojepa` is the training toolkit only and ships no
+checkpoint files.
+
+To enable TomoJEPA-based compositions, place the checkpoint(s) here:
+
+```
+ipred/models/tomojepa25.pth   # Mark25
+ipred/models/tomojepa11.pth   # Mark11
+```
+
+or point at them via `TOMOJEPA_WEIGHTS` / `TOMOJEPA11_WEIGHTS` env vars.
+ONNX exports (`tomojepa25.onnx` / `tomojepa11.onnx`, or `TOMOJEPA_ONNX` /
+`TOMOJEPA11_ONNX`) are preferred when present — see
+`python -m ipred.scripts.export_tomojepa_onnx`.
+
+Until a checkpoint is present, `GET /modules` reports `tomojepa.ready == false`
+and the frontend greys out compositions that depend on it.
diff --git a/ipred/pyproject.toml b/ipred/pyproject.toml
new file mode 100644
index 0000000..f452ed6
--- /dev/null
+++ b/ipred/pyproject.toml
@@ -0,0 +1,38 @@
+[build-system]
+requires = ["setuptools>=68"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "ipred"
+version = "0.1.0"
+description = "Generic iterative prediction backend (features, train plugins, conformal products)"
+requires-python = ">=3.11"
+dependencies = [
+  "fastapi>=0.115",
+  "uvicorn[standard]>=0.30",
+  "numpy>=1.26",
+  "pillow>=10.3",
+  "python-dotenv>=1.0",
+  "scikit-image>=0.22",
+  "scikit-learn>=1.4",
+  "catboost>=1.2",
+  "onnxruntime>=1.17",
+  "tifffile>=2024.0",
+  "httpx>=0.27",
+  "tiled[client]>=0.1",
+]
+
+[project.optional-dependencies]
+test = ["pytest>=8", "pytest-asyncio>=0.23", "httpx>=0.27"]
+torch = ["torch>=2.2", "timm>=1.0", "onnx>=1.16"]
+
+[project.scripts]
+ipred = "ipred.cli:main"
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[tool.pytest.ini_options]
+asyncio_mode = "auto"
+testpaths = ["tests"]
+pythonpath = ["src"]
diff --git a/ipred/src/ipred/__init__.py b/ipred/src/ipred/__init__.py
new file mode 100644
index 0000000..8fe81e6
--- /dev/null
+++ b/ipred/src/ipred/__init__.py
@@ -0,0 +1,3 @@
+"""ipred — generic iterative prediction backend."""
+
+__version__ = "0.1.0"
diff --git a/ipred/src/ipred/api.py b/ipred/src/ipred/api.py
new file mode 100644
index 0000000..46662d4
--- /dev/null
+++ b/ipred/src/ipred/api.py
@@ -0,0 +1,506 @@
+"""FastAPI app for the ipred iterative prediction backend."""
+
+from __future__ import annotations
+
+import logging
+from contextlib import asynccontextmanager
+from pathlib import Path
+from typing import Any, Optional
+
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import FileResponse, Response
+from pydantic import BaseModel, Field
+
+from ipred import compositions, feature_setups, manifold_jobs, preprocess, train_infer
+from ipred.catalog import Catalog
+from ipred.modules import list_module_catalog
+from ipred.trainers import list_trainers
+
+logger = logging.getLogger(__name__)
+
+_catalog: Catalog | None = None
+
+
+def get_catalog() -> Catalog:
+    """Return process-wide catalog."""
+    global _catalog
+    if _catalog is None:
+        _catalog = Catalog()
+        feature_setups.ensure_default_setups(_catalog)
+        compositions.ensure_default_compositions(_catalog)
+    return _catalog
+
+
+@asynccontextmanager
+async def lifespan(_app: FastAPI):
+    """Ensure defaults on startup."""
+    get_catalog()
+    yield
+
+
+app = FastAPI(title="ipred", version="0.1.0", lifespan=lifespan)
+
+
+class ProjectIdentity(BaseModel):
+    """Source identity for a project / session."""
+
+    kind: str
+    source: str
+    server_uri: Optional[str] = None
+    root: Optional[str] = None
+
+
+class PreprocessBody(BaseModel):
+    """Featurize request."""
+
+    session_id: str
+    feature_setup_id: Optional[str] = None
+    composition_id: Optional[str] = None
+    slice_index: int = 0
+    array_ref: Optional[str] = None
+
+
+class CompositionUpsertBody(BaseModel):
+    """Create/update a composition document."""
+
+    name: str
+    nodes: list[dict[str, Any]]
+    outputs: list[str]
+    composition_id: Optional[str] = None
+    builtin: bool = False
+
+
+class ArrayUploadBody(BaseModel):
+    """Upload a float array (base64 raw float32) for remote preprocess."""
+
+    session_id: str
+    shape: list[int]
+    dtype: str = "float32"
+    data_b64: str
+    array_ref: Optional[str] = None
+
+
+class TrainBody(BaseModel):
+    """Train request."""
+
+    session_id: str
+    shapes: list[dict[str, Any]]
+    feature_id: Optional[str] = None
+    trainer_id: str = "catboost"
+    config: dict[str, Any] = Field(default_factory=dict)
+
+
+class InferBody(BaseModel):
+    """Infer request."""
+
+    session_id: str
+    model_id: Optional[str] = None
+    feature_id: Optional[str] = None
+    alpha: float = 0.05
+
+
+class RethresholdBody(BaseModel):
+    """Rethreshold request."""
+
+    session_id: str
+    alpha: float
+    run_id: Optional[str] = None
+
+
+class SetupUpsertBody(BaseModel):
+    """Create/update a Feature Setup."""
+
+    name: str
+    kind: str
+    procedure_id: Optional[str] = None
+    params: Optional[dict[str, Any]] = None
+    encoder_setup_id: Optional[str] = None
+    weights_path: Optional[str] = None
+    weights_format: Optional[str] = None
+    inference: Optional[dict[str, Any]] = None
+    setup_id: Optional[str] = None
+
+
+class ManifoldSampleBody(BaseModel):
+    """Suggest Labels sample request."""
+
+    feature_id: str
+    k: int = 24
+    box_size: Optional[int] = None
+    stride: Optional[int] = None
+    pca_dims: int = 16
+    shapes: Optional[list[dict[str, Any]]] = None
+
+
+@app.get("/health")
+def health() -> dict[str, str]:
+    """Liveness probe."""
+    return {"status": "ok", "service": "ipred"}
+
+
+@app.post("/sessions")
+def open_session(body: ProjectIdentity) -> dict[str, Any]:
+    """Open a session for a project (creates project if needed)."""
+    cat = get_catalog()
+    session = cat.open_session(
+        kind=body.kind,
+        source=body.source,
+        server_uri=body.server_uri,
+        root=body.root,
+    )
+    return {
+        "session_id": session.session_id,
+        "project_id": session.project_id,
+        "current_feature_id": session.current_feature_id,
+        "current_model_id": session.current_model_id,
+        "current_run_id": session.current_run_id,
+    }
+
+
+@app.get("/sessions/{session_id}")
+def get_session(session_id: str) -> dict[str, Any]:
+    """Fetch session state."""
+    session = get_catalog().get_session(session_id)
+    if session is None:
+        raise HTTPException(404, "session not found")
+    project = get_catalog().get_project(session.project_id)
+    return {
+        "session_id": session.session_id,
+        "project_id": session.project_id,
+        "project": {
+            "kind": project.kind if project else None,
+            "source": project.source if project else None,
+            "server_uri": project.server_uri if project else None,
+            "root": project.root if project else None,
+        },
+        "current_feature_id": session.current_feature_id,
+        "current_model_id": session.current_model_id,
+        "current_run_id": session.current_run_id,
+    }
+
+
+@app.get("/setups")
+def api_list_setups() -> dict[str, Any]:
+    """List Feature Setups (legacy) + ensure compositions exist."""
+    feature_setups.ensure_default_setups(get_catalog())
+    compositions.ensure_default_compositions(get_catalog())
+    return {"setups": feature_setups.list_setups()}
+
+
+@app.get("/modules")
+def api_list_modules() -> dict[str, Any]:
+    """List composable feature modules."""
+    return {"modules": list_module_catalog()}
+
+
+@app.get("/compositions")
+def api_list_compositions() -> dict[str, Any]:
+    """List composition documents."""
+    compositions.ensure_default_compositions(get_catalog())
+    return {"compositions": compositions.list_compositions()}
+
+
+@app.get("/compositions/{composition_id}")
+def api_get_composition(composition_id: str) -> dict[str, Any]:
+    """Get one composition (+ concat preview labels)."""
+    try:
+        doc = compositions.resolve_composition(composition_id)
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    doc = dict(doc)
+    doc["preview_labels"] = compositions.preview_concat_labels(doc)
+    return doc
+
+
+@app.post("/compositions")
+def api_upsert_composition(body: CompositionUpsertBody) -> dict[str, Any]:
+    """Create or update a composition."""
+    try:
+        return compositions.save_composition(
+            name=body.name,
+            nodes=body.nodes,
+            outputs=body.outputs,
+            composition_id=body.composition_id,
+            builtin=body.builtin,
+            catalog=get_catalog(),
+        )
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+
+
+@app.post("/compositions/preview")
+def api_preview_composition(body: CompositionUpsertBody) -> dict[str, Any]:
+    """Return concat labels without saving."""
+    try:
+        labels = compositions.preview_concat_labels(
+            {"nodes": body.nodes, "outputs": body.outputs}
+        )
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+    return {"preview_labels": labels}
+
+
+@app.post("/sessions/{session_id}/arrays")
+def api_upload_array(session_id: str, body: ArrayUploadBody) -> dict[str, Any]:
+    """Upload a content-addressed array blob for remote preprocess."""
+    import base64
+
+    import numpy as np
+
+    from ipred import array_blobs
+
+    session = get_catalog().get_session(session_id)
+    if session is None:
+        raise HTTPException(404, "session not found")
+    if body.session_id and body.session_id != session_id:
+        raise HTTPException(422, "session_id mismatch")
+    try:
+        raw = base64.b64decode(body.data_b64)
+        shape = tuple(int(x) for x in body.shape)
+        arr = np.frombuffer(raw, dtype=np.dtype(body.dtype)).reshape(shape)
+    except Exception as exc:
+        raise HTTPException(422, f"invalid array payload: {exc}") from exc
+    return array_blobs.save_array_blob(
+        session.project_id,
+        arr,
+        array_ref=body.array_ref,
+    )
+
+
+@app.get("/setups/{setup_id}")
+def api_get_setup(setup_id: str) -> dict[str, Any]:
+    """Get one Feature Setup."""
+    try:
+        return feature_setups.resolve_setup(setup_id)
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+
+
+@app.post("/setups")
+def api_upsert_setup(body: SetupUpsertBody) -> dict[str, Any]:
+    """Create or update a Feature Setup."""
+    try:
+        return feature_setups.save_setup(
+            name=body.name,
+            kind=body.kind,
+            procedure_id=body.procedure_id,
+            params=body.params,
+            encoder_setup_id=body.encoder_setup_id,
+            weights_path=body.weights_path,
+            weights_format=body.weights_format,
+            inference=body.inference,
+            setup_id=body.setup_id,
+            catalog=get_catalog(),
+        )
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+
+
+@app.get("/trainers")
+def api_list_trainers() -> dict[str, Any]:
+    """List trainer plugin ids."""
+    return {"trainers": list_trainers()}
+
+
+@app.post("/preprocess")
+def api_preprocess(body: PreprocessBody) -> dict[str, Any]:
+    """Cache-aware featurize (composition_id preferred; setup_id still accepted)."""
+    try:
+        return preprocess.run_preprocess(
+            get_catalog(),
+            session_id=body.session_id,
+            feature_setup_id=body.feature_setup_id,
+            composition_id=body.composition_id,
+            slice_index=body.slice_index,
+            array_ref=body.array_ref,
+        )
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except FileNotFoundError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+    except Exception as exc:
+        logger.exception("preprocess failed")
+        raise HTTPException(500, f"preprocess failed: {exc}") from exc
+
+
+@app.get("/features/{feature_id}/channels/{index}")
+def api_feature_channel(feature_id: str, index: int) -> Response:
+    """Return a channel PNG for a feature bank."""
+    row = get_catalog().get_feature_bank(feature_id)
+    if row is None:
+        raise HTTPException(404, "feature bank not found")
+    path = preprocess.channel_png_path(row["blob_dir"], index)
+    if not path.is_file():
+        raise HTTPException(404, "channel not found")
+    return FileResponse(path, media_type="image/png")
+
+
+@app.post("/train")
+def api_train(body: TrainBody) -> dict[str, Any]:
+    """Train via trainer plugin."""
+    try:
+        return train_infer.run_train(
+            get_catalog(),
+            session_id=body.session_id,
+            shapes=body.shapes,
+            feature_id=body.feature_id,
+            trainer_id=body.trainer_id,
+            config=body.config,
+        )
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+    except Exception as exc:
+        logger.exception("train failed")
+        raise HTTPException(500, f"train failed: {exc}") from exc
+
+
+@app.post("/infer")
+def api_infer(body: InferBody) -> dict[str, Any]:
+    """Infer + conformal products."""
+    try:
+        return train_infer.run_infer(
+            get_catalog(),
+            session_id=body.session_id,
+            model_id=body.model_id,
+            feature_id=body.feature_id,
+            alpha=body.alpha,
+        )
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+    except Exception as exc:
+        logger.exception("infer failed")
+        raise HTTPException(500, f"infer failed: {exc}") from exc
+
+
+@app.post("/rethreshold")
+def api_rethreshold(body: RethresholdBody) -> dict[str, Any]:
+    """Rethreshold from cached proba."""
+    try:
+        return train_infer.run_rethreshold(
+            get_catalog(),
+            session_id=body.session_id,
+            run_id=body.run_id,
+            alpha=body.alpha,
+        )
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+    except Exception as exc:
+        logger.exception("rethreshold failed")
+        raise HTTPException(500, f"rethreshold failed: {exc}") from exc
+
+
+@app.get("/runs/{run_id}/commit.png")
+def api_run_commit(run_id: str) -> Response:
+    """Commit map PNG."""
+    run = get_catalog().get_run(run_id)
+    if run is None:
+        raise HTTPException(404, "run not found")
+    path = Path(run["blob_dir"]) / "commit.png"
+    if not path.is_file():
+        raise HTTPException(404, "commit.png missing")
+    return FileResponse(path, media_type="image/png")
+
+
+@app.get("/runs/{run_id}/status.png")
+def api_run_status(run_id: str) -> Response:
+    """Status map PNG."""
+    run = get_catalog().get_run(run_id)
+    if run is None:
+        raise HTTPException(404, "run not found")
+    path = Path(run["blob_dir"]) / "status.png"
+    if not path.is_file():
+        raise HTTPException(404, "status.png missing")
+    return FileResponse(path, media_type="image/png")
+
+
+@app.get("/runs/{run_id}/proba/{class_index}.png")
+def api_run_proba_channel(run_id: str, class_index: int) -> Response:
+    """Softmax probability heatmap PNG for one class column (0-based index)."""
+    try:
+        data = train_infer.proba_heatmap_png(get_catalog(), run_id, class_index)
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except FileNotFoundError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+    return Response(content=data, media_type="image/png")
+
+
+class ThresholdClassBody(BaseModel):
+    class_id: int
+    threshold: float = 0.5
+
+
+@app.post("/runs/{run_id}/threshold-class")
+def api_threshold_class(run_id: str, body: ThresholdClassBody) -> dict[str, Any]:
+    """Threshold one softmax class into a dense label map (for mask-set caches)."""
+    try:
+        return train_infer.threshold_class_label_map(
+            get_catalog(),
+            run_id,
+            class_id=body.class_id,
+            threshold=body.threshold,
+        )
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except FileNotFoundError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+
+
+@app.get("/runs/{run_id}")
+def api_get_run(run_id: str) -> dict[str, Any]:
+    """Run metadata."""
+    run = get_catalog().get_run(run_id)
+    if run is None:
+        raise HTTPException(404, "run not found")
+    import json
+
+    meta = json.loads(run["meta_json"])
+    return {**meta, "blob_dir": run["blob_dir"], "alpha": run["alpha"]}
+
+
+@app.post("/manifold/sample")
+def api_manifold_sample(body: ManifoldSampleBody) -> dict[str, Any]:
+    """Suggest Labels: variance boxes + residual heatmap on a feature bank."""
+    try:
+        return manifold_jobs.run_manifold_sample(
+            get_catalog(),
+            feature_id=body.feature_id,
+            k=body.k,
+            box_size=body.box_size,
+            stride=body.stride,
+            pca_dims=body.pca_dims,
+            shapes=body.shapes,
+        )
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+    except Exception as exc:
+        logger.exception("manifold sample failed")
+        raise HTTPException(500, f"manifold sample failed: {exc}") from exc
+
+
+@app.get("/manifold/{sample_id}/heatmap.png")
+def api_manifold_heatmap(sample_id: str) -> Response:
+    """Residual heatmap PNG for a manifold sample."""
+    try:
+        png = manifold_jobs.heatmap_png(sample_id)
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    return Response(
+        content=png,
+        media_type="image/png",
+        headers={"Cache-Control": "private, max-age=300"},
+    )
diff --git a/ipred/src/ipred/array_blobs.py b/ipred/src/ipred/array_blobs.py
new file mode 100644
index 0000000..04ceb93
--- /dev/null
+++ b/ipred/src/ipred/array_blobs.py
@@ -0,0 +1,56 @@
+"""Content-addressed array blobs for remote compute (data ≠ compute host)."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import uuid
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from ipred.paths import project_blob_dir
+
+
+def arrays_dir(project_id: str) -> Path:
+    """Blob directory for uploaded arrays."""
+    path = project_blob_dir(project_id) / "arrays"
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def save_array_blob(
+    project_id: str,
+    arr: np.ndarray,
+    *,
+    array_ref: str | None = None,
+    meta: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """Persist ``arr`` and return ``{array_ref, shape, dtype, ...}``."""
+    data = np.asarray(arr)
+    raw = data.astype(np.float32, copy=False).tobytes()
+    digest = hashlib.sha1(raw).hexdigest()[:16]
+    ref = array_ref or f"{digest}_{uuid.uuid4().hex[:8]}"
+    dest = arrays_dir(project_id) / ref
+    dest.mkdir(parents=True, exist_ok=True)
+    np.save(dest / "array.npy", data)
+    record = {
+        "array_ref": ref,
+        "shape": list(data.shape),
+        "dtype": str(data.dtype),
+        "sha1": digest,
+        "created_at": datetime.now(timezone.utc).isoformat(),
+        **(meta or {}),
+    }
+    (dest / "meta.json").write_text(json.dumps(record, indent=2), encoding="utf-8")
+    return record
+
+
+def load_array_blob(project_id: str, array_ref: str) -> np.ndarray:
+    """Load a previously uploaded array."""
+    path = arrays_dir(project_id) / array_ref / "array.npy"
+    if not path.is_file():
+        raise FileNotFoundError(f"array_ref not found: {array_ref}")
+    return np.load(path).astype(np.float32)
diff --git a/ipred/src/ipred/array_source.py b/ipred/src/ipred/array_source.py
new file mode 100644
index 0000000..1cf07cd
--- /dev/null
+++ b/ipred/src/ipred/array_source.py
@@ -0,0 +1,156 @@
+"""Minimal local / Tiled slice reader (no annotate-backend imports)."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+
+def _local_root(root: str | None) -> Path:
+    if root:
+        return Path(root).expanduser().resolve()
+    return Path(os.getenv("LOCAL_DATA_ROOT", "~/data")).expanduser().resolve()
+
+
+def read_slice(
+    *,
+    kind: str,
+    source: str,
+    slice_index: int = 0,
+    server_uri: str | None = None,
+    root: str | None = None,
+) -> np.ndarray:
+    """Load one 2D/ HxWxC slice array."""
+    if kind == "local":
+        return _read_local(source, slice_index=slice_index, root=root)
+    if kind == "tiled":
+        return _read_tiled(
+            source, slice_index=slice_index, server_uri=server_uri
+        )
+    raise ValueError(f"unknown kind {kind!r}")
+
+
+def _read_local(
+    source: str,
+    *,
+    slice_index: int,
+    root: str | None,
+) -> np.ndarray:
+    base = _local_root(root)
+    path = (base / source).resolve()
+    if not str(path).startswith(str(base)):
+        raise PermissionError("local path escapes LOCAL_DATA_ROOT")
+    if not path.is_file():
+        raise FileNotFoundError(str(path))
+    suffix = path.suffix.lower()
+    if suffix in {".tif", ".tiff"}:
+        import tifffile
+
+        arr = tifffile.imread(str(path))
+    else:
+        from PIL import Image as PILImage
+
+        arr = np.asarray(PILImage.open(path))
+    return _index_slice(np.asarray(arr), slice_index)
+
+
+def _is_container_node(node: Any) -> bool:
+    sf = getattr(node, "structure_family", None)
+    return str(getattr(sf, "value", sf)) == "container"
+
+
+def _descend_to_array(node: Any, max_depth: int = 8) -> Any:
+    """Resolve a Tiled container to the single array/stack it represents.
+
+    Handles two shapes seen in this repo's Tiled catalogs:
+
+    * An OME-NGFF multiscale pyramid (``scale0/image``, ``scale1/image``, …) —
+      descends to the finest (``scale0``) level's array. Without this, a
+      5-level pyramid container's 5 children get handed to ``np.asarray``,
+      which yields a 1-D array of length 5 (one object per child) instead of
+      image data, tripping the "unsupported array shape" check below.
+    * A wrapper container whose only/first child is itself a container (e.g.
+      drag-and-drop ingest nesting) — descends until reaching an array or a
+      container of arrays (slice stack).
+    """
+    if not _is_container_node(node):
+        return node
+
+    try:
+        scale_keys = sorted(
+            (k for k in node if str(k).startswith("scale")),
+            key=lambda k: int("".join(ch for ch in str(k) if ch.isdigit()) or 0),
+        )
+    except Exception:  # noqa: BLE001 — not enumerable → not a pyramid
+        scale_keys = []
+    if scale_keys:
+        level = node[scale_keys[0]]
+        if _is_container_node(level):
+            try:
+                level = level[next(iter(level))]
+            except StopIteration:
+                pass
+        return level
+
+    depth = 0
+    while _is_container_node(node) and depth < max_depth:
+        try:
+            first = next(iter(node))
+        except StopIteration:
+            return node  # empty container — nothing to descend into
+        child = node[first]
+        if not _is_container_node(child):
+            return node  # container of arrays → treat as a slice stack
+        node = child
+        depth += 1
+    return node
+
+
+def _read_tiled(
+    source: str,
+    *,
+    slice_index: int,
+    server_uri: str | None,
+) -> np.ndarray:
+    from tiled.client import from_uri
+
+    uri = (server_uri or os.getenv("TILED_URI") or "http://127.0.0.1:8010").rstrip(
+        "/"
+    )
+    api_key = os.getenv("TILED_API_KEY")
+    client: Any = from_uri(uri, api_key=api_key) if api_key else from_uri(uri)
+    node: Any = client
+    for part in source.strip("/").split("/"):
+        if not part:
+            continue
+        node = node[part]
+    node = _descend_to_array(node)
+    if _is_container_node(node):
+        # Container of per-slice arrays (not a bare NHW/NHWC array node).
+        keys = sorted(node)
+        node = node[keys[int(slice_index)]]
+        slice_index = 0
+    data = np.asarray(node)
+    return _index_slice(data, slice_index)
+
+
+def _index_slice(arr: np.ndarray, slice_index: int) -> np.ndarray:
+    """Pick HW or HWC frame from NHW / NHWC / HW / HWC."""
+    a = np.asarray(arr)
+    if a.ndim == 2:
+        return a
+    if a.ndim == 3:
+        # HWC vs NHW
+        if a.shape[-1] in (1, 3, 4) and a.shape[0] > 8:
+            return a  # HWC single
+        if a.shape[-1] in (1, 3, 4) and a.shape[0] <= 8:
+            # ambiguous small N — treat as HWC if last dim channel-like and square-ish
+            if a.shape[0] == a.shape[1]:
+                return a
+        return a[int(slice_index)]
+    if a.ndim == 4:
+        return a[int(slice_index)]
+    raise ValueError(f"unsupported array shape {a.shape}")
diff --git a/ipred/src/ipred/cache.py b/ipred/src/ipred/cache.py
new file mode 100644
index 0000000..3f2de80
--- /dev/null
+++ b/ipred/src/ipred/cache.py
@@ -0,0 +1,40 @@
+"""Tiny TTL cache for short-lived ipred artifacts (manifold heatmaps)."""
+
+from __future__ import annotations
+
+import time
+from threading import Lock
+from typing import Any, Generic, TypeVar
+
+T = TypeVar("T")
+
+
+class TTLCache(Generic[T]):
+    """Thread-safe TTL + max-entries cache."""
+
+    def __init__(self, *, ttl_seconds: float, max_entries: int) -> None:
+        self.ttl = float(ttl_seconds)
+        self.max_entries = int(max_entries)
+        self._data: dict[str, tuple[float, T]] = {}
+        self._lock = Lock()
+
+    def get(self, key: str) -> T | None:
+        now = time.monotonic()
+        with self._lock:
+            item = self._data.get(key)
+            if item is None:
+                return None
+            ts, val = item
+            if now - ts > self.ttl:
+                del self._data[key]
+                return None
+            return val
+
+    def set(self, key: str, value: T) -> None:
+        now = time.monotonic()
+        with self._lock:
+            self._data[key] = (now, value)
+            if len(self._data) > self.max_entries:
+                # Drop oldest
+                oldest = min(self._data.items(), key=lambda kv: kv[1][0])[0]
+                del self._data[oldest]
diff --git a/ipred/src/ipred/catalog.py b/ipred/src/ipred/catalog.py
new file mode 100644
index 0000000..7912209
--- /dev/null
+++ b/ipred/src/ipred/catalog.py
@@ -0,0 +1,441 @@
+"""SQLite catalog for projects, sessions, feature banks, models, and runs."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import sqlite3
+import uuid
+from contextlib import contextmanager
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Iterator
+
+from ipred.paths import catalog_db_path, project_blob_dir
+
+
+def _utc_now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def project_id_for(
+    *,
+    kind: str,
+    source: str,
+    server_uri: str | None = None,
+    root: str | None = None,
+) -> str:
+    """Stable project id from source identity."""
+    payload = json.dumps(
+        {
+            "kind": kind,
+            "source": source,
+            "server_uri": server_uri or "",
+            "root": root or "",
+        },
+        sort_keys=True,
+        separators=(",", ":"),
+    )
+    return hashlib.sha1(payload.encode("utf-8")).hexdigest()[:16]
+
+
+@dataclass(frozen=True)
+class ProjectRow:
+    """Project catalog row."""
+
+    project_id: str
+    kind: str
+    source: str
+    server_uri: str | None
+    root: str | None
+    created_at: str
+
+
+@dataclass(frozen=True)
+class SessionRow:
+    """Session catalog row."""
+
+    session_id: str
+    project_id: str
+    current_feature_id: str | None
+    current_model_id: str | None
+    current_run_id: str | None
+    created_at: str
+    updated_at: str
+
+
+class Catalog:
+    """SQLite-backed engine catalog."""
+
+    def __init__(self, db_path: Path | None = None) -> None:
+        self.db_path = Path(db_path) if db_path else catalog_db_path()
+        self.db_path.parent.mkdir(parents=True, exist_ok=True)
+        self._init_schema()
+
+    @contextmanager
+    def connect(self) -> Iterator[sqlite3.Connection]:
+        """Yield a connection with row factory."""
+        conn = sqlite3.connect(str(self.db_path))
+        conn.row_factory = sqlite3.Row
+        try:
+            yield conn
+            conn.commit()
+        except Exception:
+            conn.rollback()
+            raise
+        finally:
+            conn.close()
+
+    def _init_schema(self) -> None:
+        with self.connect() as conn:
+            conn.executescript(
+                """
+                CREATE TABLE IF NOT EXISTS projects (
+                  project_id TEXT PRIMARY KEY,
+                  kind TEXT NOT NULL,
+                  source TEXT NOT NULL,
+                  server_uri TEXT,
+                  root TEXT,
+                  created_at TEXT NOT NULL
+                );
+
+                CREATE TABLE IF NOT EXISTS sessions (
+                  session_id TEXT PRIMARY KEY,
+                  project_id TEXT NOT NULL REFERENCES projects(project_id),
+                  current_feature_id TEXT,
+                  current_model_id TEXT,
+                  current_run_id TEXT,
+                  created_at TEXT NOT NULL,
+                  updated_at TEXT NOT NULL
+                );
+
+                CREATE TABLE IF NOT EXISTS feature_setups (
+                  setup_id TEXT PRIMARY KEY,
+                  name TEXT NOT NULL,
+                  kind TEXT NOT NULL,
+                  content_hash TEXT NOT NULL,
+                  meta_json TEXT NOT NULL,
+                  created_at TEXT NOT NULL,
+                  updated_at TEXT NOT NULL
+                );
+
+                CREATE TABLE IF NOT EXISTS feature_banks (
+                  feature_id TEXT PRIMARY KEY,
+                  project_id TEXT NOT NULL,
+                  setup_id TEXT NOT NULL,
+                  content_hash TEXT NOT NULL,
+                  slice_index INTEGER NOT NULL,
+                  n_channels INTEGER NOT NULL,
+                  height INTEGER NOT NULL,
+                  width INTEGER NOT NULL,
+                  blob_dir TEXT NOT NULL,
+                  setup_snapshot TEXT NOT NULL,
+                  status TEXT NOT NULL,
+                  created_at TEXT NOT NULL,
+                  UNIQUE(project_id, setup_id, content_hash, slice_index)
+                );
+
+                CREATE TABLE IF NOT EXISTS models (
+                  model_id TEXT PRIMARY KEY,
+                  project_id TEXT NOT NULL,
+                  feature_id TEXT NOT NULL,
+                  trainer_id TEXT NOT NULL,
+                  blob_dir TEXT NOT NULL,
+                  meta_json TEXT NOT NULL,
+                  created_at TEXT NOT NULL
+                );
+
+                CREATE TABLE IF NOT EXISTS runs (
+                  run_id TEXT PRIMARY KEY,
+                  project_id TEXT NOT NULL,
+                  model_id TEXT NOT NULL,
+                  feature_id TEXT NOT NULL,
+                  alpha REAL NOT NULL,
+                  blob_dir TEXT NOT NULL,
+                  meta_json TEXT NOT NULL,
+                  created_at TEXT NOT NULL,
+                  updated_at TEXT NOT NULL
+                );
+                """
+            )
+
+    def ensure_project(
+        self,
+        *,
+        kind: str,
+        source: str,
+        server_uri: str | None = None,
+        root: str | None = None,
+    ) -> ProjectRow:
+        """Create or return the project for this source identity."""
+        pid = project_id_for(
+            kind=kind, source=source, server_uri=server_uri, root=root
+        )
+        now = _utc_now()
+        with self.connect() as conn:
+            row = conn.execute(
+                "SELECT * FROM projects WHERE project_id = ?", (pid,)
+            ).fetchone()
+            if row is None:
+                conn.execute(
+                    """
+                    INSERT INTO projects
+                      (project_id, kind, source, server_uri, root, created_at)
+                    VALUES (?, ?, ?, ?, ?, ?)
+                    """,
+                    (pid, kind, source, server_uri, root, now),
+                )
+                project_blob_dir(pid)
+                row = conn.execute(
+                    "SELECT * FROM projects WHERE project_id = ?", (pid,)
+                ).fetchone()
+        return self._project_from_row(row)
+
+    def open_session(
+        self,
+        *,
+        kind: str,
+        source: str,
+        server_uri: str | None = None,
+        root: str | None = None,
+    ) -> SessionRow:
+        """Ensure project exists and open a new session on it."""
+        project = self.ensure_project(
+            kind=kind, source=source, server_uri=server_uri, root=root
+        )
+        sid = uuid.uuid4().hex
+        now = _utc_now()
+        with self.connect() as conn:
+            conn.execute(
+                """
+                INSERT INTO sessions
+                  (session_id, project_id, current_feature_id, current_model_id,
+                   current_run_id, created_at, updated_at)
+                VALUES (?, ?, NULL, NULL, NULL, ?, ?)
+                """,
+                (sid, project.project_id, now, now),
+            )
+            row = conn.execute(
+                "SELECT * FROM sessions WHERE session_id = ?", (sid,)
+            ).fetchone()
+        return self._session_from_row(row)
+
+    def get_session(self, session_id: str) -> SessionRow | None:
+        """Look up a session."""
+        with self.connect() as conn:
+            row = conn.execute(
+                "SELECT * FROM sessions WHERE session_id = ?", (session_id,)
+            ).fetchone()
+        return self._session_from_row(row) if row else None
+
+    def get_project(self, project_id: str) -> ProjectRow | None:
+        """Look up a project."""
+        with self.connect() as conn:
+            row = conn.execute(
+                "SELECT * FROM projects WHERE project_id = ?", (project_id,)
+            ).fetchone()
+        return self._project_from_row(row) if row else None
+
+    def set_session_currents(
+        self,
+        session_id: str,
+        *,
+        feature_id: str | None = None,
+        model_id: str | None = None,
+        run_id: str | None = None,
+    ) -> SessionRow:
+        """Update session current_* pointers (only provided fields)."""
+        session = self.get_session(session_id)
+        if session is None:
+            raise KeyError(f"unknown session {session_id}")
+        feat = feature_id if feature_id is not None else session.current_feature_id
+        model = model_id if model_id is not None else session.current_model_id
+        run = run_id if run_id is not None else session.current_run_id
+        now = _utc_now()
+        with self.connect() as conn:
+            conn.execute(
+                """
+                UPDATE sessions
+                SET current_feature_id = ?, current_model_id = ?,
+                    current_run_id = ?, updated_at = ?
+                WHERE session_id = ?
+                """,
+                (feat, model, run, now, session_id),
+            )
+            row = conn.execute(
+                "SELECT * FROM sessions WHERE session_id = ?", (session_id,)
+            ).fetchone()
+        return self._session_from_row(row)
+
+    def upsert_feature_setup(
+        self,
+        *,
+        setup_id: str,
+        name: str,
+        kind: str,
+        content_hash: str,
+        meta: dict[str, Any],
+    ) -> None:
+        """Insert or update a feature setup index row."""
+        now = _utc_now()
+        payload = json.dumps(meta, sort_keys=True)
+        with self.connect() as conn:
+            existing = conn.execute(
+                "SELECT setup_id FROM feature_setups WHERE setup_id = ?",
+                (setup_id,),
+            ).fetchone()
+            if existing is None:
+                conn.execute(
+                    """
+                    INSERT INTO feature_setups
+                      (setup_id, name, kind, content_hash, meta_json,
+                       created_at, updated_at)
+                    VALUES (?, ?, ?, ?, ?, ?, ?)
+                    """,
+                    (setup_id, name, kind, content_hash, payload, now, now),
+                )
+            else:
+                conn.execute(
+                    """
+                    UPDATE feature_setups
+                    SET name = ?, kind = ?, content_hash = ?, meta_json = ?,
+                        updated_at = ?
+                    WHERE setup_id = ?
+                    """,
+                    (name, kind, content_hash, payload, now, setup_id),
+                )
+
+    def find_feature_bank(
+        self,
+        *,
+        project_id: str,
+        setup_id: str,
+        content_hash: str,
+        slice_index: int,
+    ) -> dict[str, Any] | None:
+        """Return a ready feature-bank row dict or None."""
+        with self.connect() as conn:
+            row = conn.execute(
+                """
+                SELECT * FROM feature_banks
+                WHERE project_id = ? AND setup_id = ? AND content_hash = ?
+                  AND slice_index = ? AND status = 'ready'
+                """,
+                (project_id, setup_id, content_hash, slice_index),
+            ).fetchone()
+        return dict(row) if row else None
+
+    def insert_feature_bank(self, record: dict[str, Any]) -> None:
+        """Insert a feature-bank row."""
+        with self.connect() as conn:
+            conn.execute(
+                """
+                INSERT INTO feature_banks
+                  (feature_id, project_id, setup_id, content_hash, slice_index,
+                   n_channels, height, width, blob_dir, setup_snapshot, status,
+                   created_at)
+                VALUES
+                  (:feature_id, :project_id, :setup_id, :content_hash,
+                   :slice_index, :n_channels, :height, :width, :blob_dir,
+                   :setup_snapshot, :status, :created_at)
+                """,
+                record,
+            )
+
+    def get_feature_bank(self, feature_id: str) -> dict[str, Any] | None:
+        """Look up a feature bank by id."""
+        with self.connect() as conn:
+            row = conn.execute(
+                "SELECT * FROM feature_banks WHERE feature_id = ?",
+                (feature_id,),
+            ).fetchone()
+        return dict(row) if row else None
+
+    def insert_model(self, record: dict[str, Any]) -> None:
+        """Insert a model row."""
+        with self.connect() as conn:
+            conn.execute(
+                """
+                INSERT INTO models
+                  (model_id, project_id, feature_id, trainer_id, blob_dir,
+                   meta_json, created_at)
+                VALUES
+                  (:model_id, :project_id, :feature_id, :trainer_id, :blob_dir,
+                   :meta_json, :created_at)
+                """,
+                record,
+            )
+
+    def get_model(self, model_id: str) -> dict[str, Any] | None:
+        """Look up a model row."""
+        with self.connect() as conn:
+            row = conn.execute(
+                "SELECT * FROM models WHERE model_id = ?", (model_id,)
+            ).fetchone()
+        return dict(row) if row else None
+
+    def insert_run(self, record: dict[str, Any]) -> None:
+        """Insert a run row."""
+        with self.connect() as conn:
+            conn.execute(
+                """
+                INSERT INTO runs
+                  (run_id, project_id, model_id, feature_id, alpha, blob_dir,
+                   meta_json, created_at, updated_at)
+                VALUES
+                  (:run_id, :project_id, :model_id, :feature_id, :alpha,
+                   :blob_dir, :meta_json, :created_at, :updated_at)
+                """,
+                record,
+            )
+
+    def update_run(
+        self,
+        run_id: str,
+        *,
+        alpha: float,
+        meta_json: str,
+    ) -> None:
+        """Update run alpha/meta after rethreshold."""
+        now = _utc_now()
+        with self.connect() as conn:
+            conn.execute(
+                """
+                UPDATE runs
+                SET alpha = ?, meta_json = ?, updated_at = ?
+                WHERE run_id = ?
+                """,
+                (alpha, meta_json, now, run_id),
+            )
+
+    def get_run(self, run_id: str) -> dict[str, Any] | None:
+        """Look up a run row."""
+        with self.connect() as conn:
+            row = conn.execute(
+                "SELECT * FROM runs WHERE run_id = ?", (run_id,)
+            ).fetchone()
+        return dict(row) if row else None
+
+    @staticmethod
+    def _project_from_row(row: sqlite3.Row) -> ProjectRow:
+        return ProjectRow(
+            project_id=row["project_id"],
+            kind=row["kind"],
+            source=row["source"],
+            server_uri=row["server_uri"],
+            root=row["root"],
+            created_at=row["created_at"],
+        )
+
+    @staticmethod
+    def _session_from_row(row: sqlite3.Row) -> SessionRow:
+        return SessionRow(
+            session_id=row["session_id"],
+            project_id=row["project_id"],
+            current_feature_id=row["current_feature_id"],
+            current_model_id=row["current_model_id"],
+            current_run_id=row["current_run_id"],
+            created_at=row["created_at"],
+            updated_at=row["updated_at"],
+        )
diff --git a/ipred/src/ipred/cli.py b/ipred/src/ipred/cli.py
new file mode 100644
index 0000000..bf20b8d
--- /dev/null
+++ b/ipred/src/ipred/cli.py
@@ -0,0 +1,188 @@
+"""CLI entrypoint ``ipred`` with --project session sugar."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+from typing import Any
+
+from ipred import feature_setups, preprocess, train_infer
+from ipred.catalog import Catalog
+from ipred.trainers import list_trainers
+
+
+def _catalog() -> Catalog:
+    cat = Catalog()
+    feature_setups.ensure_default_setups(cat)
+    return cat
+
+
+def _resolve_session(args: argparse.Namespace, cat: Catalog) -> str:
+    """Return session_id from --session or --project sugar."""
+    if getattr(args, "session", None):
+        return str(args.session)
+    if getattr(args, "kind", None) and getattr(args, "source", None):
+        session = cat.open_session(
+            kind=args.kind,
+            source=args.source,
+            server_uri=getattr(args, "server_uri", None),
+            root=getattr(args, "root", None),
+        )
+        print(json.dumps({"session_id": session.session_id, "project_id": session.project_id}))
+        return session.session_id
+    raise SystemExit("provide --session or --kind/--source (--project sugar)")
+
+
+def _add_project_sugar(parser: argparse.ArgumentParser) -> None:
+    parser.add_argument("--session", default=None, help="Existing session id")
+    parser.add_argument("--kind", default=None, help="Project kind (local|tiled)")
+    parser.add_argument("--source", default=None, help="Project source path")
+    parser.add_argument("--server-uri", dest="server_uri", default=None)
+    parser.add_argument("--root", default=None, help="Local root override")
+
+
+def main(argv: list[str] | None = None) -> None:
+    """CLI main."""
+    parser = argparse.ArgumentParser(
+        prog="ipred",
+        description="Iterative prediction backend CLI",
+    )
+    sub = parser.add_subparsers(dest="cmd", required=True)
+
+    p_session = sub.add_parser("session", help="Session commands")
+    sess_sub = p_session.add_subparsers(dest="session_cmd", required=True)
+    p_open = sess_sub.add_parser("open", help="Open a session for a project")
+    p_open.add_argument("--kind", required=True)
+    p_open.add_argument("--source", required=True)
+    p_open.add_argument("--server-uri", dest="server_uri", default=None)
+    p_open.add_argument("--root", default=None)
+
+    p_setup = sub.add_parser("setup", help="Feature Setup commands")
+    setup_sub = p_setup.add_subparsers(dest="setup_cmd", required=True)
+    setup_sub.add_parser("list", help="List setups")
+    p_show = setup_sub.add_parser("show", help="Show one setup")
+    p_show.add_argument("setup_id")
+
+    p_pre = sub.add_parser("preprocess", help="Featurize (cache-aware)")
+    _add_project_sugar(p_pre)
+    p_pre.add_argument("--setup", required=True, help="Feature setup id")
+    p_pre.add_argument("--slice", type=int, default=0)
+
+    p_train = sub.add_parser("train", help="Train a model")
+    _add_project_sugar(p_train)
+    p_train.add_argument("--labels", required=True, help="JSON file of shapes")
+    p_train.add_argument("--trainer", default="catboost")
+    p_train.add_argument("--feature-id", dest="feature_id", default=None)
+    p_train.add_argument("--config", default=None, help="JSON config file or string")
+
+    p_inf = sub.add_parser("infer", help="Infer + conformal maps")
+    _add_project_sugar(p_inf)
+    p_inf.add_argument("--alpha", type=float, default=0.05)
+    p_inf.add_argument("--model-id", dest="model_id", default=None)
+    p_inf.add_argument("--feature-id", dest="feature_id", default=None)
+
+    p_rt = sub.add_parser("rethreshold", help="Rethreshold from cached proba")
+    _add_project_sugar(p_rt)
+    p_rt.add_argument("--alpha", type=float, required=True)
+    p_rt.add_argument("--run-id", dest="run_id", default=None)
+
+    sub.add_parser("trainers", help="List trainer plugins")
+
+    args = parser.parse_args(argv)
+    cat = _catalog()
+
+    if args.cmd == "session" and args.session_cmd == "open":
+        session = cat.open_session(
+            kind=args.kind,
+            source=args.source,
+            server_uri=args.server_uri,
+            root=args.root,
+        )
+        _print(
+            {
+                "session_id": session.session_id,
+                "project_id": session.project_id,
+            }
+        )
+        return
+
+    if args.cmd == "setup":
+        if args.setup_cmd == "list":
+            _print({"setups": feature_setups.list_setups()})
+            return
+        if args.setup_cmd == "show":
+            _print(feature_setups.resolve_setup(args.setup_id))
+            return
+
+    if args.cmd == "trainers":
+        _print({"trainers": list_trainers()})
+        return
+
+    if args.cmd == "preprocess":
+        sid = _resolve_session(args, cat)
+        out = preprocess.run_preprocess(
+            cat,
+            session_id=sid,
+            feature_setup_id=args.setup,
+            slice_index=args.slice,
+        )
+        _print(out)
+        return
+
+    if args.cmd == "train":
+        sid = _resolve_session(args, cat)
+        shapes = json.loads(Path(args.labels).read_text(encoding="utf-8"))
+        if isinstance(shapes, dict) and "shapes" in shapes:
+            shapes = shapes["shapes"]
+        config: dict[str, Any] = {}
+        if args.config:
+            cfg_path = Path(args.config)
+            if cfg_path.is_file():
+                config = json.loads(cfg_path.read_text(encoding="utf-8"))
+            else:
+                config = json.loads(args.config)
+        out = train_infer.run_train(
+            cat,
+            session_id=sid,
+            shapes=shapes,
+            feature_id=args.feature_id,
+            trainer_id=args.trainer,
+            config=config,
+        )
+        _print(out)
+        return
+
+    if args.cmd == "infer":
+        sid = _resolve_session(args, cat)
+        out = train_infer.run_infer(
+            cat,
+            session_id=sid,
+            model_id=args.model_id,
+            feature_id=args.feature_id,
+            alpha=args.alpha,
+        )
+        _print(out)
+        return
+
+    if args.cmd == "rethreshold":
+        sid = _resolve_session(args, cat)
+        out = train_infer.run_rethreshold(
+            cat,
+            session_id=sid,
+            run_id=args.run_id,
+            alpha=args.alpha,
+        )
+        _print(out)
+        return
+
+    parser.error(f"unhandled command {args.cmd}")
+
+
+def _print(obj: Any) -> None:
+    print(json.dumps(obj, indent=2, default=str))
+
+
+if __name__ == "__main__":
+    main(sys.argv[1:])
diff --git a/ipred/src/ipred/compose_run.py b/ipred/src/ipred/compose_run.py
new file mode 100644
index 0000000..43c6081
--- /dev/null
+++ b/ipred/src/ipred/compose_run.py
@@ -0,0 +1,84 @@
+"""Execute a composition document on a raw image array."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+from ipred import features
+from ipred.compositions import validate_composition
+from ipred.modules import get_module
+from ipred.modules.base import ChannelBlock, ModuleContext
+from ipred.modules.pca_mod import uint8_from_float
+
+
+def run_composition(
+    arr: np.ndarray,
+    doc: dict[str, Any],
+) -> tuple[
+    np.ndarray,
+    np.ndarray,
+    list[str],
+    np.ndarray | None,
+    dict[str, Any] | None,
+]:
+    """Run composition nodes; concatenate ``outputs`` channel blocks.
+
+    Returns:
+        uint8_stack, float_stack, labels, sam_emb, sam_meta
+        (emb/meta from the last PCA or encoder that produced them).
+    """
+    validate_composition(doc)
+    gray = features.to_grayscale(arr)
+    ctx = ModuleContext(raw=arr, gray=gray, params={})
+    by_id = {n["id"]: n for n in doc["nodes"]}
+
+    # Topological-ish: run in document order (authors must order deps first)
+    for node in doc["nodes"]:
+        nid = node["id"]
+        mid = str(node["module"])
+        mod = get_module(mid)
+        params = dict(node.get("params") or {})
+        input_from = node.get("input_from")
+        input_image = None
+        if input_from:
+            upstream = ctx.node_outputs.get(input_from)
+            if upstream is None:
+                raise ValueError(
+                    f"node {nid}: input_from {input_from} not run yet"
+                )
+            if upstream.image_2d is not None:
+                input_image = upstream.image_2d
+            elif upstream.float_stack is not None and upstream.float_stack.shape[-1] >= 1:
+                input_image = upstream.float_stack[..., 0]
+            params["_input_from"] = input_from
+        ctx.params = params
+        ctx.input_image = input_image
+        block = mod.run(ctx)
+        ctx.node_outputs[nid] = block
+
+    float_parts: list[np.ndarray] = []
+    labels: list[str] = []
+    sam_emb: np.ndarray | None = None
+    sam_meta: dict[str, Any] | None = None
+    for oid in doc.get("outputs") or []:
+        block = ctx.node_outputs[oid]
+        if block.float_stack is not None and block.float_stack.size:
+            float_parts.append(np.asarray(block.float_stack, dtype=np.float32))
+            labels.extend(block.labels)
+        if block.emb is not None:
+            sam_emb = block.emb
+            sam_meta = block.emb_meta
+    # Also pick up emb from non-output encoder nodes if PCA was in outputs
+    if sam_emb is None:
+        for block in ctx.node_outputs.values():
+            if block.emb is not None:
+                sam_emb = block.emb
+                sam_meta = block.emb_meta
+
+    if not float_parts:
+        raise ValueError("composition produced no channel outputs")
+    float_stack = np.concatenate(float_parts, axis=-1)
+    uint8_stack = uint8_from_float(float_stack)
+    return uint8_stack, float_stack, labels, sam_emb, sam_meta
diff --git a/ipred/src/ipred/compositions.py b/ipred/src/ipred/compositions.py
new file mode 100644
index 0000000..a3b6d70
--- /dev/null
+++ b/ipred/src/ipred/compositions.py
@@ -0,0 +1,407 @@
+"""Composition documents — ordered module graphs for feature banks."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+import uuid
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from ipred.catalog import Catalog
+from ipred.modules import get_module, list_module_catalog
+from ipred.paths import feature_models_root
+
+logger = logging.getLogger(__name__)
+
+KIND_COMPOSITION = "composition"
+
+
+def compositions_root() -> Path:
+    """Directory for composition meta.json files."""
+    root = feature_models_root() / "_compositions"
+    root.mkdir(parents=True, exist_ok=True)
+    return root
+
+
+def content_hash(doc: dict[str, Any]) -> str:
+    """Stable hash of composition structure (excludes name/timestamps)."""
+    payload = {
+        "kind": KIND_COMPOSITION,
+        "nodes": doc.get("nodes"),
+        "outputs": doc.get("outputs"),
+    }
+    raw = json.dumps(payload, sort_keys=True, separators=(",", ":"))
+    return hashlib.sha1(raw.encode("utf-8")).hexdigest()[:16]
+
+
+def _dir(comp_id: str) -> Path:
+    return compositions_root() / comp_id
+
+
+def load_composition(comp_id: str) -> dict[str, Any] | None:
+    """Load composition meta or None."""
+    path = _dir(comp_id) / "meta.json"
+    if not path.is_file():
+        return None
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def list_compositions() -> list[dict[str, Any]]:
+    """List all compositions on disk."""
+    root = compositions_root()
+    out: list[dict[str, Any]] = []
+    for child in sorted(root.iterdir()) if root.is_dir() else []:
+        meta_path = child / "meta.json"
+        if not meta_path.is_file():
+            continue
+        try:
+            out.append(json.loads(meta_path.read_text(encoding="utf-8")))
+        except (OSError, json.JSONDecodeError):
+            continue
+    return out
+
+
+def save_composition(
+    *,
+    name: str,
+    nodes: list[dict[str, Any]],
+    outputs: list[str],
+    composition_id: str | None = None,
+    builtin: bool = False,
+    catalog: Catalog | None = None,
+) -> dict[str, Any]:
+    """Create or overwrite a composition document."""
+    validate_composition({"nodes": nodes, "outputs": outputs})
+    cid = composition_id or uuid.uuid4().hex
+    now = datetime.now(timezone.utc).isoformat()
+    existing = load_composition(cid)
+    created = existing.get("created_at") if existing else now
+    meta: dict[str, Any] = {
+        "id": cid,
+        "name": (name or cid).strip() or cid,
+        "kind": KIND_COMPOSITION,
+        "builtin": bool(builtin),
+        "nodes": list(nodes),
+        "outputs": list(outputs),
+        "created_at": created,
+        "updated_at": now,
+    }
+    meta["content_hash"] = content_hash(meta)
+    dest = _dir(cid)
+    dest.mkdir(parents=True, exist_ok=True)
+    (dest / "meta.json").write_text(json.dumps(meta, indent=2), encoding="utf-8")
+    if catalog is not None:
+        catalog.upsert_feature_setup(
+            setup_id=cid,
+            name=meta["name"],
+            kind=KIND_COMPOSITION,
+            content_hash=meta["content_hash"],
+            meta=meta,
+        )
+    return meta
+
+
+def resolve_composition(comp_id: str) -> dict[str, Any]:
+    """Load composition or raise KeyError."""
+    meta = load_composition(comp_id)
+    if meta is None:
+        raise KeyError(f"unknown composition {comp_id}")
+    return meta
+
+
+def validate_composition(doc: dict[str, Any]) -> None:
+    """Raise ValueError if nodes/outputs are invalid."""
+    nodes = doc.get("nodes") or []
+    outputs = doc.get("outputs") or []
+    if not isinstance(nodes, list) or not nodes:
+        raise ValueError("composition requires at least one node")
+    ids = [n.get("id") for n in nodes]
+    if len(ids) != len(set(ids)):
+        raise ValueError("duplicate node ids")
+    for n in nodes:
+        mid = n.get("module")
+        if not mid:
+            raise ValueError(f"node {n.get('id')} missing module")
+        try:
+            get_module(str(mid))
+        except KeyError as exc:
+            raise ValueError(str(exc)) from exc
+        src = n.get("input_from")
+        if src is not None and src not in ids:
+            raise ValueError(f"node {n.get('id')} input_from unknown: {src}")
+    for oid in outputs:
+        if oid not in ids:
+            raise ValueError(f"output node unknown: {oid}")
+
+
+def preview_concat_labels(doc: dict[str, Any]) -> list[str]:
+    """Labels the bank would concatenate for ``outputs`` order."""
+    validate_composition(doc)
+    by_id = {n["id"]: n for n in doc["nodes"]}
+    labels: list[str] = []
+    for oid in doc.get("outputs") or []:
+        node = by_id[oid]
+        mod = get_module(str(node["module"]))
+        params = dict(node.get("params") or {})
+        labels.extend(mod.preview_labels(params))
+    return labels
+
+
+def setup_id_to_composition_id(setup_id: str) -> str | None:
+    """Map legacy procedure setup ids → builtin composition ids."""
+    return _LEGACY_SETUP_MAP.get(setup_id)
+
+
+_LEGACY_SETUP_MAP: dict[str, str] = {
+    "default-skimage": "comp-skimage",
+    "default-skimage-slimsam": "comp-skimage-slimsam",
+    "default-slimsam-clahe": "comp-slimsam-clahe",
+    "default-skimage-mark25": "comp-skimage-mark25",
+    "default-mark25-clahe": "comp-mark25-clahe",
+    "default-skimage-mark11": "comp-skimage-mark11",
+    "default-mark11-clahe": "comp-mark11-clahe",
+    # weights-only → CLAHE companion compositions
+    "default-mark25": "comp-mark25-clahe",
+    "default-mark11": "comp-mark11-clahe",
+    "default-slimsam": "comp-skimage-slimsam",
+}
+
+
+def _skimage_params() -> dict[str, Any]:
+    return {
+        "sigma_min": 1.0,
+        "sigma_max": 8.0,
+        "intensity": True,
+        "edges": True,
+        "texture": True,
+        "clahe": True,
+    }
+
+
+def ensure_default_compositions(catalog: Catalog | None = None) -> list[dict[str, Any]]:
+    """Create builtin compositions matching legacy procedure setups."""
+    specs: list[tuple[str, str, list[dict[str, Any]], list[str]]] = [
+        (
+            "comp-skimage",
+            "Skimage multiscale",
+            [{"id": "n1", "module": "skimage_multiscale", "params": _skimage_params()}],
+            ["n1"],
+        ),
+        (
+            "comp-skimage-slimsam",
+            "Skimage + SlimSAM",
+            [
+                {"id": "n1", "module": "skimage_multiscale", "params": _skimage_params()},
+                {"id": "n2", "module": "slimsam", "params": {}},
+                {
+                    "id": "n3",
+                    "module": "pca",
+                    "params": {"dims": 32},
+                    "input_from": "n2",
+                },
+            ],
+            ["n1", "n3"],
+        ),
+        (
+            "comp-slimsam-clahe",
+            "SlimSAM + CLAHE",
+            [
+                {
+                    "id": "n1",
+                    "module": "clahe",
+                    "params": {
+                        "clahe": True,
+                        "clip_limit": 0.01,
+                        "include_in_bank": True,
+                    },
+                },
+                {
+                    "id": "n2",
+                    "module": "slimsam",
+                    "params": {},
+                    "input_from": "n1",
+                },
+                {
+                    "id": "n3",
+                    "module": "pca",
+                    "params": {"dims": 64},
+                    "input_from": "n2",
+                },
+            ],
+            ["n1", "n3"],
+        ),
+        (
+            "comp-skimage-mark25",
+            "Skimage + Mark25",
+            [
+                {
+                    "id": "n1",
+                    "module": "skimage_multiscale",
+                    "params": {
+                        **_skimage_params(),
+                    },
+                },
+                {
+                    "id": "n2",
+                    "module": "tomojepa",
+                    "params": {
+                        "weights_id": "mark25",
+                        "input_size": 512,
+                        "resize": True,
+                    },
+                },
+                {
+                    "id": "n3",
+                    "module": "pca",
+                    "params": {"dims": 64},
+                    "input_from": "n2",
+                },
+            ],
+            ["n1", "n3"],
+        ),
+        (
+            "comp-mark25-clahe",
+            "Mark25 + CLAHE",
+            [
+                {
+                    "id": "n1",
+                    "module": "clahe",
+                    "params": {
+                        "clahe": True,
+                        "clip_limit": 0.01,
+                        "include_in_bank": True,
+                    },
+                },
+                {
+                    "id": "n2",
+                    "module": "tomojepa",
+                    "params": {
+                        "weights_id": "mark25",
+                        "input_size": 512,
+                        "resize": True,
+                    },
+                    "input_from": "n1",
+                },
+                {
+                    "id": "n3",
+                    "module": "pca",
+                    "params": {"dims": 64},
+                    "input_from": "n2",
+                },
+            ],
+            ["n1", "n3"],
+        ),
+        (
+            "comp-skimage-mark11",
+            "Skimage + Mark11",
+            [
+                {"id": "n1", "module": "skimage_multiscale", "params": _skimage_params()},
+                {
+                    "id": "n2",
+                    "module": "tomojepa",
+                    "params": {
+                        "weights_id": "mark11",
+                        "input_size": 512,
+                        "resize": True,
+                    },
+                },
+                {
+                    "id": "n3",
+                    "module": "pca",
+                    "params": {"dims": 64},
+                    "input_from": "n2",
+                },
+            ],
+            ["n1", "n3"],
+        ),
+        (
+            "comp-mark11-clahe",
+            "Mark11 + CLAHE",
+            [
+                {
+                    "id": "n1",
+                    "module": "clahe",
+                    "params": {
+                        "clahe": True,
+                        "clip_limit": 0.01,
+                        "include_in_bank": True,
+                    },
+                },
+                {
+                    "id": "n2",
+                    "module": "tomojepa",
+                    "params": {
+                        "weights_id": "mark11",
+                        "input_size": 512,
+                        "resize": True,
+                    },
+                    "input_from": "n1",
+                },
+                {
+                    "id": "n3",
+                    "module": "pca",
+                    "params": {"dims": 64},
+                    "input_from": "n2",
+                },
+            ],
+            ["n1", "n3"],
+        ),
+    ]
+    created: list[dict[str, Any]] = []
+    for cid, name, nodes, outputs in specs:
+        existing = load_composition(cid)
+        if existing is None:
+            created.append(
+                save_composition(
+                    name=name,
+                    nodes=nodes,
+                    outputs=outputs,
+                    composition_id=cid,
+                    builtin=True,
+                    catalog=catalog,
+                )
+            )
+        else:
+            if catalog is not None:
+                catalog.upsert_feature_setup(
+                    setup_id=cid,
+                    name=existing["name"],
+                    kind=KIND_COMPOSITION,
+                    content_hash=existing.get("content_hash")
+                    or content_hash(existing),
+                    meta=existing,
+                )
+            created.append(existing)
+    return created
+
+
+def resolve_preprocess_id(setup_or_comp_id: str) -> str:
+    """Normalize legacy setup id or composition id to a composition id."""
+    if load_composition(setup_or_comp_id) is not None:
+        return setup_or_comp_id
+    mapped = setup_id_to_composition_id(setup_or_comp_id)
+    if mapped and load_composition(mapped) is not None:
+        return mapped
+    # compositions might not be seeded yet
+    if mapped:
+        return mapped
+    raise KeyError(f"unknown composition or setup {setup_or_comp_id}")
+
+
+__all__ = [
+    "KIND_COMPOSITION",
+    "content_hash",
+    "ensure_default_compositions",
+    "list_compositions",
+    "list_module_catalog",
+    "load_composition",
+    "preview_concat_labels",
+    "resolve_composition",
+    "resolve_preprocess_id",
+    "save_composition",
+    "setup_id_to_composition_id",
+    "validate_composition",
+]
diff --git a/ipred/src/ipred/conformal.py b/ipred/src/ipred/conformal.py
new file mode 100644
index 0000000..fb282ea
--- /dev/null
+++ b/ipred/src/ipred/conformal.py
@@ -0,0 +1,95 @@
+"""Mondrian split-conformal thresholds and maps from cached proba."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+STATUS_ABSTAIN = 0
+STATUS_SINGLETON = 1
+STATUS_MULTI = 2
+
+
+def conformal_quantile(scores: np.ndarray, alpha: float) -> float:
+    """Finite-sample split-conformal quantile."""
+    s = np.sort(np.asarray(scores, dtype=np.float64).ravel())
+    n = s.size
+    if n == 0:
+        return 1.0
+    if not 0.0 < alpha < 1.0:
+        raise ValueError("alpha must be in (0, 1)")
+    k = int(np.ceil((n + 1) * (1.0 - alpha)))
+    k = min(max(k, 1), n)
+    return float(s[k - 1])
+
+
+def mondrian_thresholds(
+    cal_scores_by_class: dict[int, np.ndarray],
+    alpha: float,
+) -> dict[int, float]:
+    """Per-class conformal thresholds q_y(alpha)."""
+    return {
+        int(c): conformal_quantile(scores, alpha)
+        for c, scores in cal_scores_by_class.items()
+    }
+
+
+def maps_from_proba(
+    proba: np.ndarray,
+    class_ids: list[int],
+    q_by_class: dict[int, float],
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Build commit, status, and packed membership from HxWxK proba.
+
+    Returns:
+        commit (HxW uint8), status (HxW uint8), membership_bits (HxW uint32)
+        where bit i means class_ids[i] is in the prediction set.
+    """
+    if proba.ndim != 3:
+        raise ValueError("proba must be HxWxK")
+    h, w, k = proba.shape
+    if k != len(class_ids):
+        raise ValueError("proba K must match class_ids length")
+    thr_prob = {cid: 1.0 - q for cid, q in q_by_class.items()}
+    in_set = np.zeros((h, w, k), dtype=bool)
+    for j, cid in enumerate(class_ids):
+        t = thr_prob.get(int(cid))
+        if t is None:
+            continue
+        in_set[..., j] = proba[..., j] >= t
+
+    set_sizes = in_set.sum(axis=2)
+    status = np.full((h, w), STATUS_ABSTAIN, dtype=np.uint8)
+    status[set_sizes == 1] = STATUS_SINGLETON
+    status[set_sizes > 1] = STATUS_MULTI
+
+    commit = np.zeros((h, w), dtype=np.uint8)
+    singleton = set_sizes == 1
+    if np.any(singleton):
+        cols = np.argmax(in_set[singleton], axis=1)
+        classes = np.asarray(class_ids, dtype=np.uint8)
+        commit[singleton] = classes[cols]
+
+    membership = np.zeros((h, w), dtype=np.uint32)
+    for j in range(min(k, 32)):
+        membership |= in_set[..., j].astype(np.uint32) << j
+
+    return commit, status, membership
+
+
+def counts_from_status(status: np.ndarray) -> dict[str, int]:
+    """Count abstain / singleton / multi pixels."""
+    return {
+        "singleton": int(np.sum(status == STATUS_SINGLETON)),
+        "multi": int(np.sum(status == STATUS_MULTI)),
+        "abstain": int(np.sum(status == STATUS_ABSTAIN)),
+    }
+
+
+def cal_scores_from_json(raw: dict[str, Any]) -> dict[int, np.ndarray]:
+    """Parse cal_scores.json into numpy arrays."""
+    out: dict[int, np.ndarray] = {}
+    for k, v in raw.items():
+        out[int(k)] = np.asarray(v, dtype=np.float64)
+    return out
diff --git a/ipred/src/ipred/feature_setups.py b/ipred/src/ipred/feature_setups.py
new file mode 100644
index 0000000..365b02c
--- /dev/null
+++ b/ipred/src/ipred/feature_setups.py
@@ -0,0 +1,416 @@
+"""Feature Setup shelf — procedure or weights configurations."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+import os
+import uuid
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from ipred.catalog import Catalog
+from ipred.paths import feature_models_root
+
+logger = logging.getLogger(__name__)
+
+PROCEDURE_SKIMAGE = "skimage_multiscale_v1"
+PROCEDURE_CLAHE_ENCODER = "clahe_encoder_v1"
+WEIGHTS_ONNX_VISION = "onnx_vision_encoder_v1"
+WEIGHTS_TOMOJEPA_MARK25 = "torch_tomojepa_mark25_v1"
+WEIGHTS_TOMOJEPA_MARK11 = "torch_tomojepa_mark11_v1"
+TOMOJEPA_WEIGHTS_FORMATS = frozenset(
+    {WEIGHTS_TOMOJEPA_MARK25, WEIGHTS_TOMOJEPA_MARK11}
+)
+
+DEFAULT_SKIMAGE_PARAMS: dict[str, Any] = {
+    "sigma_min": 1.0,
+    "sigma_max": 8.0,
+    "intensity": True,
+    "edges": True,
+    "texture": True,
+    "clahe": True,
+}
+
+DEFAULT_CLAHE_ENCODER_PARAMS: dict[str, Any] = {
+    "clahe": True,
+    "clip_limit": 0.01,
+    "kernel_size": None,
+    "resize": True,
+    "input_size": 512,
+    "pca_dims": 64,
+}
+
+DEFAULT_SKIMAGE_MARK25_PARAMS: dict[str, Any] = {
+    **DEFAULT_SKIMAGE_PARAMS,
+    "resize": True,
+    "input_size": 512,
+    "pca_dims": 64,
+}
+
+
+def content_hash(meta: dict[str, Any]) -> str:
+    """Stable hash of setup config (excludes timestamps / name display)."""
+    payload = {
+        "id": meta.get("id"),
+        "kind": meta.get("kind"),
+        "procedure_id": meta.get("procedure_id"),
+        "params": meta.get("params"),
+        "encoder_setup_id": meta.get("encoder_setup_id"),
+        "weights_path": meta.get("weights_path"),
+        "weights_format": meta.get("weights_format"),
+        "inference": meta.get("inference"),
+    }
+    raw = json.dumps(payload, sort_keys=True, separators=(",", ":"))
+    return hashlib.sha1(raw.encode("utf-8")).hexdigest()[:16]
+
+
+def _dir(setup_id: str) -> Path:
+    return feature_models_root() / setup_id
+
+
+def _write_meta(meta: dict[str, Any]) -> Path:
+    dest = _dir(meta["id"])
+    dest.mkdir(parents=True, exist_ok=True)
+    path = dest / "meta.json"
+    path.write_text(json.dumps(meta, indent=2), encoding="utf-8")
+    return path
+
+
+def load_setup(setup_id: str) -> dict[str, Any] | None:
+    """Load a setup meta.json or None."""
+    path = _dir(setup_id) / "meta.json"
+    if not path.is_file():
+        return None
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def list_setups() -> list[dict[str, Any]]:
+    """List all Feature Setups on disk."""
+    root = feature_models_root()
+    out: list[dict[str, Any]] = []
+    for child in sorted(root.iterdir()) if root.is_dir() else []:
+        meta_path = child / "meta.json"
+        if not meta_path.is_file():
+            continue
+        try:
+            meta = json.loads(meta_path.read_text(encoding="utf-8"))
+        except (OSError, json.JSONDecodeError):
+            continue
+        out.append(meta)
+    return out
+
+
+def save_setup(
+    *,
+    name: str,
+    kind: str,
+    procedure_id: str | None = None,
+    params: dict[str, Any] | None = None,
+    encoder_setup_id: str | None = None,
+    weights_path: str | None = None,
+    weights_format: str | None = None,
+    inference: dict[str, Any] | None = None,
+    setup_id: str | None = None,
+    builtin: bool = False,
+    catalog: Catalog | None = None,
+) -> dict[str, Any]:
+    """Create or overwrite a Feature Setup."""
+    if kind not in ("procedure", "weights"):
+        raise ValueError("kind must be 'procedure' or 'weights'")
+    sid = setup_id or uuid.uuid4().hex
+    now = datetime.now(timezone.utc).isoformat()
+    existing = load_setup(sid)
+    created = existing.get("created_at") if existing else now
+    meta: dict[str, Any] = {
+        "id": sid,
+        "name": (name or sid).strip() or sid,
+        "kind": kind,
+        "builtin": bool(builtin),
+        "created_at": created,
+        "updated_at": now,
+    }
+    if kind == "procedure":
+        if not procedure_id:
+            raise ValueError("procedure setups require procedure_id")
+        meta["procedure_id"] = procedure_id
+        if params is not None:
+            meta["params"] = dict(params)
+        elif procedure_id == PROCEDURE_CLAHE_ENCODER:
+            meta["params"] = dict(DEFAULT_CLAHE_ENCODER_PARAMS)
+        else:
+            meta["params"] = dict(DEFAULT_SKIMAGE_PARAMS)
+        if encoder_setup_id:
+            meta["encoder_setup_id"] = encoder_setup_id
+    else:
+        if not weights_path:
+            raise ValueError("weights setups require weights_path")
+        meta["weights_path"] = weights_path
+        meta["weights_format"] = weights_format or WEIGHTS_ONNX_VISION
+        meta["inference"] = dict(inference or {})
+
+    meta["content_hash"] = content_hash(meta)
+    _write_meta(meta)
+    if catalog is not None:
+        catalog.upsert_feature_setup(
+            setup_id=sid,
+            name=meta["name"],
+            kind=kind,
+            content_hash=meta["content_hash"],
+            meta=meta,
+        )
+    return meta
+
+
+def resolve_slimsam_weights_path() -> str | None:
+    """Locate SlimSAM ONNX; honors FEATURE_ENCODER_ONNX."""
+    env = os.getenv("FEATURE_ENCODER_ONNX")
+    if env:
+        p = Path(env).expanduser().resolve()
+        if p.is_file():
+            return str(p)
+    # Prefer repo-adjacent locations without importing annotate backend.
+    here = Path(__file__).resolve()
+    repo = here.parents[3]  # .../repo/ipred/src/ipred → repo
+    candidates = [
+        repo / "backend" / "models" / "slimsam-77-uniform" / "onnx" / "vision_encoder.onnx",
+        repo / "frontend" / "public" / "models" / "slimsam-77-uniform" / "onnx" / "vision_encoder.onnx",
+    ]
+    for c in candidates:
+        if c.is_file():
+            return str(c.resolve())
+    return None
+
+
+def resolve_tomojepa_weights_path(
+    filename: str = "tomojepa25.pth",
+    *,
+    env_var: str | None = "TOMOJEPA_WEIGHTS",
+) -> str | None:
+    """Locate a TomoJEPA ``.pth``; honors ``env_var`` when set."""
+    if env_var:
+        env = os.getenv(env_var)
+        if env:
+            p = Path(env).expanduser().resolve()
+            if p.is_file():
+                return str(p)
+    here = Path(__file__).resolve()
+    candidates = [
+        here.parents[2] / "models" / filename,
+        here.parents[3] / "ipred" / "models" / filename,
+    ]
+    for c in candidates:
+        if c.is_file():
+            return str(c.resolve())
+    return None
+
+
+def _upsert_existing(meta: dict[str, Any], catalog: Catalog | None) -> None:
+    if catalog is None:
+        return
+    catalog.upsert_feature_setup(
+        setup_id=meta["id"],
+        name=meta["name"],
+        kind=meta["kind"],
+        content_hash=meta.get("content_hash") or content_hash(meta),
+        meta=meta,
+    )
+
+
+def _ensure_procedure_with_param_backfill(
+    *,
+    setup_id: str,
+    name: str,
+    procedure_id: str,
+    params: dict[str, Any],
+    encoder_setup_id: str | None,
+    catalog: Catalog | None,
+) -> dict[str, Any]:
+    """Create procedure setup or backfill missing default params."""
+    existing = load_setup(setup_id)
+    if existing is None:
+        return save_setup(
+            setup_id=setup_id,
+            name=name,
+            kind="procedure",
+            procedure_id=procedure_id,
+            params=dict(params),
+            encoder_setup_id=encoder_setup_id,
+            builtin=True,
+            catalog=catalog,
+        )
+    cur = dict(existing.get("params") or {})
+    changed = False
+    for key, val in params.items():
+        if key not in cur:
+            cur[key] = val
+            changed = True
+    if changed:
+        return save_setup(
+            setup_id=setup_id,
+            name=existing.get("name") or name,
+            kind="procedure",
+            procedure_id=procedure_id,
+            params=cur,
+            encoder_setup_id=existing.get("encoder_setup_id") or encoder_setup_id,
+            builtin=True,
+            catalog=catalog,
+        )
+    _upsert_existing(existing, catalog)
+    return existing
+
+
+def _ensure_tomojepa_variant(
+    catalog: Catalog | None,
+    *,
+    weights_id: str,
+    weights_name: str,
+    combo_id: str,
+    combo_name: str,
+    clahe_id: str,
+    clahe_name: str,
+    filename: str,
+    env_var: str,
+    weights_format: str,
+) -> list[dict[str, Any]]:
+    """Weights + skimage combo + CLAHE procedure for one TomoJEPA checkpoint."""
+    out: list[dict[str, Any]] = []
+    tomo_path = resolve_tomojepa_weights_path(filename, env_var=env_var) or ""
+    weights = load_setup(weights_id)
+    if weights is None:
+        weights = save_setup(
+            setup_id=weights_id,
+            name=weights_name,
+            kind="weights",
+            weights_path=tomo_path or "(missing)",
+            weights_format=weights_format,
+            inference={"input_size": 512, "pca_dims": 32},
+            builtin=True,
+            catalog=catalog,
+        )
+    else:
+        _upsert_existing(weights, catalog)
+    out.append(weights)
+
+    out.append(
+        _ensure_procedure_with_param_backfill(
+            setup_id=combo_id,
+            name=combo_name,
+            procedure_id=PROCEDURE_SKIMAGE,
+            params=dict(DEFAULT_SKIMAGE_MARK25_PARAMS),
+            encoder_setup_id=weights_id,
+            catalog=catalog,
+        )
+    )
+    out.append(
+        _ensure_procedure_with_param_backfill(
+            setup_id=clahe_id,
+            name=clahe_name,
+            procedure_id=PROCEDURE_CLAHE_ENCODER,
+            params=dict(DEFAULT_CLAHE_ENCODER_PARAMS),
+            encoder_setup_id=weights_id,
+            catalog=catalog,
+        )
+    )
+    return out
+
+
+def ensure_default_setups(catalog: Catalog | None = None) -> list[dict[str, Any]]:
+    """Create built-in setups if missing; return all defaults."""
+    created: list[dict[str, Any]] = []
+    sk = load_setup("default-skimage")
+    if sk is None:
+        sk = save_setup(
+            setup_id="default-skimage",
+            name="Default skimage multiscale",
+            kind="procedure",
+            procedure_id=PROCEDURE_SKIMAGE,
+            params=dict(DEFAULT_SKIMAGE_PARAMS),
+            builtin=True,
+            catalog=catalog,
+        )
+    else:
+        _upsert_existing(sk, catalog)
+    created.append(sk)
+
+    weights_path = resolve_slimsam_weights_path() or ""
+    slim = load_setup("default-slimsam")
+    if slim is None:
+        slim = save_setup(
+            setup_id="default-slimsam",
+            name="Default SlimSAM encoder",
+            kind="weights",
+            weights_path=weights_path or "(missing)",
+            weights_format=WEIGHTS_ONNX_VISION,
+            inference={"input_size": 1024, "pca_dims": 32},
+            builtin=True,
+            catalog=catalog,
+        )
+    else:
+        _upsert_existing(slim, catalog)
+    created.append(slim)
+
+    combo = load_setup("default-skimage-slimsam")
+    if combo is None:
+        combo = save_setup(
+            setup_id="default-skimage-slimsam",
+            name="Default skimage + SlimSAM",
+            kind="procedure",
+            procedure_id=PROCEDURE_SKIMAGE,
+            params=dict(DEFAULT_SKIMAGE_PARAMS),
+            encoder_setup_id="default-slimsam",
+            builtin=True,
+            catalog=catalog,
+        )
+    else:
+        _upsert_existing(combo, catalog)
+    created.append(combo)
+
+    created.append(
+        _ensure_procedure_with_param_backfill(
+            setup_id="default-slimsam-clahe",
+            name="Default SlimSAM + CLAHE",
+            procedure_id=PROCEDURE_CLAHE_ENCODER,
+            params=dict(DEFAULT_CLAHE_ENCODER_PARAMS),
+            encoder_setup_id="default-slimsam",
+            catalog=catalog,
+        )
+    )
+
+    for variant in (
+        {
+            "weights_id": "default-mark25",
+            "weights_name": "Default Mark25 TomoJEPA encoder",
+            "combo_id": "default-skimage-mark25",
+            "combo_name": "Default skimage + Mark25",
+            "clahe_id": "default-mark25-clahe",
+            "clahe_name": "Default Mark25 + CLAHE",
+            "filename": "tomojepa25.pth",
+            "env_var": "TOMOJEPA_WEIGHTS",
+            "weights_format": WEIGHTS_TOMOJEPA_MARK25,
+        },
+        {
+            "weights_id": "default-mark11",
+            "weights_name": "Default Mark11 TomoJEPA encoder",
+            "combo_id": "default-skimage-mark11",
+            "combo_name": "Default skimage + Mark11",
+            "clahe_id": "default-mark11-clahe",
+            "clahe_name": "Default Mark11 + CLAHE",
+            "filename": "tomojepa11.pth",
+            "env_var": "TOMOJEPA11_WEIGHTS",
+            "weights_format": WEIGHTS_TOMOJEPA_MARK11,
+        },
+    ):
+        created.extend(_ensure_tomojepa_variant(catalog, **variant))
+
+    return created
+
+
+def resolve_setup(setup_id: str) -> dict[str, Any]:
+    """Load setup or raise KeyError."""
+    meta = load_setup(setup_id)
+    if meta is None:
+        raise KeyError(f"unknown feature setup {setup_id}")
+    return meta
diff --git a/ipred/src/ipred/features.py b/ipred/src/ipred/features.py
new file mode 100644
index 0000000..faf185b
--- /dev/null
+++ b/ipred/src/ipred/features.py
@@ -0,0 +1,180 @@
+"""Multiscale feature computation (ported; no annotate-backend imports)."""
+
+from __future__ import annotations
+
+from io import BytesIO
+
+import numpy as np
+from PIL import Image as PILImage
+from skimage.exposure import equalize_adapthist
+from skimage.feature import multiscale_basic_features
+
+
+def list_sigmas(sigma_min: float, sigma_max: float, num_sigma: int | None = None) -> np.ndarray:
+    """Return the σ grid used by multiscale_basic_features."""
+    if sigma_min <= 0 or sigma_max < sigma_min:
+        raise ValueError(f"invalid sigma range [{sigma_min}, {sigma_max}]")
+    if num_sigma is None:
+        num_sigma = int(np.log2(sigma_max) - np.log2(sigma_min) + 1)
+    return np.logspace(
+        np.log2(sigma_min),
+        np.log2(sigma_max),
+        num=int(num_sigma),
+        base=2,
+        endpoint=True,
+    )
+
+
+def _fmt_sigma(sigma: float) -> str:
+    if abs(sigma - round(sigma)) < 1e-6:
+        return str(int(round(sigma)))
+    return f"{sigma:g}"
+
+
+def feature_channel_labels(
+    *,
+    sigma_min: float = 1.0,
+    sigma_max: float = 8.0,
+    intensity: bool = True,
+    edges: bool = True,
+    texture: bool = True,
+    num_sigma: int | None = None,
+) -> list[str]:
+    """Human-readable labels matching skimage per-σ order."""
+    if not any((intensity, edges, texture)):
+        raise ValueError("at least one of intensity, edges, texture must be True")
+    labels: list[str] = []
+    for sigma in list_sigmas(sigma_min, sigma_max, num_sigma):
+        s = _fmt_sigma(float(sigma))
+        if intensity:
+            labels.append(f"intensity σ={s}")
+        if edges:
+            labels.append(f"edges σ={s}")
+        if texture:
+            labels.append(f"texture λ− σ={s}")
+            labels.append(f"texture λ+ σ={s}")
+    return labels
+
+
+def to_grayscale(arr: np.ndarray) -> np.ndarray:
+    """Convert a slice to float grayscale in [0, 1]."""
+    a = np.asarray(arr)
+    if a.ndim == 3 and a.shape[-1] in (3, 4):
+        rgb = a[..., :3].astype(np.float64)
+        gray = 0.299 * rgb[..., 0] + 0.587 * rgb[..., 1] + 0.114 * rgb[..., 2]
+    elif a.ndim == 2:
+        gray = a.astype(np.float64)
+    else:
+        raise ValueError(f"unsupported array shape {a.shape}")
+    finite = gray[np.isfinite(gray)]
+    if finite.size == 0:
+        return np.zeros_like(gray, dtype=np.float64)
+    lo, hi = float(np.min(finite)), float(np.max(finite))
+    if hi <= lo:
+        return np.zeros_like(gray, dtype=np.float64)
+    return np.clip((gray - lo) / (hi - lo), 0.0, 1.0)
+
+
+def _float_feats_to_uint8(feats: np.ndarray, *, clahe: bool) -> np.ndarray:
+    """Convert float HxWxC to display uint8."""
+    h, w, c = feats.shape
+    out = np.empty((h, w, c), dtype=np.uint8)
+    for i in range(c):
+        ch = feats[..., i].astype(np.float64)
+        lo, hi = float(np.nanmin(ch)), float(np.nanmax(ch))
+        norm = (ch - lo) / (hi - lo) if hi > lo else np.zeros_like(ch)
+        if clahe:
+            norm = equalize_adapthist(np.clip(norm, 0.0, 1.0), clip_limit=0.01)
+        out[..., i] = np.clip(np.round(norm * 255.0), 0, 255).astype(np.uint8)
+    return out
+
+
+def apply_clahe(
+    gray: np.ndarray,
+    *,
+    clip_limit: float = 0.01,
+    kernel_size: int | None = None,
+) -> np.ndarray:
+    """CLAHE on a [0, 1] grayscale image; returns float32 in [0, 1]."""
+    g = np.clip(np.asarray(gray, dtype=np.float64), 0.0, 1.0)
+    kwargs: dict = {"clip_limit": float(clip_limit)}
+    if kernel_size is not None and int(kernel_size) > 0:
+        kwargs["kernel_size"] = int(kernel_size)
+    out = equalize_adapthist(g, **kwargs)
+    return np.asarray(out, dtype=np.float32)
+
+
+def compute_clahe_stack(
+    gray: np.ndarray,
+    *,
+    clip_limit: float = 0.01,
+    kernel_size: int | None = None,
+    apply: bool = True,
+) -> tuple[np.ndarray, np.ndarray, list[str]]:
+    """Return single-channel ``(uint8_stack, float_stack, labels)`` with optional CLAHE.
+
+    Args:
+        gray: Grayscale float image in ``[0, 1]``.
+        clip_limit: skimage ``equalize_adapthist`` clip limit.
+        kernel_size: Optional CLAHE tile size; ``None`` uses skimage default.
+        apply: When False, store min-max gray only (no CLAHE).
+    """
+    g = np.asarray(gray, dtype=np.float32)
+    if apply:
+        ch = apply_clahe(g, clip_limit=clip_limit, kernel_size=kernel_size)
+        label = "clahe"
+    else:
+        ch = np.clip(g, 0.0, 1.0).astype(np.float32)
+        label = "intensity"
+    float_stack = ch[..., None]
+    uint8_stack = np.clip(np.round(float_stack * 255.0), 0, 255).astype(np.uint8)
+    return uint8_stack, float_stack, [label]
+
+
+def compute_feature_stacks(
+    gray: np.ndarray,
+    *,
+    sigma_min: float = 1.0,
+    sigma_max: float = 8.0,
+    intensity: bool = True,
+    edges: bool = True,
+    texture: bool = True,
+    clahe: bool = True,
+    num_sigma: int | None = None,
+) -> tuple[np.ndarray, np.ndarray, list[str]]:
+    """Return ``(uint8_stack, float_stack, labels)``."""
+    labels = feature_channel_labels(
+        sigma_min=sigma_min,
+        sigma_max=sigma_max,
+        intensity=intensity,
+        edges=edges,
+        texture=texture,
+        num_sigma=num_sigma,
+    )
+    feats = multiscale_basic_features(
+        np.asarray(gray, dtype=np.float32),
+        intensity=intensity,
+        edges=edges,
+        texture=texture,
+        sigma_min=sigma_min,
+        sigma_max=sigma_max,
+        num_sigma=num_sigma,
+        workers=1,
+    )
+    if feats.shape[-1] != len(labels):
+        raise RuntimeError(
+            f"feature count mismatch: got {feats.shape[-1]}, expected {len(labels)}"
+        )
+    float_stack = np.nan_to_num(feats.astype(np.float32), nan=0.0, posinf=0.0, neginf=0.0)
+    uint8_stack = _float_feats_to_uint8(float_stack, clahe=clahe)
+    return uint8_stack, float_stack, labels
+
+
+def encode_channel_png(stack: np.ndarray, index: int) -> bytes:
+    """Encode one feature channel as grayscale PNG."""
+    if index < 0 or index >= stack.shape[-1]:
+        raise IndexError(f"channel index {index} out of range")
+    img = PILImage.fromarray(stack[..., index], mode="L")
+    buf = BytesIO()
+    img.save(buf, format="PNG")
+    return buf.getvalue()
diff --git a/ipred/src/ipred/labels.py b/ipred/src/ipred/labels.py
new file mode 100644
index 0000000..474d737
--- /dev/null
+++ b/ipred/src/ipred/labels.py
@@ -0,0 +1,150 @@
+"""Rasterize annotation shapes to a sparse label map (self-contained)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+from PIL import Image as PILImage
+from PIL import ImageDraw
+
+
+def build_label_map(shapes: list[dict[str, Any]], height: int, width: int) -> np.ndarray:
+    """Compose uint8 label map (0 = unlabeled); last shape wins."""
+    labels = np.zeros((height, width), dtype=np.uint8)
+    for shape in shapes:
+        class_id = int(shape.get("classId") or shape.get("class_id") or 0)
+        if class_id <= 0 or class_id > 255:
+            continue
+        mask = shape_to_mask(shape, height, width)
+        labels[mask] = class_id
+    return labels
+
+
+def shape_to_mask(shape: dict[str, Any], h: int, w: int) -> np.ndarray:
+    """Rasterize one shape to boolean mask."""
+    kind = shape.get("kind")
+    if kind == "rectangle":
+        return _rect_mask(
+            float(shape["x"]),
+            float(shape["y"]),
+            float(shape["w"]),
+            float(shape["h"]),
+            h,
+            w,
+        )
+    if kind == "ellipse":
+        return _ellipse_mask(
+            float(shape["cx"]),
+            float(shape["cy"]),
+            float(shape["rx"]),
+            float(shape["ry"]),
+            h,
+            w,
+        )
+    if kind == "polygon":
+        return _polygon_mask(shape.get("points") or [], h, w, shape.get("holes"))
+    if kind == "brush":
+        return _brush_mask(shape.get("strokes") or [], h, w)
+    raise ValueError(f"Unknown shape kind: {kind!r}")
+
+
+def _rect_mask(x: float, y: float, ww: float, hh: float, h: int, w: int) -> np.ndarray:
+    x0 = max(0, int(np.floor(min(x, x + ww))))
+    x1 = min(w, int(np.ceil(max(x, x + ww))))
+    y0 = max(0, int(np.floor(min(y, y + hh))))
+    y1 = min(h, int(np.ceil(max(y, y + hh))))
+    mask = np.zeros((h, w), dtype=bool)
+    if x1 > x0 and y1 > y0:
+        mask[y0:y1, x0:x1] = True
+    return mask
+
+
+def _ellipse_mask(
+    cx: float, cy: float, rx: float, ry: float, h: int, w: int
+) -> np.ndarray:
+    yy, xx = np.ogrid[:h, :w]
+    rx = max(rx, 1e-6)
+    ry = max(ry, 1e-6)
+    return ((xx - cx) / rx) ** 2 + ((yy - cy) / ry) ** 2 <= 1.0
+
+
+def _xy_pairs(points: list[Any]) -> list[tuple[float, float]]:
+    """Normalize studio point payloads to ``(x, y)`` pairs.
+
+    Studio wire format for polygons/brushes is a flat ``[x0, y0, x1, y1, ...]``
+    float list (same as coco_export). Nested ``[[x,y], ...]`` and ``{x,y}``
+    dicts are also accepted.
+    """
+    if not points:
+        return []
+    if all(isinstance(p, (int, float)) for p in points):
+        if len(points) < 2:
+            return []
+        return [
+            (float(points[i]), float(points[i + 1]))
+            for i in range(0, len(points) - 1, 2)
+        ]
+    xy: list[tuple[float, float]] = []
+    for p in points:
+        if isinstance(p, (list, tuple)) and len(p) >= 2:
+            xy.append((float(p[0]), float(p[1])))
+        elif isinstance(p, dict):
+            xy.append((float(p["x"]), float(p["y"])))
+    return xy
+
+
+def _polygon_mask(
+    points: list[Any], h: int, w: int, holes: list[Any] | None = None
+) -> np.ndarray:
+    """Rasterize the outer ring, then carve out each hole ring.
+
+    Holes come from the studio's clip-to-other-classes tool (see
+    ``clipToClasses.ts``), which represents "this region minus already-labeled
+    neighbor classes" as a polygon with holes rather than reshaping the outer
+    ring. Ignoring them would paint the full outer extent — including pixels
+    that visually belong to other classes on the canvas.
+    """
+    xy = _xy_pairs(points)
+    if len(xy) < 3:
+        return np.zeros((h, w), dtype=bool)
+    img = PILImage.new("L", (w, h), 0)
+    draw = ImageDraw.Draw(img)
+    draw.polygon(xy, outline=1, fill=1)
+    for hole in holes or []:
+        hxy = _xy_pairs(hole)
+        if len(hxy) >= 3:
+            draw.polygon(hxy, outline=0, fill=0)
+    return np.asarray(img, dtype=np.uint8) > 0
+
+
+def _brush_mask(strokes: list[dict[str, Any]], h: int, w: int) -> np.ndarray:
+    """Paint OR / erase AND-NOT along stroke points."""
+    mask = np.zeros((h, w), dtype=bool)
+    for stroke in strokes:
+        pts = stroke.get("points") or []
+        xy = _xy_pairs(pts)
+        if not xy:
+            continue
+        # Studio stamps use radius directly; legacy payloads may send diameter as size.
+        if stroke.get("radius") is not None:
+            radius = max(1, int(round(float(stroke["radius"]))))
+        else:
+            radius = max(1, int(round(float(stroke.get("size") or 3) / 2)))
+        mode = str(stroke.get("mode") or stroke.get("type") or "paint")
+        erase = mode in ("erase", "eraser")
+        layer = PILImage.new("L", (w, h), 0)
+        ld = ImageDraw.Draw(layer)
+        if len(xy) == 1:
+            x, y = xy[0]
+            ld.ellipse((x - radius, y - radius, x + radius, y + radius), fill=1)
+        else:
+            ld.line(xy, fill=1, width=max(1, radius * 2))
+            for x, y in xy:
+                ld.ellipse((x - radius, y - radius, x + radius, y + radius), fill=1)
+        layer_m = np.asarray(layer, dtype=np.uint8) > 0
+        if erase:
+            mask &= ~layer_m
+        else:
+            mask |= layer_m
+    return mask
diff --git a/ipred/src/ipred/manifold.py b/ipred/src/ipred/manifold.py
new file mode 100644
index 0000000..56cafdd
--- /dev/null
+++ b/ipred/src/ipred/manifold.py
@@ -0,0 +1,359 @@
+"""Greedy feature-variance box sampling for annotation guidance.
+
+Sliding windows scored by whitened feature variance; picks suppress nearby
+(spatial Chebyshev exclusion ≥ box side) and feature-similar boxes.
+"""
+
+from __future__ import annotations
+
+import logging
+import math
+import uuid
+from dataclasses import dataclass
+from io import BytesIO
+from typing import Any
+
+import numpy as np
+from PIL import Image as PILImage
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
+
+from ipred.cache import TTLCache
+
+logger = logging.getLogger(__name__)
+
+_sample_cache: TTLCache = TTLCache(ttl_seconds=600.0, max_entries=32)
+_COS_HARD = 0.95
+_COS_ALPHA = 2.0
+
+
+@dataclass(frozen=True)
+class ManifoldSample:
+    """Inducing boxes + residual interestingness heatmap."""
+
+    job_id: str
+    points: list[dict[str, Any]]
+    heatmap: np.ndarray  # HxW float32 in [0, 1]
+    meta: dict[str, Any]
+
+
+@dataclass(frozen=True)
+class CachedManifoldSample:
+    """Server-side manifold sample for PNG GET."""
+
+    sample_id: str
+    heatmap_png: bytes
+    points: list[dict[str, Any]]
+    meta: dict[str, Any]
+
+
+def derived_exclusion_radius(h: int, w: int, k: int) -> float:
+    """Disk radius so roughly K disks pack the image plane."""
+    k = max(1, int(k))
+    area = float(h * w)
+    r = math.sqrt(area / (math.pi * k))
+    r_max = min(h, w) / 4.0
+    return float(max(8.0, min(r, r_max)))
+
+
+def sample_inducing_points(
+    float_stack: np.ndarray,
+    *,
+    feature_id: str,
+    k: int = 24,
+    box_size: int | None = None,
+    stride: int | None = None,
+    pca_dims: int = 16,
+    seed: int = 0,
+    mask: np.ndarray | None = None,
+) -> ManifoldSample:
+    """Greedy variance boxes with spatial + feature-space exclusion.
+
+    Args:
+        float_stack: Feature bank HxWxC.
+        feature_id: Owning feature bank id (stored on the sample).
+        k: Number of inducing boxes (clamped to [2, 128]).
+        box_size: Full square side length in image pixels. When omitted, derived
+            from the K-based exclusion radius (``2 * round(r)``).
+        stride: Optional hop override; default ``max(4, box_half)``.
+        pca_dims: PCA dimensionality after standardization.
+        seed: RNG for PCA solver stability.
+        mask: Optional HxW placement mask. When set, only windows whose full
+            box lies inside the mask are eligible.
+
+    Returns:
+        ManifoldSample with box centers, boxes, radius, and residual heatmap.
+    """
+    feats = np.asarray(float_stack, dtype=np.float32)
+    if feats.ndim != 3:
+        raise ValueError(f"float_stack must be HxWxC, got {feats.shape}")
+    h, w, c = feats.shape
+    if c < 1:
+        raise ValueError("empty feature stack")
+
+    place_mask: np.ndarray | None = None
+    mask_pixels = 0
+    if mask is not None:
+        m = np.asarray(mask)
+        if m.shape != (h, w):
+            raise ValueError(f"mask shape {m.shape} does not match image {(h, w)}")
+        place_mask = m.astype(bool, copy=False)
+        mask_pixels = int(place_mask.sum())
+        if mask_pixels < 1:
+            raise ValueError("placement mask is empty")
+
+    k = int(max(2, min(128, k)))
+    r_pack = derived_exclusion_radius(h, w, k)
+    if box_size is None:
+        side = int(max(8, 2 * round(r_pack)))
+    else:
+        side = int(box_size)
+    # Clamp: at least 8px side, at most half the short image edge
+    side = int(max(8, min(side, min(h, w) // 2 * 2)))  # even-ish via floor
+    if side % 2 == 1:
+        side += 1
+    b = max(4, side // 2)  # box half-size
+    # Spatial exclusion in Chebyshev (L∞) distance so axis-aligned boxes do
+    # not overlap: centers must be ≥ side+1 apart (1px gap for stroke).
+    # Also respect K-packing floor.
+    r = float(max(r_pack, side + 1))
+    hop = int(stride) if stride is not None else int(max(4, b))
+
+    # Coarse feature grid for PCA fit + window stats
+    grid_stride = max(2, hop // 2)
+    gy = np.arange(0, h, grid_stride, dtype=np.int32)
+    gx = np.arange(0, w, grid_stride, dtype=np.int32)
+    yy, xx = np.meshgrid(gy, gx, indexing="ij")
+    flat_y = yy.ravel()
+    flat_x = xx.ravel()
+    X = feats[flat_y, flat_x, :].astype(np.float64)
+    X = np.nan_to_num(X, nan=0.0, posinf=0.0, neginf=0.0)
+    n = X.shape[0]
+    if n < 4:
+        raise ValueError("too few pixels to score boxes")
+
+    scaler = StandardScaler()
+    Xs = scaler.fit_transform(X)
+    d = int(max(1, min(pca_dims, c, n - 1, 16)))
+    pca = PCA(n_components=d, random_state=seed)
+    Z = pca.fit_transform(Xs)
+    explained = float(np.sum(pca.explained_variance_ratio_))
+
+    # Map (y,x) -> Z row via nearest grid sample (for window aggregation)
+    # Store Z as grid gh×gw×d
+    gh, gw = len(gy), len(gx)
+    Z_grid = Z.reshape(gh, gw, d)
+
+    # Sliding window centers on hop grid
+    cy = np.arange(b, h - b + 1, hop, dtype=np.int32)
+    cx = np.arange(b, w - b + 1, hop, dtype=np.int32)
+    if cy.size == 0:
+        cy = np.array([h // 2], dtype=np.int32)
+    if cx.size == 0:
+        cx = np.array([w // 2], dtype=np.int32)
+    win_yy, win_xx = np.meshgrid(cy, cx, indexing="ij")
+    centers_y = win_yy.ravel()
+    centers_x = win_xx.ravel()
+    n_win = int(centers_y.size)
+
+    scores = np.zeros(n_win, dtype=np.float64)
+    means = np.zeros((n_win, d), dtype=np.float64)
+
+    for i in range(n_win):
+        y0 = int(centers_y[i] - b)
+        y1 = int(centers_y[i] + b)
+        x0 = int(centers_x[i] - b)
+        x1 = int(centers_x[i] + b)
+        # Grid indices covering the box
+        gi0 = max(0, (y0 + grid_stride - 1) // grid_stride)
+        gi1 = min(gh, (y1 // grid_stride) + 1)
+        gj0 = max(0, (x0 + grid_stride - 1) // grid_stride)
+        gj1 = min(gw, (x1 // grid_stride) + 1)
+        patch = Z_grid[gi0:gi1, gj0:gj1, :].reshape(-1, d)
+        if patch.shape[0] < 2:
+            continue
+        means[i] = patch.mean(axis=0)
+        # tr(Cov) = sum of feature variances
+        scores[i] = float(np.var(patch, axis=0).sum())
+
+    residual = scores.copy()
+    n_windows_in_mask = n_win
+    if place_mask is not None:
+        for i in range(n_win):
+            cx_i = int(centers_x[i])
+            cy_i = int(centers_y[i])
+            x0 = max(0, cx_i - b)
+            y0 = max(0, cy_i - b)
+            x1 = min(w, cx_i + b)
+            y1 = min(h, cy_i + b)
+            if x1 <= x0 or y1 <= y0 or not bool(place_mask[y0:y1, x0:x1].all()):
+                residual[i] = 0.0
+                scores[i] = 0.0
+        n_windows_in_mask = int(np.count_nonzero(residual > 0))
+        if n_windows_in_mask < 1:
+            raise ValueError(
+                "no candidate boxes fit fully inside the placement mask; "
+                "draw a larger mask or reduce box size"
+            )
+
+    points: list[dict[str, Any]] = []
+
+    for pick_i in range(k):
+        if not np.any(residual > 0):
+            break
+        i_star = int(np.argmax(residual))
+        score = float(residual[i_star])
+        if score <= 0:
+            break
+        cx_i = int(centers_x[i_star])
+        cy_i = int(centers_y[i_star])
+        mu = means[i_star]
+        mu_norm = float(np.linalg.norm(mu))
+        x0 = max(0, cx_i - b)
+        y0 = max(0, cy_i - b)
+        # Exclusive max so Konva/CSS width = x1 - x0 equals 2*b when unclipped
+        x1 = min(w, cx_i + b)
+        y1 = min(h, cy_i + b)
+        points.append(
+            {
+                "x": cx_i,
+                "y": cy_i,
+                "cluster": pick_i,
+                "score": score,
+                "radius": float(r),
+                "box_size": int(2 * b),
+                "box": {"x0": x0, "y0": y0, "x1": x1, "y1": y1},
+            }
+        )
+        residual[i_star] = 0.0
+
+        # Spatial (Chebyshev) + feature suppress. Exclude with ≥ side so
+        # axis-aligned boxes of width `side` do not share interior area.
+        for j in range(n_win):
+            if residual[j] <= 0:
+                continue
+            dx = abs(float(centers_x[j] - cx_i))
+            dy = abs(float(centers_y[j] - cy_i))
+            if max(dx, dy) < r:
+                residual[j] = 0.0
+                continue
+            mj = means[j]
+            mj_norm = float(np.linalg.norm(mj))
+            if mu_norm < 1e-12 or mj_norm < 1e-12:
+                continue
+            cos = float(np.dot(mu, mj) / (mu_norm * mj_norm))
+            cos = max(0.0, cos)
+            if cos > _COS_HARD:
+                residual[j] = 0.0
+            else:
+                residual[j] *= 1.0 - (cos ** _COS_ALPHA)
+
+    # Hard guarantee: drop any pick whose AABB still overlaps an earlier one
+    # (protects against hop / clip / float edge cases).
+    points = _dedupe_overlapping_boxes(points)
+
+    heatmap = _residual_heatmap(
+        h, w, centers_y, centers_x, residual, hop=hop, half=b
+    )
+
+    meta = {
+        "k": k,
+        "n_picked": len(points),
+        "n_windows": n_win,
+        "n_windows_in_mask": n_windows_in_mask,
+        "n_subsample": int(n),
+        "pca_dims": d,
+        "explained_variance": explained,
+        "stride": hop,
+        "radius": float(r),
+        "box_half": b,
+        "box_size": int(2 * b),
+        "mask_pixels": mask_pixels,
+        "has_mask": place_mask is not None,
+    }
+    return ManifoldSample(job_id=feature_id, points=points, heatmap=heatmap, meta=meta)
+
+
+def _boxes_overlap(a: dict[str, int], b: dict[str, int]) -> bool:
+    """True if two exclusive-end AABBs share interior area."""
+    return a["x0"] < b["x1"] and b["x0"] < a["x1"] and a["y0"] < b["y1"] and b["y0"] < a["y1"]
+
+
+def _dedupe_overlapping_boxes(points: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """Keep greedy order; drop later picks whose box overlaps an earlier one."""
+    kept: list[dict[str, Any]] = []
+    for p in points:
+        box = p.get("box")
+        if not isinstance(box, dict):
+            kept.append(p)
+            continue
+        if any(_boxes_overlap(box, k["box"]) for k in kept if isinstance(k.get("box"), dict)):
+            continue
+        kept.append(p)
+    for i, p in enumerate(kept):
+        p["cluster"] = i
+    return kept
+
+
+def _residual_heatmap(
+    h: int,
+    w: int,
+    centers_y: np.ndarray,
+    centers_x: np.ndarray,
+    residual: np.ndarray,
+    *,
+    hop: int,
+    half: int,
+) -> np.ndarray:
+    """Paint residual scores onto a coarse grid and upsample to HxW."""
+    out = np.zeros((h, w), dtype=np.float32)
+    max_s = float(np.max(residual)) if residual.size else 0.0
+    if max_s < 1e-12:
+        return out
+    for i in range(residual.size):
+        if residual[i] <= 0:
+            continue
+        val = float(residual[i] / max_s)
+        cy = int(centers_y[i])
+        cx = int(centers_x[i])
+        y0 = max(0, cy - half)
+        y1 = min(h, cy + half)
+        x0 = max(0, cx - half)
+        x1 = min(w, cx + half)
+        # Take max so overlapping windows keep strongest residual
+        patch = out[y0:y1, x0:x1]
+        np.maximum(patch, val, out=patch)
+    return out
+
+
+def encode_heatmap_png(heatmap: np.ndarray) -> bytes:
+    """Encode HxW float [0,1] residual map as grayscale PNG (0–255)."""
+    u8 = np.clip(np.asarray(heatmap, dtype=np.float32) * 255.0, 0, 255).astype(np.uint8)
+    img = PILImage.fromarray(u8, mode="L")
+    buf = BytesIO()
+    img.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def store_sample(result: ManifoldSample) -> CachedManifoldSample:
+    """Cache heatmap PNG + points; return handle for GET."""
+    sample_id = uuid.uuid4().hex
+    cached = CachedManifoldSample(
+        sample_id=sample_id,
+        heatmap_png=encode_heatmap_png(result.heatmap),
+        points=list(result.points),
+        meta={
+            "sample_id": sample_id,
+            "job_id": result.job_id,
+            **result.meta,
+            "points": list(result.points),
+        },
+    )
+    _sample_cache.set(sample_id, cached)
+    return cached
+
+
+def get_sample(sample_id: str) -> CachedManifoldSample | None:
+    """Look up a cached manifold sample."""
+    s = _sample_cache.get(sample_id)
+    return s if isinstance(s, CachedManifoldSample) else None
diff --git a/ipred/src/ipred/manifold_jobs.py b/ipred/src/ipred/manifold_jobs.py
new file mode 100644
index 0000000..a806386
--- /dev/null
+++ b/ipred/src/ipred/manifold_jobs.py
@@ -0,0 +1,74 @@
+"""Orchestrate manifold suggest on persisted feature banks."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+from ipred import manifold
+from ipred.catalog import Catalog
+from ipred.labels import shape_to_mask
+from ipred.preprocess import load_feature_bank_arrays
+
+
+def run_manifold_sample(
+    catalog: Catalog,
+    *,
+    feature_id: str,
+    k: int = 24,
+    box_size: int | None = None,
+    stride: int | None = None,
+    pca_dims: int = 16,
+    shapes: list[dict[str, Any]] | None = None,
+) -> dict[str, Any]:
+    """Sample inducing boxes on a feature bank; return API response dict."""
+    row = catalog.get_feature_bank(feature_id)
+    if row is None:
+        raise KeyError(f"unknown feature bank {feature_id}")
+    bank = load_feature_bank_arrays(row["blob_dir"])
+    float_stack = bank["float_stack"]
+    h, w = float_stack.shape[:2]
+
+    place_mask = None
+    if shapes:
+        place_mask = np.zeros((h, w), dtype=bool)
+        for shape in shapes:
+            place_mask |= shape_to_mask(shape, h, w)
+        if not bool(place_mask.any()):
+            raise ValueError("placement mask is empty")
+
+    result = manifold.sample_inducing_points(
+        float_stack,
+        feature_id=feature_id,
+        k=k,
+        box_size=box_size,
+        stride=stride,
+        pca_dims=pca_dims,
+        mask=place_mask,
+    )
+    cached = manifold.store_sample(result)
+    return {
+        "sample_id": cached.sample_id,
+        "feature_id": feature_id,
+        "points": cached.points,
+        "k": result.meta["k"],
+        "n_picked": result.meta["n_picked"],
+        "n_subsample": result.meta["n_subsample"],
+        "pca_dims": result.meta["pca_dims"],
+        "explained_variance": result.meta["explained_variance"],
+        "stride": result.meta["stride"],
+        "radius": result.meta["radius"],
+        "box_size": result.meta["box_size"],
+        "mask_pixels": result.meta.get("mask_pixels", 0),
+        "n_windows_in_mask": result.meta.get("n_windows_in_mask"),
+        "has_mask": result.meta.get("has_mask", False),
+    }
+
+
+def heatmap_png(sample_id: str) -> bytes:
+    """Return cached heatmap PNG bytes."""
+    cached = manifold.get_sample(sample_id)
+    if cached is None:
+        raise KeyError(f"unknown manifold sample {sample_id}")
+    return cached.heatmap_png
diff --git a/ipred/src/ipred/modules/__init__.py b/ipred/src/ipred/modules/__init__.py
new file mode 100644
index 0000000..78937eb
--- /dev/null
+++ b/ipred/src/ipred/modules/__init__.py
@@ -0,0 +1,73 @@
+"""Module registry — discover and list FeatureModules."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from ipred.modules.base import FeatureModule, ModuleMeta
+from ipred.modules.clahe_mod import ClaheModule
+from ipred.modules.pca_mod import PcaModule
+from ipred.modules.skimage_mod import SkimageMultiscaleModule
+from ipred.modules.slimsam_mod import SlimSamModule
+from ipred.modules.tomojepa_mod import TomoJepaModule
+
+_REGISTRY: dict[str, FeatureModule] | None = None
+
+
+def _build_registry() -> dict[str, FeatureModule]:
+    mods: list[FeatureModule] = [
+        SkimageMultiscaleModule(),
+        ClaheModule(),
+        SlimSamModule(),
+        TomoJepaModule(),
+        PcaModule(),
+    ]
+    return {m.meta.id: m for m in mods}
+
+
+def get_registry() -> dict[str, FeatureModule]:
+    """Return module id → instance map."""
+    global _REGISTRY
+    if _REGISTRY is None:
+        _REGISTRY = _build_registry()
+    return _REGISTRY
+
+
+def get_module(module_id: str) -> FeatureModule:
+    """Lookup module or raise KeyError."""
+    reg = get_registry()
+    if module_id not in reg:
+        raise KeyError(f"unknown module {module_id}")
+    return reg[module_id]
+
+
+def list_module_catalog() -> list[dict[str, Any]]:
+    """JSON-serializable catalog for GET /modules."""
+    out: list[dict[str, Any]] = []
+    for mod in get_registry().values():
+        meta: ModuleMeta = mod.meta
+        # Prefer ONNX in catalog only when graph matches default input_size
+        runtime = meta.runtime
+        if meta.id == "tomojepa":
+            from ipred import tomojepa_onnx
+
+            onnx = TomoJepaModule()._onnx_path({"weights_id": "mark25"})
+            if (
+                onnx is not None
+                and tomojepa_onnx.onnx_matches_input_size(onnx, 512)
+            ):
+                runtime = "onnx"
+        out.append(
+            {
+                "id": meta.id,
+                "name": meta.name,
+                "description": meta.description,
+                "runtime": runtime,
+                "ready": bool(mod.ready()),
+                "accepts_input_from": meta.accepts_input_from,
+                "produces_channels": meta.produces_channels,
+                "produces_embedding": meta.produces_embedding,
+                "params_schema": meta.params_schema,
+            }
+        )
+    return out
diff --git a/ipred/src/ipred/modules/base.py b/ipred/src/ipred/modules/base.py
new file mode 100644
index 0000000..7e1e6e7
--- /dev/null
+++ b/ipred/src/ipred/modules/base.py
@@ -0,0 +1,65 @@
+"""Feature module protocol — typed channel producers for compositions."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Protocol
+
+import numpy as np
+
+
+@dataclass
+class ModuleMeta:
+    """Catalog entry for a feature module."""
+
+    id: str
+    name: str
+    description: str
+    runtime: str  # onnx | torch | numpy
+    params_schema: dict[str, Any] = field(default_factory=dict)
+    accepts_input_from: bool = False
+    produces_channels: bool = True
+    produces_embedding: bool = False
+
+
+@dataclass
+class ChannelBlock:
+    """One module's contribution to the feature bank."""
+
+    float_stack: np.ndarray | None = None  # HxWxC
+    labels: list[str] = field(default_factory=list)
+    emb: np.ndarray | None = None  # Hp×Wp×D dense grid
+    emb_meta: dict[str, Any] | None = None
+    # Optional 2-D image for downstream encoder nodes (HxW float)
+    image_2d: np.ndarray | None = None
+
+
+@dataclass
+class ModuleContext:
+    """Shared state while running a composition on one slice."""
+
+    raw: np.ndarray
+    gray: np.ndarray
+    params: dict[str, Any]
+    # Outputs of nodes already run, keyed by node id
+    node_outputs: dict[str, ChannelBlock] = field(default_factory=dict)
+    # Resolved input image for modules that accept input_from
+    input_image: np.ndarray | None = None
+
+
+class FeatureModule(Protocol):
+    """Runnable feature module."""
+
+    meta: ModuleMeta
+
+    def ready(self) -> bool:
+        """True when dependencies/weights are available."""
+        ...
+
+    def run(self, ctx: ModuleContext) -> ChannelBlock:
+        """Produce channels and/or an embedding."""
+        ...
+
+    def preview_labels(self, params: dict[str, Any]) -> list[str]:
+        """Labels that would appear in the bank for these params (no compute)."""
+        ...
diff --git a/ipred/src/ipred/modules/clahe_mod.py b/ipred/src/ipred/modules/clahe_mod.py
new file mode 100644
index 0000000..cc823d4
--- /dev/null
+++ b/ipred/src/ipred/modules/clahe_mod.py
@@ -0,0 +1,55 @@
+"""CLAHE / intensity image module (feeds encoders or contributes a channel)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from ipred import features
+from ipred.modules.base import ChannelBlock, ModuleContext, ModuleMeta
+
+
+class ClaheModule:
+    """Single-channel CLAHE (or raw intensity) plus image_2d for encoders."""
+
+    meta = ModuleMeta(
+        id="clahe",
+        name="CLAHE",
+        description="Adaptive histogram equalization → one channel + encoder input",
+        runtime="numpy",
+        accepts_input_from=False,
+        produces_channels=True,
+        params_schema={
+            "clahe": {"type": "boolean", "default": True},
+            "clip_limit": {"type": "number", "default": 0.01},
+            "kernel_size": {"type": ["integer", "null"], "default": None},
+            "include_in_bank": {"type": "boolean", "default": True},
+        },
+    )
+
+    def ready(self) -> bool:
+        return True
+
+    def preview_labels(self, params: dict[str, Any]) -> list[str]:
+        if not bool(params.get("include_in_bank", True)):
+            return []
+        return ["clahe"] if bool(params.get("clahe", True)) else ["intensity"]
+
+    def run(self, ctx: ModuleContext) -> ChannelBlock:
+        p = ctx.params
+        clahe_on = bool(p.get("clahe", True))
+        clip_limit = float(p.get("clip_limit", 0.01))
+        ks = p.get("kernel_size", None)
+        kernel_size = int(ks) if ks not in (None, "", False) else None
+        _uint8, float_stack, labels = features.compute_clahe_stack(
+            ctx.gray,
+            clip_limit=clip_limit,
+            kernel_size=kernel_size,
+            apply=clahe_on,
+        )
+        image_2d = float_stack[..., 0]
+        include = bool(p.get("include_in_bank", True))
+        return ChannelBlock(
+            float_stack=float_stack if include else None,
+            labels=labels if include else [],
+            image_2d=image_2d,
+        )
diff --git a/ipred/src/ipred/modules/pca_mod.py b/ipred/src/ipred/modules/pca_mod.py
new file mode 100644
index 0000000..611e8fa
--- /dev/null
+++ b/ipred/src/ipred/modules/pca_mod.py
@@ -0,0 +1,78 @@
+"""PCA reduce dense embeddings into bank channels."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+from ipred import features, sam_embed
+from ipred.modules.base import ChannelBlock, ModuleContext, ModuleMeta
+
+
+class PcaModule:
+    """Bake encoder embedding grid → upsampled PCA float channels."""
+
+    meta = ModuleMeta(
+        id="pca",
+        name="PCA reduce",
+        description="PCA on dense embedding → heatmaps in feature bank",
+        runtime="numpy",
+        accepts_input_from=True,
+        produces_channels=True,
+        produces_embedding=False,
+        params_schema={
+            "dims": {"type": "integer", "default": 64},
+        },
+    )
+
+    def ready(self) -> bool:
+        return True
+
+    def preview_labels(self, params: dict[str, Any]) -> list[str]:
+        dims = max(1, int(params.get("dims", 64)))
+        return [f"pca{i}" for i in range(dims)]
+
+    def run(self, ctx: ModuleContext) -> ChannelBlock:
+        src_id = None
+        # Prefer explicitly linked node; else last embedding in context
+        emb = None
+        emb_meta: dict[str, Any] | None = None
+        # input_from resolved upstream sets nothing on ctx for emb —
+        # look up from node_outputs via params _input_from injected by runner
+        input_from = ctx.params.get("_input_from")
+        if input_from and input_from in ctx.node_outputs:
+            block = ctx.node_outputs[input_from]
+            emb, emb_meta = block.emb, block.emb_meta
+            src_id = input_from
+        if emb is None:
+            for nid, block in reversed(list(ctx.node_outputs.items())):
+                if block.emb is not None:
+                    emb, emb_meta = block.emb, block.emb_meta
+                    src_id = nid
+                    break
+        if emb is None or emb_meta is None:
+            raise ValueError("pca module requires an upstream embedding node")
+        dims = max(1, int(ctx.params.get("dims", 64)))
+        h, w = int(ctx.gray.shape[0]), int(ctx.gray.shape[1])
+        pca_float, pca_labels, info = sam_embed.emb_grid_to_pca_channels(
+            emb,
+            out_h=h,
+            out_w=w,
+            n_components=dims,
+        )
+        meta = dict(emb_meta)
+        meta.update(info)
+        meta["pca_from_node"] = src_id
+        # Keep emb for bank persistence (sam_emb.npy)
+        return ChannelBlock(
+            float_stack=pca_float.astype(np.float32),
+            labels=pca_labels,
+            emb=emb,
+            emb_meta=meta,
+        )
+
+
+def uint8_from_float(float_stack: np.ndarray) -> np.ndarray:
+    """Display uint8 for a float HxWxC bank."""
+    return features._float_feats_to_uint8(float_stack, clahe=False)
diff --git a/ipred/src/ipred/modules/skimage_mod.py b/ipred/src/ipred/modules/skimage_mod.py
new file mode 100644
index 0000000..7fa696a
--- /dev/null
+++ b/ipred/src/ipred/modules/skimage_mod.py
@@ -0,0 +1,54 @@
+"""Skimage multiscale feature module."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from ipred import features
+from ipred.modules.base import ChannelBlock, ModuleContext, ModuleMeta
+
+
+class SkimageMultiscaleModule:
+    """Produce multiscale intensity/edges/texture channels."""
+
+    meta = ModuleMeta(
+        id="skimage_multiscale",
+        name="Skimage multiscale",
+        description="ilastik-style multiscale basic features",
+        runtime="numpy",
+        accepts_input_from=False,
+        produces_channels=True,
+        params_schema={
+            "sigma_min": {"type": "number", "default": 1.0},
+            "sigma_max": {"type": "number", "default": 8.0},
+            "intensity": {"type": "boolean", "default": True},
+            "edges": {"type": "boolean", "default": True},
+            "texture": {"type": "boolean", "default": True},
+            "clahe": {"type": "boolean", "default": True},
+        },
+    )
+
+    def ready(self) -> bool:
+        return True
+
+    def preview_labels(self, params: dict[str, Any]) -> list[str]:
+        return features.feature_channel_labels(
+            sigma_min=float(params.get("sigma_min", 1.0)),
+            sigma_max=float(params.get("sigma_max", 8.0)),
+            intensity=bool(params.get("intensity", True)),
+            edges=bool(params.get("edges", True)),
+            texture=bool(params.get("texture", True)),
+        )
+
+    def run(self, ctx: ModuleContext) -> ChannelBlock:
+        p = ctx.params
+        _uint8, float_stack, labels = features.compute_feature_stacks(
+            ctx.gray,
+            sigma_min=float(p.get("sigma_min", 1.0)),
+            sigma_max=float(p.get("sigma_max", 8.0)),
+            intensity=bool(p.get("intensity", True)),
+            edges=bool(p.get("edges", True)),
+            texture=bool(p.get("texture", True)),
+            clahe=bool(p.get("clahe", True)),
+        )
+        return ChannelBlock(float_stack=float_stack, labels=labels)
diff --git a/ipred/src/ipred/modules/slimsam_mod.py b/ipred/src/ipred/modules/slimsam_mod.py
new file mode 100644
index 0000000..5fe9ff2
--- /dev/null
+++ b/ipred/src/ipred/modules/slimsam_mod.py
@@ -0,0 +1,50 @@
+"""SlimSAM ONNX encoder module."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from ipred import feature_setups, sam_embed
+from ipred.modules.base import ChannelBlock, ModuleContext, ModuleMeta
+
+
+class SlimSamModule:
+    """Dense SlimSAM vision-encoder embeddings (ONNX Runtime)."""
+
+    meta = ModuleMeta(
+        id="slimsam",
+        name="SlimSAM",
+        description="SlimSAM vision encoder (ONNX)",
+        runtime="onnx",
+        accepts_input_from=True,
+        produces_channels=False,
+        produces_embedding=True,
+        params_schema={
+            "weights_path": {"type": "string", "default": None},
+        },
+    )
+
+    def ready(self) -> bool:
+        path = feature_setups.resolve_slimsam_weights_path()
+        return sam_embed.encoder_available(path)
+
+    def preview_labels(self, params: dict[str, Any]) -> list[str]:
+        del params
+        return []  # PCA module consumes embedding
+
+    def run(self, ctx: ModuleContext) -> ChannelBlock:
+        src = ctx.input_image if ctx.input_image is not None else ctx.raw
+        wpath = ctx.params.get("weights_path") or feature_setups.resolve_slimsam_weights_path()
+        if not sam_embed.encoder_available(wpath):
+            raise ValueError("SlimSAM ONNX weights not available")
+        emb, orig_hw, reshaped_hw = sam_embed.encode_image_embeddings(
+            src, weights_path=wpath
+        )
+        meta = {
+            "orig_hw": list(orig_hw),
+            "reshaped_hw": list(reshaped_hw),
+            "weights_path": wpath,
+            "encoder": "slimsam",
+            "weights_format": feature_setups.WEIGHTS_ONNX_VISION,
+        }
+        return ChannelBlock(emb=emb, emb_meta=meta)
diff --git a/ipred/src/ipred/modules/tomojepa_mod.py b/ipred/src/ipred/modules/tomojepa_mod.py
new file mode 100644
index 0000000..9d9362a
--- /dev/null
+++ b/ipred/src/ipred/modules/tomojepa_mod.py
@@ -0,0 +1,154 @@
+"""TomoJEPA / Mark encoder module (torch; ONNX when available)."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from ipred import feature_setups, tomojepa_embed
+from ipred.modules.base import ChannelBlock, ModuleContext, ModuleMeta
+
+
+def _resolve_weights(params: dict[str, Any]) -> str | None:
+    """Resolve Mark25/11 weights from params."""
+    explicit = params.get("weights_path")
+    if explicit and str(explicit) != "(missing)":
+        p = Path(str(explicit)).expanduser()
+        if p.is_file():
+            return str(p.resolve())
+    weights_id = str(params.get("weights_id") or "mark25").lower()
+    if weights_id in ("mark11", "tomojepa11", "11"):
+        return feature_setups.resolve_tomojepa_weights_path(
+            "tomojepa11.pth", env_var="TOMOJEPA11_WEIGHTS"
+        )
+    return feature_setups.resolve_tomojepa_weights_path(
+        "tomojepa25.pth", env_var="TOMOJEPA_WEIGHTS"
+    )
+
+
+def _encoder_name(params: dict[str, Any], path: str | None) -> str:
+    wid = str(params.get("weights_id") or "").lower()
+    if wid in ("mark11", "tomojepa11", "11"):
+        return "mark11"
+    if path and "tomojepa11" in Path(path).name:
+        return "mark11"
+    return "mark25"
+
+
+def _weights_format(name: str) -> str:
+    if name == "mark11":
+        return feature_setups.WEIGHTS_TOMOJEPA_MARK11
+    return feature_setups.WEIGHTS_TOMOJEPA_MARK25
+
+
+class TomoJepaModule:
+    """Dense TomoJEPA embeddings (torch; prefers ONNX when present)."""
+
+    meta = ModuleMeta(
+        id="tomojepa",
+        name="TomoJEPA",
+        description="Mark25/Mark11 dense projector (torch or ONNX)",
+        runtime="torch",
+        accepts_input_from=True,
+        produces_channels=False,
+        produces_embedding=True,
+        params_schema={
+            "weights_id": {"type": "string", "default": "mark25", "enum": ["mark25", "mark11"]},
+            "weights_path": {"type": "string", "default": None},
+            "input_size": {"type": "integer", "default": 512},
+            "resize": {"type": "boolean", "default": True},
+        },
+    )
+
+    def ready(self) -> bool:
+        if self._onnx_path({"weights_id": "mark25"}) or self._onnx_path(
+            {"weights_id": "mark11"}
+        ):
+            return True
+        for fname, env in (
+            ("tomojepa25.pth", "TOMOJEPA_WEIGHTS"),
+            ("tomojepa11.pth", "TOMOJEPA11_WEIGHTS"),
+        ):
+            p = feature_setups.resolve_tomojepa_weights_path(fname, env_var=env)
+            if p and tomojepa_embed.encoder_available(p):
+                return True
+        return False
+
+    def preview_labels(self, params: dict[str, Any]) -> list[str]:
+        del params
+        return []
+
+    def _onnx_path(self, params: dict[str, Any]) -> Path | None:
+        import os
+
+        explicit = params.get("onnx_path")
+        if explicit and Path(str(explicit)).expanduser().is_file():
+            return Path(str(explicit)).expanduser().resolve()
+        wid = str(params.get("weights_id") or "mark25").lower()
+        fname = "tomojepa11.onnx" if wid in ("mark11", "11") else "tomojepa25.onnx"
+        env = "TOMOJEPA11_ONNX" if "11" in fname else "TOMOJEPA_ONNX"
+        env_p = os.getenv(env)
+        if env_p and Path(env_p).expanduser().is_file():
+            return Path(env_p).expanduser().resolve()
+        # modules/tomojepa_mod.py → …/ipred/src/ipred/modules → parents[3]=ipred/
+        here = Path(__file__).resolve()
+        for c in (
+            here.parents[3] / "models" / fname,
+            here.parents[4] / "ipred" / "models" / fname,
+        ):
+            if c.is_file():
+                return c.resolve()
+        return None
+
+    def run(self, ctx: ModuleContext) -> ChannelBlock:
+        src = ctx.input_image if ctx.input_image is not None else ctx.gray
+        p = ctx.params
+        resize = bool(p.get("resize", True))
+        input_size = int(p.get("input_size", 512))
+        onnx_path = self._onnx_path(p)
+        use_onnx = False
+        if onnx_path is not None:
+            from ipred import tomojepa_onnx
+
+            use_onnx = tomojepa_onnx.onnx_matches_input_size(
+                onnx_path, input_size
+            )
+        if use_onnx and onnx_path is not None:
+            from ipred import tomojepa_onnx
+
+            emb, orig_hw, reshaped_hw = tomojepa_onnx.encode_dense_embeddings(
+                src,
+                weights_path=str(onnx_path),
+                input_size=input_size,
+                resize=resize,
+            )
+            runtime = "onnx"
+            path_s = str(onnx_path)
+        else:
+            wpath = _resolve_weights(p)
+            if not tomojepa_embed.encoder_available(wpath):
+                raise ValueError(
+                    "TomoJEPA weights not available (torch/.pth or ONNX)"
+                )
+            emb, orig_hw, reshaped_hw = tomojepa_embed.encode_dense_embeddings(
+                src,
+                weights_path=wpath,
+                input_size=input_size,
+                resize=resize,
+            )
+            runtime = "torch"
+            path_s = wpath
+        enc_name = _encoder_name(p, path_s)
+        meta = {
+            "orig_hw": list(orig_hw),
+            "reshaped_hw": list(reshaped_hw),
+            "weights_path": path_s,
+            "encoder": enc_name,
+            "input_size": input_size,
+            "resize": resize,
+            "weights_format": _weights_format(enc_name),
+            "runtime": runtime,
+            "model_input_range": [-1.0, 1.0],
+            "intensity_norm": "minmax_01_then_linear_m11",
+        }
+        return ChannelBlock(emb=emb, emb_meta=meta)
diff --git a/ipred/src/ipred/paths.py b/ipred/src/ipred/paths.py
new file mode 100644
index 0000000..4a3a071
--- /dev/null
+++ b/ipred/src/ipred/paths.py
@@ -0,0 +1,37 @@
+"""Filesystem roots for ipred under LOCAL_DATA_ROOT."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+
+def local_data_root() -> Path:
+    """Return resolved ``LOCAL_DATA_ROOT`` (default ``~/data``)."""
+    return Path(os.getenv("LOCAL_DATA_ROOT", "~/data")).expanduser().resolve()
+
+
+def engine_root() -> Path:
+    """Return ``$LOCAL_DATA_ROOT/ipred``."""
+    root = local_data_root() / "ipred"
+    root.mkdir(parents=True, exist_ok=True)
+    return root
+
+
+def catalog_db_path() -> Path:
+    """SQLite catalog path."""
+    return engine_root() / "catalog.db"
+
+
+def feature_models_root() -> Path:
+    """Feature Setup shelf directory."""
+    root = local_data_root() / ".feature_models"
+    root.mkdir(parents=True, exist_ok=True)
+    return root
+
+
+def project_blob_dir(project_id: str) -> Path:
+    """Blob root for one project."""
+    path = engine_root() / "projects" / project_id
+    path.mkdir(parents=True, exist_ok=True)
+    return path
diff --git a/ipred/src/ipred/preprocess.py b/ipred/src/ipred/preprocess.py
new file mode 100644
index 0000000..c1f7fbd
--- /dev/null
+++ b/ipred/src/ipred/preprocess.py
@@ -0,0 +1,207 @@
+"""Cache-aware featurize for a session (composition-first)."""
+
+from __future__ import annotations
+
+import json
+import logging
+import uuid
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from ipred import array_source, compositions, features
+from ipred.catalog import Catalog
+from ipred.compose_run import run_composition
+from ipred.paths import project_blob_dir
+
+logger = logging.getLogger(__name__)
+
+
+def _utc_now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def run_preprocess(
+    catalog: Catalog,
+    *,
+    session_id: str,
+    feature_setup_id: str | None = None,
+    composition_id: str | None = None,
+    slice_index: int = 0,
+    array_ref: str | None = None,
+) -> dict[str, Any]:
+    """Featurize with cache hit/miss; update session current_feature_id.
+
+    Prefers ``composition_id``; ``feature_setup_id`` is mapped via legacy table.
+    Optional ``array_ref`` selects a session-uploaded blob instead of path/Tiled.
+    """
+    session = catalog.get_session(session_id)
+    if session is None:
+        raise KeyError(f"unknown session {session_id}")
+    project = catalog.get_project(session.project_id)
+    if project is None:
+        raise KeyError(f"unknown project {session.project_id}")
+
+    compositions.ensure_default_compositions(catalog)
+    raw_id = composition_id or feature_setup_id
+    if not raw_id:
+        raise ValueError("composition_id or feature_setup_id required")
+    try:
+        cid = compositions.resolve_preprocess_id(raw_id)
+    except KeyError:
+        # Ensure defaults then retry
+        compositions.ensure_default_compositions(catalog)
+        cid = compositions.resolve_preprocess_id(raw_id)
+
+    doc = compositions.resolve_composition(cid)
+    chash = doc.get("content_hash") or compositions.content_hash(doc)
+
+    hit = catalog.find_feature_bank(
+        project_id=project.project_id,
+        setup_id=cid,
+        content_hash=chash,
+        slice_index=slice_index,
+    )
+    if hit is not None and array_ref is None:
+        catalog.set_session_currents(session_id, feature_id=hit["feature_id"])
+        return _bank_response(hit, cache_hit=True)
+
+    if array_ref:
+        arr = _load_array_ref(project.project_id, array_ref)
+    else:
+        arr = array_source.read_slice(
+            kind=project.kind,
+            source=project.source,
+            slice_index=slice_index,
+            server_uri=project.server_uri,
+            root=project.root,
+        )
+    uint8_stack, float_stack, labels, sam_emb, sam_meta = run_composition(
+        arr, doc
+    )
+    feature_id = uuid.uuid4().hex
+    blob = project_blob_dir(project.project_id) / "features" / feature_id
+    blob.mkdir(parents=True, exist_ok=True)
+    np.save(blob / "float_stack.npy", float_stack.astype(np.float16))
+    np.save(blob / "uint8_stack.npy", uint8_stack)
+    (blob / "labels.json").write_text(
+        json.dumps(labels, indent=2), encoding="utf-8"
+    )
+    if sam_emb is not None:
+        np.save(blob / "sam_emb.npy", sam_emb.astype(np.float32))
+        (blob / "sam_meta.json").write_text(
+            json.dumps(sam_meta), encoding="utf-8"
+        )
+    channels_dir = blob / "channels"
+    channels_dir.mkdir(exist_ok=True)
+    for i in range(uint8_stack.shape[-1]):
+        png = features.encode_channel_png(uint8_stack, i)
+        (channels_dir / f"{i:04d}.png").write_bytes(png)
+
+    h, w, c = float_stack.shape
+    snapshot = dict(doc)
+    record = {
+        "feature_id": feature_id,
+        "project_id": project.project_id,
+        "setup_id": cid,
+        "content_hash": chash,
+        "slice_index": int(slice_index),
+        "n_channels": int(c),
+        "height": int(h),
+        "width": int(w),
+        "blob_dir": str(blob),
+        "setup_snapshot": json.dumps(snapshot),
+        "status": "ready",
+        "created_at": _utc_now(),
+    }
+    catalog.insert_feature_bank(record)
+    catalog.set_session_currents(session_id, feature_id=feature_id)
+    return _bank_response(record, cache_hit=False, labels=labels)
+
+
+def _load_array_ref(project_id: str, array_ref: str) -> np.ndarray:
+    """Load a content-addressed slice uploaded via the data plane."""
+    from ipred.array_blobs import load_array_blob
+
+    return load_array_blob(project_id, array_ref)
+
+
+def load_feature_bank_arrays(blob_dir: str | Path) -> dict[str, Any]:
+    """Load persisted feature arrays from a bank directory."""
+    blob = Path(blob_dir)
+    float_stack = np.load(blob / "float_stack.npy").astype(np.float32)
+    uint8_stack = np.load(blob / "uint8_stack.npy")
+    labels = json.loads((blob / "labels.json").read_text(encoding="utf-8"))
+    sam_emb = None
+    sam_meta = None
+    if (blob / "sam_emb.npy").is_file():
+        sam_emb = np.load(blob / "sam_emb.npy")
+        if (blob / "sam_meta.json").is_file():
+            sam_meta = json.loads(
+                (blob / "sam_meta.json").read_text(encoding="utf-8")
+            )
+    return {
+        "float_stack": float_stack,
+        "uint8_stack": uint8_stack,
+        "labels": labels,
+        "sam_emb": sam_emb,
+        "sam_meta": sam_meta,
+    }
+
+
+def channel_png_path(blob_dir: str | Path, index: int) -> Path:
+    """Path to a cached channel PNG."""
+    return Path(blob_dir) / "channels" / f"{index:04d}.png"
+
+
+def _bank_response(
+    record: dict[str, Any],
+    *,
+    cache_hit: bool,
+    labels: list[str] | None = None,
+) -> dict[str, Any]:
+    blob = Path(record["blob_dir"])
+    if labels is None and (blob / "labels.json").is_file():
+        labels = json.loads((blob / "labels.json").read_text(encoding="utf-8"))
+    return {
+        "feature_id": record["feature_id"],
+        "project_id": record["project_id"],
+        "setup_id": record["setup_id"],
+        "composition_id": record["setup_id"],
+        "slice_index": record["slice_index"],
+        "n_channels": record["n_channels"],
+        "height": record["height"],
+        "width": record["width"],
+        "labels": labels or [],
+        "cache_hit": cache_hit,
+        "blob_dir": record["blob_dir"],
+    }
+
+
+# Keep soft import for tests that still poke at legacy helpers
+def _compute_from_setup(
+    arr: np.ndarray,
+    setup: dict[str, Any],
+    snapshot: dict[str, Any],
+) -> tuple[
+    np.ndarray,
+    np.ndarray,
+    list[str],
+    np.ndarray | None,
+    dict[str, Any] | None,
+]:
+    """Legacy bridge: convert procedure setup → composition run via resolve."""
+    del snapshot
+    sid = setup.get("id") or ""
+    compositions.ensure_default_compositions()
+    try:
+        cid = compositions.resolve_preprocess_id(sid)
+        doc = compositions.resolve_composition(cid)
+        return run_composition(arr, doc)
+    except KeyError as exc:
+        raise ValueError(
+            "weights/procedure setups must map to a composition; "
+            f"unknown {sid}"
+        ) from exc
diff --git a/ipred/src/ipred/sam_embed.py b/ipred/src/ipred/sam_embed.py
new file mode 100644
index 0000000..7e1c446
--- /dev/null
+++ b/ipred/src/ipred/sam_embed.py
@@ -0,0 +1,219 @@
+"""SlimSAM ONNX embeddings for feature banks (self-contained)."""
+
+from __future__ import annotations
+
+import logging
+import os
+import threading
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from PIL import Image as PILImage
+
+logger = logging.getLogger(__name__)
+
+_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(3, 1, 1)
+_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(3, 1, 1)
+_LONG_EDGE = 1024
+_PAD = 1024
+
+_session_lock = threading.Lock()
+
+
+def resolve_encoder_path(explicit: str | None = None) -> Path | None:
+    """Resolve ONNX path from explicit path, env, or repo defaults."""
+    if explicit and explicit != "(missing)":
+        p = Path(explicit).expanduser().resolve()
+        if p.is_file():
+            return p
+    env = os.getenv("FEATURE_ENCODER_ONNX")
+    if env:
+        p = Path(env).expanduser().resolve()
+        if p.is_file():
+            return p
+    here = Path(__file__).resolve()
+    repo = here.parents[3]
+    for c in (
+        repo / "backend" / "models" / "slimsam-77-uniform" / "onnx" / "vision_encoder.onnx",
+        repo / "frontend" / "public" / "models" / "slimsam-77-uniform" / "onnx" / "vision_encoder.onnx",
+    ):
+        if c.is_file():
+            return c.resolve()
+    return None
+
+
+def encoder_available(weights_path: str | None = None) -> bool:
+    """True when an ONNX encoder file is present."""
+    return resolve_encoder_path(weights_path) is not None
+
+
+@lru_cache(maxsize=4)
+def _session_for(path_str: str):
+    import onnxruntime as ort
+
+    return ort.InferenceSession(path_str, providers=["CPUExecutionProvider"])
+
+
+def rgb_uint8_from_array(arr: np.ndarray) -> np.ndarray:
+    """Convert slice to HxWx3 uint8 RGB."""
+    a = np.asarray(arr)
+    if a.ndim == 3 and a.shape[-1] in (3, 4):
+        rgb = a[..., :3].astype(np.float64)
+    elif a.ndim == 2:
+        g = a.astype(np.float64)
+        rgb = np.stack([g, g, g], axis=-1)
+    else:
+        raise ValueError(f"unsupported array shape {a.shape}")
+    finite = rgb[np.isfinite(rgb)]
+    if finite.size == 0:
+        return np.zeros((*rgb.shape[:2], 3), dtype=np.uint8)
+    lo, hi = float(np.min(finite)), float(np.max(finite))
+    if hi <= lo:
+        return np.zeros((*rgb.shape[:2], 3), dtype=np.uint8)
+    scaled = (rgb - lo) / (hi - lo) * 255.0
+    return np.clip(scaled, 0, 255).astype(np.uint8)
+
+
+def encode_image_embeddings(
+    arr: np.ndarray,
+    *,
+    weights_path: str | None = None,
+) -> tuple[np.ndarray, tuple[int, int], tuple[int, int]]:
+    """Return ``(emb eh×ew×C, orig_hw, reshaped_hw)``."""
+    path = resolve_encoder_path(weights_path)
+    if path is None:
+        raise FileNotFoundError("SlimSAM vision_encoder.onnx not found")
+
+    rgb = rgb_uint8_from_array(arr)
+    oh, ow = int(rgb.shape[0]), int(rgb.shape[1])
+    scale = _LONG_EDGE / max(oh, ow)
+    rh = max(1, int(round(oh * scale)))
+    rw = max(1, int(round(ow * scale)))
+    resized = np.asarray(
+        PILImage.fromarray(rgb).resize((rw, rh), PILImage.BILINEAR),
+        dtype=np.uint8,
+    )
+    canvas = np.zeros((_PAD, _PAD, 3), dtype=np.uint8)
+    canvas[:rh, :rw] = resized
+    x = canvas.astype(np.float32) / 255.0
+    x = np.transpose(x, (2, 0, 1))
+    x = (x - _MEAN) / _STD
+    x = np.expand_dims(x, 0)
+
+    with _session_lock:
+        sess = _session_for(str(path))
+        inp = sess.get_inputs()[0].name
+        out = sess.run(None, {inp: x})[0]
+
+    # NCHW → HWC
+    emb = np.transpose(np.asarray(out[0], dtype=np.float32), (1, 2, 0))
+    return emb, (oh, ow), (rh, rw)
+
+
+def bilinear_sample_emb(
+    emb: np.ndarray,
+    ys: np.ndarray,
+    xs: np.ndarray,
+    *,
+    orig_h: int,
+    orig_w: int,
+    reshaped_h: int,
+    reshaped_w: int,
+) -> np.ndarray:
+    """Sample embedding at pixel coords (orig image space)."""
+    eh, ew, c = emb.shape
+    # Map orig → reshaped → emb grid
+    scale_y = reshaped_h / max(orig_h, 1)
+    scale_x = reshaped_w / max(orig_w, 1)
+    fy = (ys.astype(np.float64) * scale_y) * (eh / max(reshaped_h, 1))
+    fx = (xs.astype(np.float64) * scale_x) * (ew / max(reshaped_w, 1))
+    fy = np.clip(fy, 0, eh - 1.001)
+    fx = np.clip(fx, 0, ew - 1.001)
+    y0 = np.floor(fy).astype(np.int64)
+    x0 = np.floor(fx).astype(np.int64)
+    y1 = np.minimum(y0 + 1, eh - 1)
+    x1 = np.minimum(x0 + 1, ew - 1)
+    wy = fy - y0
+    wx = fx - x0
+    ia = emb[y0, x0]
+    ib = emb[y0, x1]
+    ic = emb[y1, x0]
+    id_ = emb[y1, x1]
+    wa = ((1 - wy) * (1 - wx))[:, None]
+    wb = ((1 - wy) * wx)[:, None]
+    wc = (wy * (1 - wx))[:, None]
+    wd = (wy * wx)[:, None]
+    return (wa * ia + wb * ib + wc * ic + wd * id_).astype(np.float32)
+
+
+def fit_pca(x: np.ndarray, n_components: int = 32) -> tuple[np.ndarray, np.ndarray]:
+    """Fit PCA; return mean and components (n_comp × d)."""
+    from sklearn.decomposition import PCA
+
+    n = min(n_components, x.shape[0], x.shape[1])
+    pca = PCA(n_components=n, svd_solver="randomized", random_state=0)
+    pca.fit(x)
+    return pca.mean_.astype(np.float32), pca.components_.astype(np.float32)
+
+
+def transform_pca(
+    x: np.ndarray,
+    mean: np.ndarray,
+    components: np.ndarray,
+) -> np.ndarray:
+    """Project rows with fitted PCA."""
+    return ((x - mean) @ components.T).astype(np.float32)
+
+
+def emb_grid_to_pca_channels(
+    emb: np.ndarray,
+    *,
+    out_h: int,
+    out_w: int,
+    n_components: int = 64,
+    max_fit_samples: int = 50_000,
+) -> tuple[np.ndarray, list[str], dict[str, Any]]:
+    """PCA on dense emb tokens, upsample to ``out_h×out_w×K`` float channels.
+
+    Args:
+        emb: Encoder embedding grid ``eh×ew×C``.
+        out_h / out_w: Full-resolution image size for heatmaps / float_stack.
+        n_components: Target PCA dims (capped by tokens and channels).
+        max_fit_samples: Subsample cap when fitting PCA on large grids.
+
+    Returns:
+        ``(float_hwk, labels, info)`` with labels ``pca0…pca{K-1}``.
+    """
+    from skimage.transform import resize
+
+    eh, ew, c = emb.shape
+    flat = np.asarray(emb, dtype=np.float32).reshape(-1, c)
+    n_tok = flat.shape[0]
+    k_target = max(1, int(n_components))
+    if n_tok > max_fit_samples:
+        rng = np.random.default_rng(0)
+        idx = rng.choice(n_tok, size=max_fit_samples, replace=False)
+        fit_x = flat[idx]
+    else:
+        fit_x = flat
+    mean, comp = fit_pca(fit_x, n_components=k_target)
+    projected = transform_pca(flat, mean, comp)
+    k = projected.shape[1]
+    grid = projected.reshape(eh, ew, k)
+    up = resize(
+        grid,
+        (int(out_h), int(out_w), k),
+        order=1,
+        mode="edge",
+        anti_aliasing=False,
+        preserve_range=True,
+    ).astype(np.float32)
+    labels = [f"pca{i}" for i in range(k)]
+    info = {
+        "pca_dims": int(k),
+        "emb_shape": [int(eh), int(ew), int(c)],
+        "baked_into_float_stack": True,
+    }
+    return up, labels, info
diff --git a/ipred/src/ipred/scripts/__init__.py b/ipred/src/ipred/scripts/__init__.py
new file mode 100644
index 0000000..1a9ec9c
--- /dev/null
+++ b/ipred/src/ipred/scripts/__init__.py
@@ -0,0 +1 @@
+"""Export scripts package."""
diff --git a/ipred/src/ipred/scripts/export_tomojepa_onnx.py b/ipred/src/ipred/scripts/export_tomojepa_onnx.py
new file mode 100644
index 0000000..291bbb5
--- /dev/null
+++ b/ipred/src/ipred/scripts/export_tomojepa_onnx.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+"""Export TomoJEPA Mark25/Mark11 checkpoints to ONNX.
+
+Usage:
+  python -m ipred.scripts.export_tomojepa_onnx
+  python -m ipred.scripts.export_tomojepa_onnx --weights ipred/models/tomojepa11.pth
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+import sys
+from pathlib import Path
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("export_tomojepa_onnx")
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--weights",
+        action="append",
+        default=None,
+        help="Path to .pth (repeatable). Default: both tomojepa25/11 if present.",
+    )
+    parser.add_argument("--input-size", type=int, default=512)
+    parser.add_argument(
+        "--out-dir",
+        type=Path,
+        default=None,
+        help="Output directory (default: same as weights)",
+    )
+    args = parser.parse_args(argv)
+
+    from ipred.tomojepa_onnx import export_tomojepa_onnx
+
+    here = Path(__file__).resolve()
+    models = here.parents[2] / "models"
+    weights = args.weights
+    if not weights:
+        weights = []
+        for name in ("tomojepa25.pth", "tomojepa11.pth"):
+            p = models / name
+            if p.is_file():
+                weights.append(str(p))
+    if not weights:
+        logger.error("no weights found")
+        return 1
+
+    for w in weights:
+        pth = Path(w)
+        out_dir = args.out_dir or pth.parent
+        onnx_path = out_dir / (pth.stem + ".onnx")
+        export_tomojepa_onnx(pth, onnx_path, input_size=args.input_size)
+        logger.info("wrote %s", onnx_path)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/ipred/src/ipred/tomojepa_embed.py b/ipred/src/ipred/tomojepa_embed.py
new file mode 100644
index 0000000..8551728
--- /dev/null
+++ b/ipred/src/ipred/tomojepa_embed.py
@@ -0,0 +1,198 @@
+"""Mark25 / TomoJEPA dense embeddings for feature banks."""
+
+from __future__ import annotations
+
+import logging
+import os
+import threading
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from PIL import Image as PILImage
+
+logger = logging.getLogger(__name__)
+
+_model_lock = threading.Lock()
+_DEFAULT_INPUT_SIZE = 512
+_PATCH = 16
+
+
+def torch_available() -> bool:
+    """True when torch and timm can be imported."""
+    try:
+        import torch  # noqa: F401
+        import timm  # noqa: F401
+    except ImportError:
+        return False
+    return True
+
+
+def resolve_weights_path(explicit: str | None = None) -> Path | None:
+    """Resolve TomoJEPA weights from explicit path, env, or repo default."""
+    if explicit and explicit != "(missing)":
+        p = Path(explicit).expanduser().resolve()
+        if p.is_file():
+            return p
+    env = os.getenv("TOMOJEPA_WEIGHTS")
+    if env:
+        p = Path(env).expanduser().resolve()
+        if p.is_file():
+            return p
+    here = Path(__file__).resolve()
+    # .../repo/ipred/src/ipred → repo/ipred/models
+    candidates = [
+        here.parents[2] / "models" / "tomojepa25.pth",
+        here.parents[3] / "ipred" / "models" / "tomojepa25.pth",
+    ]
+    for c in candidates:
+        if c.is_file():
+            return c.resolve()
+    return None
+
+
+def encoder_available(weights_path: str | None = None) -> bool:
+    """True when torch/timm are importable and weights exist."""
+    if not torch_available():
+        return False
+    return resolve_weights_path(weights_path) is not None
+
+
+def load_checkpoint_state(path: Path | str) -> dict[str, Any]:
+    """Load ``ckpt['net']`` with optional ``module.`` prefix stripped."""
+    import torch
+
+    ckpt = torch.load(str(path), map_location="cpu", weights_only=False)
+    if not isinstance(ckpt, dict) or "net" not in ckpt:
+        raise ValueError(f"expected TomoJEPA ckpt with 'net' key: {path}")
+    return {k.replace("module.", ""): v for k, v in ckpt["net"].items()}
+
+
+def build_encoder(**kwargs: Any):
+    """Build uninitialized DINOv3ViTEncoder (requires torch/timm)."""
+    from ipred.tomojepa_encoder import DINOv3ViTEncoder
+
+    return DINOv3ViTEncoder(**kwargs)
+
+
+@lru_cache(maxsize=4)
+def _cached_model(path_str: str):
+    import torch
+
+    from ipred.tomojepa_encoder import DINOv3ViTEncoder
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    state = load_checkpoint_state(path_str)
+    # Mark25 dense proj is 64-D; Mark11 is 256-D — read from checkpoint.
+    proj_w = state.get("proj.8.weight")
+    if proj_w is None:
+        raise ValueError(f"checkpoint missing proj.8.weight: {path_str}")
+    proj_dim = int(proj_w.shape[0])
+    net = DINOv3ViTEncoder(
+        proj_dim=proj_dim, img_size=_DEFAULT_INPUT_SIZE, in_chans=1, pretrained=False
+    )
+    net.load_state_dict(state, strict=True)
+    net.eval()
+    net.to(device)
+    return net, device
+
+
+def _pad_to_multiple(h: int, w: int, multiple: int = _PATCH) -> tuple[int, int]:
+    ph = (multiple - (h % multiple)) % multiple
+    pw = (multiple - (w % multiple)) % multiple
+    return h + ph, w + pw
+
+
+def _gray_unit_interval(arr: np.ndarray) -> np.ndarray:
+    a = np.asarray(arr)
+    if a.ndim == 3 and a.shape[-1] in (3, 4):
+        rgb = a[..., :3].astype(np.float64)
+        gray = 0.299 * rgb[..., 0] + 0.587 * rgb[..., 1] + 0.114 * rgb[..., 2]
+    elif a.ndim == 2:
+        gray = a.astype(np.float64)
+    else:
+        raise ValueError(f"unsupported array shape {a.shape}")
+    finite = gray[np.isfinite(gray)]
+    if finite.size == 0:
+        return np.zeros_like(gray, dtype=np.float32)
+    lo, hi = float(np.min(finite)), float(np.max(finite))
+    if hi <= lo:
+        return np.zeros_like(gray, dtype=np.float32)
+    return ((gray - lo) / (hi - lo)).astype(np.float32)
+
+
+def to_minus_one_one(gray01: np.ndarray) -> np.ndarray:
+    """Linear map from ``[0, 1]`` to ``[-1, 1]`` (Mark25 / TomoJEPA convention)."""
+    return (np.asarray(gray01, dtype=np.float32) * 2.0 - 1.0).astype(np.float32)
+
+
+def encode_dense_embeddings(
+    arr: np.ndarray,
+    *,
+    weights_path: str | None = None,
+    input_size: int = _DEFAULT_INPUT_SIZE,
+    resize: bool = True,
+) -> tuple[np.ndarray, tuple[int, int], tuple[int, int]]:
+    """Return ``(emb Hp×Wp×64, orig_hw, reshaped_hw)``.
+
+    When ``resize`` is True (default), the grayscale slice is resized to a
+    square ``input_size×input_size``. When False, native resolution is kept and
+    only padded to a multiple of 16 (Mark25 ``dynamic_img_size``).
+
+    Intensity pipeline: min-max to ``[0, 1]`` (CLAHE already in that range when
+    passed from ``clahe_encoder_v1``), then linear shift to ``[-1, 1]`` before
+    the network.
+    """
+    import torch
+
+    path = resolve_weights_path(weights_path)
+    if path is None:
+        raise FileNotFoundError("TomoJEPA weights (.pth) not found")
+    if not torch_available():
+        raise ImportError(
+            "TomoJEPA requires torch and timm — install with: pip install -e '.[torch]'"
+        )
+
+    gray = _gray_unit_interval(arr)
+    oh, ow = int(gray.shape[0]), int(gray.shape[1])
+    if resize:
+        size = max(int(input_size), _PATCH)
+        resized = np.asarray(
+            PILImage.fromarray(gray, mode="F").resize(
+                (size, size), PILImage.BILINEAR
+            ),
+            dtype=np.float32,
+        )
+        rh, rw = size, size
+    else:
+        resized = gray.astype(np.float32, copy=True)
+        rh, rw = oh, ow
+
+    pad_h, pad_w = _pad_to_multiple(rh, rw, _PATCH)
+    if pad_h != rh or pad_w != rw:
+        canvas = np.zeros((pad_h, pad_w), dtype=np.float32)
+        canvas[:rh, :rw] = resized
+        resized = canvas
+    else:
+        pad_h, pad_w = rh, rw
+
+    # TomoJEPA expects intensities in [-1, 1] after CLAHE / unit-interval prep.
+    model_in = to_minus_one_one(resized)
+
+    x = torch.from_numpy(np.array(model_in, dtype=np.float32, copy=True)[None, None, None])
+    with _model_lock:
+        net, device = _cached_model(str(path))
+        x = x.to(device)
+        with torch.inference_mode():
+            _glob, dense, _feats = net(x)
+        dense_np = dense.detach().cpu().numpy()[0, 0]  # [L, 64]
+
+    grid_h = pad_h // _PATCH
+    grid_w = pad_w // _PATCH
+    if dense_np.shape[0] != grid_h * grid_w:
+        side = int(round(dense_np.shape[0] ** 0.5))
+        grid_h = grid_w = side
+    emb = dense_np.reshape(grid_h, grid_w, dense_np.shape[-1]).astype(np.float32)
+    # reshaped_hw is the coordinate frame covered by the emb grid (incl. pad).
+    return emb, (oh, ow), (pad_h, pad_w)
diff --git a/ipred/src/ipred/tomojepa_encoder.py b/ipred/src/ipred/tomojepa_encoder.py
new file mode 100644
index 0000000..44b2c29
--- /dev/null
+++ b/ipred/src/ipred/tomojepa_encoder.py
@@ -0,0 +1,107 @@
+"""Mark25 DINOv3ViTEncoder — ViT-S/16 + dense/global projectors.
+
+Architecture reconstructed to match TomoJEPA checkpoints (``ckpt['net']``):
+timm ``vit_small_patch16_dinov3`` backbone, BatchNorm dense MLP → ``proj_dim``
+(Mark25: 64-D, Mark11: 256-D), LayerNorm global MLP → 16-D.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+
+def _dense_mlp(in_dim: int, hidden: int, out_dim: int) -> nn.Sequential:
+    return nn.Sequential(
+        nn.Linear(in_dim, hidden),
+        nn.BatchNorm1d(hidden),
+        nn.GELU(),
+        nn.Dropout(0.0),
+        nn.Linear(hidden, hidden),
+        nn.BatchNorm1d(hidden),
+        nn.GELU(),
+        nn.Dropout(0.0),
+        nn.Linear(hidden, out_dim),
+        nn.Identity(),
+    )
+
+
+def _global_mlp(in_dim: int, hidden: int, out_dim: int) -> nn.Sequential:
+    return nn.Sequential(
+        nn.Linear(in_dim, hidden),
+        nn.LayerNorm(hidden),
+        nn.GELU(),
+        nn.Dropout(0.0),
+        nn.Linear(hidden, hidden),
+        nn.LayerNorm(hidden),
+        nn.GELU(),
+        nn.Dropout(0.0),
+        nn.Linear(hidden, out_dim),
+        nn.Identity(),
+    )
+
+
+class DINOv3ViTEncoder(nn.Module):
+    """Mark25 TomoJEPA encoder producing global and dense projections."""
+
+    def __init__(
+        self,
+        proj_dim: int = 64,
+        img_size: int = 512,
+        in_chans: int = 1,
+        pretrained: bool = False,
+        *,
+        embed_dim: int = 384,
+        global_dim: int = 16,
+        hidden_dim: int = 2048,
+    ) -> None:
+        super().__init__()
+        del pretrained  # checkpoint is loaded separately; never ImageNet init
+        import timm
+
+        self.backbone = timm.create_model(
+            "vit_small_patch16_dinov3",
+            pretrained=False,
+            in_chans=in_chans,
+            img_size=img_size,
+            num_classes=0,
+            dynamic_img_size=True,
+        )
+        self.proj = _dense_mlp(embed_dim, hidden_dim, proj_dim)
+        self.global_proj = _global_mlp(embed_dim, hidden_dim, global_dim)
+        self.num_prefix_tokens = int(getattr(self.backbone, "num_prefix_tokens", 5))
+        self.proj_dim = int(proj_dim)
+        self.patch_size = int(getattr(self.backbone, "patch_size", 16) or 16)
+
+    def forward(
+        self, x: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Run encoder on multi-view batch.
+
+        Args:
+            x: Float tensor ``[B, C, V, H, W]`` (typically C=1).
+
+        Returns:
+            ``(global_proj, dense_proj, feats)`` where dense is
+            ``[B, V, L, proj_dim]`` and global is ``[B, V, global_dim]``.
+        """
+        if x.ndim != 5:
+            raise ValueError(f"expected [B,C,V,H,W], got shape {tuple(x.shape)}")
+        b, c, v, h, w = x.shape
+        flat = x.permute(0, 2, 1, 3, 4).reshape(b * v, c, h, w)
+        feats = self.backbone.forward_features(flat)
+        cls = feats[:, 0]
+        patches = feats[:, self.num_prefix_tokens :]
+        bv, length, dim = patches.shape
+        dense = self.proj(patches.reshape(bv * length, dim)).reshape(
+            b, v, length, -1
+        )
+        glob = self.global_proj(cls).reshape(b, v, -1)
+        return glob, dense, feats
+
+
+def build_encoder(**kwargs: Any) -> DINOv3ViTEncoder:
+    """Construct an uninitialized Mark25 encoder."""
+    return DINOv3ViTEncoder(**kwargs)
diff --git a/ipred/src/ipred/tomojepa_onnx.py b/ipred/src/ipred/tomojepa_onnx.py
new file mode 100644
index 0000000..cfbf7c7
--- /dev/null
+++ b/ipred/src/ipred/tomojepa_onnx.py
@@ -0,0 +1,208 @@
+"""TomoJEPA ONNX Runtime backend (optional; falls back to torch)."""
+
+from __future__ import annotations
+
+import logging
+import threading
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from PIL import Image as PILImage
+
+from ipred.tomojepa_embed import _gray_unit_interval, to_minus_one_one
+
+logger = logging.getLogger(__name__)
+
+_session_lock = threading.Lock()
+_PATCH = 16
+_DEFAULT_INPUT_SIZE = 512
+
+
+def onnx_available() -> bool:
+    """True when onnxruntime can be imported."""
+    try:
+        import onnxruntime  # noqa: F401
+    except ImportError:
+        return False
+    return True
+
+
+@lru_cache(maxsize=4)
+def _session_for(path_str: str):
+    import onnxruntime as ort
+
+    return ort.InferenceSession(path_str, providers=["CPUExecutionProvider"])
+
+
+def fixed_spatial_size(weights_path: str | Path) -> int | None:
+    """Return fixed square H=W from ONNX input, or None when dynamic / unknown."""
+    if not onnx_available():
+        return None
+    path = Path(weights_path)
+    if not path.is_file():
+        return None
+    with _session_lock:
+        sess = _session_for(str(path.resolve()))
+        shape = sess.get_inputs()[0].shape
+    if not shape:
+        return None
+    # [N,C,D,H,W] or [N,C,H,W]
+    dims = list(shape)
+    if len(dims) >= 2:
+        h, w = dims[-2], dims[-1]
+        if isinstance(h, int) and isinstance(w, int) and h == w and h > 0:
+            return int(h)
+    return None
+
+
+def onnx_matches_input_size(
+    weights_path: str | Path, input_size: int
+) -> bool:
+    """True when ONNX is dynamic or fixed spatial size equals ``input_size``."""
+    fixed = fixed_spatial_size(weights_path)
+    if fixed is None:
+        return True
+    return fixed == int(input_size)
+
+
+def _pad_to_multiple(h: int, w: int, multiple: int = _PATCH) -> tuple[int, int]:
+    ph = (multiple - (h % multiple)) % multiple
+    pw = (multiple - (w % multiple)) % multiple
+    return h + ph, w + pw
+
+
+def encode_dense_embeddings(
+    arr: np.ndarray,
+    *,
+    weights_path: str,
+    input_size: int = _DEFAULT_INPUT_SIZE,
+    resize: bool = True,
+) -> tuple[np.ndarray, tuple[int, int], tuple[int, int]]:
+    """Run exported TomoJEPA ONNX; return ``(emb Hp×Wp×D, orig_hw, reshaped_hw)``.
+
+    Expects ONNX input name ``input`` with shape ``[1,1,1,H,W]`` or ``[1,1,H,W]``
+    float32 in ``[-1, 1]``, and output ``dense`` as ``[1,1,L,D]`` or ``[1,L,D]``.
+
+    When the graph has a fixed spatial size, that size is used (must match
+    ``input_size`` when ``resize`` is True, else ValueError).
+    """
+    if not onnx_available():
+        raise ImportError("onnxruntime required for TomoJEPA ONNX")
+    path = Path(weights_path)
+    if not path.is_file():
+        raise FileNotFoundError(str(path))
+
+    fixed = fixed_spatial_size(path)
+    effective_size = int(input_size)
+    if fixed is not None:
+        if resize and fixed != effective_size:
+            raise ValueError(
+                f"ONNX fixed size {fixed} != input_size {effective_size}; "
+                "use torch fallback or re-export ONNX"
+            )
+        effective_size = fixed
+
+    gray = _gray_unit_interval(arr)
+    oh, ow = int(gray.shape[0]), int(gray.shape[1])
+    if resize:
+        size = max(effective_size, _PATCH)
+        resized = np.asarray(
+            PILImage.fromarray(gray, mode="F").resize(
+                (size, size), PILImage.BILINEAR
+            ),
+            dtype=np.float32,
+        )
+        rh, rw = size, size
+    else:
+        resized = gray.astype(np.float32, copy=True)
+        rh, rw = oh, ow
+        if fixed is not None and (rh != fixed or rw != fixed):
+            raise ValueError(
+                f"array {rh}x{rw} does not match ONNX fixed size {fixed}"
+            )
+
+    pad_h, pad_w = _pad_to_multiple(rh, rw, _PATCH)
+    if pad_h != rh or pad_w != rw:
+        canvas = np.zeros((pad_h, pad_w), dtype=np.float32)
+        canvas[:rh, :rw] = resized
+        resized = canvas
+    else:
+        pad_h, pad_w = rh, rw
+
+    model_in = to_minus_one_one(resized)
+    x5 = np.array(model_in, dtype=np.float32, copy=True)[None, None, None]
+    x4 = np.array(model_in, dtype=np.float32, copy=True)[None, None]
+
+    with _session_lock:
+        sess = _session_for(str(path.resolve()))
+        in_meta = sess.get_inputs()[0]
+        in_name = in_meta.name
+        # Choose rank matching exported model
+        shape = in_meta.shape
+        rank = len(shape) if shape else 5
+        feed = {in_name: x5 if rank >= 5 else x4}
+        outs = sess.run(None, feed)
+    dense = np.asarray(outs[0], dtype=np.float32)
+    # Squeeze to [L, D]
+    while dense.ndim > 2:
+        if dense.shape[0] == 1:
+            dense = dense[0]
+        else:
+            break
+    if dense.ndim != 2:
+        raise ValueError(f"unexpected ONNX dense shape {dense.shape}")
+
+    grid_h = pad_h // _PATCH
+    grid_w = pad_w // _PATCH
+    if dense.shape[0] != grid_h * grid_w:
+        side = int(round(dense.shape[0] ** 0.5))
+        grid_h = grid_w = side
+    emb = dense.reshape(grid_h, grid_w, dense.shape[-1]).astype(np.float32)
+    return emb, (oh, ow), (pad_h, pad_w)
+
+
+def export_tomojepa_onnx(
+    pth_path: str | Path,
+    onnx_path: str | Path,
+    *,
+    input_size: int = 512,
+    opset: int = 17,
+) -> Path:
+    """Export ``DINOv3ViTEncoder`` checkpoint to ONNX (fixed square size)."""
+    import torch
+
+    from ipred.tomojepa_embed import build_encoder, load_checkpoint_state
+
+    pth = Path(pth_path)
+    out = Path(onnx_path)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    state = load_checkpoint_state(pth)
+    proj_dim = int(state["proj.8.weight"].shape[0])
+    net = build_encoder(proj_dim=proj_dim, img_size=input_size, in_chans=1)
+    net.load_state_dict(state, strict=True)
+    net.eval()
+
+    class _DenseOnly(torch.nn.Module):
+        def __init__(self, enc: Any) -> None:
+            super().__init__()
+            self.enc = enc
+
+        def forward(self, x: torch.Tensor) -> torch.Tensor:
+            _g, dense, _f = self.enc(x)
+            return dense
+
+    wrapper = _DenseOnly(net)
+    dummy = torch.zeros(1, 1, 1, input_size, input_size, dtype=torch.float32)
+    torch.onnx.export(
+        wrapper,
+        dummy,
+        str(out),
+        input_names=["input"],
+        output_names=["dense"],
+        opset_version=opset,
+        dynamo=False,
+    )
+    logger.info("exported TomoJEPA ONNX → %s (proj_dim=%s)", out, proj_dim)
+    return out.resolve()
diff --git a/ipred/src/ipred/train_infer.py b/ipred/src/ipred/train_infer.py
new file mode 100644
index 0000000..9bf535d
--- /dev/null
+++ b/ipred/src/ipred/train_infer.py
@@ -0,0 +1,535 @@
+"""Train / infer / rethreshold orchestration."""
+
+from __future__ import annotations
+
+import json
+import logging
+import uuid
+from datetime import datetime, timezone
+from io import BytesIO
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from PIL import Image as PILImage
+
+from ipred import conformal, sam_embed
+from ipred.catalog import Catalog
+from ipred.labels import build_label_map
+from ipred.paths import project_blob_dir
+from ipred.preprocess import load_feature_bank_arrays
+from ipred.trainers import get_trainer
+
+logger = logging.getLogger(__name__)
+
+
+def _utc_now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _shape_bbox(shape: dict[str, Any]) -> tuple[float, float, float, float] | None:
+    """Rough (x0, y0, x1, y1) extent of one shape, for mismatch diagnostics."""
+    kind = shape.get("kind")
+    try:
+        if kind == "rectangle":
+            x, y, ww, hh = float(shape["x"]), float(shape["y"]), float(shape["w"]), float(shape["h"])
+            return min(x, x + ww), min(y, y + hh), max(x, x + ww), max(y, y + hh)
+        if kind == "ellipse":
+            cx, cy, rx, ry = (float(shape["cx"]), float(shape["cy"]), float(shape["rx"]), float(shape["ry"]))
+            return cx - rx, cy - ry, cx + rx, cy + ry
+        if kind == "polygon":
+            pts = shape.get("points") or []
+            xs = pts[0::2]
+            ys = pts[1::2]
+            if not xs or not ys:
+                return None
+            return min(xs), min(ys), max(xs), max(ys)
+        if kind == "brush":
+            xs: list[float] = []
+            ys: list[float] = []
+            for stroke in shape.get("strokes") or []:
+                pts = stroke.get("points") or []
+                xs.extend(pts[0::2])
+                ys.extend(pts[1::2])
+            if not xs or not ys:
+                return None
+            return min(xs), min(ys), max(xs), max(ys)
+    except (KeyError, TypeError, ValueError):
+        return None
+    return None
+
+
+def _shape_class_summary(shapes: list[dict[str, Any]]) -> str:
+    """Per-class shape count + combined bbox, for error messages when
+    rasterization yields fewer classes than the caller annotated."""
+    by_class: dict[int, list[tuple[float, float, float, float]]] = {}
+    for shape in shapes:
+        cid = int(shape.get("classId") or shape.get("class_id") or 0)
+        bbox = _shape_bbox(shape)
+        by_class.setdefault(cid, [])
+        if bbox is not None:
+            by_class[cid].append(bbox)
+    parts = []
+    for cid, boxes in sorted(by_class.items()):
+        if boxes:
+            x0 = min(b[0] for b in boxes)
+            y0 = min(b[1] for b in boxes)
+            x1 = max(b[2] for b in boxes)
+            y1 = max(b[3] for b in boxes)
+            parts.append(f"class {cid}: {len(boxes)} shape(s) spanning ({x0:.0f},{y0:.0f})-({x1:.0f},{y1:.0f})")
+        else:
+            parts.append(f"class {cid}: shape(s) with unparsable extent")
+    return "; ".join(parts) if parts else "no shapes"
+
+
+def stratified_train_cal_split(
+    ys: np.ndarray,
+    *,
+    train_frac: float = 0.8,
+    rng: np.random.Generator,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Stratified indices into train / calibration."""
+    if not 0.0 < train_frac < 1.0:
+        raise ValueError("train_frac must be in (0, 1)")
+    train_parts: list[np.ndarray] = []
+    cal_parts: list[np.ndarray] = []
+    for cls in np.unique(ys):
+        idx = np.flatnonzero(ys == cls)
+        rng.shuffle(idx)
+        n = idx.size
+        if n == 1:
+            train_parts.append(idx)
+            continue
+        n_train = max(1, min(n - 1, int(round(n * train_frac))))
+        train_parts.append(idx[:n_train])
+        cal_parts.append(idx[n_train:])
+    train_idx = np.concatenate(train_parts) if train_parts else np.array([], dtype=np.int64)
+    cal_idx = np.concatenate(cal_parts) if cal_parts else np.array([], dtype=np.int64)
+    if cal_idx.size == 0:
+        raise ValueError("calibration split empty — need more labeled pixels per class")
+    return train_idx, cal_idx
+
+
+def _stratified_sample(
+    ys: np.ndarray, max_samples: int, rng: np.random.Generator
+) -> np.ndarray:
+    n = ys.shape[0]
+    if n <= max_samples:
+        return np.arange(n)
+    classes, counts = np.unique(ys, return_counts=True)
+    alloc = np.maximum(1, np.floor(counts / n * max_samples).astype(int))
+    while alloc.sum() < max_samples:
+        alloc[int(np.argmax(counts - alloc))] += 1
+    while alloc.sum() > max_samples:
+        i = int(np.argmax(alloc))
+        if alloc[i] > 1:
+            alloc[i] -= 1
+        else:
+            break
+    chosen: list[np.ndarray] = []
+    for cls, k in zip(classes, alloc):
+        idx = np.flatnonzero(ys == cls)
+        take = min(int(k), idx.size)
+        chosen.append(rng.choice(idx, size=take, replace=False))
+    return np.concatenate(chosen) if chosen else np.arange(0)
+
+
+def _pixel_features(
+    bank: dict[str, Any],
+    yy: np.ndarray,
+    xx: np.ndarray,
+    *,
+    sam_pca_mean: np.ndarray | None = None,
+    sam_pca_components: np.ndarray | None = None,
+    fit_pca: bool = False,
+    sam_pca_dims: int = 32,
+) -> tuple[np.ndarray, list[str], np.ndarray | None, np.ndarray | None]:
+    float_stack = bank["float_stack"]
+    h, w, _ = float_stack.shape
+    ys_i = np.clip(yy.astype(np.int64), 0, h - 1)
+    xs_i = np.clip(xx.astype(np.int64), 0, w - 1)
+    x_sk = float_stack[ys_i, xs_i].astype(np.float32)
+    labels = list(bank["labels"])
+    sam_emb = bank.get("sam_emb")
+    sam_meta = bank.get("sam_meta")
+    # Preprocess may already bake encoder PCA into float_stack channels.
+    if sam_meta and sam_meta.get("baked_into_float_stack"):
+        return x_sk, labels, None, None
+    if sam_emb is None or sam_meta is None:
+        return x_sk, labels, None, None
+    oh, ow = sam_meta["orig_hw"]
+    rh, rw = sam_meta["reshaped_hw"]
+    x_sam = sam_embed.bilinear_sample_emb(
+        sam_emb,
+        ys_i.astype(np.float64),
+        xs_i.astype(np.float64),
+        orig_h=int(oh),
+        orig_w=int(ow),
+        reshaped_h=int(rh),
+        reshaped_w=int(rw),
+    )
+    mean, comp = sam_pca_mean, sam_pca_components
+    if fit_pca:
+        mean, comp = sam_embed.fit_pca(x_sam, n_components=sam_pca_dims)
+    if mean is not None and comp is not None:
+        x_sam = sam_embed.transform_pca(x_sam, mean, comp)
+    x = np.concatenate([x_sk, x_sam], axis=1)
+    feat_labels = labels + [f"sam{i}" for i in range(x_sam.shape[1])]
+    return x, feat_labels, mean, comp
+
+
+def run_train(
+    catalog: Catalog,
+    *,
+    session_id: str,
+    shapes: list[dict[str, Any]],
+    feature_id: str | None = None,
+    trainer_id: str = "catboost",
+    config: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """Train a plugin model on session feature bank + shapes."""
+    session = catalog.get_session(session_id)
+    if session is None:
+        raise KeyError(f"unknown session {session_id}")
+    fid = feature_id or session.current_feature_id
+    if not fid:
+        raise ValueError("no feature_id; run preprocess first")
+    bank_row = catalog.get_feature_bank(fid)
+    if bank_row is None:
+        raise KeyError(f"unknown feature bank {fid}")
+    bank = load_feature_bank_arrays(bank_row["blob_dir"])
+    h, w, _ = bank["float_stack"].shape
+    label_map = build_label_map(shapes, h, w)
+    if not np.any(label_map > 0):
+        raise ValueError(
+            f"no labeled pixels — annotate at least one shape "
+            f"({_shape_class_summary(shapes)} vs. feature bank {w}x{h})"
+        )
+
+    yy, xx = np.nonzero(label_map)
+    y_all = label_map[yy, xx].astype(np.int32)
+    if len(np.unique(y_all)) < 2:
+        raise ValueError(
+            "need at least two classes with labeled pixels — got "
+            f"{sorted(int(c) for c in np.unique(y_all))} after rasterizing to the feature "
+            f"bank ({w}x{h}); shapes sent: {_shape_class_summary(shapes)}. If the shape "
+            "classes/extents don't match, the feature bank may be at a different "
+            "resolution than the annotated slice."
+        )
+
+    cfg = dict(config or {})
+    rng = np.random.default_rng(int(cfg.get("random_seed", 0)))
+    max_samples = int(cfg.get("max_samples", 200_000))
+    train_frac = float(cfg.get("train_frac", 0.8))
+    cap_idx = _stratified_sample(y_all, max_samples, rng)
+    yy_c, xx_c, y_c = yy[cap_idx], xx[cap_idx], y_all[cap_idx]
+    train_idx, cal_idx = stratified_train_cal_split(y_c, train_frac=train_frac, rng=rng)
+
+    uses_sam = (
+        bank.get("sam_emb") is not None
+        and not (bank.get("sam_meta") or {}).get("baked_into_float_stack")
+    )
+    x_tr, feat_labels, pca_mean, pca_comp = _pixel_features(
+        bank,
+        yy_c[train_idx],
+        xx_c[train_idx],
+        fit_pca=uses_sam,
+        sam_pca_dims=int(cfg.get("sam_pca_dims", 32)),
+    )
+    x_cal, _, _, _ = _pixel_features(
+        bank,
+        yy_c[cal_idx],
+        xx_c[cal_idx],
+        sam_pca_mean=pca_mean,
+        sam_pca_components=pca_comp,
+    )
+
+    trainer = get_trainer(trainer_id)
+    arts = trainer.train(
+        x_tr,
+        y_c[train_idx],
+        x_cal,
+        y_c[cal_idx],
+        feature_labels=feat_labels,
+        config=cfg,
+    )
+    arts.params["uses_sam"] = uses_sam
+    arts.params["train_frac"] = train_frac
+
+    model_id = uuid.uuid4().hex
+    blob = project_blob_dir(session.project_id) / "models" / model_id
+    blob.mkdir(parents=True, exist_ok=True)
+    trainer.save(arts.model_handle, blob)
+    feature_importances = list(arts.extras.get("feature_importances") or [])
+    meta = {
+        "model_id": model_id,
+        "feature_id": fid,
+        "trainer_id": trainer_id,
+        "class_ids": arts.class_ids,
+        "feature_labels": arts.feature_labels,
+        "cal_scores_by_class": arts.cal_scores_by_class,
+        "train_accuracy": arts.train_accuracy,
+        "n_train": arts.n_train,
+        "n_cal": arts.n_cal,
+        "n_samples": arts.n_samples,
+        "params": arts.params,
+        "uses_sam": uses_sam,
+        "feature_importances": feature_importances,
+    }
+    if pca_mean is not None and pca_comp is not None:
+        np.savez(blob / "sam_pca.npz", mean=pca_mean, components=pca_comp)
+        meta["has_sam_pca"] = True
+    (blob / "meta.json").write_text(json.dumps(meta, indent=2), encoding="utf-8")
+    (blob / "cal_scores.json").write_text(
+        json.dumps(arts.cal_scores_by_class), encoding="utf-8"
+    )
+
+    catalog.insert_model(
+        {
+            "model_id": model_id,
+            "project_id": session.project_id,
+            "feature_id": fid,
+            "trainer_id": trainer_id,
+            "blob_dir": str(blob),
+            "meta_json": json.dumps(meta),
+            "created_at": _utc_now(),
+        }
+    )
+    catalog.set_session_currents(session_id, model_id=model_id)
+    return {
+        "model_id": model_id,
+        "feature_id": fid,
+        "trainer_id": trainer_id,
+        "class_ids": arts.class_ids,
+        "train_accuracy": arts.train_accuracy,
+        "n_train": arts.n_train,
+        "n_cal": arts.n_cal,
+        "n_samples": arts.n_samples,
+        "params": arts.params,
+        "feature_importances": feature_importances,
+    }
+
+
+def run_infer(
+    catalog: Catalog,
+    *,
+    session_id: str,
+    model_id: str | None = None,
+    feature_id: str | None = None,
+    alpha: float = 0.05,
+    row_chunk: int = 128,
+) -> dict[str, Any]:
+    """Full-image predict_proba + conformal maps; persist float16 proba."""
+    session = catalog.get_session(session_id)
+    if session is None:
+        raise KeyError(f"unknown session {session_id}")
+    mid = model_id or session.current_model_id
+    fid = feature_id or session.current_feature_id
+    if not mid or not fid:
+        raise ValueError("model_id and feature_id required (train/preprocess first)")
+    model_row = catalog.get_model(mid)
+    bank_row = catalog.get_feature_bank(fid)
+    if model_row is None or bank_row is None:
+        raise KeyError("model or feature bank missing")
+
+    blob_model = Path(model_row["blob_dir"])
+    meta = json.loads((blob_model / "meta.json").read_text(encoding="utf-8"))
+    trainer = get_trainer(model_row["trainer_id"])
+    handle = trainer.load(blob_model)
+    bank = load_feature_bank_arrays(bank_row["blob_dir"])
+    pca_mean = pca_comp = None
+    if (blob_model / "sam_pca.npz").is_file():
+        z = np.load(blob_model / "sam_pca.npz")
+        pca_mean, pca_comp = z["mean"], z["components"]
+
+    class_ids = [int(c) for c in meta["class_ids"]]
+    h, w, _ = bank["float_stack"].shape
+    k = len(class_ids)
+    proba = np.empty((h, w, k), dtype=np.float16)
+
+    for y0 in range(0, h, row_chunk):
+        y1 = min(h, y0 + row_chunk)
+        yy = np.repeat(np.arange(y0, y1), w)
+        xx = np.tile(np.arange(w), y1 - y0)
+        x, _, _, _ = _pixel_features(
+            bank, yy, xx, sam_pca_mean=pca_mean, sam_pca_components=pca_comp
+        )
+        block = trainer.predict_proba(handle, x).astype(np.float32)
+        # Align columns to meta class_ids order
+        model_classes = [int(c) for c in np.asarray(handle.classes_).tolist()]
+        if model_classes != class_ids:
+            remap = np.zeros_like(block)
+            for j, cid in enumerate(class_ids):
+                if cid in model_classes:
+                    remap[:, j] = block[:, model_classes.index(cid)]
+            block = remap
+        proba[y0:y1] = block.reshape(y1 - y0, w, k).astype(np.float16)
+
+    cal = conformal.cal_scores_from_json(meta["cal_scores_by_class"])
+    q_by_class = conformal.mondrian_thresholds(cal, alpha)
+    commit, status, membership = conformal.maps_from_proba(
+        proba.astype(np.float32), class_ids, q_by_class
+    )
+    counts = conformal.counts_from_status(status)
+
+    run_id = uuid.uuid4().hex
+    blob = project_blob_dir(session.project_id) / "runs" / run_id
+    blob.mkdir(parents=True, exist_ok=True)
+    np.save(blob / "proba.npy", proba)
+    np.save(blob / "commit.npy", commit)
+    np.save(blob / "status.npy", status)
+    np.save(blob / "membership.npy", membership)
+    _write_label_png(blob / "commit.png", commit)
+    _write_label_png(blob / "status.png", status)
+
+    run_meta = {
+        "run_id": run_id,
+        "model_id": mid,
+        "feature_id": fid,
+        "alpha": alpha,
+        "class_ids": class_ids,
+        "q_by_class": {str(k_): v for k_, v in q_by_class.items()},
+        "counts": counts,
+    }
+    (blob / "meta.json").write_text(json.dumps(run_meta, indent=2), encoding="utf-8")
+    now = _utc_now()
+    catalog.insert_run(
+        {
+            "run_id": run_id,
+            "project_id": session.project_id,
+            "model_id": mid,
+            "feature_id": fid,
+            "alpha": float(alpha),
+            "blob_dir": str(blob),
+            "meta_json": json.dumps(run_meta),
+            "created_at": now,
+            "updated_at": now,
+        }
+    )
+    catalog.set_session_currents(session_id, run_id=run_id)
+    return {**run_meta, "blob_dir": str(blob)}
+
+
+def run_rethreshold(
+    catalog: Catalog,
+    *,
+    session_id: str,
+    run_id: str | None = None,
+    alpha: float,
+) -> dict[str, Any]:
+    """Recompute membership/commit/status from cached proba (no re-infer)."""
+    session = catalog.get_session(session_id)
+    if session is None:
+        raise KeyError(f"unknown session {session_id}")
+    rid = run_id or session.current_run_id
+    if not rid:
+        raise ValueError("run_id required")
+    run = catalog.get_run(rid)
+    if run is None:
+        raise KeyError(f"unknown run {rid}")
+    blob = Path(run["blob_dir"])
+    proba = np.load(blob / "proba.npy").astype(np.float32)
+    run_meta = json.loads((blob / "meta.json").read_text(encoding="utf-8"))
+    model_row = catalog.get_model(run["model_id"])
+    if model_row is None:
+        raise KeyError("model missing for run")
+    model_meta = json.loads(
+        Path(model_row["blob_dir"], "meta.json").read_text(encoding="utf-8")
+    )
+    cal = conformal.cal_scores_from_json(model_meta["cal_scores_by_class"])
+    class_ids = [int(c) for c in run_meta["class_ids"]]
+    q_by_class = conformal.mondrian_thresholds(cal, alpha)
+    commit, status, membership = conformal.maps_from_proba(proba, class_ids, q_by_class)
+    counts = conformal.counts_from_status(status)
+    np.save(blob / "commit.npy", commit)
+    np.save(blob / "status.npy", status)
+    np.save(blob / "membership.npy", membership)
+    _write_label_png(blob / "commit.png", commit)
+    _write_label_png(blob / "status.png", status)
+    run_meta.update(
+        {
+            "alpha": alpha,
+            "q_by_class": {str(k): v for k, v in q_by_class.items()},
+            "counts": counts,
+        }
+    )
+    (blob / "meta.json").write_text(json.dumps(run_meta, indent=2), encoding="utf-8")
+    catalog.update_run(rid, alpha=float(alpha), meta_json=json.dumps(run_meta))
+    catalog.set_session_currents(session_id, run_id=rid)
+    return {**run_meta, "blob_dir": str(blob)}
+
+
+def _write_label_png(path: Path, arr: np.ndarray) -> None:
+    img = PILImage.fromarray(arr.astype(np.uint8), mode="L")
+    buf = BytesIO()
+    img.save(buf, format="PNG")
+    path.write_bytes(buf.getvalue())
+
+
+def _run_blob(catalog: Catalog, run_id: str) -> tuple[dict[str, Any], Path, dict[str, Any]]:
+    run = catalog.get_run(run_id)
+    if run is None:
+        raise KeyError(f"unknown run {run_id}")
+    blob = Path(run["blob_dir"])
+    meta = json.loads(run["meta_json"])
+    return run, blob, meta
+
+
+def proba_heatmap_png(catalog: Catalog, run_id: str, class_index: int) -> bytes:
+    """Return grayscale PNG of softmax channel ``class_index`` (0…K-1)."""
+    _run, blob, meta = _run_blob(catalog, run_id)
+    proba_path = blob / "proba.npy"
+    if not proba_path.is_file():
+        raise FileNotFoundError("proba.npy missing — run Predict first")
+    proba = np.load(proba_path)
+    if proba.ndim != 3:
+        raise ValueError("proba must be HxWxK")
+    k = int(proba.shape[2])
+    if not 0 <= int(class_index) < k:
+        raise ValueError(f"class_index {class_index} out of range 0..{k - 1}")
+    ch = np.clip(proba[:, :, int(class_index)].astype(np.float32), 0.0, 1.0)
+    uint8 = np.clip(np.round(ch * 255.0), 0, 255).astype(np.uint8)
+    buf = BytesIO()
+    PILImage.fromarray(uint8, mode="L").save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def threshold_class_label_map(
+    catalog: Catalog,
+    run_id: str,
+    *,
+    class_id: int,
+    threshold: float,
+) -> dict[str, Any]:
+    """Binary class map from softmax: ``class_id`` where ``p >= threshold``, else 0.
+
+    Returns width/height and base64-encoded uint8 label map for mask-set caches.
+    """
+    import base64
+
+    _run, blob, meta = _run_blob(catalog, run_id)
+    class_ids = [int(c) for c in meta.get("class_ids") or []]
+    if int(class_id) not in class_ids:
+        raise ValueError(f"class_id {class_id} not in run class_ids {class_ids}")
+    j = class_ids.index(int(class_id))
+    proba_path = blob / "proba.npy"
+    if not proba_path.is_file():
+        raise FileNotFoundError("proba.npy missing — run Predict first")
+    proba = np.load(proba_path).astype(np.float32)
+    t = float(threshold)
+    if not 0.0 <= t <= 1.0:
+        raise ValueError("threshold must be in [0, 1]")
+    h, w, _ = proba.shape
+    labels = np.zeros((h, w), dtype=np.uint8)
+    labels[proba[:, :, j] >= t] = np.uint8(int(class_id))
+    raw = labels.reshape(-1).tobytes()
+    return {
+        "run_id": run_id,
+        "class_id": int(class_id),
+        "class_index": int(j),
+        "threshold": t,
+        "width": int(w),
+        "height": int(h),
+        "n_positive": int(np.count_nonzero(labels)),
+        "label_map_b64": base64.b64encode(raw).decode("ascii"),
+    }
diff --git a/ipred/src/ipred/trainers/__init__.py b/ipred/src/ipred/trainers/__init__.py
new file mode 100644
index 0000000..1a360ad
--- /dev/null
+++ b/ipred/src/ipred/trainers/__init__.py
@@ -0,0 +1,25 @@
+"""Trainer plugin registry."""
+
+from __future__ import annotations
+
+from ipred.trainers.base import ModelArtifacts, TrainerPlugin
+from ipred.trainers.catboost_trainer import CatBoostTrainer
+
+_REGISTRY: dict[str, TrainerPlugin] = {
+    CatBoostTrainer.id: CatBoostTrainer(),
+}
+
+
+def get_trainer(trainer_id: str) -> TrainerPlugin:
+    """Return a registered trainer or raise KeyError."""
+    try:
+        return _REGISTRY[trainer_id]
+    except KeyError as exc:
+        raise KeyError(
+            f"unknown trainer {trainer_id!r}; available: {sorted(_REGISTRY)}"
+        ) from exc
+
+
+def list_trainers() -> list[str]:
+    """Return registered trainer ids."""
+    return sorted(_REGISTRY)
diff --git a/ipred/src/ipred/trainers/base.py b/ipred/src/ipred/trainers/base.py
new file mode 100644
index 0000000..3edd6d7
--- /dev/null
+++ b/ipred/src/ipred/trainers/base.py
@@ -0,0 +1,53 @@
+"""Trainer plugin interface."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Protocol
+
+import numpy as np
+
+
+@dataclass
+class ModelArtifacts:
+    """Artifacts produced by ``train``."""
+
+    class_ids: list[int]
+    feature_labels: list[str]
+    cal_scores_by_class: dict[int, list[float]]
+    train_accuracy: float
+    n_train: int
+    n_cal: int
+    n_samples: int
+    params: dict[str, Any]
+    extras: dict[str, Any] = field(default_factory=dict)
+    # In-memory handle for immediate predict_proba (plugin-specific)
+    model_handle: Any = None
+
+
+class TrainerPlugin(Protocol):
+    """Train / predict_proba contract for pixel classifiers."""
+
+    id: str
+
+    def train(
+        self,
+        x_train: np.ndarray,
+        y_train: np.ndarray,
+        x_cal: np.ndarray,
+        y_cal: np.ndarray,
+        *,
+        feature_labels: list[str],
+        config: dict[str, Any],
+    ) -> ModelArtifacts:
+        """Fit model and compute Mondrian calibration scores."""
+
+    def predict_proba(self, model_handle: Any, x: np.ndarray) -> np.ndarray:
+        """Return NxK probabilities aligned with training class order."""
+
+    def save(self, model_handle: Any, dest_dir: Path) -> None:
+        """Persist model weights into *dest_dir*."""
+
+    def load(self, dest_dir: Path) -> Any:
+        """Load model weights from *dest_dir*."""
diff --git a/ipred/src/ipred/trainers/catboost_trainer.py b/ipred/src/ipred/trainers/catboost_trainer.py
new file mode 100644
index 0000000..5bf01fc
--- /dev/null
+++ b/ipred/src/ipred/trainers/catboost_trainer.py
@@ -0,0 +1,107 @@
+"""CatBoost TrainerPlugin."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from catboost import CatBoostClassifier
+
+from ipred.trainers.base import ModelArtifacts
+
+
+class CatBoostTrainer:
+    """CatBoost multiclass pixel trainer with Mondrian cal scores."""
+
+    id = "catboost"
+
+    def train(
+        self,
+        x_train: np.ndarray,
+        y_train: np.ndarray,
+        x_cal: np.ndarray,
+        y_cal: np.ndarray,
+        *,
+        feature_labels: list[str],
+        config: dict[str, Any],
+    ) -> ModelArtifacts:
+        """Fit CatBoost and collect per-class nonconformity scores."""
+        iterations = int(config.get("iterations", 200))
+        depth = int(config.get("depth", 6))
+        learning_rate = float(config.get("learning_rate", 0.1))
+        random_seed = int(config.get("random_seed", 0))
+        params = {
+            "iterations": iterations,
+            "depth": depth,
+            "learning_rate": learning_rate,
+            "loss_function": "MultiClass",
+            "random_seed": random_seed,
+        }
+        clf = CatBoostClassifier(
+            iterations=iterations,
+            depth=depth,
+            learning_rate=learning_rate,
+            loss_function="MultiClass",
+            verbose=False,
+            allow_writing_files=False,
+            random_seed=random_seed,
+            thread_count=-1,
+        )
+        clf.fit(x_train, y_train)
+        pred = np.asarray(clf.predict(x_train)).reshape(-1).astype(np.int32)
+        train_accuracy = float(np.mean(pred == y_train))
+
+        proba_cal = np.asarray(clf.predict_proba(x_cal), dtype=np.float64)
+        classes = [int(c) for c in np.asarray(clf.classes_).tolist()]
+        class_to_col = {c: i for i, c in enumerate(classes)}
+        cal_scores: dict[int, list[float]] = {}
+        for cid in classes:
+            mask = y_cal == cid
+            if not np.any(mask):
+                continue
+            col = class_to_col[cid]
+            scores = 1.0 - proba_cal[mask, col]
+            cal_scores[cid] = np.sort(scores.astype(np.float64)).tolist()
+        if not cal_scores:
+            raise ValueError("no calibration scores")
+
+        labels = list(feature_labels)
+        raw_imp = np.asarray(clf.get_feature_importance(), dtype=np.float64)
+        if len(labels) != raw_imp.size:
+            labels = [f"f{i}" for i in range(raw_imp.size)]
+        order = np.argsort(-raw_imp)
+        feature_importances = [
+            {"label": labels[int(i)], "importance": float(raw_imp[int(i)])} for i in order
+        ]
+
+        return ModelArtifacts(
+            class_ids=classes,
+            feature_labels=labels,
+            cal_scores_by_class=cal_scores,
+            train_accuracy=train_accuracy,
+            n_train=int(y_train.shape[0]),
+            n_cal=int(y_cal.shape[0]),
+            n_samples=int(y_train.shape[0] + y_cal.shape[0]),
+            params=params,
+            extras={"feature_importances": feature_importances},
+            model_handle=clf,
+        )
+
+    def predict_proba(self, model_handle: Any, x: np.ndarray) -> np.ndarray:
+        """Return NxK float64 probabilities."""
+        proba = np.asarray(model_handle.predict_proba(x), dtype=np.float64)
+        if proba.ndim == 1:
+            proba = np.stack([1.0 - proba, proba], axis=1)
+        return proba
+
+    def save(self, model_handle: Any, dest_dir: Path) -> None:
+        """Write ``model.cbm``."""
+        dest_dir.mkdir(parents=True, exist_ok=True)
+        model_handle.save_model(str(dest_dir / "model.cbm"))
+
+    def load(self, dest_dir: Path) -> Any:
+        """Load ``model.cbm``."""
+        clf = CatBoostClassifier()
+        clf.load_model(str(dest_dir / "model.cbm"))
+        return clf
diff --git a/ipred/tests/test_api_health.py b/ipred/tests/test_api_health.py
new file mode 100644
index 0000000..29680bc
--- /dev/null
+++ b/ipred/tests/test_api_health.py
@@ -0,0 +1,51 @@
+"""API health + session open."""
+
+from __future__ import annotations
+
+import pytest
+from fastapi.testclient import TestClient
+
+from ipred import api
+
+
+@pytest.fixture()
+def client(tmp_path, monkeypatch: pytest.MonkeyPatch) -> TestClient:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    api._catalog = None
+    with TestClient(api.app) as c:
+        yield c
+
+
+def test_health(client: TestClient) -> None:
+    r = client.get("/health")
+    assert r.status_code == 200
+    assert r.json()["status"] == "ok"
+
+
+def test_open_session_and_list_setups(client: TestClient) -> None:
+    r = client.post(
+        "/sessions",
+        json={"kind": "local", "source": "x.png", "root": "/tmp"},
+    )
+    assert r.status_code == 200
+    body = r.json()
+    assert "session_id" in body
+    assert "project_id" in body
+
+    r2 = client.get("/setups")
+    assert r2.status_code == 200
+    ids = {s["id"] for s in r2.json()["setups"]}
+    assert "default-skimage" in ids
+    assert "default-mark25" in ids
+    assert "default-skimage-mark25" in ids
+    assert "default-mark25-clahe" in ids
+    assert "default-mark11" in ids
+    assert "default-skimage-mark11" in ids
+    assert "default-mark11-clahe" in ids
+    assert "default-slimsam-clahe" in ids
+
+    r3 = client.get("/compositions")
+    assert r3.status_code == 200
+    cids = {c["id"] for c in r3.json()["compositions"]}
+    assert "comp-skimage" in cids
+    assert "comp-skimage-mark11" in cids
diff --git a/ipred/tests/test_array_blobs.py b/ipred/tests/test_array_blobs.py
new file mode 100644
index 0000000..f590091
--- /dev/null
+++ b/ipred/tests/test_array_blobs.py
@@ -0,0 +1,77 @@
+"""Array blob data plane for remote preprocess."""
+
+from __future__ import annotations
+
+import base64
+
+import numpy as np
+import pytest
+from fastapi.testclient import TestClient
+
+from ipred import api, array_blobs, compositions
+
+
+@pytest.fixture()
+def client(tmp_path, monkeypatch: pytest.MonkeyPatch) -> TestClient:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    api._catalog = None
+    with TestClient(api.app) as c:
+        yield c
+
+
+def test_save_load_array_blob(tmp_path, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    arr = np.arange(12, dtype=np.float32).reshape(3, 4)
+    rec = array_blobs.save_array_blob("proj1", arr)
+    out = array_blobs.load_array_blob("proj1", rec["array_ref"])
+    np.testing.assert_array_equal(out, arr)
+
+
+def test_modules_and_compositions_api(client: TestClient) -> None:
+    r = client.get("/modules")
+    assert r.status_code == 200
+    ids = {m["id"] for m in r.json()["modules"]}
+    assert "tomojepa" in ids
+
+    r2 = client.get("/compositions")
+    assert r2.status_code == 200
+    cids = {c["id"] for c in r2.json()["compositions"]}
+    assert "comp-skimage" in cids
+
+    r3 = client.get("/compositions/comp-skimage")
+    assert r3.status_code == 200
+    assert "preview_labels" in r3.json()
+
+
+def test_upload_array_and_preprocess(client: TestClient) -> None:
+    sess = client.post(
+        "/sessions",
+        json={"kind": "local", "source": "x.png", "root": "/tmp"},
+    ).json()
+    arr = np.linspace(0, 1, 16 * 16, dtype=np.float32).reshape(16, 16)
+    raw = arr.astype(np.float32).tobytes()
+    up = client.post(
+        f"/sessions/{sess['session_id']}/arrays",
+        json={
+            "session_id": sess["session_id"],
+            "shape": [16, 16],
+            "dtype": "float32",
+            "data_b64": base64.b64encode(raw).decode("ascii"),
+        },
+    )
+    assert up.status_code == 200
+    ref = up.json()["array_ref"]
+    compositions.ensure_default_compositions(api.get_catalog())
+    prep = client.post(
+        "/preprocess",
+        json={
+            "session_id": sess["session_id"],
+            "composition_id": "comp-skimage",
+            "array_ref": ref,
+            "slice_index": 0,
+        },
+    )
+    assert prep.status_code == 200, prep.text
+    body = prep.json()
+    assert body["n_channels"] > 0
+    assert body["composition_id"] == "comp-skimage"
diff --git a/ipred/tests/test_array_source.py b/ipred/tests/test_array_source.py
new file mode 100644
index 0000000..2249221
--- /dev/null
+++ b/ipred/tests/test_array_source.py
@@ -0,0 +1,55 @@
+import numpy as np
+
+from ipred import array_source
+
+
+class _FakeArray:
+    def __init__(self, shape):
+        self._data = np.zeros(shape, dtype="float32")
+        self.structure_family = "array"
+
+    def __array__(self, dtype=None):
+        return self._data
+
+
+class _FakeContainer(dict):
+    structure_family = "container"
+
+
+def _fake_pyramid():
+    """Container mimicking a registered multiscale volume: scaleN -> {image: array}."""
+    return _FakeContainer(
+        {
+            "scale0": _FakeContainer({"image": _FakeArray((9, 32, 32))}),
+            "scale1": _FakeContainer({"image": _FakeArray((5, 16, 16))}),
+            "scale2": _FakeContainer({"image": _FakeArray((3, 8, 8))}),
+            "scale3": _FakeContainer({"image": _FakeArray((2, 4, 4))}),
+            "scale4": _FakeContainer({"image": _FakeArray((1, 2, 2))}),
+        }
+    )
+
+
+class TestDescendToArray:
+    def test_descends_multiscale_pyramid_to_finest_level(self) -> None:
+        node = array_source._descend_to_array(_fake_pyramid())
+        assert not array_source._is_container_node(node)
+        assert np.asarray(node).shape == (9, 32, 32)
+
+    def test_descends_wrapper_container_to_array_stack(self) -> None:
+        wrapper = _FakeContainer({"vol": _FakeContainer({"a": _FakeArray((4, 4))})})
+        node = array_source._descend_to_array(wrapper)
+        assert array_source._is_container_node(node)  # container of arrays == stack
+
+    def test_leaves_bare_array_untouched(self) -> None:
+        arr = _FakeArray((4, 4))
+        assert array_source._descend_to_array(arr) is arr
+
+    def test_five_level_pyramid_does_not_collapse_to_length_5_array(self) -> None:
+        # Regression: a naive np.asarray(container) on an un-descended 5-child
+        # pyramid container previously yielded shape (5,), tripping
+        # "unsupported array shape" in _index_slice during train/infer.
+        node = array_source._descend_to_array(_fake_pyramid())
+        data = np.asarray(node)
+        assert data.shape != (5,)
+        sliced = array_source._index_slice(data, 0)
+        assert sliced.shape == (32, 32)
diff --git a/ipred/tests/test_catalog_sessions.py b/ipred/tests/test_catalog_sessions.py
new file mode 100644
index 0000000..33e3882
--- /dev/null
+++ b/ipred/tests/test_catalog_sessions.py
@@ -0,0 +1,39 @@
+"""Catalog project/session persistence."""
+
+from __future__ import annotations
+
+import os
+
+import pytest
+
+from ipred.catalog import Catalog, project_id_for
+
+
+@pytest.fixture()
+def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return Catalog(tmp_path / "ipred" / "catalog.db")
+
+
+def test_project_id_stable() -> None:
+    a = project_id_for(kind="local", source="a.tif", root="/data")
+    b = project_id_for(kind="local", source="a.tif", root="/data")
+    c = project_id_for(kind="local", source="b.tif", root="/data")
+    assert a == b
+    assert a != c
+
+
+def test_open_session_creates_project(catalog: Catalog) -> None:
+    s1 = catalog.open_session(kind="local", source="img.tif", root=str(os.environ["LOCAL_DATA_ROOT"]))
+    s2 = catalog.open_session(kind="local", source="img.tif", root=str(os.environ["LOCAL_DATA_ROOT"]))
+    assert s1.project_id == s2.project_id
+    assert s1.session_id != s2.session_id
+    got = catalog.get_session(s1.session_id)
+    assert got is not None
+    assert got.current_feature_id is None
+
+
+def test_set_session_currents(catalog: Catalog) -> None:
+    s = catalog.open_session(kind="local", source="x.png")
+    updated = catalog.set_session_currents(s.session_id, feature_id="abc")
+    assert updated.current_feature_id == "abc"
diff --git a/ipred/tests/test_compositions.py b/ipred/tests/test_compositions.py
new file mode 100644
index 0000000..66ba5ee
--- /dev/null
+++ b/ipred/tests/test_compositions.py
@@ -0,0 +1,80 @@
+"""Composition validation, builtins, and legacy setup mapping."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from ipred import compositions, compose_run, feature_setups
+from ipred.catalog import Catalog
+from ipred.modules import list_module_catalog
+
+
+@pytest.fixture()
+def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return Catalog(tmp_path / "ipred" / "catalog.db")
+
+
+def test_module_catalog_lists_core_modules() -> None:
+    ids = {m["id"] for m in list_module_catalog()}
+    assert ids >= {
+        "skimage_multiscale",
+        "clahe",
+        "slimsam",
+        "tomojepa",
+        "pca",
+    }
+
+
+def test_ensure_default_compositions(catalog: Catalog) -> None:
+    comps = compositions.ensure_default_compositions(catalog)
+    ids = {c["id"] for c in comps}
+    assert "comp-skimage" in ids
+    assert "comp-skimage-mark11" in ids
+    assert "comp-mark25-clahe" in ids
+    assert "comp-slimsam-clahe" in ids
+
+
+def test_legacy_setup_maps_to_composition(catalog: Catalog) -> None:
+    compositions.ensure_default_compositions(catalog)
+    assert (
+        compositions.resolve_preprocess_id("default-skimage-mark25")
+        == "comp-skimage-mark25"
+    )
+    assert compositions.resolve_preprocess_id("default-mark11") == "comp-mark11-clahe"
+
+
+def test_preview_concat_skimage(catalog: Catalog) -> None:
+    compositions.ensure_default_compositions(catalog)
+    doc = compositions.resolve_composition("comp-skimage")
+    labels = compositions.preview_concat_labels(doc)
+    assert any("intensity" in lab for lab in labels)
+
+
+def test_run_composition_skimage_only(catalog: Catalog) -> None:
+    compositions.ensure_default_compositions(catalog)
+    doc = compositions.resolve_composition("comp-skimage")
+    arr = np.linspace(0, 1, 32 * 32, dtype=np.float32).reshape(32, 32)
+    uint8, floats, labels, emb, meta = compose_run.run_composition(arr, doc)
+    assert emb is None
+    assert floats.ndim == 3
+    assert uint8.shape == floats.shape
+    assert len(labels) == floats.shape[-1]
+
+
+def test_content_hash_stable() -> None:
+    doc = {
+        "nodes": [{"id": "n1", "module": "skimage_multiscale", "params": {}}],
+        "outputs": ["n1"],
+    }
+    compositions.validate_composition(doc)
+    h1 = compositions.content_hash(doc)
+    h2 = compositions.content_hash({**doc, "name": "x"})
+    assert h1 == h2
+
+
+def test_feature_setups_still_seed(catalog: Catalog) -> None:
+    feature_setups.ensure_default_setups(catalog)
+    compositions.ensure_default_compositions(catalog)
+    assert feature_setups.load_setup("default-skimage") is not None
diff --git a/ipred/tests/test_emb_pca_channels.py b/ipred/tests/test_emb_pca_channels.py
new file mode 100644
index 0000000..ecbc79f
--- /dev/null
+++ b/ipred/tests/test_emb_pca_channels.py
@@ -0,0 +1,30 @@
+"""Encoder PCA → full-res heatmap channels."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from ipred import sam_embed
+
+
+def test_emb_grid_to_pca_channels_shape() -> None:
+    rng = np.random.default_rng(0)
+    emb = rng.normal(size=(8, 8, 16)).astype(np.float32)
+    up, labels, info = sam_embed.emb_grid_to_pca_channels(
+        emb, out_h=32, out_w=40, n_components=8
+    )
+    assert up.shape == (32, 40, 8)
+    assert labels == [f"pca{i}" for i in range(8)]
+    assert info["pca_dims"] == 8
+    assert info["baked_into_float_stack"] is True
+
+
+def test_emb_grid_pca_caps_at_rank() -> None:
+    rng = np.random.default_rng(1)
+    emb = rng.normal(size=(4, 4, 5)).astype(np.float32)
+    up, labels, info = sam_embed.emb_grid_to_pca_channels(
+        emb, out_h=16, out_w=16, n_components=64
+    )
+    assert up.shape[-1] == 5
+    assert len(labels) == 5
+    assert info["pca_dims"] == 5
diff --git a/ipred/tests/test_feature_setups.py b/ipred/tests/test_feature_setups.py
new file mode 100644
index 0000000..19ea35f
--- /dev/null
+++ b/ipred/tests/test_feature_setups.py
@@ -0,0 +1,58 @@
+"""Feature Setup shelf defaults."""
+
+from __future__ import annotations
+
+import pytest
+
+from ipred.catalog import Catalog
+from ipred import feature_setups
+
+
+@pytest.fixture()
+def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return Catalog(tmp_path / "ipred" / "catalog.db")
+
+
+def test_ensure_defaults(catalog: Catalog) -> None:
+    defaults = feature_setups.ensure_default_setups(catalog)
+    assert len(defaults) == 10
+    ids = {d["id"] for d in defaults}
+    assert "default-skimage" in ids
+    assert "default-slimsam" in ids
+    assert "default-skimage-slimsam" in ids
+    assert "default-slimsam-clahe" in ids
+    assert "default-mark25" in ids
+    assert "default-skimage-mark25" in ids
+    assert "default-mark25-clahe" in ids
+    assert "default-mark11" in ids
+    assert "default-skimage-mark11" in ids
+    assert "default-mark11-clahe" in ids
+    sk = feature_setups.resolve_setup("default-skimage")
+    assert sk["kind"] == "procedure"
+    assert sk["procedure_id"] == feature_setups.PROCEDURE_SKIMAGE
+    mark = feature_setups.resolve_setup("default-mark25")
+    assert mark["kind"] == "weights"
+    assert mark["weights_format"] == feature_setups.WEIGHTS_TOMOJEPA_MARK25
+    assert mark["inference"]["input_size"] == 512
+    combo = feature_setups.resolve_setup("default-skimage-mark25")
+    assert combo["encoder_setup_id"] == "default-mark25"
+    assert combo["params"]["input_size"] == 512
+    assert combo["params"]["resize"] is True
+    assert combo["params"]["pca_dims"] == 64
+    clahe = feature_setups.resolve_setup("default-mark25-clahe")
+    assert clahe["procedure_id"] == feature_setups.PROCEDURE_CLAHE_ENCODER
+    assert clahe["encoder_setup_id"] == "default-mark25"
+    assert clahe["params"]["resize"] is True
+    assert clahe["params"]["input_size"] == 512
+    assert clahe["params"]["clahe"] is True
+    assert clahe["params"]["pca_dims"] == 64
+    mark11 = feature_setups.resolve_setup("default-mark11")
+    assert mark11["weights_format"] == feature_setups.WEIGHTS_TOMOJEPA_MARK11
+    combo11 = feature_setups.resolve_setup("default-skimage-mark11")
+    assert combo11["encoder_setup_id"] == "default-mark11"
+    clahe11 = feature_setups.resolve_setup("default-mark11-clahe")
+    assert clahe11["encoder_setup_id"] == "default-mark11"
+    slim_clahe = feature_setups.resolve_setup("default-slimsam-clahe")
+    assert slim_clahe["procedure_id"] == feature_setups.PROCEDURE_CLAHE_ENCODER
+    assert slim_clahe["encoder_setup_id"] == "default-slimsam"
diff --git a/ipred/tests/test_labels.py b/ipred/tests/test_labels.py
new file mode 100644
index 0000000..c412864
--- /dev/null
+++ b/ipred/tests/test_labels.py
@@ -0,0 +1,62 @@
+"""Label rasterization accepts studio shape wire formats."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from ipred.labels import build_label_map, shape_to_mask
+
+
+def test_polygon_flat_xy_list() -> None:
+    """Studio polygons store points as flat [x0,y0,x1,y1,...]."""
+    shape = {
+        "kind": "polygon",
+        "classId": 6,
+        "points": [10.0, 10.0, 40.0, 10.0, 40.0, 40.0, 10.0, 40.0],
+    }
+    mask = shape_to_mask(shape, 64, 64)
+    assert int(mask.sum()) > 100
+
+    labels = build_label_map([shape], 64, 64)
+    assert int((labels == 6).sum()) == int(mask.sum())
+
+
+def test_polygon_nested_pairs_still_work() -> None:
+    shape = {
+        "kind": "polygon",
+        "classId": 1,
+        "points": [[10, 10], [40, 10], [40, 40], [10, 40]],
+    }
+    assert shape_to_mask(shape, 64, 64).sum() > 100
+
+
+def test_polygon_holes_are_excluded() -> None:
+    """A background polygon clipped around another class (holes) must not
+    paint over that class's region — see clipToClasses.ts on the frontend."""
+    outer = [0.0, 0.0, 64.0, 0.0, 64.0, 64.0, 0.0, 64.0]
+    hole = [20.0, 20.0, 40.0, 20.0, 40.0, 40.0, 20.0, 40.0]
+    shape = {"kind": "polygon", "classId": 1, "points": outer, "holes": [hole]}
+    mask = shape_to_mask(shape, 64, 64)
+    solid = shape_to_mask({"kind": "polygon", "classId": 1, "points": outer}, 64, 64)
+    assert int(mask.sum()) < int(solid.sum())
+    assert not mask[30, 30]
+
+    other = {"kind": "polygon", "classId": 2, "points": hole}
+    labels = build_label_map([shape, other], 64, 64)
+    assert labels[30, 30] == 2
+    assert labels[5, 5] == 1
+
+
+def test_brush_flat_points_and_radius() -> None:
+    """Studio brush strokes use flat points + radius (not nested {x,y})."""
+    shape = {
+        "kind": "brush",
+        "classId": 2,
+        "strokes": [
+            {"points": [8.0, 8.0, 20.0, 20.0, 32.0, 8.0], "radius": 4.0, "mode": "paint"},
+        ],
+    }
+    mask = shape_to_mask(shape, 48, 48)
+    assert int(mask.sum()) > 20
+    labels = build_label_map([shape], 48, 48)
+    assert np.any(labels == 2)
diff --git a/ipred/tests/test_manifold.py b/ipred/tests/test_manifold.py
new file mode 100644
index 0000000..e32b9d6
--- /dev/null
+++ b/ipred/tests/test_manifold.py
@@ -0,0 +1,42 @@
+"""Manifold Suggest Labels on persisted ipred feature banks."""
+
+from __future__ import annotations
+
+import numpy as np
+from PIL import Image as PILImage
+import pytest
+
+from ipred.catalog import Catalog
+from ipred import feature_setups, manifold_jobs, preprocess
+
+
+@pytest.fixture()
+def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return Catalog(tmp_path / "ipred" / "catalog.db")
+
+
+def test_manifold_sample_on_feature_bank(catalog: Catalog, tmp_path) -> None:
+    feature_setups.ensure_default_setups(catalog)
+    img = np.zeros((64, 64), dtype=np.uint8)
+    img[:, 32:] = 200
+    img[16:48, 16:48] = 120
+    PILImage.fromarray(img, mode="L").save(tmp_path / "m.png")
+
+    session = catalog.open_session(kind="local", source="m.png", root=str(tmp_path))
+    bank = preprocess.run_preprocess(
+        catalog,
+        session_id=session.session_id,
+        feature_setup_id="default-skimage",
+    )
+    out = manifold_jobs.run_manifold_sample(
+        catalog,
+        feature_id=bank["feature_id"],
+        k=8,
+        box_size=16,
+    )
+    assert out["sample_id"]
+    assert out["n_picked"] >= 1
+    assert len(out["points"]) >= 1
+    png = manifold_jobs.heatmap_png(out["sample_id"])
+    assert png[:8] == b"\x89PNG\r\n\x1a\n"
diff --git a/ipred/tests/test_preprocess_cache.py b/ipred/tests/test_preprocess_cache.py
new file mode 100644
index 0000000..b98250e
--- /dev/null
+++ b/ipred/tests/test_preprocess_cache.py
@@ -0,0 +1,116 @@
+"""Cache-aware preprocess."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+from PIL import Image as PILImage
+import pytest
+
+from ipred.catalog import Catalog
+from ipred import feature_setups, preprocess, tomojepa_embed
+
+WEIGHTS = Path(__file__).resolve().parents[1] / "models" / "tomojepa25.pth"
+
+
+@pytest.fixture()
+def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return Catalog(tmp_path / "ipred" / "catalog.db")
+
+
+def test_preprocess_cache_hit(catalog: Catalog, tmp_path) -> None:
+    feature_setups.ensure_default_setups(catalog)
+    # Write a local PNG under LOCAL_DATA_ROOT
+    img = np.zeros((32, 32), dtype=np.uint8)
+    img[:, 16:] = 200
+    rel = "sample.png"
+    PILImage.fromarray(img, mode="L").save(tmp_path / rel)
+
+    session = catalog.open_session(kind="local", source=rel, root=str(tmp_path))
+    first = preprocess.run_preprocess(
+        catalog,
+        session_id=session.session_id,
+        feature_setup_id="default-skimage",
+        slice_index=0,
+    )
+    assert first["cache_hit"] is False
+    assert first["n_channels"] > 0
+
+    second = preprocess.run_preprocess(
+        catalog,
+        session_id=session.session_id,
+        feature_setup_id="default-skimage",
+        slice_index=0,
+    )
+    assert second["cache_hit"] is True
+    assert second["feature_id"] == first["feature_id"]
+
+
+@pytest.mark.skipif(
+    not WEIGHTS.is_file() or not tomojepa_embed.encoder_available(str(WEIGHTS)),
+    reason="tomojepa25.pth or torch/timm not available",
+)
+def test_preprocess_mark25_writes_dense_emb(catalog: Catalog, tmp_path) -> None:
+    feature_setups.ensure_default_setups(catalog)
+    img = np.linspace(0, 255, 64 * 48, dtype=np.float32).reshape(64, 48).astype(
+        np.uint8
+    )
+    rel = "tomo.png"
+    PILImage.fromarray(img, mode="L").save(tmp_path / rel)
+
+    session = catalog.open_session(kind="local", source=rel, root=str(tmp_path))
+    out = preprocess.run_preprocess(
+        catalog,
+        session_id=session.session_id,
+        feature_setup_id="default-skimage-mark25",
+        slice_index=0,
+    )
+    assert out["cache_hit"] is False
+    bank = preprocess.load_feature_bank_arrays(out["blob_dir"])
+    assert bank["sam_emb"] is not None
+    assert bank["sam_emb"].shape == (32, 32, 64)
+    assert bank["sam_meta"]["encoder"] == "mark25"
+    assert bank["sam_meta"]["input_size"] == 512
+
+
+@pytest.mark.skipif(
+    not WEIGHTS.is_file() or not tomojepa_embed.encoder_available(str(WEIGHTS)),
+    reason="tomojepa25.pth or torch/timm not available",
+)
+def test_preprocess_mark25_clahe_no_skimage(catalog: Catalog, tmp_path) -> None:
+    feature_setups.ensure_default_setups(catalog)
+    img = np.linspace(0, 255, 80 * 64, dtype=np.float32).reshape(80, 64).astype(
+        np.uint8
+    )
+    rel = "tomo_clahe.png"
+    PILImage.fromarray(img, mode="L").save(tmp_path / rel)
+
+    session = catalog.open_session(kind="local", source=rel, root=str(tmp_path))
+    out = preprocess.run_preprocess(
+        catalog,
+        session_id=session.session_id,
+        feature_setup_id="default-mark25-clahe",
+        slice_index=0,
+    )
+    assert out["cache_hit"] is False
+    assert out["n_channels"] == 1 + 64
+    bank = preprocess.load_feature_bank_arrays(out["blob_dir"])
+    assert bank["labels"][0] == "clahe"
+    assert bank["labels"][1:5] == ["pca0", "pca1", "pca2", "pca3"]
+    assert bank["float_stack"].shape[-1] == 65
+    assert bank["float_stack"].shape[:2] == (80, 64)
+    assert bank["sam_emb"] is not None
+    assert bank["sam_emb"].shape == (32, 32, 64)
+    assert bank["sam_meta"]["encoder"] == "mark25"
+    assert bank["sam_meta"]["resize"] is True
+    assert bank["sam_meta"]["input_size"] == 512
+    assert bank["sam_meta"]["baked_into_float_stack"] is True
+    assert bank["sam_meta"]["pca_dims"] == 64
+    # Channel PNGs written for heatmap browsing
+    from pathlib import Path
+
+    ch_dir = Path(out["blob_dir"]) / "channels"
+    assert (ch_dir / "0000.png").is_file()
+    assert (ch_dir / "0064.png").is_file()
diff --git a/ipred/tests/test_proba_channels.py b/ipred/tests/test_proba_channels.py
new file mode 100644
index 0000000..b2dccf6
--- /dev/null
+++ b/ipred/tests/test_proba_channels.py
@@ -0,0 +1,97 @@
+"""Softmax proba heatmaps + per-class threshold maps."""
+
+from __future__ import annotations
+
+import base64
+
+import numpy as np
+import pytest
+
+from ipred.catalog import Catalog
+from ipred import feature_setups, preprocess, train_infer
+from ipred.paths import project_blob_dir
+
+
+@pytest.fixture()
+def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return Catalog(tmp_path / "ipred" / "catalog.db")
+
+
+def _fake_run(catalog: Catalog, tmp_path) -> str:
+    """Insert a minimal run with synthetic HxWxK softmax proba."""
+    feature_setups.ensure_default_setups(catalog)
+    session = catalog.open_session(kind="local", source="x.png", root=str(tmp_path))
+    project_id = session.project_id
+    run_id = "runproba01"
+    blob = project_blob_dir(project_id) / "runs" / run_id
+    blob.mkdir(parents=True, exist_ok=True)
+    # Two classes; left half prefers class 1, right prefers class 2
+    h, w, k = 8, 10, 2
+    proba = np.zeros((h, w, k), dtype=np.float16)
+    proba[:, :5, 0] = 0.8
+    proba[:, :5, 1] = 0.2
+    proba[:, 5:, 0] = 0.3
+    proba[:, 5:, 1] = 0.7
+    np.save(blob / "proba.npy", proba)
+    meta = {
+        "run_id": run_id,
+        "model_id": "m",
+        "feature_id": "f",
+        "alpha": 0.05,
+        "class_ids": [1, 2],
+        "counts": {"singleton": 0, "multi": 0, "abstain": 0},
+    }
+    import json
+    from datetime import datetime, timezone
+
+    (blob / "meta.json").write_text(json.dumps(meta), encoding="utf-8")
+    now = datetime.now(timezone.utc).isoformat()
+    catalog.insert_run(
+        {
+            "run_id": run_id,
+            "project_id": project_id,
+            "model_id": "m",
+            "feature_id": "f",
+            "alpha": 0.05,
+            "blob_dir": str(blob),
+            "meta_json": json.dumps(meta),
+            "created_at": now,
+            "updated_at": now,
+        }
+    )
+    return run_id
+
+
+def test_proba_heatmap_png(catalog: Catalog, tmp_path) -> None:
+    run_id = _fake_run(catalog, tmp_path)
+    png = train_infer.proba_heatmap_png(catalog, run_id, 0)
+    assert png[:8] == b"\x89PNG\r\n\x1a\n"
+    png1 = train_infer.proba_heatmap_png(catalog, run_id, 1)
+    assert png1[:8] == b"\x89PNG\r\n\x1a\n"
+    with pytest.raises(ValueError):
+        train_infer.proba_heatmap_png(catalog, run_id, 9)
+
+
+def test_threshold_class_label_map(catalog: Catalog, tmp_path) -> None:
+    run_id = _fake_run(catalog, tmp_path)
+    out = train_infer.threshold_class_label_map(
+        catalog, run_id, class_id=1, threshold=0.5
+    )
+    assert out["width"] == 10
+    assert out["height"] == 8
+    assert out["class_id"] == 1
+    raw = base64.b64decode(out["label_map_b64"])
+    labels = np.frombuffer(raw, dtype=np.uint8).reshape(8, 10)
+    assert np.all(labels[:, :5] == 1)
+    assert np.all(labels[:, 5:] == 0)
+    assert out["n_positive"] == 8 * 5
+
+    out2 = train_infer.threshold_class_label_map(
+        catalog, run_id, class_id=2, threshold=0.6
+    )
+    labels2 = np.frombuffer(
+        base64.b64decode(out2["label_map_b64"]), dtype=np.uint8
+    ).reshape(8, 10)
+    assert np.all(labels2[:, 5:] == 2)
+    assert np.all(labels2[:, :5] == 0)
diff --git a/ipred/tests/test_tomojepa_embed.py b/ipred/tests/test_tomojepa_embed.py
new file mode 100644
index 0000000..2968e91
--- /dev/null
+++ b/ipred/tests/test_tomojepa_embed.py
@@ -0,0 +1,129 @@
+"""Mark25 / TomoJEPA dense embedding encode."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from ipred import tomojepa_embed
+
+WEIGHTS = Path(__file__).resolve().parents[1] / "models" / "tomojepa25.pth"
+WEIGHTS11 = Path(__file__).resolve().parents[1] / "models" / "tomojepa11.pth"
+needs_weights = pytest.mark.skipif(
+    not WEIGHTS.is_file() or not tomojepa_embed.torch_available(),
+    reason="tomojepa25.pth or torch/timm not available",
+)
+needs_mark11 = pytest.mark.skipif(
+    not WEIGHTS11.is_file() or not tomojepa_embed.torch_available(),
+    reason="tomojepa11.pth or torch/timm not available",
+)
+
+
+def test_resolve_weights_path_default(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.delenv("TOMOJEPA_WEIGHTS", raising=False)
+    resolved = tomojepa_embed.resolve_weights_path(None)
+    if WEIGHTS.is_file():
+        assert resolved is not None
+        assert resolved.name == "tomojepa25.pth"
+    else:
+        assert resolved is None
+
+
+def test_resolve_weights_path_env(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    fake = tmp_path / "custom.pth"
+    fake.write_bytes(b"x")
+    monkeypatch.setenv("TOMOJEPA_WEIGHTS", str(fake))
+    assert tomojepa_embed.resolve_weights_path(None) == fake.resolve()
+
+
+def test_to_minus_one_one_linear() -> None:
+    x = np.array([[0.0, 0.5, 1.0]], dtype=np.float32)
+    y = tomojepa_embed.to_minus_one_one(x)
+    np.testing.assert_allclose(y, [[-1.0, 0.0, 1.0]], atol=1e-6)
+
+
+@needs_weights
+def test_encode_feeds_model_in_minus_one_one(monkeypatch: pytest.MonkeyPatch) -> None:
+    """All Mark25 paths must pass intensities in [-1, 1] into the network."""
+    captured: list[float] = []
+
+    real_cached = tomojepa_embed._cached_model
+
+    def _wrap(path_str: str):
+        net, device = real_cached(path_str)
+        orig_forward = net.forward
+
+        def forward(x):  # noqa: ANN001
+            t = x.detach().cpu().numpy()
+            captured.append(float(t.min()))
+            captured.append(float(t.max()))
+            return orig_forward(x)
+
+        net.forward = forward  # type: ignore[method-assign]
+        return net, device
+
+    monkeypatch.setattr(tomojepa_embed, "_cached_model", _wrap)
+    arr = np.linspace(10, 200, 64 * 48, dtype=np.float32).reshape(64, 48)
+    tomojepa_embed.encode_dense_embeddings(
+        arr, weights_path=str(WEIGHTS), input_size=128
+    )
+    assert captured, "model forward was not called"
+    assert min(captured) >= -1.0 - 1e-5
+    assert max(captured) <= 1.0 + 1e-5
+    # After min-max + linear map, extremes should reach near ±1.
+    assert min(captured) < -0.9
+    assert max(captured) > 0.9
+
+
+@needs_weights
+def test_load_state_dict_strict() -> None:
+    net = tomojepa_embed.build_encoder()
+    state = tomojepa_embed.load_checkpoint_state(WEIGHTS)
+    net.load_state_dict(state, strict=True)
+
+
+@needs_weights
+def test_encode_dense_shape_512() -> None:
+    arr = np.linspace(0, 1, 400 * 300, dtype=np.float32).reshape(400, 300)
+    emb, orig_hw, reshaped_hw = tomojepa_embed.encode_dense_embeddings(
+        arr, weights_path=str(WEIGHTS), input_size=512
+    )
+    assert orig_hw == (400, 300)
+    assert reshaped_hw == (512, 512)
+    assert emb.shape == (32, 32, 64)
+    assert emb.dtype == np.float32
+
+
+@needs_weights
+def test_encode_dense_honors_input_size() -> None:
+    arr = np.random.default_rng(0).random((128, 96), dtype=np.float32)
+    emb, _, reshaped = tomojepa_embed.encode_dense_embeddings(
+        arr, weights_path=str(WEIGHTS), input_size=256
+    )
+    assert reshaped == (256, 256)
+    assert emb.shape == (16, 16, 64)
+
+
+@needs_weights
+def test_encode_native_no_resize() -> None:
+    arr = np.random.default_rng(1).random((100, 80), dtype=np.float32)
+    emb, orig_hw, reshaped = tomojepa_embed.encode_dense_embeddings(
+        arr, weights_path=str(WEIGHTS), resize=False
+    )
+    assert orig_hw == (100, 80)
+    # padded to multiple of 16
+    assert reshaped == (112, 80)
+    assert emb.shape == (7, 5, 64)
+
+
+@needs_mark11
+def test_encode_mark11_dense_is_256d() -> None:
+    """Mark11 dense projector is 256-D (Mark25 is 64-D)."""
+    tomojepa_embed._cached_model.cache_clear()
+    arr = np.linspace(0, 1, 64 * 48, dtype=np.float32).reshape(64, 48)
+    emb, _, _ = tomojepa_embed.encode_dense_embeddings(
+        arr, weights_path=str(WEIGHTS11), input_size=128
+    )
+    assert emb.shape == (8, 8, 256)
diff --git a/ipred/tests/test_tomojepa_onnx.py b/ipred/tests/test_tomojepa_onnx.py
new file mode 100644
index 0000000..5e69528
--- /dev/null
+++ b/ipred/tests/test_tomojepa_onnx.py
@@ -0,0 +1,27 @@
+"""TomoJEPA ONNX encode smoke (skips if onnx file missing)."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from ipred import tomojepa_onnx
+
+ONNX25 = Path(__file__).resolve().parents[1] / "models" / "tomojepa25.onnx"
+needs_onnx = pytest.mark.skipif(
+    not ONNX25.is_file() or not tomojepa_onnx.onnx_available(),
+    reason="tomojepa25.onnx not available",
+)
+
+
+@needs_onnx
+def test_onnx_encode_dense_shape() -> None:
+    arr = np.linspace(0, 1, 64 * 48, dtype=np.float32).reshape(64, 48)
+    emb, orig, reshaped = tomojepa_onnx.encode_dense_embeddings(
+        arr, weights_path=str(ONNX25), input_size=128
+    )
+    assert orig == (64, 48)
+    assert reshaped == (128, 128)
+    assert emb.shape == (8, 8, 64)
diff --git a/ipred/tests/test_train_rethreshold.py b/ipred/tests/test_train_rethreshold.py
new file mode 100644
index 0000000..2d896b9
--- /dev/null
+++ b/ipred/tests/test_train_rethreshold.py
@@ -0,0 +1,71 @@
+"""Train → infer → rethreshold without recompute."""
+
+from __future__ import annotations
+
+import numpy as np
+from PIL import Image as PILImage
+import pytest
+
+from ipred.catalog import Catalog
+from ipred import feature_setups, preprocess, train_infer
+
+
+@pytest.fixture()
+def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return Catalog(tmp_path / "ipred" / "catalog.db")
+
+
+def test_train_infer_rethreshold(catalog: Catalog, tmp_path) -> None:
+    feature_setups.ensure_default_setups(catalog)
+    img = np.zeros((48, 48), dtype=np.uint8)
+    img[:, 24:] = 220
+    PILImage.fromarray(img, mode="L").save(tmp_path / "blob.png")
+
+    session = catalog.open_session(
+        kind="local", source="blob.png", root=str(tmp_path)
+    )
+    preprocess.run_preprocess(
+        catalog,
+        session_id=session.session_id,
+        feature_setup_id="default-skimage",
+    )
+    shapes = [
+        {"kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 18, "h": 40},
+        {"kind": "rectangle", "classId": 2, "x": 28, "y": 2, "w": 18, "h": 40},
+    ]
+    trained = train_infer.run_train(
+        catalog,
+        session_id=session.session_id,
+        shapes=shapes,
+        trainer_id="catboost",
+        config={"iterations": 20, "depth": 4, "random_seed": 0},
+    )
+    assert trained["model_id"]
+    imps = trained.get("feature_importances") or []
+    assert imps, "CatBoost train must return ranked feature_importances"
+    assert "label" in imps[0] and "importance" in imps[0]
+    assert imps[0]["importance"] >= imps[-1]["importance"]
+
+    run = train_infer.run_infer(
+        catalog,
+        session_id=session.session_id,
+        alpha=0.2,
+    )
+    assert run["run_id"]
+    blob = run["blob_dir"]
+    assert (tmp_path / "ipred").exists() or True
+    from pathlib import Path
+
+    proba_path = Path(blob) / "proba.npy"
+    assert proba_path.is_file()
+    proba_before = np.load(proba_path).copy()
+
+    rerun = train_infer.run_rethreshold(
+        catalog,
+        session_id=session.session_id,
+        alpha=0.05,
+    )
+    assert rerun["alpha"] == 0.05
+    proba_after = np.load(proba_path)
+    np.testing.assert_array_equal(proba_before, proba_after)
diff --git a/start_all.sh b/start_all.sh
index 36c1c22..85abdf1 100755
--- a/start_all.sh
+++ b/start_all.sh
@@ -21,6 +21,7 @@ TILED_CONFIG="$SCRIPT_DIR/tiled/config.yml"
 MKDOCS_CONFIG="$SCRIPT_DIR/mkdocs.yml"
 TILED_PORT="${TILED_PORT:-8010}"
 BACKEND_PORT="${BACKEND_PORT:-8002}"
+IPRED_PORT="${IPRED_PORT:-8003}"
 FRONTEND_PORT="${FRONTEND_PORT:-5173}"
 DOCS_PORT="${DOCS_PORT:-8000}"
 # PROD=1 (or SERVE_MODE=prod): build the optimized SPA and have the backend serve
@@ -30,6 +31,7 @@ STATIC_DIR="$BACKEND_DIR/static"
 RUN_DIR="$SCRIPT_DIR/.run"
 TILED_PID_FILE="$RUN_DIR/tiled.pid"
 BACKEND_PID_FILE="$RUN_DIR/backend.pid"
+IPRED_PID_FILE="$RUN_DIR/ipred.pid"
 FRONTEND_PID_FILE="$RUN_DIR/frontend.pid"
 DOCS_PID_FILE="$RUN_DIR/docs.pid"
 ENV_DIR=""
@@ -126,6 +128,7 @@ cleanup_managed_processes() {
   stop_managed_process "$DOCS_PID_FILE" "docs"
   stop_managed_process "$FRONTEND_PID_FILE" "frontend"
   stop_managed_process "$BACKEND_PID_FILE" "backend"
+  stop_managed_process "$IPRED_PID_FILE" "iPred"
   stop_managed_process "$TILED_PID_FILE" "Tiled"
 }
 
@@ -222,6 +225,7 @@ reclaim_orphaned_repo_ports() {
   fi
   stop_repo_listener_on_port "$FRONTEND_PORT" "frontend" "$FRONTEND_DIR" "vite" ""
   stop_repo_listener_on_port "$BACKEND_PORT" "backend" "$BACKEND_DIR" "annotation_server:app" "uvicorn"
+  stop_repo_listener_on_port "$IPRED_PORT" "iPred" "$SCRIPT_DIR/ipred" "ipred.api:app" "uvicorn"
   stop_repo_listener_on_port "$DOCS_PORT" "docs" "$SCRIPT_DIR" "mkdocs" ""
   # Repo-scoped: only reclaim OUR own stale Tiled (its command line contains this
   # repo's config path). A foreign Tiled on the port is left alone — we coexist by
@@ -283,6 +287,18 @@ ensure_backend_env() {
   fi
 }
 
+ensure_ipred_env() {
+  # NOTE: probe "ipred.api", not bare "ipred" — the repo's top-level ipred/
+  # directory (sibling of ipred/src/) is itself an importable namespace package
+  # from cwd, so "import ipred" can silently succeed even when the real
+  # editable install (ipred/src/ipred) was never pip-installed.
+  if ! "$PYTHON" -c "import ipred.api" >/dev/null 2>&1; then
+    echo -e "${YELLOW}    Installing iPred (interactive segmentation service) via uv...${NC}"
+    uv pip install --python "$PYTHON" -e "$SCRIPT_DIR/ipred" >/dev/null 2>&1 || return 1
+  fi
+  "$PYTHON" -c "import ipred.api" >/dev/null 2>&1
+}
+
 ensure_frontend_runtime() {
   if command -v npm >/dev/null 2>&1 && can_run_npm "$(command -v npm)"; then
     NPM_CMD=("$(command -v npm)")
@@ -325,10 +341,11 @@ tiled_cmd() {
 cleanup() {
   echo ""
   echo -e "${YELLOW}Shutting down...${NC}"
-  kill "$TILED_PID" "$BACKEND_PID" "$FRONTEND_PID" ${DOCS_PID:+"$DOCS_PID"} 2>/dev/null || true
-  wait "$TILED_PID" "$BACKEND_PID" "$FRONTEND_PID" ${DOCS_PID:+"$DOCS_PID"} 2>/dev/null || true
+  kill "$TILED_PID" "$BACKEND_PID" "$FRONTEND_PID" ${IPRED_PID:+"$IPRED_PID"} ${DOCS_PID:+"$DOCS_PID"} 2>/dev/null || true
+  wait "$TILED_PID" "$BACKEND_PID" "$FRONTEND_PID" ${IPRED_PID:+"$IPRED_PID"} ${DOCS_PID:+"$DOCS_PID"} 2>/dev/null || true
   cleanup_pid_file "$TILED_PID_FILE"
   cleanup_pid_file "$BACKEND_PID_FILE"
+  cleanup_pid_file "$IPRED_PID_FILE"
   cleanup_pid_file "$FRONTEND_PID_FILE"
   cleanup_pid_file "$DOCS_PID_FILE"
   echo -e "${GREEN}Done.${NC}"
@@ -359,6 +376,15 @@ if [ "$BACKEND_PORT" != "$_orig_backend_port" ]; then
 fi
 export API_PROXY_TARGET="http://127.0.0.1:${BACKEND_PORT}"
 
+# iPred: fall back to the next free port if busy. Exported as IPRED_URL before
+# the backend starts, so ipred_client.py picks up the chosen port.
+_orig_ipred_port="$IPRED_PORT"
+IPRED_PORT="$(pick_free_port "$IPRED_PORT" "iPred")"
+if [ "$IPRED_PORT" != "$_orig_ipred_port" ]; then
+  echo -e "${YELLOW}    iPred port ${_orig_ipred_port} is in use — using ${IPRED_PORT} instead.${NC}"
+fi
+export IPRED_URL="http://127.0.0.1:${IPRED_PORT}"
+
 # Frontend: fall back to the next free port if busy (Vite serves on --port below).
 _orig_frontend_port="$FRONTEND_PORT"
 FRONTEND_PORT="$(pick_free_port "$FRONTEND_PORT" "Frontend")"
@@ -573,6 +599,41 @@ else
   rm -rf "$STATIC_DIR"  # ensure the backend serves API-only in dev
 fi
 
+# ---------------------------------------------------------------------------
+# iPred — standalone interactive-segmentation service (CatBoost + conformal
+# prediction on composable feature banks). Optional: the Assist/Predict stages
+# show a clear "not running" state when this is down, and everything else in
+# the app works regardless (backend/ipred_routes.py returns 503 rather than
+# erroring). Startup failures here are warnings, never fatal.
+# ---------------------------------------------------------------------------
+IPRED_PID=""
+if ensure_ipred_env; then
+  echo -e "${CYAN}==> Starting iPred (port ${IPRED_PORT})...${NC}"
+  (cd "$SCRIPT_DIR" && "$ENV_DIR/bin/uvicorn" ipred.api:app --host 127.0.0.1 --port "$IPRED_PORT") &
+  IPRED_PID=$!
+  echo "$IPRED_PID" > "$IPRED_PID_FILE"
+  echo -e "${GREEN}    iPred PID: $IPRED_PID${NC}"
+
+  echo -e "${CYAN}    Waiting for iPred...${NC}"
+  IPRED_READY=0
+  for i in $(seq 1 20); do
+    if curl -sf "http://127.0.0.1:${IPRED_PORT}/health" >/dev/null 2>&1; then
+      echo -e "${GREEN}    iPred ready at http://127.0.0.1:${IPRED_PORT}${NC}"
+      IPRED_READY=1
+      break
+    fi
+    if ! kill -0 "$IPRED_PID" 2>/dev/null; then
+      break
+    fi
+    sleep 0.5
+  done
+  if [ "$IPRED_READY" != 1 ]; then
+    echo -e "${YELLOW}    iPred did not come up in time — Assist/Predict will show a 'not running' state until it does.${NC}"
+  fi
+else
+  echo -e "${YELLOW}==> Skipping iPred (install failed) — Assist/Predict will show a 'not running' state.${NC}"
+fi
+
 # ---------------------------------------------------------------------------
 # Backend
 # ---------------------------------------------------------------------------
@@ -654,6 +715,11 @@ else
   echo -e "${GREEN}  Frontend : http://127.0.0.1:${FRONTEND_PORT}${NC}"
 fi
 echo -e "${GREEN}  Backend  : http://127.0.0.1:${BACKEND_PORT}${NC}"
+if [ "$IPRED_READY" = "1" ]; then
+  echo -e "${GREEN}  iPred    : http://127.0.0.1:${IPRED_PORT}${NC}"
+else
+  echo -e "${YELLOW}  iPred    : not running (Assist/Predict disabled)${NC}"
+fi
 if [ -n "$DOCS_PID" ]; then
   echo -e "${GREEN}  Docs     : http://127.0.0.1:${DOCS_PORT}${NC}"
 fi

From 03373f434c8c15acdc6c3daf50fcad324a306920 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Fri, 28 Aug 2026 13:21:37 -0700
Subject: [PATCH 09/38] ignoring graphify

---
 .gitignore | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 6e27af8..fc9aaf1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -70,4 +70,6 @@ peter/
 
 .claude/
 
-CLAUDE.md
\ No newline at end of file
+CLAUDE.md
+
+graphify-out/*
\ No newline at end of file

From fa8f4cf05dca117e297c04ca37489111a1c6670c Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Fri, 28 Aug 2026 13:24:31 -0700
Subject: [PATCH 10/38] allowing ipred to work across slices, allowing UI parts
 to collapse/expand, matching denoising style to the rest of the UI

---
 backend/ipred_batch_jobs.py                   |    160 +
 backend/ipred_client.py                       |     24 +
 backend/ipred_routes.py                       |     73 +
 backend/tests/test_ipred_routes.py            |     88 +
 frontend/src/app/pages/AnnotatePage.tsx       |    166 +-
 .../annotate/AnnotationCanvas/ShapesLayer.tsx |     27 +-
 .../annotate/AnnotationCanvas/index.tsx       |      2 +
 .../annotate/ClassManager/index.tsx           |     13 +-
 .../annotate/DenoisePanel/index.tsx           |     16 +-
 .../annotate/DisplayControls/index.tsx        |     15 +-
 .../annotate/FeatureChannelsPanel/index.tsx   |     17 +-
 .../components/annotate/LayersPanel/index.tsx |     40 +-
 .../annotate/MaskToolsPanel/index.tsx         |      8 +-
 .../annotate/MeasurementPanel/index.tsx       |      9 +-
 .../annotate/PixelClassifierPanel/index.tsx   |    225 +-
 .../annotate/SliceNavigator/index.tsx         |      9 +-
 .../common/CollapsibleSection.test.tsx        |     50 +
 .../components/common/CollapsibleSection.tsx  |     58 +
 frontend/src/hooks/useExportJob.ts            |     14 +-
 .../usePixelClassifier.slicePersist.test.ts   |     98 +
 frontend/src/hooks/usePixelClassifier.ts      |    173 +-
 frontend/src/lib/pixelClf.ts                  |     11 +-
 frontend/src/stores/annotationStore.ts        |     50 +
 .../src/stores/layerVisibilityStore.test.ts   |     12 +
 frontend/src/stores/layerVisibilityStore.ts   |     18 +
 graphify-out/.graphify_labels.json            |    297 -
 graphify-out/.graphify_labels.json.sig        |      1 -
 graphify-out/.graphify_root                   |      1 -
 graphify-out/2026-08-26/.graphify_labels.json |    289 -
 graphify-out/2026-08-26/GRAPH_REPORT.md       |   1159 -
 graphify-out/2026-08-26/graph.json            | 159538 --------------
 graphify-out/2026-08-26/manifest.json         |   2738 -
 graphify-out/GRAPH_REPORT.md                  |   1166 -
 ...eda9d72ac7e3b8a81aca145221cddb1cc47ed.json |      1 -
 ...0ec8affd0aa1a4eba6c5a81deb4b2604a4631.json |      1 -
 ...e89f69bbd7aff8c913f7245b5e98414a40dd9.json |      1 -
 ...4c0b0b9e8c4db87d196c7fcb9734453ea75bd.json |      1 -
 ...dec040e6e35ff891d614a1a3a43b921cc1e4c.json |      1 -
 ...ad396ec309fc8eb7cfc62c0024faef1008927.json |      1 -
 ...c1c7a7fe2b522b63d0a1569b888d0c148f9b6.json |      1 -
 ...097dcfd181ca2e321c943d059df410c830386.json |      1 -
 ...4a8bbb6aa8901979d8f60dae318f4022737c0.json |      1 -
 ...6a7c803fd0326d01fec8770e4fd8e92a3309a.json |      1 -
 ...88777ce7acd3ed464e24aed71a262172b429b.json |      1 -
 ...ba839515fdb6c8be5f06600e7e306ab79454f.json |      1 -
 ...64513b05642a2427fed778c7c26c4e9899997.json |      1 -
 ...508cb413b991ff655ea6b800c7c0b02530688.json |      1 -
 ...859bfdcb1e223d70ca40be240a60c8ba7403f.json |      1 -
 ...76978466b9df19ffe3ebba1feadf0b6ee769d.json |      1 -
 ...fab9edaa94d502da491e8c03644aa9c93b907.json |      1 -
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 create mode 100644 ipred/tests/test_api_train_multi.py
 create mode 100644 ipred/tests/test_train_multi_slice.py

diff --git a/backend/ipred_batch_jobs.py b/backend/ipred_batch_jobs.py
new file mode 100644
index 0000000..2912fe1
--- /dev/null
+++ b/backend/ipred_batch_jobs.py
@@ -0,0 +1,160 @@
+"""Background jobs for sample-scale iPred operations: multi-slice train and
+whole-volume apply.
+
+Both dispatch onto the shared ``export_jobs`` registry — mirrors
+``denoise_bake.py``'s worker pattern exactly (thread target, cooperative
+cancel, bump/log per unit of work). Poll ``GET /api/export/status/{job_id}``;
+the routes that spawn these live in ``ipred_routes.py``.
+
+The apply job deliberately stops at "run inference per slice" and does NOT
+vectorize the conformal commit/status maps into polygon shapes server-side —
+that stays client-side (``AnnotatePage.tsx`` + ``pixelClf.ts``), reusing the
+already-proven, already-tested polygon tracer instead of porting it to Python.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+import export_jobs
+import ipred_client as ipred_client_mod
+
+logger = logging.getLogger(__name__)
+
+
+def run_ipred_multi_train_job(
+    jid: str,
+    *,
+    session_id: str,
+    per_slice_shapes: dict[int, list[dict[str, Any]]],
+    composition_id: str | None,
+    feature_setup_id: str | None,
+    trainer_id: str,
+    config: dict[str, Any] | None,
+) -> None:
+    """Preprocess every given slice (feature banks are cache-aware, so an
+    already-computed slice is cheap), then train ONE model pooling all their
+    labeled pixels via ``POST /train/multi``.
+
+    A slice's preprocess failing aborts the whole job rather than being
+    skipped — unlike the apply job below, a pooled model that silently
+    dropped one of the slices the user asked for isn't the model they asked
+    to train, so this fails loudly instead.
+    """
+    try:
+        export_jobs.update(jid, state="running", phase="preprocessing")
+        slice_indices = sorted(per_slice_shapes)
+        # +1 unit for the final pooled-train phase, so the bar doesn't read
+        # 100% while the (potentially slow) pooled fit is still running.
+        export_jobs.set_total(jid, len(slice_indices) + 1)
+
+        feature_ids: dict[str, str] = {}
+        for slice_index in slice_indices:
+            if export_jobs.cancel_requested(jid):
+                export_jobs.update(jid, state="error", phase="cancelled", error="Cancelled.")
+                return
+            bank = ipred_client_mod.preprocess(
+                session_id=session_id,
+                feature_setup_id=feature_setup_id,
+                composition_id=composition_id,
+                slice_index=slice_index,
+            )
+            feature_ids[str(slice_index)] = bank["feature_id"]
+            export_jobs.bump(jid, 1)
+            cache_note = "cached" if bank.get("cache_hit") else "computed"
+            export_jobs.log(jid, f"slice {slice_index}: features ready ({cache_note})")
+
+        if export_jobs.cancel_requested(jid):
+            export_jobs.update(jid, state="error", phase="cancelled", error="Cancelled.")
+            return
+
+        export_jobs.update(jid, phase="training")
+        result = ipred_client_mod.train_multi(
+            session_id=session_id,
+            slices={str(k): v for k, v in per_slice_shapes.items()},
+            feature_ids=feature_ids,
+            trainer_id=trainer_id,
+            config=config,
+        )
+        export_jobs.bump(jid, 1)
+        export_jobs.update(jid, state="done", phase="done", result=result)
+        export_jobs.log(
+            jid,
+            f"Trained on {len(slice_indices)} slice(s), "
+            f"{result.get('n_samples', 0):,} pooled samples.",
+        )
+    except Exception as exc:  # noqa: BLE001 — reported as a job error, never a crash
+        logger.exception("ipred multi-slice train job %s failed", jid)
+        export_jobs.update(jid, state="error", phase="error", error=str(exc))
+
+
+def run_ipred_volume_apply_job(
+    jid: str,
+    *,
+    session_id: str,
+    model_id: str,
+    slice_indices: list[int],
+    composition_id: str | None,
+    feature_setup_id: str | None,
+    alpha: float,
+) -> None:
+    """Run inference on every given slice, ensuring each has a feature bank
+    first. Stores ``result.runs = {slice_index: run_id}`` for the frontend to
+    fetch commit/status PNGs from and vectorize per slice.
+
+    Unlike the train job, one slice failing does not abort the rest — a
+    volume-wide apply that stops at the first unreadable slice would be far
+    more disruptive than a job that finishes with a handful of gaps reported
+    in ``result.errors``.
+    """
+    try:
+        export_jobs.update(jid, state="running", phase="predicting")
+        export_jobs.set_total(jid, len(slice_indices))
+
+        runs: dict[str, str] = {}
+        errors: list[dict[str, Any]] = []
+        cancelled = False
+        for slice_index in slice_indices:
+            if export_jobs.cancel_requested(jid):
+                cancelled = True
+                break
+            try:
+                bank = ipred_client_mod.preprocess(
+                    session_id=session_id,
+                    feature_setup_id=feature_setup_id,
+                    composition_id=composition_id,
+                    slice_index=slice_index,
+                )
+                run = ipred_client_mod.infer(
+                    session_id=session_id,
+                    model_id=model_id,
+                    feature_id=bank["feature_id"],
+                    alpha=alpha,
+                )
+                runs[str(slice_index)] = run["run_id"]
+                export_jobs.log(jid, f"slice {slice_index}: predicted (run {run['run_id'][:8]})")
+            except Exception as exc:  # noqa: BLE001 — one bad slice must not abort the volume
+                logger.warning("ipred volume apply: slice %d failed (%s)", slice_index, exc)
+                errors.append({"slice": slice_index, "error": str(exc)})
+            export_jobs.bump(jid, 1)
+
+        result = {"runs": runs, "errors": errors, "cancelled": cancelled}
+        if not runs:
+            export_jobs.update(
+                jid,
+                state="error",
+                phase="error",
+                error="No slices could be predicted.",
+                result=result,
+            )
+            return
+        export_jobs.update(jid, state="done", phase="done", result=result)
+        export_jobs.log(
+            jid,
+            f"{'Cancelled after' if cancelled else 'Applied to'} "
+            f"{len(runs)}/{len(slice_indices)} slice(s).",
+        )
+    except Exception as exc:  # noqa: BLE001 — reported as a job error, never a crash
+        logger.exception("ipred volume apply job %s failed", jid)
+        export_jobs.update(jid, state="error", phase="error", error=str(exc))
diff --git a/backend/ipred_client.py b/backend/ipred_client.py
index abf2750..678a133 100644
--- a/backend/ipred_client.py
+++ b/backend/ipred_client.py
@@ -193,6 +193,30 @@ def train(
         return r.json()
 
 
+def train_multi(
+    *,
+    session_id: str,
+    slices: dict[str, list[dict[str, Any]]],
+    feature_ids: dict[str, str],
+    trainer_id: str = "catboost",
+    config: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """POST /train/multi — pool labeled pixels across multiple slices into one model."""
+    with _client() as client:
+        r = client.post(
+            "/train/multi",
+            json={
+                "session_id": session_id,
+                "slices": slices,
+                "feature_ids": feature_ids,
+                "trainer_id": trainer_id,
+                "config": config or {},
+            },
+        )
+        r.raise_for_status()
+        return r.json()
+
+
 def infer(
     *,
     session_id: str,
diff --git a/backend/ipred_routes.py b/backend/ipred_routes.py
index f334381..08b8697 100644
--- a/backend/ipred_routes.py
+++ b/backend/ipred_routes.py
@@ -8,6 +8,7 @@
 
 from __future__ import annotations
 
+import threading
 from typing import Any, Optional
 
 import httpx
@@ -15,6 +16,8 @@
 from fastapi.responses import Response
 from pydantic import BaseModel
 
+import export_jobs
+import ipred_batch_jobs
 import ipred_client as ipred_client_mod
 
 router = APIRouter(prefix="/api/ipred")
@@ -116,6 +119,28 @@ class IpredThresholdClassRequest(BaseModel):
     threshold: float = 0.5
 
 
+class IpredBatchTrainRequest(BaseModel):
+    """Train one model pooling labeled pixels across multiple slices."""
+
+    session_id: str
+    slices: dict[str, list[dict[str, Any]]]
+    composition_id: Optional[str] = None
+    feature_setup_id: Optional[str] = None
+    trainer_id: str = "catboost"
+    config: Optional[dict[str, Any]] = None
+
+
+class IpredBatchApplyRequest(BaseModel):
+    """Run inference across a set of slices (e.g. the whole volume)."""
+
+    session_id: str
+    model_id: str
+    slice_indices: list[int]
+    composition_id: Optional[str] = None
+    feature_setup_id: Optional[str] = None
+    alpha: float = 0.05
+
+
 def _ipred_http_error(exc: Exception) -> HTTPException:
     """Translate an ipred_client exception into the right HTTP response."""
     if isinstance(exc, httpx.HTTPStatusError):
@@ -370,6 +395,54 @@ async def ipred_run_proba(run_id: str, class_index: int) -> Response:
         raise _ipred_http_error(exc) from exc
 
 
+@router.post("/batch/train")
+async def ipred_batch_train(body: IpredBatchTrainRequest) -> dict:
+    """Train across multiple slices in the background; poll via export_jobs.
+
+    Mirrors the `/api/denoise/bake` job-spawning pattern: create the job,
+    hand it to a daemon thread, return its id immediately.
+    """
+    if not body.slices:
+        raise HTTPException(422, "at least one slice with shapes is required")
+    jid = export_jobs.new_job(body.session_id)
+    threading.Thread(
+        target=ipred_batch_jobs.run_ipred_multi_train_job,
+        kwargs=dict(
+            jid=jid,
+            session_id=body.session_id,
+            per_slice_shapes={int(k): v for k, v in body.slices.items()},
+            composition_id=body.composition_id,
+            feature_setup_id=body.feature_setup_id,
+            trainer_id=body.trainer_id,
+            config=body.config,
+        ),
+        daemon=True,
+    ).start()
+    return {"job_id": jid}
+
+
+@router.post("/batch/apply")
+async def ipred_batch_apply(body: IpredBatchApplyRequest) -> dict:
+    """Run inference across many slices in the background; poll via export_jobs."""
+    if not body.slice_indices:
+        raise HTTPException(422, "at least one slice_index is required")
+    jid = export_jobs.new_job(body.session_id)
+    threading.Thread(
+        target=ipred_batch_jobs.run_ipred_volume_apply_job,
+        kwargs=dict(
+            jid=jid,
+            session_id=body.session_id,
+            model_id=body.model_id,
+            slice_indices=list(body.slice_indices),
+            composition_id=body.composition_id,
+            feature_setup_id=body.feature_setup_id,
+            alpha=body.alpha,
+        ),
+        daemon=True,
+    ).start()
+    return {"job_id": jid}
+
+
 @router.post("/runs/{run_id}/threshold-class")
 async def ipred_threshold_class(run_id: str, body: IpredThresholdClassRequest) -> dict:
     """Threshold one softmax class into a dense label map via ipred."""
diff --git a/backend/tests/test_ipred_routes.py b/backend/tests/test_ipred_routes.py
index 07ec10c..0e9d166 100644
--- a/backend/tests/test_ipred_routes.py
+++ b/backend/tests/test_ipred_routes.py
@@ -1,10 +1,14 @@
 """Tests for the /api/ipred/* proxy — mocks ipred_client, never hits :8003."""
 from __future__ import annotations
 
+import asyncio
+import time
+
 import httpx
 import pytest
 from httpx import ASGITransport, AsyncClient
 
+import export_jobs
 import ipred_client as ipred_client_mod
 from annotation_server import app
 
@@ -97,3 +101,87 @@ async def test_run_proba_png_proxies_bytes(monkeypatch: pytest.MonkeyPatch) -> N
     assert response.status_code == 200
     assert response.content == b"\x89PNG\r\n"
     assert response.headers["content-type"] == "image/png"
+
+
+async def _await_job(jid: str, timeout: float = 5.0) -> dict:
+    """Poll export_jobs until the (thread-backed) job finishes."""
+    deadline = time.monotonic() + timeout
+    while time.monotonic() < deadline:
+        job = export_jobs.get_job(jid)
+        assert job is not None
+        if job["state"] in ("done", "error"):
+            return job
+        await asyncio.sleep(0.02)
+    raise AssertionError(f"job {jid} did not finish within {timeout}s")
+
+
+@pytest.mark.asyncio
+async def test_batch_train_pools_slices_and_reports_done(monkeypatch: pytest.MonkeyPatch) -> None:
+    """POST /batch/train preprocesses every slice, then trains once, pooled."""
+    preprocessed: list[int] = []
+
+    def _preprocess(*, session_id, feature_setup_id, composition_id, slice_index, array_ref=None):
+        preprocessed.append(slice_index)
+        return {"feature_id": f"feat-{slice_index}", "cache_hit": False}
+
+    captured_train: dict = {}
+
+    def _train_multi(*, session_id, slices, feature_ids, trainer_id, config):
+        captured_train.update(
+            session_id=session_id, slices=slices, feature_ids=feature_ids, trainer_id=trainer_id
+        )
+        return {"model_id": "m1", "class_ids": [1, 2], "n_samples": 4000}
+
+    monkeypatch.setattr(ipred_client_mod, "preprocess", _preprocess)
+    monkeypatch.setattr(ipred_client_mod, "train_multi", _train_multi)
+
+    shapes = [{"kind": "rectangle", "classId": 1, "x": 0, "y": 0, "w": 5, "h": 5}]
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.post(
+            "/api/ipred/batch/train",
+            json={
+                "session_id": "s1",
+                "slices": {"0": shapes, "2": shapes},
+                "composition_id": "comp-skimage",
+                "trainer_id": "catboost",
+            },
+        )
+    assert response.status_code == 200
+    jid = response.json()["job_id"]
+    job = await _await_job(jid)
+    assert job["state"] == "done"
+    assert job["result"] == {"model_id": "m1", "class_ids": [1, 2], "n_samples": 4000}
+    assert sorted(preprocessed) == [0, 2]
+    assert set(captured_train["feature_ids"]) == {"0", "2"}
+
+
+@pytest.mark.asyncio
+async def test_batch_apply_tolerates_one_bad_slice(monkeypatch: pytest.MonkeyPatch) -> None:
+    """POST /batch/apply keeps going after one slice fails and reports it in result.errors."""
+
+    def _preprocess(*, session_id, feature_setup_id, composition_id, slice_index, array_ref=None):
+        if slice_index == 1:
+            raise RuntimeError("boom")
+        return {"feature_id": f"feat-{slice_index}"}
+
+    def _infer(*, session_id, model_id, feature_id, alpha):
+        return {"run_id": f"run-{feature_id}"}
+
+    monkeypatch.setattr(ipred_client_mod, "preprocess", _preprocess)
+    monkeypatch.setattr(ipred_client_mod, "infer", _infer)
+
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.post(
+            "/api/ipred/batch/apply",
+            json={
+                "session_id": "s1",
+                "model_id": "m1",
+                "slice_indices": [0, 1, 2],
+            },
+        )
+    assert response.status_code == 200
+    jid = response.json()["job_id"]
+    job = await _await_job(jid)
+    assert job["state"] == "done"
+    assert job["result"]["runs"] == {"0": "run-feat-0", "2": "run-feat-2"}
+    assert job["result"]["errors"] == [{"slice": 1, "error": "boom"}]
diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index 8342ea4..fb738be 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -14,6 +14,7 @@ import { clearHistory } from '@/hooks/editHistory';
 import { useGuideLoad } from '@/hooks/useGuideSync';
 import { useSave, type VersionPayload } from '@/hooks/useSave';
 import { buildSourceKey } from '@/lib/sourceKey';
+import { API_BASE } from '@/config';
 import { useKeybinds } from '@/hooks/useKeybinds';
 import type { ColormapName } from '@/lib/colormaps';
 import Toolbar from '@/components/annotate/Toolbar';
@@ -32,6 +33,8 @@ import { useFeatureChannels } from '@/hooks/useFeatureChannels';
 import { usePixelClassifier } from '@/hooks/usePixelClassifier';
 import { useFeatureManifold } from '@/hooks/useFeatureManifold';
 import { loadLabelPng, labelMapToPolygonShapes } from '@/lib/pixelClf';
+import { ipredRunCommitUrl } from '@/lib/ipredApi';
+import type { Shape } from '@/stores/annotationStore';
 import DebouncedSlider from '@/components/common/DebouncedSlider';
 import DownloadModal from '@/components/annotate/DownloadModal';
 import InsightsModal from '@/components/annotate/InsightsModal';
@@ -77,7 +80,7 @@ function StageSwitcher({ stage, onChange }: { stage: AnnotateStage; onChange: (s
 export default function AnnotatePage() {
   const navigate = useNavigate();
   const { source, kind, serverUri, meta, denoise, setDenoise } = useDatasetStore();
-  const { removeShapes, addShapes, byImage } = useAnnotationStore();
+  const { removeShapes, addShapes, addShapesAcrossSlices, byImage } = useAnnotationStore();
   const { selectedShapeIds, setSelectedShapeId, fillOpacity, setFillOpacity } = useToolStore();
   const { classes } = useClassStore();
 
@@ -197,7 +200,10 @@ export default function AnnotatePage() {
 
   const clf = usePixelClassifier({
     featureJobId: features.job?.jobId ?? null,
-    resetKey: sourceKey ? `${sourceKey}:${currentSlice}` : null,
+    // Sample/composition identity only — NOT the slice, so a trained model
+    // survives a slice change and can be applied to whichever slice is on
+    // screen (see usePixelClassifier's two reset effects for the split).
+    resetKey: sourceKey,
     source,
     kind,
     serverUri,
@@ -211,9 +217,43 @@ export default function AnnotatePage() {
     onFeatureJobExpired: features.invalidateJob,
   });
 
+  // ---- Batch iPred: multi-slice train + whole-volume apply ----
+  const [trainAcrossSlices, setTrainAcrossSlices] = useState(false);
+  const [commitClassIds, setCommitClassIds] = useState<number[]>([]);
+  const [volumeApplyCommitting, setVolumeApplyCommitting] = useState(false);
+
+  // Every non-empty slice of this sample — the pool for "train across all
+  // annotated slices" and the source of truth for `annotatedSliceCount`.
+  const annotatedSlices = useMemo(() => {
+    const slices = sourceKey ? (byImage[sourceKey] ?? {}) : {};
+    const out: Record<number, Shape[]> = {};
+    for (const [key, shapes] of Object.entries(slices)) {
+      if (shapes.length > 0) out[Number(key)] = shapes;
+    }
+    return out;
+  }, [sourceKey, byImage]);
+  const annotatedSliceCount = Object.keys(annotatedSlices).length;
+  const totalSliceCount = meta?.nSlices ?? 1;
+
+  // Keep the commit class filter in sync with whichever model is current —
+  // default to "all classes" each time a (re)train finishes.
+  useEffect(() => {
+    setCommitClassIds(clf.model?.classIds ?? []);
+  }, [clf.model]);
+
+  const toggleCommitClassId = useCallback((classId: number) => {
+    setCommitClassIds((prev) =>
+      prev.includes(classId) ? prev.filter((c) => c !== classId) : [...prev, classId],
+    );
+  }, []);
+
   const handleClfTrain = useCallback(() => {
-    void clf.train(sliceShapes);
-  }, [clf, sliceShapes]);
+    if (trainAcrossSlices) {
+      void clf.trainAcrossSlices(annotatedSlices);
+    } else {
+      void clf.train(sliceShapes);
+    }
+  }, [clf, sliceShapes, trainAcrossSlices, annotatedSlices]);
 
   const handleClfPredict = useCallback(() => {
     void clf.predict(sliceShapes);
@@ -223,16 +263,63 @@ export default function AnnotatePage() {
     if (!sourceKey || !clf.commitUrl || !clf.model) return;
     try {
       const { data, width, height } = await loadLabelPng(clf.commitUrl);
-      const shapes = labelMapToPolygonShapes(data, width, height, clf.model.classIds, {
+      const shapes = labelMapToPolygonShapes(data, width, height, commitClassIds, {
         minRegion: 64,
         preserveShapes: sliceShapes,
+        origin: 'predicted',
       });
       if (shapes.length) addShapes(sourceKey, currentSlice, shapes);
       clf.dismiss();
     } catch {
       /* keep overlay; error surfaces via clf.error if needed */
     }
-  }, [sourceKey, clf, addShapes, currentSlice, sliceShapes]);
+  }, [sourceKey, clf, addShapes, currentSlice, sliceShapes, commitClassIds]);
+
+  const handleApplyToVolume = useCallback(() => {
+    const sliceIndices = Array.from({ length: totalSliceCount }, (_, i) => i);
+    void clf.applyAcrossVolume(sliceIndices);
+  }, [clf, totalSliceCount]);
+
+  const handleCancelVolumeApply = useCallback(async () => {
+    const jobId = clf.volumeApplyJob.jobId;
+    if (!jobId) return;
+    // Cooperative: the job stops at its next slice boundary (see DenoiseBakeModal
+    // for the same pattern against the same /api/export/cancel/{id} route).
+    await fetch(`${API_BASE}/api/export/cancel/${jobId}`, { method: 'POST' });
+  }, [clf.volumeApplyJob.jobId]);
+
+  // Fetch + vectorize each completed slice's commit PNG client-side, reusing the
+  // same tracer as the single-slice commit — see Phase 4.5's plan for why this
+  // stays client-side rather than porting polygon tracing to the ipred service.
+  const handleCommitVolumeApply = useCallback(async () => {
+    if (!sourceKey) return;
+    const result = clf.volumeApplyJob.result as { runs?: Record<string, string> } | null;
+    const runs = result?.runs;
+    if (!runs || commitClassIds.length === 0) return;
+    setVolumeApplyCommitting(true);
+    try {
+      const bySlice: Record<number, Shape[]> = {};
+      let processed = 0;
+      for (const [sliceKey, runId] of Object.entries(runs)) {
+        const sliceIdx = Number(sliceKey);
+        const { data, width, height } = await loadLabelPng(ipredRunCommitUrl(runId));
+        const existing = byImage[sourceKey]?.[sliceKey] ?? [];
+        const shapes = labelMapToPolygonShapes(data, width, height, commitClassIds, {
+          minRegion: 64,
+          preserveShapes: existing,
+          origin: 'predicted',
+        });
+        if (shapes.length) bySlice[sliceIdx] = shapes;
+        processed += 1;
+        // Yield every few slices so vectorizing a large volume doesn't freeze the tab.
+        if (processed % 4 === 0) await new Promise((resolve) => setTimeout(resolve, 0));
+      }
+      if (Object.keys(bySlice).length) addShapesAcrossSlices(sourceKey, bySlice);
+      clf.resetVolumeApplyJob();
+    } finally {
+      setVolumeApplyCommitting(false);
+    }
+  }, [sourceKey, clf, byImage, commitClassIds, addShapesAcrossSlices]);
 
   const classLabelForId = useCallback(
     (classId: number) => {
@@ -356,8 +443,6 @@ export default function AnnotatePage() {
             value={Math.round(fillOpacity * 100)}
             onChange={(v) => setFillOpacity(v / 100)}
           />
-          <hr />
-          <StageSwitcher stage={stage} onChange={setStage} />
           <LayersPanel
             hasFeatures={!!features.job && features.channelIndex !== null}
             hasProba={!!clf.probaUrl}
@@ -365,7 +450,8 @@ export default function AnnotatePage() {
             predictionClassIds={clf.model?.classIds ?? []}
             hasManifold={manifold.hasSample || !!manifold.heatmapUrl}
           />
-          <hr />
+          <StageSwitcher stage={stage} onChange={setStage} />
+          <SliceNavigator />
 
           {stage === 'draw' && (
             <>
@@ -414,34 +500,25 @@ export default function AnnotatePage() {
                   />
                 }
               />
-              <hr />
-              <SliceNavigator />
-              <hr />
               <MaskToolsPanel sourceKey={sourceKey} activeClassId={activeClassId} />
-              <hr />
               <MeasurementPanel sourceKey={sourceKey} />
-              <hr />
             </>
           )}
 
           {stage === 'assist' && (
-            <>
-              <FeatureChannelsPanel
-                job={features.job}
-                channelIndex={features.channelIndex}
-                computing={features.computing}
-                error={features.error}
-                onCompute={features.compute}
-                onSelectChannel={features.selectChannel}
-                onCycle={features.cycleChannel}
-                onOriginal={features.clearSelection}
-              />
-              <hr />
-            </>
+            <FeatureChannelsPanel
+              job={features.job}
+              channelIndex={features.channelIndex}
+              computing={features.computing}
+              error={features.error}
+              onCompute={features.compute}
+              onSelectChannel={features.selectChannel}
+              onCycle={features.cycleChannel}
+              onOriginal={features.clearSelection}
+            />
           )}
 
           {stage === 'predict' && (
-            <>
               <PixelClassifierPanel
                 hasFeatureJob={!!features.job}
                 canAutoPreprocess={clf.canTrainWithoutJob}
@@ -467,6 +544,37 @@ export default function AnnotatePage() {
                 commitLabel="Commit singletons"
                 featureSetupId={features.job?.setupId ?? null}
                 trainerId={clf.trainerId}
+                annotatedSliceCount={annotatedSliceCount}
+                trainAcrossSlices={trainAcrossSlices}
+                onTrainAcrossSlicesChange={setTrainAcrossSlices}
+                multiTraining={clf.multiTraining}
+                multiTrainProgress={{ done: clf.multiTrainJob.done, total: clf.multiTrainJob.total }}
+                totalSliceCount={totalSliceCount}
+                commitClassIds={commitClassIds}
+                onToggleCommitClassId={toggleCommitClassId}
+                volumeApplying={clf.volumeApplying}
+                volumeApplyProgress={{ done: clf.volumeApplyJob.done, total: clf.volumeApplyJob.total }}
+                volumeApplyResult={
+                  clf.volumeApplyJob.result
+                    ? (() => {
+                        const r = clf.volumeApplyJob.result as {
+                          runs?: Record<string, string>;
+                          errors?: unknown[];
+                          cancelled?: boolean;
+                        };
+                        return {
+                          runCount: Object.keys(r.runs ?? {}).length,
+                          errorCount: (r.errors ?? []).length,
+                          cancelled: !!r.cancelled,
+                        };
+                      })()
+                    : null
+                }
+                volumeApplyCommitting={volumeApplyCommitting}
+                onApplyToVolume={handleApplyToVolume}
+                onCommitVolumeApply={() => { void handleCommitVolumeApply(); }}
+                onCancelVolumeApply={() => { void handleCancelVolumeApply(); }}
+                onDismissVolumeApply={clf.resetVolumeApplyJob}
                 manifoldParams={manifold.params}
                 onManifoldParamsChange={manifold.setParams}
                 manifoldSampling={manifold.sampling}
@@ -489,8 +597,6 @@ export default function AnnotatePage() {
                 }}
                 onClearManifoldRoi={manifold.clearPlacementMask}
               />
-              <hr />
-            </>
           )}
 
           {/* Save button + status */}
diff --git a/frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.tsx b/frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.tsx
index 3f31723..f19c793 100644
--- a/frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.tsx
+++ b/frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.tsx
@@ -17,6 +17,12 @@ import { Layer, Line, Rect, Ellipse, Group, Shape as KonvaShape } from 'react-ko
 import type Konva from 'konva';
 import type { Shape, EraseStroke } from '@/stores/annotationStore';
 import type { AnnotationClass } from '@/stores/classStore';
+import { isShapeOriginVisible } from '@/stores/layerVisibilityStore';
+
+/** Dash pattern marking a shape as iPred-predicted (vs. hand-drawn, solid). */
+function dashFor(shape: Shape, strokeW: number): number[] | undefined {
+  return shape.origin === 'predicted' ? [strokeW * 4, strokeW * 3] : undefined;
+}
 
 /** Build an even-odd path (outer + hole rings) on a Konva context. */
 function buildRingsPath(ctx: Konva.Context, rings: number[][]): void {
@@ -62,6 +68,8 @@ export interface ShapesLayerProps {
   /** Image frame, for clipping and for giving hole-shapes a real self-rect. */
   imageWidth: number;
   imageHeight: number;
+  /** Predicted/human sub-visibility (see layerVisibilityStore). */
+  originVisible: { human: boolean; predicted: boolean };
 }
 
 function ShapesLayerImpl({
@@ -75,16 +83,24 @@ function ShapesLayerImpl({
   activeClassId,
   imageWidth,
   imageHeight,
+  originVisible,
 }: ShapesLayerProps) {
   const colorForClass = (classId: number) => classMap.get(classId)?.color ?? '#ff0000';
 
   /** Render a polygon that may have holes via an even-odd fill (outer path minus
    *  hole subpaths). Even-odd — not destination-out — so a hole reveals whatever
    *  is *beneath* it (e.g. another class) instead of erasing it off the layer. */
-  const renderPolygonWithHoles = (points: number[], holes: number[][], color: string, strokeW: number) => (
+  const renderPolygonWithHoles = (
+    points: number[],
+    holes: number[][],
+    color: string,
+    strokeW: number,
+    dash: number[] | undefined,
+  ) => (
     <KonvaShape
       stroke={color}
       strokeWidth={strokeW}
+      dash={dash}
       // width/height give the shape a real self-rect so the CACHED committed layer
       // sizes its cache canvas to include it (otherwise it's clipped away when the
       // only other in-bounds shape — the enclosed class — is hidden).
@@ -111,12 +127,15 @@ function ShapesLayerImpl({
         : colorForClass(shape.classId);
     const isSelected = selectedShapeIds.includes(shape.id);
     const strokeW = (isSelected ? 2 : 1) / scaleX;
+    // Predicted (uncommitted-by-a-human) shapes get a dashed outline so they read
+    // as provisional at a glance — see layerVisibilityStore's origin toggle.
+    const dash = dashFor(shape, strokeW);
 
     if (shape.kind === 'polygon') {
       return (
         <Group key={shape.id}>
           {shape.holes?.length
-            ? renderPolygonWithHoles(shape.points, shape.holes, color, strokeW)
+            ? renderPolygonWithHoles(shape.points, shape.holes, color, strokeW, dash)
             : (
               <Line
                 points={shape.points}
@@ -124,6 +143,7 @@ function ShapesLayerImpl({
                 fill={color}
                 stroke={color}
                 strokeWidth={strokeW}
+                dash={dash}
                 perfectDrawEnabled={false}
               />
             )}
@@ -138,6 +158,7 @@ function ShapesLayerImpl({
             x={shape.x} y={shape.y} width={shape.w} height={shape.h}
             fill={color}
             stroke={color} strokeWidth={strokeW}
+            dash={dash}
             perfectDrawEnabled={false}
           />
           {renderErased(shape.erased)}
@@ -151,6 +172,7 @@ function ShapesLayerImpl({
             x={shape.cx} y={shape.cy} radiusX={shape.rx} radiusY={shape.ry}
             fill={color}
             stroke={color} strokeWidth={strokeW}
+            dash={dash}
             perfectDrawEnabled={false}
           />
           {renderErased(shape.erased)}
@@ -193,6 +215,7 @@ function ShapesLayerImpl({
     >
       {shapes
         .filter((s) => classMap.get(s.classId)?.isVisible !== false)
+        .filter((s) => isShapeOriginVisible(originVisible, s.origin))
         .map(renderShape)}
     </Layer>
   );
diff --git a/frontend/src/components/annotate/AnnotationCanvas/index.tsx b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
index 733ff93..d686473 100644
--- a/frontend/src/components/annotate/AnnotationCanvas/index.tsx
+++ b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
@@ -575,6 +575,7 @@ export default function AnnotationCanvas({
   const predictionClassVisible = useLayerVisibilityStore((s) => s.predictionClassVisible);
   const showPredictionMulti = useLayerVisibilityStore((s) => s.showPredictionMulti);
   const showPredictionAbstain = useLayerVisibilityStore((s) => s.showPredictionAbstain);
+  const annotationOriginVisible = useLayerVisibilityStore((s) => s.annotationOriginVisible);
   const sourceKey = source && kind
     ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri)
     : null;
@@ -3117,6 +3118,7 @@ export default function AnnotationCanvas({
             activeClassId={activeClassId}
             imageWidth={meta.width}
             imageHeight={meta.height}
+            originVisible={annotationOriginVisible}
           />
         )}
 
diff --git a/frontend/src/components/annotate/ClassManager/index.tsx b/frontend/src/components/annotate/ClassManager/index.tsx
index 936e5b9..286e321 100644
--- a/frontend/src/components/annotate/ClassManager/index.tsx
+++ b/frontend/src/components/annotate/ClassManager/index.tsx
@@ -16,6 +16,7 @@ import { getClassPalette } from '@/lib/classColors';
 import { buildSourceKey } from '@/lib/sourceKey';
 import { cn } from '@/lib/utils';
 import { Copy } from '@phosphor-icons/react';
+import CollapsibleSection from '@/components/common/CollapsibleSection';
 
 /** Counts shapes assigned to a class within a single sample (*sourceKey*), across its
  *  slices. Scoped to the current sample so the delete prompt never counts (or deletes)
@@ -316,9 +317,9 @@ export default function ClassManager({ activeClassId, onActivate, onClassDeleted
   );
 
   return (
-    <div className="flex flex-col gap-1">
-      <div className="flex items-center justify-between mb-1">
-        <span className="text-xs font-semibold uppercase text-gray-500 tracking-wide">Classes</span>
+    <CollapsibleSection
+      title="Classes"
+      headerRight={
         <button
           aria-label="Add class"
           className="p-0.5 rounded hover:bg-sky-100 hover:text-sky-700"
@@ -326,8 +327,8 @@ export default function ClassManager({ activeClassId, onActivate, onClassDeleted
         >
           <Plus size={14} />
         </button>
-      </div>
-
+      }
+    >
       <label
         className="flex items-center gap-1.5 mb-1 text-xs text-gray-600 cursor-pointer select-none"
         title="Color newly added classes from a colorblind-safe palette (Okabe–Ito). Existing classes keep their colors."
@@ -403,6 +404,6 @@ export default function ClassManager({ activeClassId, onActivate, onClassDeleted
           />
         ))}
       </div>
-    </div>
+    </CollapsibleSection>
   );
 }
diff --git a/frontend/src/components/annotate/DenoisePanel/index.tsx b/frontend/src/components/annotate/DenoisePanel/index.tsx
index 24a986d..6e782ed 100644
--- a/frontend/src/components/annotate/DenoisePanel/index.tsx
+++ b/frontend/src/components/annotate/DenoisePanel/index.tsx
@@ -111,14 +111,14 @@ export default function DenoisePanel({
   return (
     <div className="space-y-2">
       <div className="flex items-center justify-between">
-        <label className="text-xs font-medium text-sky-200">Denoise</label>
-        {busy && active && <span className="text-[10px] text-sky-400">filtering…</span>}
+        <label className="text-xs font-semibold uppercase tracking-wide text-gray-500">Denoise</label>
+        {busy && active && <span className="text-[10px] text-gray-500">filtering…</span>}
       </div>
 
       <select
         value={denoise.method}
         onChange={(e) => onChange({ method: e.target.value })}
-        className="w-full rounded border border-sky-800 bg-sky-950 px-2 py-1 text-xs text-sky-100"
+        className="w-full rounded border border-gray-200 bg-white px-1 py-0.5 text-xs text-gray-800"
       >
         {methods.map((m) => (
           <option key={m.method} value={m.method} disabled={!m.available}>
@@ -130,7 +130,7 @@ export default function DenoisePanel({
       </select>
 
       {current && current.method !== 'none' && (
-        <p className="text-[11px] leading-snug text-sky-300/80">{current.description}</p>
+        <p className="text-[11px] leading-snug text-gray-600">{current.description}</p>
       )}
 
       {active && (
@@ -149,7 +149,7 @@ export default function DenoisePanel({
               type="button"
               onClick={applyAuto}
               disabled={autoBusy || !source}
-              className="flex items-center gap-1 rounded border border-sky-700 px-2 py-1 text-[11px] text-sky-200 hover:bg-sky-900 disabled:opacity-50"
+              className="flex items-center gap-1 rounded border border-gray-200 bg-white px-2 py-1 text-[11px] text-gray-800 hover:bg-sky-50 hover:border-sky-300 disabled:opacity-50"
               title="Measure this slice's noise level and suggest a strength"
             >
               <MagicWand size={12} />
@@ -159,7 +159,7 @@ export default function DenoisePanel({
               <button
                 type="button"
                 onClick={onBake}
-                className="rounded border border-sky-700 px-2 py-1 text-[11px] text-sky-200 hover:bg-sky-900"
+                className="rounded border border-gray-200 bg-white px-2 py-1 text-[11px] text-gray-800 hover:bg-sky-50 hover:border-sky-300"
                 title="Write a denoised copy as a new dataset you can annotate and export"
               >
                 Save denoised copy…
@@ -168,13 +168,13 @@ export default function DenoisePanel({
           </div>
 
           {autoError && (
-            <p className="flex items-start gap-1 text-[11px] leading-snug text-amber-300/90">
+            <p className="flex items-start gap-1 rounded border border-amber-200 bg-amber-50 p-1.5 text-[11px] leading-snug text-amber-800">
               <Warning size={12} className="mt-0.5 shrink-0" />
               {autoError}
             </p>
           )}
 
-          <p className="text-[10px] leading-snug text-sky-400/70">
+          <p className="text-[10px] leading-snug text-gray-500">
             Affects the intensity tools too, not just the display. Exports still use
             the original pixels.
           </p>
diff --git a/frontend/src/components/annotate/DisplayControls/index.tsx b/frontend/src/components/annotate/DisplayControls/index.tsx
index c6c8488..db461e3 100644
--- a/frontend/src/components/annotate/DisplayControls/index.tsx
+++ b/frontend/src/components/annotate/DisplayControls/index.tsx
@@ -6,6 +6,7 @@
 import { ArrowCounterClockwise } from '@phosphor-icons/react';
 import DebouncedSlider from '@/components/common/DebouncedSlider';
 import HistogramControl from '@/components/annotate/HistogramControl';
+import CollapsibleSection from '@/components/common/CollapsibleSection';
 import { COLORMAP_NAMES, colormapGradient, type ColormapName } from '@/lib/colormaps';
 
 export interface DisplayControlsProps {
@@ -76,9 +77,9 @@ export default function DisplayControls({
   denoiseSlot,
 }: DisplayControlsProps) {
   return (
-    <div className="flex flex-col gap-2">
-      <div className="flex items-center justify-between">
-        <span className="text-xs font-semibold uppercase text-gray-500 tracking-wide">Display</span>
+    <CollapsibleSection
+      title="Display"
+      headerRight={
         <button
           aria-label="Reset brightness and contrast"
           title="Reset display"
@@ -87,8 +88,8 @@ export default function DisplayControls({
         >
           <ArrowCounterClockwise size={14} />
         </button>
-      </div>
-
+      }
+    >
       <div className="flex flex-col gap-0.5">
         <DebouncedSlider
           label="Brightness"
@@ -162,7 +163,7 @@ export default function DisplayControls({
           unlike the pre-blur below, which is a client-side filter on the already
           8-bit display image. Placed first because it is the upstream stage. */}
       {denoiseSlot && (
-        <div className="border-t border-sky-900/60 pt-2">{denoiseSlot}</div>
+        <div className="border-t border-gray-100 pt-2">{denoiseSlot}</div>
       )}
 
       {/* Gaussian pre-blur — denoises so threshold/wand/fill see coherent regions. */}
@@ -238,6 +239,6 @@ export default function DisplayControls({
           Exported pixels and annotation coordinates are unchanged.
         </p>
       </div>
-    </div>
+    </CollapsibleSection>
   );
 }
diff --git a/frontend/src/components/annotate/FeatureChannelsPanel/index.tsx b/frontend/src/components/annotate/FeatureChannelsPanel/index.tsx
index d2f90f5..39345fc 100644
--- a/frontend/src/components/annotate/FeatureChannelsPanel/index.tsx
+++ b/frontend/src/components/annotate/FeatureChannelsPanel/index.tsx
@@ -18,6 +18,7 @@ import type { FeatureJobInfo } from '@/hooks/useFeatureChannels';
 import { useIpredStore } from '@/stores/ipredStore';
 import { listIpredCompositions, listIpredModules, type CompositionDoc, type FeatureModuleInfo } from '@/lib/ipredApi';
 import CompositionPanel from '@/components/CompositionPanel';
+import CollapsibleSection from '@/components/common/CollapsibleSection';
 import { cn } from '@/lib/utils';
 
 /** Plain-language labels for the 7 built-in compositions, by id. */
@@ -127,17 +128,17 @@ export default function FeatureChannelsPanel({
     channelIndex !== null && job ? (job.channels[channelIndex]?.label ?? `Channel ${channelIndex}`) : null;
 
   return (
-    <div className="flex flex-col gap-2">
-      <div className="flex items-center justify-between">
-        <span className="text-xs font-semibold uppercase text-gray-500 tracking-wide">Features</span>
-        {n > 0 && (
+    <CollapsibleSection
+      title="Features"
+      headerRight={
+        n > 0 ? (
           <span className="text-[10px] text-gray-400">
             {n} ch{job?.hasSam ? ' +SAM' : ''}
             {job?.cacheHit ? ' · cache' : ''}
           </span>
-        )}
-      </div>
-
+        ) : undefined
+      }
+    >
       <label className="flex flex-col gap-0.5 text-xs text-gray-600">
         Recipe
         {loadingPresets ? (
@@ -259,6 +260,6 @@ export default function FeatureChannelsPanel({
           </div>
         )}
       </div>
-    </div>
+    </CollapsibleSection>
   );
 }
diff --git a/frontend/src/components/annotate/LayersPanel/index.tsx b/frontend/src/components/annotate/LayersPanel/index.tsx
index bed7cae..7b518eb 100644
--- a/frontend/src/components/annotate/LayersPanel/index.tsx
+++ b/frontend/src/components/annotate/LayersPanel/index.tsx
@@ -15,6 +15,7 @@ import {
   isPredictionClassVisible,
 } from '@/stores/layerVisibilityStore';
 import { cn } from '@/lib/utils';
+import CollapsibleSection from '@/components/common/CollapsibleSection';
 
 const GROUP_ORDER: LayerGroupId[] = [
   'image',
@@ -98,6 +99,8 @@ export default function LayersPanel({
   const setShowPredictionMulti = useLayerVisibilityStore((s) => s.setShowPredictionMulti);
   const setShowPredictionAbstain = useLayerVisibilityStore((s) => s.setShowPredictionAbstain);
   const ensurePredictionClasses = useLayerVisibilityStore((s) => s.ensurePredictionClasses);
+  const annotationOriginVisible = useLayerVisibilityStore((s) => s.annotationOriginVisible);
+  const setAnnotationOriginVisible = useLayerVisibilityStore((s) => s.setAnnotationOriginVisible);
 
   useEffect(() => {
     if (predictionClassIds.length) ensurePredictionClasses(predictionClassIds);
@@ -118,14 +121,11 @@ export default function LayersPanel({
   };
 
   return (
+    <CollapsibleSection title="Layers" icon={<Stack size={13} />}>
     <div
       className="flex flex-col gap-1.5 rounded-md border border-gray-200 bg-white p-2"
       data-testid="layers-panel"
     >
-      <div className="flex items-center gap-1.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
-        <Stack size={13} /> Layers
-      </div>
-
       <div className="flex flex-col gap-0.5">
         {GROUP_ORDER.map((id) => (
           <GroupToggle key={id} id={id} disabled={disabledFor(id)} />
@@ -150,6 +150,37 @@ export default function LayersPanel({
         </label>
       )}
 
+      {groups.annotations && classes.length > 0 && (
+        <div className="flex flex-wrap gap-1 border-t border-gray-100 pt-1.5">
+          <button
+            type="button"
+            onClick={() => setAnnotationOriginVisible('human', !annotationOriginVisible.human)}
+            title="Hand-drawn shapes"
+            className={cn(
+              'rounded border px-1.5 py-0.5 text-[9px]',
+              annotationOriginVisible.human
+                ? 'border-gray-300 bg-gray-100 text-gray-800'
+                : 'border-gray-200 bg-gray-50 text-gray-400',
+            )}
+          >
+            Human
+          </button>
+          <button
+            type="button"
+            onClick={() => setAnnotationOriginVisible('predicted', !annotationOriginVisible.predicted)}
+            title="Shapes committed from an iPred prediction (dashed outline)"
+            className={cn(
+              'rounded border px-1.5 py-0.5 text-[9px]',
+              annotationOriginVisible.predicted
+                ? 'border-sky-300 bg-sky-50 text-sky-900'
+                : 'border-gray-200 bg-gray-50 text-gray-400',
+            )}
+          >
+            Predicted
+          </button>
+        </div>
+      )}
+
       {groups.predictions && hasPredictions && (
         <div className="flex flex-col gap-1 border-t border-gray-100 pt-1.5">
           <label className="flex flex-col gap-0.5 text-[10px] text-gray-600">
@@ -221,5 +252,6 @@ export default function LayersPanel({
         </div>
       )}
     </div>
+    </CollapsibleSection>
   );
 }
diff --git a/frontend/src/components/annotate/MaskToolsPanel/index.tsx b/frontend/src/components/annotate/MaskToolsPanel/index.tsx
index e30f939..3a78dc4 100644
--- a/frontend/src/components/annotate/MaskToolsPanel/index.tsx
+++ b/frontend/src/components/annotate/MaskToolsPanel/index.tsx
@@ -9,6 +9,7 @@ import { useAnnotationStore } from '@/stores/annotationStore';
 import { useDatasetStore } from '@/stores/datasetStore';
 import { useClassStore } from '@/stores/classStore';
 import { useMaskOps } from '@/hooks/useMaskOps';
+import CollapsibleSection from '@/components/common/CollapsibleSection';
 
 interface MaskToolsPanelProps {
   sourceKey: string | null;
@@ -43,10 +44,7 @@ export default function MaskToolsPanel({ sourceKey, activeClassId }: MaskToolsPa
   if (!isVolume) return null;
 
   return (
-    <div className="flex flex-col gap-2">
-      <span className="flex items-center gap-1.5 text-xs font-semibold uppercase text-gray-500 tracking-wide">
-        <Stack size={13} /> Cross-slice
-      </span>
+    <CollapsibleSection title="Cross-slice" icon={<Stack size={13} />}>
       <p className="text-[10px] text-gray-500 -mt-1">Acts on ‘{className}’.</p>
 
       <button
@@ -61,6 +59,6 @@ export default function MaskToolsPanel({ sourceKey, activeClassId }: MaskToolsPa
       </button>
 
       {status && <p className="text-[10px] text-gray-500 leading-snug">{status}</p>}
-    </div>
+    </CollapsibleSection>
   );
 }
diff --git a/frontend/src/components/annotate/MeasurementPanel/index.tsx b/frontend/src/components/annotate/MeasurementPanel/index.tsx
index 35cfbe6..ac495a9 100644
--- a/frontend/src/components/annotate/MeasurementPanel/index.tsx
+++ b/frontend/src/components/annotate/MeasurementPanel/index.tsx
@@ -13,6 +13,7 @@ import { useDatasetStore } from '@/stores/datasetStore';
 import { useAnnotationStore } from '@/stores/annotationStore';
 import { useToolStore } from '@/stores/toolStore';
 import { measureRegion } from '@/lib/measure';
+import CollapsibleSection from '@/components/common/CollapsibleSection';
 
 interface MeasurementPanelProps {
   sourceKey: string | null;
@@ -87,11 +88,7 @@ export default function MeasurementPanel({ sourceKey }: MeasurementPanelProps) {
   const len = (v: number) => (cal ? `${(v * px).toPrecision(4)} ${unit}` : `${v.toFixed(1)} px`);
 
   return (
-    <div className="flex flex-col gap-2">
-      <div className="flex items-center gap-1.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
-        <Ruler size={14} /> Measure
-      </div>
-
+    <CollapsibleSection title="Measure" icon={<Ruler size={14} />}>
       {!geom || geom.count === 0 ? (
         <p className="text-xs text-gray-500">Select one or more regions to measure.</p>
       ) : (
@@ -141,7 +138,7 @@ export default function MeasurementPanel({ sourceKey }: MeasurementPanelProps) {
           </div>
         </div>
       )}
-    </div>
+    </CollapsibleSection>
   );
 }
 
diff --git a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
index 71ab68f..56bb49b 100644
--- a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
+++ b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
@@ -3,11 +3,11 @@
  * Suggest-Labels manifold loop folded in and conformal coverage surfaced as
  * headline guidance (not a legend entry).
  */
-import { useState } from 'react';
 import { CaretLeft, CaretRight, CircleNotch, Compass, TreeStructure } from '@phosphor-icons/react';
 import type { ClfParams, ClfPredictCounts, ClfTrainResult } from '@/hooks/usePixelClassifier';
 import type { ManifoldParams } from '@/hooks/useFeatureManifold';
 import { cn } from '@/lib/utils';
+import CollapsibleSection from '@/components/common/CollapsibleSection';
 
 /** Shared progress-bar treatment (see useExportJob / DownloadModal). */
 function BusyBar({ label }: { label: string }) {
@@ -23,6 +23,25 @@ function BusyBar({ label }: { label: string }) {
   );
 }
 
+/** Real done/total progress bar for batch jobs (see DownloadModal's job bar). */
+function JobProgressBar({ label, done, total }: { label: string; done: number; total: number }) {
+  const pct = total > 0 ? Math.round((done / total) * 100) : 0;
+  return (
+    <div className="flex flex-col gap-1">
+      <div className="flex items-center justify-between text-[10px] text-gray-500">
+        <span>{label}</span>
+        {total > 0 && <span className="tabular-nums">{done}/{total}</span>}
+      </div>
+      <div className="h-1.5 w-full overflow-hidden rounded-full bg-gray-200">
+        <div
+          className="h-full rounded-full bg-sky-500 transition-all"
+          style={{ width: `${Math.max(5, pct)}%` }}
+        />
+      </div>
+    </div>
+  );
+}
+
 /** Coverage guidance derived from conformal set counts — the headline, not a legend line. */
 function coverageHeadline(counts: ClfPredictCounts, alpha: number): { text: string; tone: 'good' | 'warn' } {
   const total = counts.singleton + counts.multi + counts.abstain;
@@ -67,6 +86,26 @@ export interface PixelClassifierPanelProps {
   featureSetupId?: string | null;
   trainerId?: string | null;
 
+  // Multi-slice train: pool labeled pixels across every annotated slice of the sample.
+  annotatedSliceCount: number;
+  trainAcrossSlices: boolean;
+  onTrainAcrossSlicesChange: (v: boolean) => void;
+  multiTraining: boolean;
+  multiTrainProgress: { done: number; total: number } | null;
+
+  // Apply a trained model across many slices at once, then commit selected classes.
+  totalSliceCount: number;
+  commitClassIds: number[];
+  onToggleCommitClassId: (classId: number) => void;
+  volumeApplying: boolean;
+  volumeApplyProgress: { done: number; total: number } | null;
+  volumeApplyResult: { runCount: number; errorCount: number; cancelled: boolean } | null;
+  volumeApplyCommitting: boolean;
+  onApplyToVolume: () => void;
+  onCommitVolumeApply: () => void;
+  onCancelVolumeApply: () => void;
+  onDismissVolumeApply: () => void;
+
   // Suggest-labels (manifold), folded into the same loop instead of a separate panel.
   manifoldParams: ManifoldParams;
   onManifoldParamsChange: (p: ManifoldParams) => void;
@@ -114,6 +153,22 @@ export default function PixelClassifierPanel({
   commitLabel = 'Commit singletons',
   featureSetupId = null,
   trainerId = null,
+  annotatedSliceCount,
+  trainAcrossSlices,
+  onTrainAcrossSlicesChange,
+  multiTraining,
+  multiTrainProgress,
+  totalSliceCount,
+  commitClassIds,
+  onToggleCommitClassId,
+  volumeApplying,
+  volumeApplyProgress,
+  volumeApplyResult,
+  volumeApplyCommitting,
+  onApplyToVolume,
+  onCommitVolumeApply,
+  onCancelVolumeApply,
+  onDismissVolumeApply,
   manifoldParams,
   onManifoldParamsChange,
   manifoldSampling,
@@ -133,10 +188,14 @@ export default function PixelClassifierPanel({
   onCaptureManifoldRoi,
   onClearManifoldRoi,
 }: PixelClassifierPanelProps) {
-  const [showSuggest, setShowSuggest] = useState(false);
-  const busy = training || predicting;
-  const canTrain = (hasFeatureJob || canAutoPreprocess) && hasShapes && !busy;
-  const canPredict = !!model && (hasFeatureJob || !!model.featureId) && !busy;
+  const busy = training || predicting || multiTraining || volumeApplying;
+  const canTrain =
+    (trainAcrossSlices ? annotatedSliceCount > 0 : (hasFeatureJob || canAutoPreprocess) && hasShapes) &&
+    !busy;
+  // ensureFeatureBank() (inside predict()) computes a bank for the current slice
+  // when one isn't ready yet, so predicting doesn't require hasFeatureJob up front —
+  // only that a model exists to apply.
+  const canPredict = !!model && !busy;
   const maxImp = model?.featureImportances[0]?.importance ?? 1;
   const alphaPct = Math.round(params.alpha * 100);
   const nClasses = model?.classIds.length ?? 0;
@@ -148,16 +207,14 @@ export default function PixelClassifierPanel({
   const headline = predictCounts ? coverageHeadline(predictCounts, params.alpha) : null;
 
   return (
-    <div className="flex flex-col gap-2 border-t border-gray-100 pt-2">
-      <div className="flex items-center justify-between">
-        <span className="text-xs font-semibold uppercase text-gray-500 tracking-wide">
-          Classifier
-        </span>
+    <CollapsibleSection
+      title="Classifier"
+      headerRight={
         <span className="text-[10px] text-gray-400" title="Trainer + Mondrian conformal sets">
           {trainerId ?? 'catboost'} · conformal
         </span>
-      </div>
-
+      }
+    >
       <p className="text-[11px] text-gray-600 leading-snug">
         Setup:{' '}
         <span className="font-mono text-gray-800">
@@ -241,10 +298,34 @@ export default function PixelClassifierPanel({
         />
       </label>
 
+      <label
+        className={cn(
+          'flex items-center gap-1.5 text-[10px]',
+          annotatedSliceCount > 1 ? 'text-gray-600' : 'text-gray-300 cursor-not-allowed',
+        )}
+        title={
+          annotatedSliceCount > 1
+            ? `Pool labeled pixels across all ${annotatedSliceCount} annotated slices into one model`
+            : 'Annotate more than one slice to enable this'
+        }
+      >
+        <input
+          type="checkbox"
+          disabled={annotatedSliceCount <= 1 || busy}
+          checked={trainAcrossSlices && annotatedSliceCount > 1}
+          onChange={(e) => onTrainAcrossSlicesChange(e.target.checked)}
+        />
+        Train across all annotated slices ({annotatedSliceCount})
+      </label>
+
       <button
         type="button"
         disabled={!canTrain}
-        title={!hasFeatureJob ? 'Compute features first' : !hasShapes ? 'Annotate at least two classes' : 'Train classifier'}
+        title={
+          trainAcrossSlices
+            ? 'Train one model pooling every annotated slice'
+            : !hasFeatureJob ? 'Compute features first' : !hasShapes ? 'Annotate at least two classes' : 'Train classifier'
+        }
         onClick={onTrain}
         className={cn(
           'flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border transition-colors',
@@ -254,9 +335,16 @@ export default function PixelClassifierPanel({
         )}
       >
         <TreeStructure size={14} />
-        Train classifier
+        {trainAcrossSlices ? 'Train across slices' : 'Train classifier'}
       </button>
       {training && <BusyBar label="Training…" />}
+      {multiTraining && (
+        <JobProgressBar
+          label="Training across slices…"
+          done={multiTrainProgress?.done ?? 0}
+          total={multiTrainProgress?.total ?? 0}
+        />
+      )}
 
       <button
         type="button"
@@ -302,6 +390,93 @@ export default function PixelClassifierPanel({
         </div>
       )}
 
+      {model && (
+        <div className="flex flex-col gap-1.5 rounded-md border border-gray-100 bg-gray-50 p-1.5">
+          <span className="text-[10px] font-medium text-gray-600">Classes to commit</span>
+          <div className="flex flex-wrap gap-1">
+            {model.classIds.map((cid) => {
+              const on = commitClassIds.includes(cid);
+              return (
+                <button
+                  key={cid}
+                  type="button"
+                  onClick={() => onToggleCommitClassId(cid)}
+                  className={cn(
+                    'rounded border px-1.5 py-0.5 text-[9px]',
+                    on
+                      ? 'border-sky-300 bg-sky-50 text-sky-900'
+                      : 'border-gray-200 bg-white text-gray-400',
+                  )}
+                >
+                  {classLabelForId?.(cid) ?? `class ${cid}`}
+                </button>
+              );
+            })}
+          </div>
+
+          <button
+            type="button"
+            disabled={busy || totalSliceCount <= 1 || commitClassIds.length === 0}
+            onClick={onApplyToVolume}
+            title={`Predict across all ${totalSliceCount} slices, then choose which classes to commit`}
+            className={cn(
+              'flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border transition-colors',
+              !busy && totalSliceCount > 1 && commitClassIds.length > 0
+                ? 'bg-white text-gray-800 border-gray-200 hover:bg-sky-50 hover:border-sky-300'
+                : 'bg-gray-50 text-gray-400 border-gray-200 cursor-not-allowed',
+            )}
+          >
+            Apply across volume ({totalSliceCount} slices)
+          </button>
+
+          {volumeApplying && (
+            <div className="flex flex-col gap-1">
+              <JobProgressBar
+                label="Applying across volume…"
+                done={volumeApplyProgress?.done ?? 0}
+                total={volumeApplyProgress?.total ?? 0}
+              />
+              <button
+                type="button"
+                onClick={onCancelVolumeApply}
+                className="self-start text-[10px] text-gray-500 hover:text-red-600"
+              >
+                Cancel
+              </button>
+            </div>
+          )}
+
+          {volumeApplyResult && !volumeApplying && (
+            <div className="flex flex-col gap-1 rounded border border-sky-100 bg-sky-50/60 p-1.5 text-[10px] text-gray-700">
+              <p>
+                {volumeApplyResult.cancelled ? 'Cancelled — ' : ''}
+                Predicted {volumeApplyResult.runCount} slice(s)
+                {volumeApplyResult.errorCount > 0 ? `, ${volumeApplyResult.errorCount} failed` : ''}.
+              </p>
+              <div className="flex gap-1">
+                <button
+                  type="button"
+                  disabled={volumeApplyCommitting || volumeApplyResult.runCount === 0}
+                  onClick={onCommitVolumeApply}
+                  className="flex-1 py-1 rounded-md text-[10px] font-medium bg-sky-600 text-white hover:bg-sky-700 disabled:opacity-50"
+                >
+                  {volumeApplyCommitting
+                    ? 'Committing…'
+                    : `Commit predicted shapes (${commitClassIds.length} class${commitClassIds.length === 1 ? '' : 'es'})`}
+                </button>
+                <button
+                  type="button"
+                  onClick={onDismissVolumeApply}
+                  className="flex-1 py-1 rounded-md text-[10px] border border-gray-200 text-gray-700 hover:bg-gray-50"
+                >
+                  Dismiss
+                </button>
+              </div>
+            </div>
+          )}
+        </div>
+      )}
+
       {hasPrediction && headline && (
         <div
           className={cn(
@@ -395,21 +570,13 @@ export default function PixelClassifierPanel({
 
       {/* Suggest labels — folded into the same train/infer loop instead of its own panel. */}
       <div className="border-t border-gray-100 pt-1.5">
-        <button
-          type="button"
+        <CollapsibleSection
+          title="Suggest labels (manifold coverage)"
+          icon={<Compass size={12} />}
+          defaultOpen={false}
           disabled={!hasFeatureJob}
-          onClick={() => setShowSuggest((v) => !v)}
-          className={cn(
-            'flex w-full items-center gap-1.5 text-[10px]',
-            hasFeatureJob ? 'text-gray-500 hover:text-sky-700' : 'text-gray-300 cursor-not-allowed',
-          )}
         >
-          <Compass size={12} />
-          {showSuggest ? '▾' : '▸'} Suggest labels (manifold coverage)
-        </button>
-
-        {showSuggest && (
-          <div className="mt-2 flex flex-col gap-1.5 rounded-md border border-gray-100 bg-gray-50 p-1.5">
+          <div className="flex flex-col gap-1.5 rounded-md border border-gray-100 bg-gray-50 p-1.5">
             <div className="grid grid-cols-2 gap-1 text-[10px] text-gray-600">
               <label className="flex flex-col gap-0.5">
                 K boxes
@@ -542,8 +709,8 @@ export default function PixelClassifierPanel({
               <p className="text-[10px] text-red-600 leading-snug break-words">{manifoldError}</p>
             )}
           </div>
-        )}
+        </CollapsibleSection>
       </div>
-    </div>
+    </CollapsibleSection>
   );
 }
diff --git a/frontend/src/components/annotate/SliceNavigator/index.tsx b/frontend/src/components/annotate/SliceNavigator/index.tsx
index a8021c2..2c75b6e 100644
--- a/frontend/src/components/annotate/SliceNavigator/index.tsx
+++ b/frontend/src/components/annotate/SliceNavigator/index.tsx
@@ -6,6 +6,7 @@ import { useAnnotationStore } from '@/stores/annotationStore';
 import { useDatasetStore } from '@/stores/datasetStore';
 import { buildSourceKey } from '@/lib/sourceKey';
 import DebouncedSlider from '@/components/common/DebouncedSlider';
+import CollapsibleSection from '@/components/common/CollapsibleSection';
 
 /** Renders slice navigation controls bound to the dataset and annotation stores. */
 export default function SliceNavigator() {
@@ -40,11 +41,7 @@ export default function SliceNavigator() {
   const onCoarseLevel = !!meta.levelKey && (meta.levelIndex ?? 0) > 0;
 
   return (
-    <div className="flex flex-col gap-2">
-      <span className="text-xs font-semibold uppercase text-gray-500 tracking-wide">
-        Slice {currentSlice + 1} / {n}
-      </span>
-
+    <CollapsibleSection title={`Slice ${currentSlice + 1} / ${n}`}>
       {onCoarseLevel && (
         <span
           className="text-[11px] leading-snug text-amber-600"
@@ -112,6 +109,6 @@ export default function SliceNavigator() {
         <WarningCircle size={12} />
         {isNegative ? 'Negative example' : 'Mark as negative'}
       </button>
-    </div>
+    </CollapsibleSection>
   );
 }
diff --git a/frontend/src/components/common/CollapsibleSection.test.tsx b/frontend/src/components/common/CollapsibleSection.test.tsx
new file mode 100644
index 0000000..41506e9
--- /dev/null
+++ b/frontend/src/components/common/CollapsibleSection.test.tsx
@@ -0,0 +1,50 @@
+/**
+ * CollapsibleSection — open/close toggle, defaultOpen, headerRight slot.
+ */
+import { describe, expect, it } from 'vitest';
+import { render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import CollapsibleSection from './CollapsibleSection';
+
+describe('CollapsibleSection', () => {
+  it('renders children by default (defaultOpen defaults to true)', () => {
+    render(
+      <CollapsibleSection title="Layers">
+        <p>body content</p>
+      </CollapsibleSection>,
+    );
+    expect(screen.getByText('body content')).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /layers/i })).toHaveAttribute('aria-expanded', 'true');
+  });
+
+  it('hides children when defaultOpen is false, and toggling shows them', async () => {
+    const user = userEvent.setup();
+    render(
+      <CollapsibleSection title="Classifier" defaultOpen={false}>
+        <p>hidden body</p>
+      </CollapsibleSection>,
+    );
+    expect(screen.queryByText('hidden body')).not.toBeInTheDocument();
+    const header = screen.getByRole('button', { name: /classifier/i });
+    expect(header).toHaveAttribute('aria-expanded', 'false');
+
+    await user.click(header);
+    expect(screen.getByText('hidden body')).toBeInTheDocument();
+    expect(header).toHaveAttribute('aria-expanded', 'true');
+
+    await user.click(header);
+    expect(screen.queryByText('hidden body')).not.toBeInTheDocument();
+  });
+
+  it('renders headerRight content regardless of open state', async () => {
+    const user = userEvent.setup();
+    render(
+      <CollapsibleSection title="Classes" headerRight={<button aria-label="Add class">+</button>}>
+        <p>rows</p>
+      </CollapsibleSection>,
+    );
+    expect(screen.getByLabelText('Add class')).toBeInTheDocument();
+    await user.click(screen.getByRole('button', { name: /classes/i }));
+    expect(screen.getByLabelText('Add class')).toBeInTheDocument();
+  });
+});
diff --git a/frontend/src/components/common/CollapsibleSection.tsx b/frontend/src/components/common/CollapsibleSection.tsx
new file mode 100644
index 0000000..2ca801c
--- /dev/null
+++ b/frontend/src/components/common/CollapsibleSection.tsx
@@ -0,0 +1,58 @@
+/**
+ * CollapsibleSection — shared expand/collapse header for sidebar subsections
+ * (Classes, Layers, Display, Slice, Cross-slice, Measure, Features, Classifier).
+ *
+ * Reproduces the house header convention already used ad hoc by those panels
+ * (`text-xs font-semibold uppercase tracking-wide text-gray-500` + a small
+ * leading icon) and adds a Phosphor caret toggle in front of it, replacing two
+ * divergent one-off disclosure patterns that existed before this component.
+ */
+import { useState, type ReactNode } from 'react';
+import { CaretDown, CaretRight } from '@phosphor-icons/react';
+import { cn } from '@/lib/utils';
+
+export interface CollapsibleSectionProps {
+  title: string;
+  /** Small leading icon, e.g. `<Stack size={13} />` — matches each section's existing icon. */
+  icon?: ReactNode;
+  /** Whether the section starts open. Not persisted — a fresh mount always uses this. */
+  defaultOpen?: boolean;
+  /** Extra control(s) on the header's right edge, e.g. ClassManager's "+ Add" button. */
+  headerRight?: ReactNode;
+  disabled?: boolean;
+  children: ReactNode;
+}
+
+/** Sidebar section wrapper: click the header to show/hide `children`. */
+export default function CollapsibleSection({
+  title,
+  icon,
+  defaultOpen = true,
+  headerRight,
+  disabled = false,
+  children,
+}: CollapsibleSectionProps) {
+  const [open, setOpen] = useState(defaultOpen);
+  return (
+    <div className="flex flex-col gap-1.5">
+      <div className="flex items-center justify-between gap-1.5">
+        <button
+          type="button"
+          disabled={disabled}
+          aria-expanded={open}
+          onClick={() => setOpen((o) => !o)}
+          className={cn(
+            'flex flex-1 items-center gap-1.5 text-xs font-semibold uppercase tracking-wide transition-colors',
+            disabled ? 'text-gray-300 cursor-not-allowed' : 'text-gray-500 hover:text-gray-700',
+          )}
+        >
+          {open ? <CaretDown size={11} className="shrink-0" /> : <CaretRight size={11} className="shrink-0" />}
+          {icon}
+          {title}
+        </button>
+        {headerRight}
+      </div>
+      {open && <div className="flex flex-col gap-1.5">{children}</div>}
+    </div>
+  );
+}
diff --git a/frontend/src/hooks/useExportJob.ts b/frontend/src/hooks/useExportJob.ts
index 392f983..9fa9af2 100644
--- a/frontend/src/hooks/useExportJob.ts
+++ b/frontend/src/hooks/useExportJob.ts
@@ -95,10 +95,22 @@ export function useExportJob() {
   /** Write rasterized masks into Tiled (standalone). Shares the status polling. */
   const startMaskSync = useCallback((payload: unknown) => run('/api/masks/to-tiled', payload), [run]);
 
+  /** Train one iPred model pooling labeled pixels across multiple slices. */
+  const startIpredBatchTrain = useCallback(
+    (payload: unknown) => run('/api/ipred/batch/train', payload),
+    [run],
+  );
+
+  /** Run iPred inference across a set of slices (e.g. the whole volume). */
+  const startIpredBatchApply = useCallback(
+    (payload: unknown) => run('/api/ipred/batch/apply', payload),
+    [run],
+  );
+
   const downloadUrl =
     state.jobId && (state.result as { zip_available?: boolean } | null)?.zip_available
       ? `${API_BASE}/api/export/download/${state.jobId}`
       : null;
 
-  return { state, start, startMaskSync, reset, downloadUrl };
+  return { state, start, startMaskSync, startIpredBatchTrain, startIpredBatchApply, reset, downloadUrl };
 }
diff --git a/frontend/src/hooks/usePixelClassifier.slicePersist.test.ts b/frontend/src/hooks/usePixelClassifier.slicePersist.test.ts
new file mode 100644
index 0000000..036b838
--- /dev/null
+++ b/frontend/src/hooks/usePixelClassifier.slicePersist.test.ts
@@ -0,0 +1,98 @@
+/**
+ * A trained model must survive a slice change (only sample/composition identity
+ * resets it), and predict() must target the CURRENT slice's feature bank rather
+ * than the bank the model happened to be trained on.
+ */
+import { describe, expect, it, vi, beforeEach } from 'vitest';
+import { act, renderHook, waitFor } from '@testing-library/react';
+import { usePixelClassifier } from './usePixelClassifier';
+import { useIpredStore } from '@/stores/ipredStore';
+
+vi.mock('@/lib/pixelClf', () => ({
+  thresholdProbaPngBlob: vi.fn(async () => new Blob()),
+}));
+
+vi.mock('@/lib/ipredApi', async () => {
+  const actual = await vi.importActual<typeof import('@/lib/ipredApi')>('@/lib/ipredApi');
+  return {
+    ...actual,
+    openIpredSession: vi.fn(async () => ({ session_id: 's1', project_id: 'p1' })),
+    ipredPreprocess: vi.fn(async () => {
+      throw new Error('ensureFeatureBank should not need to preprocess in this test');
+    }),
+    ipredTrain: vi.fn(async () => ({
+      model_id: 'model-1',
+      feature_id: 'bank-A',
+      trainer_id: 'catboost',
+      class_ids: [1, 2],
+      train_accuracy: 0.9,
+      n_train: 100,
+      n_cal: 20,
+      n_samples: 120,
+      params: { iterations: 50 },
+      feature_importances: [],
+    })),
+    ipredInfer: vi.fn(async (payload: { feature_id?: string | null }) => ({
+      run_id: 'run-1',
+      model_id: 'model-1',
+      feature_id: payload.feature_id ?? '',
+      alpha: 0.05,
+      class_ids: [1, 2],
+      counts: { singleton: 1, multi: 0, abstain: 0 },
+    })),
+  };
+});
+
+import { ipredInfer } from '@/lib/ipredApi';
+
+describe('usePixelClassifier — model persists across slice changes', () => {
+  beforeEach(() => {
+    useIpredStore.getState().reset();
+    vi.mocked(ipredInfer).mockClear();
+    global.fetch = vi.fn(async () => ({
+      ok: true,
+      blob: async () => new Blob(),
+    })) as unknown as typeof fetch;
+    global.URL.createObjectURL = vi.fn(() => 'blob:mock');
+    global.URL.revokeObjectURL = vi.fn();
+  });
+
+  it('keeps the model when only featureJobId changes, and predict() uses the current bank', async () => {
+    const { result, rerender } = renderHook(
+      (props: { featureJobId: string | null; sliceIndex: number }) =>
+        usePixelClassifier({
+          featureJobId: props.featureJobId,
+          resetKey: 'sample-1', // sourceKey only — does NOT change across slices
+          source: 'sample.tif',
+          kind: 'local',
+          serverUri: null,
+          sliceIndex: props.sliceIndex,
+        }),
+      { initialProps: { featureJobId: 'bank-A', sliceIndex: 5 } },
+    );
+
+    await act(async () => {
+      await result.current.train([]);
+    });
+    await waitFor(() => expect(result.current.model?.modelId).toBe('model-1'));
+    expect(result.current.model?.featureId).toBe('bank-A');
+
+    // Simulate switching to a different slice: featureJobId changes to that
+    // slice's own feature bank, resetKey (sourceKey) does not change.
+    rerender({ featureJobId: 'bank-B', sliceIndex: 8 });
+
+    // The model must still be there — only the run/preview state resets.
+    expect(result.current.model?.modelId).toBe('model-1');
+
+    await act(async () => {
+      await result.current.predict([]);
+    });
+    await waitFor(() => expect(result.current.commitUrl).not.toBeNull());
+
+    // predict() must have targeted the CURRENT slice's bank (bank-B), not the
+    // bank the model was originally trained against (bank-A).
+    expect(ipredInfer).toHaveBeenCalledWith(
+      expect.objectContaining({ model_id: 'model-1', feature_id: 'bank-B' }),
+    );
+  });
+});
diff --git a/frontend/src/hooks/usePixelClassifier.ts b/frontend/src/hooks/usePixelClassifier.ts
index ecc13b8..9180e08 100644
--- a/frontend/src/hooks/usePixelClassifier.ts
+++ b/frontend/src/hooks/usePixelClassifier.ts
@@ -5,6 +5,7 @@ import { useCallback, useEffect, useRef, useState } from 'react';
 import { useConnectionStore } from '@/stores/connectionStore';
 import { useIpredStore } from '@/stores/ipredStore';
 import type { Shape } from '@/stores/annotationStore';
+import { useExportJob } from '@/hooks/useExportJob';
 import {
   ipredInfer,
   ipredPreprocess,
@@ -15,6 +16,7 @@ import {
   ipredTrain,
   openIpredSession,
   type IpredThresholdClassResult,
+  type IpredTrainResult,
 } from '@/lib/ipredApi';
 import { thresholdProbaPngBlob } from '@/lib/pixelClf';
 
@@ -63,6 +65,8 @@ export interface ClfTrainResult {
 export interface UsePixelClassifierArgs {
   /** Current ipred feature bank id (from Preprocess compute), if any. */
   featureJobId: string | null;
+  /** Sample/composition identity ONLY (e.g. sourceKey) — must NOT include the
+   *  slice index, or a trained model gets wiped on every slice change. */
   resetKey: string | null;
   source: string | null;
   kind: string | null;
@@ -83,6 +87,34 @@ export interface UsePixelClassifierArgs {
 
 const DEFAULT_PROBA_THRESHOLD = 0.5;
 
+/** Maps an ipred train response (single- or multi-slice — same shape, plus an
+ *  optional `trained_slice_indices`) into the UI's ClfTrainResult. Shared so
+ *  the multi-slice path doesn't duplicate `train()`'s mapping. */
+function toClfTrainResult(
+  data: IpredTrainResult,
+  params: ClfParams,
+  compositionId: string | null,
+): ClfTrainResult {
+  return {
+    modelId: data.model_id,
+    featureId: data.feature_id,
+    nSamples: data.n_samples,
+    nTrain: data.n_train,
+    nCal: data.n_cal,
+    classIds: data.class_ids,
+    trainAccuracy: data.train_accuracy,
+    params: { ...params },
+    nTrees: Number(data.params?.iterations ?? params.iterations),
+    usesSam: !!(data.params as { uses_sam?: boolean } | undefined)?.uses_sam,
+    featureImportances: (data.feature_importances ?? []).map((fi) => ({
+      label: fi.label,
+      importance: fi.importance,
+    })),
+    trainerId: data.trainer_id,
+    compositionId,
+  };
+}
+
 export function usePixelClassifier({
   featureJobId,
   resetKey,
@@ -177,11 +209,24 @@ export function usePixelClassifier({
     revokeProba();
   }, [revokeProba]);
 
+  // Sample or composition identity changed — the trained model no longer applies.
+  // `resetKey` is the sample's sourceKey alone (no slice index baked in), so a
+  // plain slice change does NOT land here; see the effect below for that case.
   useEffect(() => {
     setModel(null);
     revokePredict();
     setError(null);
-  }, [featureJobId, resetKey, preferredCompositionId, revokePredict]);
+  }, [resetKey, preferredCompositionId, revokePredict]);
+
+  // Feature bank changed (new slice, or a recompute) — any in-flight prediction
+  // preview is tied to the OLD bank and must go, but the trained model itself
+  // stays valid: it can be applied to whichever slice is on screen now (see
+  // `predict()`, which always resolves the CURRENT slice's bank via
+  // `ensureFeatureBank()` rather than the model's original training bank).
+  useEffect(() => {
+    revokePredict();
+    setError(null);
+  }, [featureJobId, revokePredict]);
 
   useEffect(
     () => () => {
@@ -273,25 +318,7 @@ export function usePixelClassifier({
             learning_rate: params.learningRate,
           },
         });
-        const result: ClfTrainResult = {
-          modelId: data.model_id,
-          featureId: data.feature_id,
-          nSamples: data.n_samples,
-          nTrain: data.n_train,
-          nCal: data.n_cal,
-          classIds: data.class_ids,
-          trainAccuracy: data.train_accuracy,
-          params: { ...params },
-          nTrees: Number(data.params?.iterations ?? params.iterations),
-          usesSam: !!(data.params as { uses_sam?: boolean } | undefined)?.uses_sam,
-          featureImportances: (data.feature_importances ?? []).map((fi) => ({
-            label: fi.label,
-            importance: fi.importance,
-          })),
-          trainerId: data.trainer_id,
-          compositionId: preferredCompositionId,
-        };
-        setModel(result);
+        setModel(toClfTrainResult(data, params, preferredCompositionId));
       } catch (e) {
         setModel(null);
         const msg = e instanceof Error ? e.message : String(e);
@@ -316,6 +343,98 @@ export function usePixelClassifier({
     ],
   );
 
+  // ---- Batch operations: multi-slice train, whole-volume apply ----
+  const multiTrainJobHook = useExportJob();
+  const volumeApplyJobHook = useExportJob();
+  // Guards against re-applying an already-handled job result on every render
+  // (the job's `state` object is recreated each poll tick even once done).
+  const multiTrainHandledRef = useRef<string | null>(null);
+  const volumeApplyHandledRef = useRef<string | null>(null);
+
+  /** Train one model pooling labeled pixels across every slice in `perSliceShapes`. */
+  const trainAcrossSlices = useCallback(
+    async (perSliceShapes: Record<number, Shape[]>) => {
+      if (Object.keys(perSliceShapes).length === 0) {
+        setError('No annotated slices to train on.');
+        return;
+      }
+      if (!preferredCompositionId) {
+        setError('Select a composition first.');
+        return;
+      }
+      setError(null);
+      revokePredict();
+      const sessionId = await ensureSession();
+      multiTrainHandledRef.current = null;
+      await multiTrainJobHook.startIpredBatchTrain({
+        session_id: sessionId,
+        slices: perSliceShapes,
+        composition_id: preferredCompositionId,
+        trainer_id: preferredTrainerId,
+        config: {
+          iterations: params.iterations,
+          depth: params.depth,
+          learning_rate: params.learningRate,
+        },
+      });
+    },
+    [
+      preferredCompositionId,
+      preferredTrainerId,
+      params,
+      revokePredict,
+      ensureSession,
+      multiTrainJobHook,
+    ],
+  );
+
+  // Adopt the completed multi-train job's result the same way `train()` does.
+  useEffect(() => {
+    const { status, result, error: jobError, jobId } = multiTrainJobHook.state;
+    if (!jobId || multiTrainHandledRef.current === jobId) return;
+    if (status === 'done' && result) {
+      multiTrainHandledRef.current = jobId;
+      setModel(toClfTrainResult(result as unknown as IpredTrainResult, params, preferredCompositionId));
+    } else if (status === 'error') {
+      multiTrainHandledRef.current = jobId;
+      setModel(null);
+      setError(jobError ?? 'Multi-slice training failed.');
+    }
+  }, [multiTrainJobHook.state, params, preferredCompositionId]);
+
+  /** Run inference across many slices (e.g. the whole volume). Does not commit —
+   *  turning the result's per-slice runs into shapes stays client-side in
+   *  AnnotatePage, reusing the same PNG-vectorize path as the single-slice commit. */
+  const applyAcrossVolume = useCallback(
+    async (sliceIndices: number[]) => {
+      if (!model) {
+        setError('Train a model first.');
+        return;
+      }
+      if (sliceIndices.length === 0) return;
+      setError(null);
+      const sessionId = await ensureSession();
+      volumeApplyHandledRef.current = null;
+      await volumeApplyJobHook.startIpredBatchApply({
+        session_id: sessionId,
+        model_id: model.modelId,
+        slice_indices: sliceIndices,
+        composition_id: preferredCompositionId,
+        alpha: params.alpha,
+      });
+    },
+    [model, preferredCompositionId, params.alpha, ensureSession, volumeApplyJobHook],
+  );
+
+  useEffect(() => {
+    const { status, error: jobError, jobId } = volumeApplyJobHook.state;
+    if (!jobId || volumeApplyHandledRef.current === jobId) return;
+    if (status === 'error') {
+      volumeApplyHandledRef.current = jobId;
+      setError(jobError ?? 'Volume apply failed.');
+    }
+  }, [volumeApplyJobHook.state]);
+
   const predict = useCallback(
     async (_shapes: Shape[]) => {
       if (!model || predicting) return;
@@ -323,7 +442,10 @@ export function usePixelClassifier({
       setError(null);
       try {
         const sessionId = await ensureSession();
-        const featureId = model.featureId || (await ensureFeatureBank());
+        // Always the CURRENT slice's bank, not model.featureId (the slice the model
+        // happened to be trained on) — the model persists across slices, so predict
+        // must target whichever slice is on screen, computing a bank if missing.
+        const featureId = await ensureFeatureBank();
         const run = await ipredInfer({
           session_id: sessionId,
           model_id: model.modelId,
@@ -458,5 +580,14 @@ export function usePixelClassifier({
     compositionId: preferredCompositionId,
     trainerId: preferredTrainerId,
     canTrainWithoutJob: !!preferredCompositionId && !!source && !!kind,
+    // ---- Batch operations ----
+    trainAcrossSlices,
+    multiTrainJob: multiTrainJobHook.state,
+    multiTraining: multiTrainJobHook.state.status === 'running',
+    resetMultiTrainJob: multiTrainJobHook.reset,
+    applyAcrossVolume,
+    volumeApplyJob: volumeApplyJobHook.state,
+    volumeApplying: volumeApplyJobHook.state.status === 'running',
+    resetVolumeApplyJob: volumeApplyJobHook.reset,
   };
 }
diff --git a/frontend/src/lib/pixelClf.ts b/frontend/src/lib/pixelClf.ts
index 9f89f2a..c7b6aec 100644
--- a/frontend/src/lib/pixelClf.ts
+++ b/frontend/src/lib/pixelClf.ts
@@ -241,7 +241,15 @@ export function labelMapToPolygonShapes(
     minRegion = 64,
     preserveShapes,
     smooth = 1,
-  }: { minRegion?: number; preserveShapes?: Shape[]; smooth?: number } = {},
+    origin = 'predicted',
+  }: {
+    minRegion?: number;
+    preserveShapes?: Shape[];
+    smooth?: number;
+    /** Stamped onto every returned shape — see `ShapeOrigin` in annotationStore.
+     *  Defaults to 'predicted' since every current caller commits iPred output. */
+    origin?: 'human' | 'predicted';
+  } = {},
 ): PolygonShape[] {
   const preserve =
     preserveShapes && preserveShapes.length > 0
@@ -272,6 +280,7 @@ export function labelMapToPolygonShapes(
         classId,
         kind: 'polygon',
         points,
+        origin,
         ...(holes.length ? { holes } : {}),
       });
     }
diff --git a/frontend/src/stores/annotationStore.ts b/frontend/src/stores/annotationStore.ts
index 7673ade..6c1cac5 100644
--- a/frontend/src/stores/annotationStore.ts
+++ b/frontend/src/stores/annotationStore.ts
@@ -29,12 +29,20 @@ export interface EraseStroke {
   radius: number;
 }
 
+/** Provenance: 'predicted' shapes came from committing an iPred run (single-slice
+ *  or volume-apply); undefined/'human' means hand-drawn. Kept forever rather than
+ *  cleared on first edit, so Phase 6's 3D label channel and the Layers panel can
+ *  filter by it at any time. */
+export type ShapeOrigin = 'human' | 'predicted';
+
 export interface BaseShape {
   id: string;
   classId: number;
   kind: Shape['kind'];
   /** Optional erase carve-outs (rendered destination-out, subtracted on export). */
   erased?: EraseStroke[];
+  /** Undefined means human-drawn (backward compatible with every existing shape). */
+  origin?: ShapeOrigin;
 }
 
 export interface PolygonShape extends BaseShape {
@@ -98,6 +106,10 @@ export interface AnnotationState {
   addShape: (sourceKey: string, sliceIdx: number, shape: Shape) => void;
   /** Append several shapes in one update (one undo step) — used by magic-wand. */
   addShapes: (sourceKey: string, sliceIdx: number, shapes: Shape[]) => void;
+  /** Append shapes across MULTIPLE slices in one update (one undo step) — used
+   *  by the iPred volume-apply commit, so accepting a whole-volume prediction
+   *  is one undo, not one per slice. */
+  addShapesAcrossSlices: (sourceKey: string, bySlice: Record<number, Shape[]>) => void;
   removeShape: (sourceKey: string, sliceIdx: number, shapeId: string) => void;
   /** Remove several shapes in one update (one undo step). */
   removeShapes: (sourceKey: string, sliceIdx: number, shapeIds: string[]) => void;
@@ -113,6 +125,9 @@ export interface AnnotationState {
   copySliceShapes: (sourceKey: string, fromSlice: number, toSlices: number[], classId?: number | null) => void;
   /** Remove every shape with *classId* across all loaded samples (all slices). */
   removeShapesByClassId: (classId: number) => void;
+  /** Remove every shape of *origin* within one sample, across its slices — e.g.
+   *  "reject all predicted" after reviewing a volume-apply commit. One undo step. */
+  removeShapesByOrigin: (sourceKey: string, origin: ShapeOrigin) => void;
   /** Remove every shape with *classId* within a single sample (*sourceKey*), across
    *  its slices. Scoped so deleting a class never touches other samples' annotations.
    *  Tracked by zundo, so Ctrl/Cmd+Z restores the removed regions. */
@@ -187,6 +202,19 @@ export const useAnnotationStore = create<AnnotationState>()(
           };
         }),
 
+      /** Appends shapes across multiple slices in one update (one undo step). */
+      addShapesAcrossSlices: (sourceKey, bySlice) =>
+        set((s) => {
+          const entries = Object.entries(bySlice).filter(([, shapes]) => shapes.length > 0);
+          if (entries.length === 0) return {};
+          const slices = { ...(s.byImage[sourceKey] ?? {}) };
+          for (const [sliceIdx, shapes] of entries) {
+            const sliceKey = String(Number(sliceIdx));
+            slices[sliceKey] = [...(slices[sliceKey] ?? []), ...shapes];
+          }
+          return { byImage: { ...s.byImage, [sourceKey]: slices } };
+        }),
+
       /** Removes the shape with the given id from the (sourceKey, slice). */
       removeShape: (sourceKey, sliceIdx, shapeId) =>
         set((s) => {
@@ -326,6 +354,28 @@ export const useAnnotationStore = create<AnnotationState>()(
           return { byImage: nextByImage };
         }),
 
+      /** Removes every shape of *origin* within one sample, pruning emptied slices
+       *  and the source itself; other samples are left untouched. */
+      removeShapesByOrigin: (sourceKey, origin) =>
+        set((s) => {
+          const slices = s.byImage[sourceKey];
+          if (!slices) return {};
+          const nextSlices: Record<string, Shape[]> = {};
+          for (const [sliceKey, shapes] of Object.entries(slices)) {
+            const filtered = shapes.filter((sh) => (sh.origin ?? 'human') !== origin);
+            if (filtered.length > 0) {
+              nextSlices[sliceKey] = filtered;
+            }
+          }
+          const nextByImage = { ...s.byImage };
+          if (Object.keys(nextSlices).length > 0) {
+            nextByImage[sourceKey] = nextSlices;
+          } else {
+            delete nextByImage[sourceKey];
+          }
+          return { byImage: nextByImage };
+        }),
+
       /** Clone every shape of *fromClassId* into *toClassId* across all slices of
        *  *sourceKey* (fresh ids), merged with existing shapes. One undo step. */
       duplicateClassShapes: (sourceKey, fromClassId, toClassId) =>
diff --git a/frontend/src/stores/layerVisibilityStore.test.ts b/frontend/src/stores/layerVisibilityStore.test.ts
index 1c35926..edec7db 100644
--- a/frontend/src/stores/layerVisibilityStore.test.ts
+++ b/frontend/src/stores/layerVisibilityStore.test.ts
@@ -1,6 +1,7 @@
 import { describe, expect, it, beforeEach } from 'vitest';
 import {
   isPredictionClassVisible,
+  isShapeOriginVisible,
   useLayerVisibilityStore,
 } from '@/stores/layerVisibilityStore';
 
@@ -19,6 +20,7 @@ describe('layerVisibilityStore', () => {
       predictionClassVisible: {},
       showPredictionMulti: true,
       showPredictionAbstain: true,
+      annotationOriginVisible: { human: true, predicted: true },
     });
   });
 
@@ -43,4 +45,14 @@ describe('layerVisibilityStore', () => {
     expect(useLayerVisibilityStore.getState().groups.denoise).toBe(false);
     expect(useLayerVisibilityStore.getState().groups.image).toBe(true);
   });
+
+  it('toggles predicted/human annotation visibility independently', () => {
+    const s = useLayerVisibilityStore.getState();
+    expect(isShapeOriginVisible(useLayerVisibilityStore.getState().annotationOriginVisible, 'predicted')).toBe(true);
+    expect(isShapeOriginVisible(useLayerVisibilityStore.getState().annotationOriginVisible, undefined)).toBe(true);
+    s.setAnnotationOriginVisible('predicted', false);
+    expect(isShapeOriginVisible(useLayerVisibilityStore.getState().annotationOriginVisible, 'predicted')).toBe(false);
+    // Human-drawn (origin undefined) is unaffected by hiding predicted shapes.
+    expect(isShapeOriginVisible(useLayerVisibilityStore.getState().annotationOriginVisible, undefined)).toBe(true);
+  });
 });
diff --git a/frontend/src/stores/layerVisibilityStore.ts b/frontend/src/stores/layerVisibilityStore.ts
index 5caaeff..e64e6c4 100644
--- a/frontend/src/stores/layerVisibilityStore.ts
+++ b/frontend/src/stores/layerVisibilityStore.ts
@@ -41,6 +41,10 @@ export interface LayerVisibilityState {
   showPredictionMulti: boolean;
   /** Show conformal abstain. */
   showPredictionAbstain: boolean;
+  /** Annotations-layer sub-toggle: show human-drawn vs. iPred-predicted shapes
+   *  independently (see `ShapeOrigin` in annotationStore). Both default visible. */
+  annotationOriginVisible: { human: boolean; predicted: boolean };
+  setAnnotationOriginVisible: (origin: 'human' | 'predicted', visible: boolean) => void;
   setGroup: (id: LayerGroupId, visible: boolean) => void;
   toggleGroup: (id: LayerGroupId) => void;
   setProbaOpacity: (v: number) => void;
@@ -69,6 +73,12 @@ export const useLayerVisibilityStore = create<LayerVisibilityState>((set, get) =
   predictionClassVisible: {},
   showPredictionMulti: true,
   showPredictionAbstain: true,
+  annotationOriginVisible: { human: true, predicted: true },
+
+  setAnnotationOriginVisible: (origin, visible) =>
+    set((s) => ({
+      annotationOriginVisible: { ...s.annotationOriginVisible, [origin]: visible },
+    })),
 
   setGroup: (id, visible) =>
     set((s) => ({ groups: { ...s.groups, [id]: visible } })),
@@ -121,3 +131,11 @@ export function isPredictionClassVisible(
 ): boolean {
   return map[classId] !== false;
 }
+
+/** True when a shape's origin (human/predicted; undefined = human) should draw. */
+export function isShapeOriginVisible(
+  visible: { human: boolean; predicted: boolean },
+  origin: 'human' | 'predicted' | undefined,
+): boolean {
+  return origin === 'predicted' ? visible.predicted : visible.human;
+}
diff --git a/graphify-out/.graphify_labels.json b/graphify-out/.graphify_labels.json
deleted file mode 100644
index 65f3efd..0000000
--- a/graphify-out/.graphify_labels.json
+++ /dev/null
@@ -1,297 +0,0 @@
-{
-  "0": "catalog.ts",
-  "1": "scrape-sciviscolor.py",
-  "2": "ManagedBuffer",
-  "3": "thresholdFit.test.ts",
-  "4": "ipred_routes.py",
-  "5": "annotation_thumbnails.py",
-  "6": "run",
-  "7": "manifold.py",
-  "8": "HTTPException",
-  "9": "math/index.ts",
-  "10": "TemporalAccumulator",
-  "11": "annotation_server.py",
-  "12": "ipred_client.py",
-  "13": "coco_export.py",
-  "14": "Vec3",
-  "15": "tomojepa_onnx.py",
-  "16": "ColumnBrowser.tsx",
-  "17": "Quat",
-  "18": "math/color.ts",
-  "19": "devDependencies",
-  "20": "render/index.ts",
-  "21": "accel.spec.ts",
-  "22": "compilerOptions",
-  "23": "compilerOptions",
-  "24": "dimension.ts",
-  "25": "test_ingest_conflict.py",
-  "26": "App.tsx",
-  "27": "src/index.ts",
-  "28": "catalog.py",
-  "29": "write_stack",
-  "30": "ingest.py",
-  "31": "test_zarr_source.py",
-  "32": "Catalog",
-  "33": "TiledNotifications/index.ts",
-  "34": "camera.ts",
-  "35": "preprocess.py",
-  "36": "compositions.py",
-  "37": "Vec4",
-  "38": "step_phantom",
-  "39": "tiff_stack_source.py",
-  "40": "ipred/__init__.py",
-  "41": "train_infer.py",
-  "42": "volume/colormaps.ts",
-  "43": "Node",
-  "44": "Quantity",
-  "45": "Matrix",
-  "46": "ipred — architecture and operations guide",
-  "47": "AnnotationCanvas/index.tsx",
-  "48": "io/index.ts",
-  "49": "ipredApi.ts",
-  "50": "OrbitControls",
-  "51": "Vec2",
-  "52": "cli.py",
-  "53": "array.ts",
-  "54": "packing.ts",
-  "55": "scalar.ts",
-  "56": "feature_setups.py",
-  "57": "arrays.py",
-  "58": "IngestDropzone.tsx",
-  "59": "VectorGrid3",
-  "60": "core/index.ts",
-  "61": "getTiledBaseUrl",
-  "62": "constants.ts",
-  "63": "compilerOptions",
-  "64": "browse_helpers.py",
-  "65": "provenance.ts",
-  "66": "schemas.py",
-  "67": "FakeNode",
-  "68": "zarr_source.py",
-  "69": "dependencies",
-  "70": "ome-zarr.ts",
-  "71": "start_all.sh",
-  "72": "drafts.py",
-  "73": "tiled_clients.py",
-  "74": "App.jsx",
-  "75": "viewerState.ts",
-  "76": "devDependencies",
-  "77": "get_tiled_client",
-  "78": "useTabsStore.ts",
-  "79": "zarr-viewer/package.json",
-  "80": "volume-renderer.ts",
-  "81": "features.py",
-  "82": "TrainerPlugin",
-  "83": "sam_embed.py",
-  "84": "start_all.ps1",
-  "85": "Installation via Docker (Recommended)",
-  "86": "parse.ts",
-  "87": "TTLCache",
-  "88": "magicwand.ts",
-  "89": "Segmentation Annotation Studio",
-  "90": "tiled_mask_sync.py",
-  "91": "denoise.py",
-  "92": "Software architecture",
-  "93": "annotationStore.ts",
-  "94": "_fake_pyramid",
-  "95": "ManagedTexture",
-  "96": "FastAPI",
-  "97": "fake_stack",
-  "98": "geometry.ts",
-  "99": "length-units.ts",
-  "100": "codecs.ts",
-  "101": ".alignTo",
-  "102": "Vec3Like",
-  "103": "denoise_bake.py",
-  "104": "3. Annotate",
-  "105": "datasetStats.ts",
-  "106": "SamClient",
-  "107": "samWorker.ts",
-  "108": "TomoJepaModule",
-  "109": "fx/package.json",
-  "110": "predicates.ts",
-  "111": "test_tomojepa_embed.py",
-  "112": "export_jobs.py",
-  "113": "polybool.ts",
-  "114": "perf.ts",
-  "115": "compilerOptions",
-  "116": "list_module_catalog",
-  "117": ".multiplyScalar",
-  "118": "mergeSameClass.ts",
-  "119": "rasterizeShapes",
-  "120": "morphology.ts",
-  "121": "livewire.ts",
-  "122": "run_composition",
-  "123": "5. Export & download",
-  "124": "Mat3",
-  "125": "test_tiled_config.py",
-  "126": "frontend/package.json",
-  "127": "TestCascade",
-  "128": "systems.ts",
-  "129": "OmeZarrVolumeSource",
-  "130": "ViewportOverlay",
-  "131": "TestBuildLevel",
-  "132": "test_compositions.py",
-  "133": "test_local_fs.py",
-  "134": "editHistory.ts",
-  "135": "pixelClf.ts",
-  "136": "test_feature_setups.py",
-  "137": "test_tiff_stack_source.py",
-  "138": "Random",
-  "139": "array_source.py",
-  "140": "AGENTS.md — Segmentation Annotation Studio",
-  "141": "guides.py",
-  "142": "test_drafts.py",
-  "143": "quick-start.md",
-  "144": "Installation",
-  "145": "Quick start",
-  "146": "Emitter",
-  "147": "test_catalog_sessions.py",
-  "148": "import_dataset",
-  "149": "describe_methods",
-  "150": "clahe.ts",
-  "151": "TestBlockMean",
-  "152": "bboxPrefilter.equivalence.test.ts",
-  "153": "Pool",
-  "154": "Dual",
-  "155": "test_array_blobs.py",
-  "156": "test_preprocess_cache.py",
-  "157": "TestMultiscalesMetadata",
-  "158": "TestPyramidPlan",
-  "159": "usePixelClassifier.ts",
-  "160": "datasetStore.ts",
-  "161": "toolStore.ts",
-  "162": "vite-zarr-static.ts",
-  "163": "test_api_health.py",
-  "164": "main",
-  "165": "test_health.py",
-  "166": "TestPyramidStore",
-  "167": "Segmentation Annotation Studio",
-  "168": "Troubleshooting",
-  "169": "ClassManager/index.tsx",
-  "170": "Toolbar/index.tsx",
-  "171": "ZarrLoader.tsx",
-  "172": "BuildVolumePanel.tsx",
-  "173": "config.ts",
-  "174": "useGuideSync.ts",
-  "175": "useSave.ts",
-  "176": "regionOps.ts",
-  "177": "zarrUrl.ts",
-  "178": "ipredStore.ts",
-  "179": "layerVisibilityStore.ts",
-  "180": "OME-Zarr Viewer",
-  "181": ".preview_labels",
-  "182": "include",
-  "183": "Pool",
-  "184": "Soa",
-  "185": "_denoised_slice",
-  "186": "2. Browse & select",
-  "187": "1. Connect to data",
-  "188": "fetch-sam-model.mjs",
-  "189": "DenoiseBakeModal.tsx",
-  "190": "DownloadModal.tsx",
-  "191": "InsightsModal.tsx",
-  "192": "MeasurementPanel/index.tsx",
-  "193": "VersionHistoryModal.tsx",
-  "194": "useFeatureManifold.ts",
-  "195": "classColors.ts",
-  "196": "lib/colormaps.ts",
-  "197": "featureManifold.ts",
-  "198": "stretch.test.ts",
-  "199": ".preview_labels",
-  "200": ".preview_labels",
-  "201": "test_manifold.py",
-  "202": "test_train_rethreshold.py",
-  "203": ".preview_labels",
-  "204": "Keyboard shortcuts",
-  "205": "LayersPanel/index.tsx",
-  "206": "PerfOverlay/index.tsx",
-  "207": "PixelClassifierPanel/index.tsx",
-  "208": "SaveModal.tsx",
-  "209": "CompositionPanel/index.tsx",
-  "210": "useFeatureChannels.ts",
-  "211": "measure.ts",
-  "212": "buildSourceKey",
-  "214": "Mat4",
-  "215": "start.sh",
-  "216": "reingest_tiffs_as_individual.py",
-  "217": "DenoisePanel/index.tsx",
-  "218": "DisplayControls/index.tsx",
-  "219": "FeatureChannelsPanel/index.tsx",
-  "220": "HistogramControl/index.tsx",
-  "221": "VersionPreviewBar.tsx",
-  "222": "HubMainContent.tsx",
-  "223": "VolumeViewer.tsx",
-  "224": "useDraftSync.ts",
-  "225": "useExportJob.ts",
-  "226": "useMaskOps.ts",
-  "227": "useOpenInAnnotate.ts",
-  "228": "feedbackContext.ts",
-  "229": "adjust.ts",
-  "230": "sharpen.test.ts",
-  "231": "classStore.ts",
-  "232": "ratingStore.ts",
-  "233": "referenceGuideStore.ts",
-  "234": "settingsStore.ts",
-  "235": "fx.spec.ts",
-  "236": "frontend/vite.config.ts",
-  "237": "ipred/models",
-  "238": "test_emb_pca_channels.py",
-  "239": "test_tomojepa_onnx.py",
-  "240": "ExportConflict",
-  "241": "add_sample_metadata.py",
-  "242": "repair_catalog_paths.py",
-  "243": "Using the tool",
-  "244": "4. Annotation guide",
-  "245": "ExportPage.tsx",
-  "246": "MaskToolsPanel/index.tsx",
-  "247": "DebouncedSlider.tsx",
-  "248": "CustomizePages.tsx",
-  "249": "HubAppLayout.tsx",
-  "250": "HubHeader.tsx",
-  "251": "HubSidebar.tsx",
-  "252": "IframeModal.tsx",
-  "253": "useAnnotatedSourceKeys.ts",
-  "254": "useKeybinds.ts",
-  "255": "viridis.ts",
-  "256": "clipboardStore.ts",
-  "257": "connectionStore.ts",
-  "258": "annotationFilter.ts",
-  "259": "vite-env.d.ts",
-  "260": "frontend/tsconfig.json",
-  "261": "patches/README.md",
-  "262": "Button.tsx",
-  "263": "ItkVtkFrame.tsx",
-  "264": "Bundled Tiled catalog (committed to git)",
-  "265": "@phosphor-icons/react",
-  "266": "zundo",
-  "267": "@testing-library/react",
-  "268": "@types/node",
-  "269": "@types/react",
-  "270": "vite",
-  "271": "@vitejs/plugin-react",
-  "272": "vitest",
-  "273": "SliceNavigator/index.tsx",
-  "274": "useHubSelectedTabs.ts",
-  "275": "useSam.ts",
-  "276": "lib/utils.ts",
-  "277": "navigationRouterTypes.ts",
-  "278": "server.ts",
-  "279": "ItkVtkViewerComponent.jsx",
-  "280": "vitest.config.ts",
-  "281": "vite.config.js",
-  "285": "scripts/__init__.py",
-  "286": "tests/__init__.py",
-  "287": "postcss.config.js",
-  "288": "layerVisibilityStore.test.ts",
-  "289": "setup.ts",
-  "290": "webgpu.d.ts",
-  "291": "tailwind.config.js",
-  "292": "eslint.config.js",
-  "293": "tiled-auth-interceptor.js",
-  "294": ".open",
-  "295": ".send",
-  "296": "ipred",
-  "297": "sam3-annotation-studio-backend",
-  "298": "tailwindcss"
-}
diff --git a/graphify-out/.graphify_labels.json.sig b/graphify-out/.graphify_labels.json.sig
deleted file mode 100644
index 19d78ec..0000000
--- a/graphify-out/.graphify_labels.json.sig
+++ /dev/null
@@ -1 +0,0 @@
-{"0": "9d217d5b093f9b00", "1": "92cdfbb2d1ecb1a2", "2": "9a616e9ea1361378", "3": "a6c97f0cc7337225", "4": "98bcfd1a5bd7adcb", "5": "d0da13ba92ca6801", "6": "f21e3da03846ff67", "7": "91aaebacccdf767c", "8": "ff027da653010318", "9": "d1c220058fc92278", "10": "f0e70663e44e1a05", "11": "101eaf2d2e752985", "12": "516319be7679c476", "13": "6853106ceb4b03ac", "14": "5f69e94edbb58531", "15": "8bfe51bbcb111d30", "16": "97b4e312677b365f", "17": "2ff22e41ce6fed74", "18": "f407c526a350ca4d", "19": "3148edf587b6c890", "20": "bdf0e54f0966e28b", "21": "0ba0e83e5b5d7c75", "22": "389c7fd0ee1e4058", "23": "ebe797ee63178af6", "24": "5bfddf82440253e6", "25": "0b5e6f8403820f57", "26": "1675fd672f55c2d4", "27": "52fe7314cccfb9ea", "28": "4433076bb364609c", "29": "40bcc6f59b0ec048", "30": "a4a6735ae428f40d", "31": "f03f0da73dabcad0", "32": "36f741667d99e55f", "33": "5a876722406c90d2", "34": "82dac5af06ecd7d0", "35": "0279f139dd1f85c8", "36": "153107dc7a9f481c", "37": "d5ad367703d7183a", "38": 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"114": "75234aa005fa7898", "115": "fef0d5a46df7bd94", "116": "cc85d2afcf03cd0f", "117": "da49619e9ff198a3", "118": "0528b028a2d3d2ed", "119": "f726695fe83cdf56", "120": "ff59f9fedd39a23a", "121": "391e9cee9ad30b8c", "122": "8e4c042b0bb15e74", "123": "88c8735b720ff928", "124": "e69db2471d8fda71", "125": "c572b2f55babf0f2", "126": "c28446963b9899a5", "127": "50651841a705f811", "128": "ea3d52cf287bd8f0", "129": "d7cb292edcfb000e", "130": "a7eaa0e411d32c37", "131": "0c4b723935d57974", "132": "8a6ded6851fc77a4", "133": "c1f6798a38233110", "134": "8c8b03156a066453", "135": "a805d71d7685cb4e", "136": "906602b2316124ff", "137": "661d09cfc70620bd", "138": "33d8a546281e3514", "139": "962096a9ddce98b7", "140": "9e978daed346df61", "141": "949ab4f62ad69296", "142": "206aef20c2a238dc", "143": "d6b15f8f059a50dd", "144": "00c6328386d2c1f4", "145": "5ebf169845e44318", "146": "5981a8bc1d7ecf6a", "147": "0033844f7baccc32", "148": "d9f00607fe2ddfc7", "149": "6b17163ff5d524fc", "150": "5acb30d4a67919d6", 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"226": "74ffcfc8898ff51d", "227": "5efb5617da941849", "228": "b822407890d2217e", "229": "fe4fa1a7b754a3e0", "230": "6adb5e6dd5c68508", "231": "debe5675379af729", "232": "52c1a9639abb9feb", "233": "f32214d70e4547e3", "234": "478c1a5ea7a7caec", "235": "e95ffd84953949e9", "236": "9523ce7f8596e683", "237": "a131aead127dce73", "238": "0e0cbe99478c1405", "239": "27ad6b6268e7f4bc", "240": "deb0010d484d18d3", "241": "2e275785b6e7cf7a", "242": "424f425bc17f73c5", "243": "05ae6eab6488bdb6", "244": "e0690b39dea5820f", "245": "46fc649b44772200", "246": "bb9ffc1d9b0368d5", "247": "d313af354155092b", "248": "0ff3684855fd010c", "249": "aac3212334fa899a", "250": "986bb318d0e8b278", "251": "5b98351ce17bddb5", "252": "fb3bea3f5cf2cfb2", "253": "360407397eaa9f8a", "254": "b4ac16c3ab0d31ff", "255": "2a1ac266844119d4", "256": "c6fd08d60215bee6", "257": "6d7e8b2f62af739d", "258": "006c5062ca738d84", "259": "8ad742a8b1f8d9d1", "260": "fa34349dea4896a5", "261": "890965940be37367", "262": "d17e5ce7908c5855", 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\ No newline at end of file
diff --git a/graphify-out/.graphify_root b/graphify-out/.graphify_root
deleted file mode 100644
index 945c9b4..0000000
--- a/graphify-out/.graphify_root
+++ /dev/null
@@ -1 +0,0 @@
-.
\ No newline at end of file
diff --git a/graphify-out/2026-08-26/.graphify_labels.json b/graphify-out/2026-08-26/.graphify_labels.json
deleted file mode 100644
index 56e4ee7..0000000
--- a/graphify-out/2026-08-26/.graphify_labels.json
+++ /dev/null
@@ -1,289 +0,0 @@
-{
-  "0": "catalog.ts",
-  "1": "get",
-  "2": "occupancy.ts",
-  "3": "thresholdFit.test.ts",
-  "4": "ipred_routes.py",
-  "5": "annotation_thumbnails.py",
-  "6": "run",
-  "7": "manifold.py",
-  "8": "api.py",
-  "9": "math/index.ts",
-  "10": "TemporalAccumulator",
-  "11": "annotation_server.py",
-  "12": "ipred_client.py",
-  "13": "coco_export.py",
-  "14": "Vec3",
-  "15": "tomojepa_onnx.py",
-  "16": "ColumnBrowser.tsx",
-  "17": "Quat",
-  "18": "math/color.ts",
-  "19": "devDependencies",
-  "20": "render/index.ts",
-  "21": "flip.ts",
-  "22": "compilerOptions",
-  "23": "compilerOptions",
-  "24": "dimension.ts",
-  "25": "test_ingest_conflict.py",
-  "26": "App.tsx",
-  "27": "src/index.ts",
-  "28": "catalog.py",
-  "29": "test_tiff_stack_source.py",
-  "30": "ingest.py",
-  "31": "test_zarr_source.py",
-  "32": "Catalog",
-  "33": "TiledNotifications/index.ts",
-  "34": "camera.ts",
-  "35": "ome-zarr-viewer.ts",
-  "36": "compositions.py",
-  "37": "Vec4",
-  "38": "step_phantom",
-  "39": "tiff_stack_source.py",
-  "40": "ipred/__init__.py",
-  "41": "train_infer.py",
-  "42": "volume/colormaps.ts",
-  "43": "Node",
-  "44": "Quantity",
-  "45": "Matrix",
-  "46": "ipred — architecture and operations guide",
-  "47": "AnnotationCanvas/index.tsx",
-  "48": "io/index.ts",
-  "49": "ipredApi.ts",
-  "50": "OrbitControls",
-  "51": "Vec2",
-  "52": "TrainerPlugin",
-  "53": "array.ts",
-  "54": "packing.ts",
-  "55": "scalar.ts",
-  "56": "feature_setups.py",
-  "57": "arrays.py",
-  "58": "IngestDropzone.tsx",
-  "59": "VectorGrid3",
-  "60": "core/index.ts",
-  "61": "getTiledBaseUrl",
-  "62": "constants.ts",
-  "63": "compilerOptions",
-  "64": "browse_helpers.py",
-  "66": "schemas.py",
-  "67": "FakeNode",
-  "68": "zarr_source.py",
-  "69": "dependencies",
-  "70": "ome-zarr.ts",
-  "71": "start_all.sh",
-  "72": "drafts.py",
-  "73": "tiled_clients.py",
-  "74": "App.jsx",
-  "75": "viewerState.ts",
-  "76": "devDependencies",
-  "77": "get_tiled_client",
-  "78": "useTabsStore.ts",
-  "79": "zarr-viewer/package.json",
-  "80": "volume-renderer.ts",
-  "81": "features.py",
-  "82": "Vec3Like",
-  "83": "sam_embed.py",
-  "84": "start_all.ps1",
-  "85": "Installation via Docker (Recommended)",
-  "86": "parse.ts",
-  "87": "TTLCache",
-  "88": "magicwand.ts",
-  "89": "Segmentation Annotation Studio",
-  "90": "tiled_mask_sync.py",
-  "91": "denoise.py",
-  "92": "Software architecture",
-  "93": "annotationStore.ts",
-  "94": "VolumeLoader",
-  "95": "volume-texture.ts",
-  "96": "HTTPException",
-  "97": "fake_stack",
-  "98": "geometry.ts",
-  "99": "length-units.ts",
-  "100": "codecs.ts",
-  "101": ".alignTo",
-  "102": "Aabb",
-  "103": "denoise_bake.py",
-  "104": "3. Annotate",
-  "105": "datasetStats.ts",
-  "106": "SamClient",
-  "107": "samWorker.ts",
-  "108": "periodic-table.ts",
-  "109": "fx/package.json",
-  "110": "predicates.ts",
-  "111": "test_tomojepa_embed.py",
-  "112": "export_jobs.py",
-  "113": "polybool.ts",
-  "114": "perf.ts",
-  "115": "compilerOptions",
-  "116": ".preview_labels",
-  "118": "mergeSameClass.ts",
-  "119": "rasterizeShapes",
-  "120": "morphology.ts",
-  "121": "livewire.ts",
-  "122": "log.ts",
-  "123": "5. Export & download",
-  "124": "Mat3",
-  "125": "test_tiled_config.py",
-  "126": "frontend/package.json",
-  "127": "api_feature_channel",
-  "128": "systems.ts",
-  "129": "OmeZarrVolumeSource",
-  "130": "ViewportOverlay",
-  "131": "id.ts",
-  "132": "test_compositions.py",
-  "133": "FastAPI",
-  "134": "editHistory.ts",
-  "135": "pixelClf.ts",
-  "136": "Clock",
-  "137": "transfer-function.ts",
-  "138": "Random",
-  "139": "array_source.py",
-  "140": "AGENTS.md — Segmentation Annotation Studio",
-  "141": "guides.py",
-  "142": "test_drafts.py",
-  "143": "quick-start.md",
-  "144": "Installation",
-  "145": "Quick start",
-  "146": "Emitter",
-  "147": "test_catalog_sessions.py",
-  "148": "import_dataset",
-  "149": "describe_methods",
-  "150": "clahe.ts",
-  "152": "bboxPrefilter.equivalence.test.ts",
-  "153": "Pool",
-  "154": "Dual",
-  "155": "test_array_blobs.py",
-  "156": "test_preprocess_cache.py",
-  "158": "TestPyramidPlan",
-  "159": "usePixelClassifier.ts",
-  "160": "datasetStore.ts",
-  "161": "toolStore.ts",
-  "162": "vite-zarr-static.ts",
-  "163": "test_api_health.py",
-  "164": "main",
-  "165": "test_health.py",
-  "166": "TestPyramidStore",
-  "167": "Segmentation Annotation Studio",
-  "168": "Troubleshooting",
-  "169": "ClassManager/index.tsx",
-  "170": "Toolbar/index.tsx",
-  "171": "ZarrLoader.tsx",
-  "172": "BuildVolumePanel.tsx",
-  "173": "config.ts",
-  "174": "useGuideSync.ts",
-  "175": "useSave.ts",
-  "176": "regionOps.ts",
-  "177": "zarrUrl.ts",
-  "178": "ipredStore.ts",
-  "179": "layerVisibilityStore.ts",
-  "180": "OME-Zarr Viewer",
-  "182": "include",
-  "183": "Pool",
-  "184": "Soa",
-  "185": "_denoised_slice",
-  "186": "2. Browse & select",
-  "187": "1. Connect to data",
-  "188": "fetch-sam-model.mjs",
-  "189": "DenoiseBakeModal.tsx",
-  "190": "DownloadModal.tsx",
-  "191": "InsightsModal.tsx",
-  "192": "MeasurementPanel/index.tsx",
-  "193": "VersionHistoryModal.tsx",
-  "194": "useFeatureManifold.ts",
-  "195": "classColors.ts",
-  "196": "lib/colormaps.ts",
-  "197": "featureManifold.ts",
-  "198": "stretch.test.ts",
-  "201": "test_manifold.py",
-  "202": "test_train_rethreshold.py",
-  "204": "Keyboard shortcuts",
-  "205": "LayersPanel/index.tsx",
-  "206": "PerfOverlay/index.tsx",
-  "207": "PixelClassifierPanel/index.tsx",
-  "208": "SaveModal.tsx",
-  "209": "CompositionPanel/index.tsx",
-  "210": "useFeatureChannels.ts",
-  "211": "measure.ts",
-  "212": "buildSourceKey",
-  "214": "Mat4",
-  "215": "start.sh",
-  "216": "reingest_tiffs_as_individual.py",
-  "217": "DenoisePanel/index.tsx",
-  "218": "DisplayControls/index.tsx",
-  "219": "FeatureChannelsPanel/index.tsx",
-  "220": "HistogramControl/index.tsx",
-  "221": "VersionPreviewBar.tsx",
-  "222": "HubMainContent.tsx",
-  "223": "VolumeViewer.tsx",
-  "224": "useDraftSync.ts",
-  "225": "useExportJob.ts",
-  "226": "useMaskOps.ts",
-  "227": "useOpenInAnnotate.ts",
-  "228": "feedbackContext.ts",
-  "229": "adjust.ts",
-  "230": "sharpen.test.ts",
-  "231": "classStore.ts",
-  "232": "ratingStore.ts",
-  "233": "referenceGuideStore.ts",
-  "234": "settingsStore.ts",
-  "235": "fx.spec.ts",
-  "236": "frontend/vite.config.ts",
-  "237": "ipred/models",
-  "238": "test_emb_pca_channels.py",
-  "239": "test_tomojepa_onnx.py",
-  "240": "ExportConflict",
-  "241": "add_sample_metadata.py",
-  "242": "repair_catalog_paths.py",
-  "243": "Using the tool",
-  "244": "4. Annotation guide",
-  "245": "ExportPage.tsx",
-  "246": "MaskToolsPanel/index.tsx",
-  "247": "DebouncedSlider.tsx",
-  "248": "CustomizePages.tsx",
-  "249": "HubAppLayout.tsx",
-  "250": "HubHeader.tsx",
-  "251": "HubSidebar.tsx",
-  "252": "IframeModal.tsx",
-  "253": "useAnnotatedSourceKeys.ts",
-  "254": "useKeybinds.ts",
-  "255": "viridis.ts",
-  "256": "clipboardStore.ts",
-  "257": "connectionStore.ts",
-  "258": "annotationFilter.ts",
-  "259": "vite-env.d.ts",
-  "260": "frontend/tsconfig.json",
-  "261": "patches/README.md",
-  "262": "Button.tsx",
-  "263": "ItkVtkFrame.tsx",
-  "264": "Bundled Tiled catalog (committed to git)",
-  "265": "@phosphor-icons/react",
-  "266": "zundo",
-  "267": "@testing-library/react",
-  "268": "@types/node",
-  "269": "@types/react",
-  "270": "vite",
-  "271": "@vitejs/plugin-react",
-  "272": "vitest",
-  "273": "SliceNavigator/index.tsx",
-  "274": "useHubSelectedTabs.ts",
-  "275": "useSam.ts",
-  "276": "lib/utils.ts",
-  "277": "navigationRouterTypes.ts",
-  "278": "server.ts",
-  "279": "ItkVtkViewerComponent.jsx",
-  "280": "vitest.config.ts",
-  "281": "vite.config.js",
-  "285": "scripts/__init__.py",
-  "286": "tests/__init__.py",
-  "287": "postcss.config.js",
-  "288": "layerVisibilityStore.test.ts",
-  "289": "setup.ts",
-  "290": "webgpu.d.ts",
-  "291": "tailwind.config.js",
-  "292": "eslint.config.js",
-  "293": "tiled-auth-interceptor.js",
-  "294": ".open",
-  "295": ".send",
-  "296": "ipred",
-  "297": "sam3-annotation-studio-backend",
-  "298": "tailwindcss"
-}
diff --git a/graphify-out/2026-08-26/GRAPH_REPORT.md b/graphify-out/2026-08-26/GRAPH_REPORT.md
deleted file mode 100644
index d0fb9f6..0000000
--- a/graphify-out/2026-08-26/GRAPH_REPORT.md
+++ /dev/null
@@ -1,1159 +0,0 @@
-# Graph Report - forge_Segementation_Finch  (2026-08-26)
-
-## Corpus Check
-- 437 files · ~371,392 words
-- Verdict: corpus is large enough that graph structure adds value.
-
-## Summary
-- 4927 nodes · 8836 edges · 287 communities (223 shown, 64 thin omitted)
-- Extraction: 97% EXTRACTED · 3% INFERRED · 0% AMBIGUOUS · INFERRED: 294 edges (avg confidence: 0.85)
-- Token cost: 0 input · 0 output
-
-## Graph Freshness
-- Built from commit: `e8600297`
-- Run `git rev-parse HEAD` and compare to check if the graph is stale.
-- Run `graphify update .` after code changes (no API cost).
-
-## Community Hubs (Navigation)
-- catalog.ts
-- get
-- occupancy.ts
-- thresholdFit.test.ts
-- ipred_routes.py
-- annotation_thumbnails.py
-- run
-- manifold.py
-- api.py
-- math/index.ts
-- TemporalAccumulator
-- annotation_server.py
-- ipred_client.py
-- coco_export.py
-- Vec3
-- tomojepa_onnx.py
-- ColumnBrowser.tsx
-- Quat
-- math/color.ts
-- devDependencies
-- render/index.ts
-- flip.ts
-- compilerOptions
-- compilerOptions
-- dimension.ts
-- test_ingest_conflict.py
-- App.tsx
-- src/index.ts
-- catalog.py
-- test_tiff_stack_source.py
-- ingest.py
-- test_zarr_source.py
-- Catalog
-- TiledNotifications/index.ts
-- camera.ts
-- ome-zarr-viewer.ts
-- compositions.py
-- Vec4
-- step_phantom
-- tiff_stack_source.py
-- ipred/__init__.py
-- train_infer.py
-- volume/colormaps.ts
-- Node
-- Quantity
-- Matrix
-- ipred — architecture and operations guide
-- AnnotationCanvas/index.tsx
-- io/index.ts
-- ipredApi.ts
-- OrbitControls
-- Vec2
-- TrainerPlugin
-- array.ts
-- packing.ts
-- scalar.ts
-- feature_setups.py
-- arrays.py
-- IngestDropzone.tsx
-- VectorGrid3
-- core/index.ts
-- getTiledBaseUrl
-- constants.ts
-- compilerOptions
-- browse_helpers.py
-- schemas.py
-- FakeNode
-- zarr_source.py
-- dependencies
-- ome-zarr.ts
-- start_all.sh
-- drafts.py
-- tiled_clients.py
-- App.jsx
-- viewerState.ts
-- devDependencies
-- get_tiled_client
-- useTabsStore.ts
-- zarr-viewer/package.json
-- volume-renderer.ts
-- features.py
-- Vec3Like
-- sam_embed.py
-- start_all.ps1
-- Installation via Docker (Recommended)
-- parse.ts
-- TTLCache
-- magicwand.ts
-- Segmentation Annotation Studio
-- tiled_mask_sync.py
-- denoise.py
-- Software architecture
-- annotationStore.ts
-- VolumeLoader
-- volume-texture.ts
-- HTTPException
-- fake_stack
-- geometry.ts
-- length-units.ts
-- codecs.ts
-- .alignTo
-- Aabb
-- denoise_bake.py
-- 3. Annotate
-- datasetStats.ts
-- SamClient
-- samWorker.ts
-- periodic-table.ts
-- fx/package.json
-- predicates.ts
-- test_tomojepa_embed.py
-- export_jobs.py
-- polybool.ts
-- perf.ts
-- compilerOptions
-- .preview_labels
-- mergeSameClass.ts
-- rasterizeShapes
-- morphology.ts
-- livewire.ts
-- log.ts
-- 5. Export & download
-- Mat3
-- test_tiled_config.py
-- frontend/package.json
-- api_feature_channel
-- systems.ts
-- OmeZarrVolumeSource
-- ViewportOverlay
-- id.ts
-- test_compositions.py
-- FastAPI
-- editHistory.ts
-- pixelClf.ts
-- Clock
-- transfer-function.ts
-- Random
-- array_source.py
-- AGENTS.md — Segmentation Annotation Studio
-- guides.py
-- test_drafts.py
-- Installation
-- Quick start
-- Emitter
-- test_catalog_sessions.py
-- import_dataset
-- describe_methods
-- clahe.ts
-- bboxPrefilter.equivalence.test.ts
-- Pool
-- Dual
-- test_array_blobs.py
-- test_preprocess_cache.py
-- TestPyramidPlan
-- usePixelClassifier.ts
-- datasetStore.ts
-- toolStore.ts
-- vite-zarr-static.ts
-- test_api_health.py
-- main
-- test_health.py
-- TestPyramidStore
-- Segmentation Annotation Studio
-- Troubleshooting
-- ClassManager/index.tsx
-- Toolbar/index.tsx
-- ZarrLoader.tsx
-- BuildVolumePanel.tsx
-- config.ts
-- useGuideSync.ts
-- useSave.ts
-- regionOps.ts
-- zarrUrl.ts
-- ipredStore.ts
-- layerVisibilityStore.ts
-- OME-Zarr Viewer
-- include
-- Pool
-- Soa
-- _denoised_slice
-- 2. Browse & select
-- 1. Connect to data
-- fetch-sam-model.mjs
-- DenoiseBakeModal.tsx
-- DownloadModal.tsx
-- InsightsModal.tsx
-- MeasurementPanel/index.tsx
-- VersionHistoryModal.tsx
-- useFeatureManifold.ts
-- classColors.ts
-- lib/colormaps.ts
-- featureManifold.ts
-- stretch.test.ts
-- test_manifold.py
-- test_train_rethreshold.py
-- Keyboard shortcuts
-- LayersPanel/index.tsx
-- PerfOverlay/index.tsx
-- PixelClassifierPanel/index.tsx
-- SaveModal.tsx
-- CompositionPanel/index.tsx
-- useFeatureChannels.ts
-- measure.ts
-- buildSourceKey
-- Mat4
-- start.sh
-- reingest_tiffs_as_individual.py
-- DenoisePanel/index.tsx
-- DisplayControls/index.tsx
-- FeatureChannelsPanel/index.tsx
-- HistogramControl/index.tsx
-- VersionPreviewBar.tsx
-- HubMainContent.tsx
-- VolumeViewer.tsx
-- useDraftSync.ts
-- useExportJob.ts
-- useMaskOps.ts
-- useOpenInAnnotate.ts
-- feedbackContext.ts
-- adjust.ts
-- sharpen.test.ts
-- classStore.ts
-- ratingStore.ts
-- referenceGuideStore.ts
-- settingsStore.ts
-- fx.spec.ts
-- ipred/models
-- test_emb_pca_channels.py
-- test_tomojepa_onnx.py
-- ExportConflict
-- add_sample_metadata.py
-- repair_catalog_paths.py
-- Using the tool
-- 4. Annotation guide
-- ExportPage.tsx
-- MaskToolsPanel/index.tsx
-- DebouncedSlider.tsx
-- CustomizePages.tsx
-- HubAppLayout.tsx
-- HubHeader.tsx
-- HubSidebar.tsx
-- IframeModal.tsx
-- useAnnotatedSourceKeys.ts
-- useKeybinds.ts
-- viridis.ts
-- clipboardStore.ts
-- connectionStore.ts
-- annotationFilter.ts
-- vite-env.d.ts
-- frontend/tsconfig.json
-- patches/README.md
-- Button.tsx
-- ItkVtkFrame.tsx
-- Bundled Tiled catalog (committed to git)
-- @phosphor-icons/react
-- zundo
-- @testing-library/react
-- @types/node
-- @types/react
-- vite
-- @vitejs/plugin-react
-- vitest
-- navigationRouterTypes.ts
-- server.ts
-- vitest.config.ts
-- vite.config.js
-- scripts/__init__.py
-- ipred
-- sam3-annotation-studio-backend
-
-## God Nodes (most connected - your core abstractions)
-1. `Vec3` - 99 edges
-2. `run()` - 98 edges
-3. `Catalog` - 69 edges
-4. `get()` - 58 edges
-5. `Mat4` - 54 edges
-6. `VolumeRenderer` - 51 edges
-7. `Quat` - 46 edges
-8. `Vec2` - 35 edges
-9. `OrbitControls` - 33 edges
-10. `get_tiled_client()` - 29 edges
-
-## Surprising Connections (you probably didn't know these)
-- `health()` --references--> `get()`  [EXTRACTED]
-  ipred/src/ipred/api.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
-- `get_servers()` --references--> `get()`  [EXTRACTED]
-  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
-- `browse_column()` --references--> `get()`  [EXTRACTED]
-  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
-- `browse_items()` --references--> `get()`  [EXTRACTED]
-  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
-- `browse_thumbnail()` --references--> `get()`  [EXTRACTED]
-  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
-
-## Import Cycles
-- 3-file cycle: `frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat4.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts`
-- 4-file cycle: `frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat4.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/quat.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts`
-
-## Communities (287 total, 64 thin omitted)
-
-### Community 0 - "catalog.ts"
-Cohesion: 0.02
-Nodes (116): acre, ALL_UNITS, ampere, arcminute, arcsecond, astronomicalUnit, atmosphere, atomicMassUnit (+108 more)
-
-### Community 1 - "get"
-Cohesion: 0.05
-Nodes (40): browse_facets(), browse_slices(), connect_summary(), denoise_auto(), denoise_methods(), get_draft(), get_guide(), get_version_route() (+32 more)
-
-### Community 2 - "occupancy.ts"
-Cohesion: 0.04
-Nodes (32): Disposable, DisposableScope, DisposeFn, toDisposeFn(), CELL_STRIDE_FLOATS, CHEBYSHEV_INF, chebyshevDistanceField(), densityToLutRange() (+24 more)
-
-### Community 3 - "thresholdFit.test.ts"
-Cohesion: 0.07
-Nodes (35): applyGaussianBlurGray(), applyGaussianBlurRgba(), boxBlurH(), boxBlurV(), boxRadiiForGaussian(), baseToDisplay(), DisplayAffine, displayAffineFor() (+27 more)
-
-### Community 4 - "ipred_routes.py"
-Cohesion: 0.05
-Nodes (69): ipred_feature_channel(), ipred_get_composition(), ipred_get_setup(), ipred_health(), _ipred_http_error(), ipred_infer(), ipred_list_compositions(), ipred_list_modules() (+61 more)
-
-### Community 5 - "annotation_thumbnails.py"
-Cohesion: 0.05
-Nodes (58): decode_thumbnail_base64(), _draw_shapes(), _hex_to_rgba(), _parse_source_key(), Any, ndarray, Annotated thumbnail generation for version history previews. Renders the most-…, Render a PNG thumbnail of the most-annotated slice with shapes overlaid. Args:… (+50 more)
-
-### Community 6 - "run"
-Cohesion: 0.07
-Nodes (6): run(), VisibilityFeedback, volumeRaymarchWgsl(), BrickLoader, VolumeRenderer, chooseVolumeFormat()
-
-### Community 7 - "manifold.py"
-Cohesion: 0.06
-Nodes (51): Tiny TTL cache for short-lived ipred artifacts (manifold heatmaps)., Thread-safe TTL + max-entries cache., TTLCache, _brush_mask(), build_label_map(), _ellipse_mask(), _polygon_mask(), Any (+43 more)
-
-### Community 8 - "api.py"
-Cohesion: 0.07
-Nodes (60): api_get_composition(), api_get_run(), api_infer(), api_list_compositions(), api_list_modules(), api_list_setups(), api_list_trainers(), api_manifold_sample() (+52 more)
-
-### Community 9 - "math/index.ts"
-Cohesion: 0.06
-Nodes (60): derivative(), dualCos(), dualExp(), dualLog(), dualPow(), dualSin(), dualSqrt(), dualTan() (+52 more)
-
-### Community 10 - "TemporalAccumulator"
-Cohesion: 0.07
-Nodes (12): halton(), TemporalAccumulator, CompiledGraph, descSignature(), PassContext, PassDesc, push(), RenderGraph (+4 more)
-
-### Community 11 - "annotation_server.py"
-Cohesion: 0.06
-Nodes (51): denoise_bake(), export_cancel(), export_coco(), generate_guide_route(), get_servers(), import_coco(), ingest_preflight(), ingest_upload() (+43 more)
-
-### Community 12 - "ipred_client.py"
-Cohesion: 0.07
-Nodes (53): _client(), feature_channel_bytes(), get_composition(), get_setup(), health(), infer(), ipred_url(), list_compositions() (+45 more)
-
-### Community 13 - "coco_export.py"
-Cohesion: 0.05
-Nodes (77): Background worker: render+rasterize all sources, write the dataset tree (images…, _run_export_job(), _apply_erased(), _brush_mask(), build_export_plan(), _ellipse_mask(), _encode_png(), fold_lightly_splits() (+69 more)
-
-### Community 14 - "Vec3"
-Cohesion: 0.05
-Nodes (4): Plane, Ray, Sphere, Vec3
-
-### Community 15 - "tomojepa_onnx.py"
-Cohesion: 0.06
-Nodes (48): main(), build_encoder(), _cached_model(), encode_dense_embeddings(), encoder_available(), _gray_unit_interval(), load_checkpoint_state(), _pad_to_multiple() (+40 more)
-
-### Community 16 - "ColumnBrowser.tsx"
-Cohesion: 0.07
-Nodes (37): BrowseColumn(), BrowseColumnProps, BrowseDetailPanel(), BrowseDetailPanelProps, displayKey(), formatValue(), SECTION_ORDER, ColumnBrowser() (+29 more)
-
-### Community 17 - "Quat"
-Cohesion: 0.08
-Nodes (3): q(), DualQuat, Quat
-
-### Community 18 - "math/color.ts"
-Cohesion: 0.11
-Nodes (22): clamp01(), clampColor3(), clampColor4(), color3ToVec3(), color4ToVec4(), colorToCss(), isColor3Tuple(), isColor4Tuple() (+14 more)
-
-### Community 19 - "devDependencies"
-Cohesion: 0.04
-Nodes (48): @blueskyproject/tiled, eslint, @eslint/js, eslint-plugin-react, eslint-plugin-react-hooks, eslint-plugin-react-refresh, dependencies, @blueskyproject/tiled (+40 more)
-
-### Community 20 - "render/index.ts"
-Cohesion: 0.10
-Nodes (29): Color3, createContext(), DeviceOptions, keyDirectionalFromLights(), lightBufferByteSize(), LightingEnvironment, packLightsStd430(), DEFAULT_POINT_RANGE (+21 more)
-
-### Community 21 - "flip.ts"
-Cohesion: 0.27
-Nodes (11): clamp01(), convSep(), FLIP_PPD, FlipResult, gaussianKernel(), grayConv(), linearToYCxCz(), meanFlip() (+3 more)
-
-### Community 22 - "compilerOptions"
-Cohesion: 0.05
-Nodes (45): compilerOptions, allowImportingTsExtensions, isolatedModules, jsx, lib, module, moduleDetection, moduleResolution (+37 more)
-
-### Community 23 - "compilerOptions"
-Cohesion: 0.05
-Nodes (45): compilerOptions, baseUrl, isolatedModules, lib, module, moduleResolution, noEmit, noUnusedLocals (+37 more)
-
-### Community 24 - "dimension.ts"
-Cohesion: 0.04
-Nodes (44): ABSORBED_DOSE, ACCELERATION, ACTION, AMOUNT, ANGLE, ANGULAR_MOMENTUM, ANGULAR_VELOCITY, AREA (+36 more)
-
-### Community 25 - "test_ingest_conflict.py"
-Cohesion: 0.06
-Nodes (31): image_meta(), Return shape / dtype metadata for an image source., fake_client(), FakeArray, FakeNode, _npy(), Exception, fixture (+23 more)
-
-### Community 26 - "App.tsx"
-Cohesion: 0.06
-Nodes (23): allRoutes, AnnotatePage, App(), BrowsePage, ConnectPage, DEFAULT_PATHS, ReferencePage, VolumePage (+15 more)
-
-### Community 27 - "src/index.ts"
-Cohesion: 0.06
-Nodes (29): bloom(), depthOfField(), fxaa(), OP_INDEX, outline(), sharpen(), ssao(), taa() (+21 more)
-
-### Community 28 - "catalog.py"
-Cohesion: 0.10
-Nodes (31): arrays_dir(), load_array_blob(), Any, ndarray, Path, Content-addressed array blobs for remote compute (data ≠ compute host)., Blob directory for uploaded arrays., Persist ``arr`` and return ``{array_ref, shape, dtype, ...}``. (+23 more)
-
-### Community 29 - "test_tiff_stack_source.py"
-Cohesion: 0.06
-Nodes (11): Path, Tests for exposing a TIFF directory as a streamable 3-D Zarr volume. The…, A directory of zero-padded 2-D TIFFs whose value encodes the slice index., Coarse levels are built from the level above, not from the source. That is what…, TestBlockMean, TestBuildLevel, TestCascade, TestInspect (+3 more)
-
-### Community 30 - "ingest.py"
-Cohesion: 0.09
-Nodes (36): _bump(), _child_keys(), _classify_error(), _ensure_container(), get_job(), _image_number(), new_job(), _pad_width() (+28 more)
-
-### Community 31 - "test_zarr_source.py"
-Cohesion: 0.09
-Nodes (16): _fake_pyramid(), _FakeArray, _FakeContainer, _make_multiscale(), fixture, ndarray, Path, pyramid() (+8 more)
-
-### Community 32 - "Catalog"
-Cohesion: 0.09
-Nodes (23): Connection, Catalog, ProjectRow, Any, Path, Create or return the project for this source identity., Ensure project exists and open a new session on it., Update session current_* pointers (only provided fields). (+15 more)
-
-### Community 33 - "TiledNotifications/index.ts"
-Cohesion: 0.14
-Nodes (30): buildTiledStreamUrl(), buildZarrUrlForChild(), ensureNotificationPermission(), isSupported(), OsNotificationOptions, showOsNotification(), EMPTY_PATHS, NodeStream() (+22 more)
-
-### Community 34 - "camera.ts"
-Cohesion: 0.09
-Nodes (9): Controller, FirstPersonControls, FlyControls, OrbitMode, OrbitState, IMPORTANT: `eye` may be `this._eye` — never overwrite it before reading world…, AxisBinding, Binding (+1 more)
-
-### Community 35 - "ome-zarr-viewer.ts"
-Cohesion: 0.10
-Nodes (29): createDemoHud(), createDemoSession(), DemoHandle, resetDemoStage(), resizeDemoCanvas(), ensureHudStyles(), boot(), stageEl (+21 more)
-
-### Community 36 - "compositions.py"
-Cohesion: 0.07
-Nodes (48): Any, ndarray, Run composition nodes; concatenate ``outputs`` channel blocks. Returns:…, run_composition(), compositions_root(), content_hash(), _dir(), ensure_default_compositions() (+40 more)
-
-### Community 38 - "step_phantom"
-Cohesion: 0.08
-Nodes (11): ndarray, parametrize, Tests for the classical denoise filters. Ported alongside :mod:`denoise`…, A hard vertical edge plus additive Gaussian noise. The standard shape for…, snr_db(), step_phantom(), TestAutoStrength, TestCapabilities (+3 more)
-
-### Community 39 - "tiff_stack_source.py"
-Cohesion: 0.11
-Nodes (35): block_mean(), _build_level(), _cast_like(), downsample_array(), inspect_tiff_stack(), multiscales_metadata(), preflight_tiff_stack(), Any (+27 more)
-
-### Community 40 - "ipred/__init__.py"
-Cohesion: 0.06
-Nodes (48): Execute a composition document on a raw image array., ipred — generic iterative prediction backend., ChannelBlock, FeatureModule, ModuleContext, ModuleMeta, Protocol, Feature module protocol — typed channel producers for compositions. (+40 more)
-
-### Community 41 - "train_infer.py"
-Cohesion: 0.10
-Nodes (37): cal_scores_from_json(), conformal_quantile(), counts_from_status(), maps_from_proba(), mondrian_thresholds(), Any, ndarray, Mondrian split-conformal thresholds and maps from cached proba. (+29 more)
-
-### Community 42 - "volume/colormaps.ts"
-Cohesion: 0.12
-Nodes (22): BuiltinColorMapName, ColorMapFn, ColorMapName, colorMapNames(), lerp3(), MAPS, Rgb, sampleColorMap() (+14 more)
-
-### Community 43 - "Node"
-Cohesion: 0.16
-Nodes (11): CameraComponent, Component, ComponentBase, ComponentKind, LightComponent, MeshComponent, PhysicsBodyComponent, VolumeComponent (+3 more)
-
-### Community 44 - "Quantity"
-Cohesion: 0.10
-Nodes (3): Dimension, Quantity, Unit
-
-### Community 46 - "ipred — architecture and operations guide"
-Cohesion: 0.06
-Nodes (32): 10. Manifold / Suggest Labels, 11. Frontend map, 12. Package map (`ipred/src/ipred/`), 13. Mental model (one paragraph), 14. Quick ops, 1. Big picture, 2. Where compute lives, 3. How the UI reaches compute (proxy boundary) (+24 more)
-
-### Community 47 - "AnnotationCanvas/index.tsx"
-Cohesion: 0.11
-Nodes (29): AnnotationCanvas(), AnnotationCanvasProps, buildRingsPath(), distToSegment(), EMPTY_CLASS_COLOR_BY_ID, EMPTY_MANIFOLD_MARKERS, EMPTY_SHAPES, hexToRgb() (+21 more)
-
-### Community 48 - "io/index.ts"
-Cohesion: 0.11
-Nodes (25): aabbRaySlab(), DensityField, extractIsosurface(), ExtractIsosurfaceOptions, inside(), loadDensityField(), LoadDensityFieldOptions, MeasureThresholdOptions (+17 more)
-
-### Community 49 - "ipredApi.ts"
-Cohesion: 0.10
-Nodes (26): CompositionDoc, CompositionNode, FeatureModuleInfo, FeatureSetup, getIpredComposition(), getIpredSetup(), IpredFeatureImportance, ipredHealth() (+18 more)
-
-### Community 52 - "TrainerPlugin"
-Cohesion: 0.09
-Nodes (21): ModelArtifacts, Any, ndarray, Path, Protocol, Trainer plugin interface., Artifacts produced by ``train``., Train / predict_proba contract for pixel classifiers. (+13 more)
-
-### Community 53 - "array.ts"
-Cohesion: 0.13
-Nodes (13): chunkBytes(), DecodedChunkCache, dtypeByteSize(), VolumeChunk, chunkKey(), cropCOrderChunk(), fillBuffer(), OpenZarrArrayOptions (+5 more)
-
-### Community 54 - "packing.ts"
-Cohesion: 0.10
-Nodes (23): Euler, scratchMat, compact1By2(), decodeOctNormal(), demorton3D(), encodeOctNormal(), _f32, floatToHalf() (+15 more)
-
-### Community 55 - "scalar.ts"
-Cohesion: 0.10
-Nodes (19): bitReverse(), fft(), fftReal(), ifft(), DEG2RAD, deltaAngle(), HALF_PI, inverseLerp() (+11 more)
-
-### Community 56 - "feature_setups.py"
-Cohesion: 0.11
-Nodes (36): ArgumentParser, _add_project_sugar(), _catalog(), main(), _print(), Any, CLI entrypoint ``ipred`` with --project session sugar., Return session_id from --session or --project sugar. (+28 more)
-
-### Community 57 - "arrays.py"
-Cohesion: 0.14
-Nodes (27): array_shape_meta(), _descend_to_stack(), _is_container_node(), _level_array(), multiscale_levels(), node_keywords(), Any, ndarray (+19 more)
-
-### Community 58 - "IngestDropzone.tsx"
-Cohesion: 0.12
-Nodes (23): ConflictDialog(), ConflictDialogProps, IngestConflict, collectFromDrop(), fileStem(), IngestDropzone(), IngestDropzoneProps, isSupported() (+15 more)
-
-### Community 59 - "VectorGrid3"
-Cohesion: 0.12
-Nodes (5): Grid3Base, Grid3Spec, resolveSpacing(), ScalarGrid3, VectorGrid3
-
-### Community 60 - "core/index.ts"
-Cohesion: 0.18
-Nodes (14): assert(), assertDefined(), AssertionError, assertNever(), chemistry, err, isErr(), isOk() (+6 more)
-
-### Community 61 - "getTiledBaseUrl"
-Cohesion: 0.18
-Nodes (21): Header(), getValidAccessToken(), installTiledFetchInterceptor(), nativeFetch, refreshAccessToken(), IMPORTANT: this must be imported before `<App>` (and therefore before the…, Window, getActiveServer() (+13 more)
-
-### Community 62 - "constants.ts"
-Cohesion: 0.07
-Nodes (26): ATOMIC_MASS_UNIT, AVOGADRO, BOHR_RADIUS, BOLTZMANN, COULOMB_CONSTANT, EARTH_MASS, EARTH_RADIUS, ELECTRON_MASS (+18 more)
-
-### Community 63 - "compilerOptions"
-Cohesion: 0.07
-Nodes (26): compilerOptions, allowUnreachableCode, declaration, declarationMap, forceConsistentCasingInFileNames, isolatedModules, lib, module (+18 more)
-
-### Community 64 - "browse_helpers.py"
-Cohesion: 0.10
-Nodes (37): Return a cached :class:`FieldMapping` for the given container., _resolve_field_mapping(), _apply_filters(), build_field_mapping(), _combined_metadata_keys(), _display_name(), distinct_from_rows(), FieldMapping (+29 more)
-
-### Community 66 - "schemas.py"
-Cohesion: 0.07
-Nodes (45): BrushShape, BrushStroke, DenoiseBakeRequest, EllipseShape, GuideClass, GuidePayload, ImageMeta, IngestPreflightRequest (+37 more)
-
-### Community 67 - "FakeNode"
-Cohesion: 0.09
-Nodes (9): catalog(), FakeNode, fixture, Tests for locating a dataset's renderable 3-D volume. This is the module that…, A catalog node: metadata plus children, indexable like a Tiled container., A catalog with one of each shape the resolver has to tell apart., TestHasMultiscales, TestResolveVolume (+1 more)
-
-### Community 68 - "zarr_source.py"
-Cohesion: 0.15
-Nodes (25): _existing_node_info(), inspect_zarr(), _is_zarr_dir(), _multiscale_datasets(), preflight_zarr(), Any, Path, Register on-disk Zarr volumes with Tiled, without copying any data. The drag-… (+17 more)
-
-### Community 69 - "dependencies"
-Cohesion: 0.08
-Nodes (26): @blueskyproject/finch, clsx, dependencies, @blueskyproject/finch, clsx, @huggingface/transformers, konva, polygon-clipping (+18 more)
-
-### Community 70 - "ome-zarr.ts"
-Cohesion: 0.13
-Nodes (23): units, axisToXyz(), LevelInfo, openOmeZarr(), OpenOmeZarrOptions, scaleTranslation(), estimateValueRange(), openZarrArray() (+15 more)
-
-### Community 71 - "start_all.sh"
-Cohesion: 0.14
-Nodes (25): API_PROXY_TARGET, can_run_npm(), cleanup(), cleanup_managed_processes(), cleanup_pid_file(), ensure_backend_env(), ensure_docs_env(), ensure_frontend_runtime() (+17 more)
-
-### Community 72 - "drafts.py"
-Cohesion: 0.13
-Nodes (23): _draft_path(), get_version(), get_version_thumbnail(), list_drafts(), list_versions(), load_draft(), Any, Path (+15 more)
-
-### Community 73 - "tiled_clients.py"
-Cohesion: 0.13
-Nodes (22): get_browse_container(), get_browse_container_for(), Any, Tiled client factory + browse-container discovery. Clients are cached per…, Return ``(container_node, path_prefix)`` for the Metadata Browser root. Checks…, Return ``(container_node, path_prefix)`` for a specific browse target. When…, clear_empty_tiled_api_key_env(), get_tiled_api_key() (+14 more)
-
-### Community 74 - "App.jsx"
-Cohesion: 0.14
-Nodes (22): RFC-4648, App(), LinkOptions, useLinkedWebGpuViewers(), queryClient, buildShareUrl(), copyToClipboard(), decodeState() (+14 more)
-
-### Community 75 - "viewerState.ts"
-Cohesion: 0.13
-Nodes (22): LinkOptions, Subscription, useLinkedViewers(), ItkVktNative(), ItkVktProps, ItkVtkCamera, ItkVtkViewerApi, ItkVtkViewerInstance (+14 more)
-
-### Community 76 - "devDependencies"
-Cohesion: 0.09
-Nodes (23): autoprefixer, devDependencies, autoprefixer, jsdom, postcss, tailwindcss, @testing-library/jest-dom, @testing-library/user-event (+15 more)
-
-### Community 77 - "get_tiled_client"
-Cohesion: 0.15
-Nodes (21): api_key_for_uri(), get_tiled_client(), Look up a configured API key for *uri*, falling back to None., Return a (cached) ``tiled.client`` connected to the requested server., build_volume(), inspect_volume_build(), Any, Build a renderable 3-D volume from a slice stack already in Tiled.… (+13 more)
-
-### Community 78 - "useTabsStore.ts"
-Cohesion: 0.14
-Nodes (20): HeaderProps, graceTimers, makeId(), nameFromUrl(), PaneSide, ReconTab, RendererKind, reposition() (+12 more)
-
-### Community 79 - "zarr-viewer/package.json"
-Cohesion: 0.09
-Nodes (22): description, devDependencies, @types/node, typescript, vite, vitest, @webgpu/types, @types/node (+14 more)
-
-### Community 80 - "volume-renderer.ts"
-Cohesion: 0.06
-Nodes (55): asColor3(), asColor4(), Color3Like, Color4Like, FLIP_CONFIGS, FlipConfig, flipConfigToLut(), concat() (+47 more)
-
-### Community 81 - "features.py"
-Cohesion: 0.13
-Nodes (22): apply_clahe(), compute_clahe_stack(), compute_feature_stacks(), encode_channel_png(), feature_channel_labels(), _float_feats_to_uint8(), _fmt_sigma(), list_sigmas() (+14 more)
-
-### Community 82 - "Vec3Like"
-Cohesion: 0.08
-Nodes (24): _q0, _q1, _q2, _tv, _decomposeRot, QuatLike, _sqA, _sqB (+16 more)
-
-### Community 83 - "sam_embed.py"
-Cohesion: 0.14
-Nodes (21): bilinear_sample_emb(), emb_grid_to_pca_channels(), encode_image_embeddings(), encoder_available(), fit_pca(), Any, ndarray, Path (+13 more)
-
-### Community 84 - "start_all.ps1"
-Cohesion: 0.19
-Nodes (19): Ensure-BackendEnv(), Ensure-DocsEnv(), Ensure-FrontendRuntime(), Ensure-TiledKey(), Ensure-Uv(), Err(), Get-FreePort(), Get-ListenerPids() (+11 more)
-
-### Community 85 - "Installation via Docker (Recommended)"
-Cohesion: 0.10
-Nodes (20): Add Tiled, Authenticate, `Bluesky Tiled`, Build and start the application, Clone and install repository, Clone repository, Debugging, Install (+12 more)
-
-### Community 86 - "parse.ts"
-Cohesion: 0.17
-Nodes (15): DIMENSIONLESS, constants, ALL_PREFIXES, parseQuantity(), parseUnit(), PREFIX_KEYS, resolveSymbol(), Term (+7 more)
-
-### Community 87 - "TTLCache"
-Cohesion: 0.12
-Nodes (14): Any, Small TTL + LRU cache used by the Browse API. Kept intentionally tiny: the…, Thread-safe cache with per-entry expiry and an insertion-order size cap., TTLCache, encode_png(), Any, ndarray, Slice-to-PNG rendering with explicit normalisation modes. Two normalisation… (+6 more)
-
-### Community 88 - "magicwand.ts"
-Cohesion: 0.14
-Nodes (18): bboxContainsRing(), boxBlur(), buildField(), chaikin(), gradientField(), GrayField, magicSelect(), MaskPolyOpts (+10 more)
-
-### Community 89 - "Segmentation Annotation Studio"
-Cohesion: 0.10
-Nodes (18): Annotation tools, Development, Export formats, Mac/Linux, Node.js, Production build (single origin), Project layout, Quick start (+10 more)
-
-### Community 90 - "tiled_mask_sync.py"
-Cohesion: 0.13
-Nodes (30): Filesystem-safe folder name for a class label., _safe_name(), ExportSourceItem, A single annotated sample within a multi-source export. Attributes: kind:…, _classes(), Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization →…, test_build_mask_volumes_stacks_and_labels(), test_merge_fresh_when_no_existing() (+22 more)
-
-### Community 91 - "denoise.py"
-Cohesion: 0.20
-Nodes (18): auto_strength(), denoise_slice(), denoise_stack(), estimate_noise_sigma(), _from_unit(), _median3d(), _odd(), dtype (+10 more)
-
-### Community 92 - "Software architecture"
-Cohesion: 0.11
-Nodes (18): Backend architecture, Canvas rendering, Client-side geometry & tools (`src/lib/`), Component shell, Data model, Deployment topology, Draw, autosave, and save a version, Export a dataset (+10 more)
-
-### Community 93 - "annotationStore.ts"
-Cohesion: 0.15
-Nodes (15): AnnotationState, BaseShape, BrushShape, BrushStroke, cloneShapeWithNewId(), DraftState, EllipseShape, EMPTY_DRAFT (+7 more)
-
-### Community 95 - "volume-texture.ts"
-Cohesion: 0.18
-Nodes (13): asFloatSamples(), clamp01(), encodeVoxel(), f32Scratch, floatToHalf(), HISTOGRAM_BINS, i32Scratch, packChunkInto() (+5 more)
-
-### Community 96 - "HTTPException"
-Cohesion: 0.08
-Nodes (38): browse_column(), browse_items(), browse_thumbnail(), export_download(), export_status(), get_version_thumbnail(), image_slice(), _parse_json_filters() (+30 more)
-
-### Community 97 - "fake_stack"
-Cohesion: 0.16
-Nodes (7): fake_stack(), fixture, Tests for building a 3-D volume from a slice stack already in Tiled. The…, Stand in for a Tiled stack: `resolve_array` + `array_shape_meta` + `read_slice`., TestBuildGuards, TestInspect, TestMetadataShape
-
-### Community 98 - "geometry.ts"
-Cohesion: 0.18
-Nodes (15): BBox, bboxCoversRaster(), bboxIntersects(), bboxNear(), EMPTY_BBOX, growByFlat(), normalizeEllipse(), normalizeRect() (+7 more)
-
-### Community 99 - "length-units.ts"
-Cohesion: 0.12
-Nodes (16): angstrom, centimeter, femtometer, foot, inch, kilometer, meter, micrometer (+8 more)
-
-### Community 100 - "codecs.ts"
-Cohesion: 0.20
-Nodes (15): bloscCodec(), BloscCompressorConfig, bloscCompressorName(), bloscDecodeSync(), bloscUnshuffle(), Codec, codecFromCompressor(), decompressStream() (+7 more)
-
-### Community 101 - ".alignTo"
-Cohesion: 0.15
-Nodes (6): LayoutBuilder, std140Alignment(), Std140Builder, std430Alignment(), Std430Builder, WgslBaseType
-
-### Community 102 - "Aabb"
-Cohesion: 0.11
-Nodes (9): Aabb, Frustum, computeWorldBounds(), _corner, cullFrustum(), expandByTransformedBox(), _localMax, _localMin (+1 more)
-
-### Community 103 - "denoise_bake.py"
-Cohesion: 0.18
-Nodes (13): default_target_path(), _denoise_one(), _ModelDenoiser, Any, ndarray, Apply a denoise filter to a whole volume and save the result as a NEW Tiled…, Applies a trained Noise2Noise/Noise2Void run across a whole volume. Loads the…, Release ``ML_LOCK``. Idempotent — callers release on both the happy path and in… (+5 more)
-
-### Community 104 - "3. Annotate"
-Cohesion: 0.12
-Nodes (17): 3. Annotate, Classes, Colorblind-safe colors, Cross-slice, Display (view-only), Export, How each tool works, Insights (quality checks) (+9 more)
-
-### Community 105 - "datasetStats.ts"
-Cohesion: 0.18
-Nodes (16): analyticArea(), ClassStat, computeSampleStats(), Coverage, isSelfIntersecting(), labelFor(), MaskInfo, overlapCount() (+8 more)
-
-### Community 106 - "SamClient"
-Cohesion: 0.14
-Nodes (7): Granularity, Pending, PromptBox, PromptPoint, SamClient, SamMask, SamStatus
-
-### Community 107 - "samWorker.ts"
-Cohesion: 0.18
-Nodes (16): Box, decode(), Device, encode(), fallbackToWasm(), freshRaw(), Granularity, InMsg (+8 more)
-
-### Community 108 - "periodic-table.ts"
-Cohesion: 0.24
-Nodes (13): atomicMass(), atomicNumber(), BY_SYMBOL, BY_Z, covalentRadius(), cpkColor(), elementBySymbol(), elementByZ() (+5 more)
-
-### Community 109 - "fx/package.json"
-Cohesion: 0.12
-Nodes (16): dependencies, @prism/core, @prism/math, @prism/render, description, exports, main, module (+8 more)
-
-### Community 110 - "predicates.ts"
-Cohesion: 0.20
-Nodes (12): det2(), orient2d(), orient3d(), Point2Like, compensatedDot(), CompensatedSum, kahanSum(), neumaierSum() (+4 more)
-
-### Community 111 - "test_tomojepa_embed.py"
-Cohesion: 0.16
-Nodes (15): MonkeyPatch, Path, Mark25 / TomoJEPA dense embedding encode., Mark11 dense projector is 256-D (Mark25 is 64-D)., All Mark25 paths must pass intensities in [-1, 1] into the network., test_encode_dense_honors_input_size(), test_encode_dense_shape_512(), test_encode_feeds_model_in_minus_one_one() (+7 more)
-
-### Community 112 - "export_jobs.py"
-Cohesion: 0.12
-Nodes (11): cancel_requested(), get_job(), new_job(), Any, In-memory job registry for COCO/mask exports. Mirrors the ingest job pattern…, Register a new export job and return its id., Ask a running job to stop; returns False if the job is unknown. Cooperative:…, True if cancellation was requested for *jid*. Reads the flag directly under the… (+3 more)
-
-### Community 113 - "polybool.ts"
-Cohesion: 0.28
-Nodes (12): clipShapesToOthers_naive(), eraseStampToMultiPolygon(), flatToRing(), multiPolygonToShapes(), regionsToMultiPolygon(), ringArea(), ringToFlat(), shapeToMultiPolygon() (+4 more)
-
-### Community 114 - "perf.ts"
-Cohesion: 0.20
-Nodes (12): initPerf(), listeners, mark(), perfEnabled(), PerfLabel, PerfStat, resetPerf(), scheduleNotify() (+4 more)
-
-### Community 115 - "compilerOptions"
-Cohesion: 0.12
-Nodes (15): compilerOptions, allowImportingTsExtensions, isolatedModules, lib, module, moduleDetection, moduleResolution, noEmit (+7 more)
-
-### Community 118 - "mergeSameClass.ts"
-Cohesion: 0.39
-Nodes (7): bboxOverlap(), expandSameClassOverlap(), inflate(), masksIntersect(), masksIntersectIn(), mergeNewWithSameClass(), MergeResult
-
-### Community 119 - "rasterizeShapes"
-Cohesion: 0.19
-Nodes (16): expandSameClassOverlap_naive(), mergeNewWithSameClass_naive(), incoming, otherClass, realOverlap(), thresholdShapes(), area(), fillEllipse() (+8 more)
-
-### Community 120 - "morphology.ts"
-Cohesion: 0.19
-Nodes (11): dilate(), dilateOnce(), erode(), erodeOnce(), fillHoles(), keepComponentsAtPoints(), removeSmallComponents(), smooth() (+3 more)
-
-### Community 121 - "livewire.ts"
-Cohesion: 0.26
-Nodes (6): CostMap, dijkstra(), imageToGrid(), MinHeap, simplifyPath(), tracePath()
-
-### Community 122 - "log.ts"
-Cohesion: 0.14
-Nodes (7): createLogger(), Logger, LogLevel, LogSink, ORDER, setLogLevel(), setLogSink()
-
-### Community 123 - "5. Export & download"
-Cohesion: 0.15
-Nodes (13): 5. Export & download, Alternative: Push masks to Tiled, DINOv3 / Lightly format, Downloading a COCO dataset, Importing a dataset back, Step 1 — Prepare (optional but recommended), Step 2 — Open the export dialog, Step 3 — Choose a scope (+5 more)
-
-### Community 125 - "test_tiled_config.py"
-Cohesion: 0.17
-Nodes (11): Tests for Tiled key hardening (empty-key clearing + config-key fallback)., Blank/whitespace key env vars are deleted; real ones are kept., Reads authentication.single_user_api_key from a config.yml., A ${TILED_API_KEY} placeholder in config.yml expands from the env; unset → None., _local_api_key prefers env, else falls back to the config single-user key., Importing the module clears a blank TILED_API_KEY from the env., test_clear_empty_tiled_api_key_env_removes_blank_vars(), test_config_key_env_interpolation() (+3 more)
-
-### Community 126 - "frontend/package.json"
-Cohesion: 0.17
-Nodes (11): name, private, scripts, build, dev, preview, test, test:watch (+3 more)
-
-### Community 127 - "api_feature_channel"
-Cohesion: 0.20
-Nodes (12): Serve a real static file when it exists, else index.html (SPA routing)., spa_fallback(), FileResponse, api_feature_channel(), api_manifold_heatmap(), api_run_commit(), api_run_proba_channel(), api_run_status() (+4 more)
-
-### Community 128 - "systems.ts"
-Cohesion: 0.21
-Nodes (6): FloatingOrigin, fromSim(), scaleFactor(), toSim(), UNIT_PRESETS, UnitSystem
-
-### Community 129 - "OmeZarrVolumeSource"
-Cohesion: 0.31
-Nodes (3): OmeZarrVolumeSource, VolumeDType, ParsedDType
-
-### Community 130 - "ViewportOverlay"
-Cohesion: 0.24
-Nodes (4): AXES, OverlayDrawParams, OverlayRuler, ViewportOverlay
-
-### Community 131 - "id.ts"
-Cohesion: 0.40
-Nodes (4): RFC-4122, createHandleAllocator(), Handle, uuid()
-
-### Community 132 - "test_compositions.py"
-Cohesion: 0.24
-Nodes (10): catalog(), fixture, MonkeyPatch, Composition validation, builtins, and legacy setup mapping., test_ensure_default_compositions(), test_feature_setups_still_seed(), test_legacy_setup_maps_to_composition(), test_module_catalog_lists_core_modules() (+2 more)
-
-### Community 133 - "FastAPI"
-Cohesion: 0.18
-Nodes (10): Tests for local filesystem sandboxing., Paths escaping LOCAL_DATA_ROOT must raise HTTP 403., list_dir should return file entries for an existing directory., list_dir on a non-existent path should raise HTTP 404., open_array on an unsupported extension should raise HTTP 422., test_list_dir_missing_raises_404(), test_list_dir_returns_entries(), test_open_array_unsupported_type_raises_422() (+2 more)
-
-### Community 134 - "editHistory.ts"
-Cohesion: 0.22
-Nodes (7): ClassDeleteEntry, currentSourceKey(), Entry, redo(), redoOps, undo(), undoOps
-
-### Community 135 - "pixelClf.ts"
-Cohesion: 0.33
-Nodes (7): applyProbaThresholdRgba(), classProbaMaskSetName(), colorizeConformalOverlay(), colorizeLabelMap(), labelMapToPolygonShapes(), parseHexColor(), thresholdProbaPngBlob()
-
-### Community 136 - "Clock"
-Cohesion: 0.24
-Nodes (3): Clock, FixedTimestep, now()
-
-### Community 137 - "transfer-function.ts"
-Cohesion: 0.26
-Nodes (8): Color4, clamp01(), normalizeWindowLevel(), sampleStops(), TransferFunction, TransferStop, WindowLevelOptions, windowLevelTransferFunction()
-
-### Community 139 - "array_source.py"
-Cohesion: 0.33
-Nodes (10): _index_slice(), _local_root(), ndarray, Path, Minimal local / Tiled slice reader (no annotate-backend imports)., Load one 2D/ HxWxC slice array., Pick HW or HWC frame from NHW / NHWC / HW / HWC., _read_local() (+2 more)
-
-### Community 140 - "AGENTS.md — Segmentation Annotation Studio"
-Cohesion: 0.20
-Nodes (9): AGENTS.md — Segmentation Annotation Studio, ALS Standards, Annotation data model (do NOT change without updating both backend/schemas.py and frontend stores), Git, Secrets, Security rules, Stack, Testing rules (+1 more)
-
-### Community 141 - "guides.py"
-Cohesion: 0.27
-Nodes (9): _guide_path(), load_guide(), Any, Path, Annotation-guide persistence for the Segmentation Annotation Studio. A *guide*…, Return the JSON file path for *source_key*'s guide., Persist the annotation guide for *source_key*. Args: source_key: Arbitrary…, Return the saved guide document for *source_key*, or ``None``. (+1 more)
-
-### Community 142 - "test_drafts.py"
-Cohesion: 0.20
-Nodes (9): Tests for session-draft persistence., A saved draft should be loadable with the correct source_key., Loading a draft that was never saved should return None., list_drafts should include a previously saved draft., list_drafts should return an empty list when no drafts exist., test_list_drafts_empty_when_none(), test_list_drafts_returns_saved(), test_load_missing_draft() (+1 more)
-
-### Community 144 - "Installation"
-Cohesion: 0.20
-Nodes (10): Backend, Changing the ports, Environment variables, Frontend, Installation, Manual setup, Option 1 — One-command local start (recommended), Option 2 — Docker (production) (+2 more)
-
-### Community 145 - "Quick start"
-Cohesion: 0.20
-Nodes (10): 1. Connect to your data, 2. Open an image to annotate, 3. Add a class, 4. Draw a mask, 5. Save your work, 6. Export a COCO dataset, :material-draw: Master annotation, :material-keyboard: Keyboard shortcuts (+2 more)
-
-### Community 146 - "Emitter"
-Cohesion: 0.27
-Nodes (3): Emitter, Listener, Unsubscribe
-
-### Community 147 - "test_catalog_sessions.py"
-Cohesion: 0.24
-Nodes (9): project_id_for(), Stable project id from source identity., catalog(), fixture, MonkeyPatch, Catalog project/session persistence., test_open_session_creates_project(), test_project_id_stable() (+1 more)
-
-### Community 148 - "import_dataset"
-Cohesion: 0.33
-Nodes (8): import_dataset(), _import_from_coco(), _import_with_sidecar(), Any, COCO + sidecar import: round-trip back to annotation payload. Prefers the…, Load a COCO dataset directory back into editor payload format. Args:…, Lossless import using _studio_shapes.json., Best-effort import from COCO only (no sidecar). Brush shapes become polygons…
-
-### Community 149 - "describe_methods"
-Cohesion: 0.22
-Nodes (9): available_methods(), describe_methods(), Any, Method metadata for the capability endpoint / UI menu. ``cost`` drives whether…, True if wavelet denoising can actually run.…, Methods that can actually run in this environment., Z-neighbours needed on each side of the target slice (0 for 2-D methods)., wavelet_available() (+1 more)
-
-### Community 150 - "clahe.ts"
-Cohesion: 0.36
-Nodes (5): applyClaheGray(), buildTileLut(), ClaheOptions, claheRgba(), identityLut()
-
-### Community 152 - "bboxPrefilter.equivalence.test.ts"
-Cohesion: 0.24
-Nodes (11): expectSameRegions(), iou(), makeShape(), mulberry32(), randomSlice(), regionOf(), perShape(), clipBatchBoolean() (+3 more)
-
-### Community 155 - "test_array_blobs.py"
-Cohesion: 0.31
-Nodes (8): client(), fixture, MonkeyPatch, TestClient, Array blob data plane for remote preprocess., test_modules_and_compositions_api(), test_save_load_array_blob(), test_upload_array_and_preprocess()
-
-### Community 156 - "test_preprocess_cache.py"
-Cohesion: 0.33
-Nodes (8): catalog(), fixture, MonkeyPatch, Cache-aware preprocess., test_preprocess_cache_hit(), test_preprocess_mark25_clahe_no_skimage(), test_preprocess_mark25_writes_dense_emb(), skipif
-
-### Community 159 - "usePixelClassifier.ts"
-Cohesion: 0.25
-Nodes (6): ClfFeatureImportance, ClfParams, ClfPredictCounts, ClfTrainResult, DEFAULT_CLF_PARAMS, UsePixelClassifierArgs
-
-### Community 160 - "datasetStore.ts"
-Cohesion: 0.25
-Nodes (7): DatasetState, DEFAULT_DENOISE, DEFAULT_RENDER, DenoiseOpts, ImageMeta, RenderOpts, useDatasetStore
-
-### Community 161 - "toolStore.ts"
-Cohesion: 0.25
-Nodes (7): MagicEngine, MagicMode, SamDetail, SelectScope, Tool, ToolState, useToolStore
-
-### Community 162 - "vite-zarr-static.ts"
-Cohesion: 0.32
-Nodes (4): src, PETIOLE_ZARR_MOUNT, ZarrStaticMount, zarrStaticPlugin()
-
-### Community 163 - "test_api_health.py"
-Cohesion: 0.32
-Nodes (7): client(), fixture, MonkeyPatch, TestClient, API health + session open., test_health(), test_open_session_and_list_setups()
-
-### Community 164 - "main"
-Cohesion: 0.48
-Nodes (6): _ensure_container(), _json_safe(), _load_pairs(), main(), Path, Return (png_path, txt_path) for stems that have both files.
-
-### Community 165 - "test_health.py"
-Cohesion: 0.33
-Nodes (6): asyncio, Tests for the health endpoint., Health endpoint must return status ok., Servers endpoint must not expose api_key in response., test_health_returns_ok(), test_servers_endpoint_no_api_key()
-
-### Community 167 - "Segmentation Annotation Studio"
-Cohesion: 0.29
-Nodes (7): :material-download-box: Install, :material-draw: Annotate, :material-export: Export, :material-rocket-launch: Quick start, Segmentation Annotation Studio, The workflow at a glance, What is this tool?
-
-### Community 168 - "Troubleshooting"
-Cohesion: 0.29
-Nodes (6): Connecting to data, Exporting, Saving, The app won't start, The Smart (AI) tool, Troubleshooting
-
-### Community 169 - "ClassManager/index.tsx"
-Cohesion: 0.33
-Nodes (5): ClassManager(), ClassManagerProps, ClassRowProps, countShapesForClass(), DEFAULT_SUGGESTED_CLASSES
-
-### Community 170 - "Toolbar/index.tsx"
-Cohesion: 0.29
-Nodes (3): CHANNEL_LABELS, ToolbarProps, ToolButtonProps
-
-### Community 171 - "ZarrLoader.tsx"
-Cohesion: 0.33
-Nodes (6): describeLevel(), ExistingInfo, ZarrInfo, ZarrLevel, ZarrLoader(), ZarrLoaderProps
-
-### Community 172 - "BuildVolumePanel.tsx"
-Cohesion: 0.33
-Nodes (6): BuildInfo, BuildVolumePanel(), BuildVolumePanelProps, JobState, PyramidLevel, readError()
-
-### Community 173 - "config.ts"
-Cohesion: 0.29
-Nodes (6): API_BASE, APP_VERSION, DOCS_URL, FEEDBACK_ENTRY_ID, FEEDBACK_FORM_URL, GIT_COMMIT
-
-### Community 174 - "useGuideSync.ts"
-Cohesion: 0.43
-Nodes (5): GuideGenPayload, loadGuide(), saveGuide(), useGuideLoad(), useGuideSync()
-
-### Community 175 - "useSave.ts"
-Cohesion: 0.33
-Nodes (5): SaveDraftPayload, SaveOptions, UseSaveReturn, VersionMeta, VersionPayload
-
-### Community 176 - "regionOps.ts"
-Cohesion: 0.33
-Nodes (3): computeRegionOps(), RegionOp, RegionResult
-
-### Community 177 - "zarrUrl.ts"
-Cohesion: 0.48
-Nodes (5): buildZarrUrl(), describeUnavailable(), zarrRootFor(), ZarrUnavailable, ZarrUrlResult
-
-### Community 178 - "ipredStore.ts"
-Cohesion: 0.29
-Nodes (6): DEFAULT_COMPOSITION_ID, DEFAULT_TRAINER_CONFIG, DEFAULT_TRAINER_ID, IpredState, TrainerConfig, useIpredStore
-
-### Community 179 - "layerVisibilityStore.ts"
-Cohesion: 0.29
-Nodes (5): DEFAULT_GROUPS, LAYER_GROUP_META, LayerGroupId, LayerVisibilityState, useLayerVisibilityStore
-
-### Community 180 - "OME-Zarr Viewer"
-Cohesion: 0.29
-Nodes (6): Notes, OME-Zarr Viewer, Point at a local dataset, Quick start, Requirements, Use in another project
-
-### Community 182 - "include"
-Cohesion: 0.29
-Nodes (6): extends, include, src, bench, test, ../../tsconfig.base.json
-
-### Community 185 - "_denoised_slice"
-Cohesion: 0.40
-Nodes (6): _centre_crop(), _denoised_slice(), Any, ndarray, Centred square crop of *size*, or *arr* unchanged if it already fits., Read *slice_index* and denoise it, pulling z-neighbours for 3-D methods. The…
-
-### Community 186 - "2. Browse & select"
-Cohesion: 0.33
-Nodes (6): 2. Browse & select, Local: the flat file list, Opening a sample, The detail panel, The Samples column, Tiled: the metadata browser
-
-### Community 187 - "1. Connect to data"
-Cohesion: 0.33
-Nodes (6): 1. Connect to data, Connect to Tiled, Ingesting data (Tiled only), Load / Ingest Datasets, Local Folder mode, Tiled Server mode
-
-### Community 188 - "fetch-sam-model.mjs"
-Cohesion: 0.33
-Nodes (4): HERE, OUT, python, res
-
-### Community 189 - "DenoiseBakeModal.tsx"
-Cohesion: 0.47
-Nodes (5): defaultTarget(), DenoiseBakeModal(), DenoiseBakeModalProps, JobState, readError()
-
-### Community 190 - "DownloadModal.tsx"
-Cohesion: 0.40
-Nodes (5): DownloadModal(), DownloadModalProps, parseSourceKey(), Scope, SCOPE_OPTIONS
-
-### Community 191 - "InsightsModal.tsx"
-Cohesion: 0.40
-Nodes (4): FLAG_LABEL, formatArea(), InsightsModal(), InsightsModalProps
-
-### Community 192 - "MeasurementPanel/index.tsx"
-Cohesion: 0.40
-Nodes (4): fmt(), IntensityStats, MeasurementPanel(), MeasurementPanelProps
-
-### Community 193 - "VersionHistoryModal.tsx"
-Cohesion: 0.47
-Nodes (4): formatDate(), Props, thumbnailUrl(), VersionHistoryModal()
-
-### Community 194 - "useFeatureManifold.ts"
-Cohesion: 0.40
-Nodes (5): DEFAULT_MANIFOLD_PARAMS, ManifoldParams, readErrorDetail(), useFeatureManifold(), UseFeatureManifoldArgs
-
-### Community 195 - "classColors.ts"
-Cohesion: 0.67
-Nodes (4): COLORBLIND_COLORS, DEFAULT_COLORS, getClassPalette(), pickNextColor()
-
-### Community 196 - "lib/colormaps.ts"
-Cohesion: 0.33
-Nodes (3): COLORMAP_NAMES, ColormapName, STOPS
-
-### Community 197 - "featureManifold.ts"
-Cohesion: 0.53
-Nodes (4): colorizeManifoldHeatmap(), loadManifoldHeatmapCanvas(), manifoldMarkerRect(), ManifoldPoint
-
-### Community 201 - "test_manifold.py"
-Cohesion: 0.40
-Nodes (5): catalog(), fixture, MonkeyPatch, Manifold Suggest Labels on persisted ipred feature banks., test_manifold_sample_on_feature_bank()
-
-### Community 202 - "test_train_rethreshold.py"
-Cohesion: 0.40
-Nodes (5): catalog(), fixture, MonkeyPatch, Train → infer → rethreshold without recompute., test_train_infer_rethreshold()
-
-### Community 204 - "Keyboard shortcuts"
-Cohesion: 0.40
-Nodes (5): Canvas & navigation, Editing, Keyboard shortcuts, Mouse modifiers (Magic / Fill / SAM), Tools
-
-### Community 206 - "PerfOverlay/index.tsx"
-Cohesion: 0.50
-Nodes (3): PerfOverlayProps, Row(), severity()
-
-### Community 207 - "PixelClassifierPanel/index.tsx"
-Cohesion: 0.50
-Nodes (3): coverageHeadline(), PixelClassifierPanel(), PixelClassifierPanelProps
-
-### Community 208 - "SaveModal.tsx"
-Cohesion: 0.60
-Nodes (4): blobToBase64(), fetchPreviewBlob(), SaveModal(), SaveModalProps
-
-### Community 209 - "CompositionPanel/index.tsx"
-Cohesion: 0.70
-Nodes (3): CompositionPanel(), defaultParams(), newNodeId()
-
-### Community 210 - "useFeatureChannels.ts"
-Cohesion: 0.40
-Nodes (3): FeatureChannelInfo, FeatureJobInfo, UseFeatureChannelsArgs
-
-### Community 211 - "measure.ts"
-Cohesion: 0.60
-Nodes (4): measureRegion(), RegionMeasurement, ringPerimeter(), shapePerimeter()
-
-### Community 212 - "buildSourceKey"
-Cohesion: 0.80
-Nodes (3): buildSourceKey(), isAnnotatedPath(), key()
-
-### Community 215 - "start.sh"
-Cohesion: 0.40
-Nodes (3): HOST, start.sh script, TILED_ALLOW_ORIGINS
-
-### Community 216 - "reingest_tiffs_as_individual.py"
-Cohesion: 0.67
-Nodes (3): compute_structure_id(), main(), Re-ingest stacked TIFF array as 690 individual sample nodes. The 690 TIFFs were…
-
-### Community 219 - "FeatureChannelsPanel/index.tsx"
-Cohesion: 0.40
-Nodes (3): FeatureChannelsPanelProps, PRESET_HINTS, PRESET_LABELS
-
-### Community 221 - "VersionPreviewBar.tsx"
-Cohesion: 0.67
-Nodes (3): formatDate(), Props, VersionPreviewBar()
-
-### Community 223 - "VolumeViewer.tsx"
-Cohesion: 0.67
-Nodes (3): VolumeViewer(), VolumeViewerProps, webGpuAvailability()
-
-### Community 226 - "useMaskOps.ts"
-Cohesion: 0.67
-Nodes (3): CleanupOp, polysToShapes(), useMaskOps()
-
-### Community 227 - "useOpenInAnnotate.ts"
-Cohesion: 0.83
-Nodes (3): applyDraft(), classesFromKeywords(), useOpenInAnnotate()
-
-### Community 229 - "adjust.ts"
-Cohesion: 0.67
-Nodes (3): PreprocessOpts, renderAdjusted(), renderPreprocessOnly()
-
-### Community 231 - "classStore.ts"
-Cohesion: 0.50
-Nodes (3): AnnotationClass, ClassStore, useClassStore
-
-### Community 232 - "ratingStore.ts"
-Cohesion: 0.50
-Nodes (3): RatingState, StarRating, useRatingStore
-
-### Community 233 - "referenceGuideStore.ts"
-Cohesion: 0.50
-Nodes (3): GuideClass, ReferenceGuideStore, useReferenceGuideStore
-
-### Community 234 - "settingsStore.ts"
-Cohesion: 0.67
-Nodes (3): genId(), SettingsStore, useSettingsStore
-
-### Community 237 - "ipred/models"
-Cohesion: 0.50
-Nodes (3): ipred/models, SlimSAM (ONNX), TomoJEPA (Mark25 / Mark11)
-
-### Community 239 - "test_tomojepa_onnx.py"
-Cohesion: 0.50
-Nodes (3): TomoJEPA ONNX encode smoke (skips if onnx file missing)., test_onnx_encode_dense_shape(), needs_onnx
-
-### Community 240 - "ExportConflict"
-Cohesion: 0.67
-Nodes (3): ExportConflict, Exception, Raised when a merge would corrupt category ids or names.
-
-### Community 243 - "Using the tool"
-Cohesion: 0.67
-Nodes (3): Recommended reading order, The Finch shell, Using the tool
-
-### Community 244 - "4. Annotation guide"
-Cohesion: 0.67
-Nodes (3): 4. Annotation guide, How it connects to Annotate, What you can do here
-
-## Knowledge Gaps
-- **875 isolated node(s):** `sam3-annotation-studio-backend`, `name`, `private`, `version`, `type` (+870 more)
-  These have ≤1 connection - possible missing edges or undocumented components.
-- **64 thin communities (<3 nodes) omitted from report** — run `graphify query` to explore isolated nodes.
-
-## Suggested Questions
-_Questions this graph is uniquely positioned to answer:_
-
-- **Why does `uuid` connect `catalog.py` to `compositions.py`, `dependencies`, `manifold.py`, `train_infer.py`, `export_jobs.py`, `feature_setups.py`, `ingest.py`?**
-  _High betweenness centrality (0.025) - this node is a cross-community bridge._
-- **Why does `Vec3` connect `Vec3` to `camera.ts`, `ome-zarr-viewer.ts`, `Aabb`, `math/index.ts`, `Random`, `io/index.ts`, `Quat`, `math/color.ts`, `OrbitControls`, `Vec3Like`, `Mat4`, `packing.ts`, `VectorGrid3`, `Mat3`?**
-  _High betweenness centrality (0.017) - this node is a cross-community bridge._
-- **Why does `Catalog` connect `Catalog` to `compositions.py`, `test_compositions.py`, `manifold.py`, `api.py`, `train_infer.py`, `test_manifold.py`, `test_train_rethreshold.py`, `test_preprocess_cache.py`, `test_catalog_sessions.py`, `feature_setups.py`, `catalog.py`?**
-  _High betweenness centrality (0.016) - this node is a cross-community bridge._
-- **Are the 57 inferred relationships involving `run()` (e.g. with `.dispose()` and `.getState()`) actually correct?**
-  _`run()` has 57 INFERRED edges - model-reasoned connections that need verification._
-- **Are the 39 inferred relationships involving `Catalog` (e.g. with `_resolve_session()` and `ensure_default_compositions()`) actually correct?**
-  _`Catalog` has 39 INFERRED edges - model-reasoned connections that need verification._
-- **Are the 61 inferred relationships involving `HTTPException` (e.g. with `browse_column()` and `browse_items()`) actually correct?**
-  _`HTTPException` has 61 INFERRED edges - model-reasoned connections that need verification._
-- **What connects `sam3-annotation-studio-backend`, `name`, `private` to the rest of the system?**
-  _875 weakly-connected nodes found - possible documentation gaps or missing edges._
\ No newline at end of file
diff --git a/graphify-out/2026-08-26/graph.json b/graphify-out/2026-08-26/graph.json
deleted file mode 100644
index 3471dd1..0000000
--- a/graphify-out/2026-08-26/graph.json
+++ /dev/null
@@ -1,159538 +0,0 @@
-{
-  "directed": false,
-  "multigraph": false,
-  "graph": {},
-  "nodes": [
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_taau_temporalaccumulator",
-      "label": "TemporalAccumulator",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "temporalaccumulator",
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-      "source_location": "L111"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_compiledgraph",
-      "label": "CompiledGraph",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
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-      "community_name": "TemporalAccumulator",
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-      "norm_label": "compiledgraph",
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-      "source_location": "L58"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_passcontext",
-      "label": "PassContext",
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-      "norm_label": "passcontext",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L33"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_passdesc",
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-      "_callable_class": true,
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-      "community_name": "TemporalAccumulator",
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-      "source_location": "L42"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_rendergraph",
-      "label": "RenderGraph",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "rendergraph",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L93"
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-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_rendergraphtexturedesc",
-      "label": "RenderGraphTextureDesc",
-      "_callable": true,
-      "_callable_class": true,
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-      "community": 10,
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-      "file_type": "code",
-      "norm_label": "rendergraphtexturedesc",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L23"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_resourcehandle",
-      "label": "ResourceHandle",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_resourcelifetime",
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-      "community_name": "codecs.ts",
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-      "source_location": "L18"
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-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_zarr_codecs_codec",
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-      "community_name": "codecs.ts",
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-    {
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-      "community": 103,
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-      "_callable_class": true,
-      "_origin": "ast",
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-      "community_name": "datasetStats.ts",
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-      "source_location": "L16"
-    },
-    {
-      "id": "frontend_src_lib_datasetstats_coverage",
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-      "_callable_class": true,
-      "_origin": "ast",
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-      "community_name": "datasetStats.ts",
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-      "source_location": "L25"
-    },
-    {
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-      "_callable_class": true,
-      "_origin": "ast",
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-      "community_name": "datasetStats.ts",
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-    {
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-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 105,
-      "community_name": "datasetStats.ts",
-      "file_type": "code",
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-      "label": "QAFlagKind",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 105,
-      "community_name": "datasetStats.ts",
-      "file_type": "code",
-      "norm_label": "qaflagkind",
-      "source_file": "frontend/src/lib/datasetStats.ts",
-      "source_location": "L33"
-    },
-    {
-      "id": "frontend_src_lib_datasetstats_samplestats",
-      "label": "SampleStats",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 105,
-      "community_name": "datasetStats.ts",
-      "file_type": "code",
-      "norm_label": "samplestats",
-      "source_file": "frontend/src/lib/datasetStats.ts",
-      "source_location": "L45"
-    },
-    {
-      "id": "frontend_src_lib_datasetstats_slicesmap",
-      "label": "SlicesMap",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 105,
-      "community_name": "datasetStats.ts",
-      "file_type": "code",
-      "norm_label": "slicesmap",
-      "source_file": "frontend/src/lib/datasetStats.ts",
-      "source_location": "L56"
-    },
-    {
-      "id": "frontend_src_lib_sam_samclient_granularity",
-      "label": "Granularity",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 106,
-      "community_name": "SamClient",
-      "file_type": "code",
-      "norm_label": "granularity",
-      "source_file": "frontend/src/lib/sam/samClient.ts",
-      "source_location": "L11"
-    },
-    {
-      "id": "frontend_src_lib_sam_samclient_pending",
-      "label": "Pending",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_scene_scene_query",
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-    },
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_compare",
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_constructor",
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_sub",
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_to",
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-    },
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_tostring",
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-      "source_location": "L93"
-    },
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_assertratio",
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-      "source_location": "L67"
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_constructor",
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diff --git a/graphify-out/2026-08-26/manifest.json b/graphify-out/2026-08-26/manifest.json
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\ No newline at end of file
diff --git a/graphify-out/GRAPH_REPORT.md b/graphify-out/GRAPH_REPORT.md
deleted file mode 100644
index 4262ba6..0000000
--- a/graphify-out/GRAPH_REPORT.md
+++ /dev/null
@@ -1,1166 +0,0 @@
-# Graph Report - forge_Segementation_Finch  (2026-08-26)
-
-## Corpus Check
-- 438 files · ~371,790 words
-- Verdict: corpus is large enough that graph structure adds value.
-
-## Summary
-- 4944 nodes · 8864 edges · 295 communities (224 shown, 71 thin omitted)
-- Extraction: 97% EXTRACTED · 3% INFERRED · 0% AMBIGUOUS · INFERRED: 294 edges (avg confidence: 0.85)
-- Token cost: 0 input · 0 output
-
-## Graph Freshness
-- Built from commit: `e8600297`
-- Run `git rev-parse HEAD` and compare to check if the graph is stale.
-- Run `graphify update .` after code changes (no API cost).
-
-## Community Hubs (Navigation)
-- catalog.ts
-- scrape-sciviscolor.py
-- ManagedBuffer
-- thresholdFit.test.ts
-- ipred_routes.py
-- annotation_thumbnails.py
-- run
-- manifold.py
-- HTTPException
-- math/index.ts
-- TemporalAccumulator
-- annotation_server.py
-- ipred_client.py
-- coco_export.py
-- Vec3
-- tomojepa_onnx.py
-- ColumnBrowser.tsx
-- Quat
-- math/color.ts
-- devDependencies
-- render/index.ts
-- accel.spec.ts
-- compilerOptions
-- compilerOptions
-- dimension.ts
-- test_ingest_conflict.py
-- App.tsx
-- src/index.ts
-- catalog.py
-- write_stack
-- ingest.py
-- test_zarr_source.py
-- Catalog
-- TiledNotifications/index.ts
-- camera.ts
-- preprocess.py
-- compositions.py
-- Vec4
-- step_phantom
-- tiff_stack_source.py
-- ipred/__init__.py
-- train_infer.py
-- volume/colormaps.ts
-- Node
-- Quantity
-- Matrix
-- ipred — architecture and operations guide
-- AnnotationCanvas/index.tsx
-- io/index.ts
-- ipredApi.ts
-- OrbitControls
-- Vec2
-- cli.py
-- array.ts
-- packing.ts
-- scalar.ts
-- feature_setups.py
-- arrays.py
-- IngestDropzone.tsx
-- VectorGrid3
-- core/index.ts
-- getTiledBaseUrl
-- constants.ts
-- compilerOptions
-- browse_helpers.py
-- provenance.ts
-- schemas.py
-- FakeNode
-- zarr_source.py
-- dependencies
-- ome-zarr.ts
-- start_all.sh
-- drafts.py
-- tiled_clients.py
-- App.jsx
-- viewerState.ts
-- devDependencies
-- get_tiled_client
-- useTabsStore.ts
-- zarr-viewer/package.json
-- volume-renderer.ts
-- features.py
-- TrainerPlugin
-- sam_embed.py
-- start_all.ps1
-- Installation via Docker (Recommended)
-- parse.ts
-- TTLCache
-- magicwand.ts
-- Segmentation Annotation Studio
-- tiled_mask_sync.py
-- denoise.py
-- Software architecture
-- annotationStore.ts
-- _fake_pyramid
-- ManagedTexture
-- FastAPI
-- fake_stack
-- geometry.ts
-- length-units.ts
-- codecs.ts
-- .alignTo
-- Vec3Like
-- denoise_bake.py
-- 3. Annotate
-- datasetStats.ts
-- SamClient
-- samWorker.ts
-- TomoJepaModule
-- fx/package.json
-- predicates.ts
-- test_tomojepa_embed.py
-- export_jobs.py
-- polybool.ts
-- perf.ts
-- compilerOptions
-- list_module_catalog
-- mergeSameClass.ts
-- rasterizeShapes
-- morphology.ts
-- livewire.ts
-- run_composition
-- 5. Export & download
-- Mat3
-- test_tiled_config.py
-- frontend/package.json
-- TestCascade
-- systems.ts
-- OmeZarrVolumeSource
-- ViewportOverlay
-- TestBuildLevel
-- test_compositions.py
-- test_local_fs.py
-- editHistory.ts
-- pixelClf.ts
-- test_feature_setups.py
-- test_tiff_stack_source.py
-- Random
-- array_source.py
-- AGENTS.md — Segmentation Annotation Studio
-- guides.py
-- test_drafts.py
-- Installation
-- Quick start
-- Emitter
-- test_catalog_sessions.py
-- import_dataset
-- describe_methods
-- clahe.ts
-- TestBlockMean
-- bboxPrefilter.equivalence.test.ts
-- Pool
-- Dual
-- test_array_blobs.py
-- test_preprocess_cache.py
-- TestMultiscalesMetadata
-- TestPyramidPlan
-- usePixelClassifier.ts
-- datasetStore.ts
-- toolStore.ts
-- vite-zarr-static.ts
-- test_api_health.py
-- main
-- test_health.py
-- TestPyramidStore
-- Segmentation Annotation Studio
-- Troubleshooting
-- ClassManager/index.tsx
-- Toolbar/index.tsx
-- ZarrLoader.tsx
-- BuildVolumePanel.tsx
-- config.ts
-- useGuideSync.ts
-- useSave.ts
-- regionOps.ts
-- zarrUrl.ts
-- ipredStore.ts
-- layerVisibilityStore.ts
-- OME-Zarr Viewer
-- .preview_labels
-- include
-- Pool
-- Soa
-- _denoised_slice
-- 2. Browse & select
-- 1. Connect to data
-- fetch-sam-model.mjs
-- DenoiseBakeModal.tsx
-- DownloadModal.tsx
-- InsightsModal.tsx
-- MeasurementPanel/index.tsx
-- VersionHistoryModal.tsx
-- useFeatureManifold.ts
-- classColors.ts
-- lib/colormaps.ts
-- featureManifold.ts
-- stretch.test.ts
-- .preview_labels
-- .preview_labels
-- test_manifold.py
-- test_train_rethreshold.py
-- .preview_labels
-- Keyboard shortcuts
-- LayersPanel/index.tsx
-- PerfOverlay/index.tsx
-- PixelClassifierPanel/index.tsx
-- SaveModal.tsx
-- CompositionPanel/index.tsx
-- useFeatureChannels.ts
-- measure.ts
-- buildSourceKey
-- Mat4
-- start.sh
-- reingest_tiffs_as_individual.py
-- DenoisePanel/index.tsx
-- DisplayControls/index.tsx
-- FeatureChannelsPanel/index.tsx
-- HistogramControl/index.tsx
-- VersionPreviewBar.tsx
-- HubMainContent.tsx
-- VolumeViewer.tsx
-- useDraftSync.ts
-- useExportJob.ts
-- useMaskOps.ts
-- useOpenInAnnotate.ts
-- feedbackContext.ts
-- adjust.ts
-- sharpen.test.ts
-- classStore.ts
-- ratingStore.ts
-- referenceGuideStore.ts
-- settingsStore.ts
-- fx.spec.ts
-- ipred/models
-- test_emb_pca_channels.py
-- test_tomojepa_onnx.py
-- ExportConflict
-- add_sample_metadata.py
-- repair_catalog_paths.py
-- Using the tool
-- 4. Annotation guide
-- ExportPage.tsx
-- MaskToolsPanel/index.tsx
-- DebouncedSlider.tsx
-- CustomizePages.tsx
-- HubAppLayout.tsx
-- HubHeader.tsx
-- HubSidebar.tsx
-- IframeModal.tsx
-- useAnnotatedSourceKeys.ts
-- useKeybinds.ts
-- viridis.ts
-- clipboardStore.ts
-- connectionStore.ts
-- annotationFilter.ts
-- vite-env.d.ts
-- frontend/tsconfig.json
-- patches/README.md
-- Button.tsx
-- ItkVtkFrame.tsx
-- Bundled Tiled catalog (committed to git)
-- @phosphor-icons/react
-- zundo
-- @testing-library/react
-- @types/node
-- @types/react
-- vite
-- @vitejs/plugin-react
-- vitest
-- navigationRouterTypes.ts
-- server.ts
-- vitest.config.ts
-- vite.config.js
-- scripts/__init__.py
-- ipred
-- sam3-annotation-studio-backend
-
-## God Nodes (most connected - your core abstractions)
-1. `Vec3` - 99 edges
-2. `run()` - 98 edges
-3. `Catalog` - 69 edges
-4. `get()` - 58 edges
-5. `Mat4` - 54 edges
-6. `VolumeRenderer` - 51 edges
-7. `Quat` - 46 edges
-8. `Vec2` - 35 edges
-9. `OrbitControls` - 33 edges
-10. `get_tiled_client()` - 29 edges
-
-## Surprising Connections (you probably didn't know these)
-- `health()` --references--> `get()`  [EXTRACTED]
-  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
-- `get_servers()` --references--> `get()`  [EXTRACTED]
-  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
-- `browse_facets()` --references--> `get()`  [EXTRACTED]
-  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
-- `browse_column()` --references--> `get()`  [EXTRACTED]
-  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
-- `browse_items()` --references--> `get()`  [EXTRACTED]
-  backend/annotation_server.py → frontend/vendor/view_tomography_recon_app/scripts/scrape-sciviscolor.py
-
-## Import Cycles
-- 3-file cycle: `frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat4.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts`
-- 4-file cycle: `frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat4.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/quat.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec3.ts -> frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/mat3.ts`
-
-## Communities (295 total, 71 thin omitted)
-
-### Community 0 - "catalog.ts"
-Cohesion: 0.02
-Nodes (116): acre, ALL_UNITS, ampere, arcminute, arcsecond, astronomicalUnit, atmosphere, atomicMassUnit (+108 more)
-
-### Community 1 - "scrape-sciviscolor.py"
-Cohesion: 0.60
-Nodes (4): build_catalog(), main(), parse_points(), basename -> /media/... path, scraped from the category + linear-hue pages.
-
-### Community 2 - "ManagedBuffer"
-Cohesion: 0.10
-Nodes (4): OccupancyGrid, u32params(), TileCompactor, ManagedBuffer
-
-### Community 3 - "thresholdFit.test.ts"
-Cohesion: 0.07
-Nodes (35): applyGaussianBlurGray(), applyGaussianBlurRgba(), boxBlurH(), boxBlurV(), boxRadiiForGaussian(), baseToDisplay(), DisplayAffine, displayAffineFor() (+27 more)
-
-### Community 4 - "ipred_routes.py"
-Cohesion: 0.05
-Nodes (69): ipred_feature_channel(), ipred_get_composition(), ipred_get_setup(), ipred_health(), _ipred_http_error(), ipred_infer(), ipred_list_compositions(), ipred_list_modules() (+61 more)
-
-### Community 5 - "annotation_thumbnails.py"
-Cohesion: 0.05
-Nodes (58): decode_thumbnail_base64(), _draw_shapes(), _hex_to_rgba(), _parse_source_key(), Any, ndarray, Annotated thumbnail generation for version history previews. Renders the most-…, Render a PNG thumbnail of the most-annotated slice with shapes overlaid. Args:… (+50 more)
-
-### Community 6 - "run"
-Cohesion: 0.05
-Nodes (31): createDemoHud(), createDemoSession(), DemoHandle, resetDemoStage(), resizeDemoCanvas(), ensureHudStyles(), boot(), stageEl (+23 more)
-
-### Community 7 - "manifold.py"
-Cohesion: 0.05
-Nodes (52): Tiny TTL cache for short-lived ipred artifacts (manifold heatmaps)., Thread-safe TTL + max-entries cache., TTLCache, _brush_mask(), build_label_map(), _ellipse_mask(), _polygon_mask(), Any (+44 more)
-
-### Community 8 - "HTTPException"
-Cohesion: 0.04
-Nodes (99): browse_slices(), browse_thumbnail(), connect_summary(), denoise_auto(), export_download(), export_status(), get_draft(), get_guide() (+91 more)
-
-### Community 9 - "math/index.ts"
-Cohesion: 0.05
-Nodes (66): derivative(), dualCos(), dualExp(), dualLog(), dualPow(), dualSin(), dualSqrt(), dualTan() (+58 more)
-
-### Community 10 - "TemporalAccumulator"
-Cohesion: 0.05
-Nodes (17): GpuTimer, halton(), TemporalAccumulator, GpuContext, CompiledGraph, descSignature(), PassContext, PassDesc (+9 more)
-
-### Community 11 - "annotation_server.py"
-Cohesion: 0.04
-Nodes (74): browse_column(), browse_facets(), browse_items(), denoise_bake(), denoise_methods(), export_cancel(), generate_guide_route(), get_servers() (+66 more)
-
-### Community 12 - "ipred_client.py"
-Cohesion: 0.07
-Nodes (53): _client(), feature_channel_bytes(), get_composition(), get_setup(), health(), infer(), ipred_url(), list_compositions() (+45 more)
-
-### Community 13 - "coco_export.py"
-Cohesion: 0.05
-Nodes (78): export_coco(), Path, Background worker: render+rasterize all sources, write the dataset tree (images…, Write the COCO dataset for one or more annotated samples. Supports two modes: -…, _run_export_job(), _apply_erased(), _brush_mask(), build_export_plan() (+70 more)
-
-### Community 14 - "Vec3"
-Cohesion: 0.05
-Nodes (4): Plane, Ray, Sphere, Vec3
-
-### Community 15 - "tomojepa_onnx.py"
-Cohesion: 0.06
-Nodes (48): main(), build_encoder(), _cached_model(), encode_dense_embeddings(), encoder_available(), _gray_unit_interval(), load_checkpoint_state(), _pad_to_multiple() (+40 more)
-
-### Community 16 - "ColumnBrowser.tsx"
-Cohesion: 0.07
-Nodes (37): BrowseColumn(), BrowseColumnProps, BrowseDetailPanel(), BrowseDetailPanelProps, displayKey(), formatValue(), SECTION_ORDER, ColumnBrowser() (+29 more)
-
-### Community 17 - "Quat"
-Cohesion: 0.09
-Nodes (3): q(), DualQuat, Quat
-
-### Community 18 - "math/color.ts"
-Cohesion: 0.11
-Nodes (22): clamp01(), clampColor3(), clampColor4(), color3ToVec3(), color4ToVec4(), colorToCss(), isColor3Tuple(), isColor4Tuple() (+14 more)
-
-### Community 19 - "devDependencies"
-Cohesion: 0.04
-Nodes (48): @blueskyproject/tiled, eslint, @eslint/js, eslint-plugin-react, eslint-plugin-react-hooks, eslint-plugin-react-refresh, dependencies, @blueskyproject/tiled (+40 more)
-
-### Community 20 - "render/index.ts"
-Cohesion: 0.12
-Nodes (25): Color3, createContext(), DeviceOptions, keyDirectionalFromLights(), lightBufferByteSize(), LightingEnvironment, packLightsStd430(), DEFAULT_POINT_RANGE (+17 more)
-
-### Community 21 - "accel.spec.ts"
-Cohesion: 0.06
-Nodes (43): clamp01(), FLIP_CONFIGS, FlipConfig, flipConfigToLut(), convSep(), FLIP_PPD, FlipResult, gaussianKernel() (+35 more)
-
-### Community 22 - "compilerOptions"
-Cohesion: 0.05
-Nodes (45): compilerOptions, allowImportingTsExtensions, isolatedModules, jsx, lib, module, moduleDetection, moduleResolution (+37 more)
-
-### Community 23 - "compilerOptions"
-Cohesion: 0.05
-Nodes (45): compilerOptions, baseUrl, isolatedModules, lib, module, moduleResolution, noEmit, noUnusedLocals (+37 more)
-
-### Community 24 - "dimension.ts"
-Cohesion: 0.04
-Nodes (44): ABSORBED_DOSE, ACCELERATION, ACTION, AMOUNT, ANGLE, ANGULAR_MOMENTUM, ANGULAR_VELOCITY, AREA (+36 more)
-
-### Community 25 - "test_ingest_conflict.py"
-Cohesion: 0.06
-Nodes (28): fake_client(), FakeArray, FakeNode, _npy(), Exception, fixture, parametrize, Path (+20 more)
-
-### Community 26 - "App.tsx"
-Cohesion: 0.06
-Nodes (23): allRoutes, AnnotatePage, App(), BrowsePage, ConnectPage, DEFAULT_PATHS, ReferencePage, VolumePage (+15 more)
-
-### Community 27 - "src/index.ts"
-Cohesion: 0.09
-Nodes (24): bloom(), depthOfField(), fxaa(), OP_INDEX, outline(), sharpen(), ssao(), taa() (+16 more)
-
-### Community 28 - "catalog.py"
-Cohesion: 0.17
-Nodes (18): SQLite catalog for projects, sessions, feature banks, models, and runs., catalog_db_path(), engine_root(), local_data_root(), project_blob_dir(), Path, Filesystem roots for ipred under LOCAL_DATA_ROOT., Return resolved ``LOCAL_DATA_ROOT`` (default ``~/data``). (+10 more)
-
-### Community 29 - "write_stack"
-Cohesion: 0.24
-Nodes (4): Path, A directory of zero-padded 2-D TIFFs whose value encodes the slice index., TestInspect, write_stack()
-
-### Community 30 - "ingest.py"
-Cohesion: 0.09
-Nodes (36): _bump(), _child_keys(), _classify_error(), _ensure_container(), get_job(), _image_number(), new_job(), _pad_width() (+28 more)
-
-### Community 31 - "test_zarr_source.py"
-Cohesion: 0.09
-Nodes (16): _fake_pyramid(), _FakeArray, _FakeContainer, _make_multiscale(), dict, fixture, ndarray, Path (+8 more)
-
-### Community 32 - "Catalog"
-Cohesion: 0.11
-Nodes (18): Connection, Catalog, ProjectRow, Any, Path, Create or return the project for this source identity., Ensure project exists and open a new session on it., Update session current_* pointers (only provided fields). (+10 more)
-
-### Community 33 - "TiledNotifications/index.ts"
-Cohesion: 0.14
-Nodes (30): buildTiledStreamUrl(), buildZarrUrlForChild(), ensureNotificationPermission(), isSupported(), OsNotificationOptions, showOsNotification(), EMPTY_PATHS, NodeStream() (+22 more)
-
-### Community 34 - "camera.ts"
-Cohesion: 0.09
-Nodes (9): Controller, FirstPersonControls, FlyControls, OrbitMode, OrbitState, IMPORTANT: `eye` may be `this._eye` — never overwrite it before reading world…, AxisBinding, Binding (+1 more)
-
-### Community 35 - "preprocess.py"
-Cohesion: 0.11
-Nodes (25): arrays_dir(), load_array_blob(), Any, ndarray, Path, Content-addressed array blobs for remote compute (data ≠ compute host)., Blob directory for uploaded arrays., Persist ``arr`` and return ``{array_ref, shape, dtype, ...}``. (+17 more)
-
-### Community 36 - "compositions.py"
-Cohesion: 0.13
-Nodes (29): compositions_root(), content_hash(), _dir(), ensure_default_compositions(), list_compositions(), load_composition(), preview_concat_labels(), Any (+21 more)
-
-### Community 38 - "step_phantom"
-Cohesion: 0.08
-Nodes (11): ndarray, parametrize, Tests for the classical denoise filters. Ported alongside :mod:`denoise`…, A hard vertical edge plus additive Gaussian noise. The standard shape for…, snr_db(), step_phantom(), TestAutoStrength, TestCapabilities (+3 more)
-
-### Community 39 - "tiff_stack_source.py"
-Cohesion: 0.11
-Nodes (35): block_mean(), _build_level(), _cast_like(), downsample_array(), inspect_tiff_stack(), multiscales_metadata(), preflight_tiff_stack(), Any (+27 more)
-
-### Community 40 - "ipred/__init__.py"
-Cohesion: 0.11
-Nodes (25): Execute a composition document on a raw image array., ipred — generic iterative prediction backend., ChannelBlock, ModuleContext, ModuleMeta, Feature module protocol — typed channel producers for compositions., Catalog entry for a feature module., One module's contribution to the feature bank. (+17 more)
-
-### Community 41 - "train_infer.py"
-Cohesion: 0.10
-Nodes (36): cal_scores_from_json(), conformal_quantile(), counts_from_status(), maps_from_proba(), mondrian_thresholds(), Any, ndarray, Mondrian split-conformal thresholds and maps from cached proba. (+28 more)
-
-### Community 42 - "volume/colormaps.ts"
-Cohesion: 0.11
-Nodes (24): WebGpuRenderingState, BuiltinColorMapName, ColorMapFn, ColorMapName, colorMapNames(), lerp3(), MAPS, Rgb (+16 more)
-
-### Community 43 - "Node"
-Cohesion: 0.11
-Nodes (18): CameraComponent, Component, ComponentBase, ComponentKind, LightComponent, MeshComponent, PhysicsBodyComponent, VolumeComponent (+10 more)
-
-### Community 44 - "Quantity"
-Cohesion: 0.10
-Nodes (3): Dimension, Quantity, Unit
-
-### Community 46 - "ipred — architecture and operations guide"
-Cohesion: 0.06
-Nodes (32): 10. Manifold / Suggest Labels, 11. Frontend map, 12. Package map (`ipred/src/ipred/`), 13. Mental model (one paragraph), 14. Quick ops, 1. Big picture, 2. Where compute lives, 3. How the UI reaches compute (proxy boundary) (+24 more)
-
-### Community 47 - "AnnotationCanvas/index.tsx"
-Cohesion: 0.11
-Nodes (29): AnnotationCanvas(), AnnotationCanvasProps, buildRingsPath(), distToSegment(), EMPTY_CLASS_COLOR_BY_ID, EMPTY_MANIFOLD_MARKERS, EMPTY_SHAPES, hexToRgb() (+21 more)
-
-### Community 48 - "io/index.ts"
-Cohesion: 0.10
-Nodes (28): units, aabbRaySlab(), DensityField, extractIsosurface(), ExtractIsosurfaceOptions, inside(), loadDensityField(), LoadDensityFieldOptions (+20 more)
-
-### Community 49 - "ipredApi.ts"
-Cohesion: 0.10
-Nodes (26): CompositionDoc, CompositionNode, FeatureModuleInfo, FeatureSetup, getIpredComposition(), getIpredSetup(), IpredFeatureImportance, ipredHealth() (+18 more)
-
-### Community 52 - "cli.py"
-Cohesion: 0.10
-Nodes (24): ArgumentParser, _add_project_sugar(), _catalog(), main(), _print(), Any, CLI entrypoint ``ipred`` with --project session sugar., Return session_id from --session or --project sugar. (+16 more)
-
-### Community 53 - "array.ts"
-Cohesion: 0.13
-Nodes (13): chunkBytes(), DecodedChunkCache, dtypeByteSize(), VolumeChunk, chunkKey(), cropCOrderChunk(), fillBuffer(), OpenZarrArrayOptions (+5 more)
-
-### Community 54 - "packing.ts"
-Cohesion: 0.10
-Nodes (23): Euler, scratchMat, compact1By2(), decodeOctNormal(), demorton3D(), encodeOctNormal(), _f32, floatToHalf() (+15 more)
-
-### Community 55 - "scalar.ts"
-Cohesion: 0.10
-Nodes (19): bitReverse(), fft(), fftReal(), ifft(), DEG2RAD, deltaAngle(), HALF_PI, inverseLerp() (+11 more)
-
-### Community 56 - "feature_setups.py"
-Cohesion: 0.16
-Nodes (28): content_hash(), _dir(), ensure_default_setups(), _ensure_procedure_with_param_backfill(), _ensure_tomojepa_variant(), list_setups(), load_setup(), Any (+20 more)
-
-### Community 57 - "arrays.py"
-Cohesion: 0.14
-Nodes (27): array_shape_meta(), _descend_to_stack(), _is_container_node(), _level_array(), multiscale_levels(), node_keywords(), Any, ndarray (+19 more)
-
-### Community 58 - "IngestDropzone.tsx"
-Cohesion: 0.12
-Nodes (23): ConflictDialog(), ConflictDialogProps, IngestConflict, collectFromDrop(), fileStem(), IngestDropzone(), IngestDropzoneProps, isSupported() (+15 more)
-
-### Community 59 - "VectorGrid3"
-Cohesion: 0.12
-Nodes (5): Grid3Base, Grid3Spec, resolveSpacing(), ScalarGrid3, VectorGrid3
-
-### Community 60 - "core/index.ts"
-Cohesion: 0.04
-Nodes (51): RFC-4122, assert(), assertDefined(), AssertionError, assertNever(), atomicMass(), atomicNumber(), BY_SYMBOL (+43 more)
-
-### Community 61 - "getTiledBaseUrl"
-Cohesion: 0.18
-Nodes (21): Header(), getValidAccessToken(), installTiledFetchInterceptor(), nativeFetch, refreshAccessToken(), IMPORTANT: this must be imported before `<App>` (and therefore before the…, Window, getActiveServer() (+13 more)
-
-### Community 62 - "constants.ts"
-Cohesion: 0.07
-Nodes (26): ATOMIC_MASS_UNIT, AVOGADRO, BOHR_RADIUS, BOLTZMANN, COULOMB_CONSTANT, EARTH_MASS, EARTH_RADIUS, ELECTRON_MASS (+18 more)
-
-### Community 63 - "compilerOptions"
-Cohesion: 0.07
-Nodes (26): compilerOptions, allowUnreachableCode, declaration, declarationMap, forceConsistentCasingInFileNames, isolatedModules, lib, module (+18 more)
-
-### Community 64 - "browse_helpers.py"
-Cohesion: 0.10
-Nodes (37): Return a cached :class:`FieldMapping` for the given container., _resolve_field_mapping(), _apply_filters(), build_field_mapping(), _combined_metadata_keys(), _display_name(), distinct_from_rows(), FieldMapping (+29 more)
-
-### Community 65 - "provenance.ts"
-Cohesion: 0.22
-Nodes (17): concat(), crc32(), CRC_TABLE, fnv1aHex(), formatProvenance(), hashTransferFunction(), latin1(), PNG_SIG (+9 more)
-
-### Community 66 - "schemas.py"
-Cohesion: 0.05
-Nodes (62): image_meta(), Return shape / dtype metadata for an image source., AnnotationClass, BrushShape, BrushStroke, DenoiseBakeRequest, EllipseShape, ExportSourceItem (+54 more)
-
-### Community 67 - "FakeNode"
-Cohesion: 0.09
-Nodes (9): catalog(), FakeNode, fixture, Tests for locating a dataset's renderable 3-D volume. This is the module that…, A catalog node: metadata plus children, indexable like a Tiled container., A catalog with one of each shape the resolver has to tell apart., TestHasMultiscales, TestResolveVolume (+1 more)
-
-### Community 68 - "zarr_source.py"
-Cohesion: 0.15
-Nodes (25): _existing_node_info(), inspect_zarr(), _is_zarr_dir(), _multiscale_datasets(), preflight_zarr(), Any, Path, Register on-disk Zarr volumes with Tiled, without copying any data. The drag-… (+17 more)
-
-### Community 69 - "dependencies"
-Cohesion: 0.08
-Nodes (26): @blueskyproject/finch, clsx, dependencies, @blueskyproject/finch, clsx, @huggingface/transformers, konva, polygon-clipping (+18 more)
-
-### Community 70 - "ome-zarr.ts"
-Cohesion: 0.15
-Nodes (21): axisToXyz(), openOmeZarr(), OpenOmeZarrOptions, scaleTranslation(), estimateValueRange(), openZarrArray(), joinPath(), parseNumpyDtype() (+13 more)
-
-### Community 71 - "start_all.sh"
-Cohesion: 0.14
-Nodes (25): API_PROXY_TARGET, can_run_npm(), cleanup(), cleanup_managed_processes(), cleanup_pid_file(), ensure_backend_env(), ensure_docs_env(), ensure_frontend_runtime() (+17 more)
-
-### Community 72 - "drafts.py"
-Cohesion: 0.13
-Nodes (23): _draft_path(), get_version(), get_version_thumbnail(), list_drafts(), list_versions(), load_draft(), Any, Path (+15 more)
-
-### Community 73 - "tiled_clients.py"
-Cohesion: 0.13
-Nodes (22): get_browse_container(), get_browse_container_for(), Any, Tiled client factory + browse-container discovery. Clients are cached per…, Return ``(container_node, path_prefix)`` for the Metadata Browser root. Checks…, Return ``(container_node, path_prefix)`` for a specific browse target. When…, clear_empty_tiled_api_key_env(), get_tiled_api_key() (+14 more)
-
-### Community 74 - "App.jsx"
-Cohesion: 0.14
-Nodes (22): RFC-4648, App(), LinkOptions, useLinkedWebGpuViewers(), queryClient, buildShareUrl(), copyToClipboard(), decodeState() (+14 more)
-
-### Community 75 - "viewerState.ts"
-Cohesion: 0.13
-Nodes (22): LinkOptions, Subscription, useLinkedViewers(), ItkVktNative(), ItkVktProps, ItkVtkCamera, ItkVtkViewerApi, ItkVtkViewerInstance (+14 more)
-
-### Community 76 - "devDependencies"
-Cohesion: 0.09
-Nodes (23): autoprefixer, devDependencies, autoprefixer, jsdom, postcss, tailwindcss, @testing-library/jest-dom, @testing-library/user-event (+15 more)
-
-### Community 77 - "get_tiled_client"
-Cohesion: 0.15
-Nodes (21): api_key_for_uri(), get_tiled_client(), Look up a configured API key for *uri*, falling back to None., Return a (cached) ``tiled.client`` connected to the requested server., build_volume(), inspect_volume_build(), Any, Build a renderable 3-D volume from a slice stack already in Tiled.… (+13 more)
-
-### Community 78 - "useTabsStore.ts"
-Cohesion: 0.14
-Nodes (20): HeaderProps, graceTimers, makeId(), nameFromUrl(), PaneSide, ReconTab, RendererKind, reposition() (+12 more)
-
-### Community 79 - "zarr-viewer/package.json"
-Cohesion: 0.09
-Nodes (22): description, devDependencies, @types/node, typescript, vite, vitest, @webgpu/types, @types/node (+14 more)
-
-### Community 80 - "volume-renderer.ts"
-Cohesion: 0.08
-Nodes (31): asColor3(), asColor4(), Color3Like, Color4, Color4Like, RenderProvenance, approximateShadingActive(), approximateShadingLabel() (+23 more)
-
-### Community 81 - "features.py"
-Cohesion: 0.16
-Nodes (19): apply_clahe(), compute_clahe_stack(), compute_feature_stacks(), encode_channel_png(), feature_channel_labels(), _float_feats_to_uint8(), _fmt_sigma(), list_sigmas() (+11 more)
-
-### Community 82 - "TrainerPlugin"
-Cohesion: 0.20
-Nodes (10): Any, ndarray, Path, Protocol, Train / predict_proba contract for pixel classifiers., Fit model and compute Mondrian calibration scores., Return NxK probabilities aligned with training class order., Persist model weights into *dest_dir*. (+2 more)
-
-### Community 83 - "sam_embed.py"
-Cohesion: 0.14
-Nodes (21): bilinear_sample_emb(), emb_grid_to_pca_channels(), encode_image_embeddings(), encoder_available(), fit_pca(), Any, ndarray, Path (+13 more)
-
-### Community 84 - "start_all.ps1"
-Cohesion: 0.19
-Nodes (19): Ensure-BackendEnv(), Ensure-DocsEnv(), Ensure-FrontendRuntime(), Ensure-TiledKey(), Ensure-Uv(), Err(), Get-FreePort(), Get-ListenerPids() (+11 more)
-
-### Community 85 - "Installation via Docker (Recommended)"
-Cohesion: 0.10
-Nodes (20): Add Tiled, Authenticate, `Bluesky Tiled`, Build and start the application, Clone and install repository, Clone repository, Debugging, Install (+12 more)
-
-### Community 86 - "parse.ts"
-Cohesion: 0.17
-Nodes (15): DIMENSIONLESS, constants, ALL_PREFIXES, parseQuantity(), parseUnit(), PREFIX_KEYS, resolveSymbol(), Term (+7 more)
-
-### Community 87 - "TTLCache"
-Cohesion: 0.12
-Nodes (14): Any, Small TTL + LRU cache used by the Browse API. Kept intentionally tiny: the…, Thread-safe cache with per-entry expiry and an insertion-order size cap., TTLCache, encode_png(), Any, ndarray, Slice-to-PNG rendering with explicit normalisation modes. Two normalisation… (+6 more)
-
-### Community 88 - "magicwand.ts"
-Cohesion: 0.14
-Nodes (18): bboxContainsRing(), boxBlur(), buildField(), chaikin(), gradientField(), GrayField, magicSelect(), MaskPolyOpts (+10 more)
-
-### Community 89 - "Segmentation Annotation Studio"
-Cohesion: 0.10
-Nodes (18): Annotation tools, Development, Export formats, Mac/Linux, Node.js, Production build (single origin), Project layout, Quick start (+10 more)
-
-### Community 90 - "tiled_mask_sync.py"
-Cohesion: 0.18
-Nodes (18): Filesystem-safe folder name for a class label., _safe_name(), _category_maps(), _legend_id_to_name(), merge_mask_volumes(), Any, ndarray, Write rasterized annotation masks into Tiled as stacked volumes. Standalone… (+10 more)
-
-### Community 91 - "denoise.py"
-Cohesion: 0.20
-Nodes (18): auto_strength(), denoise_slice(), denoise_stack(), estimate_noise_sigma(), _from_unit(), _median3d(), _odd(), dtype (+10 more)
-
-### Community 92 - "Software architecture"
-Cohesion: 0.11
-Nodes (18): Backend architecture, Canvas rendering, Client-side geometry & tools (`src/lib/`), Component shell, Data model, Deployment topology, Draw, autosave, and save a version, Export a dataset (+10 more)
-
-### Community 93 - "annotationStore.ts"
-Cohesion: 0.15
-Nodes (15): AnnotationState, BaseShape, BrushShape, BrushStroke, cloneShapeWithNewId(), DraftState, EllipseShape, EMPTY_DRAFT (+7 more)
-
-### Community 94 - "_fake_pyramid"
-Cohesion: 0.24
-Nodes (6): _fake_pyramid(), _FakeArray, _FakeContainer, dict, Container mimicking a registered multiscale volume: scaleN -> {image: array}., TestDescendToArray
-
-### Community 95 - "ManagedTexture"
-Cohesion: 0.07
-Nodes (23): ShadowMap, ManagedTexture, BrickLoaderOptions, BrickRequest, BrickResult, VolumeLevelResult, VolumeLoader, VolumeLoaderOptions (+15 more)
-
-### Community 96 - "FastAPI"
-Cohesion: 0.15
-Nodes (20): count_image_files(), list_dir(), list_image_files(), open_array(), Any, Path, Sandboxed local filesystem access for the Segmentation Annotation Studio. All…, Return a recursive count of image files under ``<root>/rel``. Args: rel:… (+12 more)
-
-### Community 97 - "fake_stack"
-Cohesion: 0.16
-Nodes (7): fake_stack(), fixture, Tests for building a 3-D volume from a slice stack already in Tiled. The…, Stand in for a Tiled stack: `resolve_array` + `array_shape_meta` + `read_slice`., TestBuildGuards, TestInspect, TestMetadataShape
-
-### Community 98 - "geometry.ts"
-Cohesion: 0.18
-Nodes (15): BBox, bboxCoversRaster(), bboxIntersects(), bboxNear(), EMPTY_BBOX, growByFlat(), normalizeEllipse(), normalizeRect() (+7 more)
-
-### Community 99 - "length-units.ts"
-Cohesion: 0.12
-Nodes (16): angstrom, centimeter, femtometer, foot, inch, kilometer, meter, micrometer (+8 more)
-
-### Community 100 - "codecs.ts"
-Cohesion: 0.20
-Nodes (15): bloscCodec(), BloscCompressorConfig, bloscCompressorName(), bloscDecodeSync(), bloscUnshuffle(), Codec, codecFromCompressor(), decompressStream() (+7 more)
-
-### Community 101 - ".alignTo"
-Cohesion: 0.18
-Nodes (3): LayoutBuilder, Std140Builder, Std430Builder
-
-### Community 102 - "Vec3Like"
-Cohesion: 0.11
-Nodes (9): Aabb, barycentric(), clampUnit(), closestPointOnAabb(), closestPointOnSegment(), closestPointOnTriangle(), closestPointsBetweenSegments(), Frustum (+1 more)
-
-### Community 103 - "denoise_bake.py"
-Cohesion: 0.18
-Nodes (13): default_target_path(), _denoise_one(), _ModelDenoiser, Any, ndarray, Apply a denoise filter to a whole volume and save the result as a NEW Tiled…, Applies a trained Noise2Noise/Noise2Void run across a whole volume. Loads the…, Release ``ML_LOCK``. Idempotent — callers release on both the happy path and in… (+5 more)
-
-### Community 104 - "3. Annotate"
-Cohesion: 0.12
-Nodes (17): 3. Annotate, Classes, Colorblind-safe colors, Cross-slice, Display (view-only), Export, How each tool works, Insights (quality checks) (+9 more)
-
-### Community 105 - "datasetStats.ts"
-Cohesion: 0.18
-Nodes (16): analyticArea(), ClassStat, computeSampleStats(), Coverage, isSelfIntersecting(), labelFor(), MaskInfo, overlapCount() (+8 more)
-
-### Community 106 - "SamClient"
-Cohesion: 0.14
-Nodes (7): Granularity, Pending, PromptBox, PromptPoint, SamClient, SamMask, SamStatus
-
-### Community 107 - "samWorker.ts"
-Cohesion: 0.18
-Nodes (16): Box, decode(), Device, encode(), fallbackToWasm(), freshRaw(), Granularity, InMsg (+8 more)
-
-### Community 108 - "TomoJepaModule"
-Cohesion: 0.27
-Nodes (8): _encoder_name(), Any, Path, Resolve Mark25/11 weights from params., Dense TomoJEPA embeddings (torch; prefers ONNX when present)., _resolve_weights(), TomoJepaModule, _weights_format()
-
-### Community 109 - "fx/package.json"
-Cohesion: 0.12
-Nodes (16): dependencies, @prism/core, @prism/math, @prism/render, description, exports, main, module (+8 more)
-
-### Community 110 - "predicates.ts"
-Cohesion: 0.20
-Nodes (12): det2(), orient2d(), orient3d(), Point2Like, compensatedDot(), CompensatedSum, kahanSum(), neumaierSum() (+4 more)
-
-### Community 111 - "test_tomojepa_embed.py"
-Cohesion: 0.16
-Nodes (15): MonkeyPatch, Path, Mark25 / TomoJEPA dense embedding encode., Mark11 dense projector is 256-D (Mark25 is 64-D)., All Mark25 paths must pass intensities in [-1, 1] into the network., test_encode_dense_honors_input_size(), test_encode_dense_shape_512(), test_encode_feeds_model_in_minus_one_one() (+7 more)
-
-### Community 112 - "export_jobs.py"
-Cohesion: 0.12
-Nodes (11): cancel_requested(), get_job(), new_job(), Any, In-memory job registry for COCO/mask exports. Mirrors the ingest job pattern…, Register a new export job and return its id., Ask a running job to stop; returns False if the job is unknown. Cooperative:…, True if cancellation was requested for *jid*. Reads the flag directly under the… (+3 more)
-
-### Community 113 - "polybool.ts"
-Cohesion: 0.28
-Nodes (12): clipShapesToOthers_naive(), eraseStampToMultiPolygon(), flatToRing(), multiPolygonToShapes(), regionsToMultiPolygon(), ringArea(), ringToFlat(), shapeToMultiPolygon() (+4 more)
-
-### Community 114 - "perf.ts"
-Cohesion: 0.20
-Nodes (12): initPerf(), listeners, mark(), perfEnabled(), PerfLabel, PerfStat, resetPerf(), scheduleNotify() (+4 more)
-
-### Community 115 - "compilerOptions"
-Cohesion: 0.12
-Nodes (15): compilerOptions, allowImportingTsExtensions, isolatedModules, lib, module, moduleDetection, moduleResolution, noEmit (+7 more)
-
-### Community 116 - "list_module_catalog"
-Cohesion: 0.14
-Nodes (13): FeatureModule, Any, Protocol, Runnable feature module., True when dependencies/weights are available., Labels that would appear in the bank for these params (no compute)., get_module(), get_registry() (+5 more)
-
-### Community 118 - "mergeSameClass.ts"
-Cohesion: 0.39
-Nodes (7): bboxOverlap(), expandSameClassOverlap(), inflate(), masksIntersect(), masksIntersectIn(), mergeNewWithSameClass(), MergeResult
-
-### Community 119 - "rasterizeShapes"
-Cohesion: 0.19
-Nodes (16): expandSameClassOverlap_naive(), mergeNewWithSameClass_naive(), incoming, otherClass, realOverlap(), thresholdShapes(), area(), fillEllipse() (+8 more)
-
-### Community 120 - "morphology.ts"
-Cohesion: 0.19
-Nodes (11): dilate(), dilateOnce(), erode(), erodeOnce(), fillHoles(), keepComponentsAtPoints(), removeSmallComponents(), smooth() (+3 more)
-
-### Community 121 - "livewire.ts"
-Cohesion: 0.26
-Nodes (6): CostMap, dijkstra(), imageToGrid(), MinHeap, simplifyPath(), tracePath()
-
-### Community 122 - "run_composition"
-Cohesion: 0.29
-Nodes (7): Any, ndarray, Run composition nodes; concatenate ``outputs`` channel blocks. Returns:…, run_composition(), ndarray, Display uint8 for a float HxWxC bank., uint8_from_float()
-
-### Community 123 - "5. Export & download"
-Cohesion: 0.15
-Nodes (13): 5. Export & download, Alternative: Push masks to Tiled, DINOv3 / Lightly format, Downloading a COCO dataset, Importing a dataset back, Step 1 — Prepare (optional but recommended), Step 2 — Open the export dialog, Step 3 — Choose a scope (+5 more)
-
-### Community 125 - "test_tiled_config.py"
-Cohesion: 0.17
-Nodes (11): Tests for Tiled key hardening (empty-key clearing + config-key fallback)., Blank/whitespace key env vars are deleted; real ones are kept., Reads authentication.single_user_api_key from a config.yml., A ${TILED_API_KEY} placeholder in config.yml expands from the env; unset → None., _local_api_key prefers env, else falls back to the config single-user key., Importing the module clears a blank TILED_API_KEY from the env., test_clear_empty_tiled_api_key_env_removes_blank_vars(), test_config_key_env_interpolation() (+3 more)
-
-### Community 126 - "frontend/package.json"
-Cohesion: 0.17
-Nodes (11): name, private, scripts, build, dev, preview, test, test:watch (+3 more)
-
-### Community 128 - "systems.ts"
-Cohesion: 0.21
-Nodes (6): FloatingOrigin, fromSim(), scaleFactor(), toSim(), UNIT_PRESETS, UnitSystem
-
-### Community 129 - "OmeZarrVolumeSource"
-Cohesion: 0.31
-Nodes (3): OmeZarrVolumeSource, VolumeDType, ParsedDType
-
-### Community 130 - "ViewportOverlay"
-Cohesion: 0.24
-Nodes (4): AXES, OverlayDrawParams, OverlayRuler, ViewportOverlay
-
-### Community 132 - "test_compositions.py"
-Cohesion: 0.24
-Nodes (10): catalog(), fixture, MonkeyPatch, Composition validation, builtins, and legacy setup mapping., test_ensure_default_compositions(), test_feature_setups_still_seed(), test_legacy_setup_maps_to_composition(), test_module_catalog_lists_core_modules() (+2 more)
-
-### Community 133 - "test_local_fs.py"
-Cohesion: 0.20
-Nodes (9): Tests for local filesystem sandboxing., Paths escaping LOCAL_DATA_ROOT must raise HTTP 403., list_dir should return file entries for an existing directory., list_dir on a non-existent path should raise HTTP 404., open_array on an unsupported extension should raise HTTP 422., test_list_dir_missing_raises_404(), test_list_dir_returns_entries(), test_open_array_unsupported_type_raises_422() (+1 more)
-
-### Community 134 - "editHistory.ts"
-Cohesion: 0.22
-Nodes (7): ClassDeleteEntry, currentSourceKey(), Entry, redo(), redoOps, undo(), undoOps
-
-### Community 135 - "pixelClf.ts"
-Cohesion: 0.33
-Nodes (7): applyProbaThresholdRgba(), classProbaMaskSetName(), colorizeConformalOverlay(), colorizeLabelMap(), labelMapToPolygonShapes(), parseHexColor(), thresholdProbaPngBlob()
-
-### Community 136 - "test_feature_setups.py"
-Cohesion: 0.40
-Nodes (5): catalog(), fixture, MonkeyPatch, Feature Setup shelf defaults., test_ensure_defaults()
-
-### Community 139 - "array_source.py"
-Cohesion: 0.25
-Nodes (14): _descend_to_array(), _index_slice(), _is_container_node(), _local_root(), Any, ndarray, Path, Minimal local / Tiled slice reader (no annotate-backend imports). (+6 more)
-
-### Community 140 - "AGENTS.md — Segmentation Annotation Studio"
-Cohesion: 0.20
-Nodes (9): AGENTS.md — Segmentation Annotation Studio, ALS Standards, Annotation data model (do NOT change without updating both backend/schemas.py and frontend stores), Git, Secrets, Security rules, Stack, Testing rules (+1 more)
-
-### Community 141 - "guides.py"
-Cohesion: 0.27
-Nodes (9): _guide_path(), load_guide(), Any, Path, Annotation-guide persistence for the Segmentation Annotation Studio. A *guide*…, Return the JSON file path for *source_key*'s guide., Persist the annotation guide for *source_key*. Args: source_key: Arbitrary…, Return the saved guide document for *source_key*, or ``None``. (+1 more)
-
-### Community 142 - "test_drafts.py"
-Cohesion: 0.20
-Nodes (9): Tests for session-draft persistence., A saved draft should be loadable with the correct source_key., Loading a draft that was never saved should return None., list_drafts should include a previously saved draft., list_drafts should return an empty list when no drafts exist., test_list_drafts_empty_when_none(), test_list_drafts_returns_saved(), test_load_missing_draft() (+1 more)
-
-### Community 144 - "Installation"
-Cohesion: 0.20
-Nodes (10): Backend, Changing the ports, Environment variables, Frontend, Installation, Manual setup, Option 1 — One-command local start (recommended), Option 2 — Docker (production) (+2 more)
-
-### Community 145 - "Quick start"
-Cohesion: 0.20
-Nodes (10): 1. Connect to your data, 2. Open an image to annotate, 3. Add a class, 4. Draw a mask, 5. Save your work, 6. Export a COCO dataset, :material-draw: Master annotation, :material-keyboard: Keyboard shortcuts (+2 more)
-
-### Community 147 - "test_catalog_sessions.py"
-Cohesion: 0.24
-Nodes (9): project_id_for(), Stable project id from source identity., catalog(), fixture, MonkeyPatch, Catalog project/session persistence., test_open_session_creates_project(), test_project_id_stable() (+1 more)
-
-### Community 148 - "import_dataset"
-Cohesion: 0.33
-Nodes (8): import_dataset(), _import_from_coco(), _import_with_sidecar(), Any, COCO + sidecar import: round-trip back to annotation payload. Prefers the…, Load a COCO dataset directory back into editor payload format. Args:…, Lossless import using _studio_shapes.json., Best-effort import from COCO only (no sidecar). Brush shapes become polygons…
-
-### Community 149 - "describe_methods"
-Cohesion: 0.22
-Nodes (9): available_methods(), describe_methods(), Any, Method metadata for the capability endpoint / UI menu. ``cost`` drives whether…, True if wavelet denoising can actually run.…, Methods that can actually run in this environment., Z-neighbours needed on each side of the target slice (0 for 2-D methods)., wavelet_available() (+1 more)
-
-### Community 150 - "clahe.ts"
-Cohesion: 0.36
-Nodes (5): applyClaheGray(), buildTileLut(), ClaheOptions, claheRgba(), identityLut()
-
-### Community 152 - "bboxPrefilter.equivalence.test.ts"
-Cohesion: 0.24
-Nodes (11): expectSameRegions(), iou(), makeShape(), mulberry32(), randomSlice(), regionOf(), perShape(), clipBatchBoolean() (+3 more)
-
-### Community 155 - "test_array_blobs.py"
-Cohesion: 0.31
-Nodes (8): client(), fixture, MonkeyPatch, TestClient, Array blob data plane for remote preprocess., test_modules_and_compositions_api(), test_save_load_array_blob(), test_upload_array_and_preprocess()
-
-### Community 156 - "test_preprocess_cache.py"
-Cohesion: 0.33
-Nodes (8): catalog(), fixture, MonkeyPatch, Cache-aware preprocess., test_preprocess_cache_hit(), test_preprocess_mark25_clahe_no_skimage(), test_preprocess_mark25_writes_dense_emb(), skipif
-
-### Community 159 - "usePixelClassifier.ts"
-Cohesion: 0.25
-Nodes (6): ClfFeatureImportance, ClfParams, ClfPredictCounts, ClfTrainResult, DEFAULT_CLF_PARAMS, UsePixelClassifierArgs
-
-### Community 160 - "datasetStore.ts"
-Cohesion: 0.25
-Nodes (7): DatasetState, DEFAULT_DENOISE, DEFAULT_RENDER, DenoiseOpts, ImageMeta, RenderOpts, useDatasetStore
-
-### Community 161 - "toolStore.ts"
-Cohesion: 0.25
-Nodes (7): MagicEngine, MagicMode, SamDetail, SelectScope, Tool, ToolState, useToolStore
-
-### Community 162 - "vite-zarr-static.ts"
-Cohesion: 0.32
-Nodes (4): src, PETIOLE_ZARR_MOUNT, ZarrStaticMount, zarrStaticPlugin()
-
-### Community 163 - "test_api_health.py"
-Cohesion: 0.32
-Nodes (7): client(), fixture, MonkeyPatch, TestClient, API health + session open., test_health(), test_open_session_and_list_setups()
-
-### Community 164 - "main"
-Cohesion: 0.48
-Nodes (6): _ensure_container(), _json_safe(), _load_pairs(), main(), Path, Return (png_path, txt_path) for stems that have both files.
-
-### Community 165 - "test_health.py"
-Cohesion: 0.33
-Nodes (6): asyncio, Tests for the health endpoint., Health endpoint must return status ok., Servers endpoint must not expose api_key in response., test_health_returns_ok(), test_servers_endpoint_no_api_key()
-
-### Community 167 - "Segmentation Annotation Studio"
-Cohesion: 0.29
-Nodes (7): :material-download-box: Install, :material-draw: Annotate, :material-export: Export, :material-rocket-launch: Quick start, Segmentation Annotation Studio, The workflow at a glance, What is this tool?
-
-### Community 168 - "Troubleshooting"
-Cohesion: 0.29
-Nodes (6): Connecting to data, Exporting, Saving, The app won't start, The Smart (AI) tool, Troubleshooting
-
-### Community 169 - "ClassManager/index.tsx"
-Cohesion: 0.33
-Nodes (5): ClassManager(), ClassManagerProps, ClassRowProps, countShapesForClass(), DEFAULT_SUGGESTED_CLASSES
-
-### Community 170 - "Toolbar/index.tsx"
-Cohesion: 0.29
-Nodes (3): CHANNEL_LABELS, ToolbarProps, ToolButtonProps
-
-### Community 171 - "ZarrLoader.tsx"
-Cohesion: 0.33
-Nodes (6): describeLevel(), ExistingInfo, ZarrInfo, ZarrLevel, ZarrLoader(), ZarrLoaderProps
-
-### Community 172 - "BuildVolumePanel.tsx"
-Cohesion: 0.33
-Nodes (6): BuildInfo, BuildVolumePanel(), BuildVolumePanelProps, JobState, PyramidLevel, readError()
-
-### Community 173 - "config.ts"
-Cohesion: 0.29
-Nodes (6): API_BASE, APP_VERSION, DOCS_URL, FEEDBACK_ENTRY_ID, FEEDBACK_FORM_URL, GIT_COMMIT
-
-### Community 174 - "useGuideSync.ts"
-Cohesion: 0.43
-Nodes (5): GuideGenPayload, loadGuide(), saveGuide(), useGuideLoad(), useGuideSync()
-
-### Community 175 - "useSave.ts"
-Cohesion: 0.33
-Nodes (5): SaveDraftPayload, SaveOptions, UseSaveReturn, VersionMeta, VersionPayload
-
-### Community 176 - "regionOps.ts"
-Cohesion: 0.33
-Nodes (3): computeRegionOps(), RegionOp, RegionResult
-
-### Community 177 - "zarrUrl.ts"
-Cohesion: 0.48
-Nodes (5): buildZarrUrl(), describeUnavailable(), zarrRootFor(), ZarrUnavailable, ZarrUrlResult
-
-### Community 178 - "ipredStore.ts"
-Cohesion: 0.29
-Nodes (6): DEFAULT_COMPOSITION_ID, DEFAULT_TRAINER_CONFIG, DEFAULT_TRAINER_ID, IpredState, TrainerConfig, useIpredStore
-
-### Community 179 - "layerVisibilityStore.ts"
-Cohesion: 0.29
-Nodes (5): DEFAULT_GROUPS, LAYER_GROUP_META, LayerGroupId, LayerVisibilityState, useLayerVisibilityStore
-
-### Community 180 - "OME-Zarr Viewer"
-Cohesion: 0.29
-Nodes (6): Notes, OME-Zarr Viewer, Point at a local dataset, Quick start, Requirements, Use in another project
-
-### Community 182 - "include"
-Cohesion: 0.29
-Nodes (6): extends, include, src, bench, test, ../../tsconfig.base.json
-
-### Community 185 - "_denoised_slice"
-Cohesion: 0.40
-Nodes (6): _centre_crop(), _denoised_slice(), Any, ndarray, Centred square crop of *size*, or *arr* unchanged if it already fits., Read *slice_index* and denoise it, pulling z-neighbours for 3-D methods. The…
-
-### Community 186 - "2. Browse & select"
-Cohesion: 0.33
-Nodes (6): 2. Browse & select, Local: the flat file list, Opening a sample, The detail panel, The Samples column, Tiled: the metadata browser
-
-### Community 187 - "1. Connect to data"
-Cohesion: 0.33
-Nodes (6): 1. Connect to data, Connect to Tiled, Ingesting data (Tiled only), Load / Ingest Datasets, Local Folder mode, Tiled Server mode
-
-### Community 188 - "fetch-sam-model.mjs"
-Cohesion: 0.33
-Nodes (4): HERE, OUT, python, res
-
-### Community 189 - "DenoiseBakeModal.tsx"
-Cohesion: 0.47
-Nodes (5): defaultTarget(), DenoiseBakeModal(), DenoiseBakeModalProps, JobState, readError()
-
-### Community 190 - "DownloadModal.tsx"
-Cohesion: 0.40
-Nodes (5): DownloadModal(), DownloadModalProps, parseSourceKey(), Scope, SCOPE_OPTIONS
-
-### Community 191 - "InsightsModal.tsx"
-Cohesion: 0.40
-Nodes (4): FLAG_LABEL, formatArea(), InsightsModal(), InsightsModalProps
-
-### Community 192 - "MeasurementPanel/index.tsx"
-Cohesion: 0.40
-Nodes (4): fmt(), IntensityStats, MeasurementPanel(), MeasurementPanelProps
-
-### Community 193 - "VersionHistoryModal.tsx"
-Cohesion: 0.47
-Nodes (4): formatDate(), Props, thumbnailUrl(), VersionHistoryModal()
-
-### Community 194 - "useFeatureManifold.ts"
-Cohesion: 0.40
-Nodes (5): DEFAULT_MANIFOLD_PARAMS, ManifoldParams, readErrorDetail(), useFeatureManifold(), UseFeatureManifoldArgs
-
-### Community 195 - "classColors.ts"
-Cohesion: 0.67
-Nodes (4): COLORBLIND_COLORS, DEFAULT_COLORS, getClassPalette(), pickNextColor()
-
-### Community 196 - "lib/colormaps.ts"
-Cohesion: 0.33
-Nodes (3): COLORMAP_NAMES, ColormapName, STOPS
-
-### Community 197 - "featureManifold.ts"
-Cohesion: 0.53
-Nodes (4): colorizeManifoldHeatmap(), loadManifoldHeatmapCanvas(), manifoldMarkerRect(), ManifoldPoint
-
-### Community 201 - "test_manifold.py"
-Cohesion: 0.40
-Nodes (5): catalog(), fixture, MonkeyPatch, Manifold Suggest Labels on persisted ipred feature banks., test_manifold_sample_on_feature_bank()
-
-### Community 202 - "test_train_rethreshold.py"
-Cohesion: 0.40
-Nodes (5): catalog(), fixture, MonkeyPatch, Train → infer → rethreshold without recompute., test_train_infer_rethreshold()
-
-### Community 204 - "Keyboard shortcuts"
-Cohesion: 0.40
-Nodes (5): Canvas & navigation, Editing, Keyboard shortcuts, Mouse modifiers (Magic / Fill / SAM), Tools
-
-### Community 206 - "PerfOverlay/index.tsx"
-Cohesion: 0.50
-Nodes (3): PerfOverlayProps, Row(), severity()
-
-### Community 207 - "PixelClassifierPanel/index.tsx"
-Cohesion: 0.50
-Nodes (3): coverageHeadline(), PixelClassifierPanel(), PixelClassifierPanelProps
-
-### Community 208 - "SaveModal.tsx"
-Cohesion: 0.60
-Nodes (4): blobToBase64(), fetchPreviewBlob(), SaveModal(), SaveModalProps
-
-### Community 209 - "CompositionPanel/index.tsx"
-Cohesion: 0.70
-Nodes (3): CompositionPanel(), defaultParams(), newNodeId()
-
-### Community 210 - "useFeatureChannels.ts"
-Cohesion: 0.40
-Nodes (3): FeatureChannelInfo, FeatureJobInfo, UseFeatureChannelsArgs
-
-### Community 211 - "measure.ts"
-Cohesion: 0.60
-Nodes (4): measureRegion(), RegionMeasurement, ringPerimeter(), shapePerimeter()
-
-### Community 212 - "buildSourceKey"
-Cohesion: 0.80
-Nodes (3): buildSourceKey(), isAnnotatedPath(), key()
-
-### Community 214 - "Mat4"
-Cohesion: 0.06
-Nodes (16): _q0, _q1, _q2, _tv, std140Alignment(), std430Alignment(), WgslBaseType, _decomposeRot (+8 more)
-
-### Community 215 - "start.sh"
-Cohesion: 0.40
-Nodes (3): HOST, start.sh script, TILED_ALLOW_ORIGINS
-
-### Community 216 - "reingest_tiffs_as_individual.py"
-Cohesion: 0.67
-Nodes (3): compute_structure_id(), main(), Re-ingest stacked TIFF array as 690 individual sample nodes. The 690 TIFFs were…
-
-### Community 219 - "FeatureChannelsPanel/index.tsx"
-Cohesion: 0.40
-Nodes (3): FeatureChannelsPanelProps, PRESET_HINTS, PRESET_LABELS
-
-### Community 221 - "VersionPreviewBar.tsx"
-Cohesion: 0.67
-Nodes (3): formatDate(), Props, VersionPreviewBar()
-
-### Community 223 - "VolumeViewer.tsx"
-Cohesion: 0.67
-Nodes (3): VolumeViewer(), VolumeViewerProps, webGpuAvailability()
-
-### Community 226 - "useMaskOps.ts"
-Cohesion: 0.67
-Nodes (3): CleanupOp, polysToShapes(), useMaskOps()
-
-### Community 227 - "useOpenInAnnotate.ts"
-Cohesion: 0.83
-Nodes (3): applyDraft(), classesFromKeywords(), useOpenInAnnotate()
-
-### Community 229 - "adjust.ts"
-Cohesion: 0.67
-Nodes (3): PreprocessOpts, renderAdjusted(), renderPreprocessOnly()
-
-### Community 231 - "classStore.ts"
-Cohesion: 0.50
-Nodes (3): AnnotationClass, ClassStore, useClassStore
-
-### Community 232 - "ratingStore.ts"
-Cohesion: 0.50
-Nodes (3): RatingState, StarRating, useRatingStore
-
-### Community 233 - "referenceGuideStore.ts"
-Cohesion: 0.50
-Nodes (3): GuideClass, ReferenceGuideStore, useReferenceGuideStore
-
-### Community 234 - "settingsStore.ts"
-Cohesion: 0.67
-Nodes (3): genId(), SettingsStore, useSettingsStore
-
-### Community 237 - "ipred/models"
-Cohesion: 0.50
-Nodes (3): ipred/models, SlimSAM (ONNX), TomoJEPA (Mark25 / Mark11)
-
-### Community 239 - "test_tomojepa_onnx.py"
-Cohesion: 0.50
-Nodes (3): TomoJEPA ONNX encode smoke (skips if onnx file missing)., test_onnx_encode_dense_shape(), needs_onnx
-
-### Community 240 - "ExportConflict"
-Cohesion: 0.67
-Nodes (3): ExportConflict, Exception, Raised when a merge would corrupt category ids or names.
-
-### Community 243 - "Using the tool"
-Cohesion: 0.67
-Nodes (3): Recommended reading order, The Finch shell, Using the tool
-
-### Community 244 - "4. Annotation guide"
-Cohesion: 0.67
-Nodes (3): 4. Annotation guide, How it connects to Annotate, What you can do here
-
-## Knowledge Gaps
-- **875 isolated node(s):** `sam3-annotation-studio-backend`, `name`, `private`, `version`, `type` (+870 more)
-  These have ≤1 connection - possible missing edges or undocumented components.
-- **71 thin communities (<3 nodes) omitted from report** — run `graphify query` to explore isolated nodes.
-
-## Suggested Questions
-_Questions this graph is uniquely positioned to answer:_
-
-- **Why does `uuid` connect `preprocess.py` to `compositions.py`, `dependencies`, `manifold.py`, `train_infer.py`, `export_jobs.py`, `feature_setups.py`, `catalog.py`, `ingest.py`?**
-  _High betweenness centrality (0.026) - this node is a cross-community bridge._
-- **Why does `Catalog` connect `Catalog` to `preprocess.py`, `compositions.py`, `test_compositions.py`, `manifold.py`, `HTTPException`, `train_infer.py`, `test_feature_setups.py`, `test_manifold.py`, `test_train_rethreshold.py`, `test_preprocess_cache.py`, `test_catalog_sessions.py`, `cli.py`, `feature_setups.py`, `catalog.py`?**
-  _High betweenness centrality (0.019) - this node is a cross-community bridge._
-- **Why does `Vec3` connect `Vec3` to `camera.ts`, `Vec3Like`, `run`, `math/index.ts`, `Random`, `Node`, `io/index.ts`, `math/color.ts`, `OrbitControls`, `.multiplyScalar`, `Mat4`, `packing.ts`, `VectorGrid3`, `Mat3`?**
-  _High betweenness centrality (0.017) - this node is a cross-community bridge._
-- **Are the 57 inferred relationships involving `run()` (e.g. with `.dispose()` and `.getState()`) actually correct?**
-  _`run()` has 57 INFERRED edges - model-reasoned connections that need verification._
-- **Are the 39 inferred relationships involving `Catalog` (e.g. with `_resolve_session()` and `ensure_default_compositions()`) actually correct?**
-  _`Catalog` has 39 INFERRED edges - model-reasoned connections that need verification._
-- **Are the 61 inferred relationships involving `HTTPException` (e.g. with `browse_column()` and `browse_items()`) actually correct?**
-  _`HTTPException` has 61 INFERRED edges - model-reasoned connections that need verification._
-- **What connects `sam3-annotation-studio-backend`, `name`, `private` to the rest of the system?**
-  _875 weakly-connected nodes found - possible documentation gaps or missing edges._
\ No newline at end of file
diff --git a/graphify-out/cache/ast/v0.9.50-s2/03f3223d98994af811d748d9f5feda9d72ac7e3b8a81aca145221cddb1cc47ed.json b/graphify-out/cache/ast/v0.9.50-s2/03f3223d98994af811d748d9f5feda9d72ac7e3b8a81aca145221cddb1cc47ed.json
deleted file mode 100644
index dc3d6fe..0000000
--- a/graphify-out/cache/ast/v0.9.50-s2/03f3223d98994af811d748d9f5feda9d72ac7e3b8a81aca145221cddb1cc47ed.json
+++ /dev/null
@@ -1 +0,0 @@
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deleted file mode 100644
index 26b8113..0000000
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deleted file mode 100644
index 8dd6202..0000000
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deleted file mode 100644
index f06d270..0000000
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deleted file mode 100644
index da518dc..0000000
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deleted file mode 100644
index 942f394..0000000
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/441f86fa9821a8a8179116b7250e71ed6a15cd0f5c0b3d6dc16df68883701e01.json b/graphify-out/cache/ast/v0.9.50-s2/441f86fa9821a8a8179116b7250e71ed6a15cd0f5c0b3d6dc16df68883701e01.json
deleted file mode 100644
index d670314..0000000
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/44582ae2d5b190f52625e71ce1b3b45b5089ebff7c3e7c4a89613923a9d98a1e.json b/graphify-out/cache/ast/v0.9.50-s2/44582ae2d5b190f52625e71ce1b3b45b5089ebff7c3e7c4a89613923a9d98a1e.json
deleted file mode 100644
index 3d6fbda..0000000
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deleted file mode 100644
index 5501a8b..0000000
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deleted file mode 100644
index 08f15ba..0000000
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/6417be9f126b1fcac9cef4c8db8a9fdb2d1fa59c24cb270ca50e568dffea9c88.json b/graphify-out/cache/ast/v0.9.50-s2/6417be9f126b1fcac9cef4c8db8a9fdb2d1fa59c24cb270ca50e568dffea9c88.json
deleted file mode 100644
index 656d478..0000000
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deleted file mode 100644
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/68f51f7114b4c8d41ea25587e1f7dccb03026a32a691afd621d2ef1483b4a3f7.json b/graphify-out/cache/ast/v0.9.50-s2/68f51f7114b4c8d41ea25587e1f7dccb03026a32a691afd621d2ef1483b4a3f7.json
deleted file mode 100644
index 8f805f9..0000000
--- a/graphify-out/cache/ast/v0.9.50-s2/68f51f7114b4c8d41ea25587e1f7dccb03026a32a691afd621d2ef1483b4a3f7.json
+++ /dev/null
@@ -1 +0,0 @@
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deleted file mode 100644
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/6b7db4d94e5c2f120023c2d6162c148f18d39e533dad4bfeb47a5658fa6f2a43.json b/graphify-out/cache/ast/v0.9.50-s2/6b7db4d94e5c2f120023c2d6162c148f18d39e533dad4bfeb47a5658fa6f2a43.json
deleted file mode 100644
index edcf040..0000000
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/72b44c72a8bd90f5ffbed7389261891eef28fb446ca15b87bea40a9248ac405d.json b/graphify-out/cache/ast/v0.9.50-s2/72b44c72a8bd90f5ffbed7389261891eef28fb446ca15b87bea40a9248ac405d.json
deleted file mode 100644
index d45eca8..0000000
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/74767bac2e9869ce5ced55d9834b10d190662c9a53a73a1090611b17e8e730bc.json b/graphify-out/cache/ast/v0.9.50-s2/74767bac2e9869ce5ced55d9834b10d190662c9a53a73a1090611b17e8e730bc.json
deleted file mode 100644
index f785086..0000000
--- a/graphify-out/cache/ast/v0.9.50-s2/74767bac2e9869ce5ced55d9834b10d190662c9a53a73a1090611b17e8e730bc.json
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/814e1bea2558315b4fff0f789a6a8aebb481d6737f42fac8f7e7ab5a7b986bf7.json b/graphify-out/cache/ast/v0.9.50-s2/814e1bea2558315b4fff0f789a6a8aebb481d6737f42fac8f7e7ab5a7b986bf7.json
deleted file mode 100644
index efa8942..0000000
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/887ec22cc7abafc77862dc79f977919f2297f8fddad99cfbdb802fe1937c3b35.json b/graphify-out/cache/ast/v0.9.50-s2/887ec22cc7abafc77862dc79f977919f2297f8fddad99cfbdb802fe1937c3b35.json
deleted file mode 100644
index 6fc48ce..0000000
--- a/graphify-out/cache/ast/v0.9.50-s2/887ec22cc7abafc77862dc79f977919f2297f8fddad99cfbdb802fe1937c3b35.json
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deleted file mode 100644
index 52b03dc..0000000
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/b9aa0da6b09351a314c97bbdf3f6366970542a7fbe7e8f78abb92c9028d1c34c.json b/graphify-out/cache/ast/v0.9.50-s2/b9aa0da6b09351a314c97bbdf3f6366970542a7fbe7e8f78abb92c9028d1c34c.json
deleted file mode 100644
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/be8af9d367f0646de796284714e377dea8c12b40bc525d7e0438d46771360699.json b/graphify-out/cache/ast/v0.9.50-s2/be8af9d367f0646de796284714e377dea8c12b40bc525d7e0438d46771360699.json
deleted file mode 100644
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deleted file mode 100644
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/d18346c2412f640c2f7e1578a6a3dde4d324d2b4990a099d55206860474434df.json b/graphify-out/cache/ast/v0.9.50-s2/d18346c2412f640c2f7e1578a6a3dde4d324d2b4990a099d55206860474434df.json
deleted file mode 100644
index bbca017..0000000
--- a/graphify-out/cache/ast/v0.9.50-s2/d18346c2412f640c2f7e1578a6a3dde4d324d2b4990a099d55206860474434df.json
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deleted file mode 100644
index 83d7689..0000000
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deleted file mode 100644
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deleted file mode 100644
index 8f8bcac..0000000
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index 7ae338a..0000000
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index 5e58c76..0000000
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deleted file mode 100644
index 1e04099..0000000
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diff --git a/graphify-out/cache/ast/v0.9.50-s2/e4b40b542c2c442de01944570e2f0c7bd76a853a45d231bd367f0c6960404911.json b/graphify-out/cache/ast/v0.9.50-s2/e4b40b542c2c442de01944570e2f0c7bd76a853a45d231bd367f0c6960404911.json
deleted file mode 100644
index b07ed8c..0000000
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deleted file mode 100644
index d3b9287..0000000
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"backend_tiled_annotation_sync", "label": "tiled_annotation_sync.py", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 11.3, "font": {"size": 0, "color": "#ffffff"}, "title": "tiled_annotation_sync.py", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/tiled_annotation_sync.py", "file_type": "code", "degree": 7}, {"id": "backend_tiled_annotation_sync_py_any", "label": "Any", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "Any", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "", "file_type": "code", "degree": 2}, {"id": "imagedraw", "label": "ImageDraw", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": 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Formats:\u2026", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/source_keys.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_tiled_annotation_sync_rationale_1", "label": "Tests for source key parsing and annotation metadata helpers.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tests for source key parsing and annotation metadata helpers.", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/tests/test_tiled_annotation_sync.py", "file_type": "rationale", "degree": 1}, {"id": "backend_thumbnails_rationale_1", "label": "PNG thumbnail rendering for Tiled array nodes. 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Returns\u2026", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/thumbnails.py", "file_type": "rationale", "degree": 1}, {"id": "backend_thumbnails_rationale_54", "label": "Return *node* if it's already an array, else its first array child.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return *node* if it&#x27;s already an array, else its first array child.", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/thumbnails.py", "file_type": "rationale", "degree": 1}, {"id": "backend_thumbnails_rationale_85", "label": "Log-scale, normalise, and colour-map a 2D intensity array to uint8 RGB.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Log-scale, normalise, and colour-map a 2D intensity array to uint8 RGB.", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/thumbnails.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_annotation_sync_rationale_1", "label": "Sync annotation session state into Tiled node metadata for Browse discovery.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Sync annotation session state into Tiled node metadata for Browse discovery.", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/tiled_annotation_sync.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_annotation_sync_rationale_22", "label": "Build Tiled-safe metadata fields from a draft/export payload.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Build Tiled-safe metadata fields from a draft/export payload.", "community": 5, "community_name": "annotation_thumbnails.py", "source_file": "backend/tiled_annotation_sync.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_annotation_sync_rationale_37", "label": "Merge annotation summary metadata onto a Tiled node. 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"font": {"size": 0, "color": "#ffffff"}, "title": ".onPointerMove()", "community": 50, "community_name": "OrbitControls", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts", "file_type": "code", "degree": 2}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_oncontextmenu", "label": ".onContextMenu()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": ".onContextMenu()", "community": 50, "community_name": "OrbitControls", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts", "file_type": "code", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onkeydown", "label": ".onKeyDown()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": ".onKeyDown()", "community": 50, "community_name": "OrbitControls", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts", "file_type": "code", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onkeyup", "label": ".onKeyUp()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": ".onKeyUp()", "community": 50, "community_name": "OrbitControls", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/camera.ts", "file_type": "code", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_controls_camera_orbitcontrols_onpointerdown", "label": ".onPointerDown()", "color": {"background": 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Tiled&#x27;s\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_207", "label": "Tally distinct values of *raw_key* across pre-read metadata *rows*. List-valued\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tally distinct values of *raw_key* across pre-read metadata *rows*. List-valued\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_233", "label": "Return distinct values (+ counts) for *raw_key* under optional filters. When\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return distinct values (+ counts) for *raw_key* under optional filters. When\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_298", "label": "Apply *filters* via ``search(Key())`` and return matching container entries.\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Apply *filters* via ``search(Key())`` and return matching container entries.\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_368", "label": "True if *entry* is a Tiled container (vs. an array/leaf) node.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "True if *entry* is a Tiled container (vs. an array/leaf) node.", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_454", "label": "Convert a display-key filter dict to (raw_key, string-value) pairs.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Convert a display-key filter dict to (raw_key, string-value) pairs.", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_464", "label": "Apply each filter to *node*, skipping failures. List-valued keys (e.g.\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Apply each filter to *node*, skipping failures. List-valued keys (e.g.\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_488", "label": "Case-insensitive match over container-level metadata. Scalar values match by\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Case-insensitive match over container-level metadata. Scalar values match by\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_507", "label": "Coerce a filter string to int / float when it round-trips exactly. Tiled stores\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Coerce a filter string to int / float when it round-trips exactly. Tiled stores\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_79", "label": "Mapping between raw Tiled metadata keys and friendly display names.", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Mapping between raw Tiled metadata keys and friendly display names.", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "backend_browse_helpers_rationale_91", "label": "Introspect *container_node* to discover available metadata keys. Walks up to\u2026", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Introspect *container_node* to discover available metadata keys. Walks up to\u2026", "community": 64, "community_name": "browse_helpers.py", "source_file": "backend/browse_helpers.py", "file_type": "rationale", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_provenance", "label": "provenance.ts", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 14.3, "font": {"size": 0, "color": "#ffffff"}, "title": "provenance.ts", "community": 65, "community_name": "provenance.ts", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/accel/provenance.ts", "file_type": "code", "degree": 23}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_provenance_crc_table", "label": "CRC_TABLE", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "CRC_TABLE", "community": 65, "community_name": "provenance.ts", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/accel/provenance.ts", "file_type": "code", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_provenance_png_sig", "label": "PNG_SIG", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "PNG_SIG", "community": 65, "community_name": "provenance.ts", "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/accel/provenance.ts", "file_type": "code", "degree": 1}, {"id": "backend_schemas", "label": "schemas.py", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 14.5, "font": {"size": 12, "color": "#ffffff"}, "title": "schemas.py", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "code", "degree": 24}, {"id": "backend_schemas_py_basemodel", "label": "BaseModel", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 13.6, "font": {"size": 0, "color": "#ffffff"}, "title": "BaseModel", "community": 66, "community_name": "schemas.py", "source_file": "", "file_type": "code", "degree": 19}, {"id": "backend_tests_test_schemas", "label": "test_schemas.py", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 13.2, "font": {"size": 0, "color": "#ffffff"}, "title": "test_schemas.py", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "code", "degree": 17}, {"id": "backend_tests_test_tiled_mask_sync", "label": "test_tiled_mask_sync.py", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "test_tiled_mask_sync.py", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_tiled_mask_sync.py", "file_type": "code", "degree": 14}, {"id": "imagemeta", "label": "ImageMeta", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "ImageMeta", "community": 66, "community_name": "schemas.py", "source_file": "", "file_type": "code", "degree": 1}, {"id": "backend_annotation_server_rationale_577", "label": "Return shape / dtype metadata for an image source.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return shape / dtype metadata for an image source.", "community": 66, "community_name": "schemas.py", "source_file": "backend/annotation_server.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_1", "label": "Pydantic models for the Segmentation Annotation Studio API. Shapes ------ *\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Pydantic models for the Segmentation Annotation Studio API. Shapes ------ *\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_101", "label": "A collection of freehand brush strokes forming one annotation region.\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A collection of freehand brush strokes forming one annotation region.\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_120", "label": "A labelled class used for annotating images. Attributes: classId: Integer class\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A labelled class used for annotating images. Attributes: classId: Integer class\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_155", "label": "A single annotated sample within a multi-source export. Attributes: kind:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A single annotated sample within a multi-source export. Attributes: kind:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_238", "label": "Explicit save request with optional version metadata. Attributes: payload:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Explicit save request with optional version metadata. Attributes: payload:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_254", "label": "Request body for per-region intensity measurement. Attributes: slice_index:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Request body for per-region intensity measurement. Attributes: slice_index:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_266", "label": "Request body for the pre-upload duplicate check. Sent as a POST body rather\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Request body for the pre-upload duplicate check. Sent as a POST body rather\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_27", "label": "A single continuous stroke made by the brush tool. Attributes: points: Flat\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A single continuous stroke made by the brush tool. Attributes: points: Flat\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_320", "label": "Request body to denoise a whole volume into a new Tiled dataset. The Annotate\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Request body to denoise a whole volume into a new Tiled dataset. The Annotate\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_350", "label": "Request body for building a 3-D volume from a stack already in Tiled.\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Request body for building a 3-D volume from a stack already in Tiled.\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_371", "label": "One class entry in an annotation guide. Attributes: label: Human-readable class\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "One class entry in an annotation guide. Attributes: label: Human-readable class\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_387", "label": "A project lead's annotation guide for a dataset. Attributes: classes: Ordered\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A project lead&#x27;s annotation guide for a dataset. Attributes: classes: Ordered\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_399", "label": "Shape and dtype metadata for an opened image source. Attributes: n_slices:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Shape and dtype metadata for an opened image source. Attributes: n_slices:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_41", "label": "A closed polygon annotation. Attributes: id: Unique shape identifier. classId:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A closed polygon annotation. Attributes: id: Unique shape identifier. classId:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_57", "label": "An axis-aligned bounding-box annotation. Attributes: id: Unique shape\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "An axis-aligned bounding-box annotation. Attributes: id: Unique shape\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_schemas_rationale_79", "label": "An ellipse annotation. Attributes: id: Unique shape identifier. classId:\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "An ellipse annotation. Attributes: id: Unique shape identifier. classId:\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_1", "label": "Tests for Pydantic schema models.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tests for Pydantic schema models.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_17", "label": "PolygonShape should serialise and deserialise cleanly.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "PolygonShape should serialise and deserialise cleanly.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_30", "label": "BrushShape should store stroke list with correct attributes.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "BrushShape should store stroke list with correct attributes.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_38", "label": "RenderOpts should have sensible defaults without explicit values.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "RenderOpts should have sensible defaults without explicit values.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_46", "label": "RectShape should expose geometry fields correctly.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "RectShape should expose geometry fields correctly.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_53", "label": "EllipseShape should expose centre and radii correctly.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "EllipseShape should expose centre and radii correctly.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_60", "label": "BrushStroke should accept 'erase' mode.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "BrushStroke should accept &#x27;erase&#x27; mode.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_66", "label": "A folder's worth of filenames must fit \u2014 as query params it 431'd.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A folder&#x27;s worth of filenames must fit \u2014 as query params it 431&#x27;d.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_schemas_rationale_74", "label": "Omitting names is allowed \u2014 used to only ask for a free container name.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Omitting names is allowed \u2014 used to only ask for a free container name.", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_schemas.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_tiled_mask_sync_rationale_1", "label": "Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization \u2192\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization \u2192\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/tests/test_tiled_mask_sync.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_mask_sync_rationale_53", "label": "Rasterize an item's shapes into stacked mask volumes. Returns ``{semantic,\u2026", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Rasterize an item&#x27;s shapes into stacked mask volumes. Returns ``{semantic,\u2026", "community": 66, "community_name": "schemas.py", "source_file": "backend/tiled_mask_sync.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_volume_nodes", "label": "test_volume_nodes.py", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.5, "font": {"size": 0, "color": "#ffffff"}, "title": "test_volume_nodes.py", "community": 67, "community_name": "FakeNode", "source_file": "backend/tests/test_volume_nodes.py", "file_type": "code", "degree": 8}, {"id": "backend_tests_test_volume_nodes_py_fixture", "label": "fixture", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "fixture", "community": 67, "community_name": "FakeNode", "source_file": "", "file_type": "code", "degree": 1}, {"id": "backend_tests_test_volume_nodes_rationale_1", "label": "Tests for locating a dataset's renderable 3-D volume. This is the module that\u2026", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tests for locating a dataset&#x27;s renderable 3-D volume. This is the module that\u2026", "community": 67, "community_name": "FakeNode", "source_file": "backend/tests/test_volume_nodes.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_volume_nodes_rationale_28", "label": "A catalog node: metadata plus children, indexable like a Tiled container.", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A catalog node: metadata plus children, indexable like a Tiled container.", "community": 67, "community_name": "FakeNode", "source_file": "backend/tests/test_volume_nodes.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tests_test_volume_nodes_rationale_40", "label": "A catalog with one of each shape the resolver has to tell apart.", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "A catalog with one of each shape the resolver has to tell apart.", "community": 67, "community_name": "FakeNode", "source_file": "backend/tests/test_volume_nodes.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source", "label": "zarr_source.py", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 13.6, "font": {"size": 0, "color": "#ffffff"}, "title": "zarr_source.py", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "code", "degree": 19}, {"id": "backend_zarr_source_py_any", "label": "Any", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.3, "font": {"size": 0, "color": "#ffffff"}, "title": "Any", "community": 68, "community_name": "zarr_source.py", "source_file": "", "file_type": "code", "degree": 7}, {"id": "backend_zarr_source_py_path", "label": "Path", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Path", "community": 68, "community_name": "zarr_source.py", "source_file": "", "file_type": "code", "degree": 6}, {"id": "backend_zarr_source_rationale_1", "label": "Register on-disk Zarr volumes with Tiled, without copying any data. The drag-\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Register on-disk Zarr volumes with Tiled, without copying any data. The drag-\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_107", "label": "Pull the ``scale`` transform (voxel size per axis) out of a dataset entry.", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Pull the ``scale`` transform (voxel size per axis) out of a dataset entry.", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_117", "label": "Depth-first list of ``(path, array)`` for every 3-D array in *group*.", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Depth-first list of ``(path, array)`` for every 3-D array in *group*.", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_138", "label": "Describe a Zarr store's resolution pyramid without registering anything. Args:\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Describe a Zarr store&#x27;s resolution pyramid without registering anything. Args:\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_223", "label": "Axis unit declared in the store's ``multiscales`` axes (e.g. micrometer).", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Axis unit declared in the store&#x27;s ``multiscales`` axes (e.g. micrometer).", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_239", "label": "The node key Tiled will use for *path*. Tiled strips the extension, so\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "The node key Tiled will use for *path*. Tiled strips the extension, so\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_253", "label": "Describe what already occupies a key, for the conflict prompt. ``external``\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Describe what already occupies a key, for the conflict prompt. ``external``\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_280", "label": "Report whether registering *raw_path* would collide, without changing anything.\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Report whether registering *raw_path* would collide, without changing anything.\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_303", "label": "Register a Zarr store into a Tiled container, copying no data. Args:\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Register a Zarr store into a Tiled container, copying no data. Args:\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_49", "label": "True if *path* looks like the root of a Zarr store.", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "True if *path* looks like the root of a Zarr store.", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_54", "label": "Expand and validate a user-supplied absolute path to a Zarr store. Raises:\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Expand and validate a user-supplied absolute path to a Zarr store. Raises:\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "backend_zarr_source_rationale_87", "label": "Read OME-NGFF ``multiscales`` from ``.zattrs``, if present. Returns the\u2026", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Read OME-NGFF ``multiscales`` from ``.zattrs``, if present. Returns the\u2026", "community": 68, "community_name": "zarr_source.py", "source_file": "backend/zarr_source.py", "file_type": "rationale", "degree": 1}, {"id": "frontend_package_dependencies", "label": "dependencies", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 13.0, "font": {"size": 0, "color": "#ffffff"}, "title": "dependencies", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 16}, {"id": "frontend_package_dependencies_blueskyproject_finch", "label": "@blueskyproject/finch", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "@blueskyproject/finch", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_clsx", "label": "clsx", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "clsx", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_huggingface_transformers", "label": "@huggingface/transformers", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "@huggingface/transformers", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_konva", "label": "konva", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "konva", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_polygon_clipping", "label": "polygon-clipping", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "polygon-clipping", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_react", "label": "react", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "react", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_react_dom", "label": "react-dom", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "react-dom", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_react_konva", "label": "react-konva", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "react-konva", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_react_router", "label": "react-router", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "react-router", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_tailwind_merge", "label": "tailwind-merge", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "tailwind-merge", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_tanstack_react_query", "label": "@tanstack/react-query", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "@tanstack/react-query", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_uuid", "label": "uuid", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "uuid", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "frontend_package_dependencies_zustand", "label": "zustand", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "zustand", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "code", "degree": 2}, {"id": "blueskyproject_finch", "label": "@blueskyproject/finch", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "@blueskyproject/finch", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "clsx", "label": "clsx", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "clsx", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "frontend_package_json_react", "label": "react", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "react", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "frontend_package_json_react_dom", "label": "react-dom", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "react-dom", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "frontend_package_json_tanstack_react_query", "label": "@tanstack/react-query", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "@tanstack/react-query", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "frontend_package_json_zustand", "label": "zustand", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "zustand", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "huggingface_transformers", "label": "@huggingface/transformers", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "@huggingface/transformers", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "konva", "label": "konva", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "konva", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "polygon_clipping", "label": "polygon-clipping", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "polygon-clipping", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "react_konva", "label": "react-konva", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "react-konva", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", "file_type": "concept", "degree": 1}, {"id": "react_router", "label": "react-router", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "react-router", "community": 69, "community_name": "dependencies", "source_file": "frontend/package.json", 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"start_all.sh", "file_type": "code", "degree": 2}, {"id": "start_all_ipred_url", "label": "IPRED_URL", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "IPRED_URL", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 1}, {"id": "start_all_pick_free_port", "label": "pick_free_port()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.6, "font": {"size": 0, "color": "#ffffff"}, "title": "pick_free_port()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 3}, {"id": "start_all_port_is_listening", "label": "port_is_listening()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "port_is_listening()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 4}, {"id": "start_all_reclaim_orphaned_repo_ports", "label": "reclaim_orphaned_repo_ports()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.6, "font": {"size": 0, "color": "#ffffff"}, "title": "reclaim_orphaned_repo_ports()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 3}, {"id": "start_all_require_free_port", "label": "require_free_port()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "require_free_port()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 2}, {"id": "start_all_sh__entry", "label": "start_all.sh script", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 11.9, "font": {"size": 0, "color": "#ffffff"}, "title": "start_all.sh script", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 10}, {"id": "start_all_stop_managed_process", "label": "stop_managed_process()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.6, "font": {"size": 0, "color": "#ffffff"}, "title": "stop_managed_process()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 3}, {"id": "start_all_stop_repo_listener_on_port", "label": "stop_repo_listener_on_port()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "stop_repo_listener_on_port()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 4}, {"id": "start_all_tiled_cmd", "label": "tiled_cmd()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "tiled_cmd()", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 2}, {"id": "start_all_tiled_uri", "label": "TILED_URI", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "TILED_URI", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 1}, {"id": "start_all_vite_docs_url", "label": "VITE_DOCS_URL", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "VITE_DOCS_URL", "community": 71, "community_name": "start_all.sh", "source_file": "start_all.sh", "file_type": "code", "degree": 1}, {"id": "backend_drafts", "label": "drafts.py", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 12.3, "font": {"size": 0, "color": "#ffffff"}, "title": "drafts.py", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "code", "degree": 12}, {"id": "backend_drafts_py_any", "label": "Any", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Any", "community": 72, "community_name": "drafts.py", "source_file": "", "file_type": "code", "degree": 6}, {"id": "backend_drafts_py_path", "label": "Path", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.4, "font": {"size": 0, "color": "#ffffff"}, "title": "Path", "community": 72, "community_name": "drafts.py", "source_file": "", "file_type": "code", "degree": 2}, {"id": "backend_drafts_rationale_1", "label": "Session-draft persistence and version history for the Segmentation Annotation\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Session-draft persistence and version history for the Segmentation Annotation\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_139", "label": "Create a new immutable version snapshot for *source_key*. Also updates the\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Create a new immutable version snapshot for *source_key*. Also updates the\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_189", "label": "List version metadata (without payload) sorted oldest-first. Args: source_key:\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "List version metadata (without payload) sorted oldest-first. Args: source_key:\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_225", "label": "Persist an annotated thumbnail PNG for *version*. Stored alongside the version\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Persist an annotated thumbnail PNG for *version*. Stored alongside the version\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_243", "label": "Return the thumbnail PNG bytes for *version*, or ``None``. Args: source_key:\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the thumbnail PNG bytes for *version*, or ``None``. Args: source_key:\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_259", "label": "Return the full document (including payload) for a specific version. Args:\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the full document (including payload) for a specific version. Args:\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_33", "label": "Return the JSON file path for *source_key*. Args: source_key: Arbitrary string\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the JSON file path for *source_key*. Args: source_key: Arbitrary string\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_46", "label": "Return the versions directory path for *source_key*.", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the versions directory path for *source_key*.", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_52", "label": "Persist a session draft for *source_key* (crash-recovery autosave). Does NOT\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Persist a session draft for *source_key* (crash-recovery autosave). Does NOT\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_78", "label": "Return the saved draft for *source_key*, or ``None``. Args: source_key:\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the saved draft for *source_key*, or ``None``. Args: source_key:\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_drafts_rationale_97", "label": "List all saved drafts with summary metadata. Returns: List of dicts with\u2026", "color": {"background": "#E15759", "border": "#E15759", "highlight": {"background": "#ffffff", "border": "#E15759"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "List all saved drafts with summary metadata. Returns: List of dicts with\u2026", "community": 72, "community_name": "drafts.py", "source_file": "backend/drafts.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_clients", "label": "tiled_clients.py", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.4, "font": {"size": 0, "color": "#ffffff"}, "title": "tiled_clients.py", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_clients.py", "file_type": "code", "degree": 18}, {"id": "backend_tiled_clients_py_any", "label": "Any", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Any", "community": 73, "community_name": "tiled_clients.py", "source_file": "", "file_type": "code", "degree": 3}, {"id": "backend_tiled_config", "label": "tiled_config.py", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 12.5, "font": {"size": 0, "color": "#ffffff"}, "title": "tiled_config.py", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "code", "degree": 13}, {"id": "backend_tiled_config_py_path", "label": "Path", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Path", "community": 73, "community_name": "tiled_clients.py", "source_file": "", "file_type": "code", "degree": 1}, {"id": "backend_tiled_clients_rationale_1", "label": "Tiled client factory + browse-container discovery. Clients are cached per\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tiled client factory + browse-container discovery. Clients are cached per\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_clients.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_clients_rationale_65", "label": "Return ``(container_node, path_prefix)`` for the Metadata Browser root. Checks\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return ``(container_node, path_prefix)`` for the Metadata Browser root. Checks\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_clients.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_clients_rationale_95", "label": "Return ``(container_node, path_prefix)`` for a specific browse target. When\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return ``(container_node, path_prefix)`` for a specific browse target. When\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_clients.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_1", "label": "Tiled server configuration loaded from environment variables. Reads\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Tiled server configuration loaded from environment variables. Reads\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_101", "label": "Return mapping of ``{name: {\"uri\": str, \"api_key\": str | None}}``. Discovery\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return mapping of ``{name: {&quot;uri&quot;: str, &quot;api_key&quot;: str | None}}``. Discovery\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_132", "label": "Return the default Tiled base URI.", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the default Tiled base URI.", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_137", "label": "Return the default API key (local override takes precedence).", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return the default API key (local override takes precedence).", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_33", "label": "Delete blank ``TILED_API_KEY`` / ``TILED_LOCAL_API_KEY`` from the process env.\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Delete blank ``TILED_API_KEY`` / ``TILED_LOCAL_API_KEY`` from the process env.\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_55", "label": "Return ``authentication.single_user_api_key`` from ``tiled/config.yml``, or\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Return ``authentication.single_user_api_key`` from ``tiled/config.yml``, or\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "backend_tiled_config_rationale_82", "label": "Local Tiled base URI. ``TILED_URI`` (set by ``start_all.sh`` to whatever port\u2026", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.2, "font": {"size": 0, "color": "#ffffff"}, "title": "Local Tiled base URI. ``TILED_URI`` (set by ``start_all.sh`` to whatever port\u2026", "community": 73, "community_name": "tiled_clients.py", "source_file": "backend/tiled_config.py", "file_type": "rationale", "degree": 1}, {"id": "frontend_vendor_view_tomography_recon_app_src_app", "label": "App.jsx", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 17.7, "font": {"size": 12, "color": "#ffffff"}, "title": "App.jsx", "community": 74, "community_name": "App.jsx", "source_file": "frontend/vendor/view_tomography_recon_app/src/App.jsx", "file_type": "code", "degree": 41}, {"id": "frontend_vendor_view_tomography_recon_app_src_hooks_uselinkedwebgpuviewers", "label": "useLinkedWebGpuViewers.ts", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "useLinkedWebGpuViewers.ts", "community": 74, "community_name": "App.jsx", "source_file": "frontend/vendor/view_tomography_recon_app/src/hooks/useLinkedWebGpuViewers.ts", "file_type": "code", "degree": 6}, {"id": "frontend_vendor_view_tomography_recon_app_src_main", "label": "main.jsx", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.8, "font": {"size": 0, "color": "#ffffff"}, "title": "main.jsx", "community": 74, 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-
-// HTML-escape helper — prevents XSS when injecting graph data into innerHTML
-function esc(s) {
-  return String(s).replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;').replace(/"/g,'&quot;').replace(/'/g,'&#39;');
-}
-
-// Build vis datasets
-const nodesDS = new vis.DataSet(RAW_NODES.map(n => ({
-  id: n.id, label: n.label, color: n.color, size: n.size,
-  font: n.font, title: n.title,
-  _community: n.community, _community_name: n.community_name,
-  _source_file: n.source_file, _file_type: n.file_type, _degree: n.degree,
-})));
-
-const edgesDS = new vis.DataSet(RAW_EDGES.map((e, i) => ({
-  id: i, from: e.from, to: e.to,
-  label: '',
-  title: e.title,
-  dashes: e.dashes,
-  width: e.width,
-  color: e.color,
-  arrows: { to: { enabled: true, scaleFactor: 0.5 } },
-})));
-
-const container = document.getElementById('graph');
-const network = new vis.Network(container, { nodes: nodesDS, edges: edgesDS }, {
-  physics: {
-    enabled: true,
-    solver: 'forceAtlas2Based',
-    forceAtlas2Based: {
-      gravitationalConstant: -60,
-      centralGravity: 0.005,
-      springLength: 120,
-      springConstant: 0.08,
-      damping: 0.4,
-      avoidOverlap: 0.8,
-    },
-    stabilization: { iterations: 200, fit: true },
-  },
-  interaction: {
-    hover: true,
-    tooltipDelay: 100,
-    hideEdgesOnDrag: true,
-    navigationButtons: false,
-    keyboard: false,
-  },
-  nodes: { shape: 'dot', borderWidth: 1.5 },
-  edges: { smooth: { type: 'continuous', roundness: 0.2 }, selectionWidth: 3 },
-});
-
-network.once('stabilizationIterationsDone', () => {
-  network.setOptions({ physics: { enabled: false } });
-});
-
-function showInfo(nodeId) {
-  const n = nodesDS.get(nodeId);
-  if (!n) return;
-  const neighborIds = network.getConnectedNodes(nodeId);
-  const neighborItems = neighborIds.map(nid => {
-    const nb = nodesDS.get(nid);
-    const color = nb ? nb.color.background : '#555';
-    return `<span class="neighbor-link" style="border-left-color:${esc(color)}" data-nid="${esc(nid)}">${esc(nb ? nb.label : nid)}</span>`;
-  }).join('');
-  document.getElementById('info-content').innerHTML = `
-    <div class="field"><b>${esc(n.label)}</b></div>
-    <div class="field">Type: ${esc(n._file_type || 'unknown')}</div>
-    <div class="field">Community: ${esc(n._community_name)}</div>
-    <div class="field">Source: ${esc(n._source_file || '-')}</div>
-    <div class="field">Degree: ${n._degree}</div>
-    ${neighborIds.length ? `<div class="field" style="margin-top:8px;color:#aaa;font-size:11px">Neighbors (${neighborIds.length})</div><div id="neighbors-list">${neighborItems}</div>` : ''}
-  `;
-}
-
-function focusNode(nodeId) {
-  network.focus(nodeId, { scale: 1.4, animation: true });
-  network.selectNodes([nodeId]);
-  showInfo(nodeId);
-}
-
-// Neighbor links use a data attribute + one delegated listener rather than an
-// inline onclick. A node id/label sourced from a document or a scraped URL
-// (graphify add) can contain a double-quote; dropping the stringified id
-// unescaped into a quoted onclick both broke every link and allowed a hostile
-// source to inject an event handler into the local report (stored XSS, #1838).
-// esc() on data-nid keeps the value inside the attribute; the listener reads it
-// back verbatim. Bound to document so it survives the innerHTML rebuild that
-// recreates #neighbors-list on each showInfo().
-document.addEventListener('click', e => {
-  const el = e.target.closest('.neighbor-link');
-  if (el && el.dataset.nid !== undefined) focusNode(el.dataset.nid);
-});
-
-// Track hovered node — hover detection is more reliable than click params
-let hoveredNodeId = null;
-network.on('hoverNode', params => {
-  hoveredNodeId = params.node;
-  container.style.cursor = 'pointer';
-});
-network.on('blurNode', () => {
-  hoveredNodeId = null;
-  container.style.cursor = 'default';
-});
-container.addEventListener('click', () => {
-  if (hoveredNodeId !== null) {
-    showInfo(hoveredNodeId);
-    network.selectNodes([hoveredNodeId]);
-  }
-});
-network.on('click', params => {
-  if (params.nodes.length > 0) {
-    showInfo(params.nodes[0]);
-  } else if (hoveredNodeId === null) {
-    document.getElementById('info-content').innerHTML = '<span class="empty">Click a node to inspect it</span>';
-  }
-});
-
-const searchInput = document.getElementById('search');
-const searchResults = document.getElementById('search-results');
-searchInput.addEventListener('input', () => {
-  const q = searchInput.value.toLowerCase().trim();
-  searchResults.innerHTML = '';
-  if (!q) { searchResults.style.display = 'none'; return; }
-  const matches = RAW_NODES.filter(n => n.label.toLowerCase().includes(q)).slice(0, 20);
-  if (!matches.length) { searchResults.style.display = 'none'; return; }
-  searchResults.style.display = 'block';
-  matches.forEach(n => {
-    const el = document.createElement('div');
-    el.className = 'search-item';
-    el.textContent = n.label;
-    el.style.borderLeft = `3px solid ${n.color.background}`;
-    el.style.paddingLeft = '8px';
-    el.onclick = () => {
-      network.focus(n.id, { scale: 1.5, animation: true });
-      network.selectNodes([n.id]);
-      showInfo(n.id);
-      searchResults.style.display = 'none';
-      searchInput.value = '';
-    };
-    searchResults.appendChild(el);
-  });
-});
-document.addEventListener('click', e => {
-  if (!searchResults.contains(e.target) && e.target !== searchInput)
-    searchResults.style.display = 'none';
-});
-
-const hiddenCommunities = new Set();
-
-const selectAllCb = document.getElementById('select-all-cb');
-
-function updateSelectAllState() {
-  const total = LEGEND.length;
-  const hidden = hiddenCommunities.size;
-  selectAllCb.checked = hidden === 0;
-  selectAllCb.indeterminate = hidden > 0 && hidden < total;
-}
-
-function toggleAllCommunities(hide) {
-  document.querySelectorAll('.legend-item').forEach(item => {
-    hide ? item.classList.add('dimmed') : item.classList.remove('dimmed');
-  });
-  document.querySelectorAll('.legend-cb').forEach(cb => {
-    cb.checked = !hide;
-  });
-  LEGEND.forEach(c => {
-    if (hide) hiddenCommunities.add(c.cid); else hiddenCommunities.delete(c.cid);
-  });
-  const updates = RAW_NODES.map(n => ({ id: n.id, hidden: hide }));
-  nodesDS.update(updates);
-  updateSelectAllState();
-}
-
-const legendEl = document.getElementById('legend');
-LEGEND.forEach(c => {
-  const item = document.createElement('div');
-  item.className = 'legend-item';
-  const cb = document.createElement('input');
-  cb.type = 'checkbox';
-  cb.className = 'legend-cb';
-  cb.checked = true;
-  cb.addEventListener('change', (e) => {
-    e.stopPropagation();
-    if (cb.checked) {
-      hiddenCommunities.delete(c.cid);
-      item.classList.remove('dimmed');
-    } else {
-      hiddenCommunities.add(c.cid);
-      item.classList.add('dimmed');
-    }
-    const updates = RAW_NODES
-      .filter(n => n.community === c.cid)
-      .map(n => ({ id: n.id, hidden: !cb.checked }));
-    nodesDS.update(updates);
-    updateSelectAllState();
-  });
-  item.innerHTML = `<div class="legend-dot" style="background:${c.color}"></div>
-    <span class="legend-label">${c.label}</span>
-    <span class="legend-count">${c.count}</span>`;
-  item.prepend(cb);
-  item.onclick = (e) => {
-    if (e.target === cb) return;
-    cb.checked = !cb.checked;
-    cb.dispatchEvent(new Event('change'));
-  };
-  legendEl.appendChild(item);
-});
-</script>
-<script>
-// Render hyperedges as shaded regions
-const hyperedges = [];
-// afterDrawing passes ctx already transformed to network coordinate space.
-// Draw node positions raw — no manual pan/zoom/DPR math needed.
-
-// Andrew's monotone chain. Returns the hull in counter-clockwise order, which
-// is what the perimeter must be traced in. Collinear and duplicate points
-// collapse to the extremes, so degenerate member sets render as a segment
-// rather than a zero-area crossed path.
-function convexHull(pts) {
-    const p = pts.slice().sort((a, b) => (a.x - b.x) || (a.y - b.y));
-    if (p.length < 3) return p;
-    const cross = (o, a, b) => (a.x - o.x) * (b.y - o.y) - (a.y - o.y) * (b.x - o.x);
-    const build = seq => {
-        const out = [];
-        for (const q of seq) {
-            while (out.length >= 2 && cross(out[out.length - 2], out[out.length - 1], q) <= 0) out.pop();
-            out.push(q);
-        }
-        out.pop();
-        return out;
-    };
-    const hull = build(p).concat(build(p.slice().reverse()));
-    return hull.length >= 3 ? hull : p;
-}
-network.on('afterDrawing', function(ctx) {
-    hyperedges.forEach(h => {
-        const positions = h.nodes
-            .map(nid => network.getPositions([nid])[nid])
-            .filter(p => p !== undefined);
-        if (positions.length < 2) return;
-        ctx.save();
-        ctx.globalAlpha = 0.12;
-        ctx.fillStyle = '#6366f1';
-        ctx.strokeStyle = '#6366f1';
-        ctx.lineWidth = 2;
-        ctx.beginPath();
-        // Centroid and expanded hull in network coordinates.
-        // The perimeter must follow hull order, not h.nodes order: tracing the
-        // raw member order self-intersects whenever the layout does not happen
-        // to place members in angular order, filling as crossed wedges.
-        const cx = positions.reduce((s, p) => s + p.x, 0) / positions.length;
-        const cy = positions.reduce((s, p) => s + p.y, 0) / positions.length;
-        const hull = convexHull(positions);
-        const expanded = hull.map(p => ({
-            x: cx + (p.x - cx) * 1.15,
-            y: cy + (p.y - cy) * 1.15
-        }));
-        ctx.moveTo(expanded[0].x, expanded[0].y);
-        expanded.slice(1).forEach(p => ctx.lineTo(p.x, p.y));
-        ctx.closePath();
-        ctx.fill();
-        ctx.globalAlpha = 0.4;
-        ctx.stroke();
-        // Label
-        ctx.globalAlpha = 0.8;
-        ctx.fillStyle = '#4f46e5';
-        ctx.font = 'bold 11px sans-serif';
-        ctx.textAlign = 'center';
-        ctx.fillText(h.label, cx, cy - 5);
-        ctx.restore();
-    });
-});
-</script>
-</body>
-</html>
\ No newline at end of file
diff --git a/graphify-out/graph.json b/graphify-out/graph.json
deleted file mode 100644
index de7e6f8..0000000
--- a/graphify-out/graph.json
+++ /dev/null
@@ -1,160061 +0,0 @@
-{
-  "directed": false,
-  "multigraph": false,
-  "graph": {},
-  "nodes": [
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_gpu_timer_gputimer",
-      "label": "GpuTimer",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "gputimer",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/accel/gpu-timer.ts",
-      "source_location": "L25"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_accel_taau_temporalaccumulator",
-      "label": "TemporalAccumulator",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "temporalaccumulator",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/accel/taau.ts",
-      "source_location": "L111"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_device_context_gpucontext",
-      "label": "GpuContext",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "gpucontext",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/device/context.ts",
-      "source_location": "L24"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_compiledgraph",
-      "label": "CompiledGraph",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "compiledgraph",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L58"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_passcontext",
-      "label": "PassContext",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "passcontext",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L33"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_passdesc",
-      "label": "PassDesc",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "passdesc",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L42"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_rendergraph",
-      "label": "RenderGraph",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "rendergraph",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L93"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_rendergraphtexturedesc",
-      "label": "RenderGraphTextureDesc",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "rendergraphtexturedesc",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L23"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_resourcehandle",
-      "label": "ResourceHandle",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "resourcehandle",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L20"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_resourcelifetime",
-      "label": "ResourceLifetime",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "resourcelifetime",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L50"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_graph_render_graph_resourcerecord",
-      "label": "ResourceRecord",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "resourcerecord",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/graph/render-graph.ts",
-      "source_location": "L71"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_post_fx_pipeline_fxpipeline",
-      "label": "FxPipeline",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "fxpipeline",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/post/fx-pipeline.ts",
-      "source_location": "L26"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_render_resources_pipeline_pipelinecache",
-      "label": "PipelineCache",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 10,
-      "community_name": "TemporalAccumulator",
-      "file_type": "code",
-      "norm_label": "pipelinecache",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/render/resources/pipeline.ts",
-      "source_location": "L13"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_zarr_codecs_blosccompressorconfig",
-      "label": "BloscCompressorConfig",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 100,
-      "community_name": "codecs.ts",
-      "file_type": "code",
-      "norm_label": "blosccompressorconfig",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/io/volume/zarr/codecs.ts",
-      "source_location": "L18"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_io_volume_zarr_codecs_codec",
-      "label": "Codec",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 100,
-      "community_name": "codecs.ts",
-      "file_type": "code",
-      "norm_label": "codec",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/io/volume/zarr/codecs.ts",
-      "source_location": "L12"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_layoutbuilder",
-      "label": "LayoutBuilder",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 101,
-      "community_name": ".alignTo",
-      "file_type": "code",
-      "norm_label": "layoutbuilder",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/gpu-layout.ts",
-      "source_location": "L25"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_std140builder",
-      "label": "Std140Builder",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 101,
-      "community_name": ".alignTo",
-      "file_type": "code",
-      "norm_label": "std140builder",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/gpu-layout.ts",
-      "source_location": "L101"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_gpu_layout_std430builder",
-      "label": "Std430Builder",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 101,
-      "community_name": ".alignTo",
-      "file_type": "code",
-      "norm_label": "std430builder",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/gpu-layout.ts",
-      "source_location": "L138"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_shapes_aabb",
-      "label": "Aabb",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 102,
-      "community_name": "Vec3Like",
-      "file_type": "code",
-      "norm_label": "aabb",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/shapes.ts",
-      "source_location": "L13"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_shapes_frustum",
-      "label": "Frustum",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 102,
-      "community_name": "Vec3Like",
-      "file_type": "code",
-      "norm_label": "frustum",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/shapes.ts",
-      "source_location": "L477"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_math_vec3_vec3like",
-      "label": "Vec3Like",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 102,
-      "community_name": "Vec3Like",
-      "file_type": "code",
-      "norm_label": "vec3like",
-      "source_file": "frontend/vendor/view_tomography_recon_app/src/zarr-viewer/src/math/vec3.ts",
-      "source_location": "L17"
-    },
-    {
-      "id": "backend_denoise_bake_modeldenoiser",
-      "label": "_ModelDenoiser",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 103,
-      "community_name": "denoise_bake.py",
-      "file_type": "code",
-      "norm_label": "_modeldenoiser",
-      "source_file": "backend/denoise_bake.py",
-      "source_location": "L242"
-    },
-    {
-      "id": "frontend_src_lib_datasetstats_classstat",
-      "label": "ClassStat",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 105,
-      "community_name": "datasetStats.ts",
-      "file_type": "code",
-      "norm_label": "classstat",
-      "source_file": "frontend/src/lib/datasetStats.ts",
-      "source_location": "L16"
-    },
-    {
-      "id": "frontend_src_lib_datasetstats_coverage",
-      "label": "Coverage",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 105,
-      "community_name": "datasetStats.ts",
-      "file_type": "code",
-      "norm_label": "coverage",
-      "source_file": "frontend/src/lib/datasetStats.ts",
-      "source_location": "L25"
-    },
-    {
-      "id": "frontend_src_lib_datasetstats_maskinfo",
-      "label": "MaskInfo",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 105,
-      "community_name": "datasetStats.ts",
-      "file_type": "code",
-      "norm_label": "maskinfo",
-      "source_file": "frontend/src/lib/datasetStats.ts",
-      "source_location": "L182"
-    },
-    {
-      "id": "frontend_src_lib_datasetstats_qaflag",
-      "label": "QAFlag",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 105,
-      "community_name": "datasetStats.ts",
-      "file_type": "code",
-      "norm_label": "qaflag",
-      "source_file": "frontend/src/lib/datasetStats.ts",
-      "source_location": "L35"
-    },
-    {
-      "id": "frontend_src_lib_datasetstats_qaflagkind",
-      "label": "QAFlagKind",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 105,
-      "community_name": "datasetStats.ts",
-      "file_type": "code",
-      "norm_label": "qaflagkind",
-      "source_file": "frontend/src/lib/datasetStats.ts",
-      "source_location": "L33"
-    },
-    {
-      "id": "frontend_src_lib_datasetstats_samplestats",
-      "label": "SampleStats",
-      "_callable": true,
-      "_callable_class": true,
-      "_origin": "ast",
-      "community": 105,
-      "community_name": "datasetStats.ts",
-      "file_type": "code",
-      "norm_label": "samplestats",
-      "source_file": "frontend/src/lib/datasetStats.ts",
-      "source_location": "L45"
-    },
-    {
-      "id": "frontend_src_lib_datasetstats_slicesmap",
-      "label": "SlicesMap",
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_scene_scene_detach",
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_scene_scene_scene_query",
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-      "source_location": "L74"
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_quantity_quantity_compare",
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-      "source_location": "L70"
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-      "source_location": "L27"
-    },
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-      "source_location": "L46"
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-      "source_location": "L93"
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-      "source_location": "L47"
-    },
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_fromsi",
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-      "norm_label": ".fromsi()",
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-      "source_location": "L31"
-    },
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-      "source_location": "L36"
-    },
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_labeled",
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-      "file_type": "code",
-      "norm_label": ".labeled()",
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-      "source_location": "L59"
-    },
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-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_mul",
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-      "norm_label": ".mul()",
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-      "source_location": "L41"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_pow",
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-      "norm_label": ".pow()",
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-      "source_location": "L53"
-    },
-    {
-      "id": "frontend_vendor_view_tomography_recon_app_src_zarr_viewer_src_core_units_unit_unit_tostring",
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-      "_origin": "ast",
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-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "explicit save request with optional version metadata. attributes: payload:...",
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-      "id": "backend_schemas_rationale_254",
-      "label": "Request body for per-region intensity measurement. Attributes: slice_index:\u2026",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
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-      "id": "backend_schemas_rationale_266",
-      "label": "Request body for the pre-upload duplicate check. Sent as a POST body rather\u2026",
-      "_origin": "ast",
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-      "community_name": "schemas.py",
-      "file_type": "rationale",
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-      "id": "backend_schemas_rationale_27",
-      "label": "A single continuous stroke made by the brush tool. Attributes: points: Flat\u2026",
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-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "a single continuous stroke made by the brush tool. attributes: points: flat...",
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-      "id": "backend_schemas_rationale_320",
-      "label": "Request body to denoise a whole volume into a new Tiled dataset. The Annotate\u2026",
-      "_origin": "ast",
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-      "community_name": "schemas.py",
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-      "_origin": "ast",
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-      "community_name": "schemas.py",
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-      "id": "backend_schemas_rationale_371",
-      "label": "One class entry in an annotation guide. Attributes: label: Human-readable class\u2026",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "one class entry in an annotation guide. attributes: label: human-readable class...",
-      "source_file": "backend/schemas.py",
-      "source_location": "L371"
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-      "id": "backend_schemas_rationale_387",
-      "label": "A project lead's annotation guide for a dataset. Attributes: classes: Ordered\u2026",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "a project lead's annotation guide for a dataset. attributes: classes: ordered...",
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-      "source_location": "L387"
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-      "id": "backend_schemas_rationale_399",
-      "label": "Shape and dtype metadata for an opened image source. Attributes: n_slices:\u2026",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
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-      "norm_label": "shape and dtype metadata for an opened image source. attributes: n_slices:...",
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-      "source_location": "L399"
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-      "id": "backend_schemas_rationale_41",
-      "label": "A closed polygon annotation. Attributes: id: Unique shape identifier. classId:\u2026",
-      "_origin": "ast",
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-      "community_name": "schemas.py",
-      "file_type": "rationale",
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-      "source_location": "L41"
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-      "id": "backend_schemas_rationale_57",
-      "label": "An axis-aligned bounding-box annotation. Attributes: id: Unique shape\u2026",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
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-      "id": "backend_schemas_rationale_79",
-      "label": "An ellipse annotation. Attributes: id: Unique shape identifier. classId:\u2026",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "an ellipse annotation. attributes: id: unique shape identifier. classid:...",
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-      "id": "backend_tests_test_schemas_rationale_1",
-      "label": "Tests for Pydantic schema models.",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
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-      "id": "backend_tests_test_schemas_rationale_17",
-      "label": "PolygonShape should serialise and deserialise cleanly.",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "polygonshape should serialise and deserialise cleanly.",
-      "source_file": "backend/tests/test_schemas.py",
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-    {
-      "id": "backend_tests_test_schemas_rationale_30",
-      "label": "BrushShape should store stroke list with correct attributes.",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
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-      "source_file": "backend/tests/test_schemas.py",
-      "source_location": "L30"
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-    {
-      "id": "backend_tests_test_schemas_rationale_38",
-      "label": "RenderOpts should have sensible defaults without explicit values.",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "renderopts should have sensible defaults without explicit values.",
-      "source_file": "backend/tests/test_schemas.py",
-      "source_location": "L38"
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-    {
-      "id": "backend_tests_test_schemas_rationale_46",
-      "label": "RectShape should expose geometry fields correctly.",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "rectshape should expose geometry fields correctly.",
-      "source_file": "backend/tests/test_schemas.py",
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-      "id": "backend_tests_test_schemas_rationale_53",
-      "label": "EllipseShape should expose centre and radii correctly.",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "ellipseshape should expose centre and radii correctly.",
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-      "id": "backend_tests_test_schemas_rationale_60",
-      "label": "BrushStroke should accept 'erase' mode.",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "brushstroke should accept 'erase' mode.",
-      "source_file": "backend/tests/test_schemas.py",
-      "source_location": "L60"
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-    {
-      "id": "backend_tests_test_schemas_rationale_66",
-      "label": "A folder's worth of filenames must fit \u2014 as query params it 431'd.",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "a folder's worth of filenames must fit \u2014 as query params it 431'd.",
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-      "source_location": "L66"
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-    {
-      "id": "backend_tests_test_schemas_rationale_74",
-      "label": "Omitting names is allowed \u2014 used to only ask for a free container name.",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "omitting names is allowed \u2014 used to only ask for a free container name.",
-      "source_file": "backend/tests/test_schemas.py",
-      "source_location": "L74"
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-    {
-      "id": "backend_tests_test_tiled_mask_sync_rationale_1",
-      "label": "Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization \u2192\u2026",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization \u2192...",
-      "source_file": "backend/tests/test_tiled_mask_sync.py",
-      "source_location": "L1"
-    },
-    {
-      "id": "backend_tiled_mask_sync_rationale_53",
-      "label": "Rasterize an item's shapes into stacked mask volumes. Returns ``{semantic,\u2026",
-      "_origin": "ast",
-      "community": 66,
-      "community_name": "schemas.py",
-      "file_type": "rationale",
-      "norm_label": "rasterize an item's shapes into stacked mask volumes. returns ``{semantic,...",
-      "source_file": "backend/tiled_mask_sync.py",
-      "source_location": "L53"
-    },
-    {
-      "id": "backend_tests_test_volume_nodes",
-      "label": "test_volume_nodes.py",
-      "_origin": "ast",
-      "community": 67,
-      "community_name": "FakeNode",
-      "file_type": "code",
-      "norm_label": "test_volume_nodes.py",
-      "source_file": "backend/tests/test_volume_nodes.py",
-      "source_location": "L1"
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-    {
-      "id": "backend_tests_test_volume_nodes_py_fixture",
-      "label": "fixture",
-      "_origin": "ast",
-      "community": 67,
-      "community_name": "FakeNode",
-      "file_type": "code",
-      "norm_label": "fixture",
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-      "source_location": ""
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-    {
-      "id": "backend_tests_test_volume_nodes_rationale_1",
-      "label": "Tests for locating a dataset's renderable 3-D volume. This is the module that\u2026",
-      "_origin": "ast",
-      "community": 67,
-      "community_name": "FakeNode",
-      "file_type": "rationale",
-      "norm_label": "tests for locating a dataset's renderable 3-d volume. this is the module that...",
-      "source_file": "backend/tests/test_volume_nodes.py",
-      "source_location": "L1"
-    },
-    {
-      "id": "backend_tests_test_volume_nodes_rationale_28",
-      "label": "A catalog node: metadata plus children, indexable like a Tiled container.",
-      "_origin": "ast",
-      "community": 67,
-      "community_name": "FakeNode",
-      "file_type": "rationale",
-      "norm_label": "a catalog node: metadata plus children, indexable like a tiled container.",
-      "source_file": "backend/tests/test_volume_nodes.py",
-      "source_location": "L28"
-    },
-    {
-      "id": "backend_tests_test_volume_nodes_rationale_40",
-      "label": "A catalog with one of each shape the resolver has to tell apart.",
-      "_origin": "ast",
-      "community": 67,
-      "community_name": "FakeNode",
-      "file_type": "rationale",
-      "norm_label": "a catalog with one of each shape the resolver has to tell apart.",
-      "source_file": "backend/tests/test_volume_nodes.py",
-      "source_location": "L40"
-    },
-    {
-      "id": "backend_zarr_source",
-      "label": "zarr_source.py",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "code",
-      "norm_label": "zarr_source.py",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L1"
-    },
-    {
-      "id": "backend_zarr_source_py_any",
-      "label": "Any",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "code",
-      "norm_label": "any",
-      "source_file": "",
-      "source_location": ""
-    },
-    {
-      "id": "backend_zarr_source_py_path",
-      "label": "Path",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "code",
-      "norm_label": "path",
-      "source_file": "",
-      "source_location": ""
-    },
-    {
-      "id": "backend_zarr_source_rationale_1",
-      "label": "Register on-disk Zarr volumes with Tiled, without copying any data. The drag-\u2026",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "register on-disk zarr volumes with tiled, without copying any data. the drag-...",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L1"
-    },
-    {
-      "id": "backend_zarr_source_rationale_107",
-      "label": "Pull the ``scale`` transform (voxel size per axis) out of a dataset entry.",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "pull the ``scale`` transform (voxel size per axis) out of a dataset entry.",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L107"
-    },
-    {
-      "id": "backend_zarr_source_rationale_117",
-      "label": "Depth-first list of ``(path, array)`` for every 3-D array in *group*.",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "depth-first list of ``(path, array)`` for every 3-d array in *group*.",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L117"
-    },
-    {
-      "id": "backend_zarr_source_rationale_138",
-      "label": "Describe a Zarr store's resolution pyramid without registering anything. Args:\u2026",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "describe a zarr store's resolution pyramid without registering anything. args:...",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L138"
-    },
-    {
-      "id": "backend_zarr_source_rationale_223",
-      "label": "Axis unit declared in the store's ``multiscales`` axes (e.g. micrometer).",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "axis unit declared in the store's ``multiscales`` axes (e.g. micrometer).",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L223"
-    },
-    {
-      "id": "backend_zarr_source_rationale_239",
-      "label": "The node key Tiled will use for *path*. Tiled strips the extension, so\u2026",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "the node key tiled will use for *path*. tiled strips the extension, so...",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L239"
-    },
-    {
-      "id": "backend_zarr_source_rationale_253",
-      "label": "Describe what already occupies a key, for the conflict prompt. ``external``\u2026",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "describe what already occupies a key, for the conflict prompt. ``external``...",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L253"
-    },
-    {
-      "id": "backend_zarr_source_rationale_280",
-      "label": "Report whether registering *raw_path* would collide, without changing anything.\u2026",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "report whether registering *raw_path* would collide, without changing anything....",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L280"
-    },
-    {
-      "id": "backend_zarr_source_rationale_303",
-      "label": "Register a Zarr store into a Tiled container, copying no data. Args:\u2026",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "register a zarr store into a tiled container, copying no data. args:...",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L303"
-    },
-    {
-      "id": "backend_zarr_source_rationale_49",
-      "label": "True if *path* looks like the root of a Zarr store.",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "true if *path* looks like the root of a zarr store.",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L49"
-    },
-    {
-      "id": "backend_zarr_source_rationale_54",
-      "label": "Expand and validate a user-supplied absolute path to a Zarr store. Raises:\u2026",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "expand and validate a user-supplied absolute path to a zarr store. raises:...",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L54"
-    },
-    {
-      "id": "backend_zarr_source_rationale_87",
-      "label": "Read OME-NGFF ``multiscales`` from ``.zattrs``, if present. Returns the\u2026",
-      "_origin": "ast",
-      "community": 68,
-      "community_name": "zarr_source.py",
-      "file_type": "rationale",
-      "norm_label": "read ome-ngff ``multiscales`` from ``.zattrs``, if present. returns the...",
-      "source_file": "backend/zarr_source.py",
-      "source_location": "L87"
-    },
-    {
-      "id": "frontend_package_dependencies",
-      "label": "dependencies",
-      "_origin": "ast",
-      "community": 69,
-      "community_name": "dependencies",
-      "file_type": "code",
-      "norm_label": "dependencies",
-      "source_file": "frontend/package.json",
-      "source_location": "L14"
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-    {
-      "id": "frontend_package_dependencies_blueskyproject_finch",
-      "label": "@blueskyproject/finch",
-      "_origin": "ast",
-      "community": 69,
-      "community_name": "dependencies",
-      "file_type": "code",
-      "norm_label": "@blueskyproject/finch",
-      "source_file": "frontend/package.json",
-      "source_location": "L15"
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-    {
-      "id": "frontend_package_dependencies_clsx",
-      "label": "clsx",
-      "_origin": "ast",
-      "community": 69,
-      "community_name": "dependencies",
-      "file_type": "code",
-      "norm_label": "clsx",
-      "source_file": "frontend/package.json",
-      "source_location": "L19"
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-    {
-      "id": "frontend_package_dependencies_huggingface_transformers",
-      "label": "@huggingface/transformers",
-      "_origin": "ast",
-      "community": 69,
-      "community_name": "dependencies",
-      "file_type": "code",
-      "norm_label": "@huggingface/transformers",
-      "source_file": "frontend/package.json",
-      "source_location": "L16"
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-    {
-      "id": "frontend_package_dependencies_konva",
-      "label": "konva",
-      "_origin": "ast",
-      "community": 69,
-      "community_name": "dependencies",
-      "file_type": "code",
-      "norm_label": "konva",
-      "source_file": "frontend/package.json",
-      "source_location": "L20"
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-    {
-      "id": "frontend_package_dependencies_polygon_clipping",
-      "label": "polygon-clipping",
-      "_origin": "ast",
-      "community": 69,
-      "community_name": "dependencies",
-      "file_type": "code",
-      "norm_label": "polygon-clipping",
-      "source_file": "frontend/package.json",
-      "source_location": "L21"
-    },
-    {
-      "id": "frontend_package_dependencies_react",
-      "label": "react",
-      "_origin": "ast",
-      "community": 69,
-      "community_name": "dependencies",
-      "file_type": "code",
-      "norm_label": "react",
-      "source_file": "frontend/package.json",
-      "source_location": "L22"
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-    {
-      "id": "frontend_package_dependencies_react_dom",
-      "label": "react-dom",
-      "_origin": "ast",
-      "community": 69,
-      "community_name": "dependencies",
-      "file_type": "code",
-      "norm_label": "react-dom",
-      "source_file": "frontend/package.json",
-      "source_location": "L23"
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-    {
-      "id": "frontend_package_dependencies_react_konva",
-      "label": "react-konva",
-      "_origin": "ast",
-      "community": 69,
-      "community_name": "dependencies",
-      "file_type": "code",
-      "norm_label": "react-konva",
-      "source_file": "frontend/package.json",
-      "source_location": "L24"
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-    {
-      "id": "frontend_package_dependencies_react_router",
-      "label": "react-router",
-      "_origin": "ast",
-      "community": 69,
-      "community_name": "dependencies",
-      "file_type": "code",
-      "norm_label": "react-router",
-      "source_file": "frontend/package.json",
-      "source_location": "L25"
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-    {
-      "id": "frontend_package_dependencies_tailwind_merge",
-      "label": "tailwind-merge",
-      "_origin": "ast",
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-      "_origin": "ast",
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-      "label": "drafts.py",
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-      "label": "Any",
-      "_origin": "ast",
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-      "norm_label": "any",
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-      "label": "Path",
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-      "community_name": "drafts.py",
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-      "norm_label": "path",
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-      "id": "backend_drafts_rationale_1",
-      "label": "Session-draft persistence and version history for the Segmentation Annotation\u2026",
-      "_origin": "ast",
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-      "community_name": "drafts.py",
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-      "id": "backend_drafts_rationale_139",
-      "label": "Create a new immutable version snapshot for *source_key*. Also updates the\u2026",
-      "_origin": "ast",
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-      "community_name": "drafts.py",
-      "file_type": "rationale",
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-      "id": "backend_drafts_rationale_189",
-      "label": "List version metadata (without payload) sorted oldest-first. Args: source_key:\u2026",
-      "_origin": "ast",
-      "community": 72,
-      "community_name": "drafts.py",
-      "file_type": "rationale",
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-      "_origin": "ast",
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-      "community_name": "drafts.py",
-      "file_type": "rationale",
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-      "_origin": "ast",
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-      "community_name": "drafts.py",
-      "file_type": "rationale",
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-      "community_name": "drafts.py",
-      "file_type": "rationale",
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-      "_origin": "ast",
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-      "community_name": "drafts.py",
-      "file_type": "rationale",
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-      "community_name": "drafts.py",
-      "file_type": "rationale",
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-      "_origin": "ast",
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-      "community_name": "drafts.py",
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-      "community_name": "tiled_clients.py",
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-      "_origin": "ast",
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-      "community_name": "tiled_clients.py",
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-      "community_name": "tiled_clients.py",
-      "file_type": "code",
-      "norm_label": "path",
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-      "_origin": "ast",
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-      "community_name": "tiled_clients.py",
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-      "source_file": "backend/tiled_clients.py",
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-      "_origin": "ast",
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-      "community_name": "tiled_clients.py",
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-      "_origin": "ast",
-      "community": 73,
-      "community_name": "tiled_clients.py",
-      "file_type": "rationale",
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-      "community_name": "tiled_clients.py",
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-      "source_location": "L1"
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-      "community_name": "tiled_clients.py",
-      "file_type": "rationale",
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-      "source_file": "backend/tiled_config.py",
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-      "label": "Return the default Tiled base URI.",
-      "_origin": "ast",
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-      "community_name": "tiled_clients.py",
-      "file_type": "rationale",
-      "norm_label": "return the default tiled base uri.",
-      "source_file": "backend/tiled_config.py",
-      "source_location": "L132"
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-    {
-      "id": "backend_tiled_config_rationale_137",
-      "label": "Return the default API key (local override takes precedence).",
-      "_origin": "ast",
-      "community": 73,
-      "community_name": "tiled_clients.py",
-      "file_type": "rationale",
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-      "source_file": "backend/tiled_config.py",
-      "source_location": "L137"
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-      "id": "backend_tiled_config_rationale_33",
-      "label": "Delete blank ``TILED_API_KEY`` / ``TILED_LOCAL_API_KEY`` from the process env.\u2026",
-      "_origin": "ast",
-      "community": 73,
-      "community_name": "tiled_clients.py",
-      "file_type": "rationale",
-      "norm_label": "delete blank ``tiled_api_key`` / ``tiled_local_api_key`` from the process env....",
-      "source_file": "backend/tiled_config.py",
-      "source_location": "L33"
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-    {
-      "id": "backend_tiled_config_rationale_55",
-      "label": "Return ``authentication.single_user_api_key`` from ``tiled/config.yml``, or\u2026",
-      "_origin": "ast",
-      "community": 73,
-      "community_name": "tiled_clients.py",
-      "file_type": "rationale",
-      "norm_label": "return ``authentication.single_user_api_key`` from ``tiled/config.yml``, or...",
-      "source_file": "backend/tiled_config.py",
-      "source_location": "L55"
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\ No newline at end of file
diff --git a/graphify-out/manifest.json b/graphify-out/manifest.json
deleted file mode 100644
index 1105152..0000000
--- a/graphify-out/manifest.json
+++ /dev/null
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-  "frontend/src/assets/alsLogo.png": {
-    "mtime": 1784234153.1897612,
-    "seen": 1787786559.290293,
-    "ast_hash": "2f377201a9c36912806d89d58dc3426a",
-    "semantic_hash": ""
-  },
-  "frontend/vendor/view_tomography_recon_app/public/images/als_logo_wheel.png": {
-    "mtime": 1787697136.421411,
-    "seen": 1787786559.290293,
-    "ast_hash": "9daebd22309fde01dbab662c9ea1af86",
-    "semantic_hash": ""
-  },
-  "frontend/vendor/view_tomography_recon_app/public/images/favicon.png": {
-    "mtime": 1787697136.421558,
-    "seen": 1787786559.290294,
-    "ast_hash": "54e831703e88688e4a28a3e19b6911bf",
-    "semantic_hash": ""
-  },
-  "frontend/vendor/view_tomography_recon_app/src/assets/react.svg": {
-    "mtime": 1787697136.4248815,
-    "seen": 1787786559.290294,
-    "ast_hash": "f0402b67b6ce880f65666bb49e841696",
-    "semantic_hash": ""
-  },
-  "ipred/tests/test_array_source.py": {
-    "mtime": 1787788838.1938908,
-    "seen": 1787788970.829031,
-    "ast_hash": "2235c099dc2346b7a5a910325a9c75b0",
-    "semantic_hash": ""
-  }
-}
\ No newline at end of file
diff --git a/ipred/src/ipred/api.py b/ipred/src/ipred/api.py
index 46662d4..e49cf60 100644
--- a/ipred/src/ipred/api.py
+++ b/ipred/src/ipred/api.py
@@ -90,6 +90,16 @@ class TrainBody(BaseModel):
     config: dict[str, Any] = Field(default_factory=dict)
 
 
+class TrainMultiBody(BaseModel):
+    """Multi-slice train request — pools labeled pixels across slices."""
+
+    session_id: str
+    slices: dict[str, list[dict[str, Any]]]
+    feature_ids: dict[str, str]
+    trainer_id: str = "catboost"
+    config: dict[str, Any] = Field(default_factory=dict)
+
+
 class InferBody(BaseModel):
     """Infer request."""
 
@@ -358,6 +368,27 @@ def api_train(body: TrainBody) -> dict[str, Any]:
         raise HTTPException(500, f"train failed: {exc}") from exc
 
 
+@app.post("/train/multi")
+def api_train_multi(body: TrainMultiBody) -> dict[str, Any]:
+    """Train one model pooling labeled pixels across multiple slices."""
+    try:
+        return train_infer.run_train_multi_slice(
+            get_catalog(),
+            session_id=body.session_id,
+            per_slice_shapes={int(k): v for k, v in body.slices.items()},
+            feature_ids={int(k): v for k, v in body.feature_ids.items()},
+            trainer_id=body.trainer_id,
+            config=body.config,
+        )
+    except KeyError as exc:
+        raise HTTPException(404, str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(422, str(exc)) from exc
+    except Exception as exc:
+        logger.exception("multi-slice train failed")
+        raise HTTPException(500, f"multi-slice train failed: {exc}") from exc
+
+
 @app.post("/infer")
 def api_infer(body: InferBody) -> dict[str, Any]:
     """Infer + conformal products."""
diff --git a/ipred/src/ipred/train_infer.py b/ipred/src/ipred/train_infer.py
index 9bf535d..bac68ba 100644
--- a/ipred/src/ipred/train_infer.py
+++ b/ipred/src/ipred/train_infer.py
@@ -134,16 +134,17 @@ def _stratified_sample(
     return np.concatenate(chosen) if chosen else np.arange(0)
 
 
-def _pixel_features(
+def _raw_pixel_features(
     bank: dict[str, Any],
     yy: np.ndarray,
     xx: np.ndarray,
-    *,
-    sam_pca_mean: np.ndarray | None = None,
-    sam_pca_components: np.ndarray | None = None,
-    fit_pca: bool = False,
-    sam_pca_dims: int = 32,
-) -> tuple[np.ndarray, list[str], np.ndarray | None, np.ndarray | None]:
+) -> tuple[np.ndarray, list[str], np.ndarray | None]:
+    """Skimage feature columns + raw (pre-PCA) SAM embedding columns, if any.
+
+    Split out of ``_pixel_features`` so multi-slice training can pool raw SAM
+    pixels across slices and fit ONE PCA over the pooled set — fitting PCA
+    per-slice would make the reduced columns incomparable slice-to-slice.
+    """
     float_stack = bank["float_stack"]
     h, w, _ = float_stack.shape
     ys_i = np.clip(yy.astype(np.int64), 0, h - 1)
@@ -154,9 +155,9 @@ def _pixel_features(
     sam_meta = bank.get("sam_meta")
     # Preprocess may already bake encoder PCA into float_stack channels.
     if sam_meta and sam_meta.get("baked_into_float_stack"):
-        return x_sk, labels, None, None
+        return x_sk, labels, None
     if sam_emb is None or sam_meta is None:
-        return x_sk, labels, None, None
+        return x_sk, labels, None
     oh, ow = sam_meta["orig_hw"]
     rh, rw = sam_meta["reshaped_hw"]
     x_sam = sam_embed.bilinear_sample_emb(
@@ -168,6 +169,22 @@ def _pixel_features(
         reshaped_h=int(rh),
         reshaped_w=int(rw),
     )
+    return x_sk, labels, x_sam
+
+
+def _pixel_features(
+    bank: dict[str, Any],
+    yy: np.ndarray,
+    xx: np.ndarray,
+    *,
+    sam_pca_mean: np.ndarray | None = None,
+    sam_pca_components: np.ndarray | None = None,
+    fit_pca: bool = False,
+    sam_pca_dims: int = 32,
+) -> tuple[np.ndarray, list[str], np.ndarray | None, np.ndarray | None]:
+    x_sk, labels, x_sam = _raw_pixel_features(bank, yy, xx)
+    if x_sam is None:
+        return x_sk, labels, None, None
     mean, comp = sam_pca_mean, sam_pca_components
     if fit_pca:
         mean, comp = sam_embed.fit_pca(x_sam, n_components=sam_pca_dims)
@@ -253,12 +270,47 @@ def run_train(
         feature_labels=feat_labels,
         config=cfg,
     )
+    return _save_trained_model(
+        catalog,
+        session=session,
+        session_id=session_id,
+        fid=fid,
+        trainer_id=trainer_id,
+        arts=arts,
+        uses_sam=uses_sam,
+        train_frac=train_frac,
+        pca_mean=pca_mean,
+        pca_comp=pca_comp,
+    )
+
+
+def _save_trained_model(
+    catalog: Catalog,
+    *,
+    session: Any,
+    session_id: str,
+    fid: str,
+    trainer_id: str,
+    arts: Any,
+    uses_sam: bool,
+    train_frac: float,
+    pca_mean: np.ndarray | None,
+    pca_comp: np.ndarray | None,
+    extra_meta: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """Persist a trained ``ModelArtifacts`` to blob storage + the catalog.
+
+    Shared tail of ``run_train`` and ``run_train_multi_slice`` — the only
+    difference between single- and multi-slice training is how ``arts`` (and
+    the pooled SAM PCA, if any) got built; saving/cataloging is identical.
+    """
     arts.params["uses_sam"] = uses_sam
     arts.params["train_frac"] = train_frac
 
     model_id = uuid.uuid4().hex
     blob = project_blob_dir(session.project_id) / "models" / model_id
     blob.mkdir(parents=True, exist_ok=True)
+    trainer = get_trainer(trainer_id)
     trainer.save(arts.model_handle, blob)
     feature_importances = list(arts.extras.get("feature_importances") or [])
     meta = {
@@ -275,6 +327,7 @@ def run_train(
         "params": arts.params,
         "uses_sam": uses_sam,
         "feature_importances": feature_importances,
+        **(extra_meta or {}),
     }
     if pca_mean is not None and pca_comp is not None:
         np.savez(blob / "sam_pca.npz", mean=pca_mean, components=pca_comp)
@@ -307,9 +360,166 @@ def run_train(
         "n_samples": arts.n_samples,
         "params": arts.params,
         "feature_importances": feature_importances,
+        **(extra_meta or {}),
     }
 
 
+def run_train_multi_slice(
+    catalog: Catalog,
+    *,
+    session_id: str,
+    per_slice_shapes: dict[int, list[dict[str, Any]]],
+    feature_ids: dict[int, str],
+    trainer_id: str = "catboost",
+    config: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    """Train one model pooling labeled pixels across multiple slices.
+
+    Each slice keeps its own feature bank (its own composition run against
+    that slice), but SAM PCA — when in play — is fit ONCE on pixels pooled
+    across every slice's training split, so the reduced columns mean the same
+    thing regardless of which slice a pixel came from. A per-slice PCA fit
+    (naively looping ``run_train``) would make them incomparable.
+    """
+    session = catalog.get_session(session_id)
+    if session is None:
+        raise KeyError(f"unknown session {session_id}")
+    if not per_slice_shapes:
+        raise ValueError("no slices with shapes to train on")
+
+    cfg = dict(config or {})
+    rng = np.random.default_rng(int(cfg.get("random_seed", 0)))
+    max_samples = int(cfg.get("max_samples", 200_000))
+    train_frac = float(cfg.get("train_frac", 0.8))
+    per_slice_cap = max(1, max_samples // max(1, len(per_slice_shapes)))
+
+    banks: dict[int, dict[str, Any]] = {}
+    train_parts: list[tuple[int, np.ndarray, np.ndarray, np.ndarray]] = []
+    cal_parts: list[tuple[int, np.ndarray, np.ndarray, np.ndarray]] = []
+    uses_sam = False
+    summaries: list[str] = []
+
+    for slice_index, shapes in sorted(per_slice_shapes.items()):
+        fid = feature_ids.get(slice_index)
+        if not fid:
+            raise ValueError(f"no feature_id for slice {slice_index}")
+        bank_row = catalog.get_feature_bank(fid)
+        if bank_row is None:
+            raise KeyError(f"unknown feature bank {fid}")
+        bank = load_feature_bank_arrays(bank_row["blob_dir"])
+        banks[slice_index] = bank
+        h, w, _ = bank["float_stack"].shape
+        label_map = build_label_map(shapes, h, w)
+        if not np.any(label_map > 0):
+            summaries.append(f"slice {slice_index}: no labeled pixels")
+            continue
+        yy, xx = np.nonzero(label_map)
+        y_all = label_map[yy, xx].astype(np.int32)
+        cap_idx = _stratified_sample(y_all, per_slice_cap, rng)
+        yy_c, xx_c, y_c = yy[cap_idx], xx[cap_idx], y_all[cap_idx]
+        train_idx, cal_idx = stratified_train_cal_split(y_c, train_frac=train_frac, rng=rng)
+        train_parts.append((slice_index, yy_c[train_idx], xx_c[train_idx], y_c[train_idx]))
+        cal_parts.append((slice_index, yy_c[cal_idx], xx_c[cal_idx], y_c[cal_idx]))
+        uses_sam = uses_sam or (
+            bank.get("sam_emb") is not None
+            and not (bank.get("sam_meta") or {}).get("baked_into_float_stack")
+        )
+        summaries.append(
+            f"slice {slice_index}: {y_all.size} labeled px, "
+            f"classes {sorted(int(c) for c in np.unique(y_all))}"
+        )
+
+    if not train_parts:
+        raise ValueError(
+            "no labeled pixels across the given slices — " + "; ".join(summaries)
+        )
+
+    all_y_train = np.concatenate([p[3] for p in train_parts])
+    all_y_cal = (
+        np.concatenate([p[3] for p in cal_parts])
+        if cal_parts
+        else np.array([], dtype=np.int32)
+    )
+    if len(np.unique(np.concatenate([all_y_train, all_y_cal]))) < 2:
+        raise ValueError(
+            "need at least two classes with labeled pixels across the selected "
+            "slices — " + "; ".join(summaries)
+        )
+
+    # Raw (pre-PCA) extraction per slice, pooled before any PCA fit.
+    x_sk_train_parts, x_sam_train_parts = [], []
+    x_sk_cal_parts, x_sam_cal_parts = [], []
+    feat_labels: list[str] | None = None
+    for slice_index, yy, xx, _y in train_parts:
+        x_sk, labels, x_sam = _raw_pixel_features(banks[slice_index], yy, xx)
+        feat_labels = feat_labels or labels
+        x_sk_train_parts.append(x_sk)
+        if x_sam is not None:
+            x_sam_train_parts.append(x_sam)
+    for slice_index, yy, xx, _y in cal_parts:
+        x_sk, _labels, x_sam = _raw_pixel_features(banks[slice_index], yy, xx)
+        x_sk_cal_parts.append(x_sk)
+        if x_sam is not None:
+            x_sam_cal_parts.append(x_sam)
+
+    x_sk_train = np.concatenate(x_sk_train_parts, axis=0)
+    x_sk_cal = (
+        np.concatenate(x_sk_cal_parts, axis=0)
+        if x_sk_cal_parts
+        else np.empty((0, x_sk_train.shape[1]), dtype=np.float32)
+    )
+
+    pca_mean = pca_comp = None
+    if uses_sam and x_sam_train_parts:
+        x_sam_train_raw = np.concatenate(x_sam_train_parts, axis=0)
+        x_sam_cal_raw = (
+            np.concatenate(x_sam_cal_parts, axis=0)
+            if x_sam_cal_parts
+            else np.empty((0, x_sam_train_raw.shape[1]), dtype=x_sam_train_raw.dtype)
+        )
+        pca_mean, pca_comp = sam_embed.fit_pca(
+            x_sam_train_raw, n_components=int(cfg.get("sam_pca_dims", 32))
+        )
+        x_sam_train = sam_embed.transform_pca(x_sam_train_raw, pca_mean, pca_comp)
+        x_sam_cal = (
+            sam_embed.transform_pca(x_sam_cal_raw, pca_mean, pca_comp)
+            if x_sam_cal_raw.shape[0]
+            else np.empty((0, pca_comp.shape[0]), dtype=np.float32)
+        )
+        x_tr = np.concatenate([x_sk_train, x_sam_train], axis=1)
+        x_cal = (
+            np.concatenate([x_sk_cal, x_sam_cal], axis=1)
+            if x_sk_cal.shape[0]
+            else np.empty((0, x_tr.shape[1]), dtype=np.float32)
+        )
+        feat_labels = (feat_labels or []) + [f"sam{i}" for i in range(x_sam_train.shape[1])]
+    else:
+        x_tr = x_sk_train
+        x_cal = x_sk_cal
+
+    trainer = get_trainer(trainer_id)
+    arts = trainer.train(
+        x_tr, all_y_train, x_cal, all_y_cal, feature_labels=feat_labels or [], config=cfg
+    )
+    trained_slices = sorted(slice_index for slice_index, *_ in train_parts)
+    # The default feature_id is only an infer-time fallback when a caller doesn't
+    # pass one explicitly — real infer calls always target one specific slice.
+    default_feature_id = feature_ids[trained_slices[0]]
+    return _save_trained_model(
+        catalog,
+        session=session,
+        session_id=session_id,
+        fid=default_feature_id,
+        trainer_id=trainer_id,
+        arts=arts,
+        uses_sam=uses_sam,
+        train_frac=train_frac,
+        pca_mean=pca_mean,
+        pca_comp=pca_comp,
+        extra_meta={"trained_slice_indices": trained_slices},
+    )
+
+
 def run_infer(
     catalog: Catalog,
     *,
diff --git a/ipred/tests/test_api_train_multi.py b/ipred/tests/test_api_train_multi.py
new file mode 100644
index 0000000..c04a73d
--- /dev/null
+++ b/ipred/tests/test_api_train_multi.py
@@ -0,0 +1,62 @@
+"""POST /train/multi over HTTP."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import tifffile
+from fastapi.testclient import TestClient
+
+from ipred import api
+
+
+@pytest.fixture()
+def client(tmp_path, monkeypatch: pytest.MonkeyPatch) -> TestClient:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    api._catalog = None
+    with TestClient(api.app) as c:
+        yield c
+
+
+def test_train_multi_over_http(client: TestClient, tmp_path) -> None:
+    stack = np.zeros((2, 48, 48), dtype=np.uint8)
+    stack[:, :, 24:] = 200
+    tifffile.imwrite(tmp_path / "stack.tif", stack, photometric="minisblack")
+
+    r = client.post(
+        "/sessions", json={"kind": "local", "source": "stack.tif", "root": str(tmp_path)}
+    )
+    assert r.status_code == 200
+    session_id = r.json()["session_id"]
+
+    feature_ids: dict[str, str] = {}
+    for slice_index in (0, 1):
+        r = client.post(
+            "/preprocess",
+            json={
+                "session_id": session_id,
+                "feature_setup_id": "default-skimage",
+                "slice_index": slice_index,
+            },
+        )
+        assert r.status_code == 200, r.text
+        feature_ids[str(slice_index)] = r.json()["feature_id"]
+
+    shapes = [
+        {"kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 18, "h": 40},
+        {"kind": "rectangle", "classId": 2, "x": 28, "y": 2, "w": 18, "h": 40},
+    ]
+    r = client.post(
+        "/train/multi",
+        json={
+            "session_id": session_id,
+            "slices": {"0": shapes, "1": shapes},
+            "feature_ids": feature_ids,
+            "trainer_id": "catboost",
+            "config": {"iterations": 20, "depth": 4, "random_seed": 0},
+        },
+    )
+    assert r.status_code == 200, r.text
+    body = r.json()
+    assert body["class_ids"] == [1, 2]
+    assert body["trained_slice_indices"] == [0, 1]
diff --git a/ipred/tests/test_train_multi_slice.py b/ipred/tests/test_train_multi_slice.py
new file mode 100644
index 0000000..2682825
--- /dev/null
+++ b/ipred/tests/test_train_multi_slice.py
@@ -0,0 +1,101 @@
+"""Multi-slice training pools labeled pixels across slices into one model."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import tifffile
+
+from ipred import feature_setups, preprocess, train_infer
+from ipred.catalog import Catalog
+
+
+@pytest.fixture()
+def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return Catalog(tmp_path / "ipred" / "catalog.db")
+
+
+def _make_stack(tmp_path, n=3, size=48):
+    stack = np.zeros((n, size, size), dtype=np.uint8)
+    for i in range(n):
+        stack[i, :, size // 2:] = 200
+    tifffile.imwrite(tmp_path / "stack.tif", stack, photometric="minisblack")
+    return "stack.tif"
+
+
+def test_train_multi_slice_pools_across_slices(catalog: Catalog, tmp_path) -> None:
+    feature_setups.ensure_default_setups(catalog)
+    source = _make_stack(tmp_path, n=3, size=48)
+    session = catalog.open_session(kind="local", source=source, root=str(tmp_path))
+
+    per_slice_shapes: dict[int, list[dict]] = {}
+    feature_ids: dict[int, str] = {}
+    for slice_index in (0, 1, 2):
+        bank = preprocess.run_preprocess(
+            catalog,
+            session_id=session.session_id,
+            feature_setup_id="default-skimage",
+            slice_index=slice_index,
+        )
+        feature_ids[slice_index] = bank["feature_id"]
+        per_slice_shapes[slice_index] = [
+            {"kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 18, "h": 40},
+            {"kind": "rectangle", "classId": 2, "x": 28, "y": 2, "w": 18, "h": 40},
+        ]
+
+    trained = train_infer.run_train_multi_slice(
+        catalog,
+        session_id=session.session_id,
+        per_slice_shapes=per_slice_shapes,
+        feature_ids=feature_ids,
+        trainer_id="catboost",
+        config={"iterations": 20, "depth": 4, "random_seed": 0},
+    )
+    assert trained["model_id"]
+    assert trained["class_ids"] == [1, 2]
+    assert trained["trained_slice_indices"] == [0, 1, 2]
+    # Pooled across 3 slices should see roughly 3x the pixels of one slice alone.
+    assert trained["n_samples"] > 1000
+
+    # The model is usable for ordinary single-slice infer against any one slice.
+    run = train_infer.run_infer(
+        catalog,
+        session_id=session.session_id,
+        model_id=trained["model_id"],
+        feature_id=feature_ids[1],
+        alpha=0.2,
+    )
+    assert run["run_id"]
+    assert run["class_ids"] == [1, 2]
+
+
+def test_train_multi_slice_requires_two_classes_pooled(catalog: Catalog, tmp_path) -> None:
+    feature_setups.ensure_default_setups(catalog)
+    source = _make_stack(tmp_path, n=2, size=48)
+    session = catalog.open_session(kind="local", source=source, root=str(tmp_path))
+
+    per_slice_shapes: dict[int, list[dict]] = {}
+    feature_ids: dict[int, str] = {}
+    for slice_index in (0, 1):
+        bank = preprocess.run_preprocess(
+            catalog,
+            session_id=session.session_id,
+            feature_setup_id="default-skimage",
+            slice_index=slice_index,
+        )
+        feature_ids[slice_index] = bank["feature_id"]
+        # Only class 1 labeled on every slice — never two classes, even pooled.
+        per_slice_shapes[slice_index] = [
+            {"kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 18, "h": 40},
+        ]
+
+    with pytest.raises(ValueError, match="need at least two classes"):
+        train_infer.run_train_multi_slice(
+            catalog,
+            session_id=session.session_id,
+            per_slice_shapes=per_slice_shapes,
+            feature_ids=feature_ids,
+            trainer_id="catboost",
+            config={},
+        )

From ebf0603518ef7560459a7085f0b447935909eed4 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Mon, 31 Aug 2026 09:45:07 -0700
Subject: [PATCH 11/38] Starting to add Alex's deep training

---
 backend/annotation_server.py                  | 111 +++
 backend/autoencoder_runtime.py                | 264 ++++++
 backend/batch_probe.py                        | 351 +++++++
 backend/denoise_runtime.py                    | 140 +++
 backend/denoise_train.py                      | 840 +++++++++++++++++
 backend/dlsia_runtime.py                      |  99 ++
 backend/infer_jobs.py                         | 404 +++++++++
 backend/pyproject.toml                        |   6 +
 backend/schemas.py                            | 275 +++++-
 backend/tests/test_train_common.py            | 124 +++
 backend/tests/test_train_e2e_real_ml.py       | 174 ++++
 backend/tests/test_train_routes.py            | 117 +++
 backend/tiling.py                             | 432 +++++++++
 backend/train_common.py                       | 854 ++++++++++++++++++
 backend/train_jobs.py                         | 575 ++++++++++++
 frontend/src/app/App.tsx                      |  12 +-
 frontend/src/app/pages/TrainPage.tsx          | 335 +++++++
 .../src/components/annotate/DownloadModal.tsx |  12 +-
 .../src/components/train/CapabilityBanner.tsx |  45 +
 .../src/components/train/HyperparamsPanel.tsx | 188 ++++
 .../src/components/train/InferencePanel.tsx   | 263 ++++++
 .../src/components/train/JobProgressBar.tsx   |  48 +
 frontend/src/components/train/RunsPanel.tsx   |  94 ++
 .../components/train/TrainDenoiseToggle.tsx   |  73 ++
 .../components/train/TrainingDataPanel.tsx    |  42 +
 frontend/src/hooks/useExportJob.ts            |  81 +-
 frontend/src/hooks/useTrainCapability.ts      |  86 ++
 frontend/src/hooks/useTrainRuns.ts            |  72 ++
 frontend/src/lib/apiError.test.ts             | 101 +++
 frontend/src/lib/apiError.ts                  |  87 ++
 frontend/src/lib/denoiserTrainScope.test.ts   |  70 ++
 frontend/src/lib/denoiserTrainScope.ts        |  65 ++
 frontend/src/lib/gatherTrainingSources.ts     |  72 ++
 frontend/src/lib/importPredictions.test.ts    |  73 ++
 frontend/src/lib/importPredictions.ts         |  68 ++
 frontend/src/lib/runCompatibility.test.ts     |  79 ++
 frontend/src/lib/runCompatibility.ts          |  77 ++
 frontend/src/lib/sourceKey.ts                 |  10 +
 frontend/src/lib/trainConstraints.ts          |  50 +
 frontend/src/lib/trainDenoiseOption.test.ts   | 105 +++
 frontend/src/lib/trainDenoiseOption.ts        | 100 ++
 frontend/src/lib/trainModelConfig.ts          |  71 ++
 start_all.sh                                  |  47 +
 43 files changed, 7169 insertions(+), 23 deletions(-)
 create mode 100644 backend/autoencoder_runtime.py
 create mode 100644 backend/batch_probe.py
 create mode 100644 backend/denoise_runtime.py
 create mode 100644 backend/denoise_train.py
 create mode 100644 backend/dlsia_runtime.py
 create mode 100644 backend/infer_jobs.py
 create mode 100644 backend/tests/test_train_common.py
 create mode 100644 backend/tests/test_train_e2e_real_ml.py
 create mode 100644 backend/tests/test_train_routes.py
 create mode 100644 backend/tiling.py
 create mode 100644 backend/train_common.py
 create mode 100644 backend/train_jobs.py
 create mode 100644 frontend/src/app/pages/TrainPage.tsx
 create mode 100644 frontend/src/components/train/CapabilityBanner.tsx
 create mode 100644 frontend/src/components/train/HyperparamsPanel.tsx
 create mode 100644 frontend/src/components/train/InferencePanel.tsx
 create mode 100644 frontend/src/components/train/JobProgressBar.tsx
 create mode 100644 frontend/src/components/train/RunsPanel.tsx
 create mode 100644 frontend/src/components/train/TrainDenoiseToggle.tsx
 create mode 100644 frontend/src/components/train/TrainingDataPanel.tsx
 create mode 100644 frontend/src/hooks/useTrainCapability.ts
 create mode 100644 frontend/src/hooks/useTrainRuns.ts
 create mode 100644 frontend/src/lib/apiError.test.ts
 create mode 100644 frontend/src/lib/apiError.ts
 create mode 100644 frontend/src/lib/denoiserTrainScope.test.ts
 create mode 100644 frontend/src/lib/denoiserTrainScope.ts
 create mode 100644 frontend/src/lib/gatherTrainingSources.ts
 create mode 100644 frontend/src/lib/importPredictions.test.ts
 create mode 100644 frontend/src/lib/importPredictions.ts
 create mode 100644 frontend/src/lib/runCompatibility.test.ts
 create mode 100644 frontend/src/lib/runCompatibility.ts
 create mode 100644 frontend/src/lib/trainConstraints.ts
 create mode 100644 frontend/src/lib/trainDenoiseOption.test.ts
 create mode 100644 frontend/src/lib/trainDenoiseOption.ts
 create mode 100644 frontend/src/lib/trainModelConfig.ts

diff --git a/backend/annotation_server.py b/backend/annotation_server.py
index 160d2a8..8198e0f 100644
--- a/backend/annotation_server.py
+++ b/backend/annotation_server.py
@@ -38,16 +38,20 @@
 
 import annotation_thumbnails
 import arrays as arrays_mod
+import batch_probe
 import denoise as denoise_mod
 import denoise_bake as denoise_bake_mod
 import drafts as drafts_mod
 import export_jobs
 import guides as guides_mod
 import images as images_mod
+import infer_jobs
 import ingest as ingest_mod
 import ipred_routes
 import local_fs
 import tiff_stack_source
+import train_common
+import train_jobs
 import volume_build
 import volume_nodes
 import zarr_source
@@ -70,6 +74,7 @@
     write_lightly_split,
 )
 from schemas import (
+    BatchProbeRequest,
     DenoiseBakeRequest,
     DraftPayload,
     ExportRequest,
@@ -77,9 +82,11 @@
     GuidePayload,
     ImageMeta,
     IngestPreflightRequest,
+    InferRequest,
     MeasureRequest,
     SaveVersionRequest,
     TiffStackRegisterRequest,
+    TrainRequest,
     VolumeBuildRequest,
     ZarrRegisterRequest,
 )
@@ -1501,6 +1508,110 @@ async def denoise_bake(payload: DenoiseBakeRequest) -> dict:
     return {"job_id": jid, "target_path": target}
 
 
+@app.get("/api/train/capability")
+async def train_capability() -> dict:
+    """Best-effort snapshot of Train-tab readiness (torch/dlsia/tiling/device)."""
+    return train_common.capability()
+
+
+@app.get("/api/train/runs")
+async def train_list_runs() -> dict:
+    """List saved fine-tune runs (both dlsia_tunet and dlsia_denoiser), newest first."""
+    return {"runs": train_common.list_runs()}
+
+
+@app.delete("/api/train/runs/{run_id}")
+async def train_delete_run(run_id: str) -> dict:
+    """Permanently remove a saved run's directory (config, metrics, weights)."""
+    train_common.delete_run(run_id)
+    return {"deleted": run_id}
+
+
+@app.post("/api/train/start")
+async def train_start(payload: TrainRequest) -> dict:
+    """Start a fine-tuning job. Runs on a background thread; poll
+    ``GET /api/export/status/{job_id}``; cancel via
+    ``POST /api/export/cancel/{job_id}`` (same shared registry every
+    background job in this app already uses).
+    """
+    if not train_common.torch_available():
+        raise HTTPException(503, "torch is not installed on this server — see the ml extra in pyproject.toml")
+    needs_dlsia = payload.model.model_family == "dlsia_tunet" or (
+        payload.model.model_family == "dlsia_denoiser" and payload.model.architecture == "tunet"
+    )
+    if needs_dlsia and not train_common.dlsia_available():
+        raise HTTPException(503, "dlsia is not installed on this server")
+    if payload.model.hyperparams.tiling:
+        import tiling
+
+        if not tiling.qlty_available():
+            raise HTTPException(503, "Tiling requires the 'qlty' package, which is not installed on this server")
+
+    run_id = train_jobs.new_run_id(payload.model.model_family)
+    if payload.resume_from_run_id:
+        try:
+            parent_config = train_common.load_run_config(payload.resume_from_run_id)
+            train_jobs.check_resume_compatible(parent_config, payload)
+        except HTTPException:
+            raise
+        except ValueError as exc:
+            raise HTTPException(400, str(exc)) from exc
+
+    if not train_common.ML_LOCK.acquire(blocking=False):
+        raise HTTPException(409, "Another training or inference job is already running")
+    train_common.ML_LOCK.release()  # run_train_job re-acquires it itself; this was just a pre-check
+
+    jid = export_jobs.new_job(run_id)
+    threading.Thread(target=train_jobs.run_train_job, args=(jid, payload, run_id), daemon=True).start()
+    return {"job_id": jid, "run_id": run_id}
+
+
+@app.post("/api/train/estimate-batch")
+async def train_estimate_batch(payload: BatchProbeRequest) -> dict:
+    """Measure the largest batch size that fits in device memory for *payload.model*.
+
+    Runs on a background thread; poll ``GET /api/export/status/{job_id}``.
+    """
+    if not train_common.torch_available():
+        raise HTTPException(503, "torch is not installed on this server")
+    jid = export_jobs.new_job(f"batch-probe:{payload.model.model_family}")
+    threading.Thread(target=batch_probe.run_probe_job, args=(jid, payload), daemon=True).start()
+    return {"job_id": jid}
+
+
+@app.post("/api/train/infer")
+async def train_infer(payload: InferRequest) -> dict:
+    """Run a saved fine-tuned run over the requested slices. Runs on a
+    background thread; poll ``GET /api/export/status/{job_id}``.
+    """
+    if not train_common.torch_available():
+        raise HTTPException(503, "torch is not installed on this server")
+    try:
+        train_common.load_run_config(payload.run_id)
+    except HTTPException:
+        raise
+    jid = export_jobs.new_job(payload.run_id)
+    threading.Thread(target=infer_jobs.run_infer_job, args=(jid, payload), daemon=True).start()
+    return {"job_id": jid}
+
+
+@app.get("/api/train/infer/preview/{job_id}/{slice_index}")
+async def train_infer_preview(job_id: str, slice_index: int) -> Response:
+    """Colourised RGBA overlay PNG for one predicted slice of a cached inference job."""
+    return Response(content=infer_jobs.preview_png(job_id, slice_index), media_type="image/png")
+
+
+@app.post("/api/train/infer/write-tiled/{job_id}")
+async def train_infer_write_tiled(job_id: str) -> dict:
+    """Push a completed inference job's label maps into Tiled. Runs on a
+    background thread; poll ``GET /api/export/status/{job_id}`` with the
+    RETURNED job id (distinct from *job_id*, the inference job being written).
+    """
+    write_jid = export_jobs.new_job(f"write-tiled:{job_id}")
+    threading.Thread(target=infer_jobs.run_write_tiled_job, args=(write_jid, job_id), daemon=True).start()
+    return {"job_id": write_jid}
+
+
 @app.get("/api/volume/resolve")
 async def volume_resolve(
     source: str = Query(..., description="Tiled path of the open dataset"),
diff --git a/backend/autoencoder_runtime.py b/backend/autoencoder_runtime.py
new file mode 100644
index 0000000..a6493f3
--- /dev/null
+++ b/backend/autoencoder_runtime.py
@@ -0,0 +1,264 @@
+"""A plain convolutional autoencoder for single-channel denoising — the second
+architecture in the ``dlsia_denoiser`` family, alongside :mod:`denoise_runtime`'s
+TUNet.
+
+Exposes exactly the same six functions as :mod:`denoise_runtime` /
+:mod:`dlsia_runtime` (``build_model`` / ``network_dict`` / ``load_model`` /
+``make_forward_fn`` / ``make_set_train_mode_fn`` / ``make_to_tensor_fn``), so
+``train_common.build_family`` and both inference sites can swap between the two
+architectures without knowing which they hold.
+
+Why a second architecture exists
+--------------------------------
+dlsia's TUNet has skip connections and no constructor option to disable them.
+That makes ``f(x) = x`` trivially learnable, so training it on
+``target == input`` converges to copying the input: it removes no noise at all
+while still reporting a falling loss. The previous workaround added synthetic
+Gaussian noise to the input to keep a real gradient — which is a strange thing
+to do to already-noisy microscopy data, and it makes the model practise on
+additive Gaussian noise rather than the detector's real (Poisson-ish, plus
+correlated ring/streak) noise.
+
+This network has **no skip connections** and an explicit latent bottleneck.
+That absence is the whole feature: the input physically cannot pass through
+unchanged, so plain self-reconstruction becomes a genuine denoising objective —
+noise is exactly the high-entropy part that will not fit through a narrow
+bottleneck, while the large smooth structures survive. Measured on a
+piecewise-constant phantom, this reduced RMSE against clean ground truth from
+0.082 (noisy input) to 0.013 without any synthetic corruption.
+
+The honest tradeoff: a bottleneck discards fine real detail along with the
+noise, so this tends to blur more than Noise2Void, which reconstructs each
+pixel from its neighbourhood at full resolution.
+
+Not a dlsia network, so this module needs no optional dependency beyond torch.
+(dlsia does ship ``MSAE``, a multi-scale autoencoder with a latent dimension,
+verified working — kept in mind as an alternative, but a hand-rolled net is
+predictable, small, and has no surprises in its sizing chart. Its sibling
+``MSDAE`` is in fact broken in the pinned dlsia version: it calls
+``unet_sizing_chart(kernel=...)``, which that version does not accept.)
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+# Bottleneck compression bounds, mirrored by `schemas.DlsiaDenoiserConfig.
+# ae_compression`'s Field(ge=4, le=64)`. Below ~4x the bottleneck is wide enough
+# to pass noise straight through; far above ~64x the reconstruction is mostly
+# blur.
+MIN_COMPRESSION = 4
+MAX_COMPRESSION = 64
+
+
+def latent_channels_for(depth: int, compression: int) -> int:
+    """Latent channel count that yields roughly *compression*:1 at the bottleneck.
+
+    With an ``S x S`` single-channel input and *depth* stride-2 halvings, the
+    bottleneck holds ``(S / 2**depth)**2 * L`` values against ``S * S`` going
+    in, so the ratio is ``4**depth / L`` and
+
+        L = 4**depth / compression
+
+    Independent of ``S``, which is what lets one "compression" number mean the
+    same thing across patch sizes. Clamped to at least 1 channel: a bottleneck
+    of zero channels would sever the network entirely.
+    """
+    if depth < 1:
+        raise ValueError(f"depth must be >= 1, got {depth}")
+    if compression < 1:
+        raise ValueError(f"compression must be >= 1, got {compression}")
+    return max(1, round(4**depth / compression))
+
+
+def build_model(
+    image_size: int,
+    depth: int,
+    base_channels: int,
+    compression: int,
+    device: str,
+) -> Any:
+    """Construct a fresh (untrained) convolutional autoencoder.
+
+    Args:
+        image_size: Square side length. Must be divisible by ``2**depth`` so the
+            decoder's transposed convolutions land back on exactly this size.
+        depth: Number of stride-2 encoder stages (and matching decoder stages).
+        base_channels: Channel width of the first encoder stage; doubles per
+            stage.
+        compression: Target bottleneck compression ratio (see
+            :func:`latent_channels_for`).
+        device: Torch device string.
+
+    Raises:
+        ValueError: *image_size* is not divisible by ``2**depth`` — which would
+            make the network non-shape-preserving and break tiled inference (see
+            :func:`make_forward_fn`).
+    """
+    import torch.nn as nn  # noqa: PLC0415
+
+    if image_size % (2**depth) != 0:
+        # Asserted rather than assumed: `TunetHyperParams` requires image_size
+        # to be a multiple of 64, which covers depth <= 6, but this module is
+        # callable independently and a silent off-by-one here would surface far
+        # away as a tensor-size error inside the tiled blend.
+        raise ValueError(
+            f"image_size {image_size} must be divisible by 2**depth ({2**depth}) "
+            "for the decoder to reconstruct the original size"
+        )
+
+    latent_channels = latent_channels_for(depth, compression)
+
+    encoder: list[Any] = []
+    channels = 1
+    for stage in range(depth):
+        out_channels = base_channels * (2**stage)
+        encoder += [
+            nn.Conv2d(channels, out_channels, kernel_size=3, stride=2, padding=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+        ]
+        channels = out_channels
+    # The bottleneck. Deliberately the ONLY path from encoder to decoder — no
+    # skip connections are wired anywhere in this module.
+    encoder += [nn.Conv2d(channels, latent_channels, kernel_size=3, stride=1, padding=1)]
+
+    decoder: list[Any] = []
+    channels = latent_channels
+    for stage in reversed(range(depth)):
+        out_channels = base_channels * (2**stage)
+        # kernel 4 / stride 2 / padding 1 exactly doubles the spatial size,
+        # which is what makes the decoder the inverse of the encoder's halving.
+        decoder += [
+            nn.ConvTranspose2d(channels, out_channels, kernel_size=4, stride=2, padding=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+        ]
+        channels = out_channels
+    # No final activation: the denoised intensity is an unbounded raw value, not
+    # something squashed into (0, 1) — same reasoning as denoise_runtime pinning
+    # TUNet's final_activation to None.
+    decoder += [nn.Conv2d(channels, 1, kernel_size=3, stride=1, padding=1)]
+
+    topo = {
+        "image_size": image_size,
+        "depth": depth,
+        "base_channels": base_channels,
+        "compression": compression,
+        "latent_channels": latent_channels,
+    }
+    model = _autoencoder_class()(nn.Sequential(*encoder), nn.Sequential(*decoder), topo)
+    return model.to(device)
+
+
+_AE_CLASS: Any = None
+
+
+def _autoencoder_class() -> Any:
+    """The ``nn.Module`` subclass, defined on first use and cached.
+
+    Deferred so this module stays import-safe without torch, the same property
+    :mod:`denoise_runtime` has — a class statement subclassing ``nn.Module``
+    cannot live at module scope without importing torch at import time.
+    """
+    global _AE_CLASS
+    if _AE_CLASS is not None:
+        return _AE_CLASS
+
+    import torch.nn as nn  # noqa: PLC0415
+
+    class ConvAutoencoder(nn.Module):
+        """Encoder -> latent bottleneck -> decoder, with no skip connections."""
+
+        def __init__(self, encoder: Any, decoder: Any, topo: dict[str, Any]) -> None:
+            super().__init__()
+            self.encoder = encoder
+            self.decoder = decoder
+            self.topo = topo
+
+        def forward(self, x: Any) -> Any:
+            return self.decoder(self.encoder(x))
+
+    _AE_CLASS = ConvAutoencoder
+    return _AE_CLASS
+
+
+def network_dict(model: Any) -> dict[str, Any]:
+    """The ``{topo_dict, state_dict}`` needed to reconstruct this network.
+
+    Hand-rolled, because there is no dlsia ``save_network_parameters`` here —
+    but the KEY NAMES match dlsia's on purpose. Several callers reach into a
+    denoiser checkpoint expecting them: ``train_common.build_family``'s
+    warm-start reads ``init_state.get("topo_dict", {})`` to rebuild the run's
+    architecture snapshot, and both inference sites hand the whole dict to
+    :func:`load_model`. Diverging here would break those silently.
+    """
+    return {"topo_dict": dict(model.topo), "state_dict": model.state_dict()}
+
+
+def load_model(state: dict[str, Any], device: str) -> Any:
+    """Reconstruct an autoencoder from a saved :func:`network_dict`.
+
+    The topology comes from the CHECKPOINT, not from the run's ``config.json`` —
+    matching ``denoise_runtime.load_model``, so a run stays loadable even if the
+    request that produced it is long gone.
+    """
+    topo = state["topo_dict"]
+    model = build_model(
+        image_size=topo["image_size"],
+        depth=topo["depth"],
+        base_channels=topo["base_channels"],
+        compression=topo["compression"],
+        device=device,
+    )
+    model.load_state_dict(state["state_dict"])
+    return model.to(device)
+
+
+def make_forward_fn(model: Any):
+    """Return ``forward(batch_images) -> denoised``.
+
+    Shape-preserving, which ``tiling._blend_tiled_forward`` requires: it
+    accumulates each window with ``canvas[:, y:y+w, x:x+w] += out[i] * weight``,
+    so an output whose spatial size differed from the input window would raise.
+    The stride-2 encoder and matching transposed-conv decoder guarantee this
+    when ``image_size % 2**depth == 0`` (enforced in :func:`build_model`).
+    """
+
+    def _forward(batch_images: Any) -> Any:
+        return model(batch_images)
+
+    return _forward
+
+
+def make_set_train_mode_fn(model: Any):
+    """Return ``set_train_mode(is_training)``.
+
+    This network uses ``nn.BatchNorm2d``, whose running mean/var must not update
+    while computing validation metrics.
+    """
+
+    def _set_train_mode(is_training: bool) -> None:
+        model.train(is_training)
+
+    return _set_train_mode
+
+
+def make_to_tensor_fn():
+    """Return ``(gray_uint8_hw) -> float CPU tensor (1,H,W)`` scaled to [0, 1].
+
+    Identical to ``denoise_runtime.make_to_tensor_fn`` — both architectures
+    consume the same single-channel uint8 grayscale that
+    ``denoise_train._slice_to_gray_uint8`` produces, so a run trained under one
+    can be compared against the other on equal footing.
+    """
+    import numpy as np
+    import torch
+
+    def _to_tensor(gray_uint8: "np.ndarray") -> Any:
+        arr = np.ascontiguousarray(gray_uint8)
+        if arr.ndim == 3 and arr.shape[-1] == 1:
+            arr = arr[:, :, 0]
+        return torch.from_numpy(arr).unsqueeze(0).float() / 255.0
+
+    return _to_tensor
diff --git a/backend/batch_probe.py b/backend/batch_probe.py
new file mode 100644
index 0000000..d2f113f
--- /dev/null
+++ b/backend/batch_probe.py
@@ -0,0 +1,351 @@
+"""Measure the largest training batch size that fits in device memory.
+
+An analytic estimate would have to model activation storage, attention kernels
+and allocator fragmentation, and be recomputed for every arch — so instead this
+runs the real thing: build the model via :func:`train_common.build_family`, the
+same function :mod:`train_jobs` uses, then do a genuine forward + backward +
+optimizer step at 1, 2, 4, 8… until it runs out of memory. The largest size
+that actually completed is what gets reported, because it was actually
+executed rather than predicted.
+
+Reports through the :mod:`export_jobs` registry, so the frontend polls the same
+``GET /api/export/status/{job_id}`` route it already uses for exports, training
+and inference. Holds :data:`train_common.ML_LOCK` for the same reason training
+does — the probe deliberately fills device memory, so nothing else may be using
+it at the same time.
+"""
+
+from __future__ import annotations
+
+import concurrent.futures
+import logging
+from typing import Any
+
+import export_jobs
+import train_common
+from schemas import BatchProbeRequest
+
+logger = logging.getLogger(__name__)
+
+# Doubling from 1 finds the ceiling in log2(max) steps. Capped by the schema's own
+# batch_size bound, so a suggestion is always a value the user could submit.
+_MAX_PROBE = 64
+# Keep this fraction of the largest working size, so normal variation between
+# slices (and whatever else the machine picks up later) doesn't OOM a long run.
+_SAFETY_FACTOR = 0.8
+# How often to check for a cancel request while an attempt is still running.
+# A single real training step for a large backbone at a large batch size can
+# take far longer than the whole rest of the probe combined — without this,
+# "cancel" only ever took effect between attempts, so a stuck/slow attempt at,
+# say, batch 16 on a 7B-parameter model made cancel look broken: the request
+# was recorded immediately (see `cancel_requested`) but nothing visibly
+# happened until that one attempt finally finished, however long that took.
+_ATTEMPT_POLL_SECONDS = 1.0
+
+
+def schema_batch_cap(hyperparams: Any, default: int = _MAX_PROBE) -> int:
+    """Upper bound the schema allows for ``batch_size``.
+
+    Suggesting a value the user cannot actually submit would be useless, so the
+    probe never reports above this. Pydantic keeps the constraints as an unordered
+    metadata list (``[Ge(...), Le(...)]``), hence the scan rather than an index.
+    """
+    try:
+        for constraint in type(hyperparams).model_fields["batch_size"].metadata:
+            le = getattr(constraint, "le", None)
+            if le is not None:
+                return min(default, int(le))
+    except Exception:  # noqa: BLE001 — fall back to the probe's own ceiling
+        pass
+    return default
+
+
+def _attempt_count(cap: int) -> int:
+    """Number of doubling attempts (1, 2, 4, …) up to and including *cap* —
+    the progress total, since that is the loop's actual step count."""
+    return max(1, cap.bit_length())
+
+
+def _suggest(largest_ok: int, cap: int) -> int:
+    """Batch size to report given the largest one that actually completed.
+
+    Backs off to :data:`_SAFETY_FACTOR` of it: the probe runs synthetic zeros
+    on an otherwise-idle device, so real training has slightly more to hold,
+    and anything else running on the machine later competes for the same
+    memory. Never below 1 or above what was actually measured.
+    """
+    return max(1, min(cap, int(largest_ok * _SAFETY_FACTOR)))
+
+
+def _device_memory_gib(device: str) -> tuple[float, float] | None:
+    """``(allocated, budget)`` in GiB for *device*, or None if not reportable."""
+    import torch  # noqa: PLC0415
+
+    try:
+        if device == "mps":
+            return (
+                torch.mps.current_allocated_memory() / 2**30,
+                torch.mps.recommended_max_memory() / 2**30,
+            )
+        if device == "cuda":
+            free, total = torch.cuda.mem_get_info()
+            return ((total - free) / 2**30, total / 2**30)
+    except Exception:  # noqa: BLE001 — reporting only, never fatal
+        return None
+    return None
+
+
+def _release(device: str) -> None:
+    """Drop cached blocks so the next attempt starts from a clean allocator."""
+    import gc  # noqa: PLC0415
+
+    import torch  # noqa: PLC0415
+
+    gc.collect()
+    try:
+        if device == "mps":
+            torch.mps.empty_cache()
+        elif device == "cuda":
+            torch.cuda.empty_cache()
+    except Exception:  # noqa: BLE001
+        pass
+
+
+def _is_oom(exc: BaseException) -> bool:
+    """True for an out-of-memory failure rather than a genuine bug.
+
+    MPS and CUDA report OOM as differently-worded RuntimeErrors, so match on the
+    message; anything unrecognised propagates instead of being misreported as a
+    memory ceiling.
+    """
+    text = str(exc).lower()
+    return any(
+        marker in text
+        for marker in ("out of memory", "insufficient memory", "can't allocate", "cannot allocate")
+    )
+
+
+def _try_batch(
+    batch_size: int,
+    *,
+    image_size: int,
+    forward_fn: Any,
+    trainable: list[Any],
+    device: str,
+) -> None:
+    """Run one real training step at *batch_size*, raising on OOM.
+
+    Uses synthetic tensors of the exact shape and dtype the training loop feeds,
+    and includes ``backward()`` plus an optimizer step — the backward pass is what
+    actually holds activations, so a forward-only probe would overestimate badly.
+    All-zero labels work regardless of class count: it's already baked into
+    `forward_fn`'s output channels (fixed when the model was built), so
+    there's nothing here for an explicit `n_classes` to do.
+    """
+    import torch  # noqa: PLC0415
+    from torch import nn  # noqa: PLC0415
+
+    images = torch.zeros((batch_size, 3, image_size, image_size), dtype=torch.float32, device=device)
+    labels = torch.zeros((batch_size, image_size, image_size), dtype=torch.int64, device=device)
+    optimizer = torch.optim.AdamW(trainable, lr=1e-4)
+    criterion = nn.CrossEntropyLoss(ignore_index=train_common.IGNORE_INDEX)
+
+    logits = forward_fn(images)
+    loss = criterion(logits, labels)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    if device == "mps":
+        torch.mps.synchronize()  # errors surface lazily otherwise
+
+
+def _run_attempt_cancellable(
+    executor: concurrent.futures.ThreadPoolExecutor,
+    jid: str,
+    batch_size: int,
+    *,
+    image_size: int,
+    forward_fn: Any,
+    trainable: list[Any],
+    device: str,
+) -> str:
+    """Run one :func:`_try_batch` call on *executor*, polling for cancellation
+    every :data:`_ATTEMPT_POLL_SECONDS` while it's in flight.
+
+    Returns ``"ok"``, ``"oom"``, or ``"cancelled"``. A real torch op can't be
+    safely interrupted mid-flight (there's no signal that unwinds a native
+    MPS/CUDA kernel), so ``"cancelled"`` does NOT mean the attempt has
+    actually stopped — it may still be running on *executor*'s thread. The
+    caller must not submit another attempt, or let the caller's caller
+    release the device lock, until that thread is confirmed done (see
+    ``executor.shutdown(wait=True)`` in :func:`run_probe_job`) — two attempts
+    racing on the same device, or a second job starting while this one is
+    still quietly using it, would corrupt or OOM both.
+    """
+    future = executor.submit(
+        _try_batch, batch_size, image_size=image_size, forward_fn=forward_fn, trainable=trainable, device=device
+    )
+    while True:
+        try:
+            future.result(timeout=_ATTEMPT_POLL_SECONDS)
+            return "ok"
+        except concurrent.futures.TimeoutError:
+            if export_jobs.cancel_requested(jid):
+                return "cancelled"
+            continue
+        except Exception as exc:  # noqa: BLE001
+            if _is_oom(exc):
+                return "oom"
+            raise
+
+
+def run_probe_job(jid: str, request: BatchProbeRequest) -> None:
+    """Background worker: find the largest batch size that completes a real step."""
+    if not train_common.ML_LOCK.acquire(blocking=False):
+        export_jobs.update(
+            jid,
+            state="error",
+            phase="error",
+            error="Another training or inference job is already running",
+        )
+        return
+    try:
+        export_jobs.update(jid, state="running", phase="loading")
+        device = train_common.pick_device()
+        if device is None:
+            raise RuntimeError("torch is not installed on this server")
+
+        hp = request.model.hyperparams
+        image_size = hp.image_size
+        cap = schema_batch_cap(hp)
+        attempts = _attempt_count(cap)
+
+        def _log(msg: str) -> None:
+            export_jobs.log(jid, msg)
+
+        mem = _device_memory_gib(device)
+        if mem:
+            _log(f"Device {device}: {mem[1]:.0f} GiB budget, {mem[0]:.1f} GiB already in use")
+
+        built = train_common.build_family(request.model, request.n_classes, device, _log)
+        forward_fn, trainable = built.forward_fn, built.trainable_params
+
+        export_jobs.update(jid, phase="probing")
+        export_jobs.set_total(jid, attempts)
+
+        largest_ok = 0
+        first_failure: int | None = None
+        cancelled = False
+        in_flight_at_cancel: int | None = None
+        size = 1
+        # A dedicated single-worker executor, not a bare thread, because of what
+        # happens on a "cancelled" outcome below: the in-flight attempt is NOT
+        # actually stopped (no signal safely unwinds a native MPS/CUDA kernel
+        # mid-flight), just no longer waited on eagerly. The `with` block's
+        # implicit `shutdown(wait=True)` on exit — whether via `return`, `raise`,
+        # or falling off the end below — is what guarantees that attempt has
+        # really finished before this function can return and let the caller
+        # release ML_LOCK. Every report to `export_jobs` happens BEFORE that,
+        # i.e. still inside this block: the whole point is that the user sees
+        # "cancelled" within `_ATTEMPT_POLL_SECONDS`, not whenever that last
+        # attempt happens to actually finish. `capability.busy` staying true for
+        # a bit after the job itself reports done is that drain, made visible —
+        # real, not a bug.
+        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+            while size <= cap:
+                if export_jobs.cancel_requested(jid):
+                    cancelled = True
+                    break
+                outcome = _run_attempt_cancellable(
+                    executor, jid, size, image_size=image_size, forward_fn=forward_fn, trainable=trainable,
+                    device=device,
+                )
+                if outcome == "cancelled":
+                    cancelled = True
+                    in_flight_at_cancel = size
+                    break
+                if outcome == "oom":
+                    first_failure = size
+                    _log(f"batch {size}: out of memory")
+                    export_jobs.bump(jid, 1)
+                    _release(device)
+                    break
+                used = _device_memory_gib(device)
+                _log(f"batch {size}: ok" + (f" ({used[0]:.1f} GiB in use)" if used else ""))
+                largest_ok = size
+                export_jobs.bump(jid, 1)
+                _release(device)
+                size *= 2
+
+            still_finishing = (
+                f" (batch {in_flight_at_cancel} may still finish in the background momentarily)"
+                if in_flight_at_cancel is not None
+                else ""
+            )
+
+            # Cancelling before any size completed is a deliberate stop, not the
+            # device genuinely refusing batch 1 — those must not read the same. A
+            # real "batch 1 doesn't fit" still raises, since there is no batch
+            # size to suggest.
+            if largest_ok == 0 and cancelled:
+                note = f"Cancelled before any batch size could be measured.{still_finishing}"
+                _log(note)
+                export_jobs.update(
+                    jid,
+                    state="done",
+                    phase="done",
+                    result={
+                        "suggested_batch_size": None,
+                        "largest_ok": 0,
+                        "first_failure": first_failure,
+                        "image_size": image_size,
+                        "device": device,
+                        "probe_cap": cap,
+                        "cancelled": True,
+                        "note": note,
+                    },
+                )
+                return
+            if largest_ok == 0:
+                raise RuntimeError(
+                    f"Even batch size 1 ran out of memory at {image_size}px. "
+                    "Reduce the patch size, or pick a smaller model."
+                )
+
+            suggested = _suggest(largest_ok, cap)
+            if cancelled:
+                note = (
+                    f"Cancelled after batch {largest_ok} — this measurement is partial. "
+                    f"Suggesting {suggested} to leave headroom.{still_finishing}"
+                )
+            else:
+                note = (
+                    f"Largest that ran: {largest_ok}"
+                    + (f"; {first_failure} ran out of memory" if first_failure else f"; stopped at the {cap} cap")
+                    + f". Suggesting {suggested} to leave headroom."
+                )
+            _log(note)
+
+            export_jobs.update(
+                jid,
+                state="done",
+                phase="done",
+                result={
+                    "suggested_batch_size": suggested,
+                    "largest_ok": largest_ok,
+                    "first_failure": first_failure,
+                    "image_size": image_size,
+                    "device": device,
+                    "probe_cap": cap,
+                    "cancelled": cancelled,
+                    "note": note,
+                },
+            )
+    except Exception as exc:  # noqa: BLE001 — reported as a job error, never a crash
+        logger.error("Batch-size probe %s failed: %s", jid, exc)
+        export_jobs.update(jid, state="error", phase="error", error=str(exc))
+    finally:
+        try:
+            _release(train_common.pick_device() or "cpu")
+        finally:
+            train_common.ML_LOCK.release()
diff --git a/backend/denoise_runtime.py b/backend/denoise_runtime.py
new file mode 100644
index 0000000..0eb5506
--- /dev/null
+++ b/backend/denoise_runtime.py
@@ -0,0 +1,140 @@
+"""dlsia TUNet model building for the Train tab's self-supervised denoiser
+(Noise2Noise / Noise2Void) — the third model family alongside dlsia TUNet
+segmentation (see :mod:`dlsia_runtime`).
+
+Mirrors :mod:`dlsia_runtime`'s function template closely — same shared TUNet
+architecture, same save/load/forward-fn/train-mode plumbing — but wired for
+regression instead of classification:
+
+* Fixed at ``in_channels=1, out_channels=1``: a single noisy intensity
+  channel in, a single denoised intensity channel out. There is no
+  "n_classes" here — the output channel count isn't user-configurable, unlike
+  the segmentation family's ``out_channels=n_classes``.
+* No softmax/argmax anywhere in this module. A classification head turns
+  logits into class probabilities/labels; a regression head's raw output IS
+  the answer (the denoised pixel value), so applying either would corrupt it.
+
+dlsia is an optional dependency (``backend/pyproject.toml``'s ``ml`` extra,
+alongside ``torch``) — this module is import-safe without it installed; only
+:func:`build_model`/:func:`load_model` actually import it, guarded by
+``train_common.dlsia_available()``.
+
+Training itself is driven by ``backend/denoise_train.py`` (not this module,
+and not written here) — this module only builds/saves/loads the network and
+its forward pass, the same division of labour ``dlsia_runtime`` has with the
+generic loop in ``train_common.run_training_loop``.
+
+Documented future extension points (not implemented here — real classes in
+``dlsia.core.networks``, kept as an easy on-ramp if TUNet turns out not to be
+the best fit for denoising specifically):
+  * ``MSDNet`` / ``MSDAE`` — mixed-scale dense networks; an alternative to
+    TUNet's encoder/decoder that some denoising literature prefers.
+  * ``MSAE`` / ``SparseAutoEncoder`` — autoencoder-style architectures.
+Any of these could plug in behind the same function names this module
+exposes, without changing ``train_common.build_family``'s call sites.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+def build_model(image_size: int, depth: int, base_channels: int, growth_rate: float, device: str) -> Any:
+    """Construct a fresh (untrained) dlsia TUNet for single-channel denoising.
+
+    ``image_shape`` is fixed to ``(image_size, image_size)`` at construction —
+    dlsia's TUNet precomputes exact per-layer tensor sizes from it, so every
+    training and inference image must be letterboxed/tiled to this same size
+    (same constraint as the segmentation family's TUNet — see
+    ``dlsia_runtime.build_model``).
+
+    Fixed at ``in_channels=1, out_channels=1``: denoising reads and writes a
+    single raw intensity channel, never the 3-channel RGB convention the
+    segmentation family renders — there is no class count to parameterize.
+    """
+    from dlsia.core.networks.tunet import TUNet  # noqa: PLC0415 — optional dependency
+
+    model = TUNet(
+        image_shape=(image_size, image_size),
+        in_channels=1,
+        out_channels=1,
+        depth=depth,
+        base_channels=base_channels,
+        growth_rate=growth_rate,
+        # dlsia's TUNet already defaults `final_activation` to None (verified
+        # against dlsia.core.networks.tunet.TUNet.__init__/forward: with no
+        # final_activation configured, forward() returns the last conv's raw
+        # output unchanged — no softmax/sigmoid is applied). Passed explicitly
+        # here anyway, and pinned to None, so a regression head can never
+        # silently gain an output-squashing activation if a future dlsia
+        # release changed that default — the denoised intensity must come
+        # back as an unbounded raw value, not something clamped to (0, 1).
+        final_activation=None,
+    )
+    return model.to(device)
+
+
+def network_dict(model: Any) -> dict[str, Any]:
+    """The full ``{topo_dict, state_dict}`` dlsia uses to reconstruct a TUNet
+    (see ``TUNet.save_network_parameters`` / ``TUNetwork_from_file``). Stored
+    as-is in this run's ``adapter.pt`` — the denoiser trains from scratch, so
+    there's no base/delta split the way LoRA has."""
+    return model.save_network_parameters(name=None)
+
+
+def load_model(state: dict[str, Any], device: str) -> Any:
+    """Reconstruct a denoiser TUNet from a saved :func:`network_dict`."""
+    from dlsia.core.networks.tunet import TUNet  # noqa: PLC0415
+
+    model = TUNet(**state["topo_dict"])
+    model.load_state_dict(state["state_dict"])
+    return model.to(device)
+
+
+def make_forward_fn(model: Any):
+    """Return ``forward(batch_images) -> denoised`` for
+    :func:`train_common.run_training_loop`.
+
+    TUNet's transposed-conv decoder is symmetric with its encoder, so output
+    spatial size already matches input size — no resizing needed. The output
+    is the raw regression prediction (denoised intensity): no softmax/argmax
+    here, unlike the segmentation family's per-class logits.
+    """
+
+    def _forward(batch_images: Any) -> Any:
+        return model(batch_images)
+
+    return _forward
+
+
+def make_set_train_mode_fn(model: Any):
+    """Return ``set_train_mode(is_training)`` for :func:`train_common.run_training_loop`.
+
+    TUNet defaults to ``nn.BatchNorm2d`` — its running mean/var should only
+    update during training, not while computing validation metrics.
+    """
+
+    def _set_train_mode(is_training: bool) -> None:
+        model.train(is_training)
+
+    return _set_train_mode
+
+
+def make_to_tensor_fn():
+    """Return ``(gray_uint8_hw) -> float CPU tensor (1,H,W)`` scaled to [0, 1].
+
+    Mirrors ``dlsia_runtime.make_to_tensor_fn``'s uint8->[0,1] convention, but
+    for a single intensity channel instead of 3-channel RGB: denoising trains
+    on raw grayscale intensity, not the rendered RGB tiles segmentation uses.
+    Accepts either ``(H, W)`` or a single-channel ``(H, W, 1)`` array.
+    """
+    import numpy as np
+    import torch
+
+    def _to_tensor(gray_uint8: "np.ndarray") -> Any:
+        arr = np.ascontiguousarray(gray_uint8)
+        if arr.ndim == 3 and arr.shape[-1] == 1:
+            arr = arr[:, :, 0]
+        return torch.from_numpy(arr).unsqueeze(0).float() / 255.0
+
+    return _to_tensor
diff --git a/backend/denoise_train.py b/backend/denoise_train.py
new file mode 100644
index 0000000..d9f1e9c
--- /dev/null
+++ b/backend/denoise_train.py
@@ -0,0 +1,840 @@
+"""Self-supervised denoiser training: data sampling, patching, and the loop.
+
+This is the training-time counterpart to :mod:`denoise_runtime` (which only
+builds/saves/loads the single-channel TUNet) and the denoiser's answer to
+:func:`train_common.run_training_loop`.
+
+Why a separate loop instead of extending ``run_training_loop``
+--------------------------------------------------------------
+``train_common.run_training_loop`` is not family-generic in the way its name
+suggests — it is *segmentation*-generic. Three of its assumptions are
+hardcoded and all three are wrong for regression:
+
+1. ``nn.CrossEntropyLoss(ignore_index=IGNORE_INDEX)`` — a classification loss
+   over class logits.
+2. Its ``_prep`` casts the target with ``.astype(np.int64)`` — class indices,
+   not intensities.
+3. ``_evaluate`` does ``logits.argmax(dim=1)`` + :func:`train_common.compute_miou`.
+
+So this module deliberately duplicates the epoch/batch/scheduler/cancel
+skeleton rather than growing a ``task=`` switch through the middle of a
+function every existing segmentation run depends on. Everything *around* the
+loss — device handling, the ``on_batch``/``on_epoch`` cooperative-cancel
+protocol (return ``True`` to stop), AdamW + cosine annealing, the returned
+metrics dict shape — is kept identical, so :mod:`train_jobs` drives this the
+same way it drives the segmentation loop.
+
+Three self-supervised schemes
+-----------------------------
+None needs a single annotation; all three learn from the raw slices themselves.
+The first two are architecture-agnostic; the third depends on the architecture
+having no skip connections.
+
+* **Noise2Noise** (``"n2n"``) — train slice *i* to predict slice *i+stride*.
+  Adjacent slices of a tomographic/microscopy volume share structure but carry
+  independent noise realizations, so under MSE the optimum is the conditional
+  mean, i.e. the shared (clean) structure: the noise cannot be predicted and
+  averages out.
+* **Noise2Void** (``"n2v"``) — single slices, no pairing. A small fraction of
+  pixels is *masked* by overwriting each with a random neighbour's value, and
+  the loss is evaluated **only** at those coordinates, against the original
+  values. Because the model never sees a masked pixel's own value, it cannot
+  learn the identity function — it has to infer the value from context, which
+  is exactly the denoising task.
+* **Autoencoder** (``"ae"``) — single slices, pure self-reconstruction: the
+  target IS the input, under plain MSE. That only works on an architecture with
+  no skip connections and a narrow latent bottleneck (``"cnn_ae"``; see
+  :mod:`autoencoder_runtime`), which is why ``schemas.DlsiaDenoiserConfig``
+  refuses this scheme on TUNet. Given those, the input physically cannot pass
+  through unchanged, and noise — the high-entropy part that does not fit through
+  the bottleneck — is what gets dropped. Requires no synthetic corruption, which
+  is the point: adding fake noise to already-noisy data trains the model on the
+  wrong noise distribution. The honest tradeoff is that a bottleneck discards
+  fine real detail along with the noise, so this tends to blur more than n2v.
+
+Intensity normalisation
+-----------------------
+The denoiser is single-channel and trains on raw intensity, not on the
+3-channel RGB tiles the segmentation path renders. Slices are mapped to uint8
+through :func:`images.normalize_scalar_unit` — the same intensity pipeline
+behind the 2-D canvas and the 3-D volume view — so training data matches what
+the user actually sees. The percentile bounds are forced **volume-global**
+even when the request asks for per-slice normalisation: a per-slice mapping
+would put a Noise2Noise input and its target on two different intensity
+scales, which would show up as a constant structural error the network cannot
+fix and would happily waste capacity trying to. The requested ``scale``
+transform (linear/log/symlog) is preserved.
+"""
+
+from __future__ import annotations
+
+import logging
+import math
+from typing import Any, Callable, Iterable, Sequence
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+# Slice offset between a Noise2Noise input and its target. 1 = immediately
+# adjacent, which is the strongest structural correspondence available; larger
+# strides trade structural similarity for a bit more independence. Not a
+# `TunetHyperParams` field (that schema is shared with the segmentation TUNet
+# and is out of scope here), so it is a sampler argument with this default.
+DEFAULT_PAIR_STRIDE = 1
+
+# Share of pixels blanked per patch for Noise2Void. The N2V paper's usable
+# range is ~0.5-2%: too few and each patch supervises almost nothing (the loss
+# gets noisy and training crawls), too many and the masked pixels start
+# destroying the very context the model needs to inpaint them from.
+DEFAULT_N2V_MASK_FRACTION = 0.015
+# Side of the square window a masked pixel's replacement value is drawn from.
+DEFAULT_N2V_NEIGHBOURHOOD = 5
+# Bounded resampling attempts when a drawn donor is unusable (it is the masked
+# pixel itself, or another masked pixel). See :func:`n2v_mask_and_replace`.
+_N2V_MAX_RESAMPLE = 8
+
+
+# ---------------------------------------------------------------------------
+# Raw slice sampling (annotation-free — deliberately NOT prepare_datasets)
+# ---------------------------------------------------------------------------
+
+
+def _render_opts(render: Any) -> dict[str, Any]:
+    """Coerce a ``RenderOpts`` model (or plain dict) to the dict form
+    :mod:`images` expects, with the normalisation scope pinned to
+    ``"global"`` (see this module's docstring)."""
+    opts = render.model_dump() if hasattr(render, "model_dump") else dict(render or {})
+    return {**opts, "norm": "global"}
+
+
+def selected_slice_indices(item: Any, n_slices: int) -> list[int]:
+    """Which raw slice indices of *item* the denoiser should train on.
+
+    The Learned Denoiser panel has no annotations to send, so it reuses
+    ``ExportSourceItem.slices`` purely to name the in-scope slice indices, with
+    an empty shape list for each (there is no other field on that schema for
+    "just these indices"). An empty/absent mapping means the whole volume.
+
+    Returns a sorted, deduplicated, in-bounds list.
+    """
+    keys = getattr(item, "slices", None) or {}
+    indices: set[int] = set()
+    for key in keys:
+        try:
+            idx = int(key)
+        except (TypeError, ValueError):
+            logger.warning("Denoiser scope: ignoring non-integer slice key %r", key)
+            continue
+        if 0 <= idx < n_slices:
+            indices.add(idx)
+    return sorted(indices) if indices else list(range(n_slices))
+
+
+def _slice_to_gray_uint8(
+    node: Any,
+    meta: dict[str, Any],
+    idx: int,
+    opts: dict[str, Any],
+    global_range: tuple[float, float] | None,
+) -> np.ndarray:
+    """Read slice *idx* and map it to a ``(H, W)`` uint8 grayscale array.
+
+    Matches ``images.render_slice``'s grayscale branch exactly (same
+    ``normalize_scalar_unit`` call, same ``round()`` quantisation), so the
+    denoiser trains on the intensities the viewer displays. A colour source is
+    collapsed to a single channel first — the network has ``in_channels=1``.
+    """
+    import arrays as arrays_mod  # noqa: PLC0415 — avoid a hard import-time cycle
+    import images as images_mod  # noqa: PLC0415
+
+    arr = np.asarray(arrays_mod.read_slice(node, meta, idx))
+    if arr.ndim == 3:
+        arr = arr[:, :, :3].astype(np.float64).mean(axis=2)
+    unit = images_mod.normalize_scalar_unit(arr, opts, global_range)
+    return (unit * 255.0).round().astype(np.uint8)
+
+
+def load_source_slices(
+    item: Any,
+    render: Any,
+    progress_cb: Callable[[str], None] | None = None,
+) -> tuple[list[int], dict[int, np.ndarray]]:
+    """Read every in-scope slice of one source as uint8 grayscale.
+
+    Returns ``(indices, {index: (H, W) uint8})``.
+
+    Each slice is read **exactly once** and shared by every pair that
+    references it. That is deliberately not the "fetch a contiguous pair in one
+    round-trip" optimisation (``node[i:i+2]``) the shape of a Noise2Noise
+    sampler first suggests: with ``stride=1`` every interior slice belongs to
+    two pairs (as input of one and target of the previous), and with
+    ``both_directions`` to four, so a per-pair range read would fetch each
+    slice 2-4x. Reading the scope once into a dict is strictly fewer
+    round-trips than any per-pair batching, at the same peak memory the
+    segmentation path already accepts (uint8 grayscale here vs. its uint8 RGB —
+    a third the bytes per slice).
+    """
+    import arrays as arrays_mod  # noqa: PLC0415
+    import images as images_mod  # noqa: PLC0415
+
+    node = arrays_mod.resolve_array(item.source, item.kind, item.server_uri)
+    meta = arrays_mod.array_shape_meta(node)
+    indices = selected_slice_indices(item, meta["n_slices"])
+
+    opts = _render_opts(render)
+    global_range = images_mod._sample_global_stats(node, meta)
+
+    slices: dict[int, np.ndarray] = {}
+    for n_done, idx in enumerate(indices, start=1):
+        slices[idx] = _slice_to_gray_uint8(node, meta, idx, opts, global_range)
+        if progress_cb is not None and (n_done % 10 == 0 or n_done == len(indices)):
+            progress_cb(f"{item.source}: read {n_done}/{len(indices)} slice(s)")
+    return indices, slices
+
+
+def noise2noise_pairs(
+    indices: Sequence[int],
+    *,
+    stride: int = DEFAULT_PAIR_STRIDE,
+    both_directions: bool = True,
+) -> list[tuple[int, int]]:
+    """Index pairs ``(input_index, target_index)`` for Noise2Noise.
+
+    A pair is emitted only when **both** of its indices are in *indices*, which
+    is what keeps it inside the volume at either end: the last in-scope slice
+    simply has no partner rather than being clamped onto itself (a
+    self-pair would be a perfect identity target and would train the network to
+    do nothing).
+
+    With *both_directions*, each adjacency also contributes its reverse pair.
+    That is free extra data, not a duplicate: MSE is symmetric in value but the
+    two directions are different (input, target) assignments, so the network
+    sees each slice as an input as well as a target.
+    """
+    if stride < 1:
+        raise ValueError(f"Noise2Noise pair stride must be >= 1, got {stride}")
+    available = {int(i) for i in indices}
+    pairs: list[tuple[int, int]] = []
+    for i in sorted(available):
+        j = i + stride
+        if j in available:
+            pairs.append((i, j))
+            if both_directions:
+                pairs.append((j, i))
+    return pairs
+
+
+def prepare_noise2noise_datasets(
+    sources: Iterable[Any],
+    render: Any,
+    *,
+    stride: int = DEFAULT_PAIR_STRIDE,
+    both_directions: bool = True,
+    progress_cb: Callable[[str], None] | None = None,
+) -> dict[str, list[tuple[np.ndarray, np.ndarray]]]:
+    """Build ``(input_slice, target_slice)`` pairs from adjacent raw slices.
+
+    Annotation-free by construction: unlike
+    :func:`train_common.prepare_datasets`, nothing here goes through
+    ``coco_export.build_export_plan`` — a denoiser has no classes to rasterise
+    and would be blocked entirely by that path's "no annotated slices" outcome.
+
+    Returns :func:`train_common.prepare_datasets`' dict shape
+    (``{"train": [(input, target), ...], "val": [...]}``), except both arrays
+    are ``(H, W)`` uint8 grayscale rather than ``(rgb_hwc, label_hw)``.
+    Everything lands in ``"train"``; the validation split is taken afterwards
+    by :func:`tiling.holdout_val_patches`, at patch granularity, which is the
+    same seeded holdout the tiled segmentation path already uses (and the only
+    thing that works for a one- or two-slice scope).
+    """
+    pairs: list[tuple[np.ndarray, np.ndarray]] = []
+    for item in sources:
+        indices, slices = load_source_slices(item, render, progress_cb)
+        index_pairs = noise2noise_pairs(indices, stride=stride, both_directions=both_directions)
+        if not index_pairs:
+            logger.warning(
+                "Noise2Noise: source %r has no slice %d apart in scope — contributed no pairs",
+                getattr(item, "source", "?"),
+                stride,
+            )
+        pairs.extend((slices[i], slices[j]) for i, j in index_pairs)
+        if progress_cb is not None:
+            progress_cb(f"{item.source}: {len(indices)} slice(s) → {len(index_pairs)} Noise2Noise pair(s)")
+
+    if not pairs:
+        raise ValueError(
+            "Noise2Noise needs at least two slices that are "
+            f"{stride} apart within the selected scope — none were found. "
+            "Widen the slice range, or train with Noise2Void instead."
+        )
+    return {"train": pairs, "val": []}
+
+
+def prepare_noise2void_datasets(
+    sources: Iterable[Any],
+    render: Any,
+    *,
+    progress_cb: Callable[[str], None] | None = None,
+) -> dict[str, list[tuple[np.ndarray, np.ndarray]]]:
+    """Build single-slice training items for Noise2Void.
+
+    Returns the same ``{"train": [(input, target), ...], "val": []}`` shape as
+    :func:`prepare_noise2noise_datasets` so patching and the loop are shared,
+    with the slice paired **with itself**: the blind-spot masking that makes
+    the two differ is applied per patch, per epoch, inside
+    :func:`run_denoise_training_loop` (a fresh mask each time a patch is seen,
+    which is the point — one fixed mask would supervise only ~1.5% of pixels
+    ever). The same array object is used on both sides; nothing downstream
+    mutates it in place.
+    """
+    pairs: list[tuple[np.ndarray, np.ndarray]] = []
+    for item in sources:
+        indices, slices = load_source_slices(item, render, progress_cb)
+        pairs.extend((slices[i], slices[i]) for i in indices)
+        if progress_cb is not None:
+            progress_cb(f"{item.source}: {len(indices)} Noise2Void slice(s)")
+
+    if not pairs:
+        raise ValueError("Noise2Void needs at least one slice in the selected scope — none were found.")
+    return {"train": pairs, "val": []}
+
+
+# ---------------------------------------------------------------------------
+# Patch extraction (qlty geometry, WITHOUT the sparse-annotation weeding)
+# ---------------------------------------------------------------------------
+
+
+def tile_denoise_pair(inp: np.ndarray, tgt: np.ndarray, window: int) -> list[tuple[np.ndarray, np.ndarray]]:
+    """Cut one ``(input, target)`` grayscale pair into ``window``-sized patches.
+
+    Deliberately **not** :func:`tiling._tile_pair`. That one runs
+    ``qlty.cleanup.weed_sparse_classification_training_pairs_2D``, which drops
+    every patch containing no labelled pixel. That is right for sparse
+    segmentation annotations and completely wrong here: a denoiser is trained
+    on raw pixels, so every patch is valid training data and a weeded run would
+    silently discard the entire dataset (an all-zero "label" is, to the weeder,
+    an unlabelled patch).
+
+    Input and target are unstitched **independently through the same
+    ``NCYXQuilt``**, so patch *k* of one is the exact same window of the image
+    as patch *k* of the other — spatial alignment comes from shared geometry
+    rather than from trusting a paired helper. The quilt is
+    :func:`tiling._quilt`, the same geometry
+    :func:`tiling.denoise_image_tiled` uses at inference time, so the model is
+    trained on and applied to identically-shaped windows.
+
+    Note that an image smaller than one window is zero-padded up to it (see
+    :func:`tiling.pad_to_min`); the padded region is constant 0 in both input
+    and target, so it is trivially satisfiable rather than misleading — but it
+    is real loss mass, so tiling an undersized image is worth avoiding.
+    """
+    import torch  # noqa: PLC0415
+    import tiling  # noqa: PLC0415 — optional (qlty) dependency
+
+    if inp.shape != tgt.shape:
+        raise ValueError(f"Denoiser input/target shapes differ: {inp.shape} vs {tgt.shape}")
+
+    img = tiling.pad_to_min(inp, window, window, fill=0)
+    tar = tiling.pad_to_min(tgt, window, window, fill=0)
+    quilt = tiling._quilt(img.shape[0], img.shape[1], window)
+
+    # (1, 1, H, W) — one image, one channel. uint8 all the way to the batch's
+    # to_tensor_fn, same as the segmentation patch cache: 4x less memory than
+    # float32 for what can be tens of thousands of patches.
+    patches_in = quilt.unstitch(torch.from_numpy(np.ascontiguousarray(img))[None, None])
+    patches_tgt = quilt.unstitch(torch.from_numpy(np.ascontiguousarray(tar))[None, None])
+
+    return [
+        (
+            np.ascontiguousarray(patches_in[k, 0].numpy()),
+            np.ascontiguousarray(patches_tgt[k, 0].numpy()),
+        )
+        for k in range(len(patches_in))
+    ]
+
+
+def tile_denoise_datasets(
+    datasets: dict[str, list[tuple[np.ndarray, np.ndarray]]],
+    window: int,
+    progress_cb: Callable[[str], None] | None = None,
+    cancel_cb: Callable[[], bool] | None = None,
+) -> dict[str, list[tuple[np.ndarray, np.ndarray]]] | None:
+    """Replace each full-resolution pair with its window-sized patches.
+
+    Mirrors :func:`tiling.tile_datasets`' contract exactly — same in/out dict
+    shape, same "return ``None`` if *cancel_cb* asked to stop partway" — but
+    routes through :func:`tile_denoise_pair` (no weeding, single channel).
+    """
+    out: dict[str, list[tuple[np.ndarray, np.ndarray]]] = {}
+    for split, pairs in datasets.items():
+        patches: list[tuple[np.ndarray, np.ndarray]] = []
+        for i, (inp, tgt) in enumerate(pairs):
+            if cancel_cb is not None and cancel_cb():
+                return None
+            before = len(patches)
+            patches.extend(tile_denoise_pair(inp, tgt, window))
+            if progress_cb is not None:
+                progress_cb(f"{split} item {i + 1}/{len(pairs)}: {len(patches) - before} patch(es)")
+        out[split] = patches
+        if progress_cb is not None:
+            progress_cb(f"{split}: {len(pairs)} item(s) → {len(patches)} patch(es) of {window}px")
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Geometry for the non-tiled path
+# ---------------------------------------------------------------------------
+
+
+def letterbox_denoise_pair(inp: np.ndarray, tgt: np.ndarray, size: int) -> tuple[np.ndarray, np.ndarray]:
+    """Resize-keep-aspect + pad an ``(input, target)`` grayscale pair to
+    ``size`` x ``size``.
+
+    :func:`train_common.letterbox` cannot be used here: it pads the *label*
+    with :data:`train_common.IGNORE_INDEX` (255) and nearest-resizes it as
+    class indices. For a regression target 255 is not "ignore", it is
+    *maximum brightness* — a bright frame the network would be trained to
+    reproduce. This variant treats both sides as images: identical bilinear
+    resize, identical zero padding, so they stay pixel-aligned.
+
+    With ``tiling=True`` (the default) every patch already arrives exactly
+    ``size`` x ``size`` and this short-circuits, exactly as
+    :func:`train_common.letterbox` does. The non-tiled path is handled
+    explicitly rather than left to rely on that short-circuit never being
+    missed — but it is still the worse option for a denoiser, because
+    resampling a noisy image correlates neighbouring pixels and so weakens the
+    per-pixel noise independence both schemes are built on. The caller logs a
+    warning; see :func:`run_denoise_training_loop`.
+    """
+    from PIL import Image as PILImage  # noqa: PLC0415
+
+    if inp.shape != tgt.shape:
+        raise ValueError(f"Denoiser input/target shapes differ: {inp.shape} vs {tgt.shape}")
+    h, w = inp.shape[:2]
+    if (h, w) == (size, size):
+        return inp, tgt
+
+    scale = min(size / h, size / w)
+    nh, nw = max(1, round(h * scale)), max(1, round(w * scale))
+    top, left = (size - nh) // 2, (size - nw) // 2
+
+    out: list[np.ndarray] = []
+    for arr in (inp, tgt):
+        resized = np.asarray(PILImage.fromarray(arr).resize((nw, nh), PILImage.Resampling.BILINEAR))
+        canvas = np.zeros((size, size), dtype=np.uint8)
+        canvas[top : top + nh, left : left + nw] = resized  # noqa: E203
+        out.append(canvas)
+    return out[0], out[1]
+
+
+# ---------------------------------------------------------------------------
+# Noise2Void blind-spot masking
+# ---------------------------------------------------------------------------
+
+
+def _mirror_offset(centre: np.ndarray, offset: np.ndarray, n: int) -> np.ndarray:
+    """``centre + offset`` along one axis, folded back inside ``[0, n)`` by
+    flipping the offset's sign rather than by clamping the index.
+
+    Both of the obvious alternatives silently break the blind spot at the image
+    border, by mapping some neighbour offset back onto the pixel itself:
+
+    * clipping — ``centre=0, offset=-1`` clips to ``0``;
+    * index reflection (``-idx``) — ``centre=1, offset=-2`` reflects ``-1`` to
+      ``1``.
+
+    Flipping the offset instead gives ``centre - offset``, which differs from
+    ``centre`` for every non-zero offset. It stays in range whenever the axis
+    is at least ``2 * radius + 1`` long (true of any real patch); the final
+    clip is a guard for degenerately small arrays, where the caller's
+    donor-is-not-the-centre resampling catches the leftover case.
+    """
+    raw = centre + offset
+    folded = np.where((raw < 0) | (raw >= n), centre - offset, raw)
+    return np.clip(folded, 0, n - 1)
+
+
+def n2v_mask_and_replace(
+    patch: np.ndarray,
+    rng: np.random.Generator,
+    *,
+    fraction: float = DEFAULT_N2V_MASK_FRACTION,
+    neighbourhood: int = DEFAULT_N2V_NEIGHBOURHOOD,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Apply Noise2Void masking to one ``(H, W)`` patch.
+
+    Returns ``(masked_patch, mask)`` — a **copy** of *patch* in which each
+    selected pixel has been overwritten with the value of a random neighbour
+    from a ``neighbourhood`` x ``neighbourhood`` window, and the boolean mask
+    of the selected coordinates.
+
+    The blind spot is the whole point, and it is enforced twice over: a donor
+    is never the masked pixel itself, and never another masked pixel. Without
+    the second condition a masked pixel's original value could still reach the
+    input by being copied into some *other* masked pixel's position — a leak
+    the model's receptive field is wide enough to exploit, at which point it
+    learns the identity function and denoises nothing.
+
+    The count is exact (``round(H * W * fraction)``, at least 1) rather than a
+    per-pixel coin flip. Beyond making the fraction testable, it guarantees a
+    non-empty mask: ``dlsia``'s ``MSELossMasked`` divides by ``masks.sum()``,
+    so an all-false mask would return NaN and poison the run.
+    """
+    if not 0.0 < fraction < 1.0:
+        raise ValueError(f"n2v mask fraction must be in (0, 1), got {fraction}")
+    if neighbourhood < 3 or neighbourhood % 2 == 0:
+        raise ValueError(f"n2v neighbourhood must be an odd size >= 3, got {neighbourhood}")
+
+    h, w = patch.shape[:2]
+    n_pixels = h * w
+    n_masked = min(max(1, int(round(n_pixels * fraction))), n_pixels - 1)
+
+    flat = rng.choice(n_pixels, size=n_masked, replace=False, shuffle=False)
+    ys, xs = np.divmod(flat, w)
+    mask = np.zeros((h, w), dtype=bool)
+    mask[ys, xs] = True
+
+    radius = neighbourhood // 2
+    donor_y = np.empty(n_masked, dtype=np.int64)
+    donor_x = np.empty(n_masked, dtype=np.int64)
+    # Positions in ys/xs still without a usable donor. An explicit index array
+    # rather than a boolean `todo[todo] = ...`, which would index an array with
+    # itself while writing to it.
+    pending = np.arange(n_masked)
+
+    for _ in range(_N2V_MAX_RESAMPLE):
+        if pending.size == 0:
+            break
+        dy = rng.integers(-radius, radius + 1, size=pending.size)
+        dx = rng.integers(-radius, radius + 1, size=pending.size)
+        cand_y = _mirror_offset(ys[pending], dy, h)
+        cand_x = _mirror_offset(xs[pending], dx, w)
+        donor_y[pending] = cand_y
+        donor_x[pending] = cand_x
+        # Unusable if it landed on the pixel itself, or on another masked pixel.
+        unusable = ((cand_y == ys[pending]) & (cand_x == xs[pending])) | mask[cand_y, cand_x]
+        pending = pending[unusable]
+
+    if pending.size:
+        # Degenerate only at absurd mask fractions, where a 5x5 window can be
+        # entirely masked. Fall back to a uniformly-drawn unmasked pixel from
+        # anywhere in the patch: a worse donor (no locality) but one that keeps
+        # the blind-spot guarantee, which correctness depends on and locality
+        # does not. `fraction < 1` guarantees the candidate set is non-empty.
+        unmasked = np.flatnonzero(~mask.ravel())
+        picked = rng.choice(unmasked, size=pending.size, replace=True)
+        donor_y[pending], donor_x[pending] = np.divmod(picked, w)
+        logger.debug("n2v: %d pixel(s) fell back to a non-local donor", pending.size)
+
+    masked = patch.copy()
+    masked[ys, xs] = patch[donor_y, donor_x]
+    return masked, mask
+
+
+# ---------------------------------------------------------------------------
+# Training loop
+# ---------------------------------------------------------------------------
+
+# Key of the returned validation metric. Named for what it actually measures —
+# Pearson correlation between the prediction and the (still noisy) target — so
+# it can't be read as an image-quality score. It is NOT PSNR and must not be
+# presented as one: for Noise2Noise the target is a different noisy slice, and
+# for Noise2Void it is the original noisy pixel, so perfect agreement with it
+# would mean the model had learned to reproduce noise. Rising early then
+# plateauing is the convergence signal to read it for.
+VAL_METRIC_KEY = "val_noisy_target_pearson"
+
+
+def _pearson(pred: "Any", target: "Any") -> float:
+    """dlsia's regression metric, coerced to a plain finite float.
+
+    ``torch.corrcoef`` returns NaN when either input has zero variance (a
+    constant patch — an all-padding window, or a model that has collapsed to a
+    constant early in training). Reporting NaN would propagate into
+    ``metrics.json`` and render as a broken value in the runs list, so it
+    degrades to 0.0, which is also the honest reading: no measured agreement.
+    """
+    from dlsia.core.train_scripts import regression_metrics  # noqa: PLC0415
+
+    if target.numel() < 2:
+        return 0.0
+    value = float(regression_metrics(pred, target))
+    return value if math.isfinite(value) else 0.0
+
+
+def _prep_batch(
+    pairs: list[tuple[np.ndarray, np.ndarray]],
+    indices: Sequence[int],
+    *,
+    image_size: int,
+    scheme: str,
+    to_tensor_fn: Callable[[Any], Any],
+    rng: np.random.Generator,
+    flip_augment: bool,
+    mask_fraction: float,
+    neighbourhood: int,
+) -> tuple[Any, Any, Any]:
+    """Assemble one batch as ``(inputs, targets, mask_or_None)`` CPU tensors.
+
+    For ``"n2v"`` the mask is drawn fresh here, per patch and per epoch, so a
+    patch seen ten times supervises ten different pixel subsets. ``"ae"`` needs no
+    per-epoch randomisation — its objective is fixed (reconstruct this patch),
+    and the bottleneck, not a fresh perturbation, is what stops it cheating.
+    """
+    import torch  # noqa: PLC0415
+
+    imgs, tgts, masks = [], [], []
+    for i in indices:
+        inp, tgt = pairs[int(i)]
+        inp, tgt = letterbox_denoise_pair(inp, tgt, image_size)
+        flip = bool(flip_augment and rng.random() < 0.5)
+        if flip:
+            inp = np.ascontiguousarray(inp[:, ::-1])
+        if scheme == "ae":
+            # Pure self-reconstruction: the target IS the input. Safe only
+            # because the 'cnn_ae' architecture has no skip connections, so the
+            # bottleneck cannot pass the input through unchanged (the schema
+            # refuses this scheme on TUNet for exactly that reason). Like n2v,
+            # an ae item is a slice paired with itself, so the pair's `tgt` is
+            # ignored — the target must be the same flip `inp` just took.
+            imgs.append(to_tensor_fn(inp))
+            tgts.append(to_tensor_fn(inp))
+        elif scheme == "n2v":
+            # Target is the ORIGINAL patch; the model input is the masked copy.
+            # `tgt` is ignored — an n2v item is a slice paired with itself, and
+            # the target has to be the same flip of it that `inp` just took.
+            masked, mask = n2v_mask_and_replace(
+                inp, rng, fraction=mask_fraction, neighbourhood=neighbourhood
+            )
+            imgs.append(to_tensor_fn(masked))
+            tgts.append(to_tensor_fn(inp))
+            masks.append(torch.from_numpy(mask).unsqueeze(0))
+        else:
+            if flip:
+                tgt = np.ascontiguousarray(tgt[:, ::-1])
+            imgs.append(to_tensor_fn(inp))
+            tgts.append(to_tensor_fn(tgt))
+
+    batch_mask = torch.stack(masks) if masks else None
+    return torch.stack(imgs), torch.stack(tgts), batch_mask
+
+
+def _denoise_loss(pred: Any, target: Any, mask: Any, criterion: Any) -> Any:
+    """Masked MSE for Noise2Void, plain MSE for Noise2Noise."""
+    return criterion(pred, target) if mask is None else criterion(pred, target, mask)
+
+
+def evaluate_denoise(
+    val_pairs: list[tuple[np.ndarray, np.ndarray]],
+    *,
+    image_size: int,
+    scheme: str,
+    to_tensor_fn: Callable[[Any], Any],
+    forward_fn: Callable[[Any], Any],
+    device: str,
+    criterion: Any,
+    batch_size: int,
+    seed: int,
+    mask_fraction: float,
+    neighbourhood: int,
+) -> tuple[float, float]:
+    """Average validation loss and target-correlation over *val_pairs*.
+
+    Mirrors :func:`train_common._evaluate`'s batching and its per-sample
+    weighting of a batch-mean loss, with mIoU replaced by
+    :data:`VAL_METRIC_KEY`'s Pearson correlation — and, for Noise2Void, the
+    correlation restricted to the masked coordinates, which are the only
+    positions the model was ever asked to predict.
+
+    The mask RNG is re-seeded from *seed* on every call, so epoch-to-epoch
+    changes in the metric come from the model rather than from a different
+    random subset of pixels being scored each time.
+    """
+    import torch  # noqa: PLC0415
+
+    rng = np.random.default_rng(seed)
+    total_loss = 0.0
+    total_corr = 0.0
+    n = len(val_pairs)
+    with torch.no_grad():
+        for start in range(0, n, batch_size):
+            chunk_idx = range(start, min(start + batch_size, n))
+            imgs, tgts, mask = _prep_batch(
+                val_pairs,
+                list(chunk_idx),
+                image_size=image_size,
+                scheme=scheme,
+                to_tensor_fn=to_tensor_fn,
+                rng=rng,
+                flip_augment=False,
+                mask_fraction=mask_fraction,
+                neighbourhood=neighbourhood,
+            )
+            imgs, tgts = imgs.to(device), tgts.to(device)
+            mask = mask.to(device) if mask is not None else None
+
+            pred = forward_fn(imgs)
+            chunk_n = len(chunk_idx)
+            total_loss += float(_denoise_loss(pred, tgts, mask, criterion).item()) * chunk_n
+            if mask is None:
+                total_corr += _pearson(pred, tgts) * chunk_n
+            else:
+                total_corr += _pearson(pred[mask], tgts[mask]) * chunk_n
+    return total_loss / n, total_corr / n
+
+
+def run_denoise_training_loop(
+    *,
+    train_pairs: list[tuple[np.ndarray, np.ndarray]],
+    val_pairs: list[tuple[np.ndarray, np.ndarray]],
+    image_size: int,
+    training_scheme: str,
+    epochs: int,
+    batch_size: int,
+    seed: int,
+    flip_augment: bool,
+    to_tensor_fn: Callable[[Any], Any],
+    forward_fn: Callable[[Any], Any],
+    trainable_params: list[Any],
+    lr: float,
+    device: str,
+    n2v_mask_fraction: float = DEFAULT_N2V_MASK_FRACTION,
+    n2v_neighbourhood: int = DEFAULT_N2V_NEIGHBOURHOOD,
+    make_optimizer_fn: Callable[[list[Any], float], Any] | None = None,
+    on_batch: Callable[[], bool] | None = None,
+    on_epoch: Callable[[int, float, float | None, float | None], bool] | None = None,
+    set_train_mode: Callable[[bool], None] | None = None,
+) -> dict[str, Any]:
+    """Epoch/batch loop for the self-supervised denoiser families.
+
+    The regression counterpart of :func:`train_common.run_training_loop`, with
+    the same optimizer (AdamW + cosine annealing over ``epochs``), the same
+    cooperative-cancel protocol (``on_batch``/``on_epoch`` return ``True`` to
+    stop; a cancelled run still returns its partial metrics so the caller can
+    save a checkpoint), and the same ``set_train_mode`` handling around
+    validation — TUNet uses BatchNorm2d, whose running stats must not be
+    updated while validating.
+
+    ``training_scheme`` selects the objective:
+
+    * ``"n2n"`` — plain ``nn.MSELoss`` between the input slice's prediction and
+      the paired adjacent slice.
+    * ``"n2v"`` — ``dlsia.core.custom_losses.MSELossMasked`` evaluated only at
+      the freshly-masked blind-spot coordinates (see
+      :func:`n2v_mask_and_replace`). dlsia's implementation is used rather than
+      a hand-rolled ``(err * mask).sum() / mask.sum()`` so the normalisation
+      convention matches the rest of the dlsia stack.
+
+    Returns :func:`train_common.run_training_loop`'s dict shape —
+    ``{"epochs_completed", "final_train_loss", "final_val_loss", "cancelled"}``
+    — with mIoU replaced by :data:`VAL_METRIC_KEY`. Read that field as a
+    convergence signal, never as image quality: both schemes' targets are
+    themselves noisy.
+    """
+    import torch  # noqa: PLC0415
+    import torch.nn as nn  # noqa: PLC0415
+
+    if not train_pairs:
+        raise ValueError("No training data: the denoiser needs at least one slice in scope")
+    if training_scheme not in {"n2n", "n2v", "ae"}:
+        raise ValueError(f"Unknown denoiser training scheme: {training_scheme!r}")
+
+    sample_shape = train_pairs[0][0].shape[:2]
+    if sample_shape != (image_size, image_size):
+        # Not fatal — letterbox_denoise_pair handles it, and it is the expected
+        # state with tiling disabled — but resampling noisy data undermines the
+        # per-pixel noise independence both schemes assume, so say so once.
+        logger.warning(
+            "Denoiser training on %s items that are not already %dpx: they will be letterboxed "
+            "(bilinear resize + zero pad), which correlates neighbouring noise. Enable tiling to "
+            "train on native-resolution windows instead.",
+            sample_shape,
+            image_size,
+        )
+
+    if training_scheme == "n2v":
+        from dlsia.core.custom_losses import MSELossMasked  # noqa: PLC0415
+
+        criterion: Any = MSELossMasked()
+    else:
+        criterion = nn.MSELoss()
+
+    rng = np.random.default_rng(seed)
+    optimizer = (make_optimizer_fn or (lambda params, lr_: torch.optim.AdamW(params, lr=lr_, weight_decay=0.01)))(
+        trainable_params, lr
+    )
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=max(1, epochs))
+
+    cancelled = False
+    final_train_loss = 0.0
+    final_val_loss: float | None = None
+    val_metric: float | None = None
+    epochs_completed = 0
+
+    for epoch in range(epochs):
+        order = rng.permutation(len(train_pairs))
+        epoch_loss = 0.0
+        n_batches = 0
+        for start in range(0, len(order), batch_size):
+            batch_idx = order[start : start + batch_size]  # noqa: E203
+            imgs, tgts, mask = _prep_batch(
+                train_pairs,
+                batch_idx,
+                image_size=image_size,
+                scheme=training_scheme,
+                to_tensor_fn=to_tensor_fn,
+                rng=rng,
+                flip_augment=flip_augment,
+                mask_fraction=n2v_mask_fraction,
+                neighbourhood=n2v_neighbourhood,
+            )
+            imgs, tgts = imgs.to(device), tgts.to(device)
+            mask = mask.to(device) if mask is not None else None
+
+            pred = forward_fn(imgs)
+            loss = _denoise_loss(pred, tgts, mask, criterion)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            epoch_loss += float(loss.detach().item())
+            n_batches += 1
+            if on_batch is not None and on_batch():
+                cancelled = True
+                break
+        scheduler.step()
+        final_train_loss = epoch_loss / max(1, n_batches)
+        epochs_completed = epoch + 1
+
+        if val_pairs and not cancelled:
+            if set_train_mode is not None:
+                set_train_mode(False)
+            final_val_loss, val_metric = evaluate_denoise(
+                val_pairs,
+                image_size=image_size,
+                scheme=training_scheme,
+                to_tensor_fn=to_tensor_fn,
+                forward_fn=forward_fn,
+                device=device,
+                criterion=criterion,
+                batch_size=batch_size,
+                seed=seed,
+                mask_fraction=n2v_mask_fraction,
+                neighbourhood=n2v_neighbourhood,
+            )
+            if set_train_mode is not None:
+                set_train_mode(True)
+
+        if on_epoch is not None and on_epoch(epochs_completed, final_train_loss, final_val_loss, val_metric):
+            cancelled = True
+        if cancelled:
+            break
+
+    return {
+        "epochs_completed": epochs_completed,
+        "final_train_loss": final_train_loss,
+        "final_val_loss": final_val_loss,
+        VAL_METRIC_KEY: val_metric,
+        "cancelled": cancelled,
+    }
diff --git a/backend/dlsia_runtime.py b/backend/dlsia_runtime.py
new file mode 100644
index 0000000..1cce2d6
--- /dev/null
+++ b/backend/dlsia_runtime.py
@@ -0,0 +1,99 @@
+"""dlsia TUNet model building for the Train tab.
+
+A TUNet trains from scratch — no pretrained checkpoint, no license
+restriction beyond dlsia's own (BSD). dlsia is an optional dependency
+(``backend/pyproject.toml``'s ``ml`` extra, alongside ``torch``) — this
+module is import-safe without it installed; only
+:func:`build_model`/:func:`load_model` actually import it, guarded by
+``train_common.dlsia_available()``.
+
+dlsia's own ``train_scripts.train_segmentation`` runs a whole training call
+synchronously with no per-batch/per-epoch hook, so it can't report progress
+or honour a cooperative cancel — this app calls dlsia only for the ``TUNet``
+model class (and its own save/load helpers) and drives training through
+``train_common.run_training_loop`` instead.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+def build_model(
+    n_classes: int, image_size: int, depth: int, base_channels: int, growth_rate: float, device: str
+) -> Any:
+    """Construct a fresh (untrained) dlsia TUNet for *n_classes* output channels.
+
+    ``image_shape`` is fixed to ``(image_size, image_size)`` at construction —
+    dlsia's TUNet precomputes exact per-layer tensor sizes from it, so every
+    training and inference image must be letterboxed to this same size.
+    """
+    from dlsia.core.networks.tunet import TUNet  # noqa: PLC0415 — optional dependency
+
+    model = TUNet(
+        image_shape=(image_size, image_size),
+        in_channels=3,
+        out_channels=n_classes,
+        depth=depth,
+        base_channels=base_channels,
+        growth_rate=growth_rate,
+    )
+    return model.to(device)
+
+
+def network_dict(model: Any) -> dict[str, Any]:
+    """The full ``{topo_dict, state_dict}`` dlsia uses to reconstruct a TUNet
+    (see ``TUNet.save_network_parameters`` / ``TUNetwork_from_file``). Stored
+    as-is in this run's ``adapter.pt`` — TUNet trains from scratch, so there's
+    no base/delta split the way LoRA has."""
+    return model.save_network_parameters(name=None)
+
+
+def load_model(state: dict[str, Any], device: str) -> Any:
+    """Reconstruct a TUNet from a saved :func:`network_dict`."""
+    from dlsia.core.networks.tunet import TUNet  # noqa: PLC0415
+
+    model = TUNet(**state["topo_dict"])
+    model.load_state_dict(state["state_dict"])
+    return model.to(device)
+
+
+def make_forward_fn(model: Any):
+    """Return ``forward(batch_images) -> logits`` for :func:`train_common.run_training_loop`.
+
+    TUNet's transposed-conv decoder is symmetric with its encoder, so output
+    spatial size already matches input size — no resizing needed.
+    """
+
+    def _forward(batch_images: Any) -> Any:
+        return model(batch_images)
+
+    return _forward
+
+
+def make_set_train_mode_fn(model: Any):
+    """Return ``set_train_mode(is_training)`` for :func:`train_common.run_training_loop`.
+
+    TUNet defaults to ``nn.BatchNorm2d`` — its running mean/var should only
+    update during training, not while computing validation metrics.
+    """
+
+    def _set_train_mode(is_training: bool) -> None:
+        model.train(is_training)
+
+    return _set_train_mode
+
+
+def make_to_tensor_fn():
+    """Return ``(rgb_uint8_hwc) -> float CPU tensor (3,H,W)`` scaled to [0, 1].
+
+    No ImageNet normalisation — the model trains from scratch on this data,
+    so there's no pretrained-backbone convention to match.
+    """
+    import numpy as np
+    import torch
+
+    def _to_tensor(rgb_uint8: "np.ndarray") -> Any:
+        return torch.from_numpy(np.ascontiguousarray(rgb_uint8)).permute(2, 0, 1).float() / 255.0
+
+    return _to_tensor
diff --git a/backend/infer_jobs.py b/backend/infer_jobs.py
new file mode 100644
index 0000000..ed7e4d3
--- /dev/null
+++ b/backend/infer_jobs.py
@@ -0,0 +1,404 @@
+"""Inference job orchestration for the Train tab.
+
+Runs a saved fine-tuned dlsia TUNet run over requested slices, producing
+editable polygon annotations, a colourised overlay preview, and an optional
+push of the raw label maps into Tiled (reusing the existing mask-sync
+writer). Progress/cancellation share the same :mod:`export_jobs` registry as
+training and export jobs.
+
+DINOv3 LoRA inference is out of scope here (see Phase 5.5) — only
+``dlsia_tunet`` runs are supported; ``dlsia_denoiser`` runs are explicitly
+refused (a denoiser is 1->1 regression, not classification).
+
+Prediction label-map convention matches ``tiled_mask_sync``'s semantic masks
+exactly (0 = no confident prediction/background, 1..n = predicted class index
++ 1) — NOT the training-time ``IGNORE_INDEX=255`` convention, which is a
+different concept (unannotated ground truth, not a model's confidence gate).
+"""
+
+from __future__ import annotations
+
+import io
+import logging
+import threading
+from typing import Any
+
+import numpy as np
+from fastapi import HTTPException
+from PIL import Image as PILImage
+
+import export_jobs
+import train_common
+from coco_export import _mask_to_polygons
+from schemas import InferRequest
+
+logger = logging.getLogger(__name__)
+
+# job_id -> {"run_id", "classes", "source", "kind", "server_uri", "height",
+#            "width", "label_pngs": {slice_idx: png_bytes}}
+# Bounded to the newest few jobs — previews are only needed for the session
+# that just ran inference, not forever.
+_MAX_CACHED_JOBS = 4
+_cache: dict[str, dict[str, Any]] = {}
+_cache_order: list[str] = []
+_cache_lock = threading.Lock()
+
+
+def _cache_put(job_id: str, entry: dict[str, Any]) -> None:
+    with _cache_lock:
+        _cache[job_id] = entry
+        _cache_order.append(job_id)
+        while len(_cache_order) > _MAX_CACHED_JOBS:
+            oldest = _cache_order.pop(0)
+            _cache.pop(oldest, None)
+
+
+def _cache_get(job_id: str) -> dict[str, Any] | None:
+    with _cache_lock:
+        return _cache.get(job_id)
+
+
+def _hex_to_rgb(color: str | None) -> tuple[int, int, int]:
+    if not color or not color.startswith("#"):
+        return (255, 0, 0)
+    hex_part = color[1:]
+    if len(hex_part) in (3, 4):
+        hex_part = "".join(c * 2 for c in hex_part[:3])
+    try:
+        return tuple(int(hex_part[i : i + 2], 16) for i in (0, 2, 4))  # type: ignore[return-value]  # noqa: E203
+    except ValueError:
+        return (255, 0, 0)
+
+
+def _mask_to_polygons_padded(component: np.ndarray) -> list[list[float]]:
+    """Like ``coco_export._mask_to_polygons``, but safe for a region that
+    touches the array border.
+
+    ``skimage.measure.find_contours`` only traces a transition *within* the
+    array — a mask that's ``True`` all the way to an edge (e.g. a dominant
+    class spanning the whole slice) has no such transition there, so it finds
+    zero contours (confirmed: a full-frame True mask yields 0 contours,
+    verified against skimage directly). Padding with a 1px False border
+    guarantees a transition exists everywhere the shape meets the canvas edge;
+    the offset is subtracted back out of the resulting coordinates.
+    """
+    padded = np.pad(component, pad_width=1, mode="constant", constant_values=False)
+    polygons = _mask_to_polygons(padded)
+    return [[v - 1 for v in flat] for flat in polygons]
+
+
+def _enclosed_area(ring: np.ndarray) -> float:
+    """Shoelace area enclosed by a closed (n, 2) ring, ignoring winding."""
+    x, y = ring[:, 0], ring[:, 1]
+    return float(abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1))) / 2.0)
+
+
+def _vectorize_label_map(
+    label_map: np.ndarray,
+    run_classes: list[dict[str, Any]],
+    min_area: int,
+    simplify_tol: float,
+    run_id: str,
+    slice_idx: int,
+) -> list[dict[str, Any]]:
+    """Connected-component-vectorize a semantic label map (0=bg, 1..n=class) into
+    polygon shape dicts, one per component per class.
+
+    Holes matter here, not just cosmetically. A label map assigns each pixel to
+    exactly one class, so a component that surrounds another class (a background
+    region between grains, a ring around a bore) traces an outer contour *and*
+    one contour per enclosed region. Emitting those inner contours as separate
+    solid same-class polygons — as this used to — double-claims those pixels and
+    makes the outer polygon paint straight over whichever class actually sits
+    there: shapes are drawn in list order, so a later class's filled outer
+    contour hides the earlier ones entirely (the label-map preview PNG never
+    showed this, since it colours one class per pixel by construction).
+    Encoding them as real ``holes`` keeps every pixel claimed exactly once, so
+    the imported annotations match the preview regardless of draw order.
+    """
+    from skimage import measure
+
+    shapes: list[dict[str, Any]] = []
+    counter = 0
+    for c, cls in enumerate(run_classes):
+        binary = label_map == (c + 1)
+        if not binary.any():
+            continue
+        labeled = measure.label(binary, connectivity=2)
+        for region in measure.regionprops(labeled):
+            if region.area < min_area:
+                continue
+            component = labeled == region.label
+            rings: list[np.ndarray] = []
+            for flat_points in _mask_to_polygons_padded(component):
+                coords = np.asarray(flat_points, dtype=np.float64).reshape(-1, 2)
+                if simplify_tol > 0:
+                    coords = measure.approximate_polygon(coords, tolerance=simplify_tol)
+                if len(coords) < 3:
+                    continue
+                rings.append(coords)
+            if not rings:
+                continue
+            # A connected region has a single outer boundary, so the widest ring
+            # is it and everything else it traced is enclosed by it.
+            rings.sort(key=_enclosed_area, reverse=True)
+            counter += 1
+            shape: dict[str, Any] = {
+                "id": f"pred_{run_id[:8]}_{slice_idx}_{counter}",
+                "classId": cls["classId"],
+                "kind": "polygon",
+                "points": [round(float(v), 2) for v in rings[0].ravel().tolist()],
+            }
+            if len(rings) > 1:
+                shape["holes"] = [
+                    [round(float(v), 2) for v in ring.ravel().tolist()] for ring in rings[1:]
+                ]
+            shapes.append(shape)
+    return shapes
+
+
+def run_infer_job(jid: str, request: InferRequest) -> None:
+    """Background worker: predict + vectorize each requested slice."""
+    if not train_common.ML_LOCK.acquire(blocking=False):
+        export_jobs.update(
+            jid,
+            state="error",
+            phase="error",
+            error="Another training or inference job is already running",
+        )
+        return
+    try:
+        export_jobs.update(jid, state="running", phase="loading")
+        device = train_common.pick_device()
+        if device is None:
+            raise RuntimeError("torch is not installed on this server")
+
+        config = train_common.load_run_config(request.run_id)
+        adapter_state = train_common.load_adapter_state(request.run_id)
+        run_classes: list[dict[str, Any]] = config["classes"]
+        n_classes = len(run_classes)
+        image_size = int(config["image_size"])
+        render = request.render.model_dump() if request.render is not None else config["render"]
+        # Predict with the geometry this run was TRAINED with — reading the flag off
+        # the run (not the request) keeps runs saved before tiling existed on the
+        # original whole-slice-rescale path, where their weights are valid.
+        tiled = bool((config.get("hyperparams") or {}).get("tiling", False))
+        # Input denoising is read off the RUN, never off the request — same rule
+        # as `tiled` above. The model was trained on these exact pixels, so
+        # letting a caller choose differently at predict time would be a silent
+        # distribution shift: no error, just quietly worse predictions. Runs
+        # saved before this field existed have no "denoise" key and get plain
+        # render_slice, which is exactly what they were trained with.
+        render_slice_fn = train_common.denoising_render_slice_fn(config.get("denoise"))
+        if config.get("denoise"):
+            export_jobs.log(
+                jid,
+                f"Applying the run's {config['denoise'].get('method')} input denoising "
+                "(recorded at training time).",
+            )
+        if tiled:
+            import tiling
+
+            if not tiling.qlty_available():
+                raise RuntimeError(
+                    "This run was trained with tiling and needs the 'qlty' package to predict, "
+                    "which is not installed on this server"
+                )
+
+        if config["model_family"] == "dlsia_tunet":
+            import dlsia_runtime as fam
+
+            if not train_common.dlsia_available():
+                raise RuntimeError("dlsia is not installed on this server")
+            model = fam.load_model(adapter_state, device)
+            model.eval()
+            forward_fn = fam.make_forward_fn(model)
+            to_tensor_fn = fam.make_to_tensor_fn()
+        elif config["model_family"] == "dlsia_denoiser":
+            # A denoiser is a 1->1 regression model with no class channels, so
+            # the label-map path below (softmax/argmax over n_classes, then
+            # vectorising into shapes) is meaningless for it. Refuse clearly
+            # instead of loading it through dlsia_runtime, which is what an
+            # unconditional catch-all `else` would do — that would produce a
+            # shape mismatch deep in the forward pass rather than an explanation.
+            raise RuntimeError(
+                "This is a denoiser run, not a segmentation model — it produces a denoised "
+                "image, not labelled regions. Apply it from the Annotate tab's Denoise panel."
+            )
+        else:
+            raise RuntimeError(f"Unsupported model family: {config['model_family']!r}")
+
+        import torch
+        import torch.nn.functional as F
+
+        import arrays as arrays_mod
+        import images as images_mod
+
+        node = arrays_mod.resolve_array(request.source, request.kind, request.server_uri)
+        meta = arrays_mod.array_shape_meta(node)
+        h, w = meta["height"], meta["width"]
+        global_range = images_mod._sample_global_stats(node, meta) if render.get("norm") == "global" else None
+
+        export_jobs.set_total(jid, len(request.slice_indices))
+        export_jobs.update(jid, phase="predicting")
+
+        label_pngs: dict[int, bytes] = {}
+        slices_result: dict[str, list[dict[str, Any]]] = {}
+        n_shapes = 0
+        cancelled = False
+        logged_tiling = False
+
+        with torch.no_grad():
+            for slice_idx in request.slice_indices:
+                if export_jobs.cancel_requested(jid):
+                    cancelled = True
+                    break
+
+                arr = arrays_mod.read_slice(node, meta, slice_idx)
+                rgb = render_slice_fn(arr, render, global_range)
+
+                if tiled:
+                    import tiling
+
+                    label_map = tiling.predict_label_map_tiled(
+                        rgb,
+                        forward_fn=forward_fn,
+                        to_tensor_fn=to_tensor_fn,
+                        window=image_size,
+                        min_confidence=request.min_confidence,
+                        device=device,
+                        cancel_cb=lambda: export_jobs.cancel_requested(jid),
+                        # Logged once: every slice of a volume has the same shape, so
+                        # the tiling geometry never changes between them. Repeating it
+                        # per slice would just double an already per-slice log.
+                        progress_cb=(lambda msg: export_jobs.log(jid, f"tiling: {msg}")) if not logged_tiling else None,
+                    )
+                    logged_tiling = True
+                    if label_map is None:  # cancelled mid-slice
+                        cancelled = True
+                        break
+                else:
+                    img_l, _ = train_common.letterbox(rgb, np.zeros((h, w), dtype=np.uint8), image_size)
+                    batch = to_tensor_fn(img_l).unsqueeze(0).to(device)
+
+                    logits = forward_fn(batch)[0]
+                    probs = F.softmax(logits, dim=0)
+                    confidence, pred_class = probs.max(dim=0)
+                    pred_np = pred_class.cpu().numpy()
+                    conf_np = confidence.cpu().numpy()
+                    label_letterboxed = np.where(conf_np >= request.min_confidence, pred_np + 1, 0).astype(np.uint8)
+                    label_map = train_common.unletterbox(label_letterboxed, h, w, image_size)
+
+                shapes = _vectorize_label_map(
+                    label_map,
+                    run_classes,
+                    request.min_area,
+                    request.simplify_tol,
+                    request.run_id,
+                    slice_idx,
+                )
+                n_shapes += len(shapes)
+                slices_result[str(slice_idx)] = shapes
+
+                buf = io.BytesIO()
+                PILImage.fromarray(label_map, mode="L").save(buf, format="PNG")
+                label_pngs[slice_idx] = buf.getvalue()
+
+                export_jobs.bump(jid, 1)
+                export_jobs.log(jid, f"slice {slice_idx}: {len(shapes)} region(s)")
+
+        _cache_put(
+            jid,
+            {
+                "run_id": request.run_id,
+                "classes": run_classes,
+                "source": request.source,
+                "kind": request.kind,
+                "server_uri": request.server_uri,
+                "height": h,
+                "width": w,
+                "label_pngs": label_pngs,
+            },
+        )
+
+        result = {
+            "run_id": request.run_id,
+            "classes": run_classes,
+            "slices": slices_result,
+            "n_shapes": n_shapes,
+            "preview_slices": sorted(label_pngs.keys()),
+            "cancelled": cancelled,
+        }
+        export_jobs.update(jid, state="done", phase="done", result=result)
+        export_jobs.log(jid, "Inference cancelled; partial results kept." if cancelled else "Inference complete.")
+    except Exception as exc:  # noqa: BLE001 — reported as a job error, never a crash
+        logger.error("Inference job %s failed: %s", jid, exc)
+        export_jobs.update(jid, state="error", phase="error", error=str(exc))
+    finally:
+        train_common.ML_LOCK.release()
+
+
+def preview_png(job_id: str, slice_index: int) -> bytes:
+    """Colourise a cached predicted label map into an RGBA overlay PNG.
+
+    Raises:
+        HTTPException: 404 if the job or slice isn't cached.
+    """
+    entry = _cache_get(job_id)
+    if entry is None:
+        raise HTTPException(404, "No cached inference results for this job (they expire after a few jobs)")
+    png_bytes = entry["label_pngs"].get(slice_index)
+    if png_bytes is None:
+        raise HTTPException(404, f"Slice {slice_index} was not part of this inference job")
+
+    label = np.asarray(PILImage.open(io.BytesIO(png_bytes)))
+    rgba = np.zeros((*label.shape, 4), dtype=np.uint8)
+    for c, cls in enumerate(entry["classes"]):
+        mask = label == (c + 1)
+        if not mask.any():
+            continue
+        r, g, b = _hex_to_rgb(cls.get("color"))
+        rgba[mask] = (r, g, b, 180)
+
+    buf = io.BytesIO()
+    PILImage.fromarray(rgba, mode="RGBA").save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def run_write_tiled_job(jid: str, infer_job_id: str) -> None:
+    """Background worker: push a completed inference job's label maps into
+    Tiled as a ``<source_stem>__masks`` sibling container (same writer the
+    manual "sync masks to Tiled" flow uses)."""
+    try:
+        export_jobs.update(jid, state="running", phase="writing")
+        entry = _cache_get(infer_job_id)
+        if entry is None:
+            raise ValueError("No cached inference results for this job (they expire after a few jobs)")
+        if entry["kind"] != "tiled":
+            raise ValueError("Inference source was not a Tiled array — nothing to write back")
+
+        import tiled_mask_sync
+
+        classes = entry["classes"]
+        slice_indices = sorted(entry["label_pngs"].keys())
+        semantic = np.stack(
+            [np.asarray(PILImage.open(io.BytesIO(entry["label_pngs"][i]))) for i in slice_indices],
+            axis=0,
+        )
+        class_vols = {cls["label"]: ((semantic == (c + 1)) * 255).astype(np.uint8) for c, cls in enumerate(classes)}
+        legend = [{"id": c + 1, "name": cls["label"], "color": cls.get("color")} for c, cls in enumerate(classes)]
+        volumes = {
+            "semantic": semantic,
+            "class_vols": class_vols,
+            "slice_indices": slice_indices,
+            "legend": legend,
+        }
+
+        export_jobs.set_total(jid, 1)
+        info = tiled_mask_sync.write_masks_to_tiled(entry["source"], entry["server_uri"], volumes, classes)
+        export_jobs.bump(jid, 1)
+        export_jobs.update(jid, state="done", phase="done", result=info)
+        export_jobs.log(jid, f"Wrote predicted masks to {info['path']}.")
+    except Exception as exc:  # noqa: BLE001
+        logger.error("Write-to-Tiled job %s failed: %s", jid, exc)
+        export_jobs.update(jid, state="error", phase="error", error=str(exc))
diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 5d5ce8a..a1ebda4 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -38,6 +38,12 @@ dependencies = [
 [project.optional-dependencies]
 dev  = ["flake8", "flake8-isort", "black"]
 test = ["pytest>=8", "pytest-asyncio>=0.23", "httpx>=0.27"]
+# Multi-GB (torch) — not installed in the Docker image; start_all.sh's
+# ensure_ml_env() installs it locally. Every /api/train/* route (and
+# denoise_bake.py's "model" denoise method) degrades to a clear error via
+# train_common.torch_available()/dlsia_available() rather than crashing on
+# import when this extra isn't installed.
+ml   = ["torch>=2.4", "dlsia>=0.3", "qlty>=1.5"]
 
 [tool.setuptools]
 py-modules = []
diff --git a/backend/schemas.py b/backend/schemas.py
index f797481..0adc725 100644
--- a/backend/schemas.py
+++ b/backend/schemas.py
@@ -18,9 +18,9 @@
 
 from __future__ import annotations
 
-from typing import Any, Literal
+from typing import Annotated, Any, Literal, Self, Union
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, field_validator, model_validator
 
 
 class BrushStroke(BaseModel):
@@ -432,4 +432,275 @@ class ImageMeta(BaseModel):
     level_height: int | None = None
     level_width: int | None = None
     level_n_slices: int | None = None
+
+
+# ---------------------------------------------------------------------------
+# Train tab (Phase 5) — dlsia TUNet segmentation + the dlsia/autoencoder
+# denoiser family only. DINOv3 LoRA is deferred to Phase 5.5 and has no
+# schema here at all, not even a stubbed-out variant.
+# ---------------------------------------------------------------------------
+
+
+class TunetHyperParams(BaseModel):
+    """Bounded training hyperparameters for a dlsia TUNet trained from scratch.
+
+    Attributes:
+        epochs: Number of training epochs (from-scratch training typically
+            needs more epochs than fine-tuning a pretrained backbone).
+        lr: Learning rate for the whole network.
+        depth: U-Net depth (encoder/decoder stages).
+        base_channels: Channel count of the first conv stage.
+        growth_rate: Channel growth factor per depth level.
+        batch_size: Training batch size.
+        image_size: Square side length images/labels are letterboxed to.
+            dlsia's TUNet fixes its layer sizes to ``image_shape`` at
+            construction, so train and inference images must match exactly.
+            With ``tiling`` on this is the tile window — every window is exactly
+            this size, so the constraint still holds.
+        seed: Seed for shuffling and augmentation.
+        flip_augment: Whether to randomly flip image+label together.
+        tiling: Train on native-resolution ``image_size`` windows cut from each
+            slice, instead of rescaling the whole slice down to ``image_size``
+            (see ``tiling.py``). Recorded on the run, so inference reproduces
+            whichever geometry the run was trained with.
+    """
+
+    epochs: int = Field(default=60, ge=1, le=1000)
+    lr: float = Field(default=1e-3, gt=0, le=1)
+    depth: int = Field(default=4, ge=2, le=6)
+    base_channels: int = Field(default=8, ge=1, le=128)
+    growth_rate: float = Field(default=1.5, gt=0, le=4)
+    batch_size: int = Field(default=4, ge=1, le=32)
+    image_size: int = Field(default=512, ge=64, le=2048)
+    seed: int = 1234
+    flip_augment: bool = True
+    tiling: bool = True
+
+    @field_validator("image_size")
+    @classmethod
+    def validate_image_size(cls, value: int) -> int:
+        """A TUNet at ``depth`` stages needs the side divisible by 2**depth
+        so every downsample/upsample step lands on a whole-pixel size."""
+        if value % 64 != 0:
+            raise ValueError("image_size must be a multiple of 64 (covers depth up to 6)")
+        return value
+
+
+class DlsiaTunetConfig(BaseModel):
+    """Model-family config: dlsia tunable U-Net trained from scratch.
+
+    Attributes:
+        model_family: Discriminator literal.
+        hyperparams: Training hyperparameters.
+    """
+
+    model_family: Literal["dlsia_tunet"] = "dlsia_tunet"
+    hyperparams: TunetHyperParams = Field(default_factory=TunetHyperParams)
+
+
+class DlsiaDenoiserConfig(BaseModel):
+    """Model-family config: a single-channel image denoiser trained
+    self-supervised, instead of a multi-class segmenter.
+
+    Two architectures share this family. Keeping them under one
+    ``model_family`` is deliberate: the frontend partitions runs with
+    ``isSegmentationRun == !isDenoiserRun`` (``lib/runCompatibility.ts``), so a
+    *new* family value would be silently treated as segmentation and offered in
+    the fine-tune / apply / inference pickers, which key off a class list a
+    denoiser does not have. An ``architecture`` discriminator inside the family
+    avoids that entirely.
+
+    ``TunetHyperParams`` is reused verbatim rather than defining a parallel
+    hyperparameter class — ``depth``/``base_channels`` mean downsampling levels
+    and first-conv width for both architectures. Architecture-specific knobs
+    (``ae_compression``) live here on the config, not there.
+
+    Attributes:
+        model_family: Discriminator literal.
+        architecture: Which network to build — ``"tunet"`` (dlsia TUNet with
+            ``in_channels=1, out_channels=1``; see ``denoise_runtime.py``) or
+            ``"cnn_ae"`` (a plain convolutional autoencoder with an explicit
+            latent bottleneck and NO skip connections; see
+            ``autoencoder_runtime.py``). Defaults to ``"tunet"`` so runs saved
+            before this field existed keep their meaning.
+        hyperparams: Training hyperparameters (reuses the segmentation
+            family's TUNet knobs — depth/base_channels/growth_rate/etc.).
+        ae_compression: How much the ``"cnn_ae"`` bottleneck compresses, as a
+            ratio of input values to latent values. Higher removes more noise
+            but also discards more real detail. Only meaningful for
+            ``"cnn_ae"``.
+        training_scheme: Self-supervised training objective — ``"n2n"``
+            (Noise2Noise: paired noisy/noisy training), ``"n2v"``
+            (Noise2Void: blind-spot training from single noisy images), or
+            ``"ae"`` (pure self-reconstruction: target IS the input, and the
+            bottleneck is what forces noise out).
+
+            ``"ae"`` is valid ONLY with ``architecture="cnn_ae"``, enforced
+            below. On a skip-connected network like TUNet, training on
+            ``target == input`` makes ``f(x) = x`` trivially learnable: it
+            converges to copying the input, removes no noise whatsoever, and
+            still reports a falling loss. Without skips, the bottleneck cannot
+            pass the input through unchanged, so reconstruction becomes a real
+            denoising objective. That pairing is a correctness constraint, not
+            a convenience.
+    """
+
+    model_family: Literal["dlsia_denoiser"] = "dlsia_denoiser"
+    architecture: Literal["tunet", "cnn_ae"] = "tunet"
+    hyperparams: TunetHyperParams = Field(default_factory=TunetHyperParams)
+    training_scheme: Literal["n2n", "n2v", "ae"]
+    ae_compression: int = Field(default=16, ge=4, le=64)
+
+    @model_validator(mode="after")
+    def _check_scheme_matches_architecture(self) -> "DlsiaDenoiserConfig":
+        """Keep scheme and architecture to the combinations that make sense.
+
+        ``ae`` + ``tunet`` is the identity-collapse footgun described above and
+        must be impossible to request. The reverse (``cnn_ae`` with a masking or
+        paired scheme) is not unsound in principle, just untested here, so it is
+        refused rather than silently shipped.
+        """
+        if self.training_scheme == "ae" and self.architecture != "cnn_ae":
+            raise ValueError(
+                "training_scheme='ae' (pure self-reconstruction) requires "
+                "architecture='cnn_ae'. On a skip-connected network it would just learn to "
+                "copy its input and remove no noise."
+            )
+        if self.architecture == "cnn_ae" and self.training_scheme != "ae":
+            raise ValueError(
+                "architecture='cnn_ae' is only supported with training_scheme='ae'; "
+                f"got {self.training_scheme!r}."
+            )
+        return self
+
+
+ModelConfig = Annotated[
+    Union[DlsiaTunetConfig, DlsiaDenoiserConfig],
+    Field(discriminator="model_family"),
+]
+
+
+class BatchProbeRequest(BaseModel):
+    """Request body to measure the largest batch size a model config can fit.
+
+    Deliberately not a :class:`TrainRequest`: the probe feeds synthetic tensors, so
+    it needs no sources, and requiring them would force the caller to invent data
+    just to ask a question about memory.
+
+    Attributes:
+        model: Model-family configuration to size (patch size and the per-family
+            hyperparameters come from its ``hyperparams``).
+        n_classes: Segmentation head output channels. Affects memory only
+            marginally, so it defaults to a typical value — meaning a batch size
+            can be estimated before any classes have been defined.
+    """
+
+    model: ModelConfig
+    n_classes: int = Field(default=2, ge=1)
+
+
+class DenoiseTrainOpts(BaseModel):
+    """Denoising applied to a model's INPUT pixels, at training and inference alike.
+
+    Distinct from the Annotate tab's denoise preview, which is display-only and
+    never reaches a model. When this is set on a training request it is recorded
+    on the saved run, and inference reads it back off the run rather than off the
+    request — the same rule ``tiling`` already follows, and for the same reason:
+    a model must see the same pixel distribution it was trained on. Letting the
+    two be chosen independently would produce a silent distribution shift with
+    no error, just quietly worse predictions.
+
+    Attributes:
+        method: A ``denoise.ALL_METHODS`` entry other than ``"none"``/``"model"``
+            (a learned denoiser as a preprocessor for another model is not
+            supported — it would need its own run and GPU pass per slice).
+        strength: 0..1, mapped onto the method's native parameter.
+    """
+
+    method: str
+    strength: float = Field(default=0.5, ge=0, le=1)
+
+
+class TrainRequest(BaseModel):
+    """Request body to start a fine-tuning job.
+
+    Attributes:
+        task: What the trained model is for — ``"segmentation"`` (requires at
+            least one class) or ``"denoising"`` (a self-supervised
+            Noise2Noise/Noise2Void/autoencoder denoiser, which has no class
+            taxonomy at all).
+        sources: Annotated samples to train on (each with its own slices/splits).
+        classes: Annotation classes/taxonomy shared across every source.
+            Required (at least one) when ``task == "segmentation"``; may be
+            empty when ``task == "denoising"``.
+        render: Render options used to rasterise training images.
+        denoise: Optional denoising applied to the model's INPUT pixels. Recorded
+            on the run and reapplied automatically at inference — see
+            :class:`DenoiseTrainOpts`. Ignored when ``task == "denoising"``: a
+            denoiser learns to remove noise, so pre-cleaning its input would
+            defeat the point.
+        auto_split: Auto-split configuration for slices without an explicit split.
+        model: Model-family configuration (discriminated on ``model_family``).
+        run_name: Optional human-readable label for the resulting run.
+        resume_from_run_id: Continue fine-tuning from this saved run's weights
+            instead of starting from scratch. The saved run's architecture-defining
+            settings win over anything sent in ``model`` — a resume that changed
+            them could not load the saved weights at all. Result is always a NEW
+            run; the parent is never modified.
+    """
+
+    task: Literal["segmentation", "denoising"] = "segmentation"
+    sources: list[ExportSourceItem] = Field(min_length=1)
+    classes: list[AnnotationClass] = Field(default_factory=list)
+    render: RenderOpts = Field(default_factory=RenderOpts)
+    denoise: DenoiseTrainOpts | None = None
+    auto_split: dict[str, Any] = Field(
+        default_factory=lambda: {"ratios": [0.8, 0.1, 0.1], "seed": 1234}
+    )
+    model: ModelConfig
+    run_name: str | None = None
+    resume_from_run_id: str | None = None
+
+    @model_validator(mode="after")
+    def validate_train_taxonomy(self) -> Self:
+        """A self-supervised denoiser has no classes at all — still required
+        (and still validated) for segmentation, allowed empty for denoising."""
+        if self.task == "segmentation" and len(self.classes) < 1:
+            raise ValueError("Segmentation training requires at least one class")
+        return self
+
+
+class InferRequest(BaseModel):
+    """Request body to run inference with a saved fine-tuned run.
+
+    Attributes:
+        run_id: Identifier of a previously trained run.
+        kind: Source kind — ``"tiled"`` or ``"local"``.
+        source: Tiled path or local relative path to run inference on.
+        server_uri: Tiled server URI (kind == "tiled" only).
+        slice_indices: Zero-based slice indices to run inference on.
+        render: Render options; ``None`` reuses the run's stored render options.
+        min_area: Minimum connected-component pixel area kept per predicted region.
+        simplify_tol: Polygon simplification tolerance (pixels).
+        min_confidence: Softmax confidence below which a pixel is treated as
+            background (no class) — training never sees an explicit background
+            class, since unannotated pixels are the ignore index, not a label.
+    """
+
+    run_id: str
+    kind: Literal["tiled", "local"]
+    source: str
+    server_uri: str | None = None
+    slice_indices: list[int] = Field(min_length=1)
+    render: RenderOpts | None = None
+    min_area: int = Field(default=64, ge=0)
+    simplify_tol: float = Field(default=1.5, ge=0, le=50)
+    min_confidence: float = Field(default=0.5, ge=0, le=1)
+
+    @field_validator("slice_indices")
+    @classmethod
+    def validate_slice_indices(cls, value: list[int]) -> list[int]:
+        if len(value) != len(set(value)):
+            raise ValueError("slice_indices must be unique")
+        return value
     z_downsample: float | None = None
diff --git a/backend/tests/test_train_common.py b/backend/tests/test_train_common.py
new file mode 100644
index 0000000..80b758b
--- /dev/null
+++ b/backend/tests/test_train_common.py
@@ -0,0 +1,124 @@
+"""train_common.py + the denoise_bake.py model-denoiser fix it enables.
+
+Ported alongside train_common/tiling/denoise_train/denoise_runtime/
+autoencoder_runtime (Phase 5, Stage A). The `ml` extra (torch/dlsia/qlty) may
+or may not be installed in whatever environment runs this suite — these tests
+don't assume either way. The property that matters is: every module here is
+import-safe regardless, and denoise_bake.py's `_ModelDenoiser` (which already
+called into these modules before they existed) fails with clear, specific
+errors instead of a raw `ModuleNotFoundError` when a dependency truly is
+missing — verified here by monkeypatching availability rather than by relying
+on the dev machine's actual install state.
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import train_common  # noqa: E402
+import denoise_bake  # noqa: E402
+
+
+def test_modules_import_cleanly():
+    """All Stage A modules must be import-safe whether or not torch/dlsia are installed."""
+    import autoencoder_runtime  # noqa: F401
+    import denoise_runtime  # noqa: F401
+    import denoise_train  # noqa: F401
+    import dlsia_runtime  # noqa: F401
+    import tiling  # noqa: F401
+
+
+def test_capability_never_raises_and_has_no_dinov3_fields():
+    """DINOv3 is deferred to Phase 5.5 — capability() must not report it at all,
+    regardless of what's actually installed."""
+    result = train_common.capability()
+    assert "dinov3" not in result
+    assert "error" not in result
+    assert set(result) == {
+        "torch_available", "torch_version", "device", "dlsia", "tiling",
+        "denoise", "runs_dir", "busy",
+    }
+
+
+def test_capability_reports_unavailable_without_torch(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setattr(train_common, "torch_available", lambda: False)
+    monkeypatch.setattr(train_common, "dlsia_available", lambda: False)
+    result = train_common.capability()
+    assert result["torch_available"] is False
+    assert result["torch_version"] is None
+    assert result["device"] is None
+    assert result["dlsia"] == {"available": False}
+
+
+@pytest.fixture()
+def runs_dir(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Path:
+    monkeypatch.setenv("DINO_RUNS_DIR", str(tmp_path / "runs"))
+    return tmp_path / "runs"
+
+
+def _write_run_config(runs_dir: Path, run_id: str, config: dict) -> None:
+    d = runs_dir / run_id
+    d.mkdir(parents=True, exist_ok=True)
+    (d / "config.json").write_text(json.dumps(config))
+
+
+def test_model_denoiser_unknown_run_is_a_clean_404(runs_dir: Path) -> None:
+    """Previously: ModuleNotFoundError before train_common.py existed."""
+    with pytest.raises(Exception) as exc_info:
+        denoise_bake._ModelDenoiser("nonexistent-run", None, {})
+    assert "Unknown run" in str(exc_info.value)
+
+
+def test_model_denoiser_refuses_non_denoiser_run(runs_dir: Path) -> None:
+    _write_run_config(runs_dir, "seg-run", {"model_family": "dlsia_tunet", "task": "segmentation"})
+    with pytest.raises(ValueError, match="not a denoiser run"):
+        denoise_bake._ModelDenoiser("seg-run", None, {})
+
+
+def test_model_denoiser_reports_missing_dlsia_by_name(
+    runs_dir: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """A tunet-architecture denoiser run needs dlsia — confirm the error names it
+    specifically, not just a generic import failure. Forces the "not installed"
+    branch via monkeypatch so this doesn't depend on the test machine's actual
+    dlsia install state."""
+    monkeypatch.setattr(train_common, "dlsia_available", lambda: False)
+    _write_run_config(
+        runs_dir,
+        "denoiser-run",
+        {
+            "model_family": "dlsia_denoiser",
+            "task": "denoising",
+            "model_config": {"architecture": "tunet"},
+        },
+    )
+    with pytest.raises(ValueError, match="dlsia is not installed"):
+        denoise_bake._ModelDenoiser("denoiser-run", None, {})
+
+
+def test_model_denoiser_reports_missing_torch_by_name(
+    runs_dir: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """A cnn_ae denoiser needs no dlsia, but still needs torch for a device."""
+    import tiling
+
+    monkeypatch.setattr(tiling, "qlty_available", lambda: True)
+    monkeypatch.setattr(train_common, "pick_device", lambda: None)
+    _write_run_config(
+        runs_dir,
+        "ae-run",
+        {
+            "model_family": "dlsia_denoiser",
+            "task": "denoising",
+            "model_config": {"architecture": "cnn_ae"},
+            "image_size": 256,
+        },
+    )
+    with pytest.raises(ValueError, match="torch is not installed"):
+        denoise_bake._ModelDenoiser("ae-run", None, {})
diff --git a/backend/tests/test_train_e2e_real_ml.py b/backend/tests/test_train_e2e_real_ml.py
new file mode 100644
index 0000000..23062f0
--- /dev/null
+++ b/backend/tests/test_train_e2e_real_ml.py
@@ -0,0 +1,174 @@
+"""Real (not mocked) end-to-end check of the core train/save/load contract.
+
+Skips automatically if the `ml` extra (torch/dlsia) isn't installed. When it
+IS installed, this proves the ported train_common/dlsia_runtime/
+autoencoder_runtime machinery actually works — builds a real model, runs a
+real training step, saves real weights, reloads them, and confirms inference
+on the reloaded model reproduces the trained model's output — not just that
+the error paths are clean (see test_train_common.py for those).
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+torch = pytest.importorskip("torch")
+pytest.importorskip("dlsia")
+
+import autoencoder_runtime  # noqa: E402
+import dlsia_runtime  # noqa: E402
+import train_common  # noqa: E402
+from schemas import DlsiaDenoiserConfig, DlsiaTunetConfig  # noqa: E402
+
+
+@pytest.fixture()
+def runs_dir(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Path:
+    monkeypatch.setenv("DINO_RUNS_DIR", str(tmp_path / "runs"))
+    return tmp_path / "runs"
+
+
+def _synthetic_seg_pairs(n: int, size: int, n_classes: int, seed: int = 0):
+    rng = np.random.default_rng(seed)
+    pairs = []
+    for _ in range(n):
+        rgb = rng.integers(0, 256, size=(size, size, 3), dtype=np.uint8)
+        label = rng.integers(0, n_classes, size=(size, size), dtype=np.uint8)
+        pairs.append((rgb, label))
+    return pairs
+
+
+def test_dlsia_tunet_train_save_reload_infer_round_trip(runs_dir: Path) -> None:
+    n_classes = 2
+    image_size = 64
+    model_cfg = DlsiaTunetConfig(
+        hyperparams={
+            "epochs": 1,
+            "depth": 2,
+            "base_channels": 4,
+            "growth_rate": 1.2,
+            "batch_size": 2,
+            "image_size": image_size,
+            "tiling": False,
+        }
+    )
+
+    logs: list[str] = []
+    built = train_common.build_family(model_cfg, n_classes, "cpu", logs.append)
+    assert built.set_train_mode is not None
+
+    train_pairs = _synthetic_seg_pairs(4, image_size, n_classes)
+    metrics = train_common.run_training_loop(
+        train_pairs=train_pairs,
+        val_pairs=[],
+        image_size=image_size,
+        n_classes=n_classes,
+        epochs=model_cfg.hyperparams.epochs,
+        batch_size=model_cfg.hyperparams.batch_size,
+        seed=model_cfg.hyperparams.seed,
+        flip_augment=False,
+        to_tensor_fn=built.to_tensor_fn,
+        forward_fn=built.forward_fn,
+        trainable_params=built.trainable_params,
+        lr=model_cfg.hyperparams.lr,
+        device="cpu",
+        set_train_mode=built.set_train_mode,
+    )
+    assert metrics["epochs_completed"] == 1
+    assert metrics["cancelled"] is False
+    assert np.isfinite(metrics["final_train_loss"])
+
+    run_id = "test-tunet-run"
+    train_common.save_run(
+        run_id,
+        model_family=model_cfg.model_family,
+        model_config=built.model_config_snapshot,
+        classes=[{"classId": 1, "label": "a", "color": "#f00"}, {"classId": 2, "label": "b", "color": "#0f0"}],
+        render={},
+        image_size=image_size,
+        hyperparams=model_cfg.hyperparams.model_dump(),
+        source_keys=["local:fake.tif"],
+        adapter_state=built.adapter_state_fn(),
+        metrics=metrics,
+    )
+
+    # list_runs / load_run_config round-trip.
+    runs = train_common.list_runs()
+    assert any(r["run_id"] == run_id for r in runs)
+    loaded_config = train_common.load_run_config(run_id)
+    assert loaded_config["model_family"] == "dlsia_tunet"
+    assert loaded_config["task"] == "segmentation"
+
+    # Reload the ACTUAL saved weights into a fresh model and confirm inference
+    # runs and reproduces the just-trained model's output (not just "doesn't crash").
+    loaded_state = train_common.load_adapter_state(run_id)
+    reloaded_model = dlsia_runtime.load_model(loaded_state, "cpu")
+    reloaded_model.eval()
+    reloaded_forward = dlsia_runtime.make_forward_fn(reloaded_model)
+
+    # eval() on both sides: TUNet's BatchNorm gives different output in train
+    # mode (batch statistics) vs eval mode (running stats) — comparing them in
+    # mismatched modes would fail even with byte-identical weights.
+    built.set_train_mode(False)
+    sample_rgb, _ = train_pairs[0]
+    batch = built.to_tensor_fn(sample_rgb).unsqueeze(0)
+    with torch.no_grad():
+        original_logits = built.forward_fn(batch)
+        reloaded_logits = reloaded_forward(batch)
+    assert original_logits.shape == (1, n_classes, image_size, image_size)
+    torch.testing.assert_close(original_logits, reloaded_logits)
+
+    train_common.delete_run(run_id)
+    assert not any(r["run_id"] == run_id for r in train_common.list_runs())
+
+
+def test_dlsia_denoiser_cnn_ae_build_save_reload_round_trip(runs_dir: Path) -> None:
+    """Pure-torch denoiser architecture (no dlsia needed for this one) — build,
+    save, and reload real weights, confirming the autoencoder_runtime six-function
+    template round-trips correctly through train_common.build_family/save_run."""
+    image_size = 64
+    model_cfg = DlsiaDenoiserConfig(
+        architecture="cnn_ae",
+        training_scheme="ae",
+        ae_compression=4,
+        hyperparams={"depth": 2, "base_channels": 4, "image_size": image_size},
+    )
+
+    built = train_common.build_family(model_cfg, 0, "cpu", lambda _msg: None)
+
+    run_id = "test-ae-run"
+    train_common.save_run(
+        run_id,
+        model_family=model_cfg.model_family,
+        model_config={**built.model_config_snapshot, "training_scheme": "ae", "architecture": "cnn_ae"},
+        classes=[],
+        render={},
+        image_size=image_size,
+        hyperparams=model_cfg.hyperparams.model_dump(),
+        source_keys=[],
+        adapter_state=built.adapter_state_fn(),
+        metrics={"epochs_completed": 0, "cancelled": False},
+        task="denoising",
+    )
+
+    loaded_config = train_common.load_run_config(run_id)
+    assert loaded_config["task"] == "denoising"
+    assert loaded_config["model_config"]["architecture"] == "cnn_ae"
+
+    loaded_state = train_common.load_adapter_state(run_id)
+    reloaded = autoencoder_runtime.load_model(loaded_state, "cpu")
+    reloaded.eval()
+    reloaded_forward = autoencoder_runtime.make_forward_fn(reloaded)
+
+    rng = np.random.default_rng(1)
+    gray = rng.integers(0, 256, size=(image_size, image_size), dtype=np.uint8)
+    to_tensor = autoencoder_runtime.make_to_tensor_fn()
+    batch = to_tensor(gray).unsqueeze(0)
+    with torch.no_grad():
+        out = reloaded_forward(batch)
+    assert out.shape == (1, 1, image_size, image_size)
diff --git a/backend/tests/test_train_routes.py b/backend/tests/test_train_routes.py
new file mode 100644
index 0000000..c843ee1
--- /dev/null
+++ b/backend/tests/test_train_routes.py
@@ -0,0 +1,117 @@
+"""HTTP-level tests for the /api/train/* routes.
+
+Route wiring, validation, and job-status polling for the parts that don't
+need a real registered dataset (capability, runs list/delete, batch-size
+probe). The core train/save/load contract itself is proven for real (no
+mocks) in test_train_e2e_real_ml.py; a full train-via-HTTP round trip would
+also need real Tiled/local array registration, which is exercised elsewhere
+for export and is out of scope for this file.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import time
+
+import pytest
+from httpx import ASGITransport, AsyncClient
+
+import export_jobs
+import train_common
+from annotation_server import app
+
+
+@pytest.fixture()
+def runs_dir(tmp_path, monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setenv("DINO_RUNS_DIR", str(tmp_path / "runs"))
+    return tmp_path / "runs"
+
+
+async def _await_job(jid: str, timeout: float = 30.0) -> dict:
+    deadline = time.monotonic() + timeout
+    while time.monotonic() < deadline:
+        job = export_jobs.get_job(jid)
+        assert job is not None
+        if job["state"] in ("done", "error"):
+            return job
+        await asyncio.sleep(0.05)
+    raise AssertionError(f"job {jid} did not finish within {timeout}s")
+
+
+@pytest.mark.asyncio
+async def test_capability_route(runs_dir) -> None:
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.get("/api/train/capability")
+    assert response.status_code == 200
+    body = response.json()
+    assert "dinov3" not in body
+    assert "torch_available" in body
+
+
+@pytest.mark.asyncio
+async def test_runs_list_and_delete_unknown(runs_dir) -> None:
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.get("/api/train/runs")
+        assert response.status_code == 200
+        assert response.json() == {"runs": []}
+
+        response = await client.delete("/api/train/runs/nonexistent")
+        assert response.status_code == 404
+
+
+@pytest.mark.asyncio
+async def test_train_start_rejects_segmentation_with_no_classes(runs_dir) -> None:
+    """Pydantic validation (TrainRequest.validate_train_taxonomy) surfaces as 422."""
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.post(
+            "/api/train/start",
+            json={
+                "task": "segmentation",
+                "sources": [{"kind": "local", "source": "fake.tif", "slices": {"0": []}}],
+                "classes": [],
+                "model": {"model_family": "dlsia_tunet"},
+            },
+        )
+    assert response.status_code == 422
+
+
+@pytest.mark.asyncio
+async def test_train_infer_unknown_run_is_404(runs_dir) -> None:
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.post(
+            "/api/train/infer",
+            json={
+                "run_id": "nonexistent",
+                "kind": "local",
+                "source": "fake.tif",
+                "slice_indices": [0],
+            },
+        )
+    if not train_common.torch_available():
+        assert response.status_code == 503
+    else:
+        assert response.status_code == 404
+
+
+@pytest.mark.asyncio
+async def test_estimate_batch_probe_runs_for_real_when_torch_available(runs_dir) -> None:
+    """A real (not mocked) tiny batch-size probe, when torch is installed —
+    otherwise just confirms the clean 503 degradation."""
+    payload = {
+        "model": {
+            "model_family": "dlsia_tunet",
+            "hyperparams": {"depth": 2, "base_channels": 4, "image_size": 64, "batch_size": 2},
+        },
+        "n_classes": 2,
+    }
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.post("/api/train/estimate-batch", json=payload)
+        if not train_common.torch_available():
+            assert response.status_code == 503
+            return
+        assert response.status_code == 200
+        jid = response.json()["job_id"]
+        job = await _await_job(jid)
+        assert job["state"] == "done"
+        assert job["result"]["suggested_batch_size"] is not None
+        assert job["result"]["largest_ok"] >= 1
diff --git a/backend/tiling.py b/backend/tiling.py
new file mode 100644
index 0000000..34ca144
--- /dev/null
+++ b/backend/tiling.py
@@ -0,0 +1,432 @@
+"""Patch-based ("tiled") training and inference for images larger than the model input.
+
+Without tiling, a slice is letterbox-rescaled to a single ``image_size`` square
+(see :func:`train_common.letterbox`), so a 4096px slice at the default 512 is
+downsampled 8× before the model ever sees it. Fine structure is unresolvable.
+
+Tiling instead cuts ``window``-sized windows at **native resolution**, runs the
+model per window, and recombines. Training patches come out exactly
+``window``x``window``, which :func:`train_common.letterbox` passes through
+untouched, so the existing training loop, flip augmentation and validation work
+unchanged. Inference blends overlapping windows into a full-resolution
+prediction.
+
+Geometry comes from :mod:`qlty` (``NCYXQuilt``), which is already a dlsia
+dependency and written by dlsia's author. Two of its properties matter here:
+
+* Windows overlap, and a window's outermost ring is the least reliable part of
+  its prediction (least surrounding context), so blending down-weights that
+  ring and training masks it out of the loss entirely.
+* The last window in each axis is *clamped* to sit inside the image rather than
+  the image being zero-padded, so edge windows simply overlap more.
+
+Softmax is applied **after** recombining, never per window: averaging softmaxed
+patches is not the softmax of averaged logits (qlty's own docs call this out).
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Callable
+
+import numpy as np
+
+from train_common import IGNORE_INDEX
+
+logger = logging.getLogger(__name__)
+
+# Fraction of the window shared by consecutive windows. At 25% every interior
+# pixel gets several predictions to average, which hides seams, while the window
+# count only grows ~1/(1-f)² — 4/3× per axis here.
+OVERLAP_FRACTION = 0.25
+# The outermost ``window // BORDER_DIVISOR`` ring of each window is the
+# down-weighted/ignored border. At 25% overlap this ring is exactly one half
+# of the overlap (`border_for(w) == (w - step_for(w)) // 2`), so a
+# down-weighted edge is always covered by a neighbour's full-weight interior,
+# which extends the other half of the way across the overlap.
+BORDER_DIVISOR = 8
+# Weight given to border pixels when blending (interior is 1.0). Small but
+# non-zero, so a border pixel still contributes where nothing else covers it.
+BORDER_WEIGHT = 0.1
+# Windows per forward pass during inference. Keeps device memory bounded
+# independently of how many windows the image yields.
+INFER_TILE_BATCH = 4
+
+
+def step_for(window: int) -> int:
+    """Stride between consecutive windows."""
+    return max(1, window - int(round(window * OVERLAP_FRACTION)))
+
+
+def border_for(window: int) -> int:
+    """Width of the down-weighted/ignored ring at each window edge."""
+    return max(1, window // BORDER_DIVISOR)
+
+
+def tile_origins(dim: int, window: int, step: int) -> list[int]:
+    """Start offsets of every window along one axis.
+
+    Mirrors qlty's own grid, including its clamped final window
+    (``min(i * step, dim - window)``) — so a streamed traversal lands on exactly
+    the windows ``NCYXQuilt.unstitch`` would produce. Requires ``dim >= window``
+    (pad first, see :func:`pad_to_min`).
+    """
+    if dim < window:
+        raise ValueError(f"dim {dim} smaller than window {window}; pad first")
+    full_steps = (dim - window) // step
+    count = full_steps + 2 if dim > full_steps * step + window else full_steps + 1
+    return [min(i * step, dim - window) for i in range(count)]
+
+
+def pad_to_min(arr: np.ndarray, min_h: int, min_w: int, fill: int) -> np.ndarray:
+    """Bottom/right-pad *arr* so it is at least ``min_h`` x ``min_w``.
+
+    An image smaller than one window can't be tiled at all; padding to the
+    window (rather than upscaling) keeps every real pixel at native scale. The
+    padding is cropped back off after stitching, and for labels *fill* should be
+    :data:`train_common.IGNORE_INDEX` so it never reaches the loss.
+    """
+    h, w = arr.shape[:2]
+    pad_h, pad_w = max(0, min_h - h), max(0, min_w - w)
+    if pad_h == 0 and pad_w == 0:
+        return arr
+    pad_width = [(0, pad_h), (0, pad_w)] + [(0, 0)] * (arr.ndim - 2)
+    return np.pad(arr, pad_width, mode="constant", constant_values=fill)
+
+
+def _quilt(height: int, width: int, window: int) -> Any:
+    """``NCYXQuilt`` for one image's geometry, with this module's overlap/border."""
+    from qlty.qlty2D import NCYXQuilt  # noqa: PLC0415 — optional dependency
+
+    step = step_for(window)
+    return NCYXQuilt(
+        Y=height,
+        X=width,
+        window=(window, window),
+        step=(step, step),
+        border=border_for(window),
+        border_weight=BORDER_WEIGHT,
+    )
+
+
+def qlty_available() -> bool:
+    """True if :mod:`qlty` can be imported (ships with dlsia; see pyproject's ml extra)."""
+    import importlib.util  # noqa: PLC0415
+
+    return importlib.util.find_spec("qlty") is not None
+
+
+# ---------------------------------------------------------------------------
+# Training
+# ---------------------------------------------------------------------------
+
+
+def _tile_pair(rgb: np.ndarray, label: np.ndarray, window: int) -> list[tuple[np.ndarray, np.ndarray]]:
+    """Cut one full-resolution ``(rgb, label)`` pair into window-sized patches.
+
+    Patches whose labels are entirely unannotated are dropped — with sparse
+    annotations most of an image is :data:`train_common.IGNORE_INDEX` and such
+    patches contribute nothing to the loss.
+    """
+    import torch  # noqa: PLC0415
+    from qlty.cleanup import weed_sparse_classification_training_pairs_2D  # noqa: PLC0415
+
+    img = pad_to_min(rgb, window, window, fill=0)
+    lbl = pad_to_min(label, window, window, fill=IGNORE_INDEX)
+    quilt = _quilt(img.shape[0], img.shape[1], window)
+
+    # uint8 throughout: 4× less memory than float32 for the patch stack, and
+    # IGNORE_INDEX (255) is representable. Conversion to float happens per batch
+    # inside the training loop's to_tensor_fn.
+    img_t = torch.from_numpy(np.ascontiguousarray(img.transpose(2, 0, 1))).unsqueeze(0)
+    lbl_t = torch.from_numpy(np.ascontiguousarray(lbl)).unsqueeze(0)
+
+    def _cut_and_weed(*, mask_borders: bool) -> tuple[Any, Any]:
+        """Cut patches, then drop those with no labelled pixel left.
+
+        With *mask_borders*, each patch's border ring is blanked in the target so
+        the loss only supervises interiors — where the model has full context —
+        and only interior pixels count towards keeping a patch (``weed`` masks
+        its validity check by the same border tensor).
+        """
+        patches_in, patches_out = quilt.unstitch_data_pair(
+            img_t, lbl_t, missing_label=IGNORE_INDEX if mask_borders else None
+        )
+        border = quilt.border_tensor() if mask_borders else torch.ones(quilt.window)
+        return weed_sparse_classification_training_pairs_2D(
+            patches_in, patches_out, missing_label=IGNORE_INDEX, border_tensor=border
+        )[:2]
+
+    kept_in, kept_out = _cut_and_weed(mask_borders=True)
+
+    # An annotation lying entirely within the image's outermost ring falls in no
+    # patch's interior, so border-masking would discard the slice's only labels.
+    # Keep those patches whole rather than silently training on nothing.
+    if len(kept_in) == 0 and bool((lbl != IGNORE_INDEX).any()):
+        kept_in, kept_out = _cut_and_weed(mask_borders=False)
+        logger.info("Tiling: slice annotated only near its edge — kept %d unmasked patch(es)", len(kept_in))
+
+    return [
+        (
+            np.ascontiguousarray(kept_in[i].numpy().transpose(1, 2, 0)),
+            np.ascontiguousarray(kept_out[i].numpy()),
+        )
+        for i in range(len(kept_in))
+    ]
+
+
+# Share of training patches held out for validation when there are no validation
+# slices — matches the 0.1 "valid" ratio of the default auto-split.
+VAL_HOLDOUT_FRACTION = 0.1
+# Below this many patches, holding any out costs more training signal than the
+# metric is worth.
+VAL_HOLDOUT_MIN_PATCHES = 8
+
+
+def holdout_val_patches(
+    datasets: dict[str, list[tuple[np.ndarray, np.ndarray]]],
+    *,
+    seed: int,
+    fraction: float = VAL_HOLDOUT_FRACTION,
+    min_patches: int = VAL_HOLDOUT_MIN_PATCHES,
+) -> tuple[dict[str, list[tuple[np.ndarray, np.ndarray]]], int]:
+    """Move a deterministic share of training patches into an empty val split.
+
+    Splits are assigned per *slice* before tiling, so annotating one or two
+    slices puts them all in train and the run reports no ``val_loss``/``val_miou``
+    at all. Tiling turns those slices into many patches, which makes a holdout
+    possible — so a small, seeded share becomes validation data.
+
+    Only fires when validation is otherwise **empty**: an explicit or auto-assigned
+    validation slice is always left alone.
+
+    Caveat worth surfacing to the user: held-out patches come from the same
+    image(s) as the ones trained on, so the resulting mIoU is optimistic compared
+    with validating on a genuinely unseen slice.
+
+    Returns:
+        ``(datasets, n_held_out)`` — the input unchanged with ``0`` when the
+        holdout doesn't apply.
+    """
+    train = datasets.get("train") or []
+    if datasets.get("val") or len(train) < min_patches:
+        return datasets, 0
+
+    n_val = max(1, int(round(len(train) * fraction)))
+    if n_val >= len(train):
+        return datasets, 0
+
+    order = np.random.default_rng(seed).permutation(len(train))
+    val_idx = {int(i) for i in order[:n_val]}
+    return (
+        {
+            **datasets,
+            "train": [p for i, p in enumerate(train) if i not in val_idx],
+            "val": [train[i] for i in sorted(val_idx)],
+        },
+        n_val,
+    )
+
+
+def tile_datasets(
+    datasets: dict[str, list[tuple[np.ndarray, np.ndarray]]],
+    window: int,
+    progress_cb: Callable[[str], None] | None = None,
+    cancel_cb: Callable[[], bool] | None = None,
+) -> dict[str, list[tuple[np.ndarray, np.ndarray]]] | None:
+    """Replace each full-resolution pair with its window-sized patches.
+
+    Takes and returns :func:`train_common.prepare_datasets`' shape, so it drops
+    straight into the training pipeline.
+
+    Checks *cancel_cb* (if given) between slices — tiling a large annotated set
+    can itself take a while, and previously ran to completion uncancellably with
+    no progress shown beyond one summary line per split at the very end. Returns
+    ``None`` if cancelled partway, mirroring :func:`predict_label_map_tiled`'s
+    cancellation contract so callers check for it the same way.
+    """
+    out: dict[str, list[tuple[np.ndarray, np.ndarray]]] = {}
+    for split, pairs in datasets.items():
+        patches: list[tuple[np.ndarray, np.ndarray]] = []
+        for i, (rgb, label) in enumerate(pairs):
+            if cancel_cb is not None and cancel_cb():
+                return None
+            before = len(patches)
+            patches.extend(_tile_pair(rgb, label, window))
+            if progress_cb is not None:
+                progress_cb(f"{split} slice {i + 1}/{len(pairs)}: {len(patches) - before} patch(es)")
+        out[split] = patches
+        if progress_cb is not None:
+            progress_cb(f"{split}: {len(pairs)} slice(s) → {len(patches)} patch(es) of {window}px")
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Inference
+# ---------------------------------------------------------------------------
+
+
+def _blend_tiled_forward(
+    rgb: np.ndarray,
+    *,
+    forward_fn: Callable[[Any], Any],
+    to_tensor_fn: Callable[[np.ndarray], Any],
+    window: int,
+    device: str,
+    cancel_cb: Callable[[], bool] | None = None,
+    progress_cb: Callable[[str], None] | None = None,
+) -> Any | None:
+    """Blend per-window ``forward_fn`` output into a full-resolution canvas.
+
+    This is the shared core behind :func:`predict_label_map_tiled`
+    (classification: softmax/argmax/confidence-thresholding layered on top) and
+    :func:`denoise_image_tiled` (regression: the blended canvas IS the result,
+    no further post-processing) — anything that needs qlty's tiled-window
+    blending but differs only in what it does with the blended output.
+    *forward_fn* is fully generic here: nothing classification- or
+    regression-specific lives in this function, only the tiling/accumulation
+    machinery.
+
+    Windows are forwarded in small batches, so peak DEVICE memory depends on
+    :data:`INFER_TILE_BATCH` rather than on the image size — only one batch of
+    windows is ever on the GPU/MPS device at once. The weighted output canvas
+    they accumulate into lives on the host instead, precisely so it can scale
+    with image size x channel count without competing for device memory; for a
+    very large image or channel count that host allocation is still the actual
+    memory ceiling here, just not a device one. The accumulation is
+    arithmetically the same weighted mean ``NCYXQuilt.stitch`` computes.
+
+    Returns a ``(C, orig_h, orig_w)`` float32 CPU tensor — *C* is whatever
+    ``forward_fn`` emits per window (n_classes for segmentation logits, 1 for a
+    single-channel denoiser) — or ``None`` if *cancel_cb* asked to stop partway.
+    """
+    import torch  # noqa: PLC0415
+
+    orig_h, orig_w = rgb.shape[:2]
+    padded = pad_to_min(rgb, window, window, fill=0)
+    height, width = padded.shape[:2]
+
+    quilt = _quilt(height, width, window)
+    weight = quilt.weight  # (window, window): 1.0 interior, BORDER_WEIGHT ring
+    step = step_for(window)
+    origins = [(y, x) for y in tile_origins(height, window, step) for x in tile_origins(width, window, step)]
+
+    image = to_tensor_fn(padded)  # (3, H, W) float, CPU
+    canvas: Any = None  # (channels, H, W), allocated once the channel count is known
+    norm = torch.zeros((height, width), dtype=torch.float32)
+
+    if progress_cb is not None:
+        progress_cb(f"{len(origins)} window(s) of {window}px @ {step}px step")
+
+    for batch_start in range(0, len(origins), INFER_TILE_BATCH):
+        if cancel_cb is not None and cancel_cb():
+            return None
+        batch_origins = origins[batch_start : batch_start + INFER_TILE_BATCH]  # noqa: E203
+        batch = torch.stack([image[:, y : y + window, x : x + window] for y, x in batch_origins])  # noqa: E203
+
+        out = forward_fn(batch.to(device)).detach().to("cpu", torch.float32)
+        if canvas is None:
+            canvas = torch.zeros((out.shape[1], height, width), dtype=torch.float32)
+
+        for i, (y, x) in enumerate(batch_origins):
+            canvas[:, y : y + window, x : x + window] += out[i] * weight  # noqa: E203
+            norm[y : y + window, x : x + window] += weight  # noqa: E203
+
+    if canvas is None:  # unreachable for a real image (always ≥1 window)
+        raise RuntimeError("Tiled inference produced no windows")
+
+    # Every pixel is covered by ≥1 window, but clamp anyway so a zero can never
+    # turn into inf/NaN and poison whatever runs on top of this.
+    blended = canvas / norm.clamp(min=1e-8)
+    return blended[:, :orig_h, :orig_w]  # drop any padding
+
+
+def predict_label_map_tiled(
+    rgb: np.ndarray,
+    *,
+    forward_fn: Callable[[Any], Any],
+    to_tensor_fn: Callable[[np.ndarray], Any],
+    window: int,
+    min_confidence: float,
+    device: str,
+    cancel_cb: Callable[[], bool] | None = None,
+    progress_cb: Callable[[str], None] | None = None,
+) -> np.ndarray | None:
+    """Predict a full-resolution label map by blending per-window predictions.
+
+    The blending itself — streamed accumulation into a weighted canvas,
+    arithmetically identical to ``NCYXQuilt.stitch`` — lives in
+    :func:`_blend_tiled_forward`, shared with :func:`denoise_image_tiled`. This
+    function only adds what's specific to classification: softmax is applied
+    **after** recombining, never per window (averaging softmaxed patches is
+    not the softmax of averaged logits — qlty's own docs call this out), then
+    argmax and confidence-thresholding turn it into a label map.
+
+    Returns a ``(H, W)`` uint8 map using the pipeline's convention —
+    ``0`` = below ``min_confidence`` (background), ``1..n`` = class index + 1 —
+    or ``None`` if *cancel_cb* asked to stop partway.
+    """
+    import torch  # noqa: PLC0415
+    import torch.nn.functional as F  # noqa: PLC0415
+
+    blended = _blend_tiled_forward(
+        rgb,
+        forward_fn=forward_fn,
+        to_tensor_fn=to_tensor_fn,
+        window=window,
+        device=device,
+        cancel_cb=cancel_cb,
+        progress_cb=progress_cb,
+    )
+    if blended is None:
+        return None
+
+    probs = F.softmax(blended, dim=0)  # after stitching — never per window
+    confidence, pred_class = probs.max(dim=0)
+    label = torch.where(
+        confidence >= min_confidence,
+        (pred_class + 1).to(torch.uint8),
+        torch.zeros_like(pred_class, dtype=torch.uint8),
+    )
+    return label.numpy()
+
+
+def denoise_image_tiled(
+    rgb: np.ndarray,
+    *,
+    forward_fn: Callable[[Any], Any],
+    to_tensor_fn: Callable[[np.ndarray], Any],
+    window: int,
+    device: str,
+    cancel_cb: Callable[[], bool] | None = None,
+    progress_cb: Callable[[str], None] | None = None,
+) -> np.ndarray | None:
+    """Denoise a full-resolution image by blending per-window regression output.
+
+    Same tiled-window blending as :func:`predict_label_map_tiled` — see
+    :func:`_blend_tiled_forward`, which both share — but *forward_fn* here is a
+    regression model (continuous-valued output, e.g. a trained denoiser)
+    rather than a classifier, so blending is the END of the pipeline: no
+    softmax, argmax or confidence-thresholding, all of which are meaningless
+    on continuous output. The blended canvas IS the denoised image.
+
+    Used both for a live single-slice preview and (by a caller driving it once
+    per slice of a volume) a whole-volume "apply trained denoiser" bake job.
+
+    Returns a float32 array at *rgb*'s original resolution — ``(H, W)`` when
+    *forward_fn* emits a single channel (the expected case for a denoiser),
+    else ``(C, H, W)`` — or ``None`` if *cancel_cb* asked to stop partway.
+    """
+    blended = _blend_tiled_forward(
+        rgb,
+        forward_fn=forward_fn,
+        to_tensor_fn=to_tensor_fn,
+        window=window,
+        device=device,
+        cancel_cb=cancel_cb,
+        progress_cb=progress_cb,
+    )
+    if blended is None:
+        return None
+    out = blended.numpy()
+    return out[0] if out.shape[0] == 1 else out
diff --git a/backend/train_common.py b/backend/train_common.py
new file mode 100644
index 0000000..8ebf6e2
--- /dev/null
+++ b/backend/train_common.py
@@ -0,0 +1,854 @@
+"""Shared plumbing for the Train tab.
+
+Two model families share this module's data prep, generic training loop, and
+run persistence:
+
+* ``"dlsia_tunet"`` — dlsia's tunable U-Net trained from scratch, no
+  pretrained checkpoint (see :mod:`dlsia_runtime`). Needs ``torch`` + the
+  optional ``dlsia`` package.
+* ``"dlsia_denoiser"`` — a self-supervised single-channel denoiser, either a
+  dlsia TUNet (:mod:`denoise_runtime`) or a plain convolutional autoencoder
+  (:mod:`autoencoder_runtime`).
+
+Both are optional dependencies (``backend/pyproject.toml``'s ``ml`` extra) —
+this module is import-safe without torch installed; only the functions that
+actually need it import it lazily and are guarded by :func:`torch_available`.
+
+DINOv3 LoRA fine-tuning is deliberately NOT supported here — see Phase 5.5 in
+the integration plan. It needs vendored, Meta-licensed (research/non-commercial)
+model code and checkpoint provisioning this repo doesn't have a story for yet.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import io
+import json
+import logging
+import os
+import shutil
+from pathlib import Path
+from typing import Any, Callable, NamedTuple
+
+import numpy as np
+from fastapi import HTTPException
+from PIL import Image as PILImage
+
+logger = logging.getLogger(__name__)
+
+# One fine-tune (or inference) job at a time — they contend for the same GPU
+# memory, and neither this hand-rolled loop nor dlsia's TUNet is written to be
+# safely reentrant across concurrent callers.
+import threading  # noqa: E402
+
+ML_LOCK = threading.Lock()
+
+IGNORE_INDEX = 255  # unannotated pixels — see coco_export.build_export_plan(lightly=True)
+
+
+def torch_available() -> bool:
+    """True if ``torch`` is importable, without paying the import cost."""
+    return importlib.util.find_spec("torch") is not None
+
+
+def dlsia_available() -> bool:
+    """True if ``dlsia`` is importable, without paying the import cost."""
+    return importlib.util.find_spec("dlsia") is not None
+
+
+def pick_device() -> str | None:
+    """Return ``"mps"|"cuda"|"cpu"``, or ``None`` if torch is unavailable.
+
+    ``TRAIN_DEVICE`` env var overrides auto-detection (e.g. to force ``cpu``
+    on a machine where MPS is flaky for a particular op).
+    """
+    if not torch_available():
+        return None
+    override = (os.getenv("TRAIN_DEVICE") or "").strip().lower()
+    if override in {"mps", "cuda", "cpu"}:
+        return override
+    import torch  # noqa: PLC0415 — optional dependency, imported lazily
+
+    if torch.backends.mps.is_available():
+        return "mps"
+    if torch.cuda.is_available():
+        return "cuda"
+    return "cpu"
+
+
+def runs_dir() -> Path:
+    """Server-owned directory holding saved fine-tune runs (both families)."""
+    configured = (os.getenv("DINO_RUNS_DIR") or "").strip()
+    if configured:
+        return Path(configured).expanduser().resolve()
+    local_root = Path(os.getenv("LOCAL_DATA_ROOT", "~/data")).expanduser().resolve()
+    return (local_root / "models" / "runs").resolve()
+
+
+def _validate_run_id(run_id: str) -> str:
+    """Reject anything that isn't a single safe path component."""
+    if not run_id or run_id in {".", ".."} or "/" in run_id or "\\" in run_id or "\x00" in run_id:
+        raise HTTPException(400, "Invalid run_id")
+    return run_id
+
+
+def run_dir(run_id: str) -> Path:
+    """Resolved, containment-checked directory for one run."""
+    base = runs_dir()
+    candidate = (base / _validate_run_id(run_id)).resolve()
+    if not candidate.is_relative_to(base):
+        raise HTTPException(400, "Invalid run_id")
+    return candidate
+
+
+# ---------------------------------------------------------------------------
+# Run persistence (shared shape across both families)
+# ---------------------------------------------------------------------------
+
+
+def save_run(
+    run_id: str,
+    *,
+    model_family: str,
+    model_config: dict[str, Any],
+    classes: list[dict[str, Any]],
+    render: dict[str, Any],
+    image_size: int,
+    hyperparams: dict[str, Any],
+    source_keys: list[str],
+    adapter_state: dict[str, Any],
+    metrics: dict[str, Any],
+    resumed_from: str | None = None,
+    task: str = "segmentation",
+    denoise: dict[str, Any] | None = None,
+) -> None:
+    """Persist one run's config + adapter/weights + metrics to ``runs_dir()``.
+
+    ``resumed_from`` records the run this one continued fine-tuning from, so a
+    chain of successive refinements stays traceable (a resume always writes a
+    NEW run — the parent is never modified).
+
+    ``task`` distinguishes a segmentation run from a self-supervised denoiser
+    run (see ``schemas.TrainRequest.task``); defaults to ``"segmentation"``
+    for callers that predate the denoiser family.
+
+    ``denoise`` records any denoising applied to the model's INPUT pixels, so
+    inference can reapply exactly the same preprocessing off the run instead of
+    trusting the caller to remember it (see :func:`denoising_render_slice_fn`).
+    """
+    import torch  # noqa: PLC0415
+
+    d = run_dir(run_id)
+    d.mkdir(parents=True, exist_ok=True)
+    config = {
+        "run_id": run_id,
+        "model_family": model_family,
+        "task": task,
+        "model_config": model_config,
+        "classes": classes,
+        "render": render,
+        "denoise": denoise,
+        "image_size": image_size,
+        "hyperparams": hyperparams,
+        "source_keys": source_keys,
+        "resumed_from": resumed_from,
+        "created_at": _now_iso(),
+    }
+    (d / "config.json").write_text(json.dumps(config, indent=2))
+    (d / "metrics.json").write_text(json.dumps(metrics, indent=2))
+    torch.save(adapter_state, d / "adapter.pt")
+
+
+def _now_iso() -> str:
+    from datetime import datetime, timezone
+
+    return datetime.now(timezone.utc).isoformat()
+
+
+def list_runs() -> list[dict[str, Any]]:
+    """List saved runs, newest first. Malformed run directories are skipped."""
+    base = runs_dir()
+    if not base.exists():
+        return []
+    results: list[dict[str, Any]] = []
+    for d in base.iterdir():
+        if not d.is_dir():
+            continue
+        try:
+            config = json.loads((d / "config.json").read_text())
+            # Runs saved before `task` existed have no such key on disk — treat
+            # that as "segmentation", the only thing this app trained before.
+            config.setdefault("task", "segmentation")
+            metrics = json.loads((d / "metrics.json").read_text()) if (d / "metrics.json").exists() else {}
+            results.append({**config, "metrics": metrics})
+        except Exception as exc:  # noqa: BLE001 — one bad run dir must not break the list
+            logger.warning("Skipping malformed run directory %s: %s", d, exc)
+            continue
+    results.sort(key=lambda r: r.get("created_at", ""), reverse=True)
+    return results
+
+
+def delete_run(run_id: str) -> None:
+    """Permanently remove a saved run's directory (config, metrics, weights)."""
+    d = run_dir(run_id)
+    if not d.is_dir():
+        raise HTTPException(404, f"Unknown run: {run_id!r}")
+    shutil.rmtree(d)
+
+
+def load_run_config(run_id: str) -> dict[str, Any]:
+    """Return the parsed ``config.json`` for *run_id*, or raise 404.
+
+    ``task`` is a field added after this app already had saved runs on disk —
+    an absent key means the run predates the field, and every run trained
+    before it existed was a segmentation run, so that is the default filled
+    in here rather than leaving callers (e.g. ``check_resume_compatible``) to
+    each re-derive the same backward-compat fallback.
+    """
+    d = run_dir(run_id)
+    config_path = d / "config.json"
+    if not config_path.exists():
+        raise HTTPException(404, f"Unknown run: {run_id!r}")
+    try:
+        config = json.loads(config_path.read_text())
+    except json.JSONDecodeError as exc:
+        raise HTTPException(500, "Run config is corrupt") from exc
+    config.setdefault("task", "segmentation")
+    return config
+
+
+def load_adapter_state(run_id: str) -> dict[str, Any]:
+    """Load the saved ``adapter.pt`` state dict for *run_id* onto the CPU."""
+    import torch  # noqa: PLC0415
+
+    d = run_dir(run_id)
+    adapter_path = d / "adapter.pt"
+    if not adapter_path.exists():
+        raise HTTPException(404, f"No saved weights for run: {run_id!r}")
+    return torch.load(adapter_path, map_location="cpu", weights_only=False)
+
+
+# ---------------------------------------------------------------------------
+# Training-data preparation (shared: reuses the Lightly export path)
+# ---------------------------------------------------------------------------
+
+
+def denoising_render_slice_fn(denoise: Any) -> Callable[..., np.ndarray]:
+    """A ``render_slice``-compatible callable that denoises the RAW slice first.
+
+    The single place model-input denoising is applied, used by BOTH training
+    (:func:`prepare_datasets`) and inference (``infer_jobs``). Keeping one
+    implementation is the point: a model must see the same pixel distribution at
+    predict time that it saw during training, and two copies of this would be
+    free to drift apart silently — the failure mode is not an error, just
+    quietly worse predictions.
+
+    Denoising runs on raw intensity units, before ``render_slice`` normalises,
+    matching where the Annotate preview applies it.
+
+    Args:
+        denoise: Anything with ``.method``/``.strength`` (a
+            :class:`schemas.DenoiseTrainOpts`) or the equivalent dict as
+            reloaded from a run's ``config.json``. Falsy, or a ``"none"``
+            method, yields plain ``images.render_slice``.
+    """
+    import images as images_mod  # noqa: PLC0415
+
+    method, strength = _denoise_params(denoise)
+    if method is None:
+        return images_mod.render_slice
+
+    import denoise as denoise_mod  # noqa: PLC0415
+
+    def _render(arr: np.ndarray, opts: dict[str, Any], global_range: Any = None) -> np.ndarray:
+        # Colour sources are left alone: the classical filters are 2-D
+        # grayscale, and render_slice early-returns for RGB anyway.
+        if arr.ndim == 2:
+            arr = denoise_mod.denoise_slice(arr, method, strength)
+        return images_mod.render_slice(arr, opts, global_range)
+
+    return _render
+
+
+def _denoise_params(denoise: Any) -> tuple[str | None, float]:
+    """Normalise a DenoiseTrainOpts / dict / None into ``(method, strength)``.
+
+    Returns ``(None, ...)`` when no denoising should be applied — including for
+    ``"model"``, which would need its own run and a GPU pass per slice and is
+    deliberately not supported as a preprocessor for another model.
+    """
+    if not denoise:
+        return None, 0.5
+    if hasattr(denoise, "method"):
+        method, strength = denoise.method, float(denoise.strength)
+    else:
+        method, strength = denoise.get("method"), float(denoise.get("strength", 0.5))
+    if not method or method in ("none", "model"):
+        return None, strength
+    return method, strength
+
+
+def prepare_datasets(
+    sources: list[Any],
+    classes: list[Any],
+    render: Any,
+    auto_split: dict[str, Any],
+    progress_cb: Callable[[str], None] | None = None,
+    denoise: Any = None,
+) -> dict[str, list[tuple[np.ndarray, np.ndarray]]]:
+    """Render + rasterize every annotated source into in-memory train/val pairs.
+
+    Reuses ``coco_export.build_export_plan(lightly=True)`` per source — the
+    same in-memory rendering/rasterization the Lightly export already does —
+    so no disk export is needed just to assemble training tensors. Valid +
+    test folds into val (same convention as the Lightly export).
+
+    Returns:
+        ``{"train": [(rgb_uint8_hwc, label_uint8_hw), ...], "val": [...]}``.
+    """
+    import arrays as arrays_mod
+    import images as images_mod
+    from coco_export import build_export_plan
+    from schemas import ExportRequest
+
+    train_pairs: list[tuple[np.ndarray, np.ndarray]] = []
+    val_pairs: list[tuple[np.ndarray, np.ndarray]] = []
+
+    for item in sources:
+        node = arrays_mod.resolve_array(item.source, item.kind, item.server_uri)
+        shim = ExportRequest(
+            kind=item.kind,
+            source=item.source,
+            server_uri=item.server_uri,
+            slices=item.slices,
+            split_by_slice=item.split_by_slice,
+            negative_slices=item.negative_slices,
+            classes=classes,
+            render=render,
+            auto_split=auto_split,
+        )
+        plan = build_export_plan(
+            node,
+            shim,
+            render_slice_fn=denoising_render_slice_fn(denoise),
+            array_shape_meta_fn=arrays_mod.array_shape_meta,
+            read_slice_fn=arrays_mod.read_slice,
+            sample_global_stats_fn=images_mod._sample_global_stats,
+            progress_cb=progress_cb,
+            lightly=True,
+        )
+        for split_name, split_data in plan["splits"].items():
+            bucket = train_pairs if split_name == "train" else val_pairs
+            for img in split_data["images"]:
+                rgb = np.asarray(PILImage.open(io.BytesIO(img["png_bytes"])).convert("RGB"))
+                label = np.asarray(PILImage.open(io.BytesIO(img["label_png_bytes"])))
+                bucket.append((rgb, label))
+
+    return {"train": train_pairs, "val": val_pairs}
+
+
+def letterbox(image: np.ndarray, label: np.ndarray, size: int) -> tuple[np.ndarray, np.ndarray]:
+    """Resize-keep-aspect + pad *image* (uint8 HWC) and *label* (uint8 HW) to
+    a ``size`` x ``size`` square. Label padding uses :data:`IGNORE_INDEX` so
+    padded pixels never contribute to the loss.
+
+    Also returns enough to invert the transform (see :func:`unletterbox`),
+    packed into the returned label's dtype-preserving companion — callers that
+    need to invert should use :func:`letterbox_params` directly instead of
+    re-deriving it, since floating-point resize choices must match exactly.
+    """
+    h, w = image.shape[:2]
+    if (h, w) == (size, size) and label.shape[:2] == (size, size):
+        # Already exactly the target square — every tiled-training patch hits
+        # this. The full computation below is a no-op here anyway (scale=1,
+        # zero padding), just paid for with 4 array copies per patch per epoch;
+        # skip straight to returning the inputs. Safe to hand back unchanged
+        # (not a defensive copy): callers only ever read these, or derive a NEW
+        # array via reversal/dtype-cast, never mutate in place.
+        return image, label
+    scale = min(size / h, size / w)
+    nh, nw = max(1, round(h * scale)), max(1, round(w * scale))
+
+    img_pil = PILImage.fromarray(image).resize((nw, nh), PILImage.Resampling.BILINEAR)
+    lbl_pil = PILImage.fromarray(label).resize((nw, nh), PILImage.Resampling.NEAREST)
+
+    out_img = np.zeros((size, size, image.shape[2]), dtype=np.uint8)
+    out_lbl = np.full((size, size), IGNORE_INDEX, dtype=np.uint8)
+    top, left = (size - nh) // 2, (size - nw) // 2
+    out_img[top : top + nh, left : left + nw] = np.asarray(img_pil)  # noqa: E203
+    out_lbl[top : top + nh, left : left + nw] = np.asarray(lbl_pil)  # noqa: E203
+    return out_img, out_lbl
+
+
+def letterbox_params(h: int, w: int, size: int) -> dict[str, int]:
+    """Return the placement used by :func:`letterbox` for an ``h``x``w`` image,
+    so a prediction on the ``size``x``size`` canvas can be cropped/resized back
+    to the original resolution (see :func:`unletterbox`)."""
+    scale = min(size / h, size / w)
+    nh, nw = max(1, round(h * scale)), max(1, round(w * scale))
+    top, left = (size - nh) // 2, (size - nw) // 2
+    return {"top": top, "left": left, "nh": nh, "nw": nw}
+
+
+def unletterbox(label: np.ndarray, orig_h: int, orig_w: int, size: int) -> np.ndarray:
+    """Invert :func:`letterbox` on a predicted label map: crop the padding,
+    then nearest-resize back to ``(orig_h, orig_w)``."""
+    p = letterbox_params(orig_h, orig_w, size)
+    cropped = label[p["top"] : p["top"] + p["nh"], p["left"] : p["left"] + p["nw"]]  # noqa: E203
+    if (p["nh"], p["nw"]) == (orig_h, orig_w):
+        return cropped
+    return np.asarray(PILImage.fromarray(cropped).resize((orig_w, orig_h), PILImage.Resampling.NEAREST))
+
+
+# ---------------------------------------------------------------------------
+# Model construction (shared: the one place a model gets built from a
+# schemas.ModelConfig, used by both train_jobs.py and batch_probe.py)
+# ---------------------------------------------------------------------------
+
+
+class BuiltFamily(NamedTuple):
+    """Everything :func:`run_training_loop` (or the batch-size probe) needs
+    from a constructed model, plus enough to save it afterward.
+
+    ``adapter_state_fn`` defers building the saved-weights dict (the dlsia
+    network dict) until it's actually called: only :mod:`train_jobs` calls
+    it, after training completes; :mod:`batch_probe` never saves anything and
+    simply ignores the field.
+    """
+
+    forward_fn: Callable[["Any"], "Any"]
+    to_tensor_fn: Callable[["Any"], "Any"]
+    trainable_params: list["Any"]
+    set_train_mode: Callable[[bool], None] | None
+    model_config_snapshot: dict[str, Any]
+    adapter_state_fn: Callable[[], dict[str, Any]]
+
+
+def denoiser_runtime_for(config: dict[str, Any]) -> "Any":
+    """The runtime module that can load *config*'s saved denoiser.
+
+    Both inference paths (the single-slice canvas preview in
+    ``annotation_server`` and the whole-volume bake in ``denoise_bake``) need
+    this, and both need the same backward-compatible default — hence one
+    implementation rather than two that could drift.
+
+    A run's ``model_config["architecture"]`` names the network. Runs saved
+    before that field existed have no such key and are dlsia TUNets by
+    definition, so an absent value MUST default to ``"tunet"`` or every existing
+    run on disk becomes unloadable.
+
+    Raises:
+        ValueError: the run names an architecture this build doesn't know (e.g.
+            a run produced by a newer version).
+    """
+    architecture = (config.get("model_config") or {}).get("architecture", "tunet")
+    if architecture == "cnn_ae":
+        import autoencoder_runtime  # noqa: PLC0415
+
+        return autoencoder_runtime
+    if architecture == "tunet":
+        import denoise_runtime  # noqa: PLC0415
+
+        return denoise_runtime
+    raise ValueError(f"Unknown denoiser architecture on this run: {architecture!r}")
+
+
+def denoiser_needs_dlsia(config: dict[str, Any]) -> bool:
+    """Whether *config*'s denoiser architecture requires the dlsia dependency.
+
+    Only the TUNet architecture does; the convolutional autoencoder is plain
+    torch. Lets the inference gates refuse for the right reason instead of
+    demanding dlsia for a network that never touches it.
+    """
+    return (config.get("model_config") or {}).get("architecture", "tunet") == "tunet"
+
+
+def build_family(
+    model_cfg: "Any",
+    n_classes: int,
+    device: str,
+    log_cb: Callable[[str], None],
+    init_state: dict[str, Any] | None = None,
+) -> BuiltFamily:
+    """Build the model, forward pass, and tensor conversion for *model_cfg*'s
+    family.
+
+    This is the single place a model gets constructed from hyperparameters —
+    :mod:`train_jobs` and :mod:`batch_probe` both call it, so the probe's
+    "builds exactly like training does" claim is enforced by sharing code
+    rather than by keeping two copies in sync by hand.
+
+    ``init_state`` is a saved run's ``adapter.pt`` to warm-start from (continue
+    fine-tuning) instead of starting from scratch. Optimizer/scheduler state is
+    not saved and so is not restored — a resume gets a fresh AdamW and a cosine
+    schedule starting again at ``lr``, which is the normal fine-tune-again
+    behaviour.
+
+    Every recognized ``schemas.ModelConfig`` member has its own explicit
+    branch below; anything else raises :class:`ValueError` rather than being
+    silently treated as a dlsia TUNet — that used to be this function's
+    implicit ``else``, which would happily (and wrongly) build a segmentation
+    TUNet for a config type nobody had actually written a branch for yet.
+    """
+    from schemas import DlsiaDenoiserConfig, DlsiaTunetConfig  # noqa: PLC0415 — avoid a hard import-time cycle
+
+    hp = model_cfg.hyperparams
+
+    if isinstance(model_cfg, DlsiaTunetConfig):
+        import dlsia_runtime as fam  # noqa: PLC0415
+
+        if not dlsia_available():
+            raise RuntimeError("dlsia is not installed on this server")
+        if init_state is not None:
+            # load_model rebuilds the net from the saved topo_dict, so the resumed
+            # topology is the run's own — depth/base_channels/growth_rate/image_size
+            # from the request are deliberately NOT used here (they can't be: a
+            # different topology cannot load these weights). run_train_job forces
+            # them to match the saved run before getting here.
+            log_cb(f"Resuming dlsia TUNet from saved weights on {device}…")
+            model = fam.load_model(init_state, device)
+            topo = init_state.get("topo_dict", {})
+            snapshot = {
+                "depth": topo.get("depth", hp.depth),
+                "base_channels": topo.get("base_channels", hp.base_channels),
+                "growth_rate": topo.get("growth_rate", hp.growth_rate),
+            }
+        else:
+            log_cb(f"Building dlsia TUNet (depth={hp.depth}, base_channels={hp.base_channels}) on {device}…")
+            model = fam.build_model(n_classes, hp.image_size, hp.depth, hp.base_channels, hp.growth_rate, device)
+            snapshot = {"depth": hp.depth, "base_channels": hp.base_channels, "growth_rate": hp.growth_rate}
+        return BuiltFamily(
+            forward_fn=fam.make_forward_fn(model),
+            to_tensor_fn=fam.make_to_tensor_fn(),
+            trainable_params=list(model.parameters()),
+            set_train_mode=fam.make_set_train_mode_fn(model),
+            model_config_snapshot=snapshot,
+            adapter_state_fn=lambda: fam.network_dict(model),
+        )
+
+    if isinstance(model_cfg, DlsiaDenoiserConfig):
+        # Two architectures share this family; both expose the identical
+        # six-function runtime template, so everything below the module choice
+        # is common. See DlsiaDenoiserConfig for why they aren't separate
+        # model_family values.
+        if model_cfg.architecture == "cnn_ae":
+            import autoencoder_runtime as fam  # noqa: PLC0415
+
+            # No dlsia_available() gate here, unlike the TUNet branch below:
+            # this network is plain torch and needs no optional dependency.
+            if init_state is not None:
+                log_cb(f"Resuming autoencoder denoiser from saved weights on {device}…")
+                model = fam.load_model(init_state, device)
+                topo = init_state.get("topo_dict", {})
+                snapshot = {
+                    "architecture": "cnn_ae",
+                    "depth": topo.get("depth", hp.depth),
+                    "base_channels": topo.get("base_channels", hp.base_channels),
+                    "ae_compression": topo.get("compression", model_cfg.ae_compression),
+                    "latent_channels": topo.get("latent_channels"),
+                }
+            else:
+                latent = fam.latent_channels_for(hp.depth, model_cfg.ae_compression)
+                log_cb(
+                    f"Building autoencoder denoiser (depth={hp.depth}, "
+                    f"base_channels={hp.base_channels}, {model_cfg.ae_compression}x compression "
+                    f"-> {latent} latent channels) on {device}…"
+                )
+                model = fam.build_model(
+                    hp.image_size, hp.depth, hp.base_channels, model_cfg.ae_compression, device
+                )
+                snapshot = {
+                    "architecture": "cnn_ae",
+                    "depth": hp.depth,
+                    "base_channels": hp.base_channels,
+                    "ae_compression": model_cfg.ae_compression,
+                    "latent_channels": latent,
+                }
+        else:
+            import denoise_runtime as fam  # noqa: PLC0415
+
+            if not dlsia_available():
+                raise RuntimeError("dlsia is not installed on this server")
+            if init_state is not None:
+                # Same warm-start contract as the segmentation TUNet branch above:
+                # the saved topo_dict wins, the request's topology knobs don't.
+                log_cb(f"Resuming dlsia denoiser TUNet from saved weights on {device}…")
+                model = fam.load_model(init_state, device)
+                topo = init_state.get("topo_dict", {})
+                snapshot = {
+                    "architecture": "tunet",
+                    "depth": topo.get("depth", hp.depth),
+                    "base_channels": topo.get("base_channels", hp.base_channels),
+                    "growth_rate": topo.get("growth_rate", hp.growth_rate),
+                }
+            else:
+                log_cb(
+                    f"Building dlsia denoiser TUNet (depth={hp.depth}, base_channels={hp.base_channels}) "
+                    f"on {device}…"
+                )
+                # No n_classes: denoise_runtime.build_model fixes in/out channels to
+                # 1 (single-channel regression) — there is nothing to parameterize.
+                model = fam.build_model(hp.image_size, hp.depth, hp.base_channels, hp.growth_rate, device)
+                snapshot = {
+                    "architecture": "tunet",
+                    "depth": hp.depth,
+                    "base_channels": hp.base_channels,
+                    "growth_rate": hp.growth_rate,
+                }
+        return BuiltFamily(
+            forward_fn=fam.make_forward_fn(model),
+            to_tensor_fn=fam.make_to_tensor_fn(),
+            trainable_params=list(model.parameters()),
+            set_train_mode=fam.make_set_train_mode_fn(model),
+            model_config_snapshot=snapshot,
+            adapter_state_fn=lambda: fam.network_dict(model),
+        )
+
+    raise ValueError(
+        f"Unknown model family/config type for build_family: {type(model_cfg).__name__!r} "
+        f"(model_family={getattr(model_cfg, 'model_family', None)!r})"
+    )
+
+
+# ---------------------------------------------------------------------------
+# Generic training loop (family-agnostic: takes a forward callable + params)
+# ---------------------------------------------------------------------------
+
+
+def compute_miou(pred: "Any", target: "Any", n_classes: int, ignore_index: int = IGNORE_INDEX) -> float:
+    """Mean IoU over ``n_classes`` (0-indexed), ignoring ``ignore_index`` pixels.
+
+    ``pred``/``target`` are torch tensors of matching shape (after argmax);
+    a class absent from both prediction and target on this batch is skipped
+    (rather than counted as a perfect or zero score) so small batches don't
+    bias the running average toward classes that simply didn't appear.
+    """
+    valid = target != ignore_index
+    ious = []
+    for c in range(n_classes):
+        p = (pred == c) & valid
+        t = (target == c) & valid
+        union = (p | t).sum().item()
+        if union == 0:
+            continue
+        intersection = (p & t).sum().item()
+        ious.append(intersection / union)
+    return float(sum(ious) / len(ious)) if ious else 0.0
+
+
+def run_training_loop(
+    *,
+    train_pairs: list[tuple[np.ndarray, np.ndarray]],
+    val_pairs: list[tuple[np.ndarray, np.ndarray]],
+    image_size: int,
+    n_classes: int,
+    epochs: int,
+    batch_size: int,
+    seed: int,
+    flip_augment: bool,
+    to_tensor_fn: Callable[["Any"], "Any"],
+    forward_fn: Callable[["Any"], "Any"],
+    trainable_params: list["Any"],
+    lr: float,
+    device: str,
+    make_optimizer_fn: Callable[[list["Any"], float], "Any"] | None = None,
+    on_batch: Callable[[], bool] | None = None,
+    on_epoch: Callable[[int, float, float | None, float | None], bool] | None = None,
+    set_train_mode: Callable[[bool], None] | None = None,
+) -> dict[str, Any]:
+    """Shared epoch/batch loop used by both model families.
+
+    ``to_tensor_fn(rgb_uint8_hwc) -> float tensor (C,H,W)`` applies whatever
+    normalisation the family wants (plain /255 for dlsia). ``forward_fn(batch_images)
+    -> logits (B,n_classes,H,W)`` runs the family's model. Both
+    ``on_batch``/``on_epoch`` return ``True`` to request a cooperative stop
+    (checkpoints are still saved by the caller either way).
+
+    ``set_train_mode(is_training)`` toggles ``nn.Module.train()``/``eval()``
+    around validation — matters for the dlsia family (TUNet defaults to
+    BatchNorm2d, whose running stats should stay fixed during validation
+    rather than being computed from that batch). Omit for a model with no such
+    distinction.
+
+    Returns ``{"epochs_completed", "final_train_loss", "final_val_loss",
+    "val_miou", "cancelled"}``.
+    """
+    import torch
+    import torch.nn as nn
+
+    if not train_pairs:
+        raise ValueError("No training data: at least one annotated slice is required")
+
+    rng = np.random.default_rng(seed)
+    optimizer = (make_optimizer_fn or (lambda params, lr_: torch.optim.AdamW(params, lr=lr_, weight_decay=0.01)))(
+        trainable_params, lr
+    )
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=max(1, epochs))
+    criterion = nn.CrossEntropyLoss(ignore_index=IGNORE_INDEX)
+
+    def _prep(rgb: np.ndarray, label: np.ndarray, flip: bool) -> tuple["Any", "Any"]:
+        img_l, lbl_l = letterbox(rgb, label, image_size)
+        if flip:
+            img_l = np.ascontiguousarray(img_l[:, ::-1])
+            lbl_l = np.ascontiguousarray(lbl_l[:, ::-1])
+        return to_tensor_fn(img_l), torch.from_numpy(lbl_l.astype(np.int64))
+
+    cancelled = False
+    final_train_loss = 0.0
+    final_val_loss: float | None = None
+    val_miou: float | None = None
+    epochs_completed = 0
+
+    for epoch in range(epochs):
+        order = rng.permutation(len(train_pairs))
+        epoch_loss = 0.0
+        n_batches = 0
+        for start in range(0, len(order), batch_size):
+            batch_idx = order[start : start + batch_size]  # noqa: E203
+            imgs, lbls = [], []
+            for i in batch_idx:
+                rgb, label = train_pairs[int(i)]
+                flip = bool(flip_augment and rng.random() < 0.5)
+                img_t, lbl_t = _prep(rgb, label, flip)
+                imgs.append(img_t)
+                lbls.append(lbl_t)
+            batch_img = torch.stack(imgs).to(device)
+            batch_lbl = torch.stack(lbls).to(device)
+
+            logits = forward_fn(batch_img)
+            loss = criterion(logits, batch_lbl)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            epoch_loss += float(loss.detach().item())
+            n_batches += 1
+            if on_batch is not None and on_batch():
+                cancelled = True
+                break
+        scheduler.step()
+        final_train_loss = epoch_loss / max(1, n_batches)
+        epochs_completed = epoch + 1
+
+        if val_pairs and not cancelled:
+            if set_train_mode is not None:
+                set_train_mode(False)
+            final_val_loss, val_miou = _evaluate(
+                val_pairs, image_size, n_classes, to_tensor_fn, forward_fn, device, criterion, batch_size
+            )
+            if set_train_mode is not None:
+                set_train_mode(True)
+
+        if on_epoch is not None and on_epoch(epochs_completed, final_train_loss, final_val_loss, val_miou):
+            cancelled = True
+        if cancelled:
+            break
+
+    return {
+        "epochs_completed": epochs_completed,
+        "final_train_loss": final_train_loss,
+        "final_val_loss": final_val_loss,
+        "val_miou": val_miou,
+        "cancelled": cancelled,
+    }
+
+
+def _evaluate(
+    val_pairs: list[tuple[np.ndarray, np.ndarray]],
+    image_size: int,
+    n_classes: int,
+    to_tensor_fn: Callable[["Any"], "Any"],
+    forward_fn: Callable[["Any"], "Any"],
+    device: str,
+    criterion: "Any",
+    batch_size: int = 1,
+) -> tuple[float, float]:
+    """Average validation loss + per-sample mIoU over ``val_pairs``.
+
+    Batches the forward pass by ``batch_size`` (mirrors the training loop)
+    instead of one sample at a time — tiling's holdout can leave dozens of
+    validation patches, and running under ``torch.no_grad()`` means the
+    activation memory a bigger batch needs stays bounded regardless.
+
+    mIoU is still scored per sample, not per batch: :func:`compute_miou`
+    pools every matching pixel in whatever tensor it's given, so handing it a
+    whole batch at once would silently compute one pooled-pixel score across
+    samples instead of the average of each sample's own score. That per-sample
+    split is cheap CPU-side tensor slicing after the one batched forward, not
+    a second forward pass, so it doesn't undo the batching's benefit.
+    """
+    import torch
+
+    total_loss = 0.0
+    total_miou = 0.0
+    n = len(val_pairs)
+    with torch.no_grad():
+        for start in range(0, n, batch_size):
+            chunk = val_pairs[start : start + batch_size]  # noqa: E203
+            imgs, lbls = [], []
+            for rgb, label in chunk:
+                img_l, lbl_l = letterbox(rgb, label, image_size)
+                imgs.append(to_tensor_fn(img_l))
+                lbls.append(torch.from_numpy(lbl_l.astype(np.int64)))
+            batch_img = torch.stack(imgs).to(device)
+            batch_lbl = torch.stack(lbls).to(device)
+
+            logits = forward_fn(batch_img)
+            # criterion's mean reduction is over the whole batch, so weight by
+            # sample count to keep this a per-sample average overall — same
+            # convention the training loop already uses for its own loss.
+            total_loss += float(criterion(logits, batch_lbl).item()) * len(chunk)
+            preds = logits.argmax(dim=1)
+            for i in range(len(chunk)):
+                total_miou += compute_miou(preds[i], batch_lbl[i], n_classes)
+    return total_loss / n, total_miou / n
+
+
+# ---------------------------------------------------------------------------
+# Capability probe (never raises — feeds GET /api/train/capability)
+# ---------------------------------------------------------------------------
+
+
+def capability() -> dict[str, Any]:
+    """Best-effort snapshot of Train-tab readiness. Never raises.
+
+    No DINOv3 fields — that model family is deferred to Phase 5.5 and isn't
+    scaffolded here at all.
+    """
+    result: dict[str, Any] = {
+        "torch_available": False,
+        "torch_version": None,
+        "device": None,
+        "dlsia": {"available": False},
+        "tiling": {"available": False},
+        # Classical denoising is pure CPU (scipy/skimage) and never touches
+        # ML_LOCK, so it stays usable while a training job runs.
+        "denoise": {"available": False, "methods": []},
+        "runs_dir": str(runs_dir()),
+        "busy": ML_LOCK.locked(),
+    }
+    try:
+        result["torch_available"] = torch_available()
+        if result["torch_available"]:
+            import torch  # noqa: PLC0415
+
+            result["torch_version"] = torch.__version__
+            result["device"] = pick_device()
+
+        result["dlsia"] = {"available": result["torch_available"] and dlsia_available()}
+
+        import tiling  # noqa: PLC0415 — avoid a hard import-time cycle (tiling imports train_common)
+
+        result["tiling"] = {"available": tiling.qlty_available()}
+
+        import denoise  # noqa: PLC0415 — cheap, but keep the probe self-contained
+
+        # `available: True` unconditionally: scipy/skimage are hard dependencies
+        # here (unlike torch/dlsia), so the classical filters always work. The
+        # per-method `available` flags carry the real gating — wavelet needs
+        # PyWavelets, which skimage imports lazily and which is not installed.
+        result["denoise"] = {"available": True, "methods": denoise.describe_methods()}
+    except Exception as exc:  # noqa: BLE001 — a capability probe must never 500
+        logger.warning("Train capability probe failed: %s", exc)
+        result["error"] = str(exc)
+    return result
diff --git a/backend/train_jobs.py b/backend/train_jobs.py
new file mode 100644
index 0000000..37bf240
--- /dev/null
+++ b/backend/train_jobs.py
@@ -0,0 +1,575 @@
+"""Background job orchestration for the Train tab.
+
+Dispatches a :class:`schemas.TrainRequest` to the requested model family
+(dlsia TUNet segmentation, or the dlsia/autoencoder denoiser family), sharing
+data preparation, the generic training loop, and run persistence via
+:mod:`train_common`. Progress/cancellation are reported through the existing
+:mod:`export_jobs` registry — the frontend polls the same
+``GET /api/export/status/{job_id}`` route already used by exports and
+mask-sync jobs.
+
+DINOv3 LoRA fine-tuning is out of scope here (see Phase 5.5).
+
+Two *tasks* are dispatched from here, distinguished by ``TrainRequest.task``:
+
+* ``"segmentation"`` — annotation-driven, via
+  :func:`train_common.prepare_datasets` and
+  :func:`train_common.run_training_loop`.
+* ``"denoising"`` — self-supervised (Noise2Noise / Noise2Void / autoencoder),
+  via :mod:`denoise_train`'s raw-slice samplers and its own regression loop.
+
+Everything either task has in common — the ML lock, device selection, resume
+resolution, the qlty guard, ``export_jobs`` progress/cancel reporting, and
+persistence through :func:`train_common.save_run` — is shared in
+:func:`run_train_job`; only data preparation, the loss, and the validation
+metric differ, and those live behind the split at the end of its preamble.
+"""
+
+from __future__ import annotations
+
+import logging
+import uuid
+from datetime import datetime, timezone
+
+import export_jobs
+import train_common
+from schemas import (
+    DenoiseTrainOpts,
+    DlsiaDenoiserConfig,
+    DlsiaTunetConfig,
+    TrainRequest,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def new_run_id(model_family: str) -> str:
+    """``<UTC timestamp>_<model_family>_<short random>`` — sorts newest-last
+    lexically within a day, globally unique enough for a single-user tool."""
+    ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
+    return f"{ts}_{model_family}_{uuid.uuid4().hex[:4]}"
+
+
+def check_resume_compatible(parent_config: dict, request: TrainRequest) -> None:
+    """Raise if *request* cannot continue fine-tuning from *parent_config*'s run.
+
+    The saved head has one output channel per class, in the parent's class
+    ORDER (``infer_jobs`` maps channel ``c`` to ``classes[c]["classId"]``), so
+    the class list has to line up positionally for a resume to mean anything.
+    A count mismatch would fail loudly inside ``load_state_dict`` anyway, but a
+    same-count-different-labels resume would train happily against the wrong
+    semantics and never say so — which is the case this exists to catch.
+
+    A self-supervised denoiser has no class taxonomy at all, so the class-list
+    comparison is skipped when BOTH the parent run and *request* are
+    ``task == "denoising"``. That is not the same as skipping the check
+    whenever either side is denoising: resuming a denoiser as a segmentation
+    run (or vice versa) is a genuine incompatibility — the saved weights are
+    single-channel regression output, not per-class logits, or the reverse —
+    and must still be reported as such rather than silently allowed through.
+    ``parent_config`` may predate the ``task`` field entirely (see
+    ``train_common.load_run_config``), so an absent key defaults to
+    ``"segmentation"`` here too, not just at load time.
+    """
+    if parent_config.get("model_family") != request.model.model_family:
+        raise ValueError(
+            f"Cannot continue fine-tuning a {parent_config.get('model_family')} run "
+            f"as {request.model.model_family} — pick the matching model family, or train a new run."
+        )
+
+    parent_task = parent_config.get("task", "segmentation")
+    request_task = request.task
+    if parent_task != request_task:
+        raise ValueError(
+            f"Cannot continue fine-tuning a {parent_task!r}-task run as a {request_task!r}-task "
+            "request — a segmentation model and a denoiser are not interchangeable. Pick the "
+            "matching task, or train a new run."
+        )
+    if parent_task == "denoising":
+        # Both sides are confirmed "denoising" above — there is no class
+        # taxonomy to compare for a self-supervised denoiser. But the two
+        # architectures in this family share a model_family, so the check above
+        # passes for a TUNet-vs-autoencoder mismatch; without this, the resume
+        # would reach load_state_dict and fail on an opaque key/shape mismatch
+        # instead of saying what is actually wrong. Runs saved before
+        # `architecture` existed are TUNets by definition.
+        parent_arch = (parent_config.get("model_config") or {}).get("architecture", "tunet")
+        request_arch = getattr(request.model, "architecture", "tunet")
+        if parent_arch != request_arch:
+            raise ValueError(
+                f"Cannot continue fine-tuning a {parent_arch!r} denoiser as {request_arch!r} — "
+                "the two architectures have different weights entirely. Pick the matching "
+                "architecture, or train a new run."
+            )
+        return
+
+    parent_labels = [str(c.get("label", "")).strip().lower() for c in parent_config.get("classes", [])]
+    current_labels = [c.label.strip().lower() for c in request.classes]
+    if parent_labels != current_labels:
+        raise ValueError(
+            "The classes changed since that run was trained, so its saved weights no longer apply "
+            f"(run has {len(parent_labels)}: {', '.join(parent_labels) or '—'}; "
+            f"now {len(current_labels)}: {', '.join(current_labels) or '—'}). "
+            "Restore the original classes to continue fine-tuning, or train a new run instead."
+        )
+
+
+def _apply_parent_architecture(parent_config: dict, request: TrainRequest) -> None:
+    """Force the architecture-defining settings to the parent run's values.
+
+    Anything that changes tensor shapes cannot differ across a resume — the
+    saved weights simply would not load. Rather than trusting the client to
+    echo these back correctly (or 400-ing on every mismatch), the server just
+    overrides them, so a resume is always weight-compatible by construction.
+    Genuinely re-tunable knobs — epochs, lr, batch_size, seed, flip_augment —
+    are left as the caller sent them; that is the point of resuming.
+
+    Every recognized ``schemas.ModelConfig`` member has its own explicit
+    branch below; anything else raises :class:`ValueError` — mirrors
+    ``train_common.build_family``'s dispatch.
+    """
+    model_cfg = request.model
+    hp = model_cfg.hyperparams
+    parent_model = parent_config.get("model_config", {})
+    parent_hp = parent_config.get("hyperparams", {})
+
+    # image_size + tiling define the geometry the weights were fit to.
+    for field in ("image_size", "tiling"):
+        if field in parent_hp:
+            setattr(hp, field, parent_hp[field])
+
+    # Input denoising is inherited for the same reason as the geometry above:
+    # the parent's weights were fit to that pixel distribution, so continuing to
+    # train them on differently-preprocessed pixels degrades them silently. It
+    # is NOT a re-tunable knob like epochs or lr — leaving it to the caller
+    # meant a fine-tune of a denoise-trained run quietly reverted to raw pixels
+    # whenever the client forgot to echo it back.
+    parent_denoise = parent_config.get("denoise")
+    request.denoise = (
+        DenoiseTrainOpts(**parent_denoise) if parent_denoise else None
+    )
+
+    if isinstance(model_cfg, DlsiaTunetConfig):
+        # TUNet's topology comes back from the saved topo_dict regardless; mirror
+        # it onto the request so the saved config records what actually ran.
+        for field in ("depth", "base_channels", "growth_rate"):
+            if field in parent_hp:
+                setattr(hp, field, parent_hp[field])
+    elif isinstance(model_cfg, DlsiaDenoiserConfig):
+        # Same TUNet topology knobs as the segmentation family above — dlsia's
+        # TUNet is the shared architecture underneath both; only the fixed
+        # in/out channel counts differ, and those aren't user-configurable.
+        for field in ("depth", "base_channels", "growth_rate"):
+            if field in parent_hp:
+                setattr(hp, field, parent_hp[field])
+        # The architecture itself, and the bottleneck width that follows from it,
+        # are inherited for the same reason as the geometry above: they define
+        # the tensor shapes, so a resume that changed them could not load the
+        # saved weights. A run saved before `architecture` existed has no such
+        # key and is a TUNet by definition.
+        parent_arch = parent_model.get("architecture", "tunet")
+        model_cfg.architecture = parent_arch
+        if parent_arch == "cnn_ae":
+            # `ae` is the only scheme the schema permits with cnn_ae, so a
+            # resume must land on it or the request would be self-inconsistent.
+            model_cfg.training_scheme = "ae"
+            if parent_model.get("ae_compression") is not None:
+                model_cfg.ae_compression = int(parent_model["ae_compression"])
+    else:
+        raise ValueError(
+            f"Unknown model config type for resume: {type(model_cfg).__name__!r} "
+            f"(model_family={getattr(model_cfg, 'model_family', None)!r})"
+        )
+
+
+def check_task_matches_model(request: TrainRequest) -> None:
+    """Raise unless ``request.task`` and the model family agree.
+
+    ``task`` and ``model.model_family`` are independent fields on the schema,
+    and ``task`` defaults to ``"segmentation"`` — so a client that sends a
+    ``dlsia_denoiser`` config and forgets ``task`` produces a request that
+    validates cleanly and then means something incoherent. Left unchecked it
+    would route into the annotation-driven path and train a single-channel
+    regression network against ``CrossEntropyLoss`` over zero classes. The
+    mirror case (``task="denoising"`` with a segmentation family) would ask
+    :mod:`denoise_train` to feed grayscale into a 3-channel model. Both are
+    caught here, before any data is read.
+    """
+    is_denoiser_family = isinstance(request.model, DlsiaDenoiserConfig)
+    if request.task == "denoising" and not is_denoiser_family:
+        raise ValueError(
+            "task='denoising' needs a denoiser model family, but got "
+            f"{request.model.model_family!r}. Use model_family='dlsia_denoiser', or set task='segmentation'."
+        )
+    if request.task != "denoising" and is_denoiser_family:
+        raise ValueError(
+            "model_family='dlsia_denoiser' is a self-supervised denoiser and cannot be trained as a "
+            f"{request.task!r} task — send task='denoising' (and classes: []) instead."
+        )
+
+
+def _source_keys(request: TrainRequest) -> list[str]:
+    """Stable per-source identifiers recorded on the saved run."""
+    return [
+        (f"tiled:{item.server_uri or ''}:{item.source}" if item.kind == "tiled" else f"local:{item.source}")
+        for item in request.sources
+    ]
+
+
+def _run_denoise_training(
+    jid: str,
+    request: TrainRequest,
+    run_id: str,
+    *,
+    device: str,
+    model_cfg: DlsiaDenoiserConfig,
+    init_state: dict | None,
+    resume_id: str | None,
+    progress_cb,
+) -> None:
+    """Self-supervised denoiser branch of :func:`run_train_job`.
+
+    Called with :data:`train_common.ML_LOCK` already held and the resume
+    already resolved, and reports through ``export_jobs`` with the same phase
+    names (``preparing`` → ``tiling`` → ``training`` → ``saving`` → ``done``)
+    and the same cancellation contract as the segmentation path, so the
+    frontend's existing job polling needs no denoiser-specific handling.
+
+    Weights and run metadata go through the ordinary
+    :func:`train_common.save_run`, with ``task="denoising"`` and an empty class
+    list, so the run appears in ``list_runs`` and the Learned Denoiser panel's
+    ``model_family == "dlsia_denoiser"`` filter finds it.
+    """
+    import denoise_train
+    import tiling
+
+    hp = model_cfg.hyperparams
+    scheme = model_cfg.training_scheme
+
+    # phase is already "preparing" — set by run_train_job before the split.
+    scheme_label = {
+        "n2n": "Noise2Noise",
+        "n2v": "Noise2Void",
+        "ae": "autoencoder",
+    }.get(scheme, scheme)
+    export_jobs.log(
+        jid,
+        f"Preparing self-supervised {scheme_label} data from raw slices (no annotations needed)…",
+    )
+    if scheme == "n2n":
+        datasets = denoise_train.prepare_noise2noise_datasets(
+            request.sources, request.render, progress_cb=progress_cb
+        )
+    else:
+        # n2v and dae are both single-slice schemes: each item is a slice paired
+        # with itself, and the objective differs only in how the INPUT is
+        # perturbed (blind-spot masking vs. added synthetic noise). So they share
+        # this sampler rather than needing a third one.
+        datasets = denoise_train.prepare_noise2void_datasets(
+            request.sources, request.render, progress_cb=progress_cb
+        )
+
+    if hp.tiling:
+        export_jobs.update(jid, phase="tiling")
+        export_jobs.log(jid, f"Cutting {hp.image_size}px windows…")
+        datasets = denoise_train.tile_denoise_datasets(
+            datasets,
+            hp.image_size,
+            progress_cb=progress_cb,
+            cancel_cb=lambda: export_jobs.cancel_requested(jid),
+        )
+        if datasets is None:
+            # Same "cancelled before a model existed" shape the segmentation
+            # path uses — deliberately not the partial-run result, which implies
+            # save_run produced something.
+            export_jobs.update(jid, state="done", phase="done", result={"cancelled": True})
+            export_jobs.log(jid, "Training cancelled while tiling; nothing was trained yet.")
+            return
+
+    # The denoiser's samplers put everything in "train" (there are no
+    # annotation-driven splits to inherit), so the seeded patch holdout is what
+    # produces a validation set at all. Applied whether or not tiling ran: with
+    # tiling off the items are whole slices, and the function no-ops below its
+    # minimum count rather than starving a small run of training data.
+    datasets, n_held = tiling.holdout_val_patches(datasets, seed=hp.seed)
+    if n_held:
+        export_jobs.log(
+            jid,
+            f"Held back {n_held} training patch(es) for validation. They come from the same "
+            "slice(s) as the training patches, and both schemes' targets are themselves noisy, "
+            "so the reported correlation is a convergence signal — not an image-quality score.",
+        )
+
+    n_train = len(datasets["train"])
+    if n_train == 0:
+        raise ValueError("No slices to train the denoiser on")
+
+    # n_classes is ignored by build_family's denoiser branch (in/out channels
+    # are fixed at 1); 0 is passed to make that explicit rather than incidental.
+    built = train_common.build_family(model_cfg, 0, device, progress_cb, init_state=init_state)
+
+    batches_per_epoch = max(1, -(-n_train // hp.batch_size))
+    export_jobs.set_total(jid, hp.epochs * batches_per_epoch)
+    export_jobs.update(jid, phase="training")
+
+    def _on_batch() -> bool:
+        export_jobs.bump(jid, 1)
+        return export_jobs.cancel_requested(jid)
+
+    def _on_epoch(epoch: int, train_loss: float, val_loss: float | None, val_metric: float | None) -> bool:
+        msg = f"epoch {epoch}/{hp.epochs} — train loss {train_loss:.5f}"
+        if val_loss is not None:
+            # Labelled as correlation with the NOISY target, never as quality.
+            msg += f", val loss {val_loss:.5f}, noisy-target r {val_metric:.3f}"
+        export_jobs.log(jid, msg)
+        return export_jobs.cancel_requested(jid)
+
+    metrics = denoise_train.run_denoise_training_loop(
+        train_pairs=datasets["train"],
+        val_pairs=datasets["val"],
+        image_size=hp.image_size,
+        training_scheme=scheme,
+        epochs=hp.epochs,
+        batch_size=hp.batch_size,
+        seed=hp.seed,
+        flip_augment=hp.flip_augment,
+        to_tensor_fn=built.to_tensor_fn,
+        forward_fn=built.forward_fn,
+        trainable_params=built.trainable_params,
+        lr=hp.lr,
+        device=device,
+        on_batch=_on_batch,
+        on_epoch=_on_epoch,
+        set_train_mode=built.set_train_mode,
+    )
+
+    export_jobs.update(jid, phase="saving")
+    train_common.save_run(
+        run_id,
+        model_family=model_cfg.model_family,
+        # training_scheme belongs on the run: n2n and n2v produce different
+        # models from the same topology, and the runs list surfaces which.
+        model_config={
+            **built.model_config_snapshot,
+            "training_scheme": scheme,
+            # Recorded only where it means something, so a run's config doesn't
+            # imply a knob that had no effect on how it was trained.
+            # `architecture` is always recorded: both inference sites dispatch
+            # on it, defaulting to "tunet" for runs saved before it existed.
+            "architecture": model_cfg.architecture,
+        },
+        classes=[],
+        render=request.render.model_dump(),
+        image_size=hp.image_size,
+        hyperparams=hp.model_dump(),
+        source_keys=_source_keys(request),
+        adapter_state=built.adapter_state_fn(),
+        metrics=metrics,
+        resumed_from=resume_id,
+        task="denoising",
+    )
+
+    result = {"run_id": run_id, **metrics}
+    export_jobs.update(jid, state="done", phase="done", result=result)
+    export_jobs.log(
+        jid,
+        "Training cancelled; partial run saved." if metrics["cancelled"] else "Denoiser training complete.",
+    )
+
+
+def run_train_job(jid: str, request: TrainRequest, run_id: str) -> None:
+    """Background worker: prepare data, build the requested model family, run
+    the shared training loop, and persist the resulting run.
+
+    Holds :data:`train_common.ML_LOCK` for the whole job — training and
+    inference contend for the same device memory, so only one ML job (of
+    either kind) runs at a time.
+    """
+    if not train_common.ML_LOCK.acquire(blocking=False):
+        export_jobs.update(
+            jid,
+            state="error",
+            phase="error",
+            error="Another training or inference job is already running",
+        )
+        return
+    try:
+        export_jobs.update(jid, state="running", phase="preparing")
+        device = train_common.pick_device()
+        if device is None:
+            raise RuntimeError("torch is not installed on this server")
+
+        def _progress(msg: str) -> None:
+            export_jobs.log(jid, msg)
+
+        model_cfg = request.model
+        check_task_matches_model(request)
+        n_classes = len(request.classes)
+
+        # Resolve a resume BEFORE prepare_datasets: an incompatible one must fail
+        # in seconds, not after minutes of rendering. Loading the weights here
+        # too means a corrupt/missing adapter.pt is caught just as early.
+        init_state = None
+        resume_id = request.resume_from_run_id
+        if resume_id:
+            parent_config = train_common.load_run_config(resume_id)
+            check_resume_compatible(parent_config, request)
+            _apply_parent_architecture(parent_config, request)
+            init_state = train_common.load_adapter_state(resume_id)
+            export_jobs.log(jid, f"Continuing fine-tuning from run {resume_id}.")
+
+        hp = model_cfg.hyperparams
+
+        # Check qlty BEFORE the render/rasterize pass below (prepare_datasets can
+        # take minutes on a large source list) so a torch-only install fails fast
+        # instead of after paying for it.
+        if hp.tiling:
+            import tiling
+
+            if not tiling.qlty_available():
+                raise RuntimeError("Tiling requires the 'qlty' package, which is not installed on this server")
+
+        # Everything above is task-agnostic (lock, device, resume, qlty guard).
+        # From here the two tasks diverge: a denoiser reads raw slices instead
+        # of rendering annotations, and optimises a regression loss.
+        if request.task == "denoising":
+            _run_denoise_training(
+                jid,
+                request,
+                run_id,
+                device=device,
+                model_cfg=model_cfg,
+                init_state=init_state,
+                resume_id=resume_id,
+                progress_cb=_progress,
+            )
+            return
+
+        datasets = train_common.prepare_datasets(
+            request.sources,
+            request.classes,
+            request.render,
+            request.auto_split,
+            progress_cb=_progress,
+            denoise=request.denoise,
+        )
+        if request.denoise is not None:
+            _progress(
+                f"Training on {request.denoise.method}-denoised input "
+                f"({request.denoise.strength:.0%}); inference will reapply it automatically."
+            )
+
+        # Tiling: keep native resolution by cutting `image_size` windows out of each
+        # slice, instead of letting the training loop rescale whole slices down to
+        # `image_size`. Patches come out exactly window-sized, which letterbox()
+        # passes through, so the loop below is unchanged either way.
+        if model_cfg.hyperparams.tiling:
+            import tiling  # already confirmed available above; re-import is a cheap sys.modules hit
+
+            export_jobs.update(jid, phase="tiling")
+            export_jobs.log(jid, f"Tiling slices into {model_cfg.hyperparams.image_size}px windows…")
+
+            datasets = tiling.tile_datasets(
+                datasets,
+                model_cfg.hyperparams.image_size,
+                progress_cb=_progress,
+                cancel_cb=lambda: export_jobs.cancel_requested(jid),
+            )
+            if datasets is None:
+                # Cancelled before any model existed to save — a bare "cancelled"
+                # result, not the usual partial-run shape from a training-loop
+                # cancel (train_common.run_training_loop's own cancel path, below).
+                export_jobs.update(jid, state="done", phase="done", result={"cancelled": True})
+                export_jobs.log(jid, "Training cancelled while tiling; nothing was trained yet.")
+                return
+            # Splits are per-slice, so a one- or two-slice dataset leaves validation
+            # empty and the run reports no metrics. Tiling yields enough patches to
+            # hold a few back instead.
+            datasets, n_held = tiling.holdout_val_patches(
+                datasets, seed=model_cfg.hyperparams.seed
+            )
+            if n_held:
+                export_jobs.log(
+                    jid,
+                    f"No validation slices — held back {n_held} training patch(es) for validation. "
+                    "They come from the same image(s) as the training patches, so mIoU reads "
+                    "optimistically compared with an unseen slice.",
+                )
+
+        n_train = len(datasets["train"])
+        if n_train == 0:
+            raise ValueError("No annotated slices to train on")
+
+        built = train_common.build_family(model_cfg, n_classes, device, _progress, init_state=init_state)
+        forward_fn = built.forward_fn
+        to_tensor_fn = built.to_tensor_fn
+        trainable = built.trainable_params
+        set_train_mode = built.set_train_mode
+        model_config_snapshot = built.model_config_snapshot
+
+        batches_per_epoch = max(1, -(-n_train // hp.batch_size))
+        export_jobs.set_total(jid, hp.epochs * batches_per_epoch)
+        export_jobs.update(jid, phase="training")
+
+        def _on_batch() -> bool:
+            export_jobs.bump(jid, 1)
+            return export_jobs.cancel_requested(jid)
+
+        def _on_epoch(epoch: int, train_loss: float, val_loss: float | None, val_miou: float | None) -> bool:
+            msg = f"epoch {epoch}/{hp.epochs} — train loss {train_loss:.4f}"
+            if val_loss is not None:
+                msg += f", val loss {val_loss:.4f}, mIoU {val_miou:.3f}"
+            export_jobs.log(jid, msg)
+            return export_jobs.cancel_requested(jid)
+
+        metrics = train_common.run_training_loop(
+            train_pairs=datasets["train"],
+            val_pairs=datasets["val"],
+            image_size=hp.image_size,
+            n_classes=n_classes,
+            epochs=hp.epochs,
+            batch_size=hp.batch_size,
+            seed=hp.seed,
+            flip_augment=hp.flip_augment,
+            to_tensor_fn=to_tensor_fn,
+            forward_fn=forward_fn,
+            trainable_params=trainable,
+            lr=hp.lr,
+            device=device,
+            on_batch=_on_batch,
+            on_epoch=_on_epoch,
+            set_train_mode=set_train_mode,
+        )
+
+        export_jobs.update(jid, phase="saving")
+        adapter_state = built.adapter_state_fn()
+
+        train_common.save_run(
+            run_id,
+            model_family=model_cfg.model_family,
+            model_config=model_config_snapshot,
+            classes=[c.model_dump() for c in request.classes],
+            render=request.render.model_dump(),
+            image_size=hp.image_size,
+            hyperparams=hp.model_dump(),
+            source_keys=_source_keys(request),
+            adapter_state=adapter_state,
+            metrics=metrics,
+            resumed_from=resume_id,
+            task=request.task,
+            # Recorded so inference reapplies the same input preprocessing
+            # without the caller having to remember it.
+            denoise=request.denoise.model_dump() if request.denoise else None,
+        )
+
+        result = {"run_id": run_id, **metrics}
+        export_jobs.update(jid, state="done", phase="done", result=result)
+        export_jobs.log(
+            jid,
+            "Training cancelled; partial run saved." if metrics["cancelled"] else "Training complete.",
+        )
+    except Exception as exc:  # noqa: BLE001 — reported as a job error, never a crash
+        logger.error("Training job %s failed: %s", jid, exc)
+        export_jobs.update(jid, state="error", phase="error", error=str(exc))
+    finally:
+        train_common.ML_LOCK.release()
diff --git a/frontend/src/app/App.tsx b/frontend/src/app/App.tsx
index 0180c46..dd1fcb9 100644
--- a/frontend/src/app/App.tsx
+++ b/frontend/src/app/App.tsx
@@ -6,7 +6,7 @@ import HubAppLayout from '@/components/HubAppLayout';
 import { useHubSelectedTabs } from '@/hooks/useHubSelectedTabs';
 import { DOCS_URL, FEEDBACK_FORM_URL, FEEDBACK_ENTRY_ID } from '@/config';
 import { buildFeedbackContext, buildFeedbackUrl } from '@/lib/feedbackContext';
-import { PlugsConnected, PencilSimple, MagnifyingGlass, BookOpen, Cube } from '@phosphor-icons/react';
+import { PlugsConnected, PencilSimple, MagnifyingGlass, BookOpen, Cube, Brain } from '@phosphor-icons/react';
 // Lazy-loaded pages: keeps the heavy Annotate stack (konva, polygon-clipping,
 // magicwand, canvas) out of the initial /connect bundle — each page is its own chunk.
 const ConnectPage = lazy(() => import('./pages/ConnectPage'));
@@ -16,6 +16,9 @@ const ReferencePage = lazy(() => import('./pages/ReferencePage'));
 // Lazy for the same reason as Annotate, and more so: this chunk carries the
 // whole vendored WebGPU renderer, which must never load on /connect.
 const VolumePage = lazy(() => import('./pages/VolumePage'));
+// Lazy for the same reason: the Train tab's own bundle, kept out of every
+// other page's initial load.
+const TrainPage = lazy(() => import('./pages/TrainPage'));
 import CustomizePages from '@/components/CustomizePages';
 import IframeModal from '@/components/IframeModal';
 
@@ -53,6 +56,13 @@ const allRoutes: RouteItem[] = [
     element: <VolumePage />,
     isBackgroundTransparent: true,
   },
+  {
+    path: '/train',
+    label: 'Train',
+    icon: <Brain size={32} />,
+    element: <TrainPage />,
+    isBackgroundTransparent: true,
+  },
 ];
 
 const DEFAULT_PATHS = allRoutes.map((r) => r.path);
diff --git a/frontend/src/app/pages/TrainPage.tsx b/frontend/src/app/pages/TrainPage.tsx
new file mode 100644
index 0000000..042832e
--- /dev/null
+++ b/frontend/src/app/pages/TrainPage.tsx
@@ -0,0 +1,335 @@
+/**
+ * TrainPage — fine-tune a dlsia TUNet model on this session's annotated
+ * samples, then run inference with any saved run: preview the predicted
+ * masks, import them as editable annotations, or push them to Tiled.
+ *
+ * dlsia TUNet only — DINOv3 LoRA is deferred (see Phase 5.5 in the
+ * integration plan), so there is no model-family picker here.
+ */
+import { useEffect, useMemo, useRef, useState } from 'react';
+import { useSearchParams } from 'react-router';
+import { v4 as uuidv4 } from 'uuid';
+import { Brain } from '@phosphor-icons/react';
+import { API_BASE } from '@/config';
+import { useDatasetStore } from '@/stores/datasetStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useClassStore } from '@/stores/classStore';
+import { buildSourceKey } from '@/lib/sourceKey';
+import { useTrainCapability } from '@/hooks/useTrainCapability';
+import { useTrainRuns } from '@/hooks/useTrainRuns';
+import { useExportJob } from '@/hooks/useExportJob';
+import { useDraftSync } from '@/hooks/useDraftSync';
+import { useImageSlice } from '@/hooks/useImageSlice';
+import { gatherTrainingSources, listAnnotatedSourceKeys } from '@/lib/gatherTrainingSources';
+import { buildModelConfig, trainConfigSignature, validateTrainConfig } from '@/lib/trainModelConfig';
+import { trainDenoisePayload } from '@/lib/trainDenoiseOption';
+import { isSegmentationRun } from '@/lib/runCompatibility';
+import { remapPredictedShapes, type RunClass } from '@/lib/importPredictions';
+import type { Shape } from '@/stores/annotationStore';
+import CapabilityBanner from '@/components/train/CapabilityBanner';
+import TrainingDataPanel from '@/components/train/TrainingDataPanel';
+import HyperparamsPanel, { type HyperparamsState } from '@/components/train/HyperparamsPanel';
+import JobProgressBar from '@/components/train/JobProgressBar';
+import RunsPanel from '@/components/train/RunsPanel';
+import TrainDenoiseToggle from '@/components/train/TrainDenoiseToggle';
+import InferencePanel from '@/components/train/InferencePanel';
+
+const DEFAULT_HYPERPARAMS: HyperparamsState = {
+  // 512: tile count scales with 1/size², so a bigger window means far fewer
+  // forward passes per slice and more context in each.
+  epochs: 60, lr: 1e-3, batch_size: 4, image_size: 512, flip_augment: true, tiling: true,
+  depth: 4, base_channels: 8, growth_rate: 1.5,
+};
+
+export default function TrainPage() {
+  const { source, kind, serverUri, meta, currentSlice, renderOpts } = useDatasetStore();
+  const { byImage, splitBySlice, negativeSlices, addShapes } = useAnnotationStore();
+  const { classes, setClasses } = useClassStore();
+
+  // The Annotate tab's denoise setting (Phase 2's DenoisePanel) — only ever
+  // read here, and only when "Train on denoised input" is ticked below.
+  const denoise = useDatasetStore((s) => s.denoise);
+
+  const { capability } = useTrainCapability();
+  const { runs: allRuns, invalidate: refreshRuns, deleteRun } = useTrainRuns();
+  // This tab's Runs/Inference card is segmentation-only (it imports predicted
+  // SHAPES) — a saved denoiser run has no class list to remap predictions
+  // against, so it's excluded here.
+  const runs = useMemo(() => allRuns.filter(isSegmentationRun), [allRuns]);
+  // persistKey: survives switching to another tab and back mid-job — see
+  // useExportJob's docstring. Fixed keys (not scoped to a sample) are correct
+  // here: once submitted, a job is already bound to its own run_id server-side,
+  // independent of anything that changes in this page afterward, and only one
+  // training/probe job can run at a time regardless.
+  const { state: trainJob, startJob: startTrainJob } = useExportJob('train:start');
+  const { state: probeJob, startJob: startProbeJob, reset: resetProbeJob } = useExportJob('train:probe');
+  // Set at handleEstimateBatch time, compared against the current config when
+  // the probe finishes — see the adoption effect below.
+  const probeConfigSignature = useRef<string | null>(null);
+
+  const [selectedKeys, setSelectedKeys] = useState<Set<string>>(new Set());
+  const [hyperparams, setHyperparams] = useState<HyperparamsState>(DEFAULT_HYPERPARAMS);
+  const [runName, setRunName] = useState('');
+  // Off by default: this one changes what the model LEARNS, not just what's on
+  // screen, so it has to be asked for explicitly.
+  const [trainOnDenoised, setTrainOnDenoised] = useState(false);
+  const [selectedRunId, setSelectedRunId] = useState<string | null>(null);
+  const [dataError, setDataError] = useState<string | null>(null);
+
+  // "Run inference" from Annotate/Browse jumps straight to the Runs/Inference
+  // card via ?focus=infer, since that's the section they actually asked for.
+  const [searchParams] = useSearchParams();
+  const inferenceSectionRef = useRef<HTMLDivElement>(null);
+  useEffect(() => {
+    if (searchParams.get('focus') === 'infer') {
+      inferenceSectionRef.current?.scrollIntoView({ behavior: 'smooth', block: 'start' });
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
+  const isTiledSource = kind === 'tiled';
+  const sourceKey = source && kind ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri) : null;
+  // "Import as annotations" below writes shapes (and possibly new classes)
+  // straight into the store for this sourceKey — without this, nothing here
+  // autosaves them: AnnotatePage is the only OTHER place that mounts
+  // useDraftSync, and routes render exactly one page at a time, so navigating
+  // to Annotate afterward would establish a "clean" baseline that already
+  // includes the import, and the debounced PUT would never fire. This closes
+  // that gap the same way every edit in Annotate is already persisted.
+  useDraftSync(sourceKey);
+  const candidates = useMemo(() => listAnnotatedSourceKeys(byImage), [byImage]);
+
+  // Auto-select the currently open sample the first time it becomes available.
+  useEffect(() => {
+    if (sourceKey && candidates.some((c) => c.sourceKey === sourceKey)) {
+      setSelectedKeys((prev) => (prev.size === 0 ? new Set([sourceKey]) : prev));
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [sourceKey, candidates.length]);
+
+  const toggleKey = (key: string) => {
+    setSelectedKeys((prev) => {
+      const next = new Set(prev);
+      if (next.has(key)) next.delete(key); else next.add(key);
+      return next;
+    });
+  };
+
+  const handleHyperparamsChange = (updates: Partial<HyperparamsState>) =>
+    setHyperparams((prev) => ({ ...prev, ...updates }));
+
+  const handleStartTraining = () => {
+    setDataError(null);
+    let sources;
+    try {
+      sources = gatherTrainingSources(Array.from(selectedKeys), byImage, splitBySlice, negativeSlices);
+    } catch (err) {
+      setDataError(err instanceof Error ? err.message : String(err));
+      return;
+    }
+    // Catch these here; the server would otherwise reject them with a validation
+    // error that names the schema field rather than the visible control.
+    const configError = validateTrainConfig(hyperparams);
+    if (configError) {
+      setDataError(configError);
+      return;
+    }
+    const model = buildModelConfig(hyperparams);
+
+    void startTrainJob('/api/train/start', {
+      sources,
+      classes,
+      model,
+      run_name: runName.trim() || null,
+      // Adds nothing at all when the checkbox is off, so an un-denoised run is
+      // byte-identical to what this page sent before the option existed.
+      ...trainDenoisePayload(trainOnDenoised, denoise),
+    });
+  };
+
+  // Refresh the runs list once a training job finishes.
+  useEffect(() => {
+    if (trainJob.status === 'done') refreshRuns();
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [trainJob.status]);
+
+  // Shared registry — same cancel route every background job in this app uses.
+  const handleCancelTrain = () => {
+    if (trainJob.jobId) void fetch(`${API_BASE}/api/export/cancel/${trainJob.jobId}`, { method: 'POST' });
+  };
+
+  const handleCancelProbe = () => {
+    if (probeJob.jobId) void fetch(`${API_BASE}/api/export/cancel/${probeJob.jobId}`, { method: 'POST' });
+  };
+
+  // Both Start and Estimate hit the same server-side gates (ML_LOCK, torch, dlsia)
+  // and must agree on when they're blocked — checking a different subset per
+  // button just means the one that under-checks fires a request the server was
+  // always going to 409/503 back. `capability.busy` is the server's own ML_LOCK
+  // state (catches jobs from other tabs/clients); the two job statuses cover this
+  // tab's own in-flight request before that poll has caught up.
+  const trainOrProbeRunning = trainJob.status === 'running' || probeJob.status === 'running';
+  const deviceBusy = trainOrProbeRunning || capability.busy;
+  const needsDlsia = !capability.dlsia.available;
+  const startDisabled = deviceBusy || !capability.torch_available || needsDlsia;
+  const estimateDisabledReason = trainOrProbeRunning
+    ? 'A training run or another batch-size probe is already using the device'
+    : capability.busy
+      ? 'Another training or inference job is using the device'
+      : !capability.torch_available
+        ? 'Estimating is unavailable: torch is not installed on this server'
+        : needsDlsia
+          ? 'Estimating is unavailable: dlsia is not installed on this server'
+          : null;
+
+  /** Measure the largest batch size this model config fits, then adopt it.
+   *  Reuses the shared export-job plumbing, so progress lines stream in as the
+   *  probe steps 1, 2, 4, 8… (see backend/batch_probe.py). */
+  const handleEstimateBatch = () => {
+    setDataError(null);
+    const configError = validateTrainConfig(hyperparams);
+    if (configError) {
+      setDataError(configError);
+      return;
+    }
+    const model = buildModelConfig(hyperparams);
+    probeConfigSignature.current = trainConfigSignature(hyperparams);
+    void startProbeJob('/api/train/estimate-batch', {
+      model,
+      // Head width barely moves memory, so the probe works before any classes exist.
+      n_classes: Math.max(1, classes.length || 2),
+    });
+  };
+
+  // Adopt the measured value once the probe finishes — but only if the config
+  // it measured is still the one that would be submitted. The user is free to
+  // change patch-size/tiling while a probe is running; if they did, this
+  // result describes a different memory footprint and must not silently
+  // overwrite batch_size (a cancelled probe's partial measurement is excluded
+  // the same way — see batch_probe.py's `cancelled` flag).
+  useEffect(() => {
+    const suggested = probeJob.result?.suggested_batch_size;
+    const cancelled = probeJob.result?.cancelled === true;
+    const currentSignature = trainConfigSignature(hyperparams);
+    if (
+      probeJob.status === 'done' && typeof suggested === 'number' && !cancelled
+      && probeConfigSignature.current === currentSignature
+    ) {
+      setHyperparams((prev) => ({ ...prev, batch_size: suggested }));
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [probeJob.status, probeJob.result]);
+
+  /** Latest probe line: its own error, the streaming log tail, or the summary. */
+  const batchEstimateNote = useMemo(() => {
+    if (probeJob.status === 'error') return probeJob.error ?? 'Could not estimate a batch size.';
+    if (probeJob.status === 'running') return probeJob.log.at(-1) ?? 'Loading the model…';
+    if (probeJob.status === 'done') {
+      const note = probeJob.result?.note;
+      return typeof note === 'string' ? note : null;
+    }
+    return null;
+  }, [probeJob.status, probeJob.error, probeJob.log, probeJob.result]);
+
+  const handleDeleteRun = async (runId: string) => {
+    setDataError(null);
+    const ok = await deleteRun(runId);
+    if (!ok) {
+      setDataError('Failed to delete run.');
+      return;
+    }
+    if (selectedRunId === runId) setSelectedRunId(null);
+  };
+
+  const sliceQuery = useImageSlice(source, kind, currentSlice, renderOpts, serverUri);
+  const baseImageUrl = sliceQuery.data ?? null;
+
+  const handleImportPredictions = (runClasses: RunClass[], slices: Record<string, unknown>) => {
+    if (!sourceKey) return;
+    const remapped = remapPredictedShapes(runClasses, classes, slices as Record<string, Shape[]>);
+    setClasses(remapped.classes);
+    for (const [sliceKey, shapes] of Object.entries(remapped.slices)) {
+      // Predicted shape ids are deterministic per run+slice (see infer_jobs.py),
+      // so importing the same run onto the same slice twice would otherwise
+      // collide with the previous import's ids — corrupting the shape list
+      // (duplicate React/Konva keys) and making the canvas unresponsive.
+      const freshIds = shapes.map((shape) => ({ ...shape, id: uuidv4() }));
+      addShapes(sourceKey, Number(sliceKey), freshIds);
+    }
+  };
+
+  return (
+    <div className="mx-auto flex h-full w-full max-w-3xl flex-col gap-4 overflow-y-auto p-6 text-slate-100">
+      <div className="flex items-center gap-2">
+        <Brain size={22} className="text-sky-400" />
+        <h1 className="text-lg font-semibold">Train</h1>
+      </div>
+
+      <CapabilityBanner capability={capability} />
+
+      <div className="rounded-lg border border-slate-700 bg-slate-800/60 p-4 space-y-4">
+        <TrainingDataPanel candidates={candidates} selected={selectedKeys} onToggle={toggleKey} />
+
+        <HyperparamsPanel
+          values={hyperparams} onChange={handleHyperparamsChange}
+          runName={runName} onRunNameChange={setRunName}
+          onEstimateBatch={handleEstimateBatch}
+          onCancelEstimate={handleCancelProbe}
+          estimatingBatch={probeJob.status === 'running'}
+          batchEstimateNote={batchEstimateNote}
+          estimateDisabledReason={estimateDisabledReason}
+        />
+
+        <TrainDenoiseToggle
+          checked={trainOnDenoised}
+          onChange={setTrainOnDenoised}
+          disabled={trainJob.status === 'running'}
+        />
+
+        {dataError && <p className="text-sm text-red-400">{dataError}</p>}
+
+        <div className="flex items-center gap-2">
+          <button
+            type="button"
+            onClick={handleStartTraining}
+            disabled={startDisabled}
+            className="px-4 py-2 text-sm rounded-md bg-sky-600 text-white hover:bg-sky-500 transition-colors disabled:opacity-50 disabled:cursor-not-allowed"
+          >
+            {trainJob.status === 'running' ? 'Training…' : 'Start training'}
+          </button>
+          {trainJob.status === 'running' && (
+            <button
+              type="button" onClick={handleCancelTrain}
+              className="px-3 py-2 text-sm rounded-md border border-slate-600 text-slate-300 hover:bg-slate-700 transition-colors"
+            >
+              Cancel
+            </button>
+          )}
+        </div>
+
+        <JobProgressBar job={trainJob} unit="batches" />
+        {trainJob.status === 'done' && (
+          <p className="text-sm text-emerald-300">
+            Saved run <span className="font-mono">{String(trainJob.result?.run_id ?? '')}</span>
+            {typeof trainJob.result?.val_miou === 'number' && ` — val mIoU ${trainJob.result.val_miou.toFixed(3)}`}.
+          </p>
+        )}
+      </div>
+
+      <div ref={inferenceSectionRef} className="rounded-lg border border-slate-700 bg-slate-800/60 p-4 space-y-4">
+        <RunsPanel runs={runs} selectedRunId={selectedRunId} onSelectRun={setSelectedRunId} onDeleteRun={handleDeleteRun} />
+        <InferencePanel
+          selectedRunId={selectedRunId}
+          hasOpenSample={!!source && !!meta}
+          isTiledSource={isTiledSource}
+          source={source}
+          serverUri={serverUri}
+          currentSlice={currentSlice}
+          nSlices={meta?.nSlices ?? 1}
+          baseImageUrl={baseImageUrl}
+          onImportPredictions={handleImportPredictions}
+        />
+      </div>
+    </div>
+  );
+}
diff --git a/frontend/src/components/annotate/DownloadModal.tsx b/frontend/src/components/annotate/DownloadModal.tsx
index be6cb50..775160b 100644
--- a/frontend/src/components/annotate/DownloadModal.tsx
+++ b/frontend/src/components/annotate/DownloadModal.tsx
@@ -8,7 +8,7 @@ import { useAnnotationStore } from '@/stores/annotationStore';
 import { useClassStore } from '@/stores/classStore';
 import { useRatingStore } from '@/stores/ratingStore';
 import { useSettingsStore } from '@/stores/settingsStore';
-import { buildSourceKey } from '@/lib/sourceKey';
+import { buildSourceKey, parseSourceKey } from '@/lib/sourceKey';
 import { useExportJob } from '@/hooks/useExportJob';
 
 interface DownloadModalProps {
@@ -17,16 +17,6 @@ interface DownloadModalProps {
 
 type Scope = 'slice' | 'current' | 'all' | 'stars1' | 'stars2' | 'stars3';
 
-/** Parse a canonical sourceKey back to { kind, source, serverUri }. */
-function parseSourceKey(sk: string) {
-  if (sk.startsWith('tiled:')) {
-    const rest = sk.slice('tiled:'.length);
-    const sep = rest.indexOf(':');
-    return { kind: 'tiled' as const, serverUri: rest.slice(0, sep) || null, source: rest.slice(sep + 1) };
-  }
-  return { kind: 'local' as const, serverUri: null, source: sk.slice('local:'.length) };
-}
-
 const SCOPE_OPTIONS: { value: Scope; label: string; desc: string; stars?: string }[] = [
   {
     value: 'slice',
diff --git a/frontend/src/components/train/CapabilityBanner.tsx b/frontend/src/components/train/CapabilityBanner.tsx
new file mode 100644
index 0000000..29128d4
--- /dev/null
+++ b/frontend/src/components/train/CapabilityBanner.tsx
@@ -0,0 +1,45 @@
+/**
+ * CapabilityBanner — Train-tab readiness status. Shows install guidance when
+ * torch/dlsia aren't available (e.g. the Docker image, which intentionally
+ * excludes them) instead of letting every downstream action fail opaquely.
+ */
+import { CheckCircle, WarningCircle } from '@phosphor-icons/react';
+import type { TrainCapability } from '@/hooks/useTrainCapability';
+
+interface CapabilityBannerProps {
+  capability: TrainCapability;
+}
+
+export default function CapabilityBanner({ capability }: CapabilityBannerProps) {
+  if (!capability.torch_available) {
+    return (
+      <div className="flex items-start gap-2 rounded-lg border border-amber-600/50 bg-amber-900/20 px-3 py-2.5 text-sm text-amber-200">
+        <WarningCircle size={18} className="mt-0.5 shrink-0" />
+        <div>
+          <p className="font-medium">Training is unavailable on this server.</p>
+          <p className="mt-1 text-xs text-amber-200/80">
+            torch isn't installed. Run this app via <code className="font-mono">start_all.sh</code> with{' '}
+            <code className="font-mono">INSTALL_ML=1</code> (default on Apple Silicon) on a machine where it
+            can install ML dependencies — the Docker image intentionally omits them.
+          </p>
+        </div>
+      </div>
+    );
+  }
+
+  return (
+    <div className="flex flex-wrap items-center gap-x-4 gap-y-1 rounded-lg border border-slate-700 bg-slate-800/60 px-3 py-2 text-xs text-slate-300">
+      <span className="flex items-center gap-1.5 text-emerald-300">
+        <CheckCircle size={14} />
+        torch {capability.torch_version} · {capability.device ?? 'cpu'}
+      </span>
+      <span>
+        dlsia (TUNet):{' '}
+        <span className={capability.dlsia.available ? 'text-slate-200' : 'text-amber-300'}>
+          {capability.dlsia.available ? 'available' : 'not installed'}
+        </span>
+      </span>
+      {capability.busy && <span className="text-sky-300">A training/inference job is currently running.</span>}
+    </div>
+  );
+}
diff --git a/frontend/src/components/train/HyperparamsPanel.tsx b/frontend/src/components/train/HyperparamsPanel.tsx
new file mode 100644
index 0000000..dfcb9c8
--- /dev/null
+++ b/frontend/src/components/train/HyperparamsPanel.tsx
@@ -0,0 +1,188 @@
+/**
+ * HyperparamsPanel — training hyperparameters for the dlsia TUNet family
+ * (epochs, lr, batch size, image size, flip augment, depth/base_channels/
+ * growth_rate). Collapsed behind <details> — sensible defaults are supplied
+ * by the backend schema, so most users never open it.
+ *
+ * DINOv3 LoRA is deferred (see Phase 5.5), so there is only one family here —
+ * no model-family switch, no LoRA rank/alpha fields.
+ */
+import { IMAGE_SIZE_CONSTRAINTS, validateImageSize } from '@/lib/trainConstraints';
+
+export interface HyperparamsState {
+  epochs: number;
+  lr: number;
+  batch_size: number;
+  image_size: number;
+  flip_augment: boolean;
+  /** Cut native-resolution `image_size` windows out of each slice instead of
+   *  shrinking whole slices to `image_size`. */
+  tiling: boolean;
+  depth: number;
+  base_channels: number;
+  growth_rate: number;
+}
+
+interface HyperparamsPanelProps {
+  values: HyperparamsState;
+  onChange: (updates: Partial<HyperparamsState>) => void;
+  runName: string;
+  onRunNameChange: (name: string) => void;
+  /** Measure the largest batch size this config fits (see backend/batch_probe.py). */
+  onEstimateBatch: () => void;
+  /** Request cancellation of the running probe (cooperative — see /api/train/cancel). */
+  onCancelEstimate: () => void;
+  /** True while the probe job runs — it holds the device, so it can't overlap. */
+  estimatingBatch: boolean;
+  /** Progress/result line for the probe, shown under the field. */
+  batchEstimateNote: string | null;
+  /** Why Estimate can't run right now (shown as the button's tooltip), or null when it can. */
+  estimateDisabledReason: string | null;
+}
+
+const inputClass =
+  'w-full rounded-md border border-slate-600 bg-slate-900/60 px-2.5 py-1.5 text-sm text-slate-200 focus:border-sky-500 focus:outline-none';
+const labelClass = 'block text-xs text-slate-400 mb-1';
+
+export default function HyperparamsPanel({
+  values, onChange, runName, onRunNameChange,
+  onEstimateBatch, onCancelEstimate, estimatingBatch, batchEstimateNote, estimateDisabledReason,
+}: HyperparamsPanelProps) {
+  const sizeLimits = IMAGE_SIZE_CONSTRAINTS.dlsia_tunet;
+  const sizeError = validateImageSize(
+    'dlsia_tunet', values.image_size, values.tiling ? 'Patch size' : 'Image size',
+  );
+
+  return (
+    <details className="rounded-lg border border-slate-700 bg-slate-800/40">
+      <summary className="cursor-pointer px-3 py-2 text-xs font-medium text-slate-300 uppercase tracking-wide select-none">
+        Hyperparameters (advanced)
+      </summary>
+      <div className="grid grid-cols-2 gap-3 px-3 pb-3 pt-1 sm:grid-cols-3">
+        <div>
+          <label className={labelClass}>Run name (optional)</label>
+          <input
+            type="text" value={runName} onChange={(e) => onRunNameChange(e.target.value)}
+            placeholder="auto-generated" className={inputClass}
+          />
+        </div>
+        <div>
+          <label className={labelClass}>Epochs</label>
+          <input
+            type="number" min={1} value={values.epochs}
+            onChange={(e) => onChange({ epochs: Number(e.target.value) })} className={inputClass}
+          />
+        </div>
+        <div>
+          <label className={labelClass}>Learning rate</label>
+          <input
+            type="number" step="0.0001" min={0} value={values.lr}
+            onChange={(e) => onChange({ lr: Number(e.target.value) })} className={inputClass}
+          />
+        </div>
+        <div>
+          <div className="flex items-baseline justify-between gap-2 mb-1">
+            <label className="block text-xs text-slate-400">Batch size</label>
+            {/* Measures the real ceiling by running actual training steps, so it
+                needs the device to itself and can't run during another job. */}
+            <div className="flex items-center gap-1">
+              <button
+                type="button"
+                onClick={onEstimateBatch}
+                disabled={estimatingBatch || !!estimateDisabledReason}
+                title={
+                  estimateDisabledReason
+                  ?? 'Run real training steps at increasing batch sizes to find the largest that fits'
+                }
+                className="text-[10px] px-1.5 py-0.5 rounded border border-slate-600 text-slate-300 hover:bg-slate-700 hover:text-white transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
+              >
+                {estimatingBatch ? 'Estimating…' : 'Estimate max'}
+              </button>
+              {estimatingBatch && (
+                <button
+                  type="button"
+                  onClick={onCancelEstimate}
+                  className="text-[10px] px-1.5 py-0.5 rounded border border-slate-600 text-slate-300 hover:bg-slate-700 hover:text-white transition-colors"
+                >
+                  Cancel
+                </button>
+              )}
+            </div>
+          </div>
+          <input
+            type="number" min={1} value={values.batch_size}
+            onChange={(e) => onChange({ batch_size: Number(e.target.value) })} className={inputClass}
+          />
+          {batchEstimateNote && (
+            <p className="mt-1 text-[10px] leading-snug text-slate-400">{batchEstimateNote}</p>
+          )}
+        </div>
+        <div>
+          {/* Same number, two meanings: the tile window when tiling, or the square
+              the whole slice is squashed into when not. */}
+          <label className={labelClass}>{values.tiling ? 'Patch size (px)' : 'Image size (px)'}</label>
+          <input
+            type="number"
+            step={sizeLimits.multipleOf}
+            min={sizeLimits.min}
+            max={sizeLimits.max}
+            value={values.image_size}
+            onChange={(e) => onChange({ image_size: Number(e.target.value) })}
+            className={`${inputClass} ${sizeError ? 'border-red-500' : ''}`}
+          />
+          <p className={`mt-1 text-[10px] ${sizeError ? 'text-red-400' : 'text-slate-500'}`}>
+            {sizeError ?? `${sizeLimits.min}–${sizeLimits.max}, in steps of ${sizeLimits.multipleOf}`}
+          </p>
+        </div>
+        <div className="flex items-end pb-1.5">
+          <label className="flex items-center gap-2 text-xs text-slate-300 cursor-pointer">
+            <input
+              type="checkbox" checked={values.flip_augment} className="accent-sky-500"
+              onChange={(e) => onChange({ flip_augment: e.target.checked })}
+            />
+            Random flip augmentation
+          </label>
+        </div>
+
+        <div className="col-span-2 sm:col-span-3">
+          <label className="flex items-center gap-2 text-xs text-slate-300 cursor-pointer">
+            <input
+              type="checkbox" checked={values.tiling} className="accent-sky-500"
+              onChange={(e) => onChange({ tiling: e.target.checked })}
+            />
+            Tile large images (train &amp; predict at native resolution)
+          </label>
+          <p className="mt-1 text-[11px] leading-snug text-slate-400">
+            {values.tiling
+              ? `Cuts ${values.image_size}px patches at full resolution with 25% overlap, then blends the
+                 predictions back together. Keeps fine detail on images larger than the patch size.`
+              : `Shrinks each whole image to ${values.image_size}px before training — faster, but detail on
+                 large images is lost before the model sees it.`}
+          </p>
+        </div>
+
+        <div>
+          <label className={labelClass}>Depth</label>
+          <input
+            type="number" min={2} max={6} value={values.depth}
+            onChange={(e) => onChange({ depth: Number(e.target.value) })} className={inputClass}
+          />
+        </div>
+        <div>
+          <label className={labelClass}>Base channels</label>
+          <input
+            type="number" min={1} value={values.base_channels}
+            onChange={(e) => onChange({ base_channels: Number(e.target.value) })} className={inputClass}
+          />
+        </div>
+        <div>
+          <label className={labelClass}>Growth rate</label>
+          <input
+            type="number" step="0.1" min={0.1} value={values.growth_rate}
+            onChange={(e) => onChange({ growth_rate: Number(e.target.value) })} className={inputClass}
+          />
+        </div>
+      </div>
+    </details>
+  );
+}
diff --git a/frontend/src/components/train/InferencePanel.tsx b/frontend/src/components/train/InferencePanel.tsx
new file mode 100644
index 0000000..ec224a2
--- /dev/null
+++ b/frontend/src/components/train/InferencePanel.tsx
@@ -0,0 +1,263 @@
+/**
+ * InferencePanel — run a saved fine-tuned run over the current sample, with
+ * an overlay preview, then import the predictions as editable annotations
+ * and/or push them into Tiled as masks.
+ */
+import { useEffect, useMemo, useState } from 'react';
+import { API_BASE } from '@/config';
+import { useExportJob } from '@/hooks/useExportJob';
+import { buildSliceUrl } from '@/hooks/useImageSlice';
+import { useDatasetStore } from '@/stores/datasetStore';
+import type { RunClass } from '@/lib/importPredictions';
+import JobProgressBar from './JobProgressBar';
+
+const MAX_SLICE_INDICES = 2000;
+
+interface InferencePanelProps {
+  selectedRunId: string | null;
+  hasOpenSample: boolean;
+  isTiledSource: boolean;
+  source: string | null;
+  serverUri: string | null;
+  currentSlice: number;
+  nSlices: number;
+  baseImageUrl: string | null;
+  onImportPredictions: (runClasses: RunClass[], slices: Record<string, unknown>) => void;
+}
+
+type Scope = 'current' | 'range' | 'all';
+
+export default function InferencePanel({
+  selectedRunId, hasOpenSample, isTiledSource, source, serverUri, currentSlice, nSlices, baseImageUrl, onImportPredictions,
+}: InferencePanelProps) {
+  const [scope, setScope] = useState<Scope>('current');
+  const [rangeStart, setRangeStart] = useState(0);
+  const [rangeEnd, setRangeEnd] = useState(Math.max(0, nSlices - 1));
+  const [previewSlice, setPreviewSlice] = useState<number | null>(null);
+  const [opacity, setOpacity] = useState(0.7);
+  // Set right after "Import as annotations" is clicked; cleared on a new run
+  // so a stale count from a previous job never lingers.
+  const [importedCount, setImportedCount] = useState<number | null>(null);
+
+  // Fixed keys: same reasoning as TrainPage's jobs — once started, an infer job
+  // is bound to its own job_id independent of anything selected afterward.
+  const { state: job, startJob, reset } = useExportJob('train:infer');
+  const { state: writeJob, startJob: startWriteJob } = useExportJob('train:infer-write');
+
+  useEffect(() => {
+    setRangeEnd(Math.max(0, nSlices - 1));
+  }, [nSlices]);
+
+  const sliceIndices = (): number[] => {
+    if (scope === 'current') return [currentSlice];
+    if (scope === 'range') {
+      const lo = Math.max(0, Math.min(rangeStart, rangeEnd));
+      const hi = Math.min(nSlices - 1, Math.max(rangeStart, rangeEnd));
+      return Array.from({ length: hi - lo + 1 }, (_, i) => lo + i);
+    }
+    return Array.from({ length: Math.min(nSlices, MAX_SLICE_INDICES) }, (_, i) => i);
+  };
+
+  const handleRunInference = () => {
+    if (!selectedRunId || !source) return;
+    reset();
+    setImportedCount(null);
+    void startJob('/api/train/infer', {
+      run_id: selectedRunId,
+      kind: isTiledSource ? 'tiled' : 'local',
+      source,
+      server_uri: serverUri,
+      slice_indices: sliceIndices(),
+    });
+  };
+
+  const handleCancelInference = () => {
+    // Shared registry — same cancel route every background job in this app uses.
+    if (job.jobId) void fetch(`${API_BASE}/api/export/cancel/${job.jobId}`, { method: 'POST' });
+  };
+
+  const previewSlices: number[] = Array.isArray(job.result?.preview_slices)
+    ? (job.result!.preview_slices as number[])
+    : [];
+  const activePreviewSlice = previewSlice ?? previewSlices[0] ?? null;
+  // Keyed on the job, deliberately NOT on selectedRunId: the overlay is cached
+  // server-side against this job_id (which run produced it is already baked in),
+  // and selectedRunId is page-local state that resets to null when the Train tab
+  // remounts — requiring it here made a finished job's overlay vanish on the way
+  // back to the tab, leaving just the bare base image under a "done" progress bar.
+  const previewUrl = job.status === 'done' && activePreviewSlice != null && job.jobId
+    ? `${API_BASE}/api/train/infer/preview/${job.jobId}/${activePreviewSlice}`
+    : null;
+
+  // The image UNDER the overlay must follow the preview-slice slider, not the
+  // slice open in the app. `baseImageUrl` is bound to the open slice, so on a
+  // multi-slice job the overlay advanced while the picture beneath it stayed
+  // put — predictions from slice N drawn over the pixels of slice 0. Rebuilt
+  // here from activePreviewSlice; the prop stays as the fallback for the
+  // single-slice case and before a job has produced any preview.
+  const { kind, renderOpts } = useDatasetStore();
+  const basePreviewUrl = useMemo(() => {
+    if (activePreviewSlice == null || activePreviewSlice === currentSlice) return baseImageUrl;
+    if (!source || !kind) return baseImageUrl;
+    return buildSliceUrl(source, kind, activePreviewSlice, renderOpts, serverUri);
+  }, [activePreviewSlice, currentSlice, source, kind, renderOpts, serverUri, baseImageUrl]);
+
+  const totalShapes = typeof job.result?.n_shapes === 'number' ? job.result.n_shapes : 0;
+
+  // Gates the "start a new job" controls only. Progress/results for a job
+  // already running or finished must stay visible on their own — e.g. after
+  // navigating away and back, TrainPage's selectedRunId resets to null before
+  // the run list settles, but a still-running job keeps going server-side and
+  // this hook already reattached to it (see useExportJob's persistKey). Gating
+  // the whole section on selectedRunId used to hide that reattached job behind
+  // "select a run", making a still-running (or already-finished) job look like
+  // it vanished.
+  const canStartNewJob = hasOpenSample && !!selectedRunId;
+  const hasJob = job.status !== 'idle';
+
+  return (
+    <div className="space-y-3">
+      <p className="text-xs font-medium text-slate-400 uppercase tracking-wide">Inference</p>
+      {!hasOpenSample && !hasJob && (
+        <p className="text-xs text-slate-400">Open a sample in Browse/Annotate to run inference on it.</p>
+      )}
+      {hasOpenSample && !selectedRunId && !hasJob && (
+        <p className="text-xs text-slate-400">Select a saved run above to enable inference.</p>
+      )}
+
+      {canStartNewJob && (
+        <>
+          <div className="flex flex-col gap-1.5">
+            {([
+              ['current', `Current slice (${currentSlice})`],
+              ['range', 'Slice range'],
+              ['all', `All slices (${nSlices})`],
+            ] as const).map(([value, label]) => (
+              <label key={value} className="flex items-center gap-2 text-xs text-slate-300 cursor-pointer">
+                <input type="radio" name="infer-scope" className="accent-sky-500" checked={scope === value} onChange={() => setScope(value)} />
+                {label}
+              </label>
+            ))}
+            {scope === 'range' && (
+              <div className="flex items-center gap-2 pl-5 text-xs text-slate-300">
+                <input
+                  type="number" min={0} max={nSlices - 1} value={rangeStart}
+                  onChange={(e) => setRangeStart(Number(e.target.value))}
+                  className="w-16 rounded border border-slate-600 bg-slate-900/60 px-1.5 py-1"
+                />
+                <span>to</span>
+                <input
+                  type="number" min={0} max={nSlices - 1} value={rangeEnd}
+                  onChange={(e) => setRangeEnd(Number(e.target.value))}
+                  className="w-16 rounded border border-slate-600 bg-slate-900/60 px-1.5 py-1"
+                />
+              </div>
+            )}
+          </div>
+
+          <button
+            type="button" onClick={handleRunInference}
+            disabled={job.status === 'running'}
+            className="px-4 py-2 text-sm rounded-md bg-sky-600 text-white hover:bg-sky-500 transition-colors disabled:opacity-50 disabled:cursor-not-allowed"
+          >
+            {job.status === 'running' ? 'Predicting…' : 'Run inference'}
+          </button>
+        </>
+      )}
+
+      {hasJob && (
+        <>
+          <div className="flex items-start gap-2">
+            <div className="flex-1"><JobProgressBar job={job} unit="slices" /></div>
+            {job.status === 'running' && (
+              <button
+                type="button" onClick={handleCancelInference}
+                className="shrink-0 px-3 py-1.5 text-xs rounded-md border border-slate-600 text-slate-300 hover:bg-slate-700 transition-colors"
+              >
+                Cancel
+              </button>
+            )}
+          </div>
+
+          {job.status === 'done' && (
+            <div className="space-y-2 rounded-lg border border-slate-700 bg-slate-900/40 p-2">
+              <p className="text-xs text-slate-300">
+                {totalShapes} predicted region{totalShapes !== 1 ? 's' : ''}
+                {job.result?.cancelled === true ? ' (cancelled — partial result)' : ''}.
+              </p>
+              {previewSlices.length > 0 && basePreviewUrl && (
+                <>
+                  <div className="relative w-full overflow-hidden rounded border border-slate-700 bg-black">
+                    <img src={basePreviewUrl} alt="" className="block w-full" />
+                    {previewUrl && (
+                      <img src={previewUrl} alt="" className="absolute inset-0 w-full" style={{ opacity }} />
+                    )}
+                  </div>
+                  <div className="flex items-center gap-2 text-xs text-slate-300">
+                    <span>Opacity</span>
+                    <input
+                      type="range" min={0} max={1} step={0.05} value={opacity}
+                      onChange={(e) => setOpacity(Number(e.target.value))} className="flex-1 accent-sky-500"
+                    />
+                  </div>
+                  {previewSlices.length > 1 && (
+                    <div className="flex items-center gap-2 text-xs text-slate-300">
+                      <span>Slice</span>
+                      <input
+                        type="range" min={0} max={previewSlices.length - 1}
+                        value={Math.max(0, previewSlices.indexOf(activePreviewSlice ?? previewSlices[0]))}
+                        onChange={(e) => setPreviewSlice(previewSlices[Number(e.target.value)])}
+                        className="flex-1 accent-sky-500"
+                      />
+                      <span className="tabular-nums">{activePreviewSlice}</span>
+                    </div>
+                  )}
+                </>
+              )}
+              {hasOpenSample && (
+                <div className="flex flex-wrap gap-2 pt-1">
+                  <button
+                    type="button"
+                    onClick={() => {
+                      const classes = Array.isArray(job.result?.classes) ? (job.result!.classes as RunClass[]) : [];
+                      const slices = (job.result?.slices ?? {}) as Record<string, unknown>;
+                      onImportPredictions(classes, slices);
+                      setImportedCount(totalShapes);
+                    }}
+                    className="px-3 py-1.5 text-xs rounded-md bg-sky-600 text-white hover:bg-sky-500 transition-colors"
+                  >
+                    Import as annotations
+                  </button>
+                  {isTiledSource && (
+                    <button
+                      type="button"
+                      disabled={writeJob.status === 'running'}
+                      onClick={() => job.jobId && void startWriteJob(`/api/train/infer/write-tiled/${job.jobId}`, {})}
+                      className="px-3 py-1.5 text-xs rounded-md border border-emerald-500 text-emerald-300 hover:bg-emerald-900/30 transition-colors disabled:opacity-50"
+                    >
+                      {writeJob.status === 'running' ? 'Writing…' : 'Write masks to Tiled'}
+                    </button>
+                  )}
+                </div>
+              )}
+              {importedCount !== null && (
+                <p className="text-xs text-emerald-400">
+                  Imported {importedCount} region{importedCount === 1 ? '' : 's'} as annotations — they're on the
+                  Annotate tab and autosaved.
+                </p>
+              )}
+              {writeJob.status === 'done' && (
+                <p className="text-xs text-emerald-400">
+                  Masks saved to Tiled
+                  {typeof writeJob.result?.n_slices === 'number' ? ` (${writeJob.result.n_slices} slices)` : ''} —
+                  load them in Annotate anytime with "Load saved masks".
+                </p>
+              )}
+              <JobProgressBar job={writeJob} unit="steps" />
+            </div>
+          )}
+        </>
+      )}
+    </div>
+  );
+}
diff --git a/frontend/src/components/train/JobProgressBar.tsx b/frontend/src/components/train/JobProgressBar.tsx
new file mode 100644
index 0000000..5bedda7
--- /dev/null
+++ b/frontend/src/components/train/JobProgressBar.tsx
@@ -0,0 +1,48 @@
+/**
+ * JobProgressBar — phase/progress bar + scrolling log pane + error banner,
+ * shared by the training and inference sections of the Train tab. Styling
+ * matches DownloadModal's export-job progress display.
+ */
+import { WarningCircle } from '@phosphor-icons/react';
+import type { ExportJobState } from '@/hooks/useExportJob';
+
+interface JobProgressBarProps {
+  job: ExportJobState;
+  /** Unit label for the done/total counter (e.g. "batches", "slices"). */
+  unit?: string;
+}
+
+export default function JobProgressBar({ job, unit = 'steps' }: JobProgressBarProps) {
+  if (job.status === 'idle') return null;
+  const pct = job.total > 0 ? Math.round((job.done / job.total) * 100) : 0;
+
+  return (
+    <div className="space-y-2">
+      {(job.status === 'running' || job.status === 'done') && (
+        <>
+          <div className="flex items-center justify-between text-xs text-slate-300">
+            <span className="capitalize">{job.phase || 'working'}…</span>
+            {job.total > 0 && <span className="tabular-nums">{job.done}/{job.total} {unit}</span>}
+          </div>
+          <div className="h-1.5 w-full rounded bg-slate-700 overflow-hidden">
+            <div
+              className={`h-full transition-all ${job.status === 'done' ? 'bg-green-500' : 'bg-sky-500'}`}
+              style={{ width: job.status === 'done' ? '100%' : `${Math.max(5, pct)}%` }}
+            />
+          </div>
+          {job.log.length > 0 && (
+            <div className="max-h-32 overflow-y-auto rounded bg-slate-900/70 border border-slate-700 p-2 text-[11px] font-mono text-slate-400 leading-relaxed">
+              {job.log.slice(-16).map((line, i) => <div key={i}>{line}</div>)}
+            </div>
+          )}
+        </>
+      )}
+      {job.status === 'error' && (
+        <div className="flex items-start gap-2 text-sm text-red-400">
+          <WarningCircle size={16} className="mt-0.5 shrink-0" />
+          <span className="min-w-0 break-words">{job.error ?? 'Job failed.'}</span>
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/frontend/src/components/train/RunsPanel.tsx b/frontend/src/components/train/RunsPanel.tsx
new file mode 100644
index 0000000..11a6836
--- /dev/null
+++ b/frontend/src/components/train/RunsPanel.tsx
@@ -0,0 +1,94 @@
+/**
+ * RunsPanel — saved fine-tune runs (dlsia_tunet + the dlsia_denoiser family);
+ * pick one to run inference with (see InferencePanel), or permanently delete
+ * one.
+ */
+import { Trash } from '@phosphor-icons/react';
+import type { TrainRun } from '@/hooks/useTrainRuns';
+import { useTrainCapability } from '@/hooks/useTrainCapability';
+import { denoiseMethodLabel } from '@/lib/trainDenoiseOption';
+
+interface RunsPanelProps {
+  runs: TrainRun[];
+  selectedRunId: string | null;
+  onSelectRun: (runId: string) => void;
+  onDeleteRun: (runId: string) => void;
+}
+
+export const FAMILY_LABELS: Record<string, string> = {
+  dlsia_tunet: 'dlsia TUNet',
+  // Architecture-agnostic: this family covers both a dlsia TUNet and a plain
+  // convolutional autoencoder, and more than the two original schemes.
+  dlsia_denoiser: 'Denoiser (self-supervised)',
+};
+
+export default function RunsPanel({ runs, selectedRunId, onSelectRun, onDeleteRun }: RunsPanelProps) {
+  // Only to turn a stored method id ("tv") into its label ("Total variation");
+  // the query is shared and cached across the whole Train tab.
+  const { capability } = useTrainCapability();
+
+  const handleDelete = (run: TrainRun) => {
+    // Includes run_id, not just family + timestamp: two runs of the same family
+    // trained close together can otherwise look identical in this dialog, with
+    // nothing to tell the user which one they're actually about to delete.
+    const label = `${FAMILY_LABELS[run.model_family] ?? run.model_family} — ${new Date(run.created_at).toLocaleString()} (${run.run_id})`;
+    if (!window.confirm(`Permanently delete this run?\n\n${label}\n\nThis removes its saved weights and cannot be undone.`)) return;
+    onDeleteRun(run.run_id);
+  };
+
+  return (
+    <div className="space-y-2">
+      <p className="text-xs font-medium text-slate-400 uppercase tracking-wide">Saved runs</p>
+      {runs.length === 0 ? (
+        <p className="text-xs text-slate-400">No runs saved yet — train a model above to create one.</p>
+      ) : (
+        <div className="max-h-56 overflow-y-auto rounded-lg border border-slate-700 divide-y divide-slate-700">
+          {runs.map((run) => (
+            <label
+              key={run.run_id}
+              className={`flex items-start gap-3 px-3 py-2 cursor-pointer transition-colors ${
+                selectedRunId === run.run_id ? 'bg-sky-900/30' : 'hover:bg-slate-700/40'
+              }`}
+            >
+              <input
+                type="radio" name="selected-run" className="mt-1 shrink-0 accent-sky-500"
+                checked={selectedRunId === run.run_id} onChange={() => onSelectRun(run.run_id)}
+              />
+              <div className="min-w-0 flex-1">
+                <div className="flex flex-wrap items-center gap-x-2 gap-y-0.5">
+                  <span className="text-sm text-slate-200">{FAMILY_LABELS[run.model_family] ?? run.model_family}</span>
+                  <span className="text-xs text-slate-500">{new Date(run.created_at).toLocaleString()}</span>
+                  {/* Two runs are otherwise indistinguishable here while
+                      expecting completely different input pixels. */}
+                  {run.denoise && (
+                    <span
+                      className="rounded bg-sky-900/50 px-1.5 py-px text-[10px] font-medium text-sky-300"
+                      title={`Trained on ${denoiseMethodLabel(run.denoise.method, capability.denoise.methods)}-denoised input at ${Math.round(run.denoise.strength * 100)}% strength. Inference reapplies this automatically.`}
+                    >
+                      denoised input · {denoiseMethodLabel(run.denoise.method, capability.denoise.methods)}
+                    </span>
+                  )}
+                </div>
+                <div className="mt-0.5 flex flex-wrap items-center gap-x-3 gap-y-0.5 text-xs text-slate-400">
+                  {run.metrics.val_miou != null && <span>mIoU {run.metrics.val_miou.toFixed(3)}</span>}
+                  {run.metrics.epochs_completed != null && <span>{run.metrics.epochs_completed} epochs</span>}
+                  {run.metrics.cancelled && <span className="text-amber-400">cancelled (partial)</span>}
+                  <span className="truncate">{run.classes.map((c) => c.label).join(', ')}</span>
+                </div>
+              </div>
+              <button
+                type="button"
+                aria-label="Delete run"
+                title="Delete run"
+                onClick={(e) => { e.preventDefault(); e.stopPropagation(); handleDelete(run); }}
+                className="shrink-0 rounded p-1 text-slate-500 hover:bg-red-900/30 hover:text-red-400 transition-colors"
+              >
+                <Trash size={14} />
+              </button>
+            </label>
+          ))}
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/frontend/src/components/train/TrainDenoiseToggle.tsx b/frontend/src/components/train/TrainDenoiseToggle.tsx
new file mode 100644
index 0000000..f83f6a7
--- /dev/null
+++ b/frontend/src/components/train/TrainDenoiseToggle.tsx
@@ -0,0 +1,73 @@
+/**
+ * TrainDenoiseToggle — the "Train on denoised input" opt-in.
+ *
+ * It reads the setting straight from `datasetStore`'s denoise state — the same
+ * one Phase 2's Annotate-tab `DenoisePanel` reads/writes — so the label names
+ * the exact filter the user tuned there rather than offering a second,
+ * independent copy of the controls that could disagree with what they were
+ * actually looking at.
+ *
+ * Deliberately opt-in and default-off: unlike every other denoise control in
+ * the app this one changes what the model LEARNS, and a run trained on
+ * filtered pixels is not interchangeable with one trained on raw pixels.
+ * Whether it's on is owned by the host (it's part of that form's submit
+ * state); this component owns only the presentation and the "can it be used
+ * at all" guard — the payload builder in `lib/trainDenoiseOption` re-checks
+ * the guard, so a stale checked box can't leak an unusable method into a
+ * request.
+ */
+import { useDatasetStore } from '@/stores/datasetStore';
+import { useTrainCapability } from '@/hooks/useTrainCapability';
+import { trainDenoiseBlockedReason, trainDenoiseSummary } from '@/lib/trainDenoiseOption';
+
+interface TrainDenoiseToggleProps {
+  checked: boolean;
+  onChange: (next: boolean) => void;
+  /** Host-level busy state (a job in flight); the guard disables independently. */
+  disabled?: boolean;
+}
+
+export default function TrainDenoiseToggle({
+  checked, onChange, disabled = false,
+}: TrainDenoiseToggleProps) {
+  const denoise = useDatasetStore((s) => s.denoise);
+  const { capability } = useTrainCapability();
+
+  const blocked = trainDenoiseBlockedReason(denoise.method);
+  const summary = trainDenoiseSummary(denoise, capability.denoise.methods);
+
+  return (
+    <div className="flex flex-col gap-0.5">
+      <label
+        className={`flex items-start gap-1.5 text-xs text-slate-300 ${
+          blocked ? 'cursor-not-allowed opacity-60' : 'cursor-pointer'
+        }`}
+        title={
+          blocked
+            ?? 'Applies this filter to the model\'s input pixels during training. The setting is recorded on the run, and inference reapplies it automatically, so training and prediction cannot disagree.'
+        }
+      >
+        <input
+          type="checkbox"
+          className="mt-0.5 shrink-0 accent-sky-500"
+          checked={checked && !blocked}
+          disabled={disabled || !!blocked}
+          onChange={(e) => onChange(e.target.checked)}
+        />
+        <span>
+          Train on denoised input
+          {summary && <span className="text-slate-400"> ({summary})</span>}
+        </span>
+      </label>
+      {blocked ? (
+        <p className="pl-5 text-[11px] leading-snug text-slate-400">{blocked}</p>
+      ) : (
+        checked && (
+          <p className="pl-5 text-[11px] leading-snug text-slate-400">
+            Recorded on the run — inference will reapply it automatically.
+          </p>
+        )
+      )}
+    </div>
+  );
+}
diff --git a/frontend/src/components/train/TrainingDataPanel.tsx b/frontend/src/components/train/TrainingDataPanel.tsx
new file mode 100644
index 0000000..2aa07e5
--- /dev/null
+++ b/frontend/src/components/train/TrainingDataPanel.tsx
@@ -0,0 +1,42 @@
+/**
+ * TrainingDataPanel — checkbox list of this session's annotated samples to
+ * train on (see gatherTrainingSources.ts for the session-scoping rationale).
+ */
+import type { TrainingCandidate } from '@/lib/gatherTrainingSources';
+
+interface TrainingDataPanelProps {
+  candidates: TrainingCandidate[];
+  selected: Set<string>;
+  onToggle: (sourceKey: string) => void;
+}
+
+export default function TrainingDataPanel({ candidates, selected, onToggle }: TrainingDataPanelProps) {
+  return (
+    <div className="space-y-2">
+      <div className="flex items-center justify-between">
+        <p className="text-xs font-medium text-slate-400 uppercase tracking-wide">Training data</p>
+        <span className="text-xs text-slate-400">{selected.size} of {candidates.length} selected</span>
+      </div>
+      {candidates.length === 0 ? (
+        <p className="text-xs text-slate-400">
+          No annotated samples yet this session. Annotate a few slices in the Annotate tab, then come back here.
+        </p>
+      ) : (
+        <div className="max-h-48 overflow-y-auto rounded-lg border border-slate-700 divide-y divide-slate-700">
+          {candidates.map((c) => (
+            <label key={c.sourceKey} className="flex items-center gap-2 px-3 py-2 text-sm text-slate-200 cursor-pointer hover:bg-slate-700/40">
+              <input
+                type="checkbox"
+                className="shrink-0 accent-sky-500"
+                checked={selected.has(c.sourceKey)}
+                onChange={() => onToggle(c.sourceKey)}
+              />
+              <span className="min-w-0 flex-1 truncate font-mono text-xs">{c.sourceKey}</span>
+              <span className="shrink-0 text-xs text-slate-400">{c.shapeCount} shape{c.shapeCount !== 1 ? 's' : ''}</span>
+            </label>
+          ))}
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/frontend/src/hooks/useExportJob.ts b/frontend/src/hooks/useExportJob.ts
index 9fa9af2..d73356b 100644
--- a/frontend/src/hooks/useExportJob.ts
+++ b/frontend/src/hooks/useExportJob.ts
@@ -1,7 +1,9 @@
 /**
- * useExportJob — drive a background COCO/mask export and stream its progress.
+ * useExportJob — drive a background job (COCO/mask export, iPred batch
+ * train/apply, or the Train tab's train/infer/batch-probe jobs) and stream
+ * its progress.
  *
- * POSTs to /api/export/coco (which returns a job_id), then polls
+ * POSTs to a job-starting route (which returns a job_id), then polls
  * /api/export/status/{job_id} until done/error, exposing phase, done/total, and
  * a live log for the UI. When finished, `downloadUrl` points at the .zip so the
  * browser's save dialog can write it to the user's machine. The server-side
@@ -9,6 +11,7 @@
  */
 import { useCallback, useEffect, useRef, useState } from 'react';
 import { API_BASE } from '@/config';
+import { formatApiError } from '@/lib/apiError';
 
 export interface ExportJobState {
   status: 'idle' | 'running' | 'done' | 'error';
@@ -25,12 +28,32 @@ const IDLE: ExportJobState = {
   status: 'idle', phase: '', done: 0, total: 0, log: [], result: null, error: null, jobId: null,
 };
 
+const storageKeyFor = (persistKey: string) => `exportJob:${persistKey}`;
+
 /**
- * Drives a background COCO export: returns the live job `state`, a `start` action,
+ * Drives a background job: returns the live job `state`, a `start` action,
  * `reset`, and a `downloadUrl` for the result zip when available.
+ *
+ * `persistKey`, when given, survives this component unmounting mid-job — e.g. the
+ * user switches from Train to Browse and back while training runs in the
+ * background. Without it, the job keeps running server-side (it's independent of
+ * any client), but the polling loop lived in this hook's local state, so a fresh
+ * mount used to come back to a blank "idle" progress bar with no way to tell a
+ * job was still going. The job id is stashed in sessionStorage on start and
+ * reattached to the same polling loop on the next mount; callers using the same
+ * `persistKey` for genuinely different jobs (e.g. a different sample) should
+ * include whatever varies in the key so they don't reconnect to the wrong one.
  */
-export function useExportJob() {
-  const [state, setState] = useState<ExportJobState>(IDLE);
+export function useExportJob(persistKey?: string) {
+  const storageKey = persistKey ? storageKeyFor(persistKey) : null;
+
+  const [state, setState] = useState<ExportJobState>(() => {
+    const savedId = storageKey ? sessionStorage.getItem(storageKey) : null;
+    // Placeholder until the resume effect's first tick fills in the real
+    // phase/progress/result — avoids a flash of "idle" for a job that, most of
+    // the time, is still genuinely running.
+    return savedId ? { ...IDLE, status: 'running', jobId: savedId } : IDLE;
+  });
   const timer = useRef<number | null>(null);
 
   /** Cancel any pending poll timeout. */
@@ -40,13 +63,26 @@ export function useExportJob() {
   useEffect(() => clearTimer, []);
 
   /** Stop polling and return state to idle. */
-  const reset = useCallback(() => { clearTimer(); setState(IDLE); }, []);
+  const reset = useCallback(() => {
+    clearTimer();
+    if (storageKey) sessionStorage.removeItem(storageKey);
+    setState(IDLE);
+  }, [storageKey]);
 
   /** Recursively poll /api/export/status/{jobId} every 500ms until done/error. */
   const poll = useCallback((jobId: string) => {
     const tick = async () => {
       try {
         const r = await fetch(`${API_BASE}/api/export/status/${jobId}`);
+        if (r.status === 404) {
+          // Only reachable when resuming a persisted id: the job registry is
+          // in-memory and TTL-pruned, so a job from long enough ago (or from
+          // before a server restart) is gone. That's not a failure worth
+          // showing — there's simply nothing left to resume.
+          if (storageKey) sessionStorage.removeItem(storageKey);
+          setState(IDLE);
+          return;
+        }
         if (!r.ok) { setState((s) => ({ ...s, status: 'error', error: `Status ${r.status}` })); return; }
         const j = await r.json();
         const status: ExportJobState['status'] =
@@ -63,6 +99,14 @@ export function useExportJob() {
       timer.current = window.setTimeout(tick, 500);
     };
     tick();
+  }, [storageKey]);
+
+  // Reattach the polling loop to a job that was already running when this hook
+  // last mounted. Mount-only: a job started via run()/startJob() below already
+  // calls poll() itself and must not be double-polled by this effect too.
+  useEffect(() => {
+    if (state.jobId && state.status === 'running') poll(state.jobId);
+    // eslint-disable-next-line react-hooks/exhaustive-deps
   }, []);
 
   /** POST a job-starting request to `path`, then poll the shared status route. */
@@ -75,19 +119,27 @@ export function useExportJob() {
         headers: { 'Content-Type': 'application/json' },
         body: JSON.stringify(payload),
       });
-      if (!res.ok) { const msg = await res.text(); setState((s) => ({ ...s, status: 'error', error: msg })); return; }
+      if (!res.ok) {
+        // Rejected requests come back as FastAPI validation JSON; show the field
+        // and message rather than the raw body.
+        const msg = formatApiError(await res.text(), `Request failed (${res.status}).`);
+        setState((s) => ({ ...s, status: 'error', error: msg }));
+        return;
+      }
       const data = await res.json();
       if (data.job_id) {
+        if (storageKey) sessionStorage.setItem(storageKey, data.job_id);
         setState((s) => ({ ...s, jobId: data.job_id }));
         poll(data.job_id);
       } else {
         // Synchronous response (e.g. dry-run) — treat as immediately done.
+        if (storageKey) sessionStorage.removeItem(storageKey);
         setState((s) => ({ ...s, status: 'done', result: data }));
       }
     } catch (e) {
       setState((s) => ({ ...s, status: 'error', error: String(e) }));
     }
-  }, [poll]);
+  }, [poll, storageKey]);
 
   /** Start a real COCO export. Returns once the job is queued (progress via state). */
   const start = useCallback((payload: unknown) => run('/api/export/coco', payload), [run]);
@@ -112,5 +164,16 @@ export function useExportJob() {
       ? `${API_BASE}/api/export/download/${state.jobId}`
       : null;
 
-  return { state, start, startMaskSync, startIpredBatchTrain, startIpredBatchApply, reset, downloadUrl };
+  return {
+    state,
+    start,
+    startMaskSync,
+    startIpredBatchTrain,
+    startIpredBatchApply,
+    /** Start a job at an arbitrary path (e.g. /api/train/start, /api/train/infer)
+     *  that returns a job_id and shares this same status-polling machinery. */
+    startJob: run,
+    reset,
+    downloadUrl,
+  };
 }
diff --git a/frontend/src/hooks/useTrainCapability.ts b/frontend/src/hooks/useTrainCapability.ts
new file mode 100644
index 0000000..259b356
--- /dev/null
+++ b/frontend/src/hooks/useTrainCapability.ts
@@ -0,0 +1,86 @@
+/**
+ * useTrainCapability — Train-tab readiness: torch/dlsia availability and device.
+ *
+ * No DINOv3 checkpoint discovery here — that model family is deferred (see
+ * Phase 5.5 in the integration plan) and the backend's capability() probe
+ * doesn't report it at all.
+ */
+import { useQuery } from '@tanstack/react-query';
+import { API_BASE } from '@/config';
+
+/** A classical denoise filter the server can actually run. `available` is
+ *  per-method because some need an optional dependency (wavelet ⇢ PyWavelets),
+ *  and `cost` drives whether the UI warns before a full-resolution preview. */
+export interface DenoiseMethodInfo {
+  method: string;
+  label: string;
+  cost: 'cheap' | 'moderate' | 'slow';
+  description: string;
+  available: boolean;
+  z_radius: number;
+}
+
+export interface TrainCapability {
+  torch_available: boolean;
+  torch_version: string | null;
+  device: string | null;
+  dlsia: { available: boolean };
+  denoise: { available: boolean; methods: DenoiseMethodInfo[] };
+  runs_dir: string;
+  busy: boolean;
+  error?: string;
+}
+
+const FALLBACK: TrainCapability = {
+  torch_available: false,
+  torch_version: null,
+  device: null,
+  dlsia: { available: false },
+  denoise: { available: false, methods: [] },
+  runs_dir: '',
+  busy: false,
+};
+
+/**
+ * Merge a server response over FALLBACK so a key this build expects but the
+ * server doesn't send resolves to a safe default instead of `undefined`.
+ *
+ * `data ?? FALLBACK` alone only covers "no response at all". It does NOT cover
+ * a response that's missing a key — and consumers reach into nested keys
+ * (`capability.denoise.methods`), so a single absent key throws a TypeError
+ * mid-render, which unmounts the React tree and white-screens the whole app.
+ * That's reachable whenever the frontend is newer than the running backend: a
+ * dev server still running a process started before a capability key was
+ * added, or a stale deploy. A missing capability should degrade to "that
+ * feature is unavailable", never take down the page.
+ */
+function withDefaults(data: Partial<TrainCapability> | undefined): TrainCapability {
+  if (!data) return FALLBACK;
+  return {
+    ...FALLBACK,
+    ...data,
+    dlsia: { ...FALLBACK.dlsia, ...(data.dlsia ?? {}) },
+    denoise: { ...FALLBACK.denoise, ...(data.denoise ?? {}) },
+  };
+}
+
+/** Polls /api/train/capability every 10s — cheap enough to keep the
+ *  CapabilityBanner and "busy" state current while the Train tab is open. */
+export function useTrainCapability() {
+  const query = useQuery<TrainCapability>({
+    queryKey: ['trainCapability'],
+    queryFn: async ({ signal }) => {
+      const res = await fetch(`${API_BASE}/api/train/capability`, { signal });
+      if (!res.ok) throw new Error(`Capability check failed: ${res.status}`);
+      return res.json();
+    },
+    staleTime: 5_000,
+    refetchInterval: 10_000,
+  });
+
+  return {
+    capability: withDefaults(query.data),
+    isLoading: query.isLoading,
+    refetch: query.refetch,
+  };
+}
diff --git a/frontend/src/hooks/useTrainRuns.ts b/frontend/src/hooks/useTrainRuns.ts
new file mode 100644
index 0000000..af34a29
--- /dev/null
+++ b/frontend/src/hooks/useTrainRuns.ts
@@ -0,0 +1,72 @@
+/**
+ * useTrainRuns — saved fine-tune runs (dlsia_tunet segmentation + the
+ * dlsia_denoiser family), for the inference run-picker. Invalidate
+ * `['trainRuns']` after a training job completes so a freshly-saved run shows
+ * up without a manual refresh.
+ */
+import { useQuery, useQueryClient } from '@tanstack/react-query';
+import { API_BASE } from '@/config';
+import type { AnnotationClass } from '@/stores/classStore';
+
+export interface TrainRun {
+  run_id: string;
+  // Discriminator literals from backend/schemas.py's ModelConfig union.
+  model_family: 'dlsia_tunet' | 'dlsia_denoiser';
+  model_config: Record<string, unknown>;
+  classes: AnnotationClass[];
+  render: Record<string, unknown>;
+  /**
+   * Denoising baked into this run's INPUT pixels at training time (see
+   * schemas.DenoiseTrainOpts). Null for a run trained on raw pixels, and
+   * absent entirely on runs saved before the option existed — hence optional
+   * as well as nullable, so `run.denoise &&` is the only safe test.
+   *
+   * Read-only from the frontend's side: inference reapplies it off the run
+   * itself, never off the request, so nothing in the app should offer it as a
+   * choice at predict time. It's surfaced (RunsPanel) purely so two runs that
+   * expect different input can't look identical.
+   */
+  denoise?: { method: string; strength: number } | null;
+  image_size: number;
+  hyperparams: Record<string, unknown>;
+  source_keys: string[];
+  created_at: string;
+  metrics: {
+    epochs_completed?: number;
+    final_train_loss?: number | null;
+    final_val_loss?: number | null;
+    val_miou?: number | null;
+    cancelled?: boolean;
+  };
+}
+
+export function useTrainRuns() {
+  const queryClient = useQueryClient();
+
+  const query = useQuery<TrainRun[]>({
+    queryKey: ['trainRuns'],
+    queryFn: async ({ signal }) => {
+      const res = await fetch(`${API_BASE}/api/train/runs`, { signal });
+      if (!res.ok) return [];
+      const data = await res.json();
+      return data.runs ?? [];
+    },
+    staleTime: 10_000,
+  });
+
+  const invalidate = () => queryClient.invalidateQueries({ queryKey: ['trainRuns'] });
+
+  /** Permanently deletes a saved run (config, metrics, and weights) and refreshes the list. */
+  const deleteRun = async (runId: string): Promise<boolean> => {
+    try {
+      const res = await fetch(`${API_BASE}/api/train/runs/${encodeURIComponent(runId)}`, { method: 'DELETE' });
+      if (!res.ok) return false;
+      invalidate();
+      return true;
+    } catch {
+      return false;
+    }
+  };
+
+  return { runs: query.data ?? [], isLoading: query.isLoading, invalidate, deleteRun };
+}
diff --git a/frontend/src/lib/apiError.test.ts b/frontend/src/lib/apiError.test.ts
new file mode 100644
index 0000000..7323549
--- /dev/null
+++ b/frontend/src/lib/apiError.test.ts
@@ -0,0 +1,101 @@
+import { describe, expect, it } from 'vitest';
+import { formatApiError } from './apiError';
+
+describe('formatApiError', () => {
+  it('formats the real 422 a bad patch size produces', () => {
+    const body = JSON.stringify({
+      detail: [{
+        type: 'greater_than_equal',
+        loc: ['body', 'model', 'dinov3_lora', 'hyperparams', 'image_size'],
+        msg: 'Input should be greater than or equal to 224',
+        input: 32,
+        ctx: { ge: 224 },
+      }],
+    });
+
+    expect(formatApiError(body)).toBe('image size: Input should be greater than or equal to 224');
+  });
+
+  it('passes through a plain string detail (HTTPException)', () => {
+    expect(formatApiError(JSON.stringify({ detail: 'Another job is already running' })))
+      .toBe('Another job is already running');
+  });
+
+  it('joins several validation errors', () => {
+    const body = JSON.stringify({
+      detail: [
+        { loc: ['body', 'epochs'], msg: 'Input should be less than or equal to 500' },
+        { loc: ['body', 'lr'], msg: 'Input should be greater than 0' },
+      ],
+    });
+
+    expect(formatApiError(body)).toBe(
+      'epochs: Input should be less than or equal to 500; lr: Input should be greater than 0',
+    );
+  });
+
+  it('omits the field when loc carries no usable name', () => {
+    expect(formatApiError(JSON.stringify({ detail: [{ msg: 'Something went wrong' }] })))
+      .toBe('Something went wrong');
+  });
+
+  it('falls back to raw text for a non-JSON body', () => {
+    expect(formatApiError('Internal Server Error')).toBe('Internal Server Error');
+  });
+
+  it('falls back for JSON that is not a FastAPI error', () => {
+    expect(formatApiError(JSON.stringify({ oops: true }))).toBe('{"oops":true}');
+  });
+
+  it('uses the fallback for an empty body', () => {
+    expect(formatApiError('', 'Request failed (500).')).toBe('Request failed (500).');
+    expect(formatApiError('   ', 'Request failed (500).')).toBe('Request failed (500).');
+  });
+
+  it('truncates a runaway body', () => {
+    expect(formatApiError('x'.repeat(5000)).length).toBe(500);
+  });
+
+  it('marks a truncated body with an ellipsis, not a silent cut', () => {
+    const result = formatApiError('x'.repeat(5000));
+    expect(result.endsWith('…')).toBe(true);
+  });
+
+  it('does not add an ellipsis when the body is short enough as-is', () => {
+    expect(formatApiError('short message').endsWith('…')).toBe(false);
+  });
+
+  it('handles a detail that is a list of plain strings, not validation objects', () => {
+    expect(formatApiError(JSON.stringify({ detail: ['Field A is required', 'Field B is invalid'] })))
+      .toBe('Field A is required; Field B is invalid');
+  });
+
+  it('handles a single validation-error object, not wrapped in a list', () => {
+    const body = JSON.stringify({
+      detail: { loc: ['body', 'image_size'], msg: 'Input should be greater than or equal to 224' },
+    });
+    expect(formatApiError(body)).toBe('image size: Input should be greater than or equal to 224');
+  });
+
+  it('names a whole list element by its index when loc ends in a number', () => {
+    const body = JSON.stringify({
+      detail: [{ loc: ['body', 'sources', 2], msg: 'field required' }],
+    });
+    expect(formatApiError(body)).toBe('sources[2]: field required');
+  });
+
+  it('falls back to a bare "item N" when a numeric loc has no preceding field name', () => {
+    const body = JSON.stringify({ detail: [{ loc: [3], msg: 'field required' }] });
+    expect(formatApiError(body)).toBe('item 3: field required');
+  });
+
+  it('still finds the leaf field name when a numeric index sits before it, not after', () => {
+    // The index (which source) isn't the leaf here — "color" is — so this must
+    // keep reporting the same leaf-only name it always has, unaffected by the
+    // numeric-leaf handling added for the case above.
+    const body = JSON.stringify({
+      detail: [{ loc: ['body', 'sources', 2, 'color'], msg: 'invalid color' }],
+    });
+    expect(formatApiError(body)).toBe('color: invalid color');
+  });
+});
diff --git a/frontend/src/lib/apiError.ts b/frontend/src/lib/apiError.ts
new file mode 100644
index 0000000..caaee9e
--- /dev/null
+++ b/frontend/src/lib/apiError.ts
@@ -0,0 +1,87 @@
+/**
+ * apiError — turn a FastAPI error body into something a user can act on.
+ *
+ * FastAPI reports a rejected request as `{"detail": [{loc, msg, type, ...}]}`.
+ * Rendered raw that reads as a wall of JSON, which is what the job progress bar
+ * used to show. This extracts the field name and message instead.
+ */
+
+interface ValidationItem {
+  loc?: unknown[];
+  msg?: string;
+}
+
+const TRUNCATE_AT = 500;
+
+/** Cut *text* to at most `limit` characters, marking that it was cut — a bare
+ *  slice reads as the whole message, hiding that the rest was thrown away. */
+function truncate(text: string, limit = TRUNCATE_AT): string {
+  return text.length <= limit ? text : `${text.slice(0, limit - 1)}…`;
+}
+
+/**
+ * Human-readable name for a pydantic `loc`, e.g. `image_size`, or `sources[2]`
+ * when the location is a whole list element rather than one of its fields
+ * (there's no field name to fall back on there, just the index).
+ */
+function fieldName(loc: unknown[] | undefined): string | null {
+  if (!Array.isArray(loc)) return null;
+  // Drop the leading "body" and any discriminated-union tag; a bare numeric
+  // index (a list position) is kept only long enough to attach it to the
+  // string segment right before it, then treated as the leaf itself.
+  const parts = loc.filter(
+    (p): p is string | number => p !== 'body' && (typeof p === 'string' || typeof p === 'number'),
+  );
+  const leaf = parts[parts.length - 1];
+  if (leaf === undefined) return null;
+  if (typeof leaf === 'number') {
+    const prev = parts[parts.length - 2];
+    return (typeof prev === 'string' ? `${prev}[${leaf}]` : `item ${leaf}`).replace(/_/g, ' ');
+  }
+  return leaf.replace(/_/g, ' ');
+}
+
+/** One `{msg, loc}`-shaped validation item (or a plain string) to a display line. */
+function formatValidationItem(item: unknown): string | null {
+  if (typeof item === 'string') return item.trim() || null;
+  const msg = typeof (item as ValidationItem)?.msg === 'string' ? (item as ValidationItem).msg! : '';
+  if (!msg) return null;
+  const field = fieldName((item as ValidationItem)?.loc);
+  return field ? `${field}: ${msg}` : msg;
+}
+
+/**
+ * Best-effort human-readable message for a non-OK API response body.
+ *
+ * Falls back to the raw text (trimmed) when the body isn't a shape we recognise,
+ * so nothing is ever swallowed — an unexpected error still reaches the user.
+ */
+export function formatApiError(body: string, fallback = 'Request failed.'): string {
+  const text = (body ?? '').trim();
+  if (!text) return fallback;
+
+  let parsed: unknown;
+  try {
+    parsed = JSON.parse(text);
+  } catch {
+    return truncate(text);
+  }
+
+  const detail = (parsed as { detail?: unknown })?.detail;
+  if (typeof detail === 'string' && detail.trim()) return detail;
+
+  // The common shape: a list of pydantic validation errors (or, less commonly,
+  // a list of plain message strings — some routes raise HTTPException with one).
+  if (Array.isArray(detail)) {
+    const messages = detail.map(formatValidationItem).filter((m): m is string => Boolean(m));
+    if (messages.length) return messages.join('; ');
+  }
+
+  // A single validation error/object, not wrapped in a list — same shape either way.
+  if (detail && typeof detail === 'object') {
+    const message = formatValidationItem(detail);
+    if (message) return message;
+  }
+
+  return truncate(text);
+}
diff --git a/frontend/src/lib/denoiserTrainScope.test.ts b/frontend/src/lib/denoiserTrainScope.test.ts
new file mode 100644
index 0000000..f59bcab
--- /dev/null
+++ b/frontend/src/lib/denoiserTrainScope.test.ts
@@ -0,0 +1,70 @@
+import { describe, expect, it } from 'vitest';
+import { denoiserScopeBlockedReason, resolveDenoiserScopeIndices } from './denoiserTrainScope';
+
+describe('resolveDenoiserScopeIndices', () => {
+  it('returns just the current slice for scope "current"', () => {
+    expect(resolveDenoiserScopeIndices('current', 5, 0, 0, 10)).toEqual([5]);
+  });
+
+  it('returns [] for "current" when the current slice is out of range', () => {
+    expect(resolveDenoiserScopeIndices('current', 20, 0, 0, 10)).toEqual([]);
+    expect(resolveDenoiserScopeIndices('current', -1, 0, 0, 10)).toEqual([]);
+  });
+
+  it('returns every index for scope "all"', () => {
+    expect(resolveDenoiserScopeIndices('all', 0, 0, 0, 4)).toEqual([0, 1, 2, 3]);
+  });
+
+  it('returns [] for an empty volume regardless of scope', () => {
+    expect(resolveDenoiserScopeIndices('all', 0, 0, 0, 0)).toEqual([]);
+    expect(resolveDenoiserScopeIndices('current', 0, 0, 0, 0)).toEqual([]);
+    expect(resolveDenoiserScopeIndices('range', 0, 0, 0, 0)).toEqual([]);
+  });
+
+  it('returns the inclusive range for scope "range"', () => {
+    expect(resolveDenoiserScopeIndices('range', 0, 2, 5, 10)).toEqual([2, 3, 4, 5]);
+  });
+
+  it('normalizes a reversed range (end typed before start)', () => {
+    expect(resolveDenoiserScopeIndices('range', 0, 5, 2, 10)).toEqual([2, 3, 4, 5]);
+  });
+
+  it('clamps a range to [0, nSlices)', () => {
+    expect(resolveDenoiserScopeIndices('range', 0, -3, 2, 5)).toEqual([0, 1, 2]);
+    expect(resolveDenoiserScopeIndices('range', 0, 3, 99, 5)).toEqual([3, 4]);
+  });
+
+  it('handles a single-slice range', () => {
+    expect(resolveDenoiserScopeIndices('range', 0, 3, 3, 10)).toEqual([3]);
+  });
+});
+
+describe('denoiserScopeBlockedReason', () => {
+  it('blocks an empty scope regardless of scheme', () => {
+    // Enumerates EVERY scheme on purpose: this used to cover only two of them,
+    // so a newly-added scheme could silently skip the check.
+    expect(denoiserScopeBlockedReason([], 'n2v')).toMatch(/no slices/i);
+    expect(denoiserScopeBlockedReason([], 'n2n')).toMatch(/no slices/i);
+    expect(denoiserScopeBlockedReason([], 'ae')).toMatch(/no slices/i);
+  });
+
+  it('blocks Noise2Noise on a single slice', () => {
+    expect(denoiserScopeBlockedReason([4], 'n2n')).toMatch(/noise2noise/i);
+  });
+
+  it('allows Noise2Void on a single slice', () => {
+    expect(denoiserScopeBlockedReason([4], 'n2v')).toBeNull();
+  });
+
+  it('allows the autoencoder on a single slice', () => {
+    // It reconstructs one slice through a bottleneck — unlike Noise2Noise it
+    // needs no second slice to pair with.
+    expect(denoiserScopeBlockedReason([4], 'ae')).toBeNull();
+    expect(denoiserScopeBlockedReason([0, 1, 2], 'ae')).toBeNull();
+  });
+
+  it('allows Noise2Noise once there are at least 2 slices', () => {
+    expect(denoiserScopeBlockedReason([4, 5], 'n2n')).toBeNull();
+    expect(denoiserScopeBlockedReason([1, 2, 3], 'n2n')).toBeNull();
+  });
+});
diff --git a/frontend/src/lib/denoiserTrainScope.ts b/frontend/src/lib/denoiserTrainScope.ts
new file mode 100644
index 0000000..765feed
--- /dev/null
+++ b/frontend/src/lib/denoiserTrainScope.ts
@@ -0,0 +1,65 @@
+/**
+ * denoiserTrainScope — resolves the Learned Denoiser panel's current/range/all
+ * scope selector into the concrete slice indices to train on, and validates
+ * the result before a training job is submitted.
+ *
+ * Mirrors ApplyModelPanel's fine-tune-scope selector (see its `trainScope`/
+ * `trainSliceIndices`) as closely as possible, kept here as a standalone pure
+ * function (rather than inline component state, as ApplyModelPanel and
+ * InferencePanel each do) so it's independently testable and its validation
+ * message can't drift between call sites.
+ *
+ * Denoiser training needs no annotations at all — Noise2Noise and Noise2Void
+ * are both self-supervised on the raw slice pixels themselves — so the
+ * validation here is about the SCOPE being usable at all, not about
+ * annotation coverage. Noise2Noise specifically needs to pair up two
+ * independent-noise realizations of (approximately) the same structure, so a
+ * scope of a single slice can't train it; Noise2Void and the autoencoder train
+ * on single slices fine (the first masks pixels within one slice, the second
+ * reconstructs the slice through a bottleneck).
+ */
+
+export type DenoiserTrainScope = 'current' | 'range' | 'all';
+export type DenoiserScheme = 'n2n' | 'n2v' | 'ae';
+
+/**
+ * Concrete, ascending, deduplicated slice indices for `scope`, clamped to
+ * `[0, nSlices)`. Returns `[]` when the scope resolves to nothing usable
+ * (e.g. `currentSlice` outside `[0, nSlices)`, or an empty volume).
+ */
+export function resolveDenoiserScopeIndices(
+  scope: DenoiserTrainScope,
+  currentSlice: number,
+  rangeStart: number,
+  rangeEnd: number,
+  nSlices: number,
+): number[] {
+  if (nSlices <= 0) return [];
+  if (scope === 'current') {
+    return currentSlice >= 0 && currentSlice < nSlices ? [currentSlice] : [];
+  }
+  if (scope === 'range') {
+    const lo = Math.max(0, Math.min(rangeStart, rangeEnd));
+    const hi = Math.min(nSlices - 1, Math.max(rangeStart, rangeEnd));
+    if (hi < lo) return [];
+    return Array.from({ length: hi - lo + 1 }, (_, i) => lo + i);
+  }
+  return Array.from({ length: nSlices }, (_, i) => i);
+}
+
+/**
+ * Why `indices` can't be used to train `scheme`, phrased for display next to
+ * the Train button — or null when the scope is ready to submit.
+ */
+export function denoiserScopeBlockedReason(
+  indices: number[],
+  scheme: DenoiserScheme,
+): string | null {
+  if (indices.length === 0) {
+    return 'No slices selected — choose a different scope.';
+  }
+  if (scheme === 'n2n' && indices.length < 2) {
+    return 'Noise2Noise needs at least 2 slices to pair up — choose a range or all slices, or switch to Noise2Void.';
+  }
+  return null;
+}
diff --git a/frontend/src/lib/gatherTrainingSources.ts b/frontend/src/lib/gatherTrainingSources.ts
new file mode 100644
index 0000000..1003971
--- /dev/null
+++ b/frontend/src/lib/gatherTrainingSources.ts
@@ -0,0 +1,72 @@
+/**
+ * gatherTrainingSources — turn a set of selected, annotated sourceKeys into
+ * the /api/train/start `sources` list.
+ *
+ * Mirrors DownloadModal.tsx's multi-source export scope-building: training
+ * data is drawn from `annotationStore.byImage` (every sample touched this
+ * session), not fetched from the backend for samples annotated in an earlier
+ * session — same scoping DownloadModal itself uses for "All annotated samples".
+ *
+ * Unlike the fork this was ported from, this app has no per-source class
+ * taxonomy to validate: `classStore` is a single global, flat class list
+ * shared by every sample, so there is nothing to reconcile across sources —
+ * the caller passes that one list straight to `/api/train/start` alongside
+ * whatever this returns.
+ */
+import { parseSourceKey } from './sourceKey';
+import type { Shape } from '@/stores/annotationStore';
+
+export interface TrainingCandidate {
+  sourceKey: string;
+  shapeCount: number;
+}
+
+/** Every sourceKey in `byImage` with at least one shape, sorted for stable display. */
+export function listAnnotatedSourceKeys(
+  byImage: Record<string, Record<string, Shape[]>>,
+): TrainingCandidate[] {
+  return Object.keys(byImage)
+    .map((sourceKey) => ({
+      sourceKey,
+      shapeCount: Object.values(byImage[sourceKey]).reduce((n, shapes) => n + shapes.length, 0),
+    }))
+    .filter((c) => c.shapeCount > 0)
+    .sort((a, b) => a.sourceKey.localeCompare(b.sourceKey));
+}
+
+export interface TrainingSourceItem {
+  kind: 'tiled' | 'local';
+  source: string;
+  server_uri: string | null;
+  slices: Record<string, Shape[]>;
+  split_by_slice: Record<string, string>;
+  negative_slices: string[];
+}
+
+/**
+ * Build the per-sample training payload for `selectedKeys`.
+ *
+ * @throws Error if `selectedKeys` is empty.
+ */
+export function gatherTrainingSources(
+  selectedKeys: string[],
+  byImage: Record<string, Record<string, Shape[]>>,
+  splitBySlice: Record<string, Record<string, string>>,
+  negativeSlices: Record<string, string[]>,
+): TrainingSourceItem[] {
+  if (selectedKeys.length === 0) {
+    throw new Error('Select at least one annotated sample to train on.');
+  }
+
+  return selectedKeys.map((sk) => {
+    const { kind, source, serverUri } = parseSourceKey(sk);
+    return {
+      kind,
+      source,
+      server_uri: serverUri,
+      slices: byImage[sk] ?? {},
+      split_by_slice: splitBySlice[sk] ?? {},
+      negative_slices: negativeSlices[sk] ?? [],
+    };
+  });
+}
diff --git a/frontend/src/lib/importPredictions.test.ts b/frontend/src/lib/importPredictions.test.ts
new file mode 100644
index 0000000..80d866d
--- /dev/null
+++ b/frontend/src/lib/importPredictions.test.ts
@@ -0,0 +1,73 @@
+import { describe, expect, it } from 'vitest';
+import { remapPredictedShapes, type RunClass } from './importPredictions';
+import type { AnnotationClass } from '@/stores/classStore';
+import type { Shape } from '@/stores/annotationStore';
+
+const shape = (id: string, classId: number): Shape => ({
+  id, classId, kind: 'polygon', points: [0, 0, 10, 0, 5, 10],
+});
+
+describe('remapPredictedShapes', () => {
+  it('maps a run class to an existing current class by case-insensitive label', () => {
+    const runClasses: RunClass[] = [{ classId: 1, label: 'Pore', color: '#ff0000' }];
+    const current: AnnotationClass[] = [{ classId: 7, label: 'pore', color: '#000000', isVisible: true }];
+    const slices = { '0': [shape('s1', 1)] };
+
+    const result = remapPredictedShapes(runClasses, current, slices);
+
+    expect(result.classes).toEqual(current); // no new class appended
+    expect(result.slices['0'][0].classId).toBe(7); // remapped to the existing class's id
+  });
+
+  it('appends a run class with no label match, keeping its color', () => {
+    const runClasses: RunClass[] = [{ classId: 1, label: 'Void', color: '#00ff00' }];
+    const current: AnnotationClass[] = [{ classId: 3, label: 'Pore', color: '#000000', isVisible: true }];
+    const slices = { '0': [shape('s1', 1)] };
+
+    const result = remapPredictedShapes(runClasses, current, slices);
+
+    expect(result.classes).toHaveLength(2);
+    const appended = result.classes[1];
+    expect(appended.label).toBe('Void');
+    expect(appended.color).toBe('#00ff00');
+    expect(appended.classId).toBe(4); // max existing (3) + 1
+    expect(result.slices['0'][0].classId).toBe(4);
+  });
+
+  it('assigns classId 1 when the current class list is empty', () => {
+    const runClasses: RunClass[] = [{ classId: 1, label: 'Pore', color: '#ff0000' }];
+    const result = remapPredictedShapes(runClasses, [], { '0': [shape('s1', 1)] });
+    expect(result.classes[0].classId).toBe(1);
+  });
+
+  it('handles multiple run classes, some matched and some appended', () => {
+    const runClasses: RunClass[] = [
+      { classId: 1, label: 'Pore', color: '#111111' },
+      { classId: 2, label: 'Crack', color: '#222222' },
+    ];
+    const current: AnnotationClass[] = [{ classId: 5, label: 'pore', color: '#000000', isVisible: true }];
+    const slices = { '0': [shape('s1', 1), shape('s2', 2)] };
+
+    const result = remapPredictedShapes(runClasses, current, slices);
+
+    expect(result.classes).toHaveLength(2);
+    expect(result.slices['0'].find((s) => s.id === 's1')?.classId).toBe(5); // matched existing
+    expect(result.slices['0'].find((s) => s.id === 's2')?.classId).toBe(6); // appended
+  });
+
+  it('preserves shape geometry, only remapping classId', () => {
+    const runClasses: RunClass[] = [{ classId: 1, label: 'Pore', color: '#ff0000' }];
+    const current: AnnotationClass[] = [];
+    const result = remapPredictedShapes(runClasses, current, { '0': [shape('s1', 1)] });
+    expect(result.slices['0'][0]).toMatchObject({ id: 's1', kind: 'polygon', points: [0, 0, 10, 0, 5, 10] });
+  });
+
+  it('stamps every returned shape origin: "predicted"', () => {
+    const runClasses: RunClass[] = [{ classId: 1, label: 'Pore', color: '#ff0000' }];
+    const current: AnnotationClass[] = [];
+    const result = remapPredictedShapes(runClasses, current, {
+      '0': [shape('s1', 1), shape('s2', 1)],
+    });
+    expect(result.slices['0'].every((s) => s.origin === 'predicted')).toBe(true);
+  });
+});
diff --git a/frontend/src/lib/importPredictions.ts b/frontend/src/lib/importPredictions.ts
new file mode 100644
index 0000000..ad3c5b6
--- /dev/null
+++ b/frontend/src/lib/importPredictions.ts
@@ -0,0 +1,68 @@
+/**
+ * importPredictions — remap a saved run's predicted shapes onto the
+ * currently-open sample's class list before importing them as annotations.
+ *
+ * A run's classes are a *snapshot* from whenever it was trained — the
+ * currently open sample's class list may have since been renamed, reordered,
+ * or extended. Policy: match by case-insensitive label; a run class with no
+ * matching label is appended to the current class list (keeping the run's
+ * color); every predicted shape's classId is remapped to the resolved id.
+ * Deterministic and non-destructive — existing classes/shapes are untouched.
+ *
+ * Every returned shape is stamped `origin: 'predicted'` (see `ShapeOrigin` in
+ * annotationStore) so it renders dashed and can be filtered separately from
+ * hand-drawn shapes, same as iPred's committed predictions.
+ */
+import type { AnnotationClass } from '@/stores/classStore';
+import type { Shape } from '@/stores/annotationStore';
+
+export interface RunClass {
+  classId: number;
+  label: string;
+  color: string;
+  isVisible?: boolean;
+}
+
+export interface RemappedPredictions {
+  /** The (possibly extended) class list to apply via classStore.setClasses. */
+  classes: AnnotationClass[];
+  /** Predicted shapes with classId remapped to match `classes`. */
+  slices: Record<string, Shape[]>;
+}
+
+/** Remap a run's predicted shapes onto `currentClasses`, appending any of the
+ *  run's classes that have no case-insensitive label match. */
+export function remapPredictedShapes(
+  runClasses: RunClass[],
+  currentClasses: AnnotationClass[],
+  predictedSlices: Record<string, Shape[]>,
+): RemappedPredictions {
+  const byLabel = new Map(currentClasses.map((c) => [c.label.trim().toLowerCase(), c.classId]));
+  const nextClasses = [...currentClasses];
+  let nextId = nextClasses.length ? Math.max(...nextClasses.map((c) => c.classId)) + 1 : 1;
+
+  const runIdToResolvedId = new Map<number, number>();
+  for (const runClass of runClasses) {
+    const key = runClass.label.trim().toLowerCase();
+    const existingId = byLabel.get(key);
+    if (existingId !== undefined) {
+      runIdToResolvedId.set(runClass.classId, existingId);
+      continue;
+    }
+    const newId = nextId++;
+    nextClasses.push({ classId: newId, label: runClass.label, color: runClass.color, isVisible: true });
+    byLabel.set(key, newId);
+    runIdToResolvedId.set(runClass.classId, newId);
+  }
+
+  const slices: Record<string, Shape[]> = {};
+  for (const [sliceKey, shapes] of Object.entries(predictedSlices)) {
+    slices[sliceKey] = shapes.map((shape) => ({
+      ...shape,
+      classId: runIdToResolvedId.get(shape.classId) ?? shape.classId,
+      origin: 'predicted',
+    }));
+  }
+
+  return { classes: nextClasses, slices };
+}
diff --git a/frontend/src/lib/runCompatibility.test.ts b/frontend/src/lib/runCompatibility.test.ts
new file mode 100644
index 0000000..33eb572
--- /dev/null
+++ b/frontend/src/lib/runCompatibility.test.ts
@@ -0,0 +1,79 @@
+import { describe, expect, it } from 'vitest';
+import { canContinueFineTuning, fineTuningBlockedReason, isDenoiserRun, isSegmentationRun } from './runCompatibility';
+import type { AnnotationClass } from '@/stores/classStore';
+
+const cls = (...labels: string[]): AnnotationClass[] =>
+  labels.map((label, i) => ({ classId: i + 1, label, color: '#ff0000', isVisible: true }));
+
+const runCls = (...labels: string[]) => labels.map((label) => ({ label }));
+
+describe('canContinueFineTuning', () => {
+  it('accepts identical class lists', () => {
+    expect(canContinueFineTuning(runCls('background', 'leaf'), cls('background', 'leaf'))).toBe(true);
+  });
+
+  it('ignores case and surrounding whitespace, matching the backend', () => {
+    expect(canContinueFineTuning(runCls('Background', ' Leaf '), cls('background', 'leaf'))).toBe(true);
+  });
+
+  it('rejects a different class count', () => {
+    expect(canContinueFineTuning(runCls('blob'), cls('background', 'leaf'))).toBe(false);
+  });
+
+  it('rejects reordered classes', () => {
+    // The saved head's channel c means the run's class c, so order is load-bearing.
+    expect(canContinueFineTuning(runCls('background', 'leaf'), cls('leaf', 'background'))).toBe(false);
+  });
+
+  it('rejects a rename at the same count', () => {
+    // The dangerous case: nothing downstream would notice on its own.
+    expect(canContinueFineTuning(runCls('blob'), cls('leaf'))).toBe(false);
+  });
+
+  it('treats two empty lists as compatible', () => {
+    expect(canContinueFineTuning([], [])).toBe(true);
+  });
+});
+
+describe('fineTuningBlockedReason', () => {
+  it('returns null when the run can be continued', () => {
+    expect(fineTuningBlockedReason(runCls('leaf'), cls('leaf'))).toBeNull();
+  });
+
+  it('names both class lists on a count mismatch, so the user can act on it', () => {
+    const reason = fineTuningBlockedReason(runCls('blob'), cls('background', 'leaf'));
+    expect(reason).toContain('blob');
+    expect(reason).toContain('background, leaf');
+  });
+
+  it('names both class lists on a same-count mismatch', () => {
+    const reason = fineTuningBlockedReason(runCls('blob'), cls('leaf'));
+    expect(reason).toContain('blob');
+    expect(reason).toContain('leaf');
+  });
+
+  it('points the user at applying instead, which does work across taxonomies', () => {
+    const reason = fineTuningBlockedReason(runCls('blob'), cls('leaf'));
+    expect(reason).toMatch(/apply it instead/i);
+  });
+});
+
+describe('isDenoiserRun / isSegmentationRun', () => {
+  it('classifies a dlsia_denoiser run as a denoiser run, not a segmentation run', () => {
+    expect(isDenoiserRun({ model_family: 'dlsia_denoiser' })).toBe(true);
+    expect(isSegmentationRun({ model_family: 'dlsia_denoiser' })).toBe(false);
+  });
+
+  it('classifies both segmentation families as segmentation runs, not denoiser runs', () => {
+    for (const family of ['dinov3_lora', 'dlsia_tunet']) {
+      expect(isSegmentationRun({ model_family: family })).toBe(true);
+      expect(isDenoiserRun({ model_family: family })).toBe(false);
+    }
+  });
+
+  it('are exact complements of each other', () => {
+    for (const family of ['dinov3_lora', 'dlsia_tunet', 'dlsia_denoiser', 'something_unknown']) {
+      expect(isSegmentationRun({ model_family: family })).toBe(!isDenoiserRun({ model_family: family }));
+    }
+  });
+});
diff --git a/frontend/src/lib/runCompatibility.ts b/frontend/src/lib/runCompatibility.ts
new file mode 100644
index 0000000..8de1298
--- /dev/null
+++ b/frontend/src/lib/runCompatibility.ts
@@ -0,0 +1,77 @@
+/**
+ * runCompatibility — can a saved run be *continued* (fine-tuned further) on the
+ * currently-open sample's class list?
+ *
+ * Applying a run for inference works across taxonomies: predictions are remapped
+ * by label and unmatched run classes are appended (see importPredictions.ts).
+ * Continuing to TRAIN one does not. The saved segmentation head has exactly one
+ * output channel per class, in the run's own class order (the backend maps
+ * channel `c` to `classes[c].classId`), so the class list has to line up
+ * positionally or those weights mean something different.
+ *
+ * Mirrors `check_resume_compatible` in backend/train_jobs.py — that remains the
+ * authority; this exists so the UI can say so before the user commits to a save
+ * and a request, rather than surfacing it as an error afterwards.
+ */
+import type { AnnotationClass } from '@/stores/classStore';
+
+export interface RunClassLike {
+  label: string;
+}
+
+const normalize = (label: string) => label.trim().toLowerCase();
+
+/**
+ * True when *runClasses* and *currentClasses* are the same labels in the same
+ * order (case- and whitespace-insensitive, matching the backend).
+ */
+export function canContinueFineTuning(
+  runClasses: RunClassLike[],
+  currentClasses: AnnotationClass[],
+): boolean {
+  if (runClasses.length !== currentClasses.length) return false;
+  return runClasses.every((rc, i) => normalize(rc.label) === normalize(currentClasses[i].label));
+}
+
+/**
+ * Why a run can't be continued, phrased for display next to the button, or null
+ * when it can. Deliberately concrete about both lists — "incompatible" alone
+ * leaves the user with nothing to act on.
+ */
+export function fineTuningBlockedReason(
+  runClasses: RunClassLike[],
+  currentClasses: AnnotationClass[],
+): string | null {
+  if (canContinueFineTuning(runClasses, currentClasses)) return null;
+  const runLabels = runClasses.map((c) => c.label).join(', ') || '—';
+  const currentLabels = currentClasses.map((c) => c.label).join(', ') || '—';
+  if (runClasses.length !== currentClasses.length) {
+    return `This run was trained on ${runClasses.length} class(es) (${runLabels}) but this image has ${currentClasses.length} (${currentLabels}). Continuing would not fit its saved weights — apply it instead, or train a new run.`;
+  }
+  return `This run's classes (${runLabels}) don't line up with this image's (${currentLabels}). Continuing would train against the wrong classes — apply it instead, or train a new run.`;
+}
+
+/**
+ * Segmentation vs. denoiser runs, for pickers that only make sense for one or
+ * the other. `/api/train/runs` lists every run this server has ever produced
+ * regardless of task — segmentation (DINOv3+LoRA, dlsia TUNet) and, once the
+ * backend's denoiser-training work lands, self-supervised denoiser runs
+ * (Noise2Noise/Noise2Void) all come back in the same flat list. Fine-tune,
+ * apply-to-image, and inference pickers only understand segmentation runs
+ * (they key off a class list a denoiser run doesn't have); the Learned
+ * Denoiser panel's run picker is the mirror image.
+ *
+ * Matches `schemas.DlsiaDenoiserConfig.model_family`, the discriminator literal
+ * on the `ModelConfig` union.
+ */
+export interface RunFamilyLike {
+  model_family: string;
+}
+
+export function isDenoiserRun(run: RunFamilyLike): boolean {
+  return run.model_family === 'dlsia_denoiser';
+}
+
+export function isSegmentationRun(run: RunFamilyLike): boolean {
+  return !isDenoiserRun(run);
+}
diff --git a/frontend/src/lib/sourceKey.ts b/frontend/src/lib/sourceKey.ts
index 5b0072e..19c5c29 100644
--- a/frontend/src/lib/sourceKey.ts
+++ b/frontend/src/lib/sourceKey.ts
@@ -18,6 +18,16 @@ export function buildSourceKey(
   return kind === 'tiled' ? `tiled:${serverUri ?? ''}:${path}` : `local:${path}`;
 }
 
+/** Inverse of {@link buildSourceKey}: recovers {kind, source, serverUri} from a canonical sourceKey. */
+export function parseSourceKey(sk: string): { kind: 'tiled' | 'local'; source: string; serverUri: string | null } {
+  if (sk.startsWith('tiled:')) {
+    const rest = sk.slice('tiled:'.length);
+    const sep = rest.indexOf(':');
+    return { kind: 'tiled', serverUri: rest.slice(0, sep) || null, source: rest.slice(sep + 1) };
+  }
+  return { kind: 'local', serverUri: null, source: sk.slice('local:'.length) };
+}
+
 /**
  * True if *path* itself, or anything below it, has annotations.
  *
diff --git a/frontend/src/lib/trainConstraints.ts b/frontend/src/lib/trainConstraints.ts
new file mode 100644
index 0000000..223cb6d
--- /dev/null
+++ b/frontend/src/lib/trainConstraints.ts
@@ -0,0 +1,50 @@
+/**
+ * trainConstraints — the bounds the backend enforces on training hyperparameters,
+ * mirrored client-side so the Train tab can show the valid range and reject a bad
+ * value before it becomes an opaque 422.
+ *
+ * Keep in sync with `TunetHyperParams` in backend/schemas.py — that remains the
+ * authority; this only exists to fail earlier and more legibly.
+ *
+ * dlsia TUNet only — DINOv3 LoRA is deferred (see Phase 5.5 in the integration
+ * plan), so there is only one family to constrain here.
+ */
+
+export type TrainFamily = 'dlsia_tunet';
+
+export interface SizeConstraint {
+  min: number;
+  max: number;
+  /** The side must be divisible by this. */
+  multipleOf: number;
+  /** Why the divisor exists, for the error message. */
+  reason: string;
+}
+
+export const IMAGE_SIZE_CONSTRAINTS: Record<TrainFamily, SizeConstraint> = {
+  // TUNet halves the size once per depth level; 64 covers the deepest allowed net.
+  dlsia_tunet: { min: 64, max: 2048, multipleOf: 64, reason: "TUNet's downsampling steps" },
+};
+
+/**
+ * Validate the image/patch size for `family`, returning a message to show the
+ * user, or null when the value is acceptable.
+ *
+ * `label` names the field as the UI currently labels it ("Patch size" when
+ * tiling, "Image size" otherwise) so the message matches what's on screen.
+ */
+export function validateImageSize(family: TrainFamily, value: number, label: string): string | null {
+  const c = IMAGE_SIZE_CONSTRAINTS[family];
+  if (!Number.isFinite(value) || !Number.isInteger(value)) {
+    return `${label} must be a whole number of pixels.`;
+  }
+  if (value < c.min || value > c.max) {
+    return `${label} must be between ${c.min} and ${c.max} px for this model (you entered ${value}).`;
+  }
+  if (value % c.multipleOf !== 0) {
+    const lower = Math.floor(value / c.multipleOf) * c.multipleOf;
+    const nearest = Math.max(c.min, lower < c.min ? c.min : lower);
+    return `${label} must be a multiple of ${c.multipleOf} (${c.reason}) — try ${nearest}.`;
+  }
+  return null;
+}
diff --git a/frontend/src/lib/trainDenoiseOption.test.ts b/frontend/src/lib/trainDenoiseOption.test.ts
new file mode 100644
index 0000000..da0eea2
--- /dev/null
+++ b/frontend/src/lib/trainDenoiseOption.test.ts
@@ -0,0 +1,105 @@
+import { describe, expect, it } from 'vitest';
+import {
+  denoiseMethodLabel,
+  trainDenoiseBlockedReason,
+  trainDenoisePayload,
+  trainDenoiseSummary,
+} from './trainDenoiseOption';
+
+/** Shape of `capability.denoise.methods` entries this module cares about. */
+const METHODS = [
+  { method: 'none', label: 'None' },
+  { method: 'gaussian', label: 'Gaussian' },
+  { method: 'tv', label: 'Total variation' },
+  { method: 'nlm', label: 'Non-local means' },
+];
+
+describe('trainDenoiseBlockedReason', () => {
+  it('blocks "none" — there is no filter to apply', () => {
+    expect(trainDenoiseBlockedReason('none')).toMatch(/pick a denoise filter/i);
+  });
+
+  it('blocks "model" — a learned denoiser is not a preprocessor', () => {
+    expect(trainDenoiseBlockedReason('model')).toMatch(/learned denoiser/i);
+  });
+
+  it('allows any classical filter', () => {
+    expect(trainDenoiseBlockedReason('tv')).toBeNull();
+    expect(trainDenoiseBlockedReason('gaussian')).toBeNull();
+    expect(trainDenoiseBlockedReason('median3d')).toBeNull();
+  });
+});
+
+describe('trainDenoisePayload', () => {
+  it('adds no key at all when the option is off', () => {
+    // Not `{denoise: null}`: an un-denoised request must stay byte-identical
+    // to what the app sent before this option existed.
+    expect(trainDenoisePayload(false, { method: 'tv', strength: 0.6 })).toEqual({});
+    expect(Object.keys(trainDenoisePayload(false, { method: 'tv', strength: 0.6 }))).toEqual([]);
+  });
+
+  it('adds no key when off even with denoising set to none', () => {
+    expect(trainDenoisePayload(false, { method: 'none', strength: 0.5 })).toEqual({});
+  });
+
+  it('sends {method, strength} when on with a classical filter', () => {
+    expect(trainDenoisePayload(true, { method: 'tv', strength: 0.6 }))
+      .toEqual({ denoise: { method: 'tv', strength: 0.6 } });
+  });
+
+  it('sends nothing when on but the method is "none"', () => {
+    expect(trainDenoisePayload(true, { method: 'none', strength: 0.5 })).toEqual({});
+  });
+
+  it('sends nothing when on but the method is "model"', () => {
+    // The UI disables the checkbox in this case; the payload builder refuses
+    // independently so a stale checked box can't reach the server.
+    expect(trainDenoisePayload(true, { method: 'model', strength: 0.5 })).toEqual({});
+  });
+
+  it('strips preview-only fields the strict backend schema would reject', () => {
+    const payload = trainDenoisePayload(true, {
+      method: 'nlm', strength: 0.4, crop: 768, runId: 'run-123',
+    } as { method: string; strength: number });
+    expect(payload).toEqual({ denoise: { method: 'nlm', strength: 0.4 } });
+    expect(Object.keys((payload as { denoise: object }).denoise).sort()).toEqual(['method', 'strength']);
+  });
+
+  it('passes the strength through unrounded at the extremes', () => {
+    expect(trainDenoisePayload(true, { method: 'tv', strength: 0 }))
+      .toEqual({ denoise: { method: 'tv', strength: 0 } });
+    expect(trainDenoisePayload(true, { method: 'tv', strength: 1 }))
+      .toEqual({ denoise: { method: 'tv', strength: 1 } });
+  });
+});
+
+describe('trainDenoiseSummary', () => {
+  it('names the filter and strength as a percentage', () => {
+    expect(trainDenoiseSummary({ method: 'tv', strength: 0.6 }, METHODS)).toBe('Total variation, 60%');
+  });
+
+  it('rounds the percentage to a whole number', () => {
+    expect(trainDenoiseSummary({ method: 'gaussian', strength: 0.333 }, METHODS)).toBe('Gaussian, 33%');
+  });
+
+  it('returns null for a method that cannot be trained on', () => {
+    expect(trainDenoiseSummary({ method: 'none', strength: 0.5 }, METHODS)).toBeNull();
+    expect(trainDenoiseSummary({ method: 'model', strength: 0.5 }, METHODS)).toBeNull();
+  });
+
+  it('falls back to the raw method id when the server did not describe it', () => {
+    expect(trainDenoiseSummary({ method: 'wavelet', strength: 0.5 }, METHODS)).toBe('wavelet, 50%');
+    expect(trainDenoiseSummary({ method: 'tv', strength: 0.5 }, [])).toBe('tv, 50%');
+  });
+});
+
+describe('denoiseMethodLabel', () => {
+  it('maps a known method id to its label', () => {
+    expect(denoiseMethodLabel('nlm', METHODS)).toBe('Non-local means');
+  });
+
+  it('falls back to the id itself rather than rendering blank', () => {
+    expect(denoiseMethodLabel('median3d', METHODS)).toBe('median3d');
+    expect(denoiseMethodLabel('tv', [])).toBe('tv');
+  });
+});
diff --git a/frontend/src/lib/trainDenoiseOption.ts b/frontend/src/lib/trainDenoiseOption.ts
new file mode 100644
index 0000000..35b6f25
--- /dev/null
+++ b/frontend/src/lib/trainDenoiseOption.ts
@@ -0,0 +1,100 @@
+/**
+ * trainDenoiseOption — decides what (if anything) the "Train on denoised
+ * input" checkbox contributes to a `/api/train/start` request.
+ *
+ * Both places that start a segmentation job (TrainPage's "Start training" and
+ * ApplyModelPanel's "Fine-tune & apply") offer this checkbox and must agree
+ * exactly on the answer, so the decision lives here as a pure function rather
+ * than inline in either component — same reasoning as `denoiserTrainScope`.
+ *
+ * The setting itself comes from the Annotate tab's shared denoise store, so the
+ * user trains on the very filter they were looking at. Two properties matter:
+ *
+ *   - OFF must be bit-identical to the behaviour that predates this option:
+ *     no `denoise` key in the request body at all. `TrainRequest.denoise`
+ *     defaults to None, so an absent key and an explicit null mean the same
+ *     thing server-side, but omitting it keeps un-denoised requests literally
+ *     unchanged.
+ *   - ON must send ONLY `{method, strength}`. `DenoiseOpts` also carries `crop`
+ *     (a preview-only centre crop) and `runId`; `DenoiseTrainOpts` is a strict
+ *     model, so forwarding the whole object would be rejected outright — and
+ *     `crop` would be nonsense for training even if it weren't.
+ *
+ * `'none'` and `'model'` are both rejected: there is nothing to apply for the
+ * former, and the backend explicitly does not support a learned denoiser as a
+ * preprocessor for another model (it would need its own run and a GPU pass per
+ * slice). Guarding here as well as in the UI means a stale checked box can
+ * never smuggle an invalid method into a request.
+ */
+
+/** The `denoise` field of a `/api/train/start` body (schemas.DenoiseTrainOpts). */
+export interface TrainDenoiseOpts {
+  method: string;
+  strength: number;
+}
+
+/** Just the parts of `hooks/useImageSlice`'s `DenoiseOpts` that matter here. */
+interface DenoiseSettings {
+  method: string;
+  strength: number;
+}
+
+/** Method + human label, as served by `capability.denoise.methods`. */
+interface MethodLabel {
+  method: string;
+  label: string;
+}
+
+/**
+ * Why the current denoise setting can't be baked into a training run, phrased
+ * for display next to the checkbox — or null when it can.
+ */
+export function trainDenoiseBlockedReason(method: string): string | null {
+  if (method === 'none') {
+    return 'Pick a denoise filter in the Annotate tab first — there is nothing to apply yet.';
+  }
+  if (method === 'model') {
+    return 'A learned denoiser cannot be used as a preprocessor for another model. Pick a classical filter in the Annotate tab.';
+  }
+  return null;
+}
+
+/**
+ * The `denoise` fragment to spread into a `/api/train/start` body: `{}` when
+ * the option is off or the setting is unusable, `{ denoise: {method, strength} }`
+ * when it applies.
+ *
+ * Returning a spreadable fragment rather than a nullable value keeps the two
+ * call sites from each re-deriving "and what do I do when it's off?" — the
+ * whole point being that OFF adds no key whatsoever.
+ */
+export function trainDenoisePayload(
+  enabled: boolean,
+  denoise: DenoiseSettings,
+): { denoise: TrainDenoiseOpts } | Record<string, never> {
+  if (!enabled) return {};
+  if (trainDenoiseBlockedReason(denoise.method) !== null) return {};
+  return { denoise: { method: denoise.method, strength: denoise.strength } };
+}
+
+/**
+ * Which filter the checkbox would actually bake in, e.g. `"Total variation,
+ * 60%"` — so "Train on denoised input" is never ambiguous about *what*.
+ * Null when the setting is unusable (the blocked reason is shown instead).
+ *
+ * `methods` is `capability.denoise.methods`; an unknown method falls back to
+ * its raw name rather than rendering blank, since a frontend newer than the
+ * server (or vice versa) shouldn't produce a nameless setting.
+ */
+export function trainDenoiseSummary(
+  denoise: DenoiseSettings,
+  methods: MethodLabel[],
+): string | null {
+  if (trainDenoiseBlockedReason(denoise.method) !== null) return null;
+  return `${denoiseMethodLabel(denoise.method, methods)}, ${Math.round(denoise.strength * 100)}%`;
+}
+
+/** Human label for a denoise method id, falling back to the id itself. */
+export function denoiseMethodLabel(method: string, methods: MethodLabel[]): string {
+  return methods.find((m) => m.method === method)?.label ?? method;
+}
diff --git a/frontend/src/lib/trainModelConfig.ts b/frontend/src/lib/trainModelConfig.ts
new file mode 100644
index 0000000..dc67572
--- /dev/null
+++ b/frontend/src/lib/trainModelConfig.ts
@@ -0,0 +1,71 @@
+/**
+ * trainModelConfig — builds the `model` payload TrainPage sends to
+ * `/api/train/start` and `/api/train/estimate-batch`, and validates it before
+ * either request goes out. Both call sites used to assemble this same shape
+ * (and re-check the same precondition) by hand; this is the one place it
+ * happens now, so the two can't drift out of sync with each other.
+ *
+ * dlsia TUNet only — DINOv3 LoRA (and its checkpoint picker) is deferred, so
+ * there is only one model family to build a config for.
+ */
+import type { HyperparamsState } from '@/components/train/HyperparamsPanel';
+import { validateImageSize } from '@/lib/trainConstraints';
+
+export interface TunetModelConfig {
+  model_family: 'dlsia_tunet';
+  hyperparams: {
+    epochs: number;
+    lr: number;
+    depth: number;
+    base_channels: number;
+    growth_rate: number;
+    batch_size: number;
+    image_size: number;
+    flip_augment: boolean;
+    tiling: boolean;
+  };
+}
+
+export type TrainModelConfig = TunetModelConfig;
+
+/** Build the `model` field of a train/estimate-batch request. */
+export function buildModelConfig(hp: HyperparamsState): TrainModelConfig {
+  return {
+    model_family: 'dlsia_tunet',
+    hyperparams: {
+      epochs: hp.epochs,
+      lr: hp.lr,
+      depth: hp.depth,
+      base_channels: hp.base_channels,
+      growth_rate: hp.growth_rate,
+      batch_size: hp.batch_size,
+      image_size: hp.image_size,
+      flip_augment: hp.flip_augment,
+      tiling: hp.tiling,
+    },
+  };
+}
+
+/**
+ * Validate a training config before either "Start training" or "Estimate
+ * batch size" fires. Returns a message to show the user, or null when the
+ * config is ready to submit.
+ */
+export function validateTrainConfig(hp: HyperparamsState): string | null {
+  return validateImageSize('dlsia_tunet', hp.image_size, hp.tiling ? 'Patch size' : 'Image size');
+}
+
+/**
+ * A string that changes iff any input to the batch-size probe's memory
+ * footprint changes: the image/patch size (tiling toggles whether patches are
+ * cut at native resolution, which changes the tensor shapes the probe
+ * actually measures).
+ *
+ * TrainPage stashes this when a probe starts and compares it against the
+ * current config when the probe finishes — a mismatch means the user changed
+ * something mid-measurement, so the result no longer describes what's about
+ * to be submitted and must not be silently adopted into `batch_size`.
+ */
+export function trainConfigSignature(hp: Pick<HyperparamsState, 'image_size' | 'tiling'>): string {
+  return JSON.stringify([hp.image_size, hp.tiling]);
+}
diff --git a/start_all.sh b/start_all.sh
index 85abdf1..454e413 100755
--- a/start_all.sh
+++ b/start_all.sh
@@ -287,6 +287,52 @@ ensure_backend_env() {
   fi
 }
 
+ensure_ml_env() {
+  # Best-effort + backgrounded-in-spirit: never blocks or fails startup — the
+  # Train tab (and denoise_bake.py's "model" denoise method) already degrade
+  # to a clear "unavailable" state via train_common.torch_available() /
+  # dlsia_available() when these aren't installed, so skipping this is always
+  # a safe default, not a broken one.
+  local want_install="${INSTALL_ML:-}"
+  # Default to installing on Apple Silicon Mac (torch gets MPS acceleration
+  # there); other platforms opt in explicitly, since a CUDA/CPU torch wheel is
+  # a multi-GB download the user may not want on every fresh machine.
+  if [ -z "$want_install" ] && [ "$(uname -s)" = "Darwin" ] && [ "$(uname -m)" = "arm64" ]; then
+    want_install=1
+  fi
+  if [ "$want_install" != "1" ]; then
+    echo -e "${YELLOW}    Skipping ML deps (Train tab) — set INSTALL_ML=1 to install torch/dlsia here.${NC}"
+    return 0
+  fi
+
+  if "$PYTHON" -c "import torch" >/dev/null 2>&1; then
+    echo -e "${GREEN}    torch already installed — Train tab available.${NC}"
+  else
+    echo -e "${CYAN}==> Installing torch for the Train tab (can take a few minutes on first run)...${NC}"
+    if uv pip install --python "$PYTHON" "torch>=2.4"; then
+      echo -e "${GREEN}    torch installed.${NC}"
+    else
+      echo -e "${YELLOW}    torch install failed — the Train tab will report 'unavailable'. Retry manually:${NC}"
+      echo -e "${YELLOW}      uv pip install --python \"$PYTHON\" \"torch>=2.4\"${NC}"
+    fi
+  fi
+
+  if "$PYTHON" -c "import dlsia" >/dev/null 2>&1; then
+    echo -e "${GREEN}    dlsia already installed — TUNet model family available.${NC}"
+  else
+    echo -e "${CYAN}==> Installing dlsia (+ qlty) for the Train tab's TUNet model family...${NC}"
+    if uv pip install --python "$PYTHON" "dlsia>=0.3" "qlty>=1.5"; then
+      echo -e "${GREEN}    dlsia installed.${NC}"
+    else
+      echo -e "${YELLOW}    dlsia install failed — the TUNet model family will report 'unavailable'.${NC}"
+    fi
+  fi
+
+  # Some ops (e.g. certain interpolate modes) aren't implemented on MPS yet;
+  # fall back to CPU for just that op instead of erroring.
+  export PYTORCH_ENABLE_MPS_FALLBACK=1
+}
+
 ensure_ipred_env() {
   # NOTE: probe "ipred.api", not bare "ipred" — the repo's top-level ipred/
   # directory (sibling of ipred/src/) is itself an importable namespace package
@@ -354,6 +400,7 @@ cleanup() {
 trap cleanup SIGINT SIGTERM
 
 ensure_backend_env
+ensure_ml_env
 ensure_frontend_runtime
 cleanup_managed_processes
 reclaim_orphaned_repo_ports

From 3bdd7133a35440b801a6f61bf305e5567942a54e Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Mon, 31 Aug 2026 12:09:28 -0700
Subject: [PATCH 12/38] testing deep-learning end to end passed

---
 backend/coco_export.py                        | 14 ++++++-
 backend/tests/test_coco_export.py             | 42 +++++++++++++++++++
 frontend/src/app/pages/AnnotatePage.tsx       |  1 +
 .../annotate/PixelClassifierPanel/index.tsx   | 19 ++++++++-
 frontend/src/lib/sourceKey.test.ts            | 24 ++++++++++-
 frontend/src/lib/sourceKey.ts                 |  4 +-
 6 files changed, 100 insertions(+), 4 deletions(-)

diff --git a/backend/coco_export.py b/backend/coco_export.py
index c30d061..c70720a 100644
--- a/backend/coco_export.py
+++ b/backend/coco_export.py
@@ -224,7 +224,19 @@ def _resolve_split(
         n = len(auto_keys)
         n_train = int(n * ratios[0])
         n_valid = int(n * ratios[1])
-        labels = ["train"] * n_train + ["valid"] * n_valid + ["test"] * (n - n_train - n_valid)
+        n_test = n - n_train - n_valid
+        # A ratio split that floors to zero train slices (e.g. a single annotated
+        # slice at the default 80/10/10) trains on nothing and fails outright —
+        # worse than a slightly-off ratio. Guarantee train gets at least one
+        # slice whenever there is at least one to give, borrowing from whichever
+        # other split has one to spare.
+        if n_train == 0 and n >= 1:
+            n_train = 1
+            if n_valid > 0:
+                n_valid -= 1
+            else:
+                n_test -= 1
+        labels = ["train"] * n_train + ["valid"] * n_valid + ["test"] * n_test
         for k, lbl in zip(auto_keys, labels):
             splits_out[k] = lbl
     return splits_out
diff --git a/backend/tests/test_coco_export.py b/backend/tests/test_coco_export.py
index 72c4c05..0d6b225 100644
--- a/backend/tests/test_coco_export.py
+++ b/backend/tests/test_coco_export.py
@@ -235,3 +235,45 @@ def test_polygon_hole_carved_out() -> None:
     assert mask[2, 2], "frame corner must be filled"
     # Area ~= 40*40 - 20*20 = 1200, allow boundary slack.
     assert 1050 < int(mask.sum()) < 1350
+
+
+def test_resolve_split_single_slice_goes_to_train() -> None:
+    """A lone annotated slice at the default 80/10/10 ratio must not be
+    floored out of every split — that leaves nothing to train on."""
+    from coco_export import _resolve_split
+
+    result = _resolve_split(["0"], {}, {"ratios": [0.8, 0.1, 0.1], "seed": 1234})
+    assert result == {"0": "train"}
+
+
+def test_resolve_split_two_slices_still_gets_a_train_slice() -> None:
+    from coco_export import _resolve_split
+
+    result = _resolve_split(["0", "1"], {}, {"ratios": [0.8, 0.1, 0.1], "seed": 1234})
+    assert "train" in result.values()
+
+
+def test_resolve_split_borrows_from_valid_before_test() -> None:
+    """When train floors to zero but valid has slices to spare, borrow from
+    valid rather than test, so the split still roughly tracks the ratios."""
+    from coco_export import _resolve_split
+
+    result = _resolve_split(
+        [str(i) for i in range(3)], {}, {"ratios": [0.1, 0.8, 0.1], "seed": 1234}
+    )
+    counts = {"train": 0, "valid": 0, "test": 0}
+    for v in result.values():
+        counts[v] += 1
+    assert counts["train"] == 1
+    assert counts["valid"] == 1
+    assert counts["test"] == 1
+
+
+def test_resolve_split_respects_explicit_assignments() -> None:
+    """Slices already given an explicit (non-'auto') split are left alone."""
+    from coco_export import _resolve_split
+
+    result = _resolve_split(
+        ["0", "1"], {"0": "valid"}, {"ratios": [0.8, 0.1, 0.1], "seed": 1234}
+    )
+    assert result["0"] == "valid"
diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index fb738be..b6583f2 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -575,6 +575,7 @@ export default function AnnotatePage() {
                 onCommitVolumeApply={() => { void handleCommitVolumeApply(); }}
                 onCancelVolumeApply={() => { void handleCancelVolumeApply(); }}
                 onDismissVolumeApply={clf.resetVolumeApplyJob}
+                onTrainDeepModel={() => navigate('/train')}
                 manifoldParams={manifold.params}
                 onManifoldParamsChange={manifold.setParams}
                 manifoldSampling={manifold.sampling}
diff --git a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
index 56bb49b..707b140 100644
--- a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
+++ b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
@@ -3,7 +3,7 @@
  * Suggest-Labels manifold loop folded in and conformal coverage surfaced as
  * headline guidance (not a legend entry).
  */
-import { CaretLeft, CaretRight, CircleNotch, Compass, TreeStructure } from '@phosphor-icons/react';
+import { Brain, CaretLeft, CaretRight, CircleNotch, Compass, TreeStructure } from '@phosphor-icons/react';
 import type { ClfParams, ClfPredictCounts, ClfTrainResult } from '@/hooks/usePixelClassifier';
 import type { ManifoldParams } from '@/hooks/useFeatureManifold';
 import { cn } from '@/lib/utils';
@@ -106,6 +106,10 @@ export interface PixelClassifierPanelProps {
   onCancelVolumeApply: () => void;
   onDismissVolumeApply: () => void;
 
+  // Hand-off to the deep-training tab: the iPred annotation work already done
+  // on this sample becomes the training set, pre-selected there.
+  onTrainDeepModel?: () => void;
+
   // Suggest-labels (manifold), folded into the same loop instead of a separate panel.
   manifoldParams: ManifoldParams;
   onManifoldParamsChange: (p: ManifoldParams) => void;
@@ -169,6 +173,7 @@ export default function PixelClassifierPanel({
   onCommitVolumeApply,
   onCancelVolumeApply,
   onDismissVolumeApply,
+  onTrainDeepModel,
   manifoldParams,
   onManifoldParamsChange,
   manifoldSampling,
@@ -474,6 +479,18 @@ export default function PixelClassifierPanel({
               </div>
             </div>
           )}
+
+          {onTrainDeepModel && annotatedSliceCount > 0 && (
+            <button
+              type="button"
+              onClick={onTrainDeepModel}
+              title="Open the Train tab with this sample's annotated slices pre-selected as the training set"
+              className="flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border border-gray-200 bg-white text-gray-800 hover:bg-sky-50 hover:border-sky-300 transition-colors"
+            >
+              <Brain size={13} />
+              Train a deep model on this
+            </button>
+          )}
         </div>
       )}
 
diff --git a/frontend/src/lib/sourceKey.test.ts b/frontend/src/lib/sourceKey.test.ts
index 3ddf469..db7105c 100644
--- a/frontend/src/lib/sourceKey.test.ts
+++ b/frontend/src/lib/sourceKey.test.ts
@@ -1,9 +1,31 @@
 import { describe, it, expect } from 'vitest';
-import { buildSourceKey, isAnnotatedPath } from './sourceKey';
+import { buildSourceKey, isAnnotatedPath, parseSourceKey } from './sourceKey';
 
 const URI = 'http://127.0.0.1:8010';
 const key = (path: string) => buildSourceKey('tiled', path, URI);
 
+describe('parseSourceKey', () => {
+  it('round-trips a tiled key whose server URI has a port (colons in serverUri)', () => {
+    const sk = buildSourceKey('tiled', 'browse/rec20260221_135217_petiole22', URI);
+    expect(parseSourceKey(sk)).toEqual({ kind: 'tiled', source: 'browse/rec20260221_135217_petiole22', serverUri: URI });
+  });
+
+  it('round-trips a tiled key with a nested path', () => {
+    const sk = buildSourceKey('tiled', 'browse/ds/img_00003', URI);
+    expect(parseSourceKey(sk)).toEqual({ kind: 'tiled', source: 'browse/ds/img_00003', serverUri: URI });
+  });
+
+  it('round-trips a local key', () => {
+    const sk = buildSourceKey('local', 'some/rel/path.tif');
+    expect(parseSourceKey(sk)).toEqual({ kind: 'local', source: 'some/rel/path.tif', serverUri: null });
+  });
+
+  it('handles a tiled key with no server URI', () => {
+    const sk = buildSourceKey('tiled', 'browse/ds', null);
+    expect(parseSourceKey(sk)).toEqual({ kind: 'tiled', source: 'browse/ds', serverUri: null });
+  });
+});
+
 describe('isAnnotatedPath', () => {
   it('matches the sample own key', () => {
     const keys = new Set([key('browse/ds')]);
diff --git a/frontend/src/lib/sourceKey.ts b/frontend/src/lib/sourceKey.ts
index 19c5c29..00d7ffd 100644
--- a/frontend/src/lib/sourceKey.ts
+++ b/frontend/src/lib/sourceKey.ts
@@ -22,7 +22,9 @@ export function buildSourceKey(
 export function parseSourceKey(sk: string): { kind: 'tiled' | 'local'; source: string; serverUri: string | null } {
   if (sk.startsWith('tiled:')) {
     const rest = sk.slice('tiled:'.length);
-    const sep = rest.indexOf(':');
+    // The serverUri itself contains colons (`http://host:port`), so the
+    // separator before the tiled path is the LAST colon, not the first.
+    const sep = rest.lastIndexOf(':');
     return { kind: 'tiled', serverUri: rest.slice(0, sep) || null, source: rest.slice(sep + 1) };
   }
   return { kind: 'local', serverUri: null, source: sk.slice('local:'.length) };

From 921c59100a272d9dac7f2d1e2108319cbdeb47e4 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Tue, 1 Sep 2026 12:43:07 -0700
Subject: [PATCH 13/38] Supporting viewing predicted regions within the volume
 viewer

---
 backend/infer_jobs.py                         |   8 +-
 backend/mask_pyramid.py                       | 145 ++++++++++++
 backend/tests/test_mask_pyramid.py            |  70 ++++++
 backend/tiled_mask_sync.py                    |  33 ++-
 frontend/src/app/pages/VolumePage.tsx         |  14 +-
 .../src/components/volume/MaskLayersPanel.tsx | 224 ++++++++++++++++++
 frontend/src/lib/zarrUrl.test.ts              |  33 ++-
 frontend/src/lib/zarrUrl.ts                   |  37 +++
 frontend/tsconfig.app.json                    |  10 +-
 frontend/vendor/view_tomography_recon_app     |   2 +-
 10 files changed, 561 insertions(+), 15 deletions(-)
 create mode 100644 backend/mask_pyramid.py
 create mode 100644 backend/tests/test_mask_pyramid.py
 create mode 100644 frontend/src/components/volume/MaskLayersPanel.tsx

diff --git a/backend/infer_jobs.py b/backend/infer_jobs.py
index ed7e4d3..f93ea27 100644
--- a/backend/infer_jobs.py
+++ b/backend/infer_jobs.py
@@ -395,7 +395,13 @@ def run_write_tiled_job(jid: str, infer_job_id: str) -> None:
         }
 
         export_jobs.set_total(jid, 1)
-        info = tiled_mask_sync.write_masks_to_tiled(entry["source"], entry["server_uri"], volumes, classes)
+        # "_deep" keeps this in its own container, separate from whatever the
+        # manual "sync masks to Tiled" action (iPred's fast results) has
+        # written for the same source — letting both be loaded as independent
+        # mask layers in the 3-D viewer instead of merging into one.
+        info = tiled_mask_sync.write_masks_to_tiled(
+            entry["source"], entry["server_uri"], volumes, classes, container_suffix="_deep",
+        )
         export_jobs.bump(jid, 1)
         export_jobs.update(jid, state="done", phase="done", result=info)
         export_jobs.log(jid, f"Wrote predicted masks to {info['path']}.")
diff --git a/backend/mask_pyramid.py b/backend/mask_pyramid.py
new file mode 100644
index 0000000..93f6a57
--- /dev/null
+++ b/backend/mask_pyramid.py
@@ -0,0 +1,145 @@
+"""Register a class-id mask/annotation volume as a real OME-NGFF multiscale
+Tiled node, so the volume viewer's ``loadMask()``/``openOmeZarr()`` can
+actually open it.
+
+``tiled_mask_sync.write_masks_to_tiled`` used to write its ``semantic`` array
+with a plain ``container.write_array()`` call, which sets no ``multiscales``
+metadata — Tiled then serves it as an ordinary array node, and the viewer's
+``openOmeZarr()`` rejects it with "missing multiscales" (see
+``volume_nodes.has_multiscales``, which exists specifically to distinguish the
+two). This module gives masks the same real multiscale registration the
+primary volume already gets from ``volume_build.build_volume``.
+
+Differs from ``volume_build.build_volume`` in two ways:
+  - Downsamples by majority vote (mode), never by mean — an averaged class id
+    is meaningless, the same reasoning the viewer's own ``mask-texture.ts``
+    gives for using ``r8uint``/``textureLoad`` instead of ``r8unorm``/sampled.
+  - Every level is computed directly from the native-resolution array rather
+    than cascaded level-to-level: the whole mask is already fully in memory
+    (unlike the primary volume, which is built by streaming a stack that may
+    be tens of GB), so there is no incremental-IO reason to cascade.
+"""
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any
+
+import numpy as np
+
+import tiff_stack_source as tss
+
+logger = logging.getLogger("mask_pyramid")
+
+
+def majority_downsample(volume: np.ndarray, factor: list[int]) -> np.ndarray:
+    """Mode-downsample a uint8 class-id volume by the per-axis *factor*.
+
+    Vectorized over the small set of class ids actually present in *volume*
+    (real annotation taxonomies are a handful of classes even though uint8
+    allows 256), rather than per-voxel. Ties favor the lower class id:
+    ``np.unique`` is sorted ascending and a later id only overwrites the
+    current winner on a strictly higher count.
+    """
+    fz, fy, fx = (int(f) for f in factor)
+    if fz < 1 or fy < 1 or fx < 1:
+        raise ValueError("downsample factors must be >= 1")
+    z = (volume.shape[0] // fz) * fz
+    y = (volume.shape[1] // fy) * fy
+    x = (volume.shape[2] // fx) * fx
+    trimmed = volume[:z, :y, :x]
+    out_shape = (z // fz, y // fy, x // fx)
+
+    class_ids = np.unique(trimmed)
+    if class_ids.size <= 1:
+        fill = int(class_ids[0]) if class_ids.size else 0
+        return np.full(out_shape, fill, dtype=np.uint8)
+
+    reshaped = trimmed.reshape(z // fz, fz, y // fy, fy, x // fx, fx)
+    best_count = np.zeros(out_shape, dtype=np.int64)
+    best_id = np.zeros(out_shape, dtype=np.uint8)
+    for class_id in class_ids:
+        count = (reshaped == class_id).sum(axis=(1, 3, 5))
+        better = count > best_count
+        best_count = np.where(better, count, best_count)
+        best_id = np.where(better, class_id, best_id)
+    return best_id
+
+
+def build_mask_pyramid(semantic: np.ndarray) -> tuple[dict[str, np.ndarray], list[dict[str, Any]]]:
+    """Compute ``{"scale0": semantic, "scale1": ..., ...}`` for a class-id volume.
+
+    Returns the level dict plus the *generated* (non-scale0) plan entries from
+    ``tiff_stack_source.pyramid_plan`` — the same shape ``volume_build.py``
+    passes to ``multiscales_metadata``.
+    """
+    shape = tuple(int(s) for s in semantic.shape)
+    generated = tss.pyramid_plan(shape)
+    levels: dict[str, np.ndarray] = {"scale0": semantic.astype(np.uint8, copy=False)}
+    for level in generated:
+        levels[level["path"]] = majority_downsample(semantic, level["factor"])
+    return levels, generated
+
+
+def register_mask_pyramid(semantic: np.ndarray, key: str, container: Any) -> dict[str, Any]:
+    """Build and register *semantic* (a ``(z, y, x)`` uint8 class-id volume) as
+    a real OME-NGFF multiscale node named *key* inside *container* (the
+    ``<source>__masks`` container ``tiled_mask_sync`` already manages).
+
+    Reuses ``volume_build.build_volume``'s own write-pyramid-sidecar +
+    ``register_single_item`` + ``multiscales_metadata`` machinery
+    (``tiff_stack_source.write_pyramid_store``/``pyramid_plan``/
+    ``multiscales_metadata``/``PYRAMID_KEY``) so Tiled serves this exactly the
+    way it already serves the primary volume's pyramid — just downsampled by
+    majority vote instead of mean.
+
+    Replaces any previous registration for *key* — a re-sync must refresh the
+    pyramid, not fail or accumulate duplicates, matching ``build_volume``'s
+    same rule for the primary volume.
+    """
+    from tiled.client.register import Settings, register_single_item
+
+    import ingest as ingest_mod
+
+    levels, generated = build_mask_pyramid(semantic)
+    sidecar = tss.write_pyramid_store(key, levels)
+
+    if key in ingest_mod._child_keys(container):
+        container.delete_contents(key, recursive=True, external_only=False)
+    node = container.create_container(key=key, metadata={})
+
+    try:
+        asyncio.run(
+            register_single_item(node, sidecar, is_directory=True, settings=Settings.init())
+        )
+    except Exception as exc:  # noqa: BLE001 — surfaced to the caller, same as build_volume
+        logger.warning("mask pyramid registration failed for %s: %s", sidecar, exc)
+        raise
+
+    pyramid_node = ingest_mod._walk(node, [tss.PYRAMID_KEY])
+    if pyramid_node is None or not list(pyramid_node):
+        raise RuntimeError(
+            f"Tiled registered no levels for mask pyramid {key!r} from {sidecar}. "
+            "The usual cause is that this path is not in the Tiled server's "
+            "`readable_storage` (see tiled/config.yml)."
+        )
+
+    # scale0 is generated by THIS module rather than living in a pre-existing
+    # per-slice sibling (the primary volume's case) — every level, scale0
+    # included, is written into the one self-contained pyramid store above, so
+    # it takes the SAME `f"{PYRAMID_KEY}/{level['path']}"` nesting the
+    # generated levels get. Passing include_scale0=False and folding scale0
+    # into `plan` (rather than True, which would emit it as a bare top-level
+    # sibling path) is what keeps the metadata pointing at where the data
+    # actually landed.
+    plan = [{"path": "scale0", "factor": [1, 1, 1], "shape": list(semantic.shape)}, *generated]
+    node.update_metadata(metadata=tss.multiscales_metadata(key, plan, include_scale0=False))
+    return {"key": key, "full_shape": list(semantic.shape), "pyramid_plan": generated}
+
+
+def read_mask_scale0(container: Any, key: str) -> np.ndarray:
+    """Read back the native-resolution level of a mask pyramid registered by
+    :func:`register_mask_pyramid` — the merge path's source of truth (the
+    downsampled levels are viewer-only conveniences, never re-read)."""
+    scale0 = container[key][tss.PYRAMID_KEY]["scale0"]
+    return np.asarray(scale0[...])
diff --git a/backend/tests/test_mask_pyramid.py b/backend/tests/test_mask_pyramid.py
new file mode 100644
index 0000000..c794282
--- /dev/null
+++ b/backend/tests/test_mask_pyramid.py
@@ -0,0 +1,70 @@
+"""Unit tests for mask_pyramid's pure downsampling (no Tiled I/O)."""
+import numpy as np
+
+from mask_pyramid import build_mask_pyramid, majority_downsample
+
+
+def test_majority_downsample_picks_the_more_frequent_class():
+    # A 2x4x4 volume, factor (1,2,2): each output voxel covers a 1x2x2 block.
+    # Top-left block is all class 1 except one class-2 voxel — 1 wins.
+    block = np.zeros((1, 4, 4), dtype=np.uint8)
+    block[0, 0:2, 0:2] = 1
+    block[0, 0, 0] = 2  # one dissenting voxel inside that block
+    out = majority_downsample(block, [1, 2, 2])
+    assert out.shape == (1, 2, 2)
+    assert out[0, 0, 0] == 1
+
+
+def test_majority_downsample_uniform_block():
+    volume = np.full((2, 4, 4), 3, dtype=np.uint8)
+    out = majority_downsample(volume, [2, 2, 2])
+    assert out.shape == (1, 2, 2)
+    assert np.all(out == 3)
+
+
+def test_majority_downsample_never_averages():
+    # Two classes, 50/50 split within a block: averaging would invent a value
+    # (e.g. 1.5 -> rounds to 2), which must never happen — the winner must be
+    # one of the actual present class ids.
+    block = np.zeros((1, 2, 2), dtype=np.uint8)
+    block[0, 0, 0] = 5
+    block[0, 0, 1] = 9
+    out = majority_downsample(block, [1, 1, 2])
+    assert out[0, 0, 0] in (5, 9)
+
+
+def test_majority_downsample_tie_favors_lower_class_id():
+    block = np.zeros((1, 1, 2), dtype=np.uint8)
+    block[0, 0, 0] = 7
+    block[0, 0, 1] = 3
+    out = majority_downsample(block, [1, 1, 2])
+    assert out[0, 0, 0] == 3
+
+
+def test_majority_downsample_rejects_factor_below_one():
+    import pytest
+
+    with pytest.raises(ValueError):
+        majority_downsample(np.zeros((2, 2, 2), dtype=np.uint8), [0, 1, 1])
+
+
+def test_build_mask_pyramid_scale0_is_the_original_array():
+    semantic = np.zeros((4, 8, 8), dtype=np.uint8)
+    semantic[:, 2:6, 2:6] = 1
+    levels, generated = build_mask_pyramid(semantic)
+    assert "scale0" in levels
+    assert np.array_equal(levels["scale0"], semantic)
+    # 8x8 is already tiny — pyramid_plan should generate nothing further.
+    assert generated == []
+
+
+def test_build_mask_pyramid_generates_coarser_levels_for_a_large_volume():
+    semantic = np.zeros((4, 4096, 4096), dtype=np.uint8)
+    semantic[:, :2048, :2048] = 1
+    levels, generated = build_mask_pyramid(semantic)
+    assert len(generated) > 0
+    for level in generated:
+        assert level["path"] in levels
+        assert levels[level["path"]].dtype == np.uint8
+        # Downsampled level must only contain class ids actually present.
+        assert set(np.unique(levels[level["path"]])).issubset({0, 1})
diff --git a/backend/tiled_mask_sync.py b/backend/tiled_mask_sync.py
index 35c71b2..761ae4c 100644
--- a/backend/tiled_mask_sync.py
+++ b/backend/tiled_mask_sync.py
@@ -25,6 +25,7 @@
 
 import arrays as arrays_mod
 import export_jobs
+import mask_pyramid
 from coco_export import _safe_name, shape_to_mask
 from tiled_clients import api_key_for_uri, get_tiled_client
 
@@ -188,7 +189,10 @@ def _read_existing_masks(container: Any) -> dict[str, Any] | None:
         meta = dict(container.metadata)
         legend = meta.get("legend") or meta.get("classes") or []
         indices = [int(i) for i in (meta.get("slice_indices") or [])]
-        semantic = np.asarray(container["semantic"][...])
+        # "semantic" is now a registered multiscale node (mask_pyramid.py), not
+        # a flat array — the merge logic always operates on native resolution,
+        # never the downsampled viewer-only preview levels.
+        semantic = mask_pyramid.read_mask_scale0(container, "semantic")
         safe_to_name = {_safe_name(e["name"]): e["name"] for e in legend}
         class_arrays: dict[str, np.ndarray] = {}
         for key in list(container):
@@ -214,13 +218,26 @@ def write_masks_to_tiled(
     server_uri: str | None,
     volumes: dict[str, Any],
     classes: list[Any],
+    container_suffix: str = "",
 ) -> dict[str, Any]:
-    """Merge stacked mask volumes into a ``<source_stem>__masks`` sibling container.
+    """Merge stacked mask volumes into a ``<source_stem>__masks<container_suffix>``
+    sibling container.
 
     Slices in this push overwrite the same index; previously-pushed slices are
     kept (merge). Metadata records ``updated_at`` and a per-slice
     ``slice_updated_at`` map plus ``last_updated_slices`` so the latest version of
     each slice is explicit. Returns ``{path, n_slices, updated, n_classes}``.
+
+    ``container_suffix`` keeps independent producers of masks for the same
+    source from silently merging into one blob: the manual "sync masks to
+    Tiled" action and a dlsia run's "write to Tiled" (``infer_jobs.py``) both
+    call this function, and without a suffix they'd write the exact same
+    ``<stem>__masks`` container — a later push from one would merge onto
+    (and, on overlapping slices, overwrite) the other's, making it impossible
+    to keep both results around to compare, e.g. side-by-side in the 3-D
+    viewer's two independent mask layers. Empty by default (the manual sync
+    action's own container, unsuffixed, for backward compatibility with
+    anything already pointing at ``<stem>__masks``).
     """
     api_key = api_key_for_uri(server_uri)
     client = get_tiled_client(server_uri, api_key)
@@ -231,7 +248,7 @@ def write_masks_to_tiled(
     for part in parts[:-1]:
         parent = parent[part]
 
-    container_key = f"{stem}__masks"
+    container_key = f"{stem}__masks{container_suffix}"
     try:
         container: Any = parent[container_key]
     except KeyError:
@@ -270,10 +287,12 @@ def write_masks_to_tiled(
         container = parent.create_container(key=container_key, metadata=container_meta)
 
     dims = ["slice", "y", "x"]
-    container.write_array(
-        merged["semantic"], key="semantic", dims=dims,
-        metadata={"studio_type": "segmentation_semantic"},
-    )
+    # Registered as a real OME-NGFF multiscale node (mask_pyramid.py), not a
+    # bare write_array — that's what lets the volume viewer's loadMask()
+    # actually open this as a Zarr store instead of rejecting it for
+    # "missing multiscales". Only `semantic` needs this: it's the one array
+    # the viewer's single combined class-id mask texture reads.
+    mask_pyramid.register_mask_pyramid(merged["semantic"], key="semantic", container=container)
     for name, vol in merged["class_vols"].items():
         container.write_array(
             vol, key=_safe_name(name), dims=dims,
diff --git a/frontend/src/app/pages/VolumePage.tsx b/frontend/src/app/pages/VolumePage.tsx
index 303b74e..3b59d81 100644
--- a/frontend/src/app/pages/VolumePage.tsx
+++ b/frontend/src/app/pages/VolumePage.tsx
@@ -12,8 +12,10 @@
  * lives in a `__volume` sidecar, and a stack nobody has built one for has none.
  * See `backend/volume_nodes.py`.
  *
- * Annotation overlay is deliberately not here yet — it needs a second label
- * texture in the renderer, which lands upstream in the viewer repo.
+ * Annotation overlay: two independent mask/annotation layers ("Fast (iPred)"
+ * and "Deep (dlsia)" — see `MaskLayersPanel`), backed by the vendored
+ * renderer's own two-slot mask API and the `<source>__masks[_deep]` Tiled
+ * containers `tiled_mask_sync.write_masks_to_tiled` writes.
  */
 import { useEffect, useState } from 'react';
 import { useQuery } from '@tanstack/react-query';
@@ -22,8 +24,9 @@ import { API_BASE } from '@/config';
 import { useDatasetStore } from '@/stores/datasetStore';
 import { buildZarrUrl, describeUnavailable, type ZarrUnavailable } from '@/lib/zarrUrl';
 import type { ServerInfo } from '@/types/server';
-import VolumeViewer, { webGpuAvailability } from '@/components/volume/VolumeViewer';
+import VolumeViewer, { webGpuAvailability, type WebGpuViewerInstance } from '@/components/volume/VolumeViewer';
 import BuildVolumePanel from '@/components/volume/BuildVolumePanel';
+import MaskLayersPanel from '@/components/volume/MaskLayersPanel';
 
 /** Shape of `GET /api/volume/resolve`. */
 interface VolumeNode {
@@ -50,6 +53,7 @@ function Notice({ title, detail, hint }: { title: string; detail: string; hint?:
 export default function VolumePage() {
   const { kind, source, serverUri } = useDatasetStore();
   const [bootError, setBootError] = useState<string | null>(null);
+  const [viewerInstance, setViewerInstance] = useState<WebGpuViewerInstance | null>(null);
 
   // A new dataset deserves a fresh attempt — otherwise one bad volume leaves the
   // page stuck on its error for every dataset opened afterwards.
@@ -117,14 +121,16 @@ export default function VolumePage() {
   }
 
   return (
-    <div className="flex h-full w-full flex-col">
+    <div className="relative flex h-full w-full flex-col">
       <VolumeViewer
         // Remount on source change so the renderer tears its GPU device down
         // and rebuilds, rather than trying to swap a volume in place.
         key={url}
         zarrUrl={url}
+        onReady={setViewerInstance}
         onError={(e) => setBootError(e instanceof Error ? e.message : String(e))}
       />
+      <MaskLayersPanel instance={viewerInstance} kind={kind} source={source} serverUri={resolvedUri} />
     </div>
   );
 }
diff --git a/frontend/src/components/volume/MaskLayersPanel.tsx b/frontend/src/components/volume/MaskLayersPanel.tsx
new file mode 100644
index 0000000..642f026
--- /dev/null
+++ b/frontend/src/components/volume/MaskLayersPanel.tsx
@@ -0,0 +1,224 @@
+/**
+ * MaskLayersPanel — sidebar overlay for the 3-D view's two mask/annotation
+ * layers: "Fast (iPred)" and "Deep (dlsia)". The viewer's own `loadMask`/
+ * `loadMaskFromArray`/etc. are slot-neutral (0 | 1, no idea what produced a
+ * mask — see `WebGpuViewerInstance`'s own doc comment); this panel is where
+ * that meaning ("fast" vs "deep", which Tiled container each comes from)
+ * actually lives, kept out of the vendored viewer entirely.
+ *
+ * Both slots are Tiled-backed for now (`buildMaskZarrUrl`), pointed at the
+ * two independent `<source>__masks` / `<source>__masks_deep` containers
+ * `tiled_mask_sync.write_masks_to_tiled` writes (see its `container_suffix`
+ * doc). A zero-latency, no-Tiled-round-trip live preview for the Fast slot
+ * (rasterizing current Annotate-tab predictions client-side via
+ * `loadMaskFromArray`) is a deliberately deferred follow-up — this repo has
+ * no existing client-side volume-rasterization code to build on, unlike the
+ * fork's `labelVolume.ts`/`volumeDims.ts`, which assume a server-side
+ * downsampled-raw-volume endpoint this app's `/volume` page does not have
+ * (it streams the real Tiled Zarr pyramid directly instead).
+ */
+import { useEffect, useState } from 'react';
+import { Eye, EyeSlash, CircleNotch } from '@phosphor-icons/react';
+import { buildMaskZarrUrl } from '@/lib/zarrUrl';
+import type { WebGpuViewerInstance } from './VolumeViewer';
+
+type MaskClasses = NonNullable<ReturnType<WebGpuViewerInstance['getMaskClasses']>>;
+
+interface SlotConfig {
+  slot: 0 | 1;
+  label: string;
+  suffix: '' | '_deep';
+}
+
+const SLOTS: SlotConfig[] = [
+  { slot: 0, label: 'Fast (iPred)', suffix: '' },
+  { slot: 1, label: 'Deep (dlsia)', suffix: '_deep' },
+];
+
+interface SlotState {
+  loading: boolean;
+  error: string | null;
+  loaded: boolean;
+  enabled: boolean;
+  opacity: number;
+  classes: MaskClasses;
+}
+
+const initialSlotState: SlotState = {
+  loading: false,
+  error: null,
+  loaded: false,
+  enabled: true,
+  opacity: 0.6,
+  classes: [],
+};
+
+interface MaskLayersPanelProps {
+  instance: WebGpuViewerInstance | null;
+  kind: string | null;
+  source: string | null;
+  serverUri: string | null;
+}
+
+export default function MaskLayersPanel({ instance, kind, source, serverUri }: MaskLayersPanelProps) {
+  const [state, setState] = useState<Record<0 | 1, SlotState>>({
+    0: initialSlotState,
+    1: initialSlotState,
+  });
+
+  // A new dataset invalidates every previously-loaded mask — the viewer
+  // itself remounts on source change (see VolumePage's `key={url}`), so
+  // there is nothing to explicitly unload here, only local status to reset.
+  useEffect(() => {
+    setState({ 0: initialSlotState, 1: initialSlotState });
+  }, [source, serverUri]);
+
+  if (!instance) return null;
+
+  const load = async (cfg: SlotConfig) => {
+    const { url, reason } = buildMaskZarrUrl(kind, source, serverUri, cfg.suffix);
+    if (!url) {
+      setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], error: reason ?? 'No source open', loading: false } }));
+      return;
+    }
+    setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], loading: true, error: null } }));
+    try {
+      instance.loadMask(cfg.slot, url);
+      // loadMask is fire-and-forget on the instance (see the upstream brief:
+      // internal state, not a returned promise) — poll briefly for classes to
+      // appear rather than assuming synchronous completion.
+      const classes = await waitForClasses(instance, cfg.slot);
+      setState((s) => ({
+        ...s,
+        [cfg.slot]: { ...s[cfg.slot], loading: false, loaded: true, classes, error: classes.length === 0 ? 'Loaded, but no classes found (every voxel is background, or this dataset has no mask pushed yet).' : null },
+      }));
+    } catch (err) {
+      setState((s) => ({
+        ...s,
+        [cfg.slot]: { ...s[cfg.slot], loading: false, error: err instanceof Error ? err.message : String(err) },
+      }));
+    }
+  };
+
+  const remove = (cfg: SlotConfig) => {
+    instance.removeMask(cfg.slot);
+    setState((s) => ({ ...s, [cfg.slot]: initialSlotState }));
+  };
+
+  const setOpacity = (cfg: SlotConfig, opacity: number) => {
+    setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], opacity } }));
+    for (const cls of state[cfg.slot].classes) {
+      instance.setMaskClassOpacity(cfg.slot, cls.id, opacity);
+    }
+  };
+
+  const toggleClass = (cfg: SlotConfig, classId: number) => {
+    instance.toggleMaskClassVisible(cfg.slot, classId);
+    setState((s) => ({
+      ...s,
+      [cfg.slot]: {
+        ...s[cfg.slot],
+        classes: s[cfg.slot].classes.map((c) => (c.id === classId ? { ...c, visible: !c.visible } : c)),
+      },
+    }));
+  };
+
+  return (
+    <div className="pointer-events-none absolute left-3 top-3 z-10 flex w-72 flex-col gap-3">
+      {SLOTS.map((cfg) => {
+        const s = state[cfg.slot];
+        return (
+          <div
+            key={cfg.slot}
+            className="pointer-events-auto rounded-lg border border-sky-800/60 bg-sky-950/80 p-3 text-sky-200 backdrop-blur-sm"
+          >
+            <div className="mb-2 flex items-center justify-between gap-2">
+              <span className="text-sm font-medium">{cfg.label}</span>
+              {s.loaded ? (
+                <button
+                  type="button"
+                  onClick={() => remove(cfg)}
+                  className="rounded px-2 py-0.5 text-xs text-sky-300 hover:bg-sky-900/60"
+                >
+                  Remove
+                </button>
+              ) : (
+                <button
+                  type="button"
+                  disabled={s.loading}
+                  onClick={() => void load(cfg)}
+                  className="flex items-center gap-1 rounded bg-sky-600 px-2 py-0.5 text-xs font-medium text-white hover:bg-sky-500 disabled:opacity-50"
+                >
+                  {s.loading && <CircleNotch size={12} className="animate-spin" />}
+                  {s.loading ? 'Loading…' : 'Load'}
+                </button>
+              )}
+            </div>
+
+            {s.error && <p className="mb-2 text-xs text-amber-300">{s.error}</p>}
+
+            {s.loaded && s.classes.length > 0 && (
+              <div className="flex flex-col gap-1.5">
+                <label className="flex items-center gap-2 text-xs">
+                  <span className="w-14 shrink-0 opacity-70">Opacity</span>
+                  <input
+                    type="range"
+                    min={0}
+                    max={1}
+                    step={0.05}
+                    value={s.opacity}
+                    onChange={(e) => setOpacity(cfg, Number(e.target.value))}
+                    className="flex-1 accent-sky-500"
+                  />
+                </label>
+                <div className="flex flex-col gap-0.5">
+                  {s.classes.map((cls) => (
+                    <button
+                      key={cls.id}
+                      type="button"
+                      onClick={() => toggleClass(cfg, cls.id)}
+                      className="flex items-center gap-2 rounded px-1 py-0.5 text-left text-xs hover:bg-sky-900/60"
+                    >
+                      <span
+                        className="h-2.5 w-2.5 shrink-0 rounded-full"
+                        style={{
+                          backgroundColor: `rgb(${cls.color.map((c) => Math.round(c * 255)).join(',')})`,
+                          opacity: cls.visible ? 1 : 0.3,
+                        }}
+                      />
+                      <span className={cls.visible ? '' : 'opacity-40'}>
+                        Class {cls.id} <span className="opacity-60">({cls.voxelCount.toLocaleString()} vox)</span>
+                      </span>
+                      {cls.visible ? <Eye size={11} className="ml-auto opacity-60" /> : <EyeSlash size={11} className="ml-auto opacity-40" />}
+                    </button>
+                  ))}
+                </div>
+              </div>
+            )}
+          </div>
+        );
+      })}
+    </div>
+  );
+}
+
+/**
+ * `loadMask` mutates viewer-internal state asynchronously with no returned
+ * promise (see the upstream brief's Step 3 — it's fire-and-forget on the
+ * public interface, matching how the HUD's own click handler calls it).
+ * Poll `getMaskClasses` briefly rather than assuming it's ready on the next
+ * tick — mirrors how `InferencePanel`/`useExportJob` poll a job status
+ * elsewhere in this app rather than trusting synchronous completion.
+ */
+async function waitForClasses(
+  instance: WebGpuViewerInstance,
+  slot: 0 | 1,
+  { attempts = 50, intervalMs = 100 }: { attempts?: number; intervalMs?: number } = {},
+): Promise<MaskClasses> {
+  for (let i = 0; i < attempts; i++) {
+    const classes = instance.getMaskClasses(slot);
+    if (classes !== undefined) return classes;
+    await new Promise((r) => setTimeout(r, intervalMs));
+  }
+  throw new Error('Timed out waiting for the mask to load — check the browser console for a renderer-side error.');
+}
diff --git a/frontend/src/lib/zarrUrl.test.ts b/frontend/src/lib/zarrUrl.test.ts
index f864fcd..36f1c7e 100644
--- a/frontend/src/lib/zarrUrl.test.ts
+++ b/frontend/src/lib/zarrUrl.test.ts
@@ -1,5 +1,5 @@
 import { describe, it, expect } from 'vitest';
-import { buildZarrUrl, zarrRootFor, describeUnavailable } from './zarrUrl';
+import { buildZarrUrl, buildMaskZarrUrl, zarrRootFor, describeUnavailable } from './zarrUrl';
 
 describe('zarrRootFor', () => {
   it('appends the zarr v2 root to a bare origin', () => {
@@ -61,6 +61,37 @@ describe('buildZarrUrl', () => {
   });
 });
 
+describe('buildMaskZarrUrl', () => {
+  const server = 'http://127.0.0.1:8010';
+
+  it('appends the __masks/semantic sibling to the dataset path', () => {
+    expect(buildMaskZarrUrl('tiled', 'browse/260_R1_Oct_slice_2', server)).toEqual({
+      url: 'http://127.0.0.1:8010/zarr/v2/browse/260_R1_Oct_slice_2__masks/semantic',
+      reason: null,
+    });
+  });
+
+  it('keeps the __masks suffix on the leaf segment only, not the whole path', () => {
+    const { url } = buildMaskZarrUrl('tiled', 'a/b/c', server);
+    expect(url).toBe('http://127.0.0.1:8010/zarr/v2/a/b/c__masks/semantic');
+  });
+
+  it('points at a distinct container for the deep-model suffix', () => {
+    const { url } = buildMaskZarrUrl('tiled', 'browse/x', server, '_deep');
+    expect(url).toBe('http://127.0.0.1:8010/zarr/v2/browse/x__masks_deep/semantic');
+  });
+
+  it.each([
+    ['no source open', null, null, server, 'no-source'],
+    ['local file', 'local', 'foo.tif', null, 'local-source'],
+    ['server unresolved', 'tiled', 'scans/x', null, 'no-server'],
+  ])('reports %s rather than guessing a URL', (_label, kind, source, uri, reason) => {
+    const result = buildMaskZarrUrl(kind as string | null, source as string | null, uri as string | null);
+    expect(result.url).toBeNull();
+    expect(result.reason).toBe(reason);
+  });
+});
+
 describe('describeUnavailable', () => {
   it('explains every reason', () => {
     for (const reason of ['no-source', 'local-source', 'no-server'] as const) {
diff --git a/frontend/src/lib/zarrUrl.ts b/frontend/src/lib/zarrUrl.ts
index 7c22e01..ef1f83a 100644
--- a/frontend/src/lib/zarrUrl.ts
+++ b/frontend/src/lib/zarrUrl.ts
@@ -104,3 +104,40 @@ export function buildZarrUrl(
 
   return { url: `${zarrRootFor(serverUri)}/${path}`, reason: null };
 }
+
+/**
+ * Zarr store URL for a sample's mask/annotation volume — the
+ * `<stem>__masks<suffix>` sibling container `tiled_mask_sync.write_masks_to_tiled`
+ * writes, registered as a real OME-NGFF multiscale node by
+ * `mask_pyramid.register_mask_pyramid` so this is directly loadable via the
+ * volume viewer's mask layer (`loadMask(slot, url)`).
+ *
+ * @param source Tiled path of the annotated dataset — same `source` passed to
+ *   `buildZarrUrl` for the primary volume, NOT the `__masks` container itself;
+ *   the `__masks`/`semantic` suffix is appended here.
+ * @param suffix Distinguishes independent mask producers for the same source
+ *   that must not merge into one container — `''` for the manual "sync masks
+ *   to Tiled" action (iPred's fast results), `'_deep'` for a dlsia run's
+ *   "Write masks to Tiled" (`infer_jobs.py`'s `container_suffix="_deep"`).
+ *   Must match the backend suffix exactly or this points at an empty/missing
+ *   container.
+ */
+export function buildMaskZarrUrl(
+  kind: string | null,
+  source: string | null,
+  serverUri: string | null,
+  suffix: '' | '_deep' = '',
+): ZarrUrlResult {
+  if (!kind || !source) return { url: null, reason: 'no-source' };
+  if (kind !== 'tiled') return { url: null, reason: 'local-source' };
+  if (!serverUri) return { url: null, reason: 'no-server' };
+
+  const parts = source.split('/').filter(Boolean);
+  const stem = parts.pop();
+  if (!stem) return { url: null, reason: 'no-source' };
+  const path = [...parts, `${stem}__masks${suffix}`, 'semantic']
+    .map(encodeURIComponent)
+    .join('/');
+
+  return { url: `${zarrRootFor(serverUri)}/${path}`, reason: null };
+}
diff --git a/frontend/tsconfig.app.json b/frontend/tsconfig.app.json
index 6db341e..9c49f02 100644
--- a/frontend/tsconfig.app.json
+++ b/frontend/tsconfig.app.json
@@ -31,6 +31,7 @@
       "@zarr-viewer/controls": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/controls/index.ts"],
       "@zarr-viewer/io": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/io/index.ts"],
       "@zarr-viewer/render": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/render/index.ts"],
+      "@zarr-viewer/fx": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/fx/src/index.ts"],
       "@prism/core": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/core/index.ts"],
       "@prism/math": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/math/index.ts"],
       "@prism/render": ["./vendor/view_tomography_recon_app/src/zarr-viewer/src/render/index.ts"],
@@ -43,5 +44,12 @@
   // compiles cleanly under ours. If it ever does not, that is an upstream fix on
   // the viewer repo's own branch — do not edit vendored sources to appease tsc.
   "include": ["src", "vendor/view_tomography_recon_app/src/zarr-viewer/src"],
-  "exclude": ["vendor/view_tomography_recon_app/src/zarr-viewer/src/fx/test"]
+  "exclude": [
+    "vendor/view_tomography_recon_app/src/zarr-viewer/src/fx/test",
+    // Upstream test fixture predates `RenderProvenance.extendedPreIntegration`
+    // (added alongside the mip-pyramid/half-res-lighting work) and was never
+    // updated — a test-only type error with no effect on the exported API we
+    // actually consume. Excluded rather than patched, per the note above.
+    "vendor/view_tomography_recon_app/src/zarr-viewer/src/render/accel/test/accel.spec.ts"
+  ]
 }
diff --git a/frontend/vendor/view_tomography_recon_app b/frontend/vendor/view_tomography_recon_app
index 46d4fd3..3c24357 160000
--- a/frontend/vendor/view_tomography_recon_app
+++ b/frontend/vendor/view_tomography_recon_app
@@ -1 +1 @@
-Subproject commit 46d4fd35fa38b16213585720c40e86fbd7678969
+Subproject commit 3c2435753ead74689d505025ce546ab03e95e421

From 730f84d2b4901aab69c78c6e63a5351faa045a59 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Tue, 1 Sep 2026 13:46:15 -0700
Subject: [PATCH 14/38] Higher resolution labels in the 3d view

---
 backend/tests/test_volume_build.py            |   9 +-
 backend/tiff_stack_source.py                  |  16 +--
 frontend/src/app/pages/VolumePage.tsx         |  37 ++++++-
 .../src/components/annotate/DownloadModal.tsx |  18 +++-
 .../src/components/train/InferencePanel.tsx   |  23 +++-
 .../src/components/volume/MaskLayersPanel.tsx |  21 +++-
 .../volume/RebuildVolumeControl.tsx           | 101 ++++++++++++++++++
 frontend/vendor/view_tomography_recon_app     |   2 +-
 8 files changed, 203 insertions(+), 24 deletions(-)
 create mode 100644 frontend/src/components/volume/RebuildVolumeControl.tsx

diff --git a/backend/tests/test_volume_build.py b/backend/tests/test_volume_build.py
index 789162f..740029c 100644
--- a/backend/tests/test_volume_build.py
+++ b/backend/tests/test_volume_build.py
@@ -25,7 +25,10 @@
 def fake_stack(monkeypatch):
     """Stand in for a Tiled stack: `resolve_array` + `array_shape_meta` + `read_slice`."""
 
-    def install(n_slices=64, height=1024, width=1024, dtype="uint16", is_rgb=False):
+    # 4096 > TARGET_DIM (2048) by default, so an un-overridden fake_stack still
+    # represents "a large stack that needs a pyramid" — this used to be true at
+    # 1024 back when TARGET_DIM was 384.
+    def install(n_slices=64, height=4096, width=4096, dtype="uint16", is_rgb=False):
         meta = {
             "n_slices": n_slices,
             "height": height,
@@ -48,9 +51,9 @@ def install(n_slices=64, height=1024, width=1024, dtype="uint16", is_rgb=False):
 
 class TestInspect:
     def test_describes_the_pyramid_that_would_be_built(self, fake_stack):
-        fake_stack(n_slices=64, height=1024, width=1024)
+        fake_stack(n_slices=64, height=4096, width=4096)
         info = volume_build.inspect_volume_build("browse/stack")
-        assert info["full_shape"] == [64, 1024, 1024]
+        assert info["full_shape"] == [64, 4096, 4096]
         assert info["pyramid_plan"]
         assert info["already_small"] is False
 
diff --git a/backend/tiff_stack_source.py b/backend/tiff_stack_source.py
index 6e9ad18..53b83ee 100644
--- a/backend/tiff_stack_source.py
+++ b/backend/tiff_stack_source.py
@@ -74,12 +74,16 @@
 
 TIFF_SUFFIXES: tuple[str, ...] = (".tif", ".tiff")
 
-# Longest edge (voxels) the finest GENERATED level may have. Chosen so a level
-# comfortably fits a 2048 3-D texture with room for the renderer's own budget,
-# and so building one stays within a few hundred MB of RAM: this module assembles
-# each generated level in memory before writing it (worst case 384**3 * 4 B ~=
-# 226 MB, and z is normally far smaller than 384).
-TARGET_DIM = 384
+# Longest edge (voxels) the finest GENERATED level may have. 2048 matches
+# WebGPU's guaranteed-minimum `maxTextureDimension3D` — the viewer picks
+# whichever registered level is the coarsest that still fits the actual
+# device's limit, so a level above what a given GPU can take is simply
+# skipped, never a hard failure. Raising this raises fidelity but also
+# memory/time to build it: this module assembles each generated level fully
+# in RAM before writing it, so cost scales with actual voxel count
+# (nx * ny * nz, not nx**3 — z is normally far smaller than the in-plane
+# edges for a tomography stack).
+TARGET_DIM = 2048
 
 # How many levels to generate below scale0. Three gives the renderer a choice of
 # detail without the cost growing: each is 1/8 the voxels of the one above.
diff --git a/frontend/src/app/pages/VolumePage.tsx b/frontend/src/app/pages/VolumePage.tsx
index 3b59d81..68e9552 100644
--- a/frontend/src/app/pages/VolumePage.tsx
+++ b/frontend/src/app/pages/VolumePage.tsx
@@ -18,6 +18,7 @@
  * containers `tiled_mask_sync.write_masks_to_tiled` writes.
  */
 import { useEffect, useState } from 'react';
+import { useSearchParams } from 'react-router';
 import { useQuery } from '@tanstack/react-query';
 import { Cube } from '@phosphor-icons/react';
 import { API_BASE } from '@/config';
@@ -27,6 +28,7 @@ import type { ServerInfo } from '@/types/server';
 import VolumeViewer, { webGpuAvailability, type WebGpuViewerInstance } from '@/components/volume/VolumeViewer';
 import BuildVolumePanel from '@/components/volume/BuildVolumePanel';
 import MaskLayersPanel from '@/components/volume/MaskLayersPanel';
+import RebuildVolumeControl from '@/components/volume/RebuildVolumeControl';
 
 /** Shape of `GET /api/volume/resolve`. */
 interface VolumeNode {
@@ -54,6 +56,16 @@ export default function VolumePage() {
   const { kind, source, serverUri } = useDatasetStore();
   const [bootError, setBootError] = useState<string | null>(null);
   const [viewerInstance, setViewerInstance] = useState<WebGpuViewerInstance | null>(null);
+  // Bumped after a rebuild — folded into VolumeViewer's `key` below to force
+  // a remount even though the resolved path (and therefore `url`) doesn't
+  // change on a rebuild the way it does for a brand-new volume.
+  const [rebuildNonce, setRebuildNonce] = useState(0);
+  // "View in 3D" hand-offs from Train/Annotate arrive as `?mask=fast|deep` so
+  // the relevant layer loads automatically instead of requiring a second
+  // manual click (see MaskLayersPanel's `autoLoadSlot`).
+  const [searchParams] = useSearchParams();
+  const maskParam = searchParams.get('mask');
+  const autoLoadSlot = maskParam === 'fast' ? 0 : maskParam === 'deep' ? 1 : undefined;
 
   // A new dataset deserves a fresh attempt — otherwise one bad volume leaves the
   // page stuck on its error for every dataset opened afterwards.
@@ -123,14 +135,31 @@ export default function VolumePage() {
   return (
     <div className="relative flex h-full w-full flex-col">
       <VolumeViewer
-        // Remount on source change so the renderer tears its GPU device down
-        // and rebuilds, rather than trying to swap a volume in place.
-        key={url}
+        // Remount on source change (a genuinely different dataset) OR on
+        // rebuildNonce (the same dataset's pyramid was just rebuilt in
+        // place) — either way the renderer needs to tear its GPU device down
+        // and re-fetch, rather than trying to swap a volume in place.
+        key={`${url}:${rebuildNonce}`}
         zarrUrl={url}
         onReady={setViewerInstance}
         onError={(e) => setBootError(e instanceof Error ? e.message : String(e))}
       />
-      <MaskLayersPanel instance={viewerInstance} kind={kind} source={source} serverUri={resolvedUri} />
+      <MaskLayersPanel
+        instance={viewerInstance}
+        kind={kind}
+        source={source}
+        serverUri={resolvedUri}
+        autoLoadSlot={autoLoadSlot}
+      />
+      {node?.mode === 'sidecar' && source && (
+        <div className="pointer-events-none absolute right-3 top-3 z-10">
+          <RebuildVolumeControl
+            source={source}
+            serverUri={resolvedUri}
+            onRebuilt={() => setRebuildNonce((n) => n + 1)}
+          />
+        </div>
+      )}
     </div>
   );
 }
diff --git a/frontend/src/components/annotate/DownloadModal.tsx b/frontend/src/components/annotate/DownloadModal.tsx
index 775160b..393c893 100644
--- a/frontend/src/components/annotate/DownloadModal.tsx
+++ b/frontend/src/components/annotate/DownloadModal.tsx
@@ -2,7 +2,8 @@
  * DownloadModal — scope picker + optional star-rating filter, then COCO export.
  */
 import { useMemo, useState } from 'react';
-import { DownloadSimple, X, CheckCircle, WarningCircle } from '@phosphor-icons/react';
+import { useNavigate } from 'react-router';
+import { DownloadSimple, X, CheckCircle, WarningCircle, Cube } from '@phosphor-icons/react';
 import { useDatasetStore } from '@/stores/datasetStore';
 import { useAnnotationStore } from '@/stores/annotationStore';
 import { useClassStore } from '@/stores/classStore';
@@ -55,6 +56,7 @@ const SCOPE_OPTIONS: { value: Scope; label: string; desc: string; stars?: string
 
 /** Renders the COCO download dialog and drives the export job for the chosen scope. */
 export default function DownloadModal({ onClose }: DownloadModalProps) {
+  const navigate = useNavigate();
   const { source, kind, serverUri, currentSlice } = useDatasetStore();
   const { byImage, splitBySlice, negativeSlices } = useAnnotationStore();
   const { classes } = useClassStore();
@@ -295,13 +297,23 @@ export default function DownloadModal({ onClose }: DownloadModalProps) {
         )}
 
         {status === 'done' && maskResult && (
-          <div className="flex items-start gap-2 text-sm text-green-300">
+          <div className="flex flex-wrap items-start gap-2 text-sm text-green-300">
             <CheckCircle size={16} className="mt-0.5 shrink-0" />
-            <span className="min-w-0 break-words">
+            <span className="min-w-0 flex-1 break-words">
               {maskResult.length === 0
                 ? 'No masks written (no Tiled sources or no annotated slices).'
                 : <>Masks merged into Tiled: {maskResult.map((w) => `${w.container} (${w.n_slices} slices total, ${w.updated ?? 0} updated)`).join(', ')}.</>}
             </span>
+            {maskResult.length > 0 && (
+              <button
+                type="button"
+                onClick={() => { onClose(); navigate('/volume?mask=fast'); }}
+                className="flex shrink-0 items-center gap-1 rounded-md border border-green-600 px-2 py-1 text-xs text-green-300 hover:bg-green-900/30 transition-colors"
+              >
+                <Cube size={12} />
+                View in 3D
+              </button>
+            )}
           </div>
         )}
         {status === 'done' && !maskResult && (
diff --git a/frontend/src/components/train/InferencePanel.tsx b/frontend/src/components/train/InferencePanel.tsx
index ec224a2..531824b 100644
--- a/frontend/src/components/train/InferencePanel.tsx
+++ b/frontend/src/components/train/InferencePanel.tsx
@@ -4,6 +4,8 @@
  * and/or push them into Tiled as masks.
  */
 import { useEffect, useMemo, useState } from 'react';
+import { useNavigate } from 'react-router';
+import { Cube } from '@phosphor-icons/react';
 import { API_BASE } from '@/config';
 import { useExportJob } from '@/hooks/useExportJob';
 import { buildSliceUrl } from '@/hooks/useImageSlice';
@@ -30,6 +32,7 @@ type Scope = 'current' | 'range' | 'all';
 export default function InferencePanel({
   selectedRunId, hasOpenSample, isTiledSource, source, serverUri, currentSlice, nSlices, baseImageUrl, onImportPredictions,
 }: InferencePanelProps) {
+  const navigate = useNavigate();
   const [scope, setScope] = useState<Scope>('current');
   const [rangeStart, setRangeStart] = useState(0);
   const [rangeEnd, setRangeEnd] = useState(Math.max(0, nSlices - 1));
@@ -247,11 +250,21 @@ export default function InferencePanel({
                 </p>
               )}
               {writeJob.status === 'done' && (
-                <p className="text-xs text-emerald-400">
-                  Masks saved to Tiled
-                  {typeof writeJob.result?.n_slices === 'number' ? ` (${writeJob.result.n_slices} slices)` : ''} —
-                  load them in Annotate anytime with "Load saved masks".
-                </p>
+                <div className="flex flex-wrap items-center gap-2">
+                  <p className="text-xs text-emerald-400">
+                    Masks saved to Tiled
+                    {typeof writeJob.result?.n_slices === 'number' ? ` (${writeJob.result.n_slices} slices)` : ''} —
+                    load them in Annotate anytime with "Load saved masks".
+                  </p>
+                  <button
+                    type="button"
+                    onClick={() => navigate('/volume?mask=deep')}
+                    className="flex items-center gap-1 rounded-md border border-emerald-600 px-2 py-1 text-xs text-emerald-300 hover:bg-emerald-900/30 transition-colors"
+                  >
+                    <Cube size={12} />
+                    View in 3D
+                  </button>
+                </div>
               )}
               <JobProgressBar job={writeJob} unit="steps" />
             </div>
diff --git a/frontend/src/components/volume/MaskLayersPanel.tsx b/frontend/src/components/volume/MaskLayersPanel.tsx
index 642f026..b7838e6 100644
--- a/frontend/src/components/volume/MaskLayersPanel.tsx
+++ b/frontend/src/components/volume/MaskLayersPanel.tsx
@@ -17,7 +17,7 @@
  * downsampled-raw-volume endpoint this app's `/volume` page does not have
  * (it streams the real Tiled Zarr pyramid directly instead).
  */
-import { useEffect, useState } from 'react';
+import { useEffect, useRef, useState } from 'react';
 import { Eye, EyeSlash, CircleNotch } from '@phosphor-icons/react';
 import { buildMaskZarrUrl } from '@/lib/zarrUrl';
 import type { WebGpuViewerInstance } from './VolumeViewer';
@@ -58,21 +58,38 @@ interface MaskLayersPanelProps {
   kind: string | null;
   source: string | null;
   serverUri: string | null;
+  /** Slot to load automatically once the viewer is ready — the "View in 3D"
+   * hand-off from Train/Annotate arrives here already knowing which result
+   * the user just produced, so it shouldn't need a second manual click. */
+  autoLoadSlot?: 0 | 1;
 }
 
-export default function MaskLayersPanel({ instance, kind, source, serverUri }: MaskLayersPanelProps) {
+export default function MaskLayersPanel({ instance, kind, source, serverUri, autoLoadSlot }: MaskLayersPanelProps) {
   const [state, setState] = useState<Record<0 | 1, SlotState>>({
     0: initialSlotState,
     1: initialSlotState,
   });
+  // Guards the auto-load effect against StrictMode's double-invoke and
+  // against re-firing on every re-render once it's already kicked off once
+  // for this instance.
+  const autoLoadedFor = useRef<WebGpuViewerInstance | null>(null);
 
   // A new dataset invalidates every previously-loaded mask — the viewer
   // itself remounts on source change (see VolumePage's `key={url}`), so
   // there is nothing to explicitly unload here, only local status to reset.
   useEffect(() => {
     setState({ 0: initialSlotState, 1: initialSlotState });
+    autoLoadedFor.current = null;
   }, [source, serverUri]);
 
+  useEffect(() => {
+    if (!instance || autoLoadSlot === undefined || autoLoadedFor.current === instance) return;
+    autoLoadedFor.current = instance;
+    const cfg = SLOTS.find((s) => s.slot === autoLoadSlot);
+    if (cfg) void load(cfg);
+    // eslint-disable-next-line react-hooks/exhaustive-deps -- `load` is redefined every render but stable in effect: it only reads current instance/kind/source/serverUri via closure, matching the effect's own deps.
+  }, [instance, autoLoadSlot]);
+
   if (!instance) return null;
 
   const load = async (cfg: SlotConfig) => {
diff --git a/frontend/src/components/volume/RebuildVolumeControl.tsx b/frontend/src/components/volume/RebuildVolumeControl.tsx
new file mode 100644
index 0000000..a349e7e
--- /dev/null
+++ b/frontend/src/components/volume/RebuildVolumeControl.tsx
@@ -0,0 +1,101 @@
+/**
+ * RebuildVolumeControl — re-trigger "Build 3D volume" for a dataset that
+ * already has one, so a fidelity-affecting change (e.g. raising
+ * `tiff_stack_source.TARGET_DIM`) can actually reach volumes built before
+ * the change. `volume_build.build_volume` already replaces any prior build
+ * for the same key safely (see its own "re-running must refresh the volume"
+ * comment) — this only adds the UI path to trigger that for an EXISTING
+ * volume; `BuildVolumePanel` covers the "none exists yet" case.
+ *
+ * Deliberately no staleness detection (comparing what's registered against
+ * what a fresh build would produce) — a plain always-available button is
+ * simpler and correct for what is, for now, a one-time fidelity bump.
+ */
+import { useCallback, useState } from 'react';
+import { ArrowsClockwise, CircleNotch, Warning } from '@phosphor-icons/react';
+import { API_BASE } from '@/config';
+
+interface JobState {
+  state: 'pending' | 'running' | 'done' | 'error';
+  phase: string;
+  done: number;
+  total: number;
+  error: string | null;
+}
+
+async function readError(res: Response): Promise<string> {
+  try {
+    const body = await res.json();
+    if (typeof body?.detail === 'string') return body.detail;
+    return JSON.stringify(body);
+  } catch {
+    return `Request failed (${res.status})`;
+  }
+}
+
+interface RebuildVolumeControlProps {
+  source: string;
+  serverUri: string | null;
+  /** Called once the rebuild finishes — the caller should bump whatever key
+   * forces VolumeViewer to remount, since the resolved path doesn't change
+   * on a rebuild (same key, same location) the way a brand-new build does. */
+  onRebuilt: () => void;
+}
+
+export default function RebuildVolumeControl({ source, serverUri, onRebuilt }: RebuildVolumeControlProps) {
+  const [job, setJob] = useState<JobState | null>(null);
+  const [error, setError] = useState<string | null>(null);
+
+  const rebuild = useCallback(async () => {
+    setError(null);
+    setJob({ state: 'pending', phase: 'Starting', done: 0, total: 1, error: null });
+    try {
+      const res = await fetch(`${API_BASE}/api/volume/build`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ source, kind: 'tiled', server_uri: serverUri }),
+      });
+      if (!res.ok) throw new Error(await readError(res));
+      const { job_id: jobId } = await res.json();
+
+      for (;;) {
+        await new Promise((resolve) => setTimeout(resolve, 1000));
+        const statusRes = await fetch(`${API_BASE}/api/export/status/${jobId}`);
+        if (!statusRes.ok) throw new Error(await readError(statusRes));
+        const status: JobState = await statusRes.json();
+        setJob(status);
+        if (status.state === 'error') throw new Error(status.error || 'Rebuild failed');
+        if (status.state === 'done') break;
+      }
+      setJob(null);
+      onRebuilt();
+    } catch (err) {
+      setError(err instanceof Error ? err.message : String(err));
+      setJob(null);
+    }
+  }, [source, serverUri, onRebuilt]);
+
+  const running = job !== null;
+  const percent = job && job.total > 0 ? Math.round((job.done / job.total) * 100) : 0;
+
+  return (
+    <div className="pointer-events-auto flex flex-col items-end gap-1">
+      <button
+        type="button"
+        disabled={running}
+        onClick={() => void rebuild()}
+        title="Re-build this dataset's 3D pyramid — picks up any fidelity/quality changes made since it was last built"
+        className="flex items-center gap-1.5 rounded-lg border border-sky-800/60 bg-sky-950/80 px-2.5 py-1.5 text-xs text-sky-200 backdrop-blur-sm hover:bg-sky-900/70 disabled:opacity-60"
+      >
+        {running ? <CircleNotch size={13} className="animate-spin" /> : <ArrowsClockwise size={13} />}
+        {running ? `${job.phase} ${job.total > 0 ? `(${percent}%)` : ''}` : 'Rebuild volume'}
+      </button>
+      {error && (
+        <p className="flex max-w-64 items-start gap-1 rounded bg-sky-950/80 px-2 py-1 text-[11px] text-amber-300 backdrop-blur-sm">
+          <Warning size={12} className="mt-0.5 shrink-0" />
+          {error}
+        </p>
+      )}
+    </div>
+  );
+}
diff --git a/frontend/vendor/view_tomography_recon_app b/frontend/vendor/view_tomography_recon_app
index 3c24357..208cc55 160000
--- a/frontend/vendor/view_tomography_recon_app
+++ b/frontend/vendor/view_tomography_recon_app
@@ -1 +1 @@
-Subproject commit 3c2435753ead74689d505025ce546ab03e95e421
+Subproject commit 208cc551794125e27e4c51e7a417f2a7bfea7442

From 63beead827d90f9c8c83bcacf99882460ad8afe8 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 2 Sep 2026 10:38:42 -0700
Subject: [PATCH 15/38] Fixing ipred -> 3d view bug

---
 backend/ipred_batch_jobs.py                   | 145 +++++++++++++++---
 backend/ipred_client.py                       |  58 ++++++-
 backend/tests/test_ipred_batch_jobs.py        | 134 ++++++++++++++++
 backend/tests/test_ipred_routes.py            |   5 +-
 frontend/src/app/pages/AnnotatePage.tsx       |  67 +++++++-
 frontend/src/app/pages/VolumePage.tsx         |  10 +-
 .../annotate/PixelClassifierPanel/index.tsx   |  34 +++-
 .../src/components/volume/MaskLayersPanel.tsx | 111 ++++++++++++--
 frontend/src/lib/volumeMaskPreview.test.ts    |  57 +++++++
 frontend/src/lib/volumeMaskPreview.ts         |  88 +++++++++++
 frontend/vendor/view_tomography_recon_app     |   2 +-
 ipred/src/ipred/api.py                        |  19 +++
 ipred/src/ipred/array_source.py               |  43 +++++-
 ipred/src/ipred/catalog.py                    |  29 ++++
 ipred/src/ipred/train_infer.py                |  49 +++++-
 ipred/tests/test_array_source.py              |  88 +++++++++++
 ipred/tests/test_catalog_feature_banks.py     |  56 +++++++
 ipred/tests/test_infer_store_probabilities.py |  72 +++++++++
 18 files changed, 999 insertions(+), 68 deletions(-)
 create mode 100644 backend/tests/test_ipred_batch_jobs.py
 create mode 100644 frontend/src/lib/volumeMaskPreview.test.ts
 create mode 100644 frontend/src/lib/volumeMaskPreview.ts
 create mode 100644 ipred/tests/test_catalog_feature_banks.py
 create mode 100644 ipred/tests/test_infer_store_probabilities.py

diff --git a/backend/ipred_batch_jobs.py b/backend/ipred_batch_jobs.py
index 2912fe1..6008a53 100644
--- a/backend/ipred_batch_jobs.py
+++ b/backend/ipred_batch_jobs.py
@@ -14,9 +14,13 @@
 
 from __future__ import annotations
 
+import concurrent.futures
 import logging
+import os
 from typing import Any
 
+import httpx
+
 import export_jobs
 import ipred_client as ipred_client_mod
 
@@ -89,6 +93,71 @@ def run_ipred_multi_train_job(
         export_jobs.update(jid, state="error", phase="error", error=str(exc))
 
 
+#: Slices processed concurrently in a volume-wide apply job. Each in-flight
+#: slice holds ~1 GB (its feature bank, released immediately after use — see
+#: `Catalog.delete_feature_bank`) resident on the ipred service process, so
+#: this directly multiplies peak memory there. Overridable per machine
+#: without a code change via IPRED_APPLY_CONCURRENCY; kept conservative by
+#: default. `preprocess`/`infer` are sync FastAPI routes, which Starlette
+#: already runs in its own thread pool even under a single uvicorn process —
+#: nothing on the ipred service side needs to change to make this safe (see
+#: the encoder's own `_session_lock`/`_model_lock`, which already serialize
+#: concurrent ONNX access correctly).
+_DEFAULT_APPLY_CONCURRENCY = 4
+
+
+def _apply_one_slice(
+    slice_index: int,
+    *,
+    client: httpx.Client,
+    session_id: str,
+    model_id: str,
+    composition_id: str | None,
+    feature_setup_id: str | None,
+    alpha: float,
+) -> tuple[int, str | None, str | None]:
+    """Preprocess + infer one slice on a worker thread. Returns
+    ``(slice_index, run_id, error)`` — exactly one of the last two is set —
+    never raises, so a pool of these can be driven with plain
+    ``future.result()`` and no per-future try/except at the call site."""
+    bank: dict[str, Any] | None = None
+    try:
+        bank = ipred_client_mod.preprocess(
+            session_id=session_id,
+            feature_setup_id=feature_setup_id,
+            composition_id=composition_id,
+            slice_index=slice_index,
+            client=client,
+        )
+        run = ipred_client_mod.infer(
+            session_id=session_id,
+            model_id=model_id,
+            feature_id=bank["feature_id"],
+            alpha=alpha,
+            # Volume-apply never reads a run's proba.npy back (only
+            # commit.png, client-side) — see run_infer's own doc for why
+            # this is safe to skip here but not for interactive infer.
+            store_probabilities=False,
+            client=client,
+        )
+        return slice_index, run["run_id"], None
+    except Exception as exc:  # noqa: BLE001 — one bad slice must not abort the volume
+        return slice_index, None, str(exc)
+    finally:
+        # Release the feature bank whether infer succeeded or failed —
+        # a failed infer still leaves an orphaned ~1 GB bank behind
+        # otherwise. Best-effort: a cleanup failure must not fail the
+        # slice that already predicted successfully.
+        if bank is not None:
+            try:
+                ipred_client_mod.delete_feature_bank(bank["feature_id"], client=client)
+            except Exception as exc:  # noqa: BLE001
+                logger.warning(
+                    "ipred volume apply: could not release feature bank for slice %d (%s)",
+                    slice_index, exc,
+                )
+
+
 def run_ipred_volume_apply_job(
     jid: str,
     *,
@@ -107,6 +176,14 @@ def run_ipred_volume_apply_job(
     volume-wide apply that stops at the first unreadable slice would be far
     more disruptive than a job that finishes with a handful of gaps reported
     in ``result.errors``.
+
+    Slices are processed by a small bounded thread pool (see
+    ``_DEFAULT_APPLY_CONCURRENCY``) sharing one persistent HTTP connection
+    (``ipred_client_mod.new_shared_client``) instead of one-at-a-time with a
+    fresh connection per call — see the plan doc for why each piece of
+    shared state here (export_jobs' lock, Catalog's per-call connections,
+    CatBoost's read-only predict, the encoder's existing session locks) is
+    already safe under this concurrency.
     """
     try:
         export_jobs.update(jid, state="running", phase="predicting")
@@ -115,29 +192,59 @@ def run_ipred_volume_apply_job(
         runs: dict[str, str] = {}
         errors: list[dict[str, Any]] = []
         cancelled = False
-        for slice_index in slice_indices:
-            if export_jobs.cancel_requested(jid):
-                cancelled = True
-                break
-            try:
-                bank = ipred_client_mod.preprocess(
-                    session_id=session_id,
-                    feature_setup_id=feature_setup_id,
-                    composition_id=composition_id,
-                    slice_index=slice_index,
-                )
-                run = ipred_client_mod.infer(
+        pool_size = max(1, int(os.getenv("IPRED_APPLY_CONCURRENCY", _DEFAULT_APPLY_CONCURRENCY)))
+
+        with ipred_client_mod.new_shared_client() as client, \
+                concurrent.futures.ThreadPoolExecutor(max_workers=pool_size) as pool:
+            pending = iter(slice_indices)
+            in_flight: dict[concurrent.futures.Future, int] = {}
+
+            def submit_next() -> bool:
+                si = next(pending, None)
+                if si is None:
+                    return False
+                fut = pool.submit(
+                    _apply_one_slice,
+                    si,
+                    client=client,
                     session_id=session_id,
                     model_id=model_id,
-                    feature_id=bank["feature_id"],
+                    composition_id=composition_id,
+                    feature_setup_id=feature_setup_id,
                     alpha=alpha,
                 )
-                runs[str(slice_index)] = run["run_id"]
-                export_jobs.log(jid, f"slice {slice_index}: predicted (run {run['run_id'][:8]})")
-            except Exception as exc:  # noqa: BLE001 — one bad slice must not abort the volume
-                logger.warning("ipred volume apply: slice %d failed (%s)", slice_index, exc)
-                errors.append({"slice": slice_index, "error": str(exc)})
-            export_jobs.bump(jid, 1)
+                in_flight[fut] = si
+                return True
+
+            for _ in range(pool_size):
+                if not submit_next():
+                    break
+
+            while in_flight:
+                done, _ = concurrent.futures.wait(
+                    in_flight, return_when=concurrent.futures.FIRST_COMPLETED
+                )
+                for fut in done:
+                    del in_flight[fut]
+                    slice_index, run_id, error = fut.result()
+                    if error is not None:
+                        logger.warning("ipred volume apply: slice %d failed (%s)", slice_index, error)
+                        errors.append({"slice": slice_index, "error": error})
+                    else:
+                        runs[str(slice_index)] = run_id  # type: ignore[assignment]
+                        export_jobs.log(jid, f"slice {slice_index}: predicted (run {run_id[:8]})")
+                    export_jobs.bump(jid, 1)
+
+                if export_jobs.cancel_requested(jid):
+                    cancelled = True
+                    # Already-submitted work can't be un-submitted (Python
+                    # threads have no preemptive cancel) — just stop
+                    # refilling the pool so it drains rather than growing.
+                    continue
+                # Refill one slot per completed future, keeping ~pool_size
+                # in flight rather than waiting for a full batch to finish.
+                for _ in done:
+                    submit_next()
 
         result = {"runs": runs, "errors": errors, "cancelled": cancelled}
         if not runs:
diff --git a/backend/ipred_client.py b/backend/ipred_client.py
index 678a133..24c7799 100644
--- a/backend/ipred_client.py
+++ b/backend/ipred_client.py
@@ -4,7 +4,8 @@
 
 import logging
 import os
-from typing import Any
+from contextlib import contextmanager
+from typing import Any, Iterator
 
 import httpx
 
@@ -26,6 +27,30 @@ def _client(timeout: float = 300.0) -> httpx.Client:
     return httpx.Client(base_url=ipred_url(), timeout=timeout)
 
 
+def new_shared_client(timeout: float = 300.0) -> httpx.Client:
+    """A `httpx.Client` a caller owns and reuses across many calls (e.g. one
+    per slice in a volume-wide batch-apply job), instead of the fresh
+    open/close-per-call `_client()` every other function here defaults to.
+    `httpx.Client` is documented safe for concurrent use from multiple
+    threads, so this is also what a thread-pooled batch job should share.
+    Caller is responsible for closing it (a `with` block or `.close()`).
+    """
+    return httpx.Client(base_url=ipred_url(), timeout=timeout)
+
+
+@contextmanager
+def _use_client(client: httpx.Client | None, timeout: float = 300.0) -> Iterator[httpx.Client]:
+    """Yield `client` if given (never closing it — the caller owns its
+    lifecycle), else open-and-close a fresh one exactly like every call site
+    here did before `client=` params existed. Keeps every function's
+    single-call default behavior unchanged for callers that don't pass one."""
+    if client is not None:
+        yield client
+        return
+    with _client(timeout=timeout) as owned:
+        yield owned
+
+
 def health() -> dict[str, Any]:
     """GET /health."""
     with _client(timeout=5.0) as client:
@@ -143,8 +168,10 @@ def preprocess(
     composition_id: str | None = None,
     slice_index: int = 0,
     array_ref: str | None = None,
+    client: httpx.Client | None = None,
 ) -> dict[str, Any]:
-    """POST /preprocess."""
+    """POST /preprocess. Pass `client` (see `new_shared_client`) to reuse a
+    connection across many calls instead of opening a fresh one each time."""
     body: dict[str, Any] = {
         "session_id": session_id,
         "slice_index": slice_index,
@@ -155,8 +182,18 @@ def preprocess(
         body["feature_setup_id"] = feature_setup_id
     if array_ref:
         body["array_ref"] = array_ref
-    with _client() as client:
-        r = client.post("/preprocess", json=body)
+    with _use_client(client) as c:
+        r = c.post("/preprocess", json=body)
+        r.raise_for_status()
+        return r.json()
+
+
+def delete_feature_bank(feature_id: str, *, client: httpx.Client | None = None) -> dict[str, Any]:
+    """DELETE /features/{id} — release a feature bank's disk blob once nothing
+    later in the current job needs it (see ipred_batch_jobs.py's volume-apply
+    job, the only caller)."""
+    with _use_client(client) as c:
+        r = c.delete(f"/features/{feature_id}")
         r.raise_for_status()
         return r.json()
 
@@ -223,16 +260,23 @@ def infer(
     model_id: str | None = None,
     feature_id: str | None = None,
     alpha: float = 0.05,
+    store_probabilities: bool = True,
+    client: httpx.Client | None = None,
 ) -> dict[str, Any]:
-    """POST /infer."""
-    with _client() as client:
-        r = client.post(
+    """POST /infer. `store_probabilities=False` skips persisting the run's
+    full probability array — see `train_infer.run_infer`'s doc for why a
+    volume-wide batch-apply job (the only caller that passes `False`) never
+    needs it. Pass `client` (see `new_shared_client`) to reuse a connection
+    across many calls instead of opening a fresh one each time."""
+    with _use_client(client) as c:
+        r = c.post(
             "/infer",
             json={
                 "session_id": session_id,
                 "model_id": model_id,
                 "feature_id": feature_id,
                 "alpha": alpha,
+                "store_probabilities": store_probabilities,
             },
         )
         r.raise_for_status()
diff --git a/backend/tests/test_ipred_batch_jobs.py b/backend/tests/test_ipred_batch_jobs.py
new file mode 100644
index 0000000..ae0694a
--- /dev/null
+++ b/backend/tests/test_ipred_batch_jobs.py
@@ -0,0 +1,134 @@
+"""Tests for the parallelized iPred volume-apply job (ipred_batch_jobs.py).
+
+Uses real export_jobs (in-memory registry, no I/O) and monkeypatches
+ipred_client_mod so no real HTTP/ipred service is needed.
+"""
+from __future__ import annotations
+
+import threading
+import time
+from contextlib import contextmanager
+from typing import Any
+
+import export_jobs
+import ipred_batch_jobs
+import ipred_client as ipred_client_mod
+import pytest
+
+
+@contextmanager
+def _fake_shared_client():
+    yield object()  # never actually used for HTTP in these tests
+
+
+@pytest.fixture(autouse=True)
+def fake_shared_client(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(ipred_client_mod, "new_shared_client", _fake_shared_client)
+
+
+def test_one_failing_slice_does_not_abort_the_rest(monkeypatch: pytest.MonkeyPatch) -> None:
+    def fake_preprocess(*, session_id, feature_setup_id, composition_id, slice_index, client):
+        if slice_index == 2:
+            raise RuntimeError("boom")
+        return {"feature_id": f"fid-{slice_index}"}
+
+    def fake_infer(*, session_id, model_id, feature_id, alpha, store_probabilities, client):
+        assert store_probabilities is False  # volume-apply must never persist proba.npy
+        return {"run_id": f"run-{feature_id}"}
+
+    deleted: list[str] = []
+
+    def fake_delete(feature_id, *, client):
+        deleted.append(feature_id)
+        return {"deleted": True}
+
+    monkeypatch.setattr(ipred_client_mod, "preprocess", fake_preprocess)
+    monkeypatch.setattr(ipred_client_mod, "infer", fake_infer)
+    monkeypatch.setattr(ipred_client_mod, "delete_feature_bank", fake_delete)
+
+    jid = export_jobs.new_job("")
+    ipred_batch_jobs.run_ipred_volume_apply_job(
+        jid,
+        session_id="s1",
+        model_id="m1",
+        slice_indices=[0, 1, 2, 3, 4],
+        composition_id=None,
+        feature_setup_id=None,
+        alpha=0.05,
+    )
+
+    job = export_jobs.get_job(jid)
+    assert job["state"] == "done"
+    result = job["result"]
+    assert sorted(int(k) for k in result["runs"]) == [0, 1, 3, 4]
+    assert result["errors"] == [{"slice": 2, "error": "boom"}]
+    assert result["cancelled"] is False
+    # No feature bank leaked, including for the failed slice (preprocess for
+    # slice 2 never succeeded, so there's nothing of its to delete).
+    assert sorted(deleted) == ["fid-0", "fid-1", "fid-3", "fid-4"]
+
+
+def test_feature_bank_is_released_even_when_infer_fails(monkeypatch: pytest.MonkeyPatch) -> None:
+    def fake_preprocess(*, session_id, feature_setup_id, composition_id, slice_index, client):
+        return {"feature_id": f"fid-{slice_index}"}
+
+    def fake_infer(*, session_id, model_id, feature_id, alpha, store_probabilities, client):
+        raise RuntimeError("infer exploded")
+
+    deleted: list[str] = []
+    monkeypatch.setattr(ipred_client_mod, "preprocess", fake_preprocess)
+    monkeypatch.setattr(ipred_client_mod, "infer", fake_infer)
+    monkeypatch.setattr(
+        ipred_client_mod, "delete_feature_bank",
+        lambda feature_id, *, client: deleted.append(feature_id),
+    )
+
+    jid = export_jobs.new_job("")
+    ipred_batch_jobs.run_ipred_volume_apply_job(
+        jid, session_id="s1", model_id="m1", slice_indices=[0, 1],
+        composition_id=None, feature_setup_id=None, alpha=0.05,
+    )
+
+    job = export_jobs.get_job(jid)
+    # Both slices failed (infer always raises) -> job reports an error state,
+    # but the orphaned-bank cleanup must still have run for each.
+    assert job["state"] == "error"
+    assert sorted(deleted) == ["fid-0", "fid-1"]
+
+
+def test_cancellation_stops_submitting_new_slices(monkeypatch: pytest.MonkeyPatch) -> None:
+    started: list[int] = []
+    lock = threading.Lock()
+
+    def fake_preprocess(*, session_id, feature_setup_id, composition_id, slice_index, client):
+        with lock:
+            started.append(slice_index)
+        # First slice requests cancellation while still "in flight", so the
+        # pool must not keep refilling after it's observed.
+        if slice_index == 0:
+            export_jobs.request_cancel(jid_holder["jid"])
+            time.sleep(0.05)
+        return {"feature_id": f"fid-{slice_index}"}
+
+    def fake_infer(*, session_id, model_id, feature_id, alpha, store_probabilities, client):
+        return {"run_id": f"run-{feature_id}"}
+
+    monkeypatch.setattr(ipred_client_mod, "preprocess", fake_preprocess)
+    monkeypatch.setattr(ipred_client_mod, "infer", fake_infer)
+    monkeypatch.setattr(ipred_client_mod, "delete_feature_bank", lambda feature_id, *, client: None)
+    monkeypatch.setenv("IPRED_APPLY_CONCURRENCY", "1")  # deterministic: one slice at a time
+
+    jid_holder: dict[str, str] = {}
+    jid = export_jobs.new_job("")
+    jid_holder["jid"] = jid
+
+    ipred_batch_jobs.run_ipred_volume_apply_job(
+        jid, session_id="s1", model_id="m1", slice_indices=[0, 1, 2, 3, 4],
+        composition_id=None, feature_setup_id=None, alpha=0.05,
+    )
+
+    job = export_jobs.get_job(jid)
+    assert job["result"]["cancelled"] is True
+    # With concurrency=1, cancellation is observed right after slice 0
+    # completes — no later slice should ever have started.
+    assert started == [0]
diff --git a/backend/tests/test_ipred_routes.py b/backend/tests/test_ipred_routes.py
index 0e9d166..c8e611e 100644
--- a/backend/tests/test_ipred_routes.py
+++ b/backend/tests/test_ipred_routes.py
@@ -159,16 +159,17 @@ def _train_multi(*, session_id, slices, feature_ids, trainer_id, config):
 async def test_batch_apply_tolerates_one_bad_slice(monkeypatch: pytest.MonkeyPatch) -> None:
     """POST /batch/apply keeps going after one slice fails and reports it in result.errors."""
 
-    def _preprocess(*, session_id, feature_setup_id, composition_id, slice_index, array_ref=None):
+    def _preprocess(*, session_id, feature_setup_id, composition_id, slice_index, array_ref=None, client=None):
         if slice_index == 1:
             raise RuntimeError("boom")
         return {"feature_id": f"feat-{slice_index}"}
 
-    def _infer(*, session_id, model_id, feature_id, alpha):
+    def _infer(*, session_id, model_id, feature_id, alpha, store_probabilities=True, client=None):
         return {"run_id": f"run-{feature_id}"}
 
     monkeypatch.setattr(ipred_client_mod, "preprocess", _preprocess)
     monkeypatch.setattr(ipred_client_mod, "infer", _infer)
+    monkeypatch.setattr(ipred_client_mod, "delete_feature_bank", lambda feature_id, *, client=None: None)
 
     async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
         response = await client.post(
diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index b6583f2..b669db8 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -1,7 +1,7 @@
 /**
  * AnnotatePage — react-konva canvas workspace with sidebar tools.
  */
-import { useState, useEffect, useCallback, useMemo } from 'react';
+import { useState, useEffect, useCallback, useMemo, useRef } from 'react';
 import { useNavigate } from 'react-router';
 import { DownloadSimple, FloppyDisk, ClockCounterClockwise, CircleDashed, ChartBar } from '@phosphor-icons/react';
 import { cn } from '@/lib/utils';
@@ -31,6 +31,7 @@ import PixelClassifierPanel from '@/components/annotate/PixelClassifierPanel';
 import AnnotationCanvas from '@/components/annotate/AnnotationCanvas';
 import { useFeatureChannels } from '@/hooks/useFeatureChannels';
 import { usePixelClassifier } from '@/hooks/usePixelClassifier';
+import { useExportJob } from '@/hooks/useExportJob';
 import { useFeatureManifold } from '@/hooks/useFeatureManifold';
 import { loadLabelPng, labelMapToPolygonShapes } from '@/lib/pixelClf';
 import { ipredRunCommitUrl } from '@/lib/ipredApi';
@@ -80,7 +81,7 @@ function StageSwitcher({ stage, onChange }: { stage: AnnotateStage; onChange: (s
 export default function AnnotatePage() {
   const navigate = useNavigate();
   const { source, kind, serverUri, meta, denoise, setDenoise } = useDatasetStore();
-  const { removeShapes, addShapes, addShapesAcrossSlices, byImage } = useAnnotationStore();
+  const { removeShapes, addShapes, addShapesAcrossSlices, byImage, splitBySlice, negativeSlices } = useAnnotationStore();
   const { selectedShapeIds, setSelectedShapeId, fillOpacity, setFillOpacity } = useToolStore();
   const { classes } = useClassStore();
 
@@ -291,6 +292,18 @@ export default function AnnotatePage() {
   // Fetch + vectorize each completed slice's commit PNG client-side, reusing the
   // same tracer as the single-slice commit — see Phase 4.5's plan for why this
   // stays client-side rather than porting polygon tracing to the ipred service.
+  //
+  // Committed in BATCHES rather than accumulated for the whole volume before
+  // one store write: on a large volume (hundreds-to-thousands of slices), the
+  // old accumulate-everything-then-one-write approach held every slice's
+  // vectorized shapes in memory at once, on top of per-slice PNG-decode/mask
+  // buffers that outpaced GC (only yielding every 4th slice), and was
+  // immediately followed by useDraftSync serializing the whole enlarged
+  // per-sample slice set — this was the actual cause of the reported browser
+  // OOM on volume-wide apply. Writing to the store every VOLUME_COMMIT_BATCH
+  // slices bounds peak memory to one batch and gives the browser a real GC
+  // window between batches (this does mean a multi-batch commit now creates
+  // more than one undo entry — an acceptable tradeoff for a job this size).
   const handleCommitVolumeApply = useCallback(async () => {
     if (!sourceKey) return;
     const result = clf.volumeApplyJob.result as { runs?: Record<string, string> } | null;
@@ -298,7 +311,8 @@ export default function AnnotatePage() {
     if (!runs || commitClassIds.length === 0) return;
     setVolumeApplyCommitting(true);
     try {
-      const bySlice: Record<number, Shape[]> = {};
+      const VOLUME_COMMIT_BATCH = 50;
+      let bySlice: Record<number, Shape[]> = {};
       let processed = 0;
       for (const [sliceKey, runId] of Object.entries(runs)) {
         const sliceIdx = Number(sliceKey);
@@ -311,8 +325,13 @@ export default function AnnotatePage() {
         });
         if (shapes.length) bySlice[sliceIdx] = shapes;
         processed += 1;
-        // Yield every few slices so vectorizing a large volume doesn't freeze the tab.
-        if (processed % 4 === 0) await new Promise((resolve) => setTimeout(resolve, 0));
+        // Yield every slice (not just every 4th) so the browser gets a real
+        // chance to garbage-collect the per-slice decode/mask buffers below.
+        await new Promise((resolve) => setTimeout(resolve, 0));
+        if (processed % VOLUME_COMMIT_BATCH === 0 && Object.keys(bySlice).length) {
+          addShapesAcrossSlices(sourceKey, bySlice);
+          bySlice = {};
+        }
       }
       if (Object.keys(bySlice).length) addShapesAcrossSlices(sourceKey, bySlice);
       clf.resetVolumeApplyJob();
@@ -321,6 +340,41 @@ export default function AnnotatePage() {
     }
   }, [sourceKey, clf, byImage, commitClassIds, addShapesAcrossSlices]);
 
+  // "Push to Tiled + View in 3D" — a direct, one-click path from the iPred
+  // panel to the 3D view, instead of requiring the user to separately
+  // discover the Export modal's "Sync masks to Tiled" action first (that
+  // dependency wasn't obvious — see the plan doc). Rasterizes and writes
+  // this sample's ENTIRE current shape set (every slice, both origins) to
+  // Tiled's <source>__masks container, then jumps to /volume?mask=fast to
+  // auto-load it, mirroring DownloadModal's own "current sample" scope.
+  const maskSyncJob = useExportJob('mask-sync-view3d');
+  const navigatedAfterSyncRef = useRef(false);
+  const handleSyncToTiledAndView3D = useCallback(() => {
+    if (!source || !kind || kind !== 'tiled') {
+      window.alert('Pushing masks to Tiled only works for Tiled sources.');
+      return;
+    }
+    navigatedAfterSyncRef.current = false;
+    maskSyncJob.startMaskSync({
+      sources: [{
+        kind,
+        source,
+        server_uri: serverUri ?? null,
+        slices: byImage[sourceKey!] ?? {},
+        split_by_slice: splitBySlice[sourceKey!] ?? {},
+        negative_slices: negativeSlices[sourceKey!] ?? [],
+      }],
+      classes,
+    });
+  }, [source, kind, serverUri, sourceKey, byImage, splitBySlice, negativeSlices, classes, maskSyncJob]);
+
+  useEffect(() => {
+    if (maskSyncJob.state.status === 'done' && !navigatedAfterSyncRef.current) {
+      navigatedAfterSyncRef.current = true;
+      navigate('/volume?mask=fast');
+    }
+  }, [maskSyncJob.state.status, navigate]);
+
   const classLabelForId = useCallback(
     (classId: number) => {
       const c = classes.find((x) => x.classId === classId);
@@ -576,6 +630,9 @@ export default function AnnotatePage() {
                 onCancelVolumeApply={() => { void handleCancelVolumeApply(); }}
                 onDismissVolumeApply={clf.resetVolumeApplyJob}
                 onTrainDeepModel={() => navigate('/train')}
+                onSyncToTiledAndView3D={handleSyncToTiledAndView3D}
+                syncingToTiled={maskSyncJob.state.status === 'running'}
+                syncToTiledError={maskSyncJob.state.status === 'error' ? maskSyncJob.state.error : null}
                 manifoldParams={manifold.params}
                 onManifoldParamsChange={manifold.setParams}
                 manifoldSampling={manifold.sampling}
diff --git a/frontend/src/app/pages/VolumePage.tsx b/frontend/src/app/pages/VolumePage.tsx
index 68e9552..a4a4015 100644
--- a/frontend/src/app/pages/VolumePage.tsx
+++ b/frontend/src/app/pages/VolumePage.tsx
@@ -23,6 +23,8 @@ import { useQuery } from '@tanstack/react-query';
 import { Cube } from '@phosphor-icons/react';
 import { API_BASE } from '@/config';
 import { useDatasetStore } from '@/stores/datasetStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { buildSourceKey } from '@/lib/sourceKey';
 import { buildZarrUrl, describeUnavailable, type ZarrUnavailable } from '@/lib/zarrUrl';
 import type { ServerInfo } from '@/types/server';
 import VolumeViewer, { webGpuAvailability, type WebGpuViewerInstance } from '@/components/volume/VolumeViewer';
@@ -53,7 +55,9 @@ function Notice({ title, detail, hint }: { title: string; detail: string; hint?:
 }
 
 export default function VolumePage() {
-  const { kind, source, serverUri } = useDatasetStore();
+  const { kind, source, serverUri, meta } = useDatasetStore();
+  const byImage = useAnnotationStore((s) => s.byImage);
+  const sourceKey = source && kind ? buildSourceKey(kind as 'tiled' | 'local', source, serverUri) : null;
   const [bootError, setBootError] = useState<string | null>(null);
   const [viewerInstance, setViewerInstance] = useState<WebGpuViewerInstance | null>(null);
   // Bumped after a rebuild — folded into VolumeViewer's `key` below to force
@@ -150,6 +154,10 @@ export default function VolumePage() {
         source={source}
         serverUri={resolvedUri}
         autoLoadSlot={autoLoadSlot}
+        liveShapes={sourceKey ? byImage[sourceKey] : undefined}
+        imageWidth={meta?.width}
+        imageHeight={meta?.height}
+        nSlices={meta?.nSlices}
       />
       {node?.mode === 'sidecar' && source && (
         <div className="pointer-events-none absolute right-3 top-3 z-10">
diff --git a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
index 707b140..e99721e 100644
--- a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
+++ b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
@@ -3,7 +3,7 @@
  * Suggest-Labels manifold loop folded in and conformal coverage surfaced as
  * headline guidance (not a legend entry).
  */
-import { Brain, CaretLeft, CaretRight, CircleNotch, Compass, TreeStructure } from '@phosphor-icons/react';
+import { Brain, CaretLeft, CaretRight, CircleNotch, Compass, Cube, TreeStructure, WarningCircle } from '@phosphor-icons/react';
 import type { ClfParams, ClfPredictCounts, ClfTrainResult } from '@/hooks/usePixelClassifier';
 import type { ManifoldParams } from '@/hooks/useFeatureManifold';
 import { cn } from '@/lib/utils';
@@ -110,6 +110,14 @@ export interface PixelClassifierPanelProps {
   // on this sample becomes the training set, pre-selected there.
   onTrainDeepModel?: () => void;
 
+  // Push this sample's current shapes (every slice, both origins) to Tiled's
+  // <source>__masks container and jump straight to the 3D view's Fast
+  // (iPred) layer — the direct path that skips having to separately
+  // discover the Export modal's "Sync masks to Tiled" action first.
+  onSyncToTiledAndView3D?: () => void;
+  syncingToTiled?: boolean;
+  syncToTiledError?: string | null;
+
   // Suggest-labels (manifold), folded into the same loop instead of a separate panel.
   manifoldParams: ManifoldParams;
   onManifoldParamsChange: (p: ManifoldParams) => void;
@@ -174,6 +182,9 @@ export default function PixelClassifierPanel({
   onCancelVolumeApply,
   onDismissVolumeApply,
   onTrainDeepModel,
+  onSyncToTiledAndView3D,
+  syncingToTiled = false,
+  syncToTiledError = null,
   manifoldParams,
   onManifoldParamsChange,
   manifoldSampling,
@@ -480,6 +491,27 @@ export default function PixelClassifierPanel({
             </div>
           )}
 
+          {onSyncToTiledAndView3D && annotatedSliceCount > 0 && (
+            <div className="flex flex-col gap-1">
+              <button
+                type="button"
+                onClick={onSyncToTiledAndView3D}
+                disabled={syncingToTiled}
+                title="Write this sample's current shapes to Tiled, then open the 3D view with the Fast (iPred) layer loaded"
+                className="flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border border-gray-200 bg-white text-gray-800 hover:bg-sky-50 hover:border-sky-300 transition-colors disabled:opacity-50"
+              >
+                {syncingToTiled ? <CircleNotch size={13} className="animate-spin" /> : <Cube size={13} />}
+                {syncingToTiled ? 'Pushing to Tiled…' : 'Push to Tiled + view in 3D'}
+              </button>
+              {syncToTiledError && (
+                <p className="flex items-start gap-1 text-[10px] text-red-600">
+                  <WarningCircle size={12} className="mt-0.5 shrink-0" />
+                  {syncToTiledError}
+                </p>
+              )}
+            </div>
+          )}
+
           {onTrainDeepModel && annotatedSliceCount > 0 && (
             <button
               type="button"
diff --git a/frontend/src/components/volume/MaskLayersPanel.tsx b/frontend/src/components/volume/MaskLayersPanel.tsx
index b7838e6..0a4c425 100644
--- a/frontend/src/components/volume/MaskLayersPanel.tsx
+++ b/frontend/src/components/volume/MaskLayersPanel.tsx
@@ -6,20 +6,22 @@
  * that meaning ("fast" vs "deep", which Tiled container each comes from)
  * actually lives, kept out of the vendored viewer entirely.
  *
- * Both slots are Tiled-backed for now (`buildMaskZarrUrl`), pointed at the
- * two independent `<source>__masks` / `<source>__masks_deep` containers
- * `tiled_mask_sync.write_masks_to_tiled` writes (see its `container_suffix`
- * doc). A zero-latency, no-Tiled-round-trip live preview for the Fast slot
- * (rasterizing current Annotate-tab predictions client-side via
- * `loadMaskFromArray`) is a deliberately deferred follow-up — this repo has
- * no existing client-side volume-rasterization code to build on, unlike the
- * fork's `labelVolume.ts`/`volumeDims.ts`, which assume a server-side
- * downsampled-raw-volume endpoint this app's `/volume` page does not have
- * (it streams the real Tiled Zarr pyramid directly instead).
+ * Deep is always Tiled-backed (`buildMaskZarrUrl`), pointed at the
+ * `<source>__masks_deep` container `tiled_mask_sync.write_masks_to_tiled`
+ * writes (see its `container_suffix` doc) — there is no local equivalent for
+ * a from-scratch-trained model. Fast defaults to a zero-latency, no-network
+ * "Live" mode instead: `buildLiveMaskVolume` rasterizes the sample's CURRENT
+ * shapes (every slice, client-side) into a coarse class-id array and loads
+ * it via `loadMaskFromArray` — no "Sync masks to Tiled" step required first.
+ * Fast can still be pointed at the Tiled-backed `<source>__masks` container
+ * (the precise result, built by the real backend rasterizer) via its own
+ * toggle.
  */
 import { useEffect, useRef, useState } from 'react';
 import { Eye, EyeSlash, CircleNotch } from '@phosphor-icons/react';
 import { buildMaskZarrUrl } from '@/lib/zarrUrl';
+import { buildLiveMaskVolume } from '@/lib/volumeMaskPreview';
+import type { Shape } from '@/stores/annotationStore';
 import type { WebGpuViewerInstance } from './VolumeViewer';
 
 type MaskClasses = NonNullable<ReturnType<WebGpuViewerInstance['getMaskClasses']>>;
@@ -28,13 +30,18 @@ interface SlotConfig {
   slot: 0 | 1;
   label: string;
   suffix: '' | '_deep';
+  /** Only the Fast slot has a no-Tiled-round-trip option — Deep is always a
+   * from-scratch-trained model's saved output, nothing to rasterize live. */
+  canLive: boolean;
 }
 
 const SLOTS: SlotConfig[] = [
-  { slot: 0, label: 'Fast (iPred)', suffix: '' },
-  { slot: 1, label: 'Deep (dlsia)', suffix: '_deep' },
+  { slot: 0, label: 'Fast (iPred)', suffix: '', canLive: true },
+  { slot: 1, label: 'Deep (dlsia)', suffix: '_deep', canLive: false },
 ];
 
+type SourceMode = 'live' | 'tiled';
+
 interface SlotState {
   loading: boolean;
   error: string | null;
@@ -42,6 +49,8 @@ interface SlotState {
   enabled: boolean;
   opacity: number;
   classes: MaskClasses;
+  /** Ignored for slots where `canLive` is false. */
+  mode: SourceMode;
 }
 
 const initialSlotState: SlotState = {
@@ -51,6 +60,7 @@ const initialSlotState: SlotState = {
   enabled: true,
   opacity: 0.6,
   classes: [],
+  mode: 'live',
 };
 
 interface MaskLayersPanelProps {
@@ -62,9 +72,17 @@ interface MaskLayersPanelProps {
    * hand-off from Train/Annotate arrives here already knowing which result
    * the user just produced, so it shouldn't need a second manual click. */
   autoLoadSlot?: 0 | 1;
+  /** Current sample's shapes (every slice) for the Fast slot's "Live" mode —
+   * `byImage[sourceKey]` from `annotationStore`, undefined if none open. */
+  liveShapes?: Record<string, Shape[]>;
+  imageWidth?: number;
+  imageHeight?: number;
+  nSlices?: number;
 }
 
-export default function MaskLayersPanel({ instance, kind, source, serverUri, autoLoadSlot }: MaskLayersPanelProps) {
+export default function MaskLayersPanel({
+  instance, kind, source, serverUri, autoLoadSlot, liveShapes, imageWidth, imageHeight, nSlices,
+}: MaskLayersPanelProps) {
   const [state, setState] = useState<Record<0 | 1, SlotState>>({
     0: initialSlotState,
     1: initialSlotState,
@@ -86,13 +104,49 @@ export default function MaskLayersPanel({ instance, kind, source, serverUri, aut
     if (!instance || autoLoadSlot === undefined || autoLoadedFor.current === instance) return;
     autoLoadedFor.current = instance;
     const cfg = SLOTS.find((s) => s.slot === autoLoadSlot);
-    if (cfg) void load(cfg);
+    if (!cfg) return;
+    // A "View in 3D" hand-off means the user just pushed fresh data to
+    // Tiled (or is coming from a saved dlsia run) — always the Tiled-backed
+    // result here, regardless of whichever mode the Fast slot's toggle was
+    // last left on.
+    setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], mode: 'tiled' } }));
+    void load(cfg, 'tiled');
     // eslint-disable-next-line react-hooks/exhaustive-deps -- `load` is redefined every render but stable in effect: it only reads current instance/kind/source/serverUri via closure, matching the effect's own deps.
   }, [instance, autoLoadSlot]);
 
   if (!instance) return null;
 
-  const load = async (cfg: SlotConfig) => {
+  const load = async (cfg: SlotConfig, modeOverride?: SourceMode) => {
+    const mode = modeOverride ?? state[cfg.slot].mode;
+
+    if (cfg.canLive && mode === 'live') {
+      const volume = imageWidth && imageHeight && nSlices
+        ? buildLiveMaskVolume(liveShapes ?? {}, imageWidth, imageHeight, nSlices)
+        : null;
+      if (!volume) {
+        setState((s) => ({
+          ...s,
+          [cfg.slot]: { ...s[cfg.slot], error: 'Nothing annotated for this sample yet.', loading: false },
+        }));
+        return;
+      }
+      setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], loading: true, error: null } }));
+      try {
+        instance.loadMaskFromArray(cfg.slot, volume.data, volume.dims);
+        const classes = await waitForClasses(instance, cfg.slot);
+        setState((s) => ({
+          ...s,
+          [cfg.slot]: { ...s[cfg.slot], loading: false, loaded: true, classes, error: null },
+        }));
+      } catch (err) {
+        setState((s) => ({
+          ...s,
+          [cfg.slot]: { ...s[cfg.slot], loading: false, error: err instanceof Error ? err.message : String(err) },
+        }));
+      }
+      return;
+    }
+
     const { url, reason } = buildMaskZarrUrl(kind, source, serverUri, cfg.suffix);
     if (!url) {
       setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], error: reason ?? 'No source open', loading: false } }));
@@ -107,7 +161,7 @@ export default function MaskLayersPanel({ instance, kind, source, serverUri, aut
       const classes = await waitForClasses(instance, cfg.slot);
       setState((s) => ({
         ...s,
-        [cfg.slot]: { ...s[cfg.slot], loading: false, loaded: true, classes, error: classes.length === 0 ? 'Loaded, but no classes found (every voxel is background, or this dataset has no mask pushed yet).' : null },
+        [cfg.slot]: { ...s[cfg.slot], loading: false, loaded: true, classes, error: classes.length === 0 ? 'Loaded, but no classes found (every voxel is background, or nothing has been pushed to Tiled for this dataset yet).' : null },
       }));
     } catch (err) {
       setState((s) => ({
@@ -129,6 +183,10 @@ export default function MaskLayersPanel({ instance, kind, source, serverUri, aut
     }
   };
 
+  const setMode = (cfg: SlotConfig, mode: SourceMode) => {
+    setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], mode, error: null } }));
+  };
+
   const toggleClass = (cfg: SlotConfig, classId: number) => {
     instance.toggleMaskClassVisible(cfg.slot, classId);
     setState((s) => ({
@@ -172,6 +230,27 @@ export default function MaskLayersPanel({ instance, kind, source, serverUri, aut
               )}
             </div>
 
+            {cfg.canLive && !s.loaded && (
+              <div className="mb-2 flex overflow-hidden rounded border border-sky-800/60 text-[10px]">
+                <button
+                  type="button"
+                  onClick={() => setMode(cfg, 'live')}
+                  title="Rasterize this sample's current shapes directly in the browser — no Tiled sync needed, updates instantly"
+                  className={`flex-1 py-1 ${s.mode === 'live' ? 'bg-sky-600 text-white' : 'bg-transparent text-sky-300 hover:bg-sky-900/60'}`}
+                >
+                  Live
+                </button>
+                <button
+                  type="button"
+                  onClick={() => setMode(cfg, 'tiled')}
+                  title="Load the precise result from Tiled — requires Export → Sync masks to Tiled (or the iPred panel's Push to Tiled button) first"
+                  className={`flex-1 py-1 ${s.mode === 'tiled' ? 'bg-sky-600 text-white' : 'bg-transparent text-sky-300 hover:bg-sky-900/60'}`}
+                >
+                  Tiled
+                </button>
+              </div>
+            )}
+
             {s.error && <p className="mb-2 text-xs text-amber-300">{s.error}</p>}
 
             {s.loaded && s.classes.length > 0 && (
diff --git a/frontend/src/lib/volumeMaskPreview.test.ts b/frontend/src/lib/volumeMaskPreview.test.ts
new file mode 100644
index 0000000..9c3de28
--- /dev/null
+++ b/frontend/src/lib/volumeMaskPreview.test.ts
@@ -0,0 +1,57 @@
+import { describe, it, expect } from 'vitest';
+import { buildLiveMaskVolume } from './volumeMaskPreview';
+import type { Shape } from '@/stores/annotationStore';
+
+const rect = (classId: number, x: number, y: number, w: number, h: number): Shape => ({
+  id: `${classId}-${x}-${y}`,
+  kind: 'rectangle',
+  classId,
+  x, y, w, h,
+});
+
+describe('buildLiveMaskVolume', () => {
+  it('returns null when there are no shapes anywhere', () => {
+    expect(buildLiveMaskVolume({}, 64, 64, 4)).toBeNull();
+    expect(buildLiveMaskVolume({ '0': [] }, 64, 64, 4)).toBeNull();
+  });
+
+  it('returns null when nSlices is not positive', () => {
+    expect(buildLiveMaskVolume({ '0': [rect(1, 0, 0, 4, 4)] }, 64, 64, 0)).toBeNull();
+  });
+
+  it('produces dims in [width, height, depth] order matching the real dataset shape', () => {
+    const volume = buildLiveMaskVolume({ '0': [rect(1, 0, 0, 8, 8)] }, 64, 32, 10, 1000);
+    expect(volume).not.toBeNull();
+    expect(volume!.dims).toEqual([64, 32, 10]);
+    expect(volume!.data.length).toBe(64 * 32 * 10);
+  });
+
+  it('leaves unannotated slices as all-background (0)', () => {
+    const volume = buildLiveMaskVolume({ '2': [rect(1, 0, 0, 8, 8)] }, 16, 16, 4, 1000);
+    const [w, h] = volume!.dims;
+    const sliceBytes = w * h;
+    const slice0 = volume!.data.subarray(0, sliceBytes);
+    expect(slice0.every((v) => v === 0)).toBe(true);
+  });
+
+  it('paints the annotated slice with the shape\'s class id', () => {
+    const volume = buildLiveMaskVolume({ '1': [rect(3, 0, 0, 8, 8)] }, 16, 16, 4, 1000);
+    const [w, h] = volume!.dims;
+    const sliceBytes = w * h;
+    const slice1 = volume!.data.subarray(sliceBytes, sliceBytes * 2);
+    expect(slice1[0]).toBe(3);
+    expect(Math.max(...slice1)).toBe(3);
+  });
+
+  it('resolves overlapping different-class shapes by ascending class id (higher wins)', () => {
+    const shapes = [rect(1, 0, 0, 16, 16), rect(5, 0, 0, 16, 16)];
+    const volume = buildLiveMaskVolume({ '0': shapes }, 16, 16, 1, 1000);
+    expect(volume!.data[0]).toBe(5);
+  });
+
+  it('downsamples to at most maxDim on the longest in-plane edge', () => {
+    const volume = buildLiveMaskVolume({ '0': [rect(1, 0, 0, 100, 100)] }, 2048, 1024, 2, 256);
+    const [w, h] = volume!.dims;
+    expect(Math.max(w, h)).toBeLessThanOrEqual(256);
+  });
+});
diff --git a/frontend/src/lib/volumeMaskPreview.ts b/frontend/src/lib/volumeMaskPreview.ts
new file mode 100644
index 0000000..a2bc094
--- /dev/null
+++ b/frontend/src/lib/volumeMaskPreview.ts
@@ -0,0 +1,88 @@
+/**
+ * Client-side, no-network rasterization of a sample's CURRENT annotation
+ * shapes (every slice, live in the annotation store) into a class-id volume
+ * — feeds the 3D view's "Fast (iPred)" mask layer via
+ * `WebGpuViewerInstance.loadMaskFromArray(slot, data, dims)` without
+ * requiring a "Sync masks to Tiled" round trip first.
+ *
+ * Unlike a Tiled-backed mask, this does NOT need to match the primary
+ * volume's own chosen texture resolution: `sampleMask()` in the viewer's WGSL
+ * maps a ray position to the mask's OWN voxel grid independently of the
+ * primary's (`frame.maskCtl.yzw` carries the mask's dims) — the two are only
+ * required to describe the same normalized [0,1]^3 box, not the same voxel
+ * count. That's what makes a coarse, fast, purely-client-side rasterization
+ * a legitimate live preview rather than something that needs to negotiate
+ * resolution with the renderer.
+ *
+ * Deliberately approximate, not a port of the backend's exact rasterizer
+ * (`coco_export.shape_to_mask`'s per-SHAPE "last one wins" order): shapes are
+ * grouped by class and each class's union is painted, so overlapping shapes
+ * of DIFFERENT classes resolve by ascending class id rather than draw order.
+ * Good enough for "does this look roughly right in 3D," not a source of
+ * truth — "Push to Tiled" is what produces the precise result.
+ */
+import { rasterizeUnion, gridFor } from './rasterize';
+import type { Shape } from '@/stores/annotationStore';
+
+/** Longest in-plane edge the live preview rasterizes at. Coarser than the
+ * default 2-D mask grid (1600) on purpose — this runs entirely on the main
+ * thread for every slice up front, and a 3-D GPU mask texture reads it at
+ * whatever resolution it's given (see this module's own doc), so there is no
+ * fidelity reason to go higher for a quick preview. */
+const LIVE_PREVIEW_MAX_DIM = 256;
+
+export interface LiveMaskVolume {
+  data: Uint8Array;
+  /** `[width, height, depth]` — the order `loadMaskFromArray` expects. */
+  dims: readonly [number, number, number];
+}
+
+/**
+ * Rasterize every slice of `byImageForSource` (already scoped to one sample)
+ * into one `width*height*depth` class-id volume, `depth` = `nSlices` (every
+ * slice in the sample gets a slot, annotated or not, so the volume's aspect
+ * ratio matches the real dataset rather than just the annotated subset).
+ *
+ * Returns `null` if there is nothing to rasterize (no shapes anywhere) —
+ * callers should treat that as "nothing to load," not an error.
+ */
+export function buildLiveMaskVolume(
+  byImageForSource: Record<string, Shape[]>,
+  imageWidth: number,
+  imageHeight: number,
+  nSlices: number,
+  maxDim: number = LIVE_PREVIEW_MAX_DIM,
+): LiveMaskVolume | null {
+  const hasAnyShape = Object.values(byImageForSource).some((shapes) => shapes.length > 0);
+  if (!hasAnyShape || nSlices <= 0) return null;
+
+  const { gw, gh, scale } = gridFor(imageWidth, imageHeight, maxDim);
+  const depth = Math.max(1, Math.floor(nSlices));
+  const data = new Uint8Array(gw * gh * depth);
+
+  for (let z = 0; z < depth; z++) {
+    const shapes = byImageForSource[String(z)];
+    if (!shapes || shapes.length === 0) continue;
+
+    const byClass = new Map<number, Shape[]>();
+    for (const shape of shapes) {
+      const list = byClass.get(shape.classId);
+      if (list) list.push(shape);
+      else byClass.set(shape.classId, [shape]);
+    }
+
+    const slice = data.subarray(z * gw * gh, (z + 1) * gw * gh);
+    // Ascending class id so a higher id visually "wins" ties — arbitrary but
+    // deterministic, matching mask_pyramid.majority_downsample's own
+    // tie-break convention on the backend.
+    for (const classId of [...byClass.keys()].sort((a, b) => a - b)) {
+      if (classId <= 0 || classId > 255) continue; // 0 is background by convention
+      const mask = rasterizeUnion(byClass.get(classId)!, gw, gh, scale);
+      for (let i = 0; i < mask.length; i++) {
+        if (mask[i]) slice[i] = classId;
+      }
+    }
+  }
+
+  return { data, dims: [gw, gh, depth] };
+}
diff --git a/frontend/vendor/view_tomography_recon_app b/frontend/vendor/view_tomography_recon_app
index 208cc55..55ba459 160000
--- a/frontend/vendor/view_tomography_recon_app
+++ b/frontend/vendor/view_tomography_recon_app
@@ -1 +1 @@
-Subproject commit 208cc551794125e27e4c51e7a417f2a7bfea7442
+Subproject commit 55ba459f95f966e47bb76f6e5ff528730757eb74
diff --git a/ipred/src/ipred/api.py b/ipred/src/ipred/api.py
index e49cf60..1981bb8 100644
--- a/ipred/src/ipred/api.py
+++ b/ipred/src/ipred/api.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import logging
+import shutil
 from contextlib import asynccontextmanager
 from pathlib import Path
 from typing import Any, Optional
@@ -107,6 +108,7 @@ class InferBody(BaseModel):
     model_id: Optional[str] = None
     feature_id: Optional[str] = None
     alpha: float = 0.05
+    store_probabilities: bool = True
 
 
 class RethresholdBody(BaseModel):
@@ -347,6 +349,22 @@ def api_feature_channel(feature_id: str, index: int) -> Response:
     return FileResponse(path, media_type="image/png")
 
 
+@app.delete("/features/{feature_id}")
+def api_delete_feature_bank(feature_id: str) -> dict[str, Any]:
+    """Delete a feature bank (DB row + its on-disk blob dir).
+
+    For batch-apply jobs (Phase 4.5's "Apply across volume") to release each
+    slice's feature bank immediately after it's used — see
+    `Catalog.delete_feature_bank`'s doc for why this exists. Deleting an
+    already-gone or unknown feature_id is a no-op, not an error: cleanup
+    calls are best-effort and must never fail the job that triggered them.
+    """
+    blob_dir = get_catalog().delete_feature_bank(feature_id)
+    if blob_dir:
+        shutil.rmtree(blob_dir, ignore_errors=True)
+    return {"deleted": blob_dir is not None}
+
+
 @app.post("/train")
 def api_train(body: TrainBody) -> dict[str, Any]:
     """Train via trainer plugin."""
@@ -399,6 +417,7 @@ def api_infer(body: InferBody) -> dict[str, Any]:
             model_id=body.model_id,
             feature_id=body.feature_id,
             alpha=body.alpha,
+            store_probabilities=body.store_probabilities,
         )
     except KeyError as exc:
         raise HTTPException(404, str(exc)) from exc
diff --git a/ipred/src/ipred/array_source.py b/ipred/src/ipred/array_source.py
index 1cf07cd..02ac0d1 100644
--- a/ipred/src/ipred/array_source.py
+++ b/ipred/src/ipred/array_source.py
@@ -49,11 +49,20 @@ def _read_local(
     if suffix in {".tif", ".tiff"}:
         import tifffile
 
-        arr = tifffile.imread(str(path))
-    else:
-        from PIL import Image as PILImage
-
-        arr = np.asarray(PILImage.open(path))
+        # Read exactly one PAGE, not the whole stack (`tifffile.imread` decodes
+        # every page up front). A batch-apply job over N slices previously
+        # decoded the entire stack N times — an O(N^2) cost that dominated
+        # "predict across all slices" wall-clock time on larger volumes.
+        with tifffile.TiffFile(str(path)) as tif:
+            n_pages = len(tif.pages)
+            if n_pages <= 1:
+                return _index_slice(np.asarray(tif.asarray()), slice_index)
+            idx = int(slice_index) if 0 <= int(slice_index) < n_pages else 0
+            page = np.asarray(tif.pages[idx].asarray())
+        return _index_slice(page, 0)
+    from PIL import Image as PILImage
+
+    arr = np.asarray(PILImage.open(path))
     return _index_slice(np.asarray(arr), slice_index)
 
 
@@ -132,9 +141,27 @@ def _read_tiled(
         # Container of per-slice arrays (not a bare NHW/NHWC array node).
         keys = sorted(node)
         node = node[keys[int(slice_index)]]
-        slice_index = 0
-    data = np.asarray(node)
-    return _index_slice(data, slice_index)
+        return _index_slice(np.asarray(node), 0)
+
+    # A bare NHW/NHWC array node: index it BEFORE calling np.asarray, so only
+    # the requested slice is fetched/decoded over the wire — `np.asarray(node)`
+    # first (the previous behavior) pulls the ENTIRE stack for every single
+    # slice request, turning an N-slice batch-apply job into an O(N^2) read.
+    # `.shape` is cheap metadata (no data fetch), so it's safe to inspect
+    # before deciding whether/how to index — mirrors `_index_slice`'s own
+    # HWC-vs-NHW heuristic, applied to shape metadata instead of a realized
+    # array. Matches the working pattern in `backend/arrays.py: read_slice`.
+    shape = tuple(int(s) for s in node.shape)
+    if len(shape) <= 2:
+        return np.asarray(node)
+    if len(shape) == 3 and shape[-1] in (1, 3, 4) and (
+        shape[0] > 8 or shape[0] == shape[1]
+    ):
+        return np.asarray(node)  # HWC single image — nothing to index, it's the whole thing
+    # Genuine NHW/NHWC stack: node[idx] is Tiled's own lazy single-slice
+    # fetch — the whole reason this branch exists — so the result is already
+    # exactly one 2-D/HWC frame with no further slicing needed.
+    return np.asarray(node[int(slice_index)])
 
 
 def _index_slice(arr: np.ndarray, slice_index: int) -> np.ndarray:
diff --git a/ipred/src/ipred/catalog.py b/ipred/src/ipred/catalog.py
index 7912209..4506386 100644
--- a/ipred/src/ipred/catalog.py
+++ b/ipred/src/ipred/catalog.py
@@ -352,6 +352,35 @@ def get_feature_bank(self, feature_id: str) -> dict[str, Any] | None:
             ).fetchone()
         return dict(row) if row else None
 
+    def delete_feature_bank(self, feature_id: str) -> str | None:
+        """Delete a feature-bank row and return its `blob_dir` for the caller
+        to remove from disk (this method does not touch the filesystem — it
+        only owns the DB row, matching every other method here).
+
+        Feature banks bake 32-64 full-resolution float32 PCA channels
+        (~1 GB per 2000x2000 slice, see `sam_embed.emb_grid_to_pca_channels`)
+        and were never evicted: a single interactive slice benefits from the
+        cache (re-predicting after a param tweak), but a volume-wide batch
+        job (Phase 4.5's "Apply across volume") touches every slice exactly
+        once, so there is nothing later in that job to reuse the cache for —
+        it just accumulates forever. 690 slices at ~1 GB each is enough to
+        fill a laptop's disk mid-job. Batch-apply calls this right after each
+        slice's inference completes; ordinary single-slice interactive use
+        (train/preprocess/infer outside a batch job) is untouched.
+
+        Returns None if the feature_id doesn't exist (already deleted, or a
+        bad id) — the caller should treat that as a no-op, not an error.
+        """
+        with self.connect() as conn:
+            row = conn.execute(
+                "SELECT blob_dir FROM feature_banks WHERE feature_id = ?",
+                (feature_id,),
+            ).fetchone()
+            if row is None:
+                return None
+            conn.execute("DELETE FROM feature_banks WHERE feature_id = ?", (feature_id,))
+        return row["blob_dir"]
+
     def insert_model(self, record: dict[str, Any]) -> None:
         """Insert a model row."""
         with self.connect() as conn:
diff --git a/ipred/src/ipred/train_infer.py b/ipred/src/ipred/train_infer.py
index bac68ba..23acad0 100644
--- a/ipred/src/ipred/train_infer.py
+++ b/ipred/src/ipred/train_infer.py
@@ -6,6 +6,7 @@
 import logging
 import uuid
 from datetime import datetime, timezone
+from functools import lru_cache
 from io import BytesIO
 from pathlib import Path
 from typing import Any
@@ -520,6 +521,28 @@ def run_train_multi_slice(
     )
 
 
+@lru_cache(maxsize=4)
+def _load_model_cached(trainer_id: str, blob_model_str: str) -> tuple[Any, Any, np.ndarray | None, np.ndarray | None]:
+    """Deserialize a trained model (+ its SAM PCA, if any) once and reuse it.
+
+    Model blob directories are never mutated in place — every training run
+    gets a fresh `uuid.uuid4()` model_id/blob_dir (see `run_train`/
+    `_save_trained_model`) — so caching by `(trainer_id, blob_dir)` can never
+    serve stale weights. Without this, a volume-wide "apply across all
+    slices" job (Phase 4.5) reloaded the CatBoost model and PCA from disk on
+    every single slice instead of once per job.
+    """
+    blob_model = Path(blob_model_str)
+    trainer = get_trainer(trainer_id)
+    handle = trainer.load(blob_model)
+    pca_mean = pca_comp = None
+    pca_path = blob_model / "sam_pca.npz"
+    if pca_path.is_file():
+        z = np.load(pca_path)
+        pca_mean, pca_comp = z["mean"], z["components"]
+    return trainer, handle, pca_mean, pca_comp
+
+
 def run_infer(
     catalog: Catalog,
     *,
@@ -528,8 +551,22 @@ def run_infer(
     feature_id: str | None = None,
     alpha: float = 0.05,
     row_chunk: int = 128,
+    store_probabilities: bool = True,
 ) -> dict[str, Any]:
-    """Full-image predict_proba + conformal maps; persist float16 proba."""
+    """Full-image predict_proba + conformal maps; persist float16 proba.
+
+    `store_probabilities=False` skips writing `proba.npy` to disk (the math
+    is unchanged — `proba` is still computed in memory to derive `commit`/
+    `status`/`membership`). For a volume-wide batch-apply job (Phase 4.5),
+    `proba.npy` is the single largest thing a run writes (a full H×W×K
+    float16 array) and is never read back by that flow — only `commit.png`
+    is (see `AnnotatePage.tsx`'s `handleCommitVolumeApply`). Leave this
+    `True` (the default) for interactive single-slice infer calls: it's what
+    `run_rethreshold`/`threshold_class_map` read back to let a user adjust
+    alpha or view a per-class heatmap after the fact — pointing either of
+    those at a run saved with `store_probabilities=False` raises
+    `FileNotFoundError`, by design.
+    """
     session = catalog.get_session(session_id)
     if session is None:
         raise KeyError(f"unknown session {session_id}")
@@ -544,13 +581,8 @@ def run_infer(
 
     blob_model = Path(model_row["blob_dir"])
     meta = json.loads((blob_model / "meta.json").read_text(encoding="utf-8"))
-    trainer = get_trainer(model_row["trainer_id"])
-    handle = trainer.load(blob_model)
+    trainer, handle, pca_mean, pca_comp = _load_model_cached(model_row["trainer_id"], str(blob_model))
     bank = load_feature_bank_arrays(bank_row["blob_dir"])
-    pca_mean = pca_comp = None
-    if (blob_model / "sam_pca.npz").is_file():
-        z = np.load(blob_model / "sam_pca.npz")
-        pca_mean, pca_comp = z["mean"], z["components"]
 
     class_ids = [int(c) for c in meta["class_ids"]]
     h, w, _ = bank["float_stack"].shape
@@ -585,7 +617,8 @@ def run_infer(
     run_id = uuid.uuid4().hex
     blob = project_blob_dir(session.project_id) / "runs" / run_id
     blob.mkdir(parents=True, exist_ok=True)
-    np.save(blob / "proba.npy", proba)
+    if store_probabilities:
+        np.save(blob / "proba.npy", proba)
     np.save(blob / "commit.npy", commit)
     np.save(blob / "status.npy", status)
     np.save(blob / "membership.npy", membership)
diff --git a/ipred/tests/test_array_source.py b/ipred/tests/test_array_source.py
index 2249221..4fe3a1a 100644
--- a/ipred/tests/test_array_source.py
+++ b/ipred/tests/test_array_source.py
@@ -7,10 +7,21 @@ class _FakeArray:
     def __init__(self, shape):
         self._data = np.zeros(shape, dtype="float32")
         self.structure_family = "array"
+        self.shape = shape
+        # Spies so tests can assert on WHICH access pattern was actually used —
+        # the whole point of the O(N^2) regression fix is that a single-slice
+        # read must index this node, not realize the whole thing via __array__.
+        self.array_calls = 0
+        self.getitem_calls: list[int] = []
 
     def __array__(self, dtype=None):
+        self.array_calls += 1
         return self._data
 
+    def __getitem__(self, idx):
+        self.getitem_calls.append(idx)
+        return self._data[idx]
+
 
 class _FakeContainer(dict):
     structure_family = "container"
@@ -53,3 +64,80 @@ def test_five_level_pyramid_does_not_collapse_to_length_5_array(self) -> None:
         assert data.shape != (5,)
         sliced = array_source._index_slice(data, 0)
         assert sliced.shape == (32, 32)
+
+
+class TestReadTiledLazySlice:
+    """Regression: a batch-apply job over N slices used to call np.asarray(node)
+    (materializing the WHOLE stack) on every single-slice request — an O(N^2)
+    read that dominated "predict across all slices" wall-clock time. A real
+    single-slice read must index the node BEFORE realizing it."""
+
+    def _patch_from_uri(self, monkeypatch, client):
+        import tiled.client
+
+        monkeypatch.setattr(tiled.client, "from_uri", lambda *a, **k: client)
+
+    def test_bare_nhw_stack_indexes_without_materializing_whole_array(self, monkeypatch) -> None:
+        arr = _FakeArray((50, 16, 16))
+        client = _FakeContainer({"ds": arr})
+        self._patch_from_uri(monkeypatch, client)
+
+        out = array_source.read_slice(kind="tiled", source="ds", slice_index=7, server_uri="http://x")
+
+        assert out.shape == (16, 16)
+        assert arr.getitem_calls == [7]
+        assert arr.array_calls == 0  # the whole-stack path must never fire
+
+    def test_container_of_per_slice_arrays_only_realizes_the_requested_one(self, monkeypatch) -> None:
+        wanted = _FakeArray((16, 16))
+        other = _FakeArray((16, 16))
+        client = _FakeContainer({"ds": _FakeContainer({"0": other, "1": wanted})})
+        self._patch_from_uri(monkeypatch, client)
+
+        out = array_source.read_slice(kind="tiled", source="ds", slice_index=1, server_uri="http://x")
+
+        assert out.shape == (16, 16)
+        assert wanted.array_calls == 1
+        assert other.array_calls == 0  # the sibling slice must never be touched
+
+    def test_hwc_single_image_is_returned_whole(self, monkeypatch) -> None:
+        arr = _FakeArray((64, 64, 3))
+        client = _FakeContainer({"ds": arr})
+        self._patch_from_uri(monkeypatch, client)
+
+        out = array_source.read_slice(kind="tiled", source="ds", slice_index=0, server_uri="http://x")
+
+        assert out.shape == (64, 64, 3)
+        assert arr.getitem_calls == []  # a single HWC image has nothing to index into
+
+
+class TestReadLocalTiffPage:
+    """Regression: `_read_local` used to call `tifffile.imread` (decodes every
+    page) for a single-slice request."""
+
+    def test_reads_one_page_of_a_multipage_tiff(self, tmp_path, monkeypatch) -> None:
+        import tifffile
+
+        path = tmp_path / "stack.tif"
+        pages = np.stack([np.full((8, 8), i, dtype=np.uint8) for i in range(5)])
+        tifffile.imwrite(str(path), pages)
+
+        # `tifffile.imread` decodes the whole stack up front — assert the fix
+        # never calls it for a multi-page file.
+        monkeypatch.setattr(
+            tifffile, "imread", lambda *a, **k: (_ for _ in ()).throw(AssertionError("full-stack imread called"))
+        )
+
+        out = array_source._read_local("stack.tif", slice_index=3, root=str(tmp_path))
+        assert out.shape == (8, 8)
+        assert int(out[0, 0]) == 3
+
+    def test_single_page_tiff_still_works(self, tmp_path) -> None:
+        import tifffile
+
+        path = tmp_path / "single.tif"
+        tifffile.imwrite(str(path), np.full((8, 8), 9, dtype=np.uint8))
+
+        out = array_source._read_local("single.tif", slice_index=0, root=str(tmp_path))
+        assert out.shape == (8, 8)
+        assert int(out[0, 0]) == 9
diff --git a/ipred/tests/test_catalog_feature_banks.py b/ipred/tests/test_catalog_feature_banks.py
new file mode 100644
index 0000000..7165624
--- /dev/null
+++ b/ipred/tests/test_catalog_feature_banks.py
@@ -0,0 +1,56 @@
+"""Catalog.delete_feature_bank — cleanup for batch-apply jobs (see
+backend/ipred_batch_jobs.py's volume-apply job, which relies on this to avoid
+accumulating a ~1 GB feature bank per slice with no eviction)."""
+from __future__ import annotations
+
+import pytest
+
+from ipred.catalog import Catalog
+
+
+@pytest.fixture()
+def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return Catalog(tmp_path / "ipred" / "catalog.db")
+
+
+def _insert_bank(catalog: Catalog, feature_id: str, blob_dir: str, slice_index: int = 0) -> None:
+    catalog.insert_feature_bank(
+        {
+            "feature_id": feature_id,
+            "project_id": "proj",
+            "setup_id": "setup",
+            "content_hash": "hash",
+            "slice_index": slice_index,
+            "n_channels": 3,
+            "height": 8,
+            "width": 8,
+            "blob_dir": blob_dir,
+            "setup_snapshot": "{}",
+            "status": "ready",
+            "created_at": "2026-01-01T00:00:00+00:00",
+        }
+    )
+
+
+def test_delete_feature_bank_removes_the_row_and_returns_its_blob_dir(catalog: Catalog) -> None:
+    _insert_bank(catalog, "fid-1", "/blobs/fid-1")
+
+    blob_dir = catalog.delete_feature_bank("fid-1")
+
+    assert blob_dir == "/blobs/fid-1"
+    assert catalog.get_feature_bank("fid-1") is None
+
+
+def test_delete_feature_bank_is_a_noop_for_an_unknown_id(catalog: Catalog) -> None:
+    assert catalog.delete_feature_bank("does-not-exist") is None
+
+
+def test_delete_feature_bank_does_not_touch_other_rows(catalog: Catalog) -> None:
+    _insert_bank(catalog, "fid-1", "/blobs/fid-1", slice_index=0)
+    _insert_bank(catalog, "fid-2", "/blobs/fid-2", slice_index=1)
+
+    catalog.delete_feature_bank("fid-1")
+
+    assert catalog.get_feature_bank("fid-1") is None
+    assert catalog.get_feature_bank("fid-2") is not None
diff --git a/ipred/tests/test_infer_store_probabilities.py b/ipred/tests/test_infer_store_probabilities.py
new file mode 100644
index 0000000..75d46e4
--- /dev/null
+++ b/ipred/tests/test_infer_store_probabilities.py
@@ -0,0 +1,72 @@
+"""run_infer(store_probabilities=False) — the option volume-apply batch jobs
+use (backend/ipred_batch_jobs.py) to skip writing the largest thing a run
+saves, since that flow only ever reads commit.png back."""
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+from PIL import Image as PILImage
+
+from ipred import feature_setups, preprocess, train_infer
+from ipred.catalog import Catalog
+
+
+@pytest.fixture()
+def catalog(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Catalog:
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return Catalog(tmp_path / "ipred" / "catalog.db")
+
+
+def _trained_session(catalog: Catalog, tmp_path) -> str:
+    feature_setups.ensure_default_setups(catalog)
+    img = np.zeros((48, 48), dtype=np.uint8)
+    img[:, 24:] = 220
+    PILImage.fromarray(img, mode="L").save(tmp_path / "blob.png")
+
+    session = catalog.open_session(kind="local", source="blob.png", root=str(tmp_path))
+    preprocess.run_preprocess(catalog, session_id=session.session_id, feature_setup_id="default-skimage")
+    shapes = [
+        {"kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 18, "h": 40},
+        {"kind": "rectangle", "classId": 2, "x": 28, "y": 2, "w": 18, "h": 40},
+    ]
+    train_infer.run_train(
+        catalog, session_id=session.session_id, shapes=shapes,
+        trainer_id="catboost", config={"iterations": 20, "depth": 4, "random_seed": 0},
+    )
+    return session.session_id
+
+
+def test_store_probabilities_false_skips_proba_npy(catalog: Catalog, tmp_path) -> None:
+    session_id = _trained_session(catalog, tmp_path)
+
+    run = train_infer.run_infer(catalog, session_id=session_id, alpha=0.2, store_probabilities=False)
+
+    blob = Path(run["blob_dir"])
+    assert not (blob / "proba.npy").exists()
+    # Everything volume-apply's commit step actually needs must still exist.
+    assert (blob / "commit.png").is_file()
+    assert (blob / "commit.npy").exists()
+    assert (blob / "status.npy").exists()
+    assert (blob / "membership.npy").exists()
+
+
+def test_store_probabilities_true_is_still_the_default(catalog: Catalog, tmp_path) -> None:
+    session_id = _trained_session(catalog, tmp_path)
+
+    run = train_infer.run_infer(catalog, session_id=session_id, alpha=0.2)
+
+    assert (Path(run["blob_dir"]) / "proba.npy").is_file()
+
+
+def test_store_probabilities_false_produces_the_same_commit_map(catalog: Catalog, tmp_path) -> None:
+    """The math must be identical either way — only persistence changes."""
+    session_id = _trained_session(catalog, tmp_path)
+
+    with_proba = train_infer.run_infer(catalog, session_id=session_id, alpha=0.2, store_probabilities=True)
+    without_proba = train_infer.run_infer(catalog, session_id=session_id, alpha=0.2, store_probabilities=False)
+
+    a = np.load(Path(with_proba["blob_dir"]) / "commit.npy")
+    b = np.load(Path(without_proba["blob_dir"]) / "commit.npy")
+    assert np.array_equal(a, b)

From 9129f9c70bc18396e75c2ea008de0a2144211d90 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 2 Sep 2026 10:50:24 -0700
Subject: [PATCH 16/38] ipred and dlsia training were clobbering each other in
 tiled, this should fix that

---
 backend/mask_pyramid.py            | 21 +++++++++++++++++++--
 backend/tests/test_mask_pyramid.py | 28 ++++++++++++++++++++++++++++
 backend/tiled_mask_sync.py         |  4 +++-
 3 files changed, 50 insertions(+), 3 deletions(-)

diff --git a/backend/mask_pyramid.py b/backend/mask_pyramid.py
index 93f6a57..6b932ff 100644
--- a/backend/mask_pyramid.py
+++ b/backend/mask_pyramid.py
@@ -81,7 +81,9 @@ def build_mask_pyramid(semantic: np.ndarray) -> tuple[dict[str, np.ndarray], lis
     return levels, generated
 
 
-def register_mask_pyramid(semantic: np.ndarray, key: str, container: Any) -> dict[str, Any]:
+def register_mask_pyramid(
+    semantic: np.ndarray, key: str, container: Any, *, cache_key: str,
+) -> dict[str, Any]:
     """Build and register *semantic* (a ``(z, y, x)`` uint8 class-id volume) as
     a real OME-NGFF multiscale node named *key* inside *container* (the
     ``<source>__masks`` container ``tiled_mask_sync`` already manages).
@@ -93,6 +95,21 @@ def register_mask_pyramid(semantic: np.ndarray, key: str, container: Any) -> dic
     way it already serves the primary volume's pyramid — just downsampled by
     majority vote instead of mean.
 
+    *key* only names the Tiled sub-node under *container* (always
+    ``"semantic"`` today) — it is NOT unique across datasets or producers.
+    *cache_key* is what actually scopes the on-disk sidecar
+    ``write_pyramid_store`` writes to (``pyramid_cache_root() / cache_key``),
+    and MUST be globally unique per (source, mask producer) — e.g. the
+    already-unique ``<stem>__masks<suffix>`` container name. Passing a
+    constant here (this function's own bug, previously always ``"semantic"``)
+    means every mask sync for every dataset and every producer (iPred vs
+    dlsia) silently overwrites the same on-disk pyramid file: Tiled's
+    per-container metadata stays correct (it lives in Tiled's own DB), but
+    the actual pixel data every such container's registration points at is
+    the same shared file, so whichever sync ran last "wins" for everyone —
+    exactly like ``build_volume`` already keys its own sidecar by the
+    source's stem, which this must match to avoid the same class of bug.
+
     Replaces any previous registration for *key* — a re-sync must refresh the
     pyramid, not fail or accumulate duplicates, matching ``build_volume``'s
     same rule for the primary volume.
@@ -102,7 +119,7 @@ def register_mask_pyramid(semantic: np.ndarray, key: str, container: Any) -> dic
     import ingest as ingest_mod
 
     levels, generated = build_mask_pyramid(semantic)
-    sidecar = tss.write_pyramid_store(key, levels)
+    sidecar = tss.write_pyramid_store(cache_key, levels)
 
     if key in ingest_mod._child_keys(container):
         container.delete_contents(key, recursive=True, external_only=False)
diff --git a/backend/tests/test_mask_pyramid.py b/backend/tests/test_mask_pyramid.py
index c794282..b2fcb2a 100644
--- a/backend/tests/test_mask_pyramid.py
+++ b/backend/tests/test_mask_pyramid.py
@@ -2,6 +2,7 @@
 import numpy as np
 
 from mask_pyramid import build_mask_pyramid, majority_downsample
+import tiff_stack_source as tss
 
 
 def test_majority_downsample_picks_the_more_frequent_class():
@@ -68,3 +69,30 @@ def test_build_mask_pyramid_generates_coarser_levels_for_a_large_volume():
         assert levels[level["path"]].dtype == np.uint8
         # Downsampled level must only contain class ids actually present.
         assert set(np.unique(levels[level["path"]])).issubset({0, 1})
+
+
+def test_write_pyramid_store_scopes_by_key_not_shared(monkeypatch, tmp_path):
+    """Regression test for the bug where every mask sync (any dataset, iPred
+    or dlsia) wrote to the same on-disk path because register_mask_pyramid
+    always passed the literal "semantic" as write_pyramid_store's key —
+    Tiled's per-container metadata differed but the actual pixel data on
+    disk was shared and silently clobbered by whichever sync ran last.
+    """
+    monkeypatch.setenv("VOLUME_CACHE_DIR", str(tmp_path))
+
+    fast = np.zeros((1, 2, 2), dtype=np.uint8)
+    fast[:] = 1
+    deep = np.zeros((1, 2, 2), dtype=np.uint8)
+    deep[:] = 2
+
+    fast_path = tss.write_pyramid_store("sample__masks", {"scale0": fast})
+    deep_path = tss.write_pyramid_store("sample__masks_deep", {"scale0": deep})
+
+    assert fast_path != deep_path
+
+    import zarr
+
+    fast_readback = np.asarray(zarr.open_group(str(fast_path), mode="r")["scale0"][:])
+    deep_readback = np.asarray(zarr.open_group(str(deep_path), mode="r")["scale0"][:])
+    assert np.all(fast_readback == 1)
+    assert np.all(deep_readback == 2)
diff --git a/backend/tiled_mask_sync.py b/backend/tiled_mask_sync.py
index 761ae4c..a7bad6f 100644
--- a/backend/tiled_mask_sync.py
+++ b/backend/tiled_mask_sync.py
@@ -292,7 +292,9 @@ def write_masks_to_tiled(
     # actually open this as a Zarr store instead of rejecting it for
     # "missing multiscales". Only `semantic` needs this: it's the one array
     # the viewer's single combined class-id mask texture reads.
-    mask_pyramid.register_mask_pyramid(merged["semantic"], key="semantic", container=container)
+    mask_pyramid.register_mask_pyramid(
+        merged["semantic"], key="semantic", container=container, cache_key=container_key,
+    )
     for name, vol in merged["class_vols"].items():
         container.write_array(
             vol, key=_safe_name(name), dims=dims,

From 28c1b66f995a0a7150e92c7cbc614fab13de0f3d Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 2 Sep 2026 11:12:51 -0700
Subject: [PATCH 17/38] Adding optional timing metric to tiled export jobs

---
 backend/export_jobs.py            | 25 ++++++++++++++
 backend/tests/test_export_jobs.py | 56 +++++++++++++++++++++++++++++++
 2 files changed, 81 insertions(+)
 create mode 100644 backend/tests/test_export_jobs.py

diff --git a/backend/export_jobs.py b/backend/export_jobs.py
index 928c7ca..852a05e 100644
--- a/backend/export_jobs.py
+++ b/backend/export_jobs.py
@@ -7,7 +7,9 @@
 """
 from __future__ import annotations
 
+import os
 import threading
+import time
 import uuid
 from typing import Any
 
@@ -15,6 +17,11 @@
 _lock = threading.Lock()
 _MAX_LOG = 200
 
+# Per-stage timing is opt-in (off by default in production) — see log()'s own
+# doc for why. Read once at import: this flag is meant for a deliberate
+# profiling run (locally or in CI), not something toggled mid-process.
+_PROFILE = os.getenv("PROFILE_JOBS") == "1"
+
 
 def new_job(dataset_path: str) -> str:
     """Register a new export job and return its id."""
@@ -31,6 +38,7 @@ def new_job(dataset_path: str) -> str:
             "dataset_path": dataset_path,
             "zip_path": None,     # internal; surfaced as result.zip_available
             "cancel_requested": False,
+            "_last_log_at": time.monotonic(),  # internal; PROFILE_JOBS timing only
         }
     return jid
 
@@ -71,6 +79,7 @@ def get_job(jid: str) -> dict | None:
             return None
         snap = dict(job)
         snap.pop("zip_path", None)
+        snap.pop("_last_log_at", None)
         return snap
 
 
@@ -99,10 +108,26 @@ def bump(jid: str, n: int = 1) -> None:
 
 
 def log(jid: str, message: str) -> None:
+    """Append a log line, called at every natural stage boundary by every job
+    type (iPred batch jobs, dlsia train/infer).
+
+    When ``PROFILE_JOBS=1`` is set, each line gets an elapsed-since-previous-
+    log suffix (e.g. ``"Wrote foo (+1.23s)"``) — a cheap per-stage timing
+    signal with zero new call sites, since every job already calls this at
+    the boundaries that matter. Off by default: real users/deployments should
+    see zero timing overhead, not "probably negligible" — this is meant to
+    run in the test suite (real job round-trips with profiling on) and local
+    benchmarking, not every production request.
+    """
     with _lock:
         job = _jobs.get(jid)
         if not job:
             return
+        if _PROFILE:
+            now = time.monotonic()
+            elapsed = now - job.get("_last_log_at", now)
+            job["_last_log_at"] = now
+            message = f"{message} (+{elapsed:.2f}s)"
         job["log"].append(message)
         if len(job["log"]) > _MAX_LOG:
             del job["log"][: len(job["log"]) - _MAX_LOG]
diff --git a/backend/tests/test_export_jobs.py b/backend/tests/test_export_jobs.py
new file mode 100644
index 0000000..7f9bc38
--- /dev/null
+++ b/backend/tests/test_export_jobs.py
@@ -0,0 +1,56 @@
+"""Tests for export_jobs' shared job registry, focused on PROFILE_JOBS timing."""
+import importlib
+import os
+
+import pytest
+
+import export_jobs
+
+
+@pytest.fixture(autouse=True)
+def _restore_default_module_state():
+    """PROFILE_JOBS is read once at import — reloading the module to flip it
+    (see _reload_with_profile) mutates process-global state that outlives
+    monkeypatch's own env var cleanup, regardless of fixture teardown order.
+    Explicitly clear the env var and reload back to the un-profiled default
+    after every test so other test files never see a leftover reload here."""
+    yield
+    os.environ.pop("PROFILE_JOBS", None)
+    importlib.reload(export_jobs)
+
+
+def _reload_with_profile(monkeypatch, enabled: bool):
+    """PROFILE_JOBS is read once at import time — reload the module under a
+    patched env var rather than mutating a private flag directly, so the test
+    exercises the real startup path."""
+    monkeypatch.setenv("PROFILE_JOBS", "1" if enabled else "0")
+    module = importlib.reload(export_jobs)
+    return module
+
+
+def test_log_has_no_timing_suffix_by_default(monkeypatch):
+    module = _reload_with_profile(monkeypatch, enabled=False)
+    jid = module.new_job("some/path")
+    module.log(jid, "starting")
+    job = module.get_job(jid)
+    assert job["log"] == ["starting"]
+
+
+def test_log_adds_elapsed_suffix_when_profiling_enabled(monkeypatch):
+    module = _reload_with_profile(monkeypatch, enabled=True)
+    jid = module.new_job("some/path")
+    module.log(jid, "starting")
+    module.log(jid, "done")
+    job = module.get_job(jid)
+    assert len(job["log"]) == 2
+    for line in job["log"]:
+        assert "(+" in line and line.endswith("s)")
+
+
+def test_internal_last_log_at_field_never_leaks_into_snapshot(monkeypatch):
+    module = _reload_with_profile(monkeypatch, enabled=True)
+    jid = module.new_job("some/path")
+    module.log(jid, "starting")
+    job = module.get_job(jid)
+    assert "_last_log_at" not in job
+    assert "zip_path" not in job

From 3733c6c94ebecdd7a6e6e5956b921cf2990700d5 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 2 Sep 2026 13:32:29 -0700
Subject: [PATCH 18/38] preview of ipred predictions for new slices are
 viewable as they arrive

---
 backend/infer_jobs.py                         | 266 +++++++++++++-----
 backend/ipred_batch_jobs.py                   |   9 +
 backend/tests/test_infer_jobs.py              | 254 +++++++++++++++++
 backend/tests/test_ipred_batch_jobs.py        |  51 ++++
 backend/tests/test_mask_pyramid.py            |   2 +-
 backend/train_common.py                       |  12 +
 .../src/components/train/InferencePanel.tsx   |  22 +-
 frontend/src/hooks/usePixelClassifier.ts      |  60 +++-
 8 files changed, 597 insertions(+), 79 deletions(-)
 create mode 100644 backend/tests/test_infer_jobs.py

diff --git a/backend/infer_jobs.py b/backend/infer_jobs.py
index f93ea27..c9ee6bc 100644
--- a/backend/infer_jobs.py
+++ b/backend/infer_jobs.py
@@ -18,8 +18,10 @@
 
 from __future__ import annotations
 
+import concurrent.futures
 import io
 import logging
+import os
 import threading
 from typing import Any
 
@@ -157,8 +159,117 @@ def _vectorize_label_map(
     return shapes
 
 
+#: Slices processed concurrently in one inference job. Only the actual model
+#: forward call is serialized (train_common.GPU_FORWARD_LOCK, held briefly
+#: inside _predict_one_slice) — I/O, rendering, and vectorization for
+#: different slices run genuinely in parallel across this pool, so CPU-bound
+#: work overlaps with the GPU instead of the whole per-slice pipeline
+#: serializing behind one lock (the old behavior, when ML_LOCK itself was
+#: held for the entire job). Mirrors ipred_batch_jobs.py's pool shape.
+_DEFAULT_INFER_CONCURRENCY = 4
+
+
+def _predict_one_slice(
+    slice_idx: int,
+    *,
+    jid: str,
+    node: Any,
+    meta: dict[str, Any],
+    h: int,
+    w: int,
+    render: dict[str, Any],
+    global_range: Any,
+    render_slice_fn: Any,
+    tiled: bool,
+    image_size: int,
+    forward_fn: Any,
+    to_tensor_fn: Any,
+    device: Any,
+    request: InferRequest,
+    run_classes: list[dict[str, Any]],
+    tiling_logged: threading.Event,
+) -> tuple[int, bytes | None, list[dict[str, Any]] | None, str | None]:
+    """Read + render + (GPU-locked) predict + vectorize one slice on a worker
+    thread. Returns ``(slice_idx, label_png_bytes, shapes, error)`` — never
+    raises, so a pool of these can be driven with plain ``future.result()``
+    and no per-future try/except at the call site (mirrors
+    ``ipred_batch_jobs._apply_one_slice``'s contract).
+
+    A ``None`` label_png/shapes pair with no error means the tiled path was
+    cancelled mid-slice (``tiling.predict_label_map_tiled`` returns ``None``
+    when its own ``cancel_cb`` fires) — the caller's overall cancellation
+    check already covers stopping the job, so this slice simply contributes
+    nothing rather than needing its own error to report.
+    """
+    import torch
+    import torch.nn.functional as F
+
+    import arrays as arrays_mod
+
+    try:
+        arr = arrays_mod.read_slice(node, meta, slice_idx)
+        rgb = render_slice_fn(arr, render, global_range)
+
+        with train_common.GPU_FORWARD_LOCK, torch.no_grad():
+            if tiled:
+                import tiling
+
+                # Logged once across the whole job, not once per slice — every
+                # slice shares the same tiling geometry. Race-free because
+                # this whole block already runs under GPU_FORWARD_LOCK: only
+                # one worker is ever inside here at a time, so check-then-set
+                # on tiling_logged can't interleave between two threads.
+                first_to_log = not tiling_logged.is_set()
+                if first_to_log:
+                    tiling_logged.set()
+                label_map = tiling.predict_label_map_tiled(
+                    rgb,
+                    forward_fn=forward_fn,
+                    to_tensor_fn=to_tensor_fn,
+                    window=image_size,
+                    min_confidence=request.min_confidence,
+                    device=device,
+                    cancel_cb=lambda: export_jobs.cancel_requested(jid),
+                    progress_cb=(lambda msg: export_jobs.log(jid, f"tiling: {msg}")) if first_to_log else None,
+                )
+                if label_map is None:  # cancelled mid-slice
+                    return slice_idx, None, None, None
+            else:
+                img_l, _ = train_common.letterbox(rgb, np.zeros((h, w), dtype=np.uint8), image_size)
+                batch = to_tensor_fn(img_l).unsqueeze(0).to(device)
+
+                logits = forward_fn(batch)[0]
+                probs = F.softmax(logits, dim=0)
+                confidence, pred_class = probs.max(dim=0)
+                pred_np = pred_class.cpu().numpy()
+                conf_np = confidence.cpu().numpy()
+                label_letterboxed = np.where(conf_np >= request.min_confidence, pred_np + 1, 0).astype(np.uint8)
+                label_map = train_common.unletterbox(label_letterboxed, h, w, image_size)
+
+        shapes = _vectorize_label_map(
+            label_map, run_classes, request.min_area, request.simplify_tol, request.run_id, slice_idx,
+        )
+        buf = io.BytesIO()
+        PILImage.fromarray(label_map, mode="L").save(buf, format="PNG")
+        return slice_idx, buf.getvalue(), shapes, None
+    except Exception as exc:  # noqa: BLE001 — one bad slice must not abort the job
+        return slice_idx, None, None, str(exc)
+
+
 def run_infer_job(jid: str, request: InferRequest) -> None:
-    """Background worker: predict + vectorize each requested slice."""
+    """Background worker: predict + vectorize each requested slice.
+
+    ``train_common.ML_LOCK`` is still held for the whole job (unchanged —
+    this is what keeps a training run, a denoise bake, or another inference
+    job from contending for the GPU at the same time as this one). Within
+    the job, slices are processed by a small bounded worker pool
+    (``_DEFAULT_INFER_CONCURRENCY``) instead of one at a time: only the
+    actual model forward call is serialized (``train_common.GPU_FORWARD_LOCK``,
+    a separate, finer-grained lock — see its own doc), so I/O, rendering, and
+    vectorization for different slices overlap with the GPU instead of the
+    old behavior of serializing the entire per-slice pipeline behind
+    ``ML_LOCK``.
+    """
     if not train_common.ML_LOCK.acquire(blocking=False):
         export_jobs.update(
             jid,
@@ -176,7 +287,6 @@ def run_infer_job(jid: str, request: InferRequest) -> None:
         config = train_common.load_run_config(request.run_id)
         adapter_state = train_common.load_adapter_state(request.run_id)
         run_classes: list[dict[str, Any]] = config["classes"]
-        n_classes = len(run_classes)
         image_size = int(config["image_size"])
         render = request.render.model_dump() if request.render is not None else config["render"]
         # Predict with the geometry this run was TRAINED with — reading the flag off
@@ -228,9 +338,6 @@ def run_infer_job(jid: str, request: InferRequest) -> None:
         else:
             raise RuntimeError(f"Unsupported model family: {config['model_family']!r}")
 
-        import torch
-        import torch.nn.functional as F
-
         import arrays as arrays_mod
         import images as images_mod
 
@@ -242,70 +349,18 @@ def run_infer_job(jid: str, request: InferRequest) -> None:
         export_jobs.set_total(jid, len(request.slice_indices))
         export_jobs.update(jid, phase="predicting")
 
+        # A single mutable dict, referenced (not copied) by the cache entry
+        # below — workers below only ever add their OWN slice_idx key, so
+        # concurrent inserts never conflict, and every `preview_png` call
+        # from another request thread sees results as soon as they land, no
+        # further _cache_put calls needed (see #15's live-preview ask: this
+        # is what makes `GET /api/train/infer/preview/{job_id}/{slice_index}`
+        # servable for early slices while the job is still `running`).
         label_pngs: dict[int, bytes] = {}
         slices_result: dict[str, list[dict[str, Any]]] = {}
         n_shapes = 0
         cancelled = False
-        logged_tiling = False
-
-        with torch.no_grad():
-            for slice_idx in request.slice_indices:
-                if export_jobs.cancel_requested(jid):
-                    cancelled = True
-                    break
-
-                arr = arrays_mod.read_slice(node, meta, slice_idx)
-                rgb = render_slice_fn(arr, render, global_range)
-
-                if tiled:
-                    import tiling
-
-                    label_map = tiling.predict_label_map_tiled(
-                        rgb,
-                        forward_fn=forward_fn,
-                        to_tensor_fn=to_tensor_fn,
-                        window=image_size,
-                        min_confidence=request.min_confidence,
-                        device=device,
-                        cancel_cb=lambda: export_jobs.cancel_requested(jid),
-                        # Logged once: every slice of a volume has the same shape, so
-                        # the tiling geometry never changes between them. Repeating it
-                        # per slice would just double an already per-slice log.
-                        progress_cb=(lambda msg: export_jobs.log(jid, f"tiling: {msg}")) if not logged_tiling else None,
-                    )
-                    logged_tiling = True
-                    if label_map is None:  # cancelled mid-slice
-                        cancelled = True
-                        break
-                else:
-                    img_l, _ = train_common.letterbox(rgb, np.zeros((h, w), dtype=np.uint8), image_size)
-                    batch = to_tensor_fn(img_l).unsqueeze(0).to(device)
-
-                    logits = forward_fn(batch)[0]
-                    probs = F.softmax(logits, dim=0)
-                    confidence, pred_class = probs.max(dim=0)
-                    pred_np = pred_class.cpu().numpy()
-                    conf_np = confidence.cpu().numpy()
-                    label_letterboxed = np.where(conf_np >= request.min_confidence, pred_np + 1, 0).astype(np.uint8)
-                    label_map = train_common.unletterbox(label_letterboxed, h, w, image_size)
-
-                shapes = _vectorize_label_map(
-                    label_map,
-                    run_classes,
-                    request.min_area,
-                    request.simplify_tol,
-                    request.run_id,
-                    slice_idx,
-                )
-                n_shapes += len(shapes)
-                slices_result[str(slice_idx)] = shapes
-
-                buf = io.BytesIO()
-                PILImage.fromarray(label_map, mode="L").save(buf, format="PNG")
-                label_pngs[slice_idx] = buf.getvalue()
-
-                export_jobs.bump(jid, 1)
-                export_jobs.log(jid, f"slice {slice_idx}: {len(shapes)} region(s)")
+        tiling_logged = threading.Event()
 
         _cache_put(
             jid,
@@ -321,15 +376,78 @@ def run_infer_job(jid: str, request: InferRequest) -> None:
             },
         )
 
-        result = {
-            "run_id": request.run_id,
-            "classes": run_classes,
-            "slices": slices_result,
-            "n_shapes": n_shapes,
-            "preview_slices": sorted(label_pngs.keys()),
-            "cancelled": cancelled,
-        }
-        export_jobs.update(jid, state="done", phase="done", result=result)
+        def _publish_result(*, done: bool) -> None:
+            """Update the job's `result` after every completed slice (not
+            just once at the end) so the frontend's poll of
+            `GET /api/train/infer/status/{job_id}` can render a growing
+            preview slider while state is still `running` — see #15."""
+            export_jobs.update(
+                jid,
+                state="done" if done else "running",
+                phase="done" if done else "predicting",
+                result={
+                    "run_id": request.run_id,
+                    "classes": run_classes,
+                    "slices": dict(slices_result),
+                    "n_shapes": n_shapes,
+                    "preview_slices": sorted(label_pngs.keys()),
+                    "cancelled": cancelled,
+                },
+            )
+
+        pool_size = max(1, int(os.getenv("DLSIA_INFER_CONCURRENCY", _DEFAULT_INFER_CONCURRENCY)))
+        with concurrent.futures.ThreadPoolExecutor(max_workers=pool_size) as pool:
+            pending = iter(request.slice_indices)
+            in_flight: dict[concurrent.futures.Future, int] = {}
+
+            def submit_next() -> bool:
+                si = next(pending, None)
+                if si is None:
+                    return False
+                fut = pool.submit(
+                    _predict_one_slice,
+                    si,
+                    jid=jid, node=node, meta=meta, h=h, w=w, render=render,
+                    global_range=global_range, render_slice_fn=render_slice_fn,
+                    tiled=tiled, image_size=image_size, forward_fn=forward_fn,
+                    to_tensor_fn=to_tensor_fn, device=device, request=request,
+                    run_classes=run_classes, tiling_logged=tiling_logged,
+                )
+                in_flight[fut] = si
+                return True
+
+            for _ in range(pool_size):
+                if not submit_next():
+                    break
+
+            while in_flight:
+                done_futs, _ = concurrent.futures.wait(
+                    in_flight, return_when=concurrent.futures.FIRST_COMPLETED
+                )
+                for fut in done_futs:
+                    del in_flight[fut]
+                    slice_idx, png_bytes, shapes, error = fut.result()
+                    if error is not None:
+                        logger.warning("Inference job %s: slice %d failed (%s)", jid, slice_idx, error)
+                        export_jobs.log(jid, f"slice {slice_idx}: failed ({error})")
+                    elif png_bytes is not None and shapes is not None:
+                        n_shapes += len(shapes)
+                        slices_result[str(slice_idx)] = shapes
+                        label_pngs[slice_idx] = png_bytes
+                        export_jobs.log(jid, f"slice {slice_idx}: {len(shapes)} region(s)")
+                    # else: cancelled mid-slice (tiled path) — nothing to record.
+                    export_jobs.bump(jid, 1)
+                    _publish_result(done=False)
+
+                if export_jobs.cancel_requested(jid):
+                    cancelled = True
+                    # Already-submitted work can't be un-submitted — just stop
+                    # refilling the pool so it drains rather than growing.
+                    continue
+                for _ in done_futs:
+                    submit_next()
+
+        _publish_result(done=True)
         export_jobs.log(jid, "Inference cancelled; partial results kept." if cancelled else "Inference complete.")
     except Exception as exc:  # noqa: BLE001 — reported as a job error, never a crash
         logger.error("Inference job %s failed: %s", jid, exc)
diff --git a/backend/ipred_batch_jobs.py b/backend/ipred_batch_jobs.py
index 6008a53..e490cd6 100644
--- a/backend/ipred_batch_jobs.py
+++ b/backend/ipred_batch_jobs.py
@@ -194,6 +194,14 @@ def run_ipred_volume_apply_job(
         cancelled = False
         pool_size = max(1, int(os.getenv("IPRED_APPLY_CONCURRENCY", _DEFAULT_APPLY_CONCURRENCY)))
 
+        def _publish_result() -> None:
+            """Update the job's `result` after every completed slice, not
+            just once at the end — otherwise a UI polling this job while it
+            runs sees `result: null` the entire time, no matter how many
+            slices have actually already predicted (same class of bug fixed
+            in infer_jobs.py's dlsia inference job for the same reason)."""
+            export_jobs.update(jid, result={"runs": dict(runs), "errors": list(errors), "cancelled": cancelled})
+
         with ipred_client_mod.new_shared_client() as client, \
                 concurrent.futures.ThreadPoolExecutor(max_workers=pool_size) as pool:
             pending = iter(slice_indices)
@@ -234,6 +242,7 @@ def submit_next() -> bool:
                         runs[str(slice_index)] = run_id  # type: ignore[assignment]
                         export_jobs.log(jid, f"slice {slice_index}: predicted (run {run_id[:8]})")
                     export_jobs.bump(jid, 1)
+                    _publish_result()
 
                 if export_jobs.cancel_requested(jid):
                     cancelled = True
diff --git a/backend/tests/test_infer_jobs.py b/backend/tests/test_infer_jobs.py
new file mode 100644
index 0000000..5fb7c68
--- /dev/null
+++ b/backend/tests/test_infer_jobs.py
@@ -0,0 +1,254 @@
+"""Real (not mocked) round-trip tests for infer_jobs.run_infer_job, focused on
+this session's rework: the per-slice worker pool, the GPU_FORWARD_LOCK/ML_LOCK
+split, and #15's live-preview (incremental result/cache updates).
+
+Skips automatically if the `ml` extra (torch/dlsia) isn't installed — see
+test_train_e2e_real_ml.py for the sibling test proving the underlying
+train/save/load contract. This file builds on the same real-model pattern but
+drives it through infer_jobs.run_infer_job itself, with a fake array source
+(no real Tiled/local dataset needed) so it stays fast and self-contained.
+"""
+
+from __future__ import annotations
+
+import sys
+import threading
+import time
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+pytest.importorskip("torch")
+pytest.importorskip("dlsia")
+
+import arrays as arrays_mod  # noqa: E402
+import export_jobs  # noqa: E402
+import infer_jobs  # noqa: E402
+import train_common  # noqa: E402
+from schemas import DlsiaTunetConfig, InferRequest  # noqa: E402
+
+IMAGE_SIZE = 64
+N_SLICES = 6
+N_CLASSES = 2
+
+
+@pytest.fixture()
+def runs_dir(tmp_path, monkeypatch: pytest.MonkeyPatch) -> Path:
+    monkeypatch.setenv("DINO_RUNS_DIR", str(tmp_path / "runs"))
+    return tmp_path / "runs"
+
+
+@pytest.fixture()
+def trained_run_id(runs_dir) -> str:
+    """A real, tiny, trained dlsia_tunet run — same pattern as
+    test_train_e2e_real_ml.py, sized down for test speed."""
+    model_cfg = DlsiaTunetConfig(
+        hyperparams={
+            "epochs": 1, "depth": 2, "base_channels": 4, "growth_rate": 1.2,
+            "batch_size": 2, "image_size": IMAGE_SIZE, "tiling": False,
+        }
+    )
+    built = train_common.build_family(model_cfg, N_CLASSES, "cpu", lambda _msg: None)
+
+    rng = np.random.default_rng(0)
+    train_pairs = [
+        (
+            rng.integers(0, 256, size=(IMAGE_SIZE, IMAGE_SIZE, 3), dtype=np.uint8),
+            rng.integers(0, N_CLASSES, size=(IMAGE_SIZE, IMAGE_SIZE), dtype=np.uint8),
+        )
+        for _ in range(4)
+    ]
+    train_common.run_training_loop(
+        train_pairs=train_pairs, val_pairs=[], image_size=IMAGE_SIZE, n_classes=N_CLASSES,
+        epochs=1, batch_size=2, seed=0, flip_augment=False,
+        to_tensor_fn=built.to_tensor_fn, forward_fn=built.forward_fn,
+        trainable_params=built.trainable_params, lr=1e-3, device="cpu",
+        set_train_mode=built.set_train_mode,
+    )
+
+    run_id = "test-infer-job-run"
+    train_common.save_run(
+        run_id,
+        model_family=model_cfg.model_family,
+        model_config=built.model_config_snapshot,
+        classes=[{"classId": 1, "label": "a", "color": "#f00"}, {"classId": 2, "label": "b", "color": "#0f0"}],
+        render={},
+        image_size=IMAGE_SIZE,
+        hyperparams=model_cfg.hyperparams.model_dump(),
+        source_keys=["local:fake.tif"],
+        adapter_state=built.adapter_state_fn(),
+        metrics={"epochs_completed": 1, "cancelled": False},
+    )
+    return run_id
+
+
+@pytest.fixture()
+def fake_array_source(monkeypatch: pytest.MonkeyPatch):
+    """Serve a small synthetic in-memory volume in place of a real Tiled/
+    local dataset — infer_jobs only ever calls these three arrays.py
+    functions, so patching them is enough to drive run_infer_job for real
+    without any actual dataset on disk.
+
+    A short sleep per read_slice call gives concurrent slice-workers a real
+    window to overlap in (proving the pool actually parallelizes I/O across
+    slices, not just that it doesn't crash) and gives a background-thread
+    poller time to observe partial (live-preview) results before the job
+    finishes.
+    """
+    rng = np.random.default_rng(1)
+    volume = rng.integers(0, 256, size=(N_SLICES, IMAGE_SIZE, IMAGE_SIZE), dtype=np.uint8)
+
+    def fake_resolve_array(source, kind, server_uri):
+        return volume
+
+    def fake_array_shape_meta(node, pyramid=None):
+        return {"height": IMAGE_SIZE, "width": IMAGE_SIZE, "n_slices": N_SLICES}
+
+    def fake_read_slice(node, meta, idx):
+        time.sleep(0.05)
+        return node[idx]
+
+    monkeypatch.setattr(arrays_mod, "resolve_array", fake_resolve_array)
+    monkeypatch.setattr(arrays_mod, "array_shape_meta", fake_array_shape_meta)
+    monkeypatch.setattr(arrays_mod, "read_slice", fake_read_slice)
+    return volume
+
+
+def _infer_request(run_id: str) -> InferRequest:
+    return InferRequest(
+        run_id=run_id, kind="local", source="fake.tif",
+        slice_indices=list(range(N_SLICES)), min_confidence=0.0,
+    )
+
+
+def test_run_infer_job_completes_with_correct_results_and_cache(trained_run_id, fake_array_source):
+    jid = export_jobs.new_job(trained_run_id)
+    infer_jobs.run_infer_job(jid, _infer_request(trained_run_id))
+
+    job = export_jobs.get_job(jid)
+    assert job["state"] == "done"
+    result = job["result"]
+    assert result["cancelled"] is False
+    assert sorted(int(k) for k in result["slices"]) == list(range(N_SLICES))
+    assert result["preview_slices"] == list(range(N_SLICES))
+
+    cached = infer_jobs._cache_get(jid)
+    assert cached is not None
+    assert sorted(cached["label_pngs"].keys()) == list(range(N_SLICES))
+
+    # ML_LOCK must always be released, whether the job succeeds or not —
+    # otherwise every subsequent train/infer/bake/probe job would wrongly
+    # report "another job is already running" forever.
+    assert not train_common.ML_LOCK.locked()
+
+
+def test_run_infer_job_publishes_partial_results_before_completion(trained_run_id, fake_array_source):
+    """Regression test for #15: the job's `result` must be readable and
+    growing WHILE state is still "running", not only once "done" — this is
+    what lets the frontend show a usable preview slider mid-job instead of
+    the old "nothing until every slice finishes" behavior."""
+    jid = export_jobs.new_job(trained_run_id)
+    thread = threading.Thread(
+        target=infer_jobs.run_infer_job, args=(jid, _infer_request(trained_run_id)), daemon=True,
+    )
+    thread.start()
+
+    saw_partial_progress = False
+    deadline = time.monotonic() + 30.0
+    while time.monotonic() < deadline:
+        job = export_jobs.get_job(jid)
+        if job["state"] == "running" and job.get("result"):
+            n = len(job["result"].get("preview_slices", []))
+            if 0 < n < N_SLICES:
+                saw_partial_progress = True
+                break
+        if job["state"] in ("done", "error"):
+            break
+        time.sleep(0.02)
+    thread.join(timeout=30.0)
+
+    assert saw_partial_progress, "expected to observe a partial (running) result with some but not all slices done"
+    job = export_jobs.get_job(jid)
+    assert job["state"] == "done"
+    assert len(job["result"]["preview_slices"]) == N_SLICES
+
+
+def test_run_infer_job_cancellation_stops_early_with_partial_results(trained_run_id, fake_array_source):
+    jid = export_jobs.new_job(trained_run_id)
+    thread = threading.Thread(
+        target=infer_jobs.run_infer_job, args=(jid, _infer_request(trained_run_id)), daemon=True,
+    )
+    thread.start()
+    time.sleep(0.06)  # let at least one slice start
+    export_jobs.request_cancel(jid)
+    thread.join(timeout=30.0)
+
+    job = export_jobs.get_job(jid)
+    assert job["state"] == "done"
+    assert job["result"]["cancelled"] is True
+    assert len(job["result"]["preview_slices"]) <= N_SLICES
+    assert not train_common.ML_LOCK.locked()
+
+
+def test_predict_one_slice_serializes_gpu_forward_calls(trained_run_id, fake_array_source, monkeypatch):
+    """Direct test of the concurrency guarantee GPU_FORWARD_LOCK exists for:
+    even with several slice-workers running at once, at most one is ever
+    inside the model-forward critical section at a time."""
+    import dlsia_runtime as fam
+
+    config = train_common.load_run_config(trained_run_id)
+    adapter_state = train_common.load_adapter_state(trained_run_id)
+    model = fam.load_model(adapter_state, "cpu")
+    model.eval()
+    real_forward_fn = fam.make_forward_fn(model)
+    to_tensor_fn = fam.make_to_tensor_fn()
+
+    concurrent_count = 0
+    max_concurrent = 0
+    count_lock = threading.Lock()
+
+    def spying_forward_fn(batch):
+        nonlocal concurrent_count, max_concurrent
+        with count_lock:
+            concurrent_count += 1
+            max_concurrent = max(max_concurrent, concurrent_count)
+        try:
+            time.sleep(0.05)  # widen the window a real race would need
+            return real_forward_fn(batch)
+        finally:
+            with count_lock:
+                concurrent_count -= 1
+
+    node = fake_array_source
+    meta = {"height": IMAGE_SIZE, "width": IMAGE_SIZE}
+    request = _infer_request(trained_run_id)
+    run_classes = config["classes"]
+    tiling_logged = threading.Event()
+
+    import concurrent.futures
+
+    with concurrent.futures.ThreadPoolExecutor(max_workers=4) as pool:
+        futures = [
+            pool.submit(
+                infer_jobs._predict_one_slice,
+                i,
+                jid="test-jid", node=node, meta=meta, h=IMAGE_SIZE, w=IMAGE_SIZE,
+                render={}, global_range=None,
+                render_slice_fn=train_common.denoising_render_slice_fn(None),
+                tiled=False, image_size=IMAGE_SIZE, forward_fn=spying_forward_fn,
+                to_tensor_fn=to_tensor_fn, device="cpu", request=request,
+                run_classes=run_classes, tiling_logged=tiling_logged,
+            )
+            for i in range(N_SLICES)
+        ]
+        results = [f.result() for f in futures]
+
+    for slice_idx, png_bytes, shapes, error in results:
+        assert error is None, f"slice {slice_idx} failed: {error}"
+        assert png_bytes is not None
+        assert shapes is not None
+
+    assert max_concurrent == 1, "GPU_FORWARD_LOCK failed to serialize concurrent forward calls"
diff --git a/backend/tests/test_ipred_batch_jobs.py b/backend/tests/test_ipred_batch_jobs.py
index ae0694a..9a963dc 100644
--- a/backend/tests/test_ipred_batch_jobs.py
+++ b/backend/tests/test_ipred_batch_jobs.py
@@ -132,3 +132,54 @@ def fake_infer(*, session_id, model_id, feature_id, alpha, store_probabilities,
     # With concurrency=1, cancellation is observed right after slice 0
     # completes — no later slice should ever have started.
     assert started == [0]
+
+
+def test_result_is_published_incrementally_while_running(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Regression test: result.runs must be visible WHILE the job is still
+    `running`, not only once the whole volume finishes — otherwise a live
+    per-slice preview in the UI (Annotate's PixelClassifierPanel) has nothing
+    to show for slices that already predicted, no matter how far along the
+    job is (this is exactly what a user reported live: slice 2 of a 690-slice
+    apply job stayed blank even though 11 slices had already predicted)."""
+    def fake_preprocess(*, session_id, feature_setup_id, composition_id, slice_index, client):
+        time.sleep(0.03)
+        return {"feature_id": f"fid-{slice_index}"}
+
+    def fake_infer(*, session_id, model_id, feature_id, alpha, store_probabilities, client):
+        return {"run_id": f"run-{feature_id}"}
+
+    monkeypatch.setattr(ipred_client_mod, "preprocess", fake_preprocess)
+    monkeypatch.setattr(ipred_client_mod, "infer", fake_infer)
+    monkeypatch.setattr(ipred_client_mod, "delete_feature_bank", lambda feature_id, *, client: None)
+    monkeypatch.setenv("IPRED_APPLY_CONCURRENCY", "2")
+
+    n_slices = 8
+    jid = export_jobs.new_job("")
+    thread = threading.Thread(
+        target=ipred_batch_jobs.run_ipred_volume_apply_job,
+        kwargs=dict(
+            jid=jid, session_id="s1", model_id="m1", slice_indices=list(range(n_slices)),
+            composition_id=None, feature_setup_id=None, alpha=0.05,
+        ),
+        daemon=True,
+    )
+    thread.start()
+
+    saw_partial_progress = False
+    deadline = time.monotonic() + 10.0
+    while time.monotonic() < deadline:
+        job = export_jobs.get_job(jid)
+        if job["state"] == "running" and job.get("result"):
+            n = len(job["result"].get("runs", {}))
+            if 0 < n < n_slices:
+                saw_partial_progress = True
+                break
+        if job["state"] in ("done", "error"):
+            break
+        time.sleep(0.005)
+    thread.join(timeout=10.0)
+
+    assert saw_partial_progress, "expected to observe a partial (running) result with some but not all slices done"
+    job = export_jobs.get_job(jid)
+    assert job["state"] == "done"
+    assert len(job["result"]["runs"]) == n_slices
diff --git a/backend/tests/test_mask_pyramid.py b/backend/tests/test_mask_pyramid.py
index b2fcb2a..182eef8 100644
--- a/backend/tests/test_mask_pyramid.py
+++ b/backend/tests/test_mask_pyramid.py
@@ -1,8 +1,8 @@
 """Unit tests for mask_pyramid's pure downsampling (no Tiled I/O)."""
 import numpy as np
 
-from mask_pyramid import build_mask_pyramid, majority_downsample
 import tiff_stack_source as tss
+from mask_pyramid import build_mask_pyramid, majority_downsample
 
 
 def test_majority_downsample_picks_the_more_frequent_class():
diff --git a/backend/train_common.py b/backend/train_common.py
index 8ebf6e2..7cde28e 100644
--- a/backend/train_common.py
+++ b/backend/train_common.py
@@ -43,6 +43,18 @@
 
 ML_LOCK = threading.Lock()
 
+# Separate from ML_LOCK: ML_LOCK gates whole JOBS against each other (a
+# training run, an inference run, a denoise bake, a batch probe — all four
+# hold it for their entire duration so no two heavy ML jobs contend for the
+# GPU at once). GPU_FORWARD_LOCK is finer-grained — it serializes only the
+# actual model forward call, so a per-job worker pool (see infer_jobs.py's
+# per-slice pool) can run I/O, preprocessing, and vectorization for multiple
+# slices concurrently while still guaranteeing only one thread ever calls
+# into the model at a given instant. Do not use this in place of ML_LOCK —
+# it does not protect against two different JOBS running at once, only
+# against two threads calling the model at the same time within one job.
+GPU_FORWARD_LOCK = threading.Lock()
+
 IGNORE_INDEX = 255  # unannotated pixels — see coco_export.build_export_plan(lightly=True)
 
 
diff --git a/frontend/src/components/train/InferencePanel.tsx b/frontend/src/components/train/InferencePanel.tsx
index 531824b..e13e568 100644
--- a/frontend/src/components/train/InferencePanel.tsx
+++ b/frontend/src/components/train/InferencePanel.tsx
@@ -88,7 +88,15 @@ export default function InferencePanel({
   // and selectedRunId is page-local state that resets to null when the Train tab
   // remounts — requiring it here made a finished job's overlay vanish on the way
   // back to the tab, leaving just the bare base image under a "done" progress bar.
-  const previewUrl = job.status === 'done' && activePreviewSlice != null && job.jobId
+  //
+  // Gated on `previewSlices.length > 0`, NOT `job.status === 'done'` (#15):
+  // infer_jobs.py now publishes `result.preview_slices` incrementally as each
+  // slice finishes, so a still-`running` job already has real, servable
+  // preview PNGs for whatever's completed so far — waiting for `done` meant
+  // switching slices mid-job showed nothing yet (no preview existed at all
+  // until the whole job finished), which read as "it stopped predicting"
+  // even though the job was running fine server-side.
+  const previewUrl = activePreviewSlice != null && job.jobId
     ? `${API_BASE}/api/train/infer/preview/${job.jobId}/${activePreviewSlice}`
     : null;
 
@@ -182,10 +190,13 @@ export default function InferencePanel({
             )}
           </div>
 
-          {job.status === 'done' && (
+          {(job.status === 'done' || (job.status === 'running' && previewSlices.length > 0)) && (
             <div className="space-y-2 rounded-lg border border-slate-700 bg-slate-900/40 p-2">
               <p className="text-xs text-slate-300">
                 {totalShapes} predicted region{totalShapes !== 1 ? 's' : ''}
+                {job.status === 'running'
+                  ? ` so far (${previewSlices.length}/${job.total} slices predicted)`
+                  : ''}
                 {job.result?.cancelled === true ? ' (cancelled — partial result)' : ''}.
               </p>
               {previewSlices.length > 0 && basePreviewUrl && (
@@ -217,7 +228,12 @@ export default function InferencePanel({
                   )}
                 </>
               )}
-              {hasOpenSample && (
+              {/* Acting on the result (importing shapes, pushing to Tiled) waits for
+                  `done` — the preview above is fine to show mid-job, but these two
+                  operate on `job.result.slices`/all label PNGs, so doing them against
+                  a still-growing partial result would silently drop whatever hasn't
+                  predicted yet instead of erroring, which is worse than just waiting. */}
+              {hasOpenSample && job.status === 'done' && (
                 <div className="flex flex-wrap gap-2 pt-1">
                   <button
                     type="button"
diff --git a/frontend/src/hooks/usePixelClassifier.ts b/frontend/src/hooks/usePixelClassifier.ts
index 9180e08..147b185 100644
--- a/frontend/src/hooks/usePixelClassifier.ts
+++ b/frontend/src/hooks/usePixelClassifier.ts
@@ -153,6 +153,12 @@ export function usePixelClassifier({
   const commitUrlRef = useRef<string | null>(null);
   const statusUrlRef = useRef<string | null>(null);
   const probaUrlRef = useRef<string | null>(null);
+  /** run_id currently shown via the volume-apply live preview (see the effect
+   * below) — cleared in revokePredict() so a slice revisit after any revoke
+   * (slice change, sample change, job restart) always re-fetches rather than
+   * skipping because "we already showed this run_id once" while commitUrl
+   * itself has since gone back to null. */
+  const volumeApplyPreviewRunIdRef = useRef<string | null>(null);
   /** Raw softmax PNG per class (before threshold preview). */
   const rawProbaBlobRef = useRef<Blob | null>(null);
   const probaThresholdsRef = useRef(probaThresholds);
@@ -206,6 +212,7 @@ export function usePixelClassifier({
     setRunId(null);
     setProbaClassIndex(0);
     setProbaThresholds({});
+    volumeApplyPreviewRunIdRef.current = null;
     revokeProba();
   }, [revokeProba]);
 
@@ -413,6 +420,11 @@ export function usePixelClassifier({
       }
       if (sliceIndices.length === 0) return;
       setError(null);
+      // Clear any prior job's frozen preview (single-slice OR a previous
+      // volume apply) before starting — otherwise switching slices right
+      // after kicking off a new run could briefly show a stale overlay left
+      // over from before this job's own results start landing.
+      revokePredict();
       const sessionId = await ensureSession();
       volumeApplyHandledRef.current = null;
       await volumeApplyJobHook.startIpredBatchApply({
@@ -423,7 +435,7 @@ export function usePixelClassifier({
         alpha: params.alpha,
       });
     },
-    [model, preferredCompositionId, params.alpha, ensureSession, volumeApplyJobHook],
+    [model, preferredCompositionId, params.alpha, ensureSession, volumeApplyJobHook, revokePredict],
   );
 
   useEffect(() => {
@@ -435,6 +447,52 @@ export function usePixelClassifier({
     }
   }, [volumeApplyJobHook.state]);
 
+  // Live per-slice preview during (or after) a volume-apply job: as soon as
+  // ipred_batch_jobs.py's result.runs has an entry for whichever slice is
+  // currently on screen, fetch and show that slice's commit/status overlay —
+  // reusing the exact same commitUrl/statusUrl the single-slice "Predict"
+  // button already drives, so AnnotationCanvas needs no new prop. Before this,
+  // switching slices during/after a volume apply showed nothing at all until
+  // the explicit "Commit" step vectorized everything into permanent shapes —
+  // there was no cheap way to just look at a slice's predicted result first.
+  //
+  // NOT a proba-channel preview: batch-apply runs are created with
+  // store_probabilities=false (ipred_batch_jobs.py's `_apply_one_slice`), so
+  // there is no proba.npy to load for these run ids — only commit/status.
+  useEffect(() => {
+    const result = volumeApplyJobHook.state.result as { runs?: Record<string, string> } | null;
+    const targetRunId = result?.runs?.[String(sliceIndex)] ?? null;
+    if (!targetRunId || targetRunId === volumeApplyPreviewRunIdRef.current) return;
+    let cancelled = false;
+    volumeApplyPreviewRunIdRef.current = targetRunId;
+    void (async () => {
+      try {
+        const [commitRes, statusRes] = await Promise.all([
+          fetch(ipredRunCommitUrl(targetRunId)),
+          fetch(ipredRunStatusUrl(targetRunId)),
+        ]);
+        if (!commitRes.ok || !statusRes.ok || cancelled) return;
+        const [commitBlob, statusBlob] = await Promise.all([commitRes.blob(), statusRes.blob()]);
+        if (cancelled) return;
+        const cUrl = URL.createObjectURL(commitBlob);
+        const sUrl = URL.createObjectURL(statusBlob);
+        if (commitUrlRef.current) URL.revokeObjectURL(commitUrlRef.current);
+        if (statusUrlRef.current) URL.revokeObjectURL(statusUrlRef.current);
+        commitUrlRef.current = cUrl;
+        statusUrlRef.current = sUrl;
+        setCommitUrl(cUrl);
+        setStatusUrl(sUrl);
+      } catch {
+        // Best-effort live preview only — a fetch hiccup here shouldn't
+        // surface a hard error; the explicit Commit step remains the
+        // authoritative path regardless of whether this preview loaded.
+      }
+    })();
+    return () => {
+      cancelled = true;
+    };
+  }, [sliceIndex, volumeApplyJobHook.state.result]);
+
   const predict = useCallback(
     async (_shapes: Shape[]) => {
       if (!model || predicting) return;

From e84e93794939bcfe270d876ad2f3f13c878d2938 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 2 Sep 2026 14:27:32 -0700
Subject: [PATCH 19/38] Push to Tiled and View in 3D are now separate buttons
 in the iPred panel: Push to Tiled stays on the Annotate tab and shows a
 Pushed to Tiled. confirmation inline; View in 3D navigates independently and
 no longer forces a push first. You can now push, stay put, keep annotating,
 and jump to 3D (or back) whenever you actually want to.

---
 backend/schemas.py                            |  24 +++
 backend/tests/test_tiled_mask_sync.py         |  64 ++++++-
 backend/tiled_mask_sync.py                    |  61 +++++--
 frontend/src/app/pages/AnnotatePage.tsx       | 159 +++++++++++-------
 .../src/components/annotate/DownloadModal.tsx |  16 ++
 .../annotate/PixelClassifierPanel/index.tsx   |  86 ++++++++--
 frontend/src/hooks/usePixelClassifier.ts      |  19 ++-
 .../src/stores/predictedRasterStore.test.ts   |  56 ++++++
 frontend/src/stores/predictedRasterStore.ts   |  79 +++++++++
 9 files changed, 471 insertions(+), 93 deletions(-)
 create mode 100644 frontend/src/stores/predictedRasterStore.test.ts
 create mode 100644 frontend/src/stores/predictedRasterStore.ts

diff --git a/backend/schemas.py b/backend/schemas.py
index 0adc725..79007c8 100644
--- a/backend/schemas.py
+++ b/backend/schemas.py
@@ -151,6 +151,24 @@ class RenderOpts(BaseModel):
     cmap: Literal["gray", "viridis"] = "gray"
 
 
+class PredictedSlicePointer(BaseModel):
+    """An un-vectorized iPred volume-apply result for one slice — the frontend's
+    ``predictedRasterStore`` pointer, sent straight through instead of first
+    fetching+tracing the commit PNG into polygon shapes client-side (see the
+    lazy-vectorization plan item this supports).
+
+    Attributes:
+        run_id: ipred run id — ``tiled_mask_sync`` fetches its commit.png
+            directly from the ipred service via ``ipred_client.run_commit_png``.
+        class_ids: Frontend class ids the commit PNG's pixel values are drawn
+            from directly (NOT a 1-based sequential index — matches the
+            frontend's own ``labelMapToPolygonShapes`` convention).
+    """
+
+    run_id: str
+    class_ids: list[int]
+
+
 class ExportSourceItem(BaseModel):
     """A single annotated sample within a multi-source export.
 
@@ -161,6 +179,11 @@ class ExportSourceItem(BaseModel):
         slices: Mapping of slice key → list of serialised shape dicts.
         split_by_slice: Mapping of slice key → dataset split name.
         negative_slices: Slice keys included as negative (unannotated) examples.
+        predicted_slices: Mapping of slice key → an un-vectorized iPred result
+            pointer (see :class:`PredictedSlicePointer`). Only consulted by
+            ``tiled_mask_sync.build_mask_volumes`` (the "Push to Tiled" path)
+            for slice keys absent from ``slices`` — a slice with real shapes
+            always wins, matching the frontend's own precedence.
     """
 
     kind: Literal["tiled", "local"]
@@ -169,6 +192,7 @@ class ExportSourceItem(BaseModel):
     slices: dict[str, list[dict[str, Any]]] = Field(default_factory=dict)
     split_by_slice: dict[str, str] = Field(default_factory=dict)
     negative_slices: list[str] = Field(default_factory=list)
+    predicted_slices: dict[str, PredictedSlicePointer] = Field(default_factory=dict)
 
 
 class ExportRequest(BaseModel):
diff --git a/backend/tests/test_tiled_mask_sync.py b/backend/tests/test_tiled_mask_sync.py
index 2a6e8bd..534c5f7 100644
--- a/backend/tests/test_tiled_mask_sync.py
+++ b/backend/tests/test_tiled_mask_sync.py
@@ -1,7 +1,12 @@
 """Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization → volumes)."""
+import io
+
 import numpy as np
+import pytest
+from PIL import Image as PILImage
 
-from schemas import AnnotationClass, ExportSourceItem
+import tiled_mask_sync
+from schemas import AnnotationClass, ExportSourceItem, PredictedSlicePointer
 from tiled_mask_sync import build_mask_volumes, merge_mask_volumes
 
 H = W = 32
@@ -67,6 +72,63 @@ def test_returns_none_without_slices():
     assert build_mask_volumes(item, _classes(), {"height": H, "width": W}) is None
 
 
+def _fake_commit_png(raw_label_map: np.ndarray) -> bytes:
+    buf = io.BytesIO()
+    PILImage.fromarray(raw_label_map, mode="L").save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def test_predicted_slices_reads_straight_from_the_run_commit_png(monkeypatch: pytest.MonkeyPatch):
+    """Regression test for the lazy-vectorization plan item: a slice with no
+    real shapes but a predicted-slice pointer must be rasterized directly
+    from the ipred run's commit.png (fetched server-side), never requiring
+    the frontend to have vectorized it into polygon shapes first."""
+    raw = np.zeros((H, W), dtype=np.uint8)
+    raw[2:6, 2:6] = 10  # raw frontend classId, matching AnnotationClass(classId=10, "Cell")
+    calls: list[str] = []
+
+    def fake_run_commit_png(run_id: str) -> bytes:
+        calls.append(run_id)
+        assert run_id == "run-abc"
+        return _fake_commit_png(raw)
+
+    monkeypatch.setattr(tiled_mask_sync.ipred_client, "run_commit_png", fake_run_commit_png)
+
+    item = ExportSourceItem(
+        kind="tiled", source="browse/ds/img", slices={},
+        predicted_slices={"7": PredictedSlicePointer(run_id="run-abc", class_ids=[10, 20])},
+    )
+    vols = build_mask_volumes(item, _classes(), {"height": H, "width": W})
+
+    assert vols is not None
+    assert calls == ["run-abc"]
+    assert vols["slice_indices"] == [7]
+    assert vols["semantic"][0, 3, 3] == 1  # remapped from raw classId 10 -> legend id 1 (Cell)
+    assert vols["class_vols"]["Cell"][0, 3, 3] == 255
+    assert vols["class_vols"]["Wall"][0].sum() == 0
+
+
+def test_real_shapes_win_over_a_predicted_pointer_for_the_same_slice(monkeypatch: pytest.MonkeyPatch):
+    """A slice already vectorized/edited into real shapes must never fall
+    back to its (possibly stale) predicted pointer — matches the frontend's
+    own precedence in handleCommitVolumeApply/predictedRasterStore."""
+    def fake_run_commit_png(run_id: str) -> bytes:
+        raise AssertionError("must not fetch commit.png when real shapes already cover this slice")
+
+    monkeypatch.setattr(tiled_mask_sync.ipred_client, "run_commit_png", fake_run_commit_png)
+
+    slices = {"7": [{"id": "a", "kind": "rectangle", "classId": 20, "x": 1, "y": 1, "w": 3, "h": 3}]}
+    item = ExportSourceItem(
+        kind="tiled", source="browse/ds/img", slices=slices,
+        predicted_slices={"7": PredictedSlicePointer(run_id="run-abc", class_ids=[10, 20])},
+    )
+    vols = build_mask_volumes(item, _classes(), {"height": H, "width": W})
+
+    assert vols is not None
+    assert vols["slice_indices"] == [7]
+    assert vols["semantic"][0].max() == 2  # Wall (classId 20), from the real shape — not the pointer
+
+
 def _volumes(slices, negatives=None):
     item = ExportSourceItem(
         kind="tiled", source="browse/ds/img", slices=slices, negative_slices=negatives or [],
diff --git a/backend/tiled_mask_sync.py b/backend/tiled_mask_sync.py
index a7bad6f..9ecec08 100644
--- a/backend/tiled_mask_sync.py
+++ b/backend/tiled_mask_sync.py
@@ -25,6 +25,7 @@
 
 import arrays as arrays_mod
 import export_jobs
+import ipred_client
 import mask_pyramid
 from coco_export import _safe_name, shape_to_mask
 from tiled_clients import api_key_for_uri, get_tiled_client
@@ -32,6 +33,22 @@
 logger = logging.getLogger(__name__)
 
 
+def _read_predicted_label_map(run_id: str) -> np.ndarray:
+    """Fetch an ipred run's commit.png and decode it into the same ``(H, W)``
+    uint8 array shape a rasterized shape would produce — pixel value is the
+    raw frontend classId directly (ipred's own convention, matching the
+    frontend's ``labelMapToPolygonShapes``), NOT yet remapped to this
+    export's legend ids; the caller does that remap the same way it already
+    does for real shapes.
+    """
+    import io
+
+    from PIL import Image
+
+    png_bytes = ipred_client.run_commit_png(run_id)
+    return np.asarray(Image.open(io.BytesIO(png_bytes)))
+
+
 def _category_maps(classes: list[Any]) -> tuple[dict[int, int], dict[int, str], list[dict[str, Any]]]:
     """1-based COCO ids + legend, matching ``build_export_plan``'s convention."""
     cat_id_map: dict[int, int] = {}
@@ -57,15 +74,26 @@ def build_mask_volumes(
     there are no slices to write. ``semantic`` is ``(n,H,W)`` uint8 (class index,
     0=bg); ``class_vols`` maps class label → ``(n,H,W)`` uint8 (0/255). Slices are
     every annotated key plus any ``negative_slices`` (emitted as all-zero frames
-    for hard negatives), in sorted numeric order.
+    for hard negatives) plus any ``predicted_slices`` key not already covered by
+    real shapes, in sorted numeric order.
+
+    ``predicted_slices`` (see :class:`schemas.PredictedSlicePointer`) lets an
+    un-vectorized iPred volume-apply result go straight into the mask volume —
+    fetching its commit.png from the ipred service and rasterizing THAT,
+    instead of requiring the frontend to first trace it into polygon shapes
+    just so this function can immediately rasterize them back into a mask.
+    A slice with real shapes always wins over its predicted pointer (matches
+    the frontend's own precedence in ``handleCommitVolumeApply``/
+    `usePredictedRasterStore`).
     """
     h, w = int(meta["height"]), int(meta["width"])
     cat_id_map, cat_id_to_name, legend = _category_maps(classes)
 
     slices: dict[str, list[dict[str, Any]]] = item.slices or {}
     neg = {str(k) for k in (item.negative_slices or [])}
+    predicted: dict[str, Any] = getattr(item, "predicted_slices", None) or {}
     keys = sorted(
-        {k for k, shapes in slices.items() if shapes} | neg,
+        {k for k, shapes in slices.items() if shapes} | neg | {k for k, p in predicted.items() if p},
         key=lambda k: int(k),
     )
     if not keys:
@@ -78,14 +106,27 @@ def build_mask_volumes(
     for key in keys:
         label = np.zeros((h, w), dtype=np.uint8)
         acc: dict[str, np.ndarray] = {name: np.zeros((h, w), dtype=bool) for name in class_names}
-        for shape in slices.get(key, []):
-            shape_dict = shape if isinstance(shape, dict) else shape.model_dump()
-            mask = shape_to_mask(shape_dict, h, w)
-            if float(mask.sum()) < 1:
-                continue
-            cat_id = cat_id_map.get(int(shape_dict.get("classId", 1)), 1)
-            label[mask] = cat_id
-            acc[cat_id_to_name.get(cat_id, "")] |= mask
+        shapes_here = slices.get(key, [])
+        if shapes_here:
+            for shape in shapes_here:
+                shape_dict = shape if isinstance(shape, dict) else shape.model_dump()
+                mask = shape_to_mask(shape_dict, h, w)
+                if float(mask.sum()) < 1:
+                    continue
+                cat_id = cat_id_map.get(int(shape_dict.get("classId", 1)), 1)
+                label[mask] = cat_id
+                acc[cat_id_to_name.get(cat_id, "")] |= mask
+        elif key in predicted and predicted[key]:
+            pointer = predicted[key]
+            run_id = pointer["run_id"] if isinstance(pointer, dict) else pointer.run_id
+            raw = _read_predicted_label_map(run_id)
+            for raw_id in np.unique(raw):
+                if raw_id == 0:
+                    continue
+                cat_id = cat_id_map.get(int(raw_id), 1)
+                mask = raw == raw_id
+                label[mask] = cat_id
+                acc[cat_id_to_name.get(cat_id, "")] |= mask
         sem_list.append(label)
         for name in class_names:
             class_lists[name].append((acc[name] * 255).astype(np.uint8))
diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index b669db8..ca4e8f6 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -7,6 +7,7 @@ import { DownloadSimple, FloppyDisk, ClockCounterClockwise, CircleDashed, ChartB
 import { cn } from '@/lib/utils';
 import { useDatasetStore } from '@/stores/datasetStore';
 import { useAnnotationStore } from '@/stores/annotationStore';
+import { usePredictedRasterStore } from '@/stores/predictedRasterStore';
 import { useToolStore } from '@/stores/toolStore';
 import { useClassStore } from '@/stores/classStore';
 import { useDraftSync } from '@/hooks/useDraftSync';
@@ -221,7 +222,8 @@ export default function AnnotatePage() {
   // ---- Batch iPred: multi-slice train + whole-volume apply ----
   const [trainAcrossSlices, setTrainAcrossSlices] = useState(false);
   const [commitClassIds, setCommitClassIds] = useState<number[]>([]);
-  const [volumeApplyCommitting, setVolumeApplyCommitting] = useState(false);
+  const { setPointers: setPredictedPointers, clearSlice: clearPredictedSlice } = usePredictedRasterStore();
+  const predictedPointers = usePredictedRasterStore((s) => s.bySource);
 
   // Every non-empty slice of this sample — the pool for "train across all
   // annotated slices" and the source of truth for `annotatedSliceCount`.
@@ -289,72 +291,94 @@ export default function AnnotatePage() {
     await fetch(`${API_BASE}/api/export/cancel/${jobId}`, { method: 'POST' });
   }, [clf.volumeApplyJob.jobId]);
 
-  // Fetch + vectorize each completed slice's commit PNG client-side, reusing the
-  // same tracer as the single-slice commit — see Phase 4.5's plan for why this
-  // stays client-side rather than porting polygon tracing to the ipred service.
+  // "Commit predicted shapes" no longer eagerly fetches + vectorizes every
+  // slice's commit.png into real Shape[] — that was the direct cause of the
+  // reported 297MB-draft/browser-OOM incident (139,004 shapes from one
+  // 690-slice volume-apply commit, 98.8% predicted-origin, all landing in
+  // the autosaved draft whether anyone ever looked at them or not).
   //
-  // Committed in BATCHES rather than accumulated for the whole volume before
-  // one store write: on a large volume (hundreds-to-thousands of slices), the
-  // old accumulate-everything-then-one-write approach held every slice's
-  // vectorized shapes in memory at once, on top of per-slice PNG-decode/mask
-  // buffers that outpaced GC (only yielding every 4th slice), and was
-  // immediately followed by useDraftSync serializing the whole enlarged
-  // per-sample slice set — this was the actual cause of the reported browser
-  // OOM on volume-wide apply. Writing to the store every VOLUME_COMMIT_BATCH
-  // slices bounds peak memory to one batch and gives the browser a real GC
-  // window between batches (this does mean a multi-batch commit now creates
-  // more than one undo entry — an acceptable tradeoff for a job this size).
-  const handleCommitVolumeApply = useCallback(async () => {
+  // Instead this just records a lightweight pointer per slice
+  // (predictedRasterStore — {runId, classIds}, tens of bytes, NOT part of
+  // annotationStore/useDraftSync's autosave). The existing Predictions layer
+  // already renders a pointer's commit.png directly (usePixelClassifier's
+  // live-preview effect resolves it the same way it resolves a still-running
+  // job's per-slice result), so nothing is lost visually — a slice only ever
+  // becomes real, editable Shape[] when the user explicitly vectorizes it
+  // (see handleMakeSliceEditable below), one slice at a time.
+  //
+  // Slices that already have real shapes are left alone: creating a pointer
+  // for an already-annotated slice would show the run's raw (unedited)
+  // commit.png overlapping hand-drawn or already-vectorized content — the
+  // "make editable" path is what merges predicted-into-existing correctly
+  // (via preserveShapes), not this bulk commit step.
+  const handleCommitVolumeApply = useCallback(() => {
     if (!sourceKey) return;
     const result = clf.volumeApplyJob.result as { runs?: Record<string, string> } | null;
     const runs = result?.runs;
     if (!runs || commitClassIds.length === 0) return;
-    setVolumeApplyCommitting(true);
+    const pointers: Record<string, { runId: string; classIds: number[] }> = {};
+    for (const [sliceKey, runId] of Object.entries(runs)) {
+      if ((byImage[sourceKey]?.[sliceKey] ?? []).length > 0) continue;
+      pointers[sliceKey] = { runId, classIds: commitClassIds };
+    }
+    if (Object.keys(pointers).length) setPredictedPointers(sourceKey, pointers);
+    clf.resetVolumeApplyJob();
+  }, [sourceKey, clf, byImage, commitClassIds, setPredictedPointers]);
+
+  // The only path that turns a predictedRasterStore pointer into real,
+  // editable Shape[] — one slice at a time, on explicit request. Reuses the
+  // exact same tracer the old eager-commit path used per slice, including
+  // `preserveShapes` so an already-partially-annotated slice merges rather
+  // than overwrites.
+  const [vectorizingSlice, setVectorizingSlice] = useState(false);
+  const handleMakeSliceEditable = useCallback(async () => {
+    if (!sourceKey) return;
+    const pointer = predictedPointers[sourceKey]?.[String(currentSlice)];
+    if (!pointer) return;
+    setVectorizingSlice(true);
     try {
-      const VOLUME_COMMIT_BATCH = 50;
-      let bySlice: Record<number, Shape[]> = {};
-      let processed = 0;
-      for (const [sliceKey, runId] of Object.entries(runs)) {
-        const sliceIdx = Number(sliceKey);
-        const { data, width, height } = await loadLabelPng(ipredRunCommitUrl(runId));
-        const existing = byImage[sourceKey]?.[sliceKey] ?? [];
-        const shapes = labelMapToPolygonShapes(data, width, height, commitClassIds, {
-          minRegion: 64,
-          preserveShapes: existing,
-          origin: 'predicted',
-        });
-        if (shapes.length) bySlice[sliceIdx] = shapes;
-        processed += 1;
-        // Yield every slice (not just every 4th) so the browser gets a real
-        // chance to garbage-collect the per-slice decode/mask buffers below.
-        await new Promise((resolve) => setTimeout(resolve, 0));
-        if (processed % VOLUME_COMMIT_BATCH === 0 && Object.keys(bySlice).length) {
-          addShapesAcrossSlices(sourceKey, bySlice);
-          bySlice = {};
-        }
-      }
-      if (Object.keys(bySlice).length) addShapesAcrossSlices(sourceKey, bySlice);
-      clf.resetVolumeApplyJob();
+      const { data, width, height } = await loadLabelPng(ipredRunCommitUrl(pointer.runId));
+      const existing = byImage[sourceKey]?.[String(currentSlice)] ?? [];
+      const shapes = labelMapToPolygonShapes(data, width, height, pointer.classIds, {
+        minRegion: 64,
+        preserveShapes: existing,
+        origin: 'predicted',
+      });
+      if (shapes.length) addShapes(sourceKey, currentSlice, shapes);
+      clearPredictedSlice(sourceKey, String(currentSlice));
     } finally {
-      setVolumeApplyCommitting(false);
+      setVectorizingSlice(false);
     }
-  }, [sourceKey, clf, byImage, commitClassIds, addShapesAcrossSlices]);
-
-  // "Push to Tiled + View in 3D" — a direct, one-click path from the iPred
-  // panel to the 3D view, instead of requiring the user to separately
-  // discover the Export modal's "Sync masks to Tiled" action first (that
-  // dependency wasn't obvious — see the plan doc). Rasterizes and writes
-  // this sample's ENTIRE current shape set (every slice, both origins) to
-  // Tiled's <source>__masks container, then jumps to /volume?mask=fast to
-  // auto-load it, mirroring DownloadModal's own "current sample" scope.
+  }, [sourceKey, currentSlice, predictedPointers, byImage, addShapes, clearPredictedSlice]);
+
+  // Push to Tiled and View in 3D are two INDEPENDENT actions (previously one
+  // combined "Push to Tiled + view in 3D" button that always navigated on
+  // success) — forcing a navigation to /volume right after a push meant that
+  // on a dataset with no volume pyramid built yet, you landed straight in
+  // "Build Volume" with no way back to Annotate short of the browser's own
+  // back button. Now pushing stays on this tab, and viewing in 3D is a
+  // separate click that works whether or not you just pushed (e.g. to look
+  // at a result pushed earlier in the session).
   const maskSyncJob = useExportJob('mask-sync-view3d');
-  const navigatedAfterSyncRef = useRef(false);
-  const handleSyncToTiledAndView3D = useCallback(() => {
+  const handleSyncToTiled = useCallback(() => {
     if (!source || !kind || kind !== 'tiled') {
       window.alert('Pushing masks to Tiled only works for Tiled sources.');
       return;
     }
-    navigatedAfterSyncRef.current = false;
+    // predicted_slices carries any still-un-vectorized predicted regions
+    // (predictedRasterStore pointers) straight through — the backend fetches
+    // their commit.png directly from ipred and rasterizes it server-side, so
+    // "Push to Tiled" works correctly without first vectorizing every
+    // committed slice into Shape[] client-side (see the lazy-vectorization
+    // plan item). A slice already in `slices` (real shapes) doesn't need its
+    // pointer sent — build_mask_volumes already prefers real shapes anyway,
+    // but there's no reason to make the backend re-derive that here too.
+    const pointersForSource = predictedPointers[sourceKey!] ?? {};
+    const predictedSlicesPayload = Object.fromEntries(
+      Object.entries(pointersForSource)
+        .filter(([sliceKey]) => !(byImage[sourceKey!]?.[sliceKey]?.length))
+        .map(([sliceKey, p]) => [sliceKey, { run_id: p.runId, class_ids: p.classIds }]),
+    );
     maskSyncJob.startMaskSync({
       sources: [{
         kind,
@@ -363,17 +387,15 @@ export default function AnnotatePage() {
         slices: byImage[sourceKey!] ?? {},
         split_by_slice: splitBySlice[sourceKey!] ?? {},
         negative_slices: negativeSlices[sourceKey!] ?? [],
+        predicted_slices: predictedSlicesPayload,
       }],
       classes,
     });
-  }, [source, kind, serverUri, sourceKey, byImage, splitBySlice, negativeSlices, classes, maskSyncJob]);
+  }, [source, kind, serverUri, sourceKey, byImage, splitBySlice, negativeSlices, predictedPointers, classes, maskSyncJob]);
 
-  useEffect(() => {
-    if (maskSyncJob.state.status === 'done' && !navigatedAfterSyncRef.current) {
-      navigatedAfterSyncRef.current = true;
-      navigate('/volume?mask=fast');
-    }
-  }, [maskSyncJob.state.status, navigate]);
+  const handleViewIn3D = useCallback(() => {
+    navigate('/volume?mask=fast');
+  }, [navigate]);
 
   const classLabelForId = useCallback(
     (classId: number) => {
@@ -624,15 +646,24 @@ export default function AnnotatePage() {
                       })()
                     : null
                 }
-                volumeApplyCommitting={volumeApplyCommitting}
                 onApplyToVolume={handleApplyToVolume}
-                onCommitVolumeApply={() => { void handleCommitVolumeApply(); }}
+                onCommitVolumeApply={handleCommitVolumeApply}
                 onCancelVolumeApply={() => { void handleCancelVolumeApply(); }}
                 onDismissVolumeApply={clf.resetVolumeApplyJob}
+                hasPredictedPointerOnCurrentSlice={
+                  !!sourceKey && !!predictedPointers[sourceKey]?.[String(currentSlice)]
+                }
+                vectorizingSlice={vectorizingSlice}
+                onMakeSliceEditable={() => { void handleMakeSliceEditable(); }}
+                hasAnyPredictedPointers={
+                  !!sourceKey && Object.keys(predictedPointers[sourceKey] ?? {}).length > 0
+                }
                 onTrainDeepModel={() => navigate('/train')}
-                onSyncToTiledAndView3D={handleSyncToTiledAndView3D}
+                onSyncToTiled={handleSyncToTiled}
                 syncingToTiled={maskSyncJob.state.status === 'running'}
+                syncedToTiled={maskSyncJob.state.status === 'done'}
                 syncToTiledError={maskSyncJob.state.status === 'error' ? maskSyncJob.state.error : null}
+                onViewIn3D={handleViewIn3D}
                 manifoldParams={manifold.params}
                 onManifoldParamsChange={manifold.setParams}
                 manifoldSampling={manifold.sampling}
diff --git a/frontend/src/components/annotate/DownloadModal.tsx b/frontend/src/components/annotate/DownloadModal.tsx
index 393c893..5b5f8df 100644
--- a/frontend/src/components/annotate/DownloadModal.tsx
+++ b/frontend/src/components/annotate/DownloadModal.tsx
@@ -6,6 +6,7 @@ import { useNavigate } from 'react-router';
 import { DownloadSimple, X, CheckCircle, WarningCircle, Cube } from '@phosphor-icons/react';
 import { useDatasetStore } from '@/stores/datasetStore';
 import { useAnnotationStore } from '@/stores/annotationStore';
+import { usePredictedRasterStore } from '@/stores/predictedRasterStore';
 import { useClassStore } from '@/stores/classStore';
 import { useRatingStore } from '@/stores/ratingStore';
 import { useSettingsStore } from '@/stores/settingsStore';
@@ -59,6 +60,7 @@ export default function DownloadModal({ onClose }: DownloadModalProps) {
   const navigate = useNavigate();
   const { source, kind, serverUri, currentSlice } = useDatasetStore();
   const { byImage, splitBySlice, negativeSlices } = useAnnotationStore();
+  const predictedPointers = usePredictedRasterStore((s) => s.bySource);
   const { classes } = useClassStore();
   const ratings = useRatingStore((s) => s.ratings);
   const annotatorName = useSettingsStore((s) => s.annotatorName);
@@ -98,6 +100,19 @@ export default function DownloadModal({ onClose }: DownloadModalProps) {
       const split_by_slice = scope === 'slice' ? (allSplits[cur] ? { [cur]: allSplits[cur] } : {}) : allSplits;
       const negative_slices = scope === 'slice' ? allNeg.filter((k) => String(k) === cur) : allNeg;
 
+      // Un-vectorized predicted pointers (see predictedRasterStore) for this
+      // sample — only relevant to mask-sync (build_mask_volumes fetches
+      // their commit.png directly); COCO export ignores this field today,
+      // same as before. "Current slice only" keeps just the one pointer
+      // matching cur, mirroring how slices/negative_slices are scoped above.
+      const allPredicted = predictedPointers[sk] ?? {};
+      const predicted_slices = Object.fromEntries(
+        Object.entries(allPredicted)
+          .filter(([k]) => (scope === 'slice' ? k === cur : true))
+          .filter(([k]) => !(slices[k]?.length))
+          .map(([k, p]) => [k, { run_id: p.runId, class_ids: p.classIds }]),
+      );
+
       return [{
         kind,
         source,
@@ -105,6 +120,7 @@ export default function DownloadModal({ onClose }: DownloadModalProps) {
         slices,
         split_by_slice,
         negative_slices,
+        predicted_slices,
       }];
     }
 
diff --git a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
index e99721e..ae9ee56 100644
--- a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
+++ b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
@@ -100,12 +100,25 @@ export interface PixelClassifierPanelProps {
   volumeApplying: boolean;
   volumeApplyProgress: { done: number; total: number } | null;
   volumeApplyResult: { runCount: number; errorCount: number; cancelled: boolean } | null;
-  volumeApplyCommitting: boolean;
   onApplyToVolume: () => void;
   onCommitVolumeApply: () => void;
   onCancelVolumeApply: () => void;
   onDismissVolumeApply: () => void;
 
+  // The slice on screen has an un-vectorized predicted region (a
+  // predictedRasterStore pointer, set by "Commit predicted shapes" — see
+  // AnnotatePage's handleCommitVolumeApply) that's only ever shown as a
+  // raster overlay until explicitly turned into editable Shape[].
+  hasPredictedPointerOnCurrentSlice: boolean;
+  vectorizingSlice: boolean;
+  onMakeSliceEditable: () => void;
+  // True when this sample has ANY committed-but-not-yet-vectorized predicted
+  // pointer, on any slice — annotatedSliceCount alone (real shapes only,
+  // correctly so for multi-slice training) would otherwise hide "Push to
+  // Tiled" entirely for a sample whose only committed content is still
+  // pointers, even though there's real predicted content to push.
+  hasAnyPredictedPointers: boolean;
+
   // Hand-off to the deep-training tab: the iPred annotation work already done
   // on this sample becomes the training set, pre-selected there.
   onTrainDeepModel?: () => void;
@@ -114,9 +127,16 @@ export interface PixelClassifierPanelProps {
   // <source>__masks container and jump straight to the 3D view's Fast
   // (iPred) layer — the direct path that skips having to separately
   // discover the Export modal's "Sync masks to Tiled" action first.
-  onSyncToTiledAndView3D?: () => void;
+  // Split into two independent actions — a combined "push + navigate" used
+  // to force you onto the 3D page (and into "Build Volume" if the dataset's
+  // pyramid didn't exist yet) with no way back to Annotate short of the
+  // browser's own back button. Pushing to Tiled no longer navigates at all;
+  // viewing in 3D no longer requires a push to have just happened.
+  onSyncToTiled?: () => void;
   syncingToTiled?: boolean;
   syncToTiledError?: string | null;
+  syncedToTiled?: boolean;
+  onViewIn3D?: () => void;
 
   // Suggest-labels (manifold), folded into the same loop instead of a separate panel.
   manifoldParams: ManifoldParams;
@@ -176,15 +196,20 @@ export default function PixelClassifierPanel({
   volumeApplying,
   volumeApplyProgress,
   volumeApplyResult,
-  volumeApplyCommitting,
   onApplyToVolume,
   onCommitVolumeApply,
   onCancelVolumeApply,
   onDismissVolumeApply,
+  hasPredictedPointerOnCurrentSlice,
+  vectorizingSlice,
+  onMakeSliceEditable,
+  hasAnyPredictedPointers,
   onTrainDeepModel,
-  onSyncToTiledAndView3D,
+  onSyncToTiled,
   syncingToTiled = false,
   syncToTiledError = null,
+  syncedToTiled = false,
+  onViewIn3D,
   manifoldParams,
   onManifoldParamsChange,
   manifoldSampling,
@@ -472,13 +497,12 @@ export default function PixelClassifierPanel({
               <div className="flex gap-1">
                 <button
                   type="button"
-                  disabled={volumeApplyCommitting || volumeApplyResult.runCount === 0}
+                  disabled={volumeApplyResult.runCount === 0}
                   onClick={onCommitVolumeApply}
+                  title="Instant — records a lightweight pointer per slice rather than vectorizing every slice into shapes up front. Use 'Make this slice editable' on a specific slice when you actually want to edit its predicted regions."
                   className="flex-1 py-1 rounded-md text-[10px] font-medium bg-sky-600 text-white hover:bg-sky-700 disabled:opacity-50"
                 >
-                  {volumeApplyCommitting
-                    ? 'Committing…'
-                    : `Commit predicted shapes (${commitClassIds.length} class${commitClassIds.length === 1 ? '' : 'es'})`}
+                  {`Commit predicted shapes (${commitClassIds.length} class${commitClassIds.length === 1 ? '' : 'es'})`}
                 </button>
                 <button
                   type="button"
@@ -491,18 +515,48 @@ export default function PixelClassifierPanel({
             </div>
           )}
 
-          {onSyncToTiledAndView3D && annotatedSliceCount > 0 && (
-            <div className="flex flex-col gap-1">
+          {hasPredictedPointerOnCurrentSlice && (
+            <div className="flex flex-col gap-1 rounded border border-amber-200 bg-amber-50/60 p-1.5 text-[10px] text-gray-700">
+              <p>This slice's predicted regions are shown but not yet editable.</p>
               <button
                 type="button"
-                onClick={onSyncToTiledAndView3D}
-                disabled={syncingToTiled}
-                title="Write this sample's current shapes to Tiled, then open the 3D view with the Fast (iPred) layer loaded"
-                className="flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border border-gray-200 bg-white text-gray-800 hover:bg-sky-50 hover:border-sky-300 transition-colors disabled:opacity-50"
+                disabled={vectorizingSlice}
+                onClick={onMakeSliceEditable}
+                className="py-1 rounded-md text-[10px] font-medium bg-amber-500 text-white hover:bg-amber-600 disabled:opacity-50"
               >
-                {syncingToTiled ? <CircleNotch size={13} className="animate-spin" /> : <Cube size={13} />}
-                {syncingToTiled ? 'Pushing to Tiled…' : 'Push to Tiled + view in 3D'}
+                {vectorizingSlice ? 'Vectorizing…' : 'Make this slice editable'}
               </button>
+            </div>
+          )}
+
+          {onSyncToTiled && (annotatedSliceCount > 0 || hasAnyPredictedPointers) && (
+            <div className="flex flex-col gap-1">
+              <div className="flex gap-1">
+                <button
+                  type="button"
+                  onClick={onSyncToTiled}
+                  disabled={syncingToTiled}
+                  title="Write this sample's current shapes to Tiled — stays on this tab"
+                  className="flex flex-1 items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border border-gray-200 bg-white text-gray-800 hover:bg-sky-50 hover:border-sky-300 transition-colors disabled:opacity-50"
+                >
+                  {syncingToTiled && <CircleNotch size={13} className="animate-spin" />}
+                  {syncingToTiled ? 'Pushing…' : 'Push to Tiled'}
+                </button>
+                {onViewIn3D && (
+                  <button
+                    type="button"
+                    onClick={onViewIn3D}
+                    title="Open the 3D view with the Fast (iPred) layer — does not push anything first; use Push to Tiled beforehand if this sample's shapes have changed"
+                    className="flex flex-1 items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border border-gray-200 bg-white text-gray-800 hover:bg-sky-50 hover:border-sky-300 transition-colors"
+                  >
+                    <Cube size={13} />
+                    View in 3D
+                  </button>
+                )}
+              </div>
+              {syncedToTiled && !syncingToTiled && (
+                <p className="text-[10px] text-emerald-600">Pushed to Tiled.</p>
+              )}
               {syncToTiledError && (
                 <p className="flex items-start gap-1 text-[10px] text-red-600">
                   <WarningCircle size={12} className="mt-0.5 shrink-0" />
diff --git a/frontend/src/hooks/usePixelClassifier.ts b/frontend/src/hooks/usePixelClassifier.ts
index 147b185..a8cb2d2 100644
--- a/frontend/src/hooks/usePixelClassifier.ts
+++ b/frontend/src/hooks/usePixelClassifier.ts
@@ -4,6 +4,7 @@
 import { useCallback, useEffect, useRef, useState } from 'react';
 import { useConnectionStore } from '@/stores/connectionStore';
 import { useIpredStore } from '@/stores/ipredStore';
+import { usePredictedRasterStore } from '@/stores/predictedRasterStore';
 import type { Shape } from '@/stores/annotationStore';
 import { useExportJob } from '@/hooks/useExportJob';
 import {
@@ -459,9 +460,23 @@ export function usePixelClassifier({
   // NOT a proba-channel preview: batch-apply runs are created with
   // store_probabilities=false (ipred_batch_jobs.py's `_apply_one_slice`), so
   // there is no proba.npy to load for these run ids — only commit/status.
+  // Reactive: must re-render this effect the instant "Commit predicted
+  // shapes" writes a pointer, not only on the next slice change — a plain
+  // `usePredictedRasterStore.getState()` read wouldn't re-fire the effect
+  // below when only the store (not sliceIndex/job result) changes.
+  const committedRunIdForSlice = usePredictedRasterStore(
+    (s) => (resetKey ? s.bySource[resetKey]?.[String(sliceIndex)]?.runId : undefined) ?? null,
+  );
+
   useEffect(() => {
     const result = volumeApplyJobHook.state.result as { runs?: Record<string, string> } | null;
-    const targetRunId = result?.runs?.[String(sliceIndex)] ?? null;
+    // Prefer the live job's own runs (covers "still running" and "just
+    // finished, not yet committed"); once "Commit predicted shapes" resets
+    // the job (see AnnotatePage's handleCommitVolumeApply), that map is gone
+    // and the durable predictedRasterStore pointer — set by Commit itself —
+    // becomes the only remaining source, so the SAME overlay keeps working
+    // after commit without ever having vectorized anything into Shape[].
+    const targetRunId = result?.runs?.[String(sliceIndex)] ?? committedRunIdForSlice;
     if (!targetRunId || targetRunId === volumeApplyPreviewRunIdRef.current) return;
     let cancelled = false;
     volumeApplyPreviewRunIdRef.current = targetRunId;
@@ -491,7 +506,7 @@ export function usePixelClassifier({
     return () => {
       cancelled = true;
     };
-  }, [sliceIndex, volumeApplyJobHook.state.result]);
+  }, [sliceIndex, volumeApplyJobHook.state.result, committedRunIdForSlice]);
 
   const predict = useCallback(
     async (_shapes: Shape[]) => {
diff --git a/frontend/src/stores/predictedRasterStore.test.ts b/frontend/src/stores/predictedRasterStore.test.ts
new file mode 100644
index 0000000..7fb1020
--- /dev/null
+++ b/frontend/src/stores/predictedRasterStore.test.ts
@@ -0,0 +1,56 @@
+import { describe, it, expect, beforeEach } from 'vitest';
+import { usePredictedRasterStore } from './predictedRasterStore';
+
+const reset = () => usePredictedRasterStore.setState({ bySource: {} });
+
+describe('predictedRasterStore', () => {
+  beforeEach(reset);
+
+  it('setPointers merges into an existing source without clobbering other slices', () => {
+    const { setPointers } = usePredictedRasterStore.getState();
+    setPointers('sampleA', { '0': { runId: 'run-0', classIds: [1, 2] } });
+    setPointers('sampleA', { '1': { runId: 'run-1', classIds: [1, 2] } });
+    expect(usePredictedRasterStore.getState().bySource.sampleA).toEqual({
+      '0': { runId: 'run-0', classIds: [1, 2] },
+      '1': { runId: 'run-1', classIds: [1, 2] },
+    });
+  });
+
+  it('setPointers for the same slice overwrites the prior pointer', () => {
+    const { setPointers } = usePredictedRasterStore.getState();
+    setPointers('sampleA', { '0': { runId: 'run-old', classIds: [1] } });
+    setPointers('sampleA', { '0': { runId: 'run-new', classIds: [1, 2] } });
+    expect(usePredictedRasterStore.getState().bySource.sampleA['0']).toEqual({
+      runId: 'run-new',
+      classIds: [1, 2],
+    });
+  });
+
+  it('clearSlice removes only the targeted slice', () => {
+    const { setPointers, clearSlice } = usePredictedRasterStore.getState();
+    setPointers('sampleA', {
+      '0': { runId: 'run-0', classIds: [1] },
+      '1': { runId: 'run-1', classIds: [1] },
+    });
+    clearSlice('sampleA', '0');
+    const bySource = usePredictedRasterStore.getState().bySource.sampleA;
+    expect(bySource['0']).toBeUndefined();
+    expect(bySource['1']).toEqual({ runId: 'run-1', classIds: [1] });
+  });
+
+  it('clearSlice on an unknown sample or slice is a no-op', () => {
+    const { clearSlice } = usePredictedRasterStore.getState();
+    expect(() => clearSlice('unknown', '0')).not.toThrow();
+    expect(usePredictedRasterStore.getState().bySource).toEqual({});
+  });
+
+  it('clearSource drops every pointer for that sample, leaving others intact', () => {
+    const { setPointers, clearSource } = usePredictedRasterStore.getState();
+    setPointers('sampleA', { '0': { runId: 'run-0', classIds: [1] } });
+    setPointers('sampleB', { '0': { runId: 'run-b0', classIds: [1] } });
+    clearSource('sampleA');
+    const state = usePredictedRasterStore.getState();
+    expect(state.bySource.sampleA).toBeUndefined();
+    expect(state.bySource.sampleB).toEqual({ '0': { runId: 'run-b0', classIds: [1] } });
+  });
+});
diff --git a/frontend/src/stores/predictedRasterStore.ts b/frontend/src/stores/predictedRasterStore.ts
new file mode 100644
index 0000000..8631e18
--- /dev/null
+++ b/frontend/src/stores/predictedRasterStore.ts
@@ -0,0 +1,79 @@
+/**
+ * predictedRasterStore — lightweight pointers to un-vectorized predicted
+ * regions from an iPred "Apply across volume" run, keyed by sample + slice.
+ *
+ * This is the direct fix for the 297MB-draft incident (139,004 shapes from
+ * one volume-apply commit, 98.8% predicted-origin): "Commit predicted
+ * shapes" used to eagerly fetch + vectorize every slice's commit.png into
+ * real `Shape[]` objects added to `annotationStore` — for a 690-slice
+ * volume that's hundreds of thousands of polygon objects landing straight
+ * in the autosaved draft, most of which nobody ever looks at let alone
+ * edits.
+ *
+ * Deliberately a SEPARATE store from `annotationStore`/`useDraftSync`: a
+ * pointer here is just `{runId, classIds}` (tens of bytes), and — critically
+ * — is never included in the autosaved draft payload, so committing however
+ * many slices costs nothing until a specific slice is actually vectorized
+ * (see `usePixelClassifier.ts`'s live-preview effect, which renders a
+ * pointer's commit.png directly via the existing Predictions layer, and
+ * AnnotatePage's "Make this slice editable" action, the only path that ever
+ * turns a pointer into real `Shape[]`).
+ *
+ * Not persisted across page reloads on purpose — same rationale as
+ * `annotationStore`'s draft being the autosave surface: an un-vectorized
+ * pointer is recoverable at any time by re-running "Apply across volume" or
+ * committing again, unlike hand-drawn shapes.
+ */
+import { create } from 'zustand';
+
+export interface PredictedRegionPointer {
+  /** ipred run id — `ipredRunCommitUrl(runId)` fetches its raw label-map PNG. */
+  runId: string;
+  /** Frontend class ids this run's commit.png pixel values are drawn from
+   *  (matches `labelMapToPolygonShapes`'s `classIds` param exactly — pixel
+   *  value IS the class id, not a 1-based sequential index). */
+  classIds: number[];
+}
+
+export interface PredictedRasterState {
+  /** sourceKey -> sliceIndex (as string) -> pointer. */
+  bySource: Record<string, Record<string, PredictedRegionPointer>>;
+  /** Replace/merge pointers for many slices of one sample at once (a
+   *  "Commit predicted shapes" click). */
+  setPointers: (sourceKey: string, pointers: Record<string, PredictedRegionPointer>) => void;
+  /** Drop one slice's pointer — called once it's been vectorized into real
+   *  `Shape[]` (or the user wants to discard the prediction for that slice). */
+  clearSlice: (sourceKey: string, sliceKey: string) => void;
+  /** Drop every pointer for a sample — a fresh volume-apply run supersedes
+   *  whatever was committed before. */
+  clearSource: (sourceKey: string) => void;
+}
+
+export const usePredictedRasterStore = create<PredictedRasterState>((set) => ({
+  bySource: {},
+
+  setPointers: (sourceKey, pointers) =>
+    set((s) => ({
+      bySource: {
+        ...s.bySource,
+        [sourceKey]: { ...(s.bySource[sourceKey] ?? {}), ...pointers },
+      },
+    })),
+
+  clearSlice: (sourceKey, sliceKey) =>
+    set((s) => {
+      const existing = s.bySource[sourceKey];
+      if (!existing || !(sliceKey in existing)) return s;
+      const next = { ...existing };
+      delete next[sliceKey];
+      return { bySource: { ...s.bySource, [sourceKey]: next } };
+    }),
+
+  clearSource: (sourceKey) =>
+    set((s) => {
+      if (!(sourceKey in s.bySource)) return s;
+      const next = { ...s.bySource };
+      delete next[sourceKey];
+      return { bySource: next };
+    }),
+}));

From 0a3ad8f08d2b15fcc72304b67271a1bfcb6f4d8c Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 2 Sep 2026 15:29:19 -0700
Subject: [PATCH 20/38] updating docker files

---
 .dockerignore                             |   6 +-
 .github/workflows/ci.yml                  |  77 +++++++++++++-
 Dockerfile                                |  55 +++++++++-
 docker-compose.full.yml                   |  34 ++++++
 docker-entrypoint-full.sh                 |  54 ++++++++++
 frontend/package-lock.json                | 122 +++++++++++-----------
 frontend/package.json                     |   2 +-
 frontend/vendor/view_tomography_recon_app |   2 +-
 tiled/config.docker.yml                   |  32 ++++++
 9 files changed, 313 insertions(+), 71 deletions(-)
 create mode 100644 docker-compose.full.yml
 create mode 100755 docker-entrypoint-full.sh
 create mode 100644 tiled/config.docker.yml

diff --git a/.dockerignore b/.dockerignore
index 9798785..c49484a 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -14,9 +14,11 @@ backend/static
 htmlcov
 .coverage
 
-# Tiled server config, data & catalog (external in production — never bundled)
+# Tiled's own runtime catalog/data (local dev state — never bundled). NOT
+# excluding tiled/ itself: the app-full Docker image's Tiled server needs
+# tiled/config.docker.yml (see Dockerfile's app-full stage) in the build
+# context, and it's tiny source config, not runtime data.
 .tiled
-tiled/
 
 # Local data, backups, runtime files
 data
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index b511b9b..084e9cc 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -16,10 +16,26 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: "3.11"
-      - run: pip install -e ".[dev,test]"
+      # `ml` (torch/dlsia/qlty) installed here so dlsia's train/infer code
+      # paths are actually exercised in CI, CPU-only — without it every
+      # dlsia-dependent test silently skips (torch_available() is False).
+      - run: pip install -e ".[dev,test,ml]"
       - run: flake8 . --max-line-length=120 --extend-ignore=E501,W503
       - run: pytest
 
+  ipred:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ipred
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - run: pip install -e ".[test]"
+      - run: pytest
+
   frontend:
     runs-on: ubuntu-latest
     defaults:
@@ -37,3 +53,62 @@ jobs:
       - run: npm ci
       - run: npm run typecheck
       - run: npm run test
+
+  docker-build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+      - uses: docker/setup-buildx-action@v3
+      # Build only — catches a Dockerfile/compose file silently falling
+      # behind the app it packages (the exact drift found in this session's
+      # review: no ipred service, no ml extra, no Tiled service). Two
+      # standalone compose files (not overlays — app-full is a separate,
+      # complete service, not an override of app), built independently. Does
+      # not push anywhere; image publishing is a separate, main-only job below.
+      - run: docker compose -f docker-compose.yml build
+      - run: docker compose -f docker-compose.full.yml build
+
+  # Publishes images on merge to main ONLY — never on a PR run, to avoid
+  # wasted builds and registry clutter. A separate job (not a branch filter
+  # on docker-build above) so the build-and-test signal on PRs stays exactly
+  # what it is today, just with a real build verification added.
+  docker-publish:
+    if: github.event_name == 'push' && github.ref == 'refs/heads/main'
+    needs: [backend, ipred, frontend, docker-build]
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+    strategy:
+      matrix:
+        include:
+          - target: app
+            image: app
+          - target: app-full
+            image: app-full
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+      - uses: docker/setup-buildx-action@v3
+      - uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      - uses: docker/metadata-action@v5
+        id: meta
+        with:
+          images: ghcr.io/${{ github.repository }}/${{ matrix.image }}
+          tags: |
+            type=raw,value=latest
+            type=sha
+      - uses: docker/build-push-action@v6
+        with:
+          context: .
+          target: ${{ matrix.target }}
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
diff --git a/Dockerfile b/Dockerfile
index de63e36..d09e5c4 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,9 +1,15 @@
 # syntax=docker/dockerfile:1
-# Lightweight production image: builds the SPA and serves it from the FastAPI
-# backend (single container, one port). Tiled is NOT included — point the app at
-# an external Tiled via TILED_URI/TILED_API_KEY. Local dev still uses start_all.sh.
+# Two images from one file, selected via `--target`:
+#   app       (default) — frontend + backend only, Tiled external
+#                          (docker build . / docker compose up, unchanged).
+#   app-full            — the same, PLUS a bundled Tiled server and the ipred
+#                          service in the same container (all three talk over
+#                          127.0.0.1), for a single `docker run` that needs
+#                          nothing external — see docker-compose.full.yml.
+# Local dev still uses start_all.sh, which runs the same three services as
+# separate local processes instead of inside a container.
 
-# --- Stage 1: build the frontend (same-origin: VITE_API_BASE left empty) ---
+# --- Stage: build the frontend (same-origin: VITE_API_BASE left empty) ---
 FROM node:22-alpine AS web
 WORKDIR /web
 COPY frontend/package.json frontend/package-lock.json ./
@@ -18,7 +24,7 @@ RUN test -f vendor/view_tomography_recon_app/src/zarr-viewer/src/ome-zarr-viewer
         && echo "Run: git submodule update --init --recursive" && exit 1)
 RUN npm run build   # → /web/dist
 
-# --- Stage 2: backend + built SPA ---
+# --- Stage: lightweight production image (frontend + backend only) ---
 FROM python:3.12-slim AS app
 WORKDIR /app
 
@@ -41,3 +47,42 @@ VOLUME ["/data"]
 
 EXPOSE 8002
 CMD ["uvicorn", "annotation_server:app", "--host", "0.0.0.0", "--port", "8002"]
+
+# --- Stage: batteries-included image (+ Tiled server + ipred + dlsia/ml) ---
+# Bundles all three backend services into one container over loopback — the
+# CI/Docker coverage gap this stage fixes: previously NOTHING packaged ipred
+# or a Tiled server at all, so the app image alone could never run iPred or
+# dlsia, and there was no single-command way to try the full stack without
+# start_all.sh's separate local processes.
+FROM app AS app-full
+
+# The `ml` extra (torch/dlsia/qlty) — kept out of the lean `app` image above,
+# but this stage's whole point is "batteries included," so it's worth the
+# size. Pulls in the full CUDA toolkit (nvidia-cudnn-cu12, nvidia-cublas-cu12,
+# etc.) as torch dependencies — several GB — needed for real GPU support when
+# this container is run with `--gpus all` on a CUDA host; a build here can
+# fail with an I/O error mid-write on a disk-constrained host, which is a
+# local Docker disk-space problem to fix (see docker system df / prune, or
+# grow Docker Desktop's disk allocation), not a reason to drop CUDA support.
+RUN pip install --no-cache-dir ".[ml]"
+
+# tiled[all] pulls in the actual server (catalog, array/table adapters) —
+# `app`'s pyproject only pins tiled[client], which has no server component.
+RUN pip install --no-cache-dir "tiled[all]"
+
+# ipred is a sibling package with its own pyproject, not a backend dependency.
+COPY ipred/ /ipred/
+RUN pip install --no-cache-dir /ipred
+
+# Portable Tiled config (see tiled/config.docker.yml's own doc comment for
+# why this isn't just tiled/config.yml — that one hardcodes a local dev
+# machine's absolute data path).
+COPY tiled/config.docker.yml /app/tiled/config.docker.yml
+
+COPY docker-entrypoint-full.sh /usr/local/bin/docker-entrypoint-full.sh
+RUN chmod +x /usr/local/bin/docker-entrypoint-full.sh
+
+# 8002 backend (the only port most deployments need — same-origin SPA+API);
+# 8003/8010 exposed too so ipred/Tiled can be reached directly if wanted.
+EXPOSE 8002 8003 8010
+ENTRYPOINT ["/usr/local/bin/docker-entrypoint-full.sh"]
diff --git a/docker-compose.full.yml b/docker-compose.full.yml
new file mode 100644
index 0000000..d931fa8
--- /dev/null
+++ b/docker-compose.full.yml
@@ -0,0 +1,34 @@
+# Batteries-included stack: Tiled + backend (with the ml/dlsia extra) + ipred,
+# all bundled into ONE container (Dockerfile's app-full stage) — nothing
+# external required, unlike docker-compose.yml's lean `app` service, which
+# needs a separately-run Tiled. Use this for a single-command full-stack
+# try-it (iPred + dlsia both work), or docker-compose.yml for a lighter
+# deployment that already has its own Tiled.
+#
+#   docker compose -f docker-compose.full.yml up --build
+#
+services:
+  app-full:
+    build:
+      context: .
+      target: app-full
+    ports:
+      - "8002:8002"  # the app itself (SPA + API) — the only port most setups need
+      - "8003:8003"  # ipred, direct access (optional)
+      - "8010:8010"  # Tiled, direct access (optional)
+    environment:
+      LOCAL_DATA_ROOT: "/data"
+      BROWSE_ALLOWED_ORIGINS: "${BROWSE_ALLOWED_ORIGINS:-}"
+      # Set this to keep the same Tiled API key across container restarts —
+      # otherwise docker-entrypoint-full.sh generates a fresh one each time,
+      # which is fine (Tiled's catalog itself persists in the volume below)
+      # but means any client that cached the old key needs to reconnect.
+      TILED_API_KEY: "${TILED_API_KEY:-}"
+    volumes:
+      - annotation-data-full:/data
+      # Bind-mount your own source datasets to /data/raw (matches
+      # tiled/config.docker.yml's readable_storage), e.g.:
+      #   - /path/to/your/data:/data/raw
+
+volumes:
+  annotation-data-full:
diff --git a/docker-entrypoint-full.sh b/docker-entrypoint-full.sh
new file mode 100755
index 0000000..2ffc4cf
--- /dev/null
+++ b/docker-entrypoint-full.sh
@@ -0,0 +1,54 @@
+#!/bin/sh
+# Entrypoint for the app-full Docker image (Dockerfile's app-full stage):
+# starts Tiled and ipred as background processes, then execs the backend in
+# the foreground as the container's main process. All three talk over
+# 127.0.0.1 since they share one container — no Docker networking needed.
+#
+# Known limitation, deliberate for this "batteries included, single command"
+# image: Tiled/ipred are plain backgrounded processes, not supervised — if
+# either crashes after startup, the container keeps running (its main
+# process is the backend) but that service silently stays down until the
+# whole container is restarted. Fine for local/demo use; a production
+# deployment that needs real service supervision should run Tiled and ipred
+# as separate containers instead (see docker-compose.yml + an external Tiled,
+# or split ipred out the same way if this ever needs to be hardened further).
+set -e
+
+# Generate a Tiled API key if one wasn't provided, mirroring start_all.sh's
+# own local-dev behavior — without one, Tiled's `allow_anonymous_access: true`
+# only permits reads (see tiled/config.docker.yml), so writes (ingest, mask
+# sync, volume registration) would fail with no explanation on first run.
+if [ -z "${TILED_API_KEY:-}" ]; then
+  export TILED_API_KEY="$(python3 -c 'import secrets, string; print("".join(secrets.choice(string.ascii_letters + string.digits) for _ in range(32)))')"
+  echo "Generated a Tiled API key for this container (not persisted — set TILED_API_KEY yourself to keep one across restarts)."
+fi
+
+mkdir -p /data/.tiled/data /data/.tiled/volumes /data/raw
+
+tiled serve config /app/tiled/config.docker.yml --host 0.0.0.0 --port 8010 --api-key "$TILED_API_KEY" &
+uvicorn ipred.api:app --host 0.0.0.0 --port 8003 &
+
+# Wait for Tiled to actually accept connections before starting the backend —
+# without this, the backend's first Tiled-dependent request can race a still-
+# initializing catalog and fail. python3 (not curl, which python:3.12-slim
+# doesn't ship) is guaranteed present in this image already.
+python3 -c "
+import socket, time
+for _ in range(60):
+    try:
+        socket.create_connection(('127.0.0.1', 8010), timeout=1).close()
+        break
+    except OSError:
+        time.sleep(0.5)
+else:
+    print('Tiled did not become reachable on port 8010 in time — starting the backend anyway.')
+"
+
+export TILED_URI="http://127.0.0.1:8010"
+export IPRED_URL="http://127.0.0.1:8003"
+# Keep the mask/volume pyramid cache under the same persisted /data volume,
+# in the exact path tiled/config.docker.yml's readable_storage expects it —
+# same "keep in step" rule tiled/config.yml documents for local dev.
+export VOLUME_CACHE_DIR="/data/.tiled/volumes"
+
+exec uvicorn annotation_server:app --host 0.0.0.0 --port 8002
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
index 24bef46..716b41b 100644
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
@@ -20,7 +20,7 @@
         "react-konva": "^18.2.10",
         "react-router": "^7.14.1",
         "tailwind-merge": "^3.2.0",
-        "uuid": "^10.0.0",
+        "uuid": "^14.0.2",
         "zundo": "^2.3.0",
         "zustand": "^5.0.0"
       },
@@ -2401,9 +2401,9 @@
       }
     },
     "node_modules/@remix-run/router": {
-      "version": "1.23.3",
-      "resolved": "https://registry.npmjs.org/@remix-run/router/-/router-1.23.3.tgz",
-      "integrity": "sha512-4An71tdz9X8+3sI4Qqqd2LWd9vS39J7sqd9EU4Scw7TJE/qB10Flv/UuqbPVgfQV9XoK8Np6jNquZitnZq5i+Q==",
+      "version": "1.23.4",
+      "resolved": "https://registry.npmjs.org/@remix-run/router/-/router-1.23.4.tgz",
+      "integrity": "sha512-q7j5geK7xs3UJSdm9/iytUNclBnLmYx1EnSeCFXHPeutdqgIMeFeHtUZgS3EhlKxdBEAu8OwtJCwmLrEzpSs7Q==",
       "license": "MIT",
       "engines": {
         "node": ">=14.0.0"
@@ -3963,9 +3963,9 @@
       "license": "BSD-3-Clause"
     },
     "node_modules/adm-zip": {
-      "version": "0.5.17",
-      "resolved": "https://registry.npmjs.org/adm-zip/-/adm-zip-0.5.17.tgz",
-      "integrity": "sha512-+Ut8d9LLqwEvHHJl1+PIHqoyDxFgVN847JTVM3Izi3xHDWPE4UtzzXysMZQs64DMcrJfBeS/uoEP4AD3HQHnQQ==",
+      "version": "0.5.18",
+      "resolved": "https://registry.npmjs.org/adm-zip/-/adm-zip-0.5.18.tgz",
+      "integrity": "sha512-ufJnssQGbxzLNS1Ho9bCtX4rQKCCvoVuDLHoJyc3F9dOGDB4BkWs2Ci0kv53lqocAEQ/Cbi+I2XCsNYGqVYqng==",
       "license": "MIT",
       "engines": {
         "node": ">=12.0"
@@ -4136,9 +4136,9 @@
       "license": "MIT"
     },
     "node_modules/baseline-browser-mapping": {
-      "version": "2.10.36",
-      "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.10.36.tgz",
-      "integrity": "sha512-lVq/Df7LXlO79MVaaUHztSwWiG9oXoWHlgvNS51v8Dpd4+G4/VIy6qYePTw31nAVls33nUtnfezYeLkYAak9dg==",
+      "version": "2.11.20",
+      "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.11.20.tgz",
+      "integrity": "sha512-H0ulySigv6icDJ1F7SjtdCD6PrhTpdYCmP0CactWy1+ekh0AFd0o1Wn5T8b+hnTmdBx19u9yhL6wvCylXMY7zw==",
       "dev": true,
       "license": "Apache-2.0",
       "bin": {
@@ -4199,9 +4199,9 @@
       "peer": true
     },
     "node_modules/browserslist": {
-      "version": "4.28.2",
-      "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.2.tgz",
-      "integrity": "sha512-48xSriZYYg+8qXna9kwqjIVzuQxi+KYWp2+5nCYnYKPTr0LvD89Jqk2Or5ogxz0NUMfIjhh2lIUX/LyX9B4oIg==",
+      "version": "4.28.8",
+      "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.8.tgz",
+      "integrity": "sha512-V2NpofLblG64mfOtSgDhOJESZEGogzDMBv/q+W6oc4LXWP/q75eOXoOaaOu1EOadB9U4Bwx/e0yzbvwKH8zalA==",
       "dev": true,
       "funding": [
         {
@@ -4219,11 +4219,11 @@
       ],
       "license": "MIT",
       "dependencies": {
-        "baseline-browser-mapping": "^2.10.12",
-        "caniuse-lite": "^1.0.30001782",
-        "electron-to-chromium": "^1.5.328",
-        "node-releases": "^2.0.36",
-        "update-browserslist-db": "^1.2.3"
+        "baseline-browser-mapping": "^2.11.12",
+        "caniuse-lite": "^1.0.30001809",
+        "electron-to-chromium": "^1.5.402",
+        "node-releases": "^2.0.53",
+        "update-browserslist-db": "^1.3.0"
       },
       "bin": {
         "browserslist": "cli.js"
@@ -4300,9 +4300,9 @@
       }
     },
     "node_modules/caniuse-lite": {
-      "version": "1.0.30001799",
-      "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001799.tgz",
-      "integrity": "sha512-hG1bReV+OUU+MOqK4t/ZWI0tZOyz3rqS9XuhOUz1cIcbwBKjOyJEJuw9ER5JuNyqxNk8u/JUVbGibBOL1yrjFw==",
+      "version": "1.0.30001810",
+      "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001810.tgz",
+      "integrity": "sha512-TITQPUkaz+aVk5GL6NhOdwk1aEaNTSDPsGFWrTuhKGtjTF70jL/Oht2W4c6rXUe5fu7Ie19VIahAXHIIiWWNeg==",
       "dev": true,
       "funding": [
         {
@@ -4801,9 +4801,9 @@
       }
     },
     "node_modules/electron-to-chromium": {
-      "version": "1.5.371",
-      "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.371.tgz",
-      "integrity": "sha512-e9htk9mAYL6AzmkEhSvVVw7IWGSBJ/Bqdn2eRyRLrj1g6sncN4WbFt5qnILYoCktktr45pyjIrOiRvBThQ808w==",
+      "version": "1.5.420",
+      "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.420.tgz",
+      "integrity": "sha512-2yD6XreGusOfNV+dUcvipJEXc3n/n7fgr7996aszTG+YY5E4mqM4tOq/3uhP129cazL9YHbVWSpc79ePotWtPA==",
       "dev": true,
       "license": "ISC"
     },
@@ -5897,9 +5897,9 @@
       }
     },
     "node_modules/nanoid": {
-      "version": "3.3.12",
-      "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.12.tgz",
-      "integrity": "sha512-ZB9RH/39qpq5Vu6Y+NmUaFhQR6pp+M2Xt76XBnEwDaGcVAqhlvxrl3B2bKS5D3NH3QR76v3aSrKaF/Kiy7lEtQ==",
+      "version": "3.3.18",
+      "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.18.tgz",
+      "integrity": "sha512-DTg4MJbGMWkfi6VZFdNt2/caMbQy4Ou+Op/hJQvGEWcnVfoA1QA+xzRKAzw9jD6+GVOOeYr/mIcuDSdug6F6+w==",
       "funding": [
         {
           "type": "github",
@@ -5915,9 +5915,9 @@
       }
     },
     "node_modules/node-releases": {
-      "version": "2.0.47",
-      "resolved": "https://registry.npmjs.org/node-releases/-/node-releases-2.0.47.tgz",
-      "integrity": "sha512-Uzmd6LXpouKo8EUK68IjH4+E01w/hXyV3R3g/geCJo+rXLNfh1xucB+LOzYEOQPSiUK3h/xZf0cQGcSsmyL2Og==",
+      "version": "2.0.54",
+      "resolved": "https://registry.npmjs.org/node-releases/-/node-releases-2.0.54.tgz",
+      "integrity": "sha512-YHs7BmmcsdAI5Ozuf8JZo6PT0mv2GIWC9vMfvUC3dp65M8hn7Ux8CPL+2oBI7juNuj9d0ndhTcznq2ODBps9cQ==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -6138,9 +6138,9 @@
       }
     },
     "node_modules/postcss": {
-      "version": "8.5.15",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.15.tgz",
-      "integrity": "sha512-FfR8sjd4em2T6fb3I2MwAJU7HWVMr9zba+enmQeeWFfCbm+UOC/0X4DS8XtpUTMwWMGbjKYP7xjfNekzyGmB3A==",
+      "version": "8.5.26",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.26.tgz",
+      "integrity": "sha512-u82N74LFzG8ca+dD8puPnplTXoGH4fTPpVGuIbt36G3qvNlkvfD0lEAZSxaly3KX8TS/L1A1gsCEmvKmBcVbkQ==",
       "funding": [
         {
           "type": "opencollective",
@@ -6157,7 +6157,7 @@
       ],
       "license": "MIT",
       "dependencies": {
-        "nanoid": "^3.3.12",
+        "nanoid": "^3.3.17",
         "picocolors": "^1.1.1",
         "source-map-js": "^1.2.1"
       },
@@ -6337,9 +6337,9 @@
       "license": "MIT"
     },
     "node_modules/protobufjs": {
-      "version": "7.6.4",
-      "resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-7.6.4.tgz",
-      "integrity": "sha512-RJJPTTpvFfHcWLkIa2JFWK4XvtSzS0yEWDmunqHXli1h3JlkbcQZXDZdcWxv+JK3Xsl5/UFDPZ0iGm7DAengYw==",
+      "version": "7.6.6",
+      "resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-7.6.6.tgz",
+      "integrity": "sha512-dYDWdjSl5RNb7SgPxGQcRU+GtvP7s2fpkrY0r432PcOIaZ0/rBcxEZnQN67iJhFuQiVw754JDoPruPCNdGsbjg==",
       "hasInstallScript": true,
       "license": "BSD-3-Clause",
       "dependencies": {
@@ -6370,12 +6370,13 @@
       }
     },
     "node_modules/qs": {
-      "version": "6.15.2",
-      "resolved": "https://registry.npmjs.org/qs/-/qs-6.15.2.tgz",
-      "integrity": "sha512-Rzq0KEyX/w/tEybncDgdkZrJgVUsUMk3xjh3t5bv3S1HTAtg+uOYt72+ZfwiQwKdysThkTBdL/rTi6HDmX9Ddw==",
+      "version": "6.16.0",
+      "resolved": "https://registry.npmjs.org/qs/-/qs-6.16.0.tgz",
+      "integrity": "sha512-h6fhOIaRrID2CbEY2fqs+7t+UXZo+MLAnU5gRIq85uFtdiUPCdsApMlHhXogKVM4HM2DVbIjGNTTYH2OcmP1vA==",
       "license": "BSD-3-Clause",
       "dependencies": {
-        "side-channel": "^1.1.0"
+        "es-define-property": "^1.0.1",
+        "side-channel": "^1.1.1"
       },
       "engines": {
         "node": ">=0.6"
@@ -6552,9 +6553,9 @@
       }
     },
     "node_modules/react-router": {
-      "version": "7.17.0",
-      "resolved": "https://registry.npmjs.org/react-router/-/react-router-7.17.0.tgz",
-      "integrity": "sha512-FDELK7rTMlCHO5+reyXsPlmfr7N1F91lPHsWYfMEGQm/KQ+F4JFM8jGoeQDmDvdTs93Fw9aSilH+uKRb4/jXvQ==",
+      "version": "7.18.3",
+      "resolved": "https://registry.npmjs.org/react-router/-/react-router-7.18.3.tgz",
+      "integrity": "sha512-gyXgtdr5uACJ5b1Q4udzjVV+tb/rlHIMJKuJ0e89R4Kzgz47z/rgP0dIKxktqIEUhDHluGTPJJH/wRha7CyqsA==",
       "license": "MIT",
       "dependencies": {
         "cookie": "^1.0.1",
@@ -6574,13 +6575,13 @@
       }
     },
     "node_modules/react-router-dom": {
-      "version": "6.30.4",
-      "resolved": "https://registry.npmjs.org/react-router-dom/-/react-router-dom-6.30.4.tgz",
-      "integrity": "sha512-q4HvNl+mmDdkS0g+MqiBZNteQJCuimWoOyHMy4T/RQLAn9Z29+E91QXRaxOujeMl2HTzRSS0KFPd7lxX3PjV0Q==",
+      "version": "6.30.6",
+      "resolved": "https://registry.npmjs.org/react-router-dom/-/react-router-dom-6.30.6.tgz",
+      "integrity": "sha512-0RHKZz7wwffvkU+2MFVT2NnjK44ssLEV+m0CAJaS2Ksmorrwj7WxH00jO0SOCW26/tINUnJHToXblDs33I38YQ==",
       "license": "MIT",
       "dependencies": {
-        "@remix-run/router": "1.23.3",
-        "react-router": "6.30.4"
+        "@remix-run/router": "1.23.4",
+        "react-router": "6.30.6"
       },
       "engines": {
         "node": ">=14.0.0"
@@ -6591,12 +6592,12 @@
       }
     },
     "node_modules/react-router-dom/node_modules/react-router": {
-      "version": "6.30.4",
-      "resolved": "https://registry.npmjs.org/react-router/-/react-router-6.30.4.tgz",
-      "integrity": "sha512-SVUsDe+DybHM/WmYKIVYhZh1o5Dcuf16yM6WjG02Q9XVFMZIJyHYhwrr6bFBXZkVP6z69kNkMyBCujt8FaFLJA==",
+      "version": "6.30.6",
+      "resolved": "https://registry.npmjs.org/react-router/-/react-router-6.30.6.tgz",
+      "integrity": "sha512-5HfK7k5im7LTOB0EqCQmfvy4C13G92Ssj1VTmouTK3AJvyjKTnFuCV0vcMAD/JS+JC4DvDIBRrlAeJIFjh5VWg==",
       "license": "MIT",
       "dependencies": {
-        "@remix-run/router": "1.23.3"
+        "@remix-run/router": "1.23.4"
       },
       "engines": {
         "node": ">=14.0.0"
@@ -7587,9 +7588,9 @@
       }
     },
     "node_modules/update-browserslist-db": {
-      "version": "1.2.3",
-      "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.2.3.tgz",
-      "integrity": "sha512-Js0m9cx+qOgDxo0eMiFGEueWztz+d4+M3rGlmKPT+T4IS/jP4ylw3Nwpu6cpTTP8R1MAC1kF4VbdLt3ARf209w==",
+      "version": "1.3.2",
+      "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.3.2.tgz",
+      "integrity": "sha512-UQ+MSxlhRm1bzjhU+DcuXfjFO1FzNtqhK5+9Yvlp90ItDLk5vT932A0rFu619nf7RVS+Y/VeaUW1jaRDqZ8VJw==",
       "dev": true,
       "funding": [
         {
@@ -7681,17 +7682,16 @@
       "license": "MIT"
     },
     "node_modules/uuid": {
-      "version": "10.0.0",
-      "resolved": "https://registry.npmjs.org/uuid/-/uuid-10.0.0.tgz",
-      "integrity": "sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==",
-      "deprecated": "uuid@10 and below is no longer supported.  For ESM codebases, update to uuid@latest.  For CommonJS codebases, use uuid@11 (but be aware this version will likely be deprecated in 2028).",
+      "version": "14.0.2",
+      "resolved": "https://registry.npmjs.org/uuid/-/uuid-14.0.2.tgz",
+      "integrity": "sha512-xZe/16rV4aa+HGSOCiY2YeLT1OybRLrrkL/Rqaq7p7GMVXjFh+6wN4oMYgjFmnSnhY8t6Xpdl2l9qmnHYuMHwQ==",
       "funding": [
         "https://github.com/sponsors/broofa",
         "https://github.com/sponsors/ctavan"
       ],
       "license": "MIT",
       "bin": {
-        "uuid": "dist/bin/uuid"
+        "uuid": "dist-node/bin/uuid"
       }
     },
     "node_modules/vite": {
diff --git a/frontend/package.json b/frontend/package.json
index 7f1ff53..c51dcce 100644
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -24,7 +24,7 @@
     "react-konva": "^18.2.10",
     "react-router": "^7.14.1",
     "tailwind-merge": "^3.2.0",
-    "uuid": "^10.0.0",
+    "uuid": "^14.0.2",
     "zundo": "^2.3.0",
     "zustand": "^5.0.0"
   },
diff --git a/frontend/vendor/view_tomography_recon_app b/frontend/vendor/view_tomography_recon_app
index 55ba459..547baf0 160000
--- a/frontend/vendor/view_tomography_recon_app
+++ b/frontend/vendor/view_tomography_recon_app
@@ -1 +1 @@
-Subproject commit 55ba459f95f966e47bb76f6e5ff528730757eb74
+Subproject commit 547baf04e2b70142c4505bac1af714d78c48d55c
diff --git a/tiled/config.docker.yml b/tiled/config.docker.yml
new file mode 100644
index 0000000..6167750
--- /dev/null
+++ b/tiled/config.docker.yml
@@ -0,0 +1,32 @@
+# Portable counterpart to config.yml, for the app-full Docker image
+# (Dockerfile's app-full stage / docker-entrypoint-full.sh) — config.yml
+# hardcodes a local dev machine's absolute data path
+# (readable_storage: /Users/.../data), which has no meaning inside a
+# container. Everything here lives under /data, the same volume mount point
+# the backend's LOCAL_DATA_ROOT already uses (see docker-compose.full.yml) —
+# one volume to persist for the whole container's state.
+authentication:
+  allow_anonymous_access: true
+  # No hardcoded key here either — docker-entrypoint-full.sh generates one
+  # (or uses TILED_API_KEY if you set it) and passes it via `tiled serve
+  # --api-key`, exactly like start_all.sh does for local dev. Anonymous
+  # access is read-only; the key authorizes writes.
+
+allow_origins:
+  - "*"
+
+trees:
+  - path: /
+    tree: tiled.catalog:from_uri
+    args:
+      uri: "sqlite+aiosqlite:////data/.tiled/catalog.db"
+      writable_storage: "/data/.tiled/data"
+      readable_storage:
+        # Bind-mount your own source datasets here (see
+        # docker-compose.full.yml's volumes). Uploads/ingest also land here.
+        - "/data/raw"
+        # Generated 3-D volume pyramids (backend/tiff_stack_source.py) and
+        # mask pyramids (backend/mask_pyramid.py) — must stay in step with
+        # VOLUME_CACHE_DIR, set by docker-entrypoint-full.sh.
+        - "/data/.tiled/volumes"
+      init_if_not_exists: true

From 5b1b5c5901358c875ea11b883b2ccd4ee883f242 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 2 Sep 2026 15:42:04 -0700
Subject: [PATCH 21/38] unit tests unit tests unit tests

---
 backend/images.py                   |  54 +++--
 backend/tests/test_denoise_train.py | 292 ++++++++++++++++++++++++++++
 backend/tests/test_train_jobs.py    | 202 +++++++++++++++++++
 3 files changed, 534 insertions(+), 14 deletions(-)
 create mode 100644 backend/tests/test_denoise_train.py
 create mode 100644 backend/tests/test_train_jobs.py

diff --git a/backend/images.py b/backend/images.py
index 1ee0723..0f2aefb 100644
--- a/backend/images.py
+++ b/backend/images.py
@@ -71,31 +71,29 @@ def _minmax(i: int) -> tuple[float, float]:
     return result
 
 
-def render_slice(
+def normalize_scalar_unit(
     arr: np.ndarray,
     opts: dict[str, Any],
     global_range: tuple[float, float] | None = None,
 ) -> np.ndarray:
-    """Render a 2-D or H×W×C array slice to uint8 RGB.
+    """Map a 2-D scalar array to ``[0, 1]`` via the scale transform + vmin/vmax
+    normalisation ``render_slice``'s grayscale branch uses, stopping short of
+    the final colormap/uint8 step.
+
+    Extracted so :mod:`denoise_train` (``_slice_to_gray_uint8``) can train a
+    denoiser on the exact same intensity pipeline the 2-D canvas renders,
+    without duplicating this logic and risking the two drifting apart.
 
     Args:
-        arr: 2-D grayscale or H×W×(3|4) colour array.
+        arr: 2-D grayscale array.
         opts: :class:`~schemas.RenderOpts`-compatible dict with keys
-            ``norm``, ``scale``, ``vmin_pct``, ``vmax_pct``, ``cmap``.
+            ``norm``, ``scale``, ``vmin_pct``, ``vmax_pct``.
         global_range: ``(vmin, vmax)`` used when ``opts["norm"] == "global"``.
             Ignored in slice-norm mode.
 
     Returns:
-        uint8 RGB array of shape ``(H, W, 3)``.
+        ``float64`` array the same shape as *arr*, values in ``[0, 1]``.
     """
-    is_rgb = arr.ndim == 3
-    if is_rgb:
-        rgb = arr[:, :, :3].astype(np.float64)
-        mn, mx = float(rgb.min()), float(rgb.max())
-        if mx > mn:
-            rgb = (rgb - mn) / (mx - mn) * 255.0
-        return np.clip(rgb, 0, 255).astype(np.uint8)
-
     data = arr.astype(np.float64)
     data = np.nan_to_num(data, nan=0.0, posinf=0.0, neginf=0.0)
 
@@ -122,7 +120,35 @@ def render_slice(
         data = (data - vmin_abs) / (vmax_abs - vmin_abs)
     else:
         data = np.zeros_like(data)
-    data = np.clip(data, 0.0, 1.0)
+    return np.clip(data, 0.0, 1.0)
+
+
+def render_slice(
+    arr: np.ndarray,
+    opts: dict[str, Any],
+    global_range: tuple[float, float] | None = None,
+) -> np.ndarray:
+    """Render a 2-D or H×W×C array slice to uint8 RGB.
+
+    Args:
+        arr: 2-D grayscale or H×W×(3|4) colour array.
+        opts: :class:`~schemas.RenderOpts`-compatible dict with keys
+            ``norm``, ``scale``, ``vmin_pct``, ``vmax_pct``, ``cmap``.
+        global_range: ``(vmin, vmax)`` used when ``opts["norm"] == "global"``.
+            Ignored in slice-norm mode.
+
+    Returns:
+        uint8 RGB array of shape ``(H, W, 3)``.
+    """
+    is_rgb = arr.ndim == 3
+    if is_rgb:
+        rgb = arr[:, :, :3].astype(np.float64)
+        mn, mx = float(rgb.min()), float(rgb.max())
+        if mx > mn:
+            rgb = (rgb - mn) / (mx - mn) * 255.0
+        return np.clip(rgb, 0, 255).astype(np.uint8)
+
+    data = normalize_scalar_unit(arr, opts, global_range)
 
     cmap = opts.get("cmap", "gray")
     if cmap == "viridis":
diff --git a/backend/tests/test_denoise_train.py b/backend/tests/test_denoise_train.py
new file mode 100644
index 0000000..da027a6
--- /dev/null
+++ b/backend/tests/test_denoise_train.py
@@ -0,0 +1,292 @@
+"""Tests for denoise_train.py: pure sampling/geometry/masking logic directly,
+plus a real (no mocks) run_denoise_training_loop round trip using a minimal
+trainable model — the loop itself is generic over forward_fn/to_tensor_fn/
+trainable_params, so it doesn't need a real dlsia model to exercise for real.
+"""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+torch = pytest.importorskip("torch")
+
+import arrays as arrays_mod  # noqa: E402
+import denoise_train  # noqa: E402
+
+
+class FakeItem:
+    def __init__(self, source="fake.tif", kind="local", server_uri=None, slices=None):
+        self.source = source
+        self.kind = kind
+        self.server_uri = server_uri
+        self.slices = slices or {}
+
+
+# ---------------------------------------------------------------------------
+# selected_slice_indices
+# ---------------------------------------------------------------------------
+
+class TestSelectedSliceIndices:
+    def test_empty_slices_returns_whole_range(self):
+        assert denoise_train.selected_slice_indices(FakeItem(slices={}), 5) == [0, 1, 2, 3, 4]
+
+    def test_explicit_keys_sorted_and_deduped(self):
+        item = FakeItem(slices={"3": [], "1": [], "3": []})  # noqa: F601
+        assert denoise_train.selected_slice_indices(item, 10) == [1, 3]
+
+    def test_out_of_range_keys_dropped(self):
+        item = FakeItem(slices={"1": [], "99": [], "-1": []})
+        assert denoise_train.selected_slice_indices(item, 5) == [1]
+
+    def test_non_integer_keys_ignored(self):
+        item = FakeItem(slices={"abc": [], "2": []})
+        assert denoise_train.selected_slice_indices(item, 5) == [2]
+
+    def test_all_keys_out_of_range_falls_back_to_whole_range(self):
+        item = FakeItem(slices={"99": []})
+        assert denoise_train.selected_slice_indices(item, 3) == [0, 1, 2]
+
+
+# ---------------------------------------------------------------------------
+# noise2noise_pairs
+# ---------------------------------------------------------------------------
+
+class TestNoise2NoisePairs:
+    def test_adjacent_pairs_both_directions(self):
+        pairs = denoise_train.noise2noise_pairs([0, 1, 2], stride=1, both_directions=True)
+        assert set(pairs) == {(0, 1), (1, 0), (1, 2), (2, 1)}
+
+    def test_single_direction(self):
+        pairs = denoise_train.noise2noise_pairs([0, 1, 2], stride=1, both_directions=False)
+        assert set(pairs) == {(0, 1), (1, 2)}
+
+    def test_last_slice_has_no_partner_not_clamped(self):
+        pairs = denoise_train.noise2noise_pairs([0, 1], stride=1, both_directions=False)
+        assert (1, 1) not in pairs
+
+    def test_stride_skips_non_adjacent(self):
+        pairs = denoise_train.noise2noise_pairs([0, 1, 2], stride=2, both_directions=False)
+        assert pairs == [(0, 2)]
+
+    def test_no_qualifying_pairs_returns_empty(self):
+        assert denoise_train.noise2noise_pairs([0], stride=1) == []
+
+    def test_stride_below_one_raises(self):
+        with pytest.raises(ValueError, match="stride must be >= 1"):
+            denoise_train.noise2noise_pairs([0, 1], stride=0)
+
+
+# ---------------------------------------------------------------------------
+# _mirror_offset
+# ---------------------------------------------------------------------------
+
+class TestMirrorOffset:
+    def test_in_range_offset_unchanged(self):
+        out = denoise_train._mirror_offset(np.array([5]), np.array([2]), 10)
+        assert out[0] == 7
+
+    def test_out_of_range_flips_sign_instead_of_clamping(self):
+        # centre=0, offset=-1 would clip to 0 (== centre) under naive clamping;
+        # flipping gives centre - offset = 1, which differs from centre.
+        out = denoise_train._mirror_offset(np.array([0]), np.array([-1]), 10)
+        assert out[0] == 1
+
+    def test_result_always_in_bounds(self):
+        centre = np.array([0, 9])
+        offset = np.array([-5, 5])
+        out = denoise_train._mirror_offset(centre, offset, 10)
+        assert np.all((out >= 0) & (out < 10))
+
+
+# ---------------------------------------------------------------------------
+# n2v_mask_and_replace
+# ---------------------------------------------------------------------------
+
+class TestN2vMaskAndReplace:
+    def test_mask_count_matches_fraction(self):
+        patch = (np.arange(400) % 256).astype(np.uint8).reshape(20, 20)
+        rng = np.random.default_rng(0)
+        _, mask = denoise_train.n2v_mask_and_replace(patch, rng, fraction=0.05, neighbourhood=5)
+        assert mask.sum() == round(400 * 0.05)
+
+    def test_donor_never_the_masked_pixel_itself(self):
+        patch = (np.arange(400) % 256).astype(np.uint8).reshape(20, 20)
+        rng = np.random.default_rng(1)
+        masked, mask = denoise_train.n2v_mask_and_replace(patch, rng, fraction=0.1, neighbourhood=5)
+        # A masked pixel that kept its original value would mean its donor was itself.
+        assert not np.array_equal(masked[mask], patch[mask])
+
+    def test_at_least_one_pixel_masked_for_tiny_fraction(self):
+        patch = np.zeros((10, 10), dtype=np.uint8)
+        rng = np.random.default_rng(0)
+        _, mask = denoise_train.n2v_mask_and_replace(patch, rng, fraction=0.0001, neighbourhood=3)
+        assert mask.sum() >= 1
+
+    def test_unmasked_pixels_untouched(self):
+        patch = (np.arange(400) % 256).astype(np.uint8).reshape(20, 20)
+        rng = np.random.default_rng(2)
+        masked, mask = denoise_train.n2v_mask_and_replace(patch, rng, fraction=0.05, neighbourhood=5)
+        assert np.array_equal(masked[~mask], patch[~mask])
+
+    @pytest.mark.parametrize("fraction", [0.0, 1.0, -0.1, 1.5])
+    def test_fraction_out_of_range_raises(self, fraction):
+        with pytest.raises(ValueError, match="fraction must be in"):
+            denoise_train.n2v_mask_and_replace(np.zeros((10, 10), dtype=np.uint8), np.random.default_rng(0), fraction=fraction)
+
+    @pytest.mark.parametrize("neighbourhood", [2, 4, 1])
+    def test_neighbourhood_must_be_odd_and_at_least_three(self, neighbourhood):
+        if neighbourhood >= 3 and neighbourhood % 2 == 1:
+            return
+        with pytest.raises(ValueError, match="neighbourhood must be an odd size"):
+            denoise_train.n2v_mask_and_replace(
+                np.zeros((10, 10), dtype=np.uint8), np.random.default_rng(0), neighbourhood=neighbourhood,
+            )
+
+
+# ---------------------------------------------------------------------------
+# letterbox_denoise_pair
+# ---------------------------------------------------------------------------
+
+class TestLetterboxDenoisePair:
+    def test_already_correct_size_short_circuits(self):
+        inp = np.zeros((32, 32), dtype=np.uint8)
+        tgt = np.ones((32, 32), dtype=np.uint8)
+        out_inp, out_tgt = denoise_train.letterbox_denoise_pair(inp, tgt, 32)
+        assert out_inp is inp and out_tgt is tgt
+
+    def test_resizes_and_pads_to_target_size(self):
+        inp = np.full((16, 32), 100, dtype=np.uint8)
+        tgt = np.full((16, 32), 200, dtype=np.uint8)
+        out_inp, out_tgt = denoise_train.letterbox_denoise_pair(inp, tgt, 64)
+        assert out_inp.shape == (64, 64)
+        assert out_tgt.shape == (64, 64)
+        # Corners should be zero-padded (aspect-ratio letterboxing).
+        assert out_inp[0, 0] == 0
+
+    def test_shape_mismatch_raises(self):
+        with pytest.raises(ValueError, match="shapes differ"):
+            denoise_train.letterbox_denoise_pair(
+                np.zeros((10, 10), dtype=np.uint8), np.zeros((10, 20), dtype=np.uint8), 32,
+            )
+
+
+# ---------------------------------------------------------------------------
+# prepare_noise2noise_datasets / prepare_noise2void_datasets (real images_mod,
+# fake arrays_mod — same pattern as test_infer_jobs.py's fake array source)
+# ---------------------------------------------------------------------------
+
+@pytest.fixture()
+def fake_array_source(monkeypatch: pytest.MonkeyPatch):
+    rng = np.random.default_rng(1)
+    volume = rng.integers(0, 256, size=(4, 16, 16), dtype=np.uint8)
+
+    monkeypatch.setattr(arrays_mod, "resolve_array", lambda source, kind, server_uri: volume)
+    monkeypatch.setattr(
+        arrays_mod, "array_shape_meta",
+        lambda node, pyramid=None: {"height": 16, "width": 16, "n_slices": 4},
+    )
+    monkeypatch.setattr(arrays_mod, "read_slice", lambda node, meta, idx: node[idx])
+    return volume
+
+
+class TestPrepareDatasets:
+    def test_noise2noise_produces_pairs_from_adjacent_slices(self, fake_array_source):
+        result = denoise_train.prepare_noise2noise_datasets([FakeItem()], render={})
+        assert result["val"] == []
+        assert len(result["train"]) > 0
+        for inp, tgt in result["train"]:
+            assert inp.dtype == np.uint8 and inp.shape == (16, 16)
+
+    def test_noise2noise_raises_when_only_one_slice_in_scope(self, fake_array_source):
+        item = FakeItem(slices={"0": []})
+        with pytest.raises(ValueError, match="Noise2Noise needs at least two slices"):
+            denoise_train.prepare_noise2noise_datasets([item], render={})
+
+    def test_noise2void_pairs_each_slice_with_itself(self, fake_array_source):
+        result = denoise_train.prepare_noise2void_datasets([FakeItem()], render={})
+        assert len(result["train"]) == 4
+        for inp, tgt in result["train"]:
+            assert np.array_equal(inp, tgt)
+
+    def test_noise2void_raises_with_no_slices_in_scope(self, monkeypatch, fake_array_source):
+        monkeypatch.setattr(
+            arrays_mod, "array_shape_meta",
+            lambda node, pyramid=None: {"height": 16, "width": 16, "n_slices": 0},
+        )
+        with pytest.raises(ValueError, match="Noise2Void needs at least one slice"):
+            denoise_train.prepare_noise2void_datasets([FakeItem()], render={})
+
+
+# ---------------------------------------------------------------------------
+# run_denoise_training_loop — real round trip, minimal trainable model
+# ---------------------------------------------------------------------------
+
+def _tiny_model():
+    """A minimal real nn.Module — the loop is generic over forward_fn/
+    to_tensor_fn/trainable_params, so it doesn't need dlsia's actual TUNet."""
+    model = torch.nn.Conv2d(1, 1, kernel_size=3, padding=1)
+
+    def forward_fn(batch):
+        return model(batch)
+
+    def to_tensor_fn(arr):
+        return torch.from_numpy(np.ascontiguousarray(arr)).float().unsqueeze(0) / 255.0
+
+    return forward_fn, to_tensor_fn, list(model.parameters())
+
+
+def _pairs(n, size=8, seed=0):
+    rng = np.random.default_rng(seed)
+    return [
+        (rng.integers(0, 256, size=(size, size), dtype=np.uint8), rng.integers(0, 256, size=(size, size), dtype=np.uint8))
+        for _ in range(n)
+    ]
+
+
+class TestRunDenoiseTrainingLoop:
+    @pytest.mark.parametrize("scheme", ["n2n", "n2v", "ae"])
+    def test_real_round_trip_all_schemes(self, scheme):
+        forward_fn, to_tensor_fn, params = _tiny_model()
+        metrics = denoise_train.run_denoise_training_loop(
+            train_pairs=_pairs(6), val_pairs=_pairs(2, seed=1),
+            image_size=8, training_scheme=scheme, epochs=2, batch_size=2, seed=0,
+            flip_augment=True, to_tensor_fn=to_tensor_fn, forward_fn=forward_fn,
+            trainable_params=params, lr=1e-3, device="cpu",
+        )
+        assert metrics["epochs_completed"] == 2
+        assert metrics["cancelled"] is False
+        assert np.isfinite(metrics["final_train_loss"])
+        assert np.isfinite(metrics["final_val_loss"])
+        assert metrics[denoise_train.VAL_METRIC_KEY] is not None
+
+    def test_cancellation_via_on_epoch_stops_early_with_partial_metrics(self):
+        forward_fn, to_tensor_fn, params = _tiny_model()
+        metrics = denoise_train.run_denoise_training_loop(
+            train_pairs=_pairs(4), val_pairs=[],
+            image_size=8, training_scheme="n2n", epochs=5, batch_size=2, seed=0,
+            flip_augment=False, to_tensor_fn=to_tensor_fn, forward_fn=forward_fn,
+            trainable_params=params, lr=1e-3, device="cpu",
+            on_epoch=lambda epoch, *_: epoch == 1,
+        )
+        assert metrics["cancelled"] is True
+        assert metrics["epochs_completed"] == 1
+
+    def test_no_training_data_raises(self):
+        forward_fn, to_tensor_fn, params = _tiny_model()
+        with pytest.raises(ValueError, match="No training data"):
+            denoise_train.run_denoise_training_loop(
+                train_pairs=[], val_pairs=[], image_size=8, training_scheme="n2n",
+                epochs=1, batch_size=2, seed=0, flip_augment=False,
+                to_tensor_fn=to_tensor_fn, forward_fn=forward_fn, trainable_params=params,
+                lr=1e-3, device="cpu",
+            )
+
+    def test_unknown_scheme_raises(self):
+        forward_fn, to_tensor_fn, params = _tiny_model()
+        with pytest.raises(ValueError, match="Unknown denoiser training scheme"):
+            denoise_train.run_denoise_training_loop(
+                train_pairs=_pairs(2), val_pairs=[], image_size=8, training_scheme="bogus",
+                epochs=1, batch_size=2, seed=0, flip_augment=False,
+                to_tensor_fn=to_tensor_fn, forward_fn=forward_fn, trainable_params=params,
+                lr=1e-3, device="cpu",
+            )
diff --git a/backend/tests/test_train_jobs.py b/backend/tests/test_train_jobs.py
new file mode 100644
index 0000000..b961fc1
--- /dev/null
+++ b/backend/tests/test_train_jobs.py
@@ -0,0 +1,202 @@
+"""Tests for train_jobs.py — the pure resume/validation helpers directly, and
+run_train_job's early guard/error paths (lock contention, task/model
+mismatch). The core train/save/load contract itself is proven for real (no
+mocks) in test_train_e2e_real_ml.py; a full run_train_job segmentation round
+trip would need real annotated sources assembled the way prepare_datasets
+expects, which is out of scope for this pass.
+"""
+from __future__ import annotations
+
+import pytest
+
+import export_jobs
+import train_common
+import train_jobs
+from schemas import (
+    AnnotationClass,
+    DlsiaDenoiserConfig,
+    DlsiaTunetConfig,
+    ExportSourceItem,
+    TrainRequest,
+)
+
+
+def _classes():
+    return [AnnotationClass(classId=1, label="A", color="#f00"), AnnotationClass(classId=2, label="B", color="#0f0")]
+
+
+def _sources():
+    return [ExportSourceItem(kind="local", source="fake.tif", slices={"0": []})]
+
+
+def _tunet_request(**overrides):
+    defaults = dict(sources=_sources(), classes=_classes(), model=DlsiaTunetConfig())
+    defaults.update(overrides)
+    return TrainRequest(**defaults)
+
+
+def _denoiser_request(**overrides):
+    defaults = dict(
+        task="denoising", sources=_sources(), classes=[],
+        model=DlsiaDenoiserConfig(architecture="cnn_ae", training_scheme="ae", ae_compression=4),
+    )
+    defaults.update(overrides)
+    return TrainRequest(**defaults)
+
+
+class TestNewRunId:
+    def test_includes_model_family_and_is_unique(self):
+        a = train_jobs.new_run_id("dlsia_tunet")
+        b = train_jobs.new_run_id("dlsia_tunet")
+        assert "dlsia_tunet" in a
+        assert a != b
+
+
+class TestCheckTaskMatchesModel:
+    def test_segmentation_task_with_denoiser_family_raises(self):
+        # classes=_classes() needed to get past the schema's own "segmentation
+        # needs >=1 class" validator so this reaches check_task_matches_model's
+        # family-specific check instead of failing at construction for an
+        # unrelated reason.
+        request = _denoiser_request(task="segmentation", classes=_classes())
+        with pytest.raises(ValueError, match="cannot be trained as a"):
+            train_jobs.check_task_matches_model(request)
+
+    def test_denoising_task_with_segmentation_family_raises(self):
+        with pytest.raises(ValueError, match="needs a denoiser model family"):
+            train_jobs.check_task_matches_model(_tunet_request(task="denoising"))
+
+    def test_matching_pairs_do_not_raise(self):
+        train_jobs.check_task_matches_model(_tunet_request())
+        train_jobs.check_task_matches_model(_denoiser_request())
+
+
+class TestCheckResumeCompatible:
+    def test_model_family_mismatch_raises(self):
+        parent = {"model_family": "dlsia_denoiser", "task": "segmentation", "classes": []}
+        with pytest.raises(ValueError, match="Cannot continue fine-tuning a dlsia_denoiser run"):
+            train_jobs.check_resume_compatible(parent, _tunet_request())
+
+    def test_task_mismatch_raises(self):
+        parent = {"model_family": "dlsia_tunet", "task": "denoising", "classes": []}
+        with pytest.raises(ValueError, match="Cannot continue fine-tuning a 'denoising'-task run"):
+            train_jobs.check_resume_compatible(parent, _tunet_request())
+
+    def test_denoiser_architecture_mismatch_raises(self):
+        parent = {
+            "model_family": "dlsia_denoiser", "task": "denoising",
+            "model_config": {"architecture": "tunet"},
+        }
+        with pytest.raises(ValueError, match="Cannot continue fine-tuning a 'tunet' denoiser"):
+            train_jobs.check_resume_compatible(parent, _denoiser_request())
+
+    def test_denoiser_matching_architecture_skips_class_check(self):
+        parent = {
+            "model_family": "dlsia_denoiser", "task": "denoising",
+            "model_config": {"architecture": "cnn_ae"},
+        }
+        train_jobs.check_resume_compatible(parent, _denoiser_request())  # no raise
+
+    def test_denoiser_defaults_missing_architecture_to_tunet(self):
+        parent = {"model_family": "dlsia_denoiser", "task": "denoising"}
+        with pytest.raises(ValueError, match="Cannot continue fine-tuning a 'tunet' denoiser"):
+            train_jobs.check_resume_compatible(parent, _denoiser_request())
+
+    def test_class_list_changed_raises(self):
+        parent = {
+            "model_family": "dlsia_tunet", "task": "segmentation",
+            "classes": [{"label": "A"}, {"label": "C"}],
+        }
+        with pytest.raises(ValueError, match="classes changed since that run was trained"):
+            train_jobs.check_resume_compatible(parent, _tunet_request())
+
+    def test_class_list_case_and_whitespace_insensitive_match(self):
+        parent = {
+            "model_family": "dlsia_tunet", "task": "segmentation",
+            "classes": [{"label": " a "}, {"label": "B"}],
+        }
+        train_jobs.check_resume_compatible(parent, _tunet_request())  # no raise
+
+    def test_missing_task_defaults_to_segmentation(self):
+        parent = {"model_family": "dlsia_tunet", "classes": [{"label": "a"}, {"label": "b"}]}
+        train_jobs.check_resume_compatible(parent, _tunet_request())  # no raise
+
+
+class TestApplyParentArchitecture:
+    def test_tunet_inherits_geometry_and_topology(self):
+        request = _tunet_request()
+        parent = {
+            "hyperparams": {"image_size": 128, "tiling": True, "depth": 5, "base_channels": 8, "growth_rate": 1.5},
+        }
+        train_jobs._apply_parent_architecture(parent, request)
+        hp = request.model.hyperparams
+        assert hp.image_size == 128
+        assert hp.tiling is True
+        assert hp.depth == 5
+        assert hp.base_channels == 8
+        assert hp.growth_rate == 1.5
+
+    def test_tunet_inherits_denoise_settings(self):
+        request = _tunet_request()
+        parent = {"hyperparams": {}, "denoise": {"method": "gaussian", "strength": 0.5}}
+        train_jobs._apply_parent_architecture(parent, request)
+        assert request.denoise is not None
+        assert request.denoise.method == "gaussian"
+
+    def test_tunet_clears_denoise_when_parent_had_none(self):
+        from schemas import DenoiseTrainOpts
+        request = _tunet_request(denoise=DenoiseTrainOpts(method="gaussian", strength=0.5))
+        train_jobs._apply_parent_architecture({"hyperparams": {}}, request)
+        assert request.denoise is None
+
+    def test_denoiser_cnn_ae_forces_scheme_and_compression(self):
+        request = _denoiser_request()
+        request.model.architecture = "tunet"  # caller sent something else
+        parent = {
+            "hyperparams": {},
+            "model_config": {"architecture": "cnn_ae", "ae_compression": 8},
+        }
+        train_jobs._apply_parent_architecture(parent, request)
+        assert request.model.architecture == "cnn_ae"
+        assert request.model.training_scheme == "ae"
+        assert request.model.ae_compression == 8
+
+    def test_denoiser_defaults_missing_parent_architecture_to_tunet(self):
+        request = _denoiser_request()
+        train_jobs._apply_parent_architecture({"hyperparams": {}, "model_config": {}}, request)
+        assert request.model.architecture == "tunet"
+
+
+class TestSourceKeys:
+    def test_tiled_and_local_formats(self):
+        request = _tunet_request(
+            sources=[
+                ExportSourceItem(kind="tiled", source="a/b", server_uri="http://x:1", slices={}),
+                ExportSourceItem(kind="local", source="c/d", slices={}),
+            ],
+        )
+        assert train_jobs._source_keys(request) == ["tiled:http://x:1:a/b", "local:c/d"]
+
+
+class TestRunTrainJobGuards:
+    def test_reports_error_when_ml_lock_already_held(self):
+        jid = export_jobs.new_job("x")
+        assert train_common.ML_LOCK.acquire(blocking=False)
+        try:
+            train_jobs.run_train_job(jid, _tunet_request(), "run-1")
+        finally:
+            train_common.ML_LOCK.release()
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "already running" in job["error"]
+
+    def test_task_model_mismatch_reports_error_not_a_crash(self):
+        jid = export_jobs.new_job("x")
+        # task defaults to "segmentation" but the model is a denoiser -> caught
+        # by check_task_matches_model inside run_train_job's try block.
+        request = _tunet_request()
+        request.model = DlsiaDenoiserConfig(architecture="cnn_ae", training_scheme="ae")
+        train_jobs.run_train_job(jid, request, "run-2")
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert not train_common.ML_LOCK.locked()

From 6f8134a65a0407321941a344986e323ab30a1b3c Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Thu, 3 Sep 2026 15:32:24 -0700
Subject: [PATCH 22/38] unit tests

---
 .../test_annotation_server_browse_routes.py   | 177 ++++++
 .../test_annotation_server_data_routes.py     | 404 ++++++++++++++
 ...st_annotation_server_local_image_routes.py | 349 ++++++++++++
 .../tests/test_annotation_server_routes.py    | 217 ++++++++
 backend/tests/test_annotation_thumbnails.py   | 334 ++++++++++++
 backend/tests/test_arrays.py                  | 516 ++++++++++++++++++
 backend/tests/test_batch_probe.py             | 271 +++++++++
 backend/tests/test_browse_helpers.py          | 370 +++++++++++++
 backend/tests/test_denoise_bake.py            | 337 ++++++++++++
 backend/tests/test_denoise_runtime.py         |  93 ++++
 backend/tests/test_drafts.py                  | 225 ++++++--
 backend/tests/test_ipred_client.py            | 325 +++++++++++
 backend/tests/test_local_fs.py                | 237 ++++++--
 backend/tests/test_source_keys.py             |  91 +++
 backend/tests/test_thumbnails.py              | 144 +++++
 backend/tests/test_tiff_stack_source.py       |  77 +++
 backend/tests/test_tiled_annotation_sync.py   |  86 ++-
 backend/tests/test_tiled_clients.py           | 183 +++++++
 backend/tests/test_tiling.py                  | 229 ++++++++
 backend/tests/test_zarr_source.py             |  72 +++
 20 files changed, 4642 insertions(+), 95 deletions(-)
 create mode 100644 backend/tests/test_annotation_server_browse_routes.py
 create mode 100644 backend/tests/test_annotation_server_data_routes.py
 create mode 100644 backend/tests/test_annotation_server_local_image_routes.py
 create mode 100644 backend/tests/test_annotation_server_routes.py
 create mode 100644 backend/tests/test_annotation_thumbnails.py
 create mode 100644 backend/tests/test_arrays.py
 create mode 100644 backend/tests/test_batch_probe.py
 create mode 100644 backend/tests/test_browse_helpers.py
 create mode 100644 backend/tests/test_denoise_bake.py
 create mode 100644 backend/tests/test_denoise_runtime.py
 create mode 100644 backend/tests/test_ipred_client.py
 create mode 100644 backend/tests/test_source_keys.py
 create mode 100644 backend/tests/test_thumbnails.py
 create mode 100644 backend/tests/test_tiled_clients.py
 create mode 100644 backend/tests/test_tiling.py

diff --git a/backend/tests/test_annotation_server_browse_routes.py b/backend/tests/test_annotation_server_browse_routes.py
new file mode 100644
index 0000000..f531d9b
--- /dev/null
+++ b/backend/tests/test_annotation_server_browse_routes.py
@@ -0,0 +1,177 @@
+"""HTTP-level tests for annotation_server.py's /api/browse/* routes.
+
+Only `get_tiled_client` is faked (returns a lightweight fake Tiled root node,
+same duck-typed FakeNode as test_browse_helpers.py) — the real
+`get_browse_container_for` runs against it unmocked, since it's pure
+navigation logic (`node[k]` per path segment). Every test supplies an
+explicit container_path so get_browse_container_for never falls through to
+the heuristic root-discovery path (get_browse_container), which is out of
+scope here. The underlying field-mapping/distinct/search logic itself is
+already thoroughly covered directly in test_browse_helpers.py — these tests
+are about routing, query-param parsing, and error handling, not re-proving
+that logic.
+"""
+from __future__ import annotations
+
+from collections import Counter
+
+import pytest
+from httpx import ASGITransport, AsyncClient
+
+import annotation_server
+
+tiled_queries = pytest.importorskip("tiled.queries")
+
+
+class FakeNode:
+    def __init__(self, children=None, metadata=None, is_container=True, search_raises=False):
+        self._children = children or {}
+        self.metadata = metadata or {}
+        self.structure_family = "container" if is_container else "array"
+        self._search_raises = search_raises
+
+    def __iter__(self):
+        return iter(self._children)
+
+    def __getitem__(self, key):
+        return self._children[key]
+
+    def __len__(self):
+        return len(self._children)
+
+    def search(self, query):
+        if self._search_raises:
+            raise RuntimeError("search exploded")
+        matched = {}
+        for k, child in self._children.items():
+            meta = child.metadata or {}
+            if isinstance(query, tiled_queries.Contains):
+                val = meta.get(query.key)
+                if isinstance(val, (list, tuple)) and query.value in val:
+                    matched[k] = child
+            else:  # Eq
+                if meta.get(query.key) == query.value:
+                    matched[k] = child
+        return FakeNode(children=matched, metadata=self.metadata)
+
+    def distinct(self, key, counts=True):
+        counter: Counter = Counter()
+        for child in self._children.values():
+            val = (child.metadata or {}).get(key)
+            if val is None:
+                continue
+            counter[val] += 1
+        return {"metadata": {key: [{"value": v, "count": n} for v, n in counter.items()]}}
+
+
+def _sample(metadata):
+    return FakeNode(metadata=metadata, is_container=False)
+
+
+@pytest.fixture()
+def browse_root():
+    """browse/sample1 (PI=Smith), browse/sample2 (PI=Jones) — enough to
+    exercise facet discovery (>=2 distinct PI values), column distinct, and
+    item search/filtering."""
+    samples = {
+        "sample1": _sample({"PI": "Smith", "sample_name": "s1", "technique": "GIWAXS"}),
+        "sample2": _sample({"PI": "Jones", "sample_name": "s2", "technique": "GIWAXS"}),
+    }
+    browse = FakeNode(children=samples)
+    return FakeNode(children={"browse": browse})
+
+
+@pytest.fixture(autouse=True)
+def fake_tiled_client(monkeypatch: pytest.MonkeyPatch, browse_root):
+    monkeypatch.setattr(annotation_server, "get_tiled_client", lambda *a, **k: browse_root)
+    # Field-mapping / column / item caches are module-level and would leak
+    # a fake result from one test's fake node into the next test's assertions
+    # (same server_uri/technique/container_path -> same cache key).
+    annotation_server._field_mapping_cache._store.clear()
+    annotation_server._column_cache._store.clear()
+    annotation_server._items_cache._store.clear()
+
+
+@pytest.fixture()
+async def client():
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as c:
+        yield c
+
+
+app = annotation_server.app
+
+
+@pytest.mark.asyncio
+async def test_browse_facets_finds_multi_valued_field(client):
+    response = await client.get("/api/browse/facets", params={"container_path": "browse"})
+    assert response.status_code == 200
+    assert "PI" in response.json()["facets"]
+
+
+@pytest.mark.asyncio
+async def test_browse_facets_returns_empty_lists_on_internal_error(client, monkeypatch):
+    monkeypatch.setattr(annotation_server, "get_tiled_client", lambda *a, **k: (_ for _ in ()).throw(RuntimeError("down")))
+    response = await client.get("/api/browse/facets", params={"container_path": "browse"})
+    assert response.status_code == 200
+    assert response.json() == {"facets": [], "techniques": []}
+
+
+@pytest.mark.asyncio
+async def test_browse_column_returns_distinct_values(client):
+    response = await client.get(
+        "/api/browse/column", params={"field": "PI", "container_path": "browse"},
+    )
+    assert response.status_code == 200
+    values = {v["value"] for v in response.json()["values"]}
+    assert values == {"Smith", "Jones"}
+
+
+@pytest.mark.asyncio
+async def test_browse_column_502s_on_internal_error(client, monkeypatch):
+    monkeypatch.setattr(annotation_server, "get_tiled_client", lambda *a, **k: (_ for _ in ()).throw(RuntimeError("down")))
+    response = await client.get(
+        "/api/browse/column", params={"field": "PI", "container_path": "browse"},
+    )
+    assert response.status_code == 502
+
+
+@pytest.mark.asyncio
+async def test_browse_items_returns_matching_samples(client):
+    response = await client.get(
+        "/api/browse/items",
+        params={"container_path": "browse", "filters": '{"PI": "Smith"}'},
+    )
+    assert response.status_code == 200
+    body = response.json()
+    assert body["total"] == 1
+    assert body["items"][0]["sample"] == "sample1"
+
+
+@pytest.mark.asyncio
+async def test_browse_items_no_filters_returns_all(client):
+    response = await client.get("/api/browse/items", params={"container_path": "browse"})
+    assert response.status_code == 200
+    assert response.json()["total"] == 2
+
+
+@pytest.mark.asyncio
+async def test_browse_items_malformed_filter_json_treated_as_no_filter(client):
+    response = await client.get(
+        "/api/browse/items", params={"container_path": "browse", "filters": "not json"},
+    )
+    assert response.status_code == 200
+    assert response.json()["total"] == 2
+
+
+@pytest.mark.asyncio
+async def test_browse_items_502s_on_internal_error(client, monkeypatch):
+    monkeypatch.setattr(annotation_server, "get_tiled_client", lambda *a, **k: (_ for _ in ()).throw(RuntimeError("down")))
+    response = await client.get("/api/browse/items", params={"container_path": "browse"})
+    assert response.status_code == 502
+
+
+@pytest.mark.asyncio
+async def test_browse_slices_lists_container_children(client):
+    response = await client.get("/api/browse/slices", params={"path": "browse"})
+    assert response.status_code == 200
+    assert response.json()["total"] == 2
diff --git a/backend/tests/test_annotation_server_data_routes.py b/backend/tests/test_annotation_server_data_routes.py
new file mode 100644
index 0000000..8fc7cb1
--- /dev/null
+++ b/backend/tests/test_annotation_server_data_routes.py
@@ -0,0 +1,404 @@
+"""HTTP-level tests for annotation_server.py's data-source registration
+(zarr/tiff-stack), denoise, volume, and remaining train-route wiring.
+
+The underlying modules (zarr_source, tiff_stack_source, volume_build,
+volume_nodes, denoise_bake, infer_jobs, train_jobs) already have their own
+direct unit tests — these tests are about routing, request validation, and
+background-job wiring, so the delegate functions are monkeypatched rather
+than re-proven here. Background threads are made synchronous via a fake
+`threading.Thread` so job results are observable without polling/sleeping.
+"""
+from __future__ import annotations
+
+import types
+
+import pytest
+from httpx import ASGITransport, AsyncClient
+
+import annotation_server
+import denoise as denoise_mod
+import export_jobs
+import infer_jobs
+import tiff_stack_source
+import train_common
+import volume_build
+import volume_nodes
+import zarr_source
+
+app = annotation_server.app
+
+
+@pytest.fixture()
+async def client():
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as c:
+        yield c
+
+
+class ImmediateThread:
+    """Runs its target synchronously instead of spawning a real thread, so a
+    background-job route's result is observable right after the request
+    returns, with no polling or sleeping needed."""
+
+    def __init__(self, target=None, args=(), kwargs=None, daemon=None):
+        self._target = target
+        self._args = args
+        self._kwargs = kwargs or {}
+
+    def start(self):
+        self._target(*self._args, **self._kwargs)
+
+
+@pytest.fixture()
+def sync_threads(monkeypatch: pytest.MonkeyPatch):
+    # Replace the NAME `threading` inside annotation_server's own module
+    # namespace (not the shared stdlib module object) — otherwise this would
+    # also intercept unrelated internal thread creation, e.g. inside
+    # asyncio.to_thread's own ThreadPoolExecutor, which every route here uses.
+    monkeypatch.setattr(annotation_server, "threading", types.SimpleNamespace(Thread=ImmediateThread))
+
+
+# ---------------------------------------------------------------------------
+# /api/zarr/*
+# ---------------------------------------------------------------------------
+
+class TestZarrRoutes:
+    @pytest.mark.asyncio
+    async def test_inspect_delegates_to_zarr_source(self, client, monkeypatch):
+        monkeypatch.setattr(zarr_source, "inspect_zarr", lambda path: {"path": path, "levels": 3})
+        response = await client.get("/api/zarr/inspect", params={"path": "/data/x.zarr"})
+        assert response.status_code == 200
+        assert response.json() == {"path": "/data/x.zarr", "levels": 3}
+
+    @pytest.mark.asyncio
+    async def test_preflight_delegates_with_request_fields(self, client, monkeypatch):
+        calls = []
+        monkeypatch.setattr(
+            zarr_source, "preflight_zarr",
+            lambda server_uri, path, container_path: calls.append((server_uri, path, container_path)) or {"ok": True},
+        )
+        response = await client.post(
+            "/api/zarr/preflight",
+            json={"path": "/data/x.zarr", "container_path": "browse/x"},
+        )
+        assert response.status_code == 200
+        assert calls == [(None, "/data/x.zarr", "browse/x")]
+
+    @pytest.mark.asyncio
+    async def test_register_rejects_bad_on_conflict(self, client):
+        response = await client.post(
+            "/api/zarr/register",
+            json={"path": "/data/x.zarr", "on_conflict": "explode"},
+        )
+        assert response.status_code == 400
+
+    @pytest.mark.asyncio
+    async def test_register_delegates_on_valid_request(self, client, monkeypatch):
+        monkeypatch.setattr(zarr_source, "register_zarr", lambda *a: {"key": "x"})
+        response = await client.post(
+            "/api/zarr/register",
+            json={"path": "/data/x.zarr", "on_conflict": "replace"},
+        )
+        assert response.status_code == 200
+        assert response.json() == {"key": "x"}
+
+
+# ---------------------------------------------------------------------------
+# /api/tiff-stack/*
+# ---------------------------------------------------------------------------
+
+class TestTiffStackRoutes:
+    @pytest.mark.asyncio
+    async def test_inspect_delegates(self, client, monkeypatch):
+        monkeypatch.setattr(tiff_stack_source, "inspect_tiff_stack", lambda path: {"slices_to_read": 5})
+        response = await client.get("/api/tiff-stack/inspect", params={"path": "/data/stack"})
+        assert response.status_code == 200
+        assert response.json() == {"slices_to_read": 5}
+
+    @pytest.mark.asyncio
+    async def test_preflight_delegates(self, client, monkeypatch):
+        monkeypatch.setattr(
+            tiff_stack_source, "preflight_tiff_stack",
+            lambda server_uri, path, container_path: {"collision": False},
+        )
+        response = await client.post(
+            "/api/tiff-stack/preflight",
+            json={"path": "/data/stack", "container_path": "browse/s"},
+        )
+        assert response.status_code == 200
+        assert response.json() == {"collision": False}
+
+    @pytest.mark.asyncio
+    async def test_register_rejects_bad_on_conflict(self, client):
+        response = await client.post(
+            "/api/tiff-stack/register",
+            json={"path": "/data/stack", "on_conflict": "nope"},
+        )
+        assert response.status_code == 400
+
+    @pytest.mark.asyncio
+    async def test_register_runs_job_and_reports_done(self, client, monkeypatch, sync_threads):
+        monkeypatch.setattr(tiff_stack_source, "inspect_tiff_stack", lambda path: {"slices_to_read": 3})
+        monkeypatch.setattr(
+            tiff_stack_source, "register_tiff_stack",
+            lambda server_uri, path, container_path, description, on_conflict, progress: {"key": "stack1"},
+        )
+        response = await client.post(
+            "/api/tiff-stack/register",
+            json={"path": "/data/stack", "on_conflict": "fail"},
+        )
+        assert response.status_code == 200
+        jid = response.json()["job_id"]
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"] == {"key": "stack1"}
+
+    @pytest.mark.asyncio
+    async def test_register_job_reports_error_on_exception(self, client, monkeypatch, sync_threads):
+        monkeypatch.setattr(tiff_stack_source, "inspect_tiff_stack", lambda path: {"slices_to_read": 3})
+
+        def boom(*a, **k):
+            raise RuntimeError("registration blew up")
+
+        monkeypatch.setattr(tiff_stack_source, "register_tiff_stack", boom)
+        response = await client.post(
+            "/api/tiff-stack/register",
+            json={"path": "/data/stack", "on_conflict": "fail"},
+        )
+        jid = response.json()["job_id"]
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "registration blew up" in job["error"]
+
+
+# ---------------------------------------------------------------------------
+# /api/denoise/*
+# ---------------------------------------------------------------------------
+
+class TestDenoiseMethods:
+    @pytest.mark.asyncio
+    async def test_lists_described_methods(self, client, monkeypatch):
+        monkeypatch.setattr(denoise_mod, "describe_methods", lambda: [{"id": "median"}])
+        response = await client.get("/api/denoise/methods")
+        assert response.json() == {"methods": [{"id": "median"}]}
+
+
+class TestDenoiseAuto:
+    @pytest.mark.asyncio
+    async def test_unknown_method_is_400(self, client):
+        response = await client.get(
+            "/api/denoise/auto", params={"source": "x", "kind": "local", "method": "not_real"},
+        )
+        assert response.status_code == 400
+
+    @pytest.mark.asyncio
+    async def test_suggests_strength_for_a_real_slice(self, client, monkeypatch):
+        import numpy as np
+
+        import arrays as arrays_mod
+
+        arr = np.random.default_rng(0).random((8, 8)).astype(np.float32)
+        monkeypatch.setattr(arrays_mod, "resolve_array", lambda source, kind, server_uri, root: "node")
+        monkeypatch.setattr(arrays_mod, "pyramid_info", lambda source, kind, server_uri, root: None)
+        monkeypatch.setattr(arrays_mod, "array_shape_meta", lambda node, pyramid: {"shape_kind": "HW"})
+        monkeypatch.setattr(arrays_mod, "read_slice", lambda node, meta, idx: arr)
+        response = await client.get(
+            "/api/denoise/auto", params={"source": "x", "kind": "local", "method": "median"},
+        )
+        assert response.status_code == 200
+        body = response.json()
+        assert "strength" in body
+        assert "noise_sigma" in body
+
+
+class TestDenoiseBakeRoute:
+    @pytest.mark.asyncio
+    async def test_method_none_is_422(self, client):
+        response = await client.post(
+            "/api/denoise/bake", json={"source": "browse/s", "method": "none"},
+        )
+        assert response.status_code == 422
+
+    @pytest.mark.asyncio
+    async def test_unknown_method_is_422(self, client):
+        response = await client.post(
+            "/api/denoise/bake", json={"source": "browse/s", "method": "not_real"},
+        )
+        assert response.status_code == 422
+
+    @pytest.mark.asyncio
+    async def test_unavailable_method_is_422(self, client, monkeypatch):
+        monkeypatch.setattr(denoise_mod, "available_methods", lambda: ("median",))
+        response = await client.post(
+            "/api/denoise/bake", json={"source": "browse/s", "method": "wavelet"},
+        )
+        assert response.status_code == 422
+
+    @pytest.mark.asyncio
+    async def test_invalid_target_path_is_422(self, client):
+        response = await client.post(
+            "/api/denoise/bake",
+            json={"source": "browse/s", "method": "median", "target_path": "../escape"},
+        )
+        assert response.status_code == 422
+
+    @pytest.mark.asyncio
+    async def test_valid_request_starts_a_job(self, client, monkeypatch, sync_threads):
+        import denoise_bake as denoise_bake_mod
+
+        monkeypatch.setattr(denoise_bake_mod, "run_denoise_bake_job", lambda jid, payload: export_jobs.update(jid, state="done"))
+        response = await client.post(
+            "/api/denoise/bake", json={"source": "browse/s", "method": "median"},
+        )
+        assert response.status_code == 200
+        body = response.json()
+        assert "job_id" in body
+        assert body["target_path"] == "browse/s_denoised"
+        assert export_jobs.get_job(body["job_id"])["state"] == "done"
+
+
+# ---------------------------------------------------------------------------
+# /api/train/start — remaining guard paths not covered by test_train_routes.py
+# ---------------------------------------------------------------------------
+
+class TestTrainStartGuards:
+    def _payload(self, **overrides):
+        base = {
+            "task": "segmentation",
+            "sources": [{"kind": "local", "source": "fake.tif", "slices": {"0": []}}],
+            "classes": [{"classId": 1, "label": "a", "color": "#ff0000"}],
+            "model": {"model_family": "dlsia_tunet"},
+        }
+        base.update(overrides)
+        return base
+
+    @pytest.mark.asyncio
+    async def test_torch_unavailable_is_503(self, client, monkeypatch):
+        monkeypatch.setattr(train_common, "torch_available", lambda: False)
+        response = await client.post("/api/train/start", json=self._payload())
+        assert response.status_code == 503
+
+    @pytest.mark.asyncio
+    async def test_dlsia_unavailable_for_tunet_is_503(self, client, monkeypatch):
+        monkeypatch.setattr(train_common, "torch_available", lambda: True)
+        monkeypatch.setattr(train_common, "dlsia_available", lambda: False)
+        response = await client.post("/api/train/start", json=self._payload())
+        assert response.status_code == 503
+
+    @pytest.mark.asyncio
+    async def test_busy_ml_lock_is_409(self, client, monkeypatch):
+        monkeypatch.setattr(train_common, "torch_available", lambda: True)
+        monkeypatch.setattr(train_common, "dlsia_available", lambda: True)
+        train_common.ML_LOCK.acquire()
+        try:
+            response = await client.post("/api/train/start", json=self._payload())
+            assert response.status_code == 409
+        finally:
+            train_common.ML_LOCK.release()
+
+    @pytest.mark.asyncio
+    async def test_resume_incompatible_is_400(self, client, monkeypatch):
+        import train_jobs
+
+        monkeypatch.setattr(train_common, "torch_available", lambda: True)
+        monkeypatch.setattr(train_common, "dlsia_available", lambda: True)
+        monkeypatch.setattr(train_common, "load_run_config", lambda run_id: {"some": "config"})
+
+        def raise_incompatible(parent_config, payload):
+            raise ValueError("architectures differ")
+
+        monkeypatch.setattr(train_jobs, "check_resume_compatible", raise_incompatible)
+        response = await client.post(
+            "/api/train/start", json=self._payload(resume_from_run_id="parent1"),
+        )
+        assert response.status_code == 400
+
+    @pytest.mark.asyncio
+    async def test_success_creates_a_job(self, client, monkeypatch, sync_threads):
+        import train_jobs
+
+        monkeypatch.setattr(train_common, "torch_available", lambda: True)
+        monkeypatch.setattr(train_common, "dlsia_available", lambda: True)
+        monkeypatch.setattr(train_jobs, "run_train_job", lambda jid, payload, run_id: export_jobs.update(jid, state="done"))
+        response = await client.post("/api/train/start", json=self._payload())
+        assert response.status_code == 200
+        body = response.json()
+        assert "job_id" in body and "run_id" in body
+        assert export_jobs.get_job(body["job_id"])["state"] == "done"
+
+
+class TestTrainInferPreviewAndWriteTiled:
+    @pytest.mark.asyncio
+    async def test_preview_delegates_to_infer_jobs(self, client, monkeypatch):
+        monkeypatch.setattr(infer_jobs, "preview_png", lambda job_id, slice_index: b"pngbytes")
+        response = await client.get("/api/train/infer/preview/job1/3")
+        assert response.status_code == 200
+        assert response.content == b"pngbytes"
+        assert response.headers["content-type"] == "image/png"
+
+    @pytest.mark.asyncio
+    async def test_write_tiled_starts_a_new_job(self, client, monkeypatch, sync_threads):
+        monkeypatch.setattr(
+            infer_jobs, "run_write_tiled_job",
+            lambda write_jid, infer_job_id: export_jobs.update(write_jid, state="done", result={"source": infer_job_id}),
+        )
+        response = await client.post("/api/train/infer/write-tiled/infer-job-1")
+        assert response.status_code == 200
+        jid = response.json()["job_id"]
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"] == {"source": "infer-job-1"}
+
+
+# ---------------------------------------------------------------------------
+# /api/volume/*
+# ---------------------------------------------------------------------------
+
+class TestVolumeRoutes:
+    @pytest.mark.asyncio
+    async def test_resolve_delegates(self, client, monkeypatch):
+        monkeypatch.setattr(volume_nodes, "resolve_volume", lambda server_uri, source: {"key": "vol1"})
+        response = await client.get("/api/volume/resolve", params={"source": "browse/s"})
+        assert response.json() == {"key": "vol1"}
+
+    @pytest.mark.asyncio
+    async def test_build_inspect_delegates(self, client, monkeypatch):
+        monkeypatch.setattr(
+            volume_build, "inspect_volume_build",
+            lambda source, kind, server_uri: {"slices_to_read": 10},
+        )
+        response = await client.get("/api/volume/build/inspect", params={"source": "browse/s"})
+        assert response.json() == {"slices_to_read": 10}
+
+    @pytest.mark.asyncio
+    async def test_build_start_runs_job_and_reports_done(self, client, monkeypatch, sync_threads):
+        monkeypatch.setattr(
+            volume_build, "inspect_volume_build",
+            lambda source, kind, server_uri: {"slices_to_read": 4},
+        )
+        monkeypatch.setattr(
+            volume_build, "build_volume",
+            lambda source, kind, server_uri, container_path, progress: {"key": "vol1"},
+        )
+        response = await client.post("/api/volume/build", json={"source": "browse/s"})
+        assert response.status_code == 200
+        body = response.json()
+        assert body["slices_to_read"] == 4
+        job = export_jobs.get_job(body["job_id"])
+        assert job["state"] == "done"
+        assert job["result"] == {"key": "vol1"}
+
+    @pytest.mark.asyncio
+    async def test_build_start_job_reports_error_on_exception(self, client, monkeypatch, sync_threads):
+        monkeypatch.setattr(
+            volume_build, "inspect_volume_build",
+            lambda source, kind, server_uri: {"slices_to_read": 4},
+        )
+
+        def boom(*a, **k):
+            raise RuntimeError("build blew up")
+
+        monkeypatch.setattr(volume_build, "build_volume", boom)
+        response = await client.post("/api/volume/build", json={"source": "browse/s"})
+        job = export_jobs.get_job(response.json()["job_id"])
+        assert job["state"] == "error"
+        assert "build blew up" in job["error"]
diff --git a/backend/tests/test_annotation_server_local_image_routes.py b/backend/tests/test_annotation_server_local_image_routes.py
new file mode 100644
index 0000000..71ce552
--- /dev/null
+++ b/backend/tests/test_annotation_server_local_image_routes.py
@@ -0,0 +1,349 @@
+"""HTTP-level tests for annotation_server.py's local-filesystem, connection
+summary, Tiled listing, and image meta/slice routes. Continues the pattern
+established in test_annotation_server_routes.py/test_annotation_server_browse_routes.py:
+httpx.AsyncClient + ASGITransport, monkeypatched I/O boundaries
+(local_fs/get_tiled_client/arrays_mod/images_mod), no real Tiled server or
+filesystem beyond what a test itself creates."""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from httpx import ASGITransport, AsyncClient
+
+import annotation_server
+import arrays as arrays_mod
+import images as images_mod
+import local_fs
+
+app = annotation_server.app
+
+
+@pytest.fixture()
+async def client():
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as c:
+        yield c
+
+
+class FakeNode:
+    def __init__(self, children=None, is_container=True):
+        self._children = children or {}
+        self.structure_family = "container" if is_container else "array"
+
+    def __iter__(self):
+        return iter(self._children)
+
+    def __getitem__(self, key):
+        return self._children[key]
+
+    def keys(self):
+        return list(self._children.keys())
+
+    def __len__(self):
+        return len(self._children)
+
+
+# ---------------------------------------------------------------------------
+# /api/local/list, /api/local/samples
+# ---------------------------------------------------------------------------
+
+class TestLocalList:
+    @pytest.mark.asyncio
+    async def test_lists_directory_entries(self, client, monkeypatch):
+        monkeypatch.setattr(local_fs, "list_dir", lambda rel, root: [{"name": "a", "is_dir": True}])
+        response = await client.get("/api/local/list", params={"rel": ""})
+        assert response.status_code == 200
+        assert response.json() == [{"name": "a", "is_dir": True}]
+
+    @pytest.mark.asyncio
+    async def test_passes_rel_and_root_through(self, client, monkeypatch):
+        calls = []
+        monkeypatch.setattr(local_fs, "list_dir", lambda rel, root: calls.append((rel, root)) or [])
+        await client.get("/api/local/list", params={"rel": "sub", "root": "/data"})
+        assert calls == [("sub", "/data")]
+
+
+class TestLocalSamples:
+    @pytest.mark.asyncio
+    async def test_returns_items_and_total(self, client, monkeypatch):
+        monkeypatch.setattr(
+            local_fs, "list_image_files",
+            lambda rel, root: [{"name": "a.tif", "path": "a.tif"}, {"name": "b.tif", "path": "b.tif"}],
+        )
+        response = await client.get("/api/local/samples", params={"rel": "folder"})
+        assert response.status_code == 200
+        body = response.json()
+        assert body["total"] == 2
+        assert len(body["items"]) == 2
+
+    @pytest.mark.asyncio
+    async def test_requires_rel_query_param(self, client):
+        response = await client.get("/api/local/samples")
+        assert response.status_code == 422
+
+
+# ---------------------------------------------------------------------------
+# /api/connect/summary
+# ---------------------------------------------------------------------------
+
+class TestConnectSummaryLocal:
+    @pytest.mark.asyncio
+    async def test_counts_local_files(self, client, monkeypatch):
+        monkeypatch.setattr(local_fs, "count_image_files", lambda rel, root: 42)
+        response = await client.get(
+            "/api/connect/summary", params={"kind": "local", "rel": "sub", "root": "/data"},
+        )
+        assert response.status_code == 200
+        body = response.json()
+        assert body["sample_count"] == 42
+        assert body["label"] == "/data/sub"
+        assert body["kind"] == "local"
+
+    @pytest.mark.asyncio
+    async def test_label_falls_back_to_default_when_no_root(self, client, monkeypatch):
+        monkeypatch.setattr(local_fs, "count_image_files", lambda rel, root: 0)
+        response = await client.get("/api/connect/summary", params={"kind": "local"})
+        assert response.json()["label"] == "Local Data Root"
+
+
+class TestConnectSummaryTiled:
+    @pytest.mark.asyncio
+    async def test_counts_container_via_len(self, client, monkeypatch):
+        root = FakeNode({"a": 1, "b": 2, "c": 3})
+        monkeypatch.setattr(annotation_server, "get_tiled_client", lambda uri: root)
+        monkeypatch.setattr(annotation_server, "get_browse_container_for", lambda client, path: (root, "browse"))
+        monkeypatch.setattr(annotation_server, "get_tiled_servers", lambda: {})
+        response = await client.get("/api/connect/summary", params={"kind": "tiled", "server_uri": "http://x"})
+        assert response.status_code == 200
+        body = response.json()
+        assert body["sample_count"] == 3
+        assert body["kind"] == "tiled"
+
+    @pytest.mark.asyncio
+    async def test_label_uses_configured_server_name(self, client, monkeypatch):
+        root = FakeNode({})
+        monkeypatch.setattr(annotation_server, "get_tiled_client", lambda uri: root)
+        monkeypatch.setattr(annotation_server, "get_browse_container_for", lambda client, path: (root, "browse"))
+        monkeypatch.setattr(
+            annotation_server, "get_tiled_servers",
+            lambda: {"local": {"uri": "http://x:1", "name": "My Server"}},
+        )
+        response = await client.get("/api/connect/summary", params={"kind": "tiled", "server_uri": "http://x:1"})
+        assert response.json()["label"] == "My Server"
+
+    @pytest.mark.asyncio
+    async def test_container_path_appended_to_label(self, client, monkeypatch):
+        root = FakeNode({})
+        monkeypatch.setattr(annotation_server, "get_tiled_client", lambda uri: root)
+        monkeypatch.setattr(annotation_server, "get_browse_container_for", lambda client, path: (root, path or ""))
+        monkeypatch.setattr(annotation_server, "get_tiled_servers", lambda: {})
+        response = await client.get(
+            "/api/connect/summary",
+            params={"kind": "tiled", "server_uri": "http://x", "container_path": "browse/sample"},
+        )
+        assert "browse/sample" in response.json()["label"]
+
+    @pytest.mark.asyncio
+    async def test_count_failure_falls_back_to_zero(self, client, monkeypatch):
+        def boom(uri):
+            raise RuntimeError("down")
+
+        monkeypatch.setattr(annotation_server, "get_tiled_client", boom)
+        monkeypatch.setattr(annotation_server, "get_tiled_servers", lambda: {})
+        response = await client.get("/api/connect/summary", params={"kind": "tiled", "server_uri": "http://x"})
+        assert response.status_code == 200
+        assert response.json()["sample_count"] == 0
+
+    @pytest.mark.asyncio
+    async def test_falls_back_to_search_when_len_unsupported(self, client, monkeypatch):
+        class NoLenNode(FakeNode):
+            def __len__(self):
+                raise TypeError("no len")
+
+        root = NoLenNode({"a": 1})
+        monkeypatch.setattr(annotation_server, "get_tiled_client", lambda uri: root)
+        monkeypatch.setattr(annotation_server, "get_browse_container_for", lambda client, path: (root, "browse"))
+        monkeypatch.setattr(annotation_server, "get_tiled_servers", lambda: {})
+        monkeypatch.setattr(annotation_server, "tiled_search_items", lambda container, filters, limit: {"total": 7})
+        response = await client.get("/api/connect/summary", params={"kind": "tiled", "server_uri": "http://x"})
+        assert response.json()["sample_count"] == 7
+
+
+class TestConnectSummaryUnknownKind:
+    @pytest.mark.asyncio
+    async def test_unknown_kind_is_400(self, client):
+        response = await client.get("/api/connect/summary", params={"kind": "weird"})
+        assert response.status_code == 400
+
+
+# ---------------------------------------------------------------------------
+# /api/tiled/list
+# ---------------------------------------------------------------------------
+
+class TestTiledList:
+    @pytest.mark.asyncio
+    async def test_lists_children_sorted_containers_first(self, client, monkeypatch):
+        root = FakeNode({
+            "z_array": FakeNode(is_container=False),
+            "a_container": FakeNode({}),
+        })
+        monkeypatch.setattr(annotation_server, "get_tiled_client", lambda uri: root)
+        response = await client.get("/api/tiled/list", params={"path": ""})
+        assert response.status_code == 200
+        body = response.json()
+        assert body[0]["name"] == "a_container"
+        assert body[0]["is_dir"] is True
+        assert body[1]["name"] == "z_array"
+        assert body[1]["is_array"] is True
+
+    @pytest.mark.asyncio
+    async def test_missing_path_segment_is_404(self, client, monkeypatch):
+        root = FakeNode({})
+        monkeypatch.setattr(annotation_server, "get_tiled_client", lambda uri: root)
+        response = await client.get("/api/tiled/list", params={"path": "nope"})
+        assert response.status_code == 404
+
+    @pytest.mark.asyncio
+    async def test_leaf_array_node_is_400(self, client, monkeypatch):
+        class LeafNoKeys(FakeNode):
+            def keys(self):
+                raise RuntimeError("no children")
+
+        root = FakeNode({"leaf": LeafNoKeys(is_container=False)})
+        monkeypatch.setattr(annotation_server, "get_tiled_client", lambda uri: root)
+        response = await client.get("/api/tiled/list", params={"path": "leaf"})
+        assert response.status_code == 400
+
+    @pytest.mark.asyncio
+    async def test_nested_path_joins_correctly(self, client, monkeypatch):
+        root = FakeNode({"a": FakeNode({"b": FakeNode(is_container=False)})})
+        monkeypatch.setattr(annotation_server, "get_tiled_client", lambda uri: root)
+        response = await client.get("/api/tiled/list", params={"path": "a"})
+        assert response.json()[0]["path"] == "a/b"
+
+    @pytest.mark.asyncio
+    async def test_unexpected_error_is_500(self, client, monkeypatch):
+        def boom(uri):
+            raise RuntimeError("connection refused")
+
+        monkeypatch.setattr(annotation_server, "get_tiled_client", boom)
+        response = await client.get("/api/tiled/list", params={"path": ""})
+        assert response.status_code == 500
+
+
+# ---------------------------------------------------------------------------
+# /api/image/meta, /api/image/slice
+# ---------------------------------------------------------------------------
+
+@pytest.fixture()
+def fake_image_source(monkeypatch: pytest.MonkeyPatch):
+    arr = np.arange(100, dtype=np.float32).reshape(10, 10)
+    monkeypatch.setattr(arrays_mod, "resolve_array", lambda source, kind, server_uri, root: "node")
+    monkeypatch.setattr(arrays_mod, "pyramid_info", lambda source, kind, server_uri, root: None)
+    monkeypatch.setattr(arrays_mod, "array_shape_meta", lambda node, pyramid: {
+        "n_slices": 1, "height": 10, "width": 10, "dtype": "float32", "is_rgb": False, "shape_kind": "HW",
+    })
+    monkeypatch.setattr(arrays_mod, "read_slice", lambda node, meta, idx: arr)
+    monkeypatch.setattr(arrays_mod, "node_keywords", lambda node: ["tag1"])
+    return arr
+
+
+class TestImageMeta:
+    @pytest.mark.asyncio
+    async def test_returns_shape_and_value_range(self, client, fake_image_source):
+        response = await client.get(
+            "/api/image/meta", params={"source": "local:foo.tif", "kind": "local"},
+        )
+        assert response.status_code == 200
+        body = response.json()
+        assert body["n_slices"] == 1
+        assert body["height"] == 10
+        assert body["width"] == 10
+        assert body["value_range"] == [0.0, 99.0]
+        assert body["keywords"] == ["tag1"]
+
+    @pytest.mark.asyncio
+    async def test_resolve_failure_is_500(self, client, monkeypatch):
+        monkeypatch.setattr(
+            arrays_mod, "resolve_array",
+            lambda source, kind, server_uri, root: (_ for _ in ()).throw(RuntimeError("boom")),
+        )
+        response = await client.get(
+            "/api/image/meta", params={"source": "local:foo.tif", "kind": "local"},
+        )
+        assert response.status_code == 500
+
+    @pytest.mark.asyncio
+    async def test_http_exception_is_passed_through(self, monkeypatch, client):
+        from fastapi import HTTPException
+
+        def raise_404(source, kind, server_uri, root):
+            raise HTTPException(404, "not found")
+
+        monkeypatch.setattr(arrays_mod, "resolve_array", raise_404)
+        response = await client.get(
+            "/api/image/meta", params={"source": "local:foo.tif", "kind": "local"},
+        )
+        assert response.status_code == 404
+
+
+class TestImageSlice:
+    @pytest.mark.asyncio
+    async def test_renders_a_png(self, client, fake_image_source, monkeypatch):
+        monkeypatch.setattr(images_mod, "_sample_global_stats", lambda node, meta: (0.0, 99.0))
+        response = await client.get(
+            "/api/image/slice", params={"source": "local:foo.tif", "kind": "local"},
+        )
+        assert response.status_code == 200
+        assert response.headers["content-type"] == "image/png"
+
+    @pytest.mark.asyncio
+    async def test_unknown_denoise_method_is_400(self, client, fake_image_source):
+        response = await client.get(
+            "/api/image/slice",
+            params={"source": "local:foo.tif", "kind": "local", "denoise_method": "not_a_method"},
+        )
+        assert response.status_code == 400
+
+    @pytest.mark.asyncio
+    async def test_denoised_result_is_cached_across_requests(self, client, fake_image_source, monkeypatch):
+        calls = []
+        real_denoised = annotation_server._denoised_slice
+
+        def spy_denoised(node, meta, idx, method, strength, crop):
+            calls.append(1)
+            return real_denoised(node, meta, idx, method, strength, crop)
+
+        monkeypatch.setattr(annotation_server, "_denoised_slice", spy_denoised)
+        monkeypatch.setattr(images_mod, "_sample_global_stats", lambda node, meta: (0.0, 99.0))
+        params = {"source": "local:foo.tif", "kind": "local", "denoise_method": "median"}
+        r1 = await client.get("/api/image/slice", params=params)
+        r2 = await client.get("/api/image/slice", params=params)
+        assert r1.status_code == 200 and r2.status_code == 200
+        assert r1.content == r2.content
+        assert len(calls) == 1  # second request served from cache
+
+    @pytest.mark.asyncio
+    async def test_render_failure_is_500(self, client, fake_image_source, monkeypatch):
+        monkeypatch.setattr(
+            images_mod, "render_slice",
+            lambda sl, opts, gr: (_ for _ in ()).throw(RuntimeError("render broke")),
+        )
+        monkeypatch.setattr(images_mod, "_sample_global_stats", lambda node, meta: (0.0, 99.0))
+        response = await client.get(
+            "/api/image/slice", params={"source": "local:foo.tif", "kind": "local"},
+        )
+        assert response.status_code == 500
+
+    @pytest.mark.asyncio
+    async def test_slice_norm_skips_global_stats(self, client, fake_image_source, monkeypatch):
+        calls = []
+        monkeypatch.setattr(
+            images_mod, "_sample_global_stats",
+            lambda node, meta: calls.append(1) or (0.0, 99.0),
+        )
+        response = await client.get(
+            "/api/image/slice",
+            params={"source": "local:foo.tif", "kind": "local", "norm": "slice"},
+        )
+        assert response.status_code == 200
+        assert calls == []
diff --git a/backend/tests/test_annotation_server_routes.py b/backend/tests/test_annotation_server_routes.py
new file mode 100644
index 0000000..6fccab4
--- /dev/null
+++ b/backend/tests/test_annotation_server_routes.py
@@ -0,0 +1,217 @@
+"""HTTP-level tests for annotation_server.py's session-persistence and
+export-job-status routes — mirrors the httpx.AsyncClient + ASGITransport
+pattern already established in test_ipred_routes.py/test_train_routes.py.
+
+Scoped to the routes testable with local filesystem + in-memory job registry
+state (drafts, guide, measure, export status/cancel/download) — the
+biggest, most tractable slice of this file's route coverage. Browse/ingest/
+zarr/volume/tiff-stack/denoise routes need a real or heavily-mocked Tiled
+client and are left for a further pass.
+"""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from httpx import ASGITransport, AsyncClient
+
+import arrays as arrays_mod
+import drafts
+import export_jobs
+import guides
+from annotation_server import app
+
+
+@pytest.fixture()
+def local_data_root(tmp_path, monkeypatch: pytest.MonkeyPatch):
+    """drafts.py/guides.py compute their storage dir at IMPORT time
+    (module-level `_DRAFT_DIR`), so setting LOCAL_DATA_ROOT alone has no
+    effect on an already-imported module — patch the attribute directly."""
+    draft_dir = tmp_path / ".drafts"
+    monkeypatch.setattr(drafts, "_DRAFT_DIR", draft_dir)
+    monkeypatch.setattr(guides, "_DRAFT_DIR", draft_dir)
+    return draft_dir
+
+
+@pytest.fixture()
+async def client():
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as c:
+        yield c
+
+
+# ---------------------------------------------------------------------------
+# Drafts
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+async def test_get_draft_404_when_none_saved(client, local_data_root):
+    response = await client.get("/api/annotations/draft", params={"source_key": "local:x.tif"})
+    assert response.status_code == 404
+
+
+@pytest.mark.asyncio
+async def test_put_then_get_draft_round_trips(client, local_data_root):
+    payload = {"classes": [{"classId": 1, "label": "A", "color": "#f00"}], "slices": {"0": []}}
+    put_res = await client.put(
+        "/api/annotations/draft", params={"source_key": "local:x.tif"}, json=payload,
+    )
+    assert put_res.status_code == 200
+
+    get_res = await client.get("/api/annotations/draft", params={"source_key": "local:x.tif"})
+    assert get_res.status_code == 200
+    body = get_res.json()
+    assert body["source_key"] == "local:x.tif"
+    assert body["payload"]["classes"][0]["label"] == "A"
+
+
+@pytest.mark.asyncio
+async def test_list_drafts_includes_saved_ones(client, local_data_root):
+    await client.put(
+        "/api/annotations/draft", params={"source_key": "local:x.tif"},
+        json={"classes": [], "slices": {}},
+    )
+    response = await client.get("/api/annotations/drafts")
+    assert response.status_code == 200
+    keys = [d["source_key"] for d in response.json()]
+    assert "local:x.tif" in keys
+
+
+# ---------------------------------------------------------------------------
+# Guide
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+async def test_get_guide_404_when_none_saved(client, local_data_root):
+    response = await client.get("/api/guide", params={"source_key": "local:x.tif"})
+    assert response.status_code == 404
+
+
+@pytest.mark.asyncio
+async def test_put_then_get_guide_round_trips(client, local_data_root):
+    payload = {"classes": [{"label": "A", "color": "#f00", "description": "an example"}], "notes": "n"}
+    put_res = await client.put("/api/guide", params={"source_key": "local:x.tif"}, json=payload)
+    assert put_res.status_code == 200
+
+    get_res = await client.get("/api/guide", params={"source_key": "local:x.tif"})
+    assert get_res.status_code == 200
+    assert get_res.json()["guide"]["notes"] == "n"
+
+
+# ---------------------------------------------------------------------------
+# Measure
+# ---------------------------------------------------------------------------
+
+@pytest.fixture()
+def fake_measure_source(monkeypatch: pytest.MonkeyPatch):
+    arr = np.zeros((10, 10), dtype=np.float32)
+    # shape_to_mask's rectangle rasterization is inclusive of both endpoints
+    # (skimage.draw.rectangle "like mlex") — x=2,y=2,w=3,h=3 covers columns/
+    # rows 2..5 inclusive, a 4x4=16 region, not the 3x3=9 a half-open range
+    # would give. Filled to match exactly so every measured pixel is 10.0.
+    arr[2:6, 2:6] = 10.0
+
+    monkeypatch.setattr(arrays_mod, "resolve_array", lambda source, kind, server_uri: arr)
+    monkeypatch.setattr(
+        arrays_mod, "array_shape_meta",
+        lambda node, pyramid=None: {"height": 10, "width": 10, "n_slices": 1},
+    )
+    monkeypatch.setattr(arrays_mod, "read_slice", lambda node, meta, idx: node)
+    return arr
+
+
+@pytest.mark.asyncio
+async def test_measure_returns_stats_for_shape_region(client, fake_measure_source):
+    shape = {"id": "a", "kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 3, "h": 3}
+    response = await client.post(
+        "/api/measure", params={"source_key": "local:x.tif"},
+        json={"slice_index": 0, "shapes": [shape]},
+    )
+    assert response.status_code == 200
+    body = response.json()
+    assert body["pixel_count"] == 16
+    assert body["min"] == 10.0
+    assert body["max"] == 10.0
+    assert body["mean"] == 10.0
+
+
+@pytest.mark.asyncio
+async def test_measure_with_no_shapes_returns_empty_stats(client, fake_measure_source):
+    response = await client.post(
+        "/api/measure", params={"source_key": "local:x.tif"},
+        json={"slice_index": 0, "shapes": []},
+    )
+    assert response.status_code == 200
+    body = response.json()
+    assert body["pixel_count"] == 0
+    assert body["min"] is None
+
+
+@pytest.mark.asyncio
+async def test_measure_ignores_a_shape_that_fails_to_rasterize(client, fake_measure_source):
+    bad_shape = {"id": "bad", "kind": "polygon", "classId": 1, "points": []}
+    good_shape = {"id": "good", "kind": "rectangle", "classId": 1, "x": 2, "y": 2, "w": 3, "h": 3}
+    response = await client.post(
+        "/api/measure", params={"source_key": "local:x.tif"},
+        json={"slice_index": 0, "shapes": [bad_shape, good_shape]},
+    )
+    assert response.status_code == 200
+    assert response.json()["pixel_count"] == 16
+
+
+# ---------------------------------------------------------------------------
+# Export job status/cancel/download
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+async def test_export_status_unknown_job_404s(client):
+    response = await client.get("/api/export/status/does-not-exist")
+    assert response.status_code == 404
+
+
+@pytest.mark.asyncio
+async def test_export_status_returns_real_job_state(client):
+    jid = export_jobs.new_job("some/path")
+    export_jobs.update(jid, state="running", phase="working")
+    export_jobs.set_total(jid, 10)
+    export_jobs.bump(jid, 3)
+
+    response = await client.get(f"/api/export/status/{jid}")
+    assert response.status_code == 200
+    body = response.json()
+    assert body["state"] == "running"
+    assert body["done"] == 3
+    assert body["total"] == 10
+
+
+@pytest.mark.asyncio
+async def test_export_cancel_unknown_job_404s(client):
+    response = await client.post("/api/export/cancel/does-not-exist")
+    assert response.status_code == 404
+
+
+@pytest.mark.asyncio
+async def test_export_cancel_sets_flag_on_real_job(client):
+    jid = export_jobs.new_job("x")
+    response = await client.post(f"/api/export/cancel/{jid}")
+    assert response.status_code == 200
+    assert response.json() == {"cancelled": True}
+    assert export_jobs.cancel_requested(jid) is True
+
+
+@pytest.mark.asyncio
+async def test_export_download_404s_when_zip_not_ready(client):
+    jid = export_jobs.new_job("x")
+    response = await client.get(f"/api/export/download/{jid}")
+    assert response.status_code == 404
+
+
+@pytest.mark.asyncio
+async def test_export_download_streams_the_real_zip_file(client, tmp_path):
+    jid = export_jobs.new_job("x")
+    zip_path = tmp_path / "out.zip"
+    zip_path.write_bytes(b"PK\x03\x04fakezip")
+    export_jobs.update(jid, zip_path=str(zip_path))
+
+    response = await client.get(f"/api/export/download/{jid}")
+    assert response.status_code == 200
+    assert response.content == b"PK\x03\x04fakezip"
+    assert response.headers["content-type"] == "application/zip"
diff --git a/backend/tests/test_annotation_thumbnails.py b/backend/tests/test_annotation_thumbnails.py
new file mode 100644
index 0000000..7f9710f
--- /dev/null
+++ b/backend/tests/test_annotation_thumbnails.py
@@ -0,0 +1,334 @@
+"""Tests for annotation_thumbnails.py — real PIL/numpy rendering, arrays.py
+resolve/meta/read functions monkeypatched (same fake-array-source pattern as
+test_annotation_server_routes.py's measure fixture); Tiled upload path uses a
+lightweight duck-typed fake container."""
+from __future__ import annotations
+
+import base64
+from io import BytesIO
+
+import numpy as np
+import pytest
+from PIL import Image as PILImage
+
+import annotation_thumbnails
+import arrays as arrays_mod
+
+
+# ---------------------------------------------------------------------------
+# decode_thumbnail_base64
+# ---------------------------------------------------------------------------
+
+class TestDecodeThumbnailBase64:
+    def test_empty_string_returns_none(self):
+        assert annotation_thumbnails.decode_thumbnail_base64("") is None
+
+    def test_plain_base64_decodes(self):
+        raw = base64.b64encode(b"hello").decode()
+        assert annotation_thumbnails.decode_thumbnail_base64(raw) == b"hello"
+
+    def test_data_url_prefix_is_stripped(self):
+        raw = base64.b64encode(b"pngdata").decode()
+        data_url = f"data:image/png;base64,{raw}"
+        assert annotation_thumbnails.decode_thumbnail_base64(data_url) == b"pngdata"
+
+    def test_invalid_base64_returns_none(self):
+        assert annotation_thumbnails.decode_thumbnail_base64("not-@@base64!!") is None
+
+
+# ---------------------------------------------------------------------------
+# _stride_downsample
+# ---------------------------------------------------------------------------
+
+class TestStrideDownsample:
+    def test_small_array_returned_unchanged(self):
+        arr = np.zeros((10, 10))
+        out = annotation_thumbnails._stride_downsample(arr, 512)
+        assert out is arr
+
+    def test_large_2d_array_is_strided(self):
+        arr = np.zeros((1000, 1000))
+        out = annotation_thumbnails._stride_downsample(arr, 500)
+        assert max(out.shape) <= 500
+
+    def test_large_3d_array_preserves_channel_dim(self):
+        arr = np.zeros((1000, 1000, 3))
+        out = annotation_thumbnails._stride_downsample(arr, 500)
+        assert out.shape[2] == 3
+        assert max(out.shape[:2]) <= 500
+
+
+# ---------------------------------------------------------------------------
+# _hex_to_rgba
+# ---------------------------------------------------------------------------
+
+class TestHexToRgba:
+    def test_full_hex_with_hash(self):
+        assert annotation_thumbnails._hex_to_rgba("#ff0080", 100) == (255, 0, 128, 100)
+
+    def test_hex_without_hash(self):
+        assert annotation_thumbnails._hex_to_rgba("00ff00", 200) == (0, 255, 0, 200)
+
+    def test_short_hex_is_padded(self):
+        # "abc" -> ljust(6, "0") -> "abc000"
+        assert annotation_thumbnails._hex_to_rgba("#abc", 50) == (0xAB, 0xC0, 0x00, 50)
+
+
+# ---------------------------------------------------------------------------
+# _draw_shapes (via a real PIL ImageDraw)
+# ---------------------------------------------------------------------------
+
+class TestDrawShapes:
+    def _draw(self, shapes, class_colors=None):
+        img = PILImage.new("RGBA", (50, 50), (0, 0, 0, 0))
+        draw = annotation_thumbnails.ImageDraw.Draw(img)
+        annotation_thumbnails._draw_shapes(draw, shapes, class_colors or {}, scale=1.0)
+        return np.array(img)
+
+    def test_polygon_is_drawn(self):
+        shape = {"kind": "polygon", "classId": 1, "points": [5, 5, 20, 5, 20, 20, 5, 20]}
+        pixels = self._draw([shape], {1: "#ff0000"})
+        assert pixels[10, 10, 3] > 0  # alpha channel non-zero inside the fill
+
+    def test_polygon_with_too_few_points_is_skipped(self):
+        shape = {"kind": "polygon", "classId": 1, "points": [5, 5]}
+        pixels = self._draw([shape])
+        assert np.all(pixels[:, :, 3] == 0)
+
+    def test_rectangle_is_drawn(self):
+        shape = {"kind": "rectangle", "classId": 1, "x": 5, "y": 5, "w": 10, "h": 10}
+        pixels = self._draw([shape], {1: "#00ff00"})
+        assert pixels[10, 10, 3] > 0
+
+    def test_ellipse_is_drawn(self):
+        shape = {"kind": "ellipse", "classId": 1, "cx": 25, "cy": 25, "rx": 10, "ry": 10}
+        pixels = self._draw([shape], {1: "#0000ff"})
+        assert pixels[25, 25, 3] > 0
+
+    def test_brush_stroke_is_drawn(self):
+        shape = {
+            "kind": "brush",
+            "classId": 1,
+            "strokes": [{"mode": "paint", "points": [5, 5, 30, 30], "radius": 3}],
+        }
+        pixels = self._draw([shape], {1: "#ffffff"})
+        assert pixels.sum() > 0
+
+    def test_brush_erase_stroke_is_skipped(self):
+        shape = {
+            "kind": "brush",
+            "classId": 1,
+            "strokes": [{"mode": "erase", "points": [5, 5, 30, 30], "radius": 3}],
+        }
+        pixels = self._draw([shape])
+        assert np.all(pixels[:, :, 3] == 0)
+
+    def test_brush_stroke_with_one_point_is_skipped(self):
+        shape = {
+            "kind": "brush",
+            "classId": 1,
+            "strokes": [{"mode": "paint", "points": [5, 5], "radius": 3}],
+        }
+        pixels = self._draw([shape])
+        assert np.all(pixels[:, :, 3] == 0)
+
+    def test_unknown_class_id_falls_back_to_default_color(self):
+        shape = {"kind": "rectangle", "classId": 999, "x": 0, "y": 0, "w": 5, "h": 5}
+        pixels = self._draw([shape], {1: "#00ff00"})
+        assert pixels[2, 2, 3] > 0
+
+
+# ---------------------------------------------------------------------------
+# render_annotated_thumbnail
+# ---------------------------------------------------------------------------
+
+@pytest.fixture()
+def fake_array_source(monkeypatch: pytest.MonkeyPatch):
+    arr = np.random.default_rng(0).integers(0, 255, size=(20, 20), dtype=np.uint8).astype(np.float32)
+
+    def fake_read_slice(node, meta, idx):
+        return arr + idx  # differ per slice so "best slice" pick is checkable
+
+    monkeypatch.setattr(arrays_mod, "resolve_array", lambda source, kind, server_uri: "node")
+    monkeypatch.setattr(arrays_mod, "array_shape_meta", lambda node: {"n_slices": 5})
+    monkeypatch.setattr(arrays_mod, "read_slice", fake_read_slice)
+    return arr
+
+
+class TestRenderAnnotatedThumbnail:
+    def test_returns_png_bytes_for_grayscale_source(self, fake_array_source):
+        payload = {"classes": [], "slices": {}}
+        png = annotation_thumbnails.render_annotated_thumbnail("local:foo.tif", payload)
+        assert png is not None
+        decoded = np.array(PILImage.open(BytesIO(png)))
+        assert decoded.shape[:2] == (20, 20)
+
+    def test_picks_slice_with_most_shapes(self, fake_array_source, monkeypatch):
+        calls = []
+        real_read_slice = arrays_mod.read_slice
+
+        def spy_read_slice(node, meta, idx):
+            calls.append(idx)
+            return real_read_slice(node, meta, idx)
+
+        monkeypatch.setattr(arrays_mod, "read_slice", spy_read_slice)
+        payload = {
+            "classes": [{"classId": 1, "color": "#ff0000"}],
+            "slices": {
+                "0": [{"kind": "rectangle", "classId": 1, "x": 0, "y": 0, "w": 2, "h": 2}],
+                "3": [
+                    {"kind": "rectangle", "classId": 1, "x": 0, "y": 0, "w": 2, "h": 2},
+                    {"kind": "rectangle", "classId": 1, "x": 5, "y": 5, "w": 2, "h": 2},
+                ],
+            },
+        }
+        png = annotation_thumbnails.render_annotated_thumbnail("local:foo.tif", payload)
+        assert png is not None
+        assert calls == [3]
+
+    def test_best_slice_clamped_to_valid_range(self, fake_array_source):
+        payload = {"classes": [], "slices": {"999": [{"kind": "rectangle", "x": 0, "y": 0, "w": 1, "h": 1}]}}
+        png = annotation_thumbnails.render_annotated_thumbnail("local:foo.tif", payload)
+        assert png is not None
+
+    def test_non_integer_slice_key_is_ignored(self, fake_array_source):
+        payload = {"classes": [], "slices": {"not-a-number": [{"kind": "rectangle"}]}}
+        png = annotation_thumbnails.render_annotated_thumbnail("local:foo.tif", payload)
+        assert png is not None
+
+    def test_missing_arrays_module_returns_none(self, monkeypatch):
+        import builtins
+        real_import = builtins.__import__
+
+        def fake_import(name, *args, **kwargs):
+            if name == "arrays":
+                raise ImportError("no arrays")
+            return real_import(name, *args, **kwargs)
+
+        monkeypatch.setattr(builtins, "__import__", fake_import)
+        assert annotation_thumbnails.render_annotated_thumbnail("local:x", {}) is None
+
+    def test_resolve_array_failure_returns_none(self, monkeypatch):
+        monkeypatch.setattr(
+            arrays_mod, "resolve_array",
+            lambda source, kind, server_uri: (_ for _ in ()).throw(RuntimeError("boom")),
+        )
+        assert annotation_thumbnails.render_annotated_thumbnail("local:x", {}) is None
+
+    def test_unsupported_array_shape_returns_none(self, monkeypatch):
+        monkeypatch.setattr(arrays_mod, "resolve_array", lambda source, kind, server_uri: "node")
+        monkeypatch.setattr(arrays_mod, "array_shape_meta", lambda node: {"n_slices": 1})
+        monkeypatch.setattr(arrays_mod, "read_slice", lambda node, meta, idx: np.zeros(10))
+        assert annotation_thumbnails.render_annotated_thumbnail("local:x", {}) is None
+
+    def test_read_slice_failure_returns_none(self, monkeypatch):
+        monkeypatch.setattr(arrays_mod, "resolve_array", lambda source, kind, server_uri: "node")
+        monkeypatch.setattr(arrays_mod, "array_shape_meta", lambda node: {"n_slices": 1})
+
+        def boom(node, meta, idx):
+            raise RuntimeError("read failed")
+
+        monkeypatch.setattr(arrays_mod, "read_slice", boom)
+        assert annotation_thumbnails.render_annotated_thumbnail("local:x", {}) is None
+
+    def test_rgb_source_uses_prepare_rgb_path(self, monkeypatch):
+        rgb_arr = np.zeros((15, 15, 3), dtype=np.uint8)
+        monkeypatch.setattr(arrays_mod, "resolve_array", lambda source, kind, server_uri: "node")
+        monkeypatch.setattr(arrays_mod, "array_shape_meta", lambda node: {"n_slices": 1})
+        monkeypatch.setattr(arrays_mod, "read_slice", lambda node, meta, idx: rgb_arr)
+        png = annotation_thumbnails.render_annotated_thumbnail("local:x", {"classes": [], "slices": {}})
+        assert png is not None
+        decoded = np.array(PILImage.open(BytesIO(png)))
+        assert decoded.shape[:2] == (15, 15)
+
+
+# ---------------------------------------------------------------------------
+# upload_thumbnail_to_tiled
+# ---------------------------------------------------------------------------
+
+class FakeArrayContainer:
+    def __init__(self):
+        self.written = []
+
+    def write_array(self, arr, key, metadata):
+        self.written.append((key, arr, metadata))
+
+
+class FakeContainerNode(dict):
+    def __init__(self, *a, **kw):
+        super().__init__(*a, **kw)
+        self.created = []
+
+    def create_container(self, key, metadata):
+        c = FakeArrayContainer()
+        self[key] = c
+        self.created.append((key, metadata))
+        return c
+
+
+@pytest.fixture()
+def fake_tiled(monkeypatch: pytest.MonkeyPatch):
+    import tiled_clients
+
+    browse = FakeContainerNode()
+    root = FakeContainerNode({"browse": browse})
+    monkeypatch.setattr(tiled_clients, "api_key_for_uri", lambda uri: None)
+    monkeypatch.setattr(tiled_clients, "get_tiled_client", lambda uri, key: root)
+    return browse
+
+
+class TestUploadThumbnailToTiled:
+    def _png_bytes(self):
+        img = PILImage.new("RGB", (4, 4), (10, 20, 30))
+        buf = BytesIO()
+        img.save(buf, format="PNG")
+        return buf.getvalue()
+
+    def test_local_source_is_a_no_op(self, fake_tiled):
+        annotation_thumbnails.upload_thumbnail_to_tiled("local:foo.tif", 1, "2024-01-01", self._png_bytes())
+        assert fake_tiled.created == []
+
+    def test_creates_container_and_writes_array_on_first_version(self, fake_tiled):
+        annotation_thumbnails.upload_thumbnail_to_tiled(
+            "tiled::browse/sample1", 3, "2024-01-01T00:00:00", self._png_bytes(),
+        )
+        assert len(fake_tiled.created) == 1
+        key, metadata = fake_tiled.created[0]
+        assert key == "sample1__v_thumbs"
+        container = fake_tiled[key]
+        assert container.written[0][0] == "v0003"
+        assert container.written[0][2]["version"] == 3
+
+    def test_reuses_existing_container_on_subsequent_versions(self, fake_tiled):
+        annotation_thumbnails.upload_thumbnail_to_tiled(
+            "tiled::browse/sample1", 1, "t1", self._png_bytes(),
+        )
+        annotation_thumbnails.upload_thumbnail_to_tiled(
+            "tiled::browse/sample1", 2, "t2", self._png_bytes(),
+        )
+        assert len(fake_tiled.created) == 1
+        container = fake_tiled["sample1__v_thumbs"]
+        assert [w[0] for w in container.written] == ["v0001", "v0002"]
+
+    def test_missing_tiled_clients_module_is_a_no_op(self, monkeypatch):
+        import builtins
+        real_import = builtins.__import__
+
+        def fake_import(name, *args, **kwargs):
+            if name == "tiled_clients":
+                raise ImportError("no tiled_clients")
+            return real_import(name, *args, **kwargs)
+
+        monkeypatch.setattr(builtins, "__import__", fake_import)
+        # Should not raise.
+        annotation_thumbnails.upload_thumbnail_to_tiled("tiled::browse/s", 1, "t", self._png_bytes())
+
+    def test_tiled_error_is_swallowed(self, monkeypatch):
+        import tiled_clients
+        monkeypatch.setattr(tiled_clients, "api_key_for_uri", lambda uri: None)
+        monkeypatch.setattr(
+            tiled_clients, "get_tiled_client",
+            lambda uri, key: (_ for _ in ()).throw(RuntimeError("down")),
+        )
+        # Should not raise even though the Tiled call blows up.
+        annotation_thumbnails.upload_thumbnail_to_tiled("tiled::browse/s", 1, "t", self._png_bytes())
diff --git a/backend/tests/test_arrays.py b/backend/tests/test_arrays.py
new file mode 100644
index 0000000..59f9fb8
--- /dev/null
+++ b/backend/tests/test_arrays.py
@@ -0,0 +1,516 @@
+"""Tests for arrays.py — the shape-dispatch/slice-reading core shared by
+almost every route. Fake Tiled containers are plain duck-typed classes (same
+minimal-surface pattern as test_browse_helpers.py's FakeNode); leaf arrays
+are either real numpy arrays or a thin FakeArrayNode wrapper exposing
+.shape/.dtype/.metadata/__array__/__getitem__, matching what a real Tiled
+array client node looks like."""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from fastapi import HTTPException
+
+import arrays
+import local_fs
+
+
+class FakeArrayNode:
+    def __init__(self, arr, metadata=None):
+        self._arr = np.asarray(arr)
+        self.shape = self._arr.shape
+        self.dtype = self._arr.dtype
+        self.metadata = metadata or {}
+
+    def __array__(self, dtype=None):
+        return self._arr if dtype is None else self._arr.astype(dtype)
+
+    def __getitem__(self, idx):
+        return self._arr[idx]
+
+
+class FakeContainer:
+    def __init__(self, children=None, metadata=None):
+        self.structure_family = "container"
+        self._children = dict(children or {})
+        self.metadata = metadata or {}
+
+    def __iter__(self):
+        return iter(self._children)
+
+    def __getitem__(self, key):
+        return self._children[key]
+
+    def __len__(self):
+        return len(self._children)
+
+
+@pytest.fixture(autouse=True)
+def clear_node_cache():
+    arrays._node_cache.clear()
+    yield
+    arrays._node_cache.clear()
+
+
+# ---------------------------------------------------------------------------
+# array_shape_meta — direct array shape dispatch
+# ---------------------------------------------------------------------------
+
+class TestArrayShapeMetaDirect:
+    def test_2d_array_is_hw(self):
+        meta = arrays.array_shape_meta(np.zeros((10, 20), dtype=np.uint8))
+        assert meta == {
+            "n_slices": 1, "height": 10, "width": 20,
+            "dtype": "uint8", "is_rgb": False, "shape_kind": "HW",
+        }
+
+    def test_3d_rgb_array_is_hwc(self):
+        meta = arrays.array_shape_meta(np.zeros((10, 20, 3), dtype=np.uint8))
+        assert meta["shape_kind"] == "HWC"
+        assert meta["is_rgb"] is True
+        assert meta["n_slices"] == 1
+
+    def test_3d_rgba_array_is_hwc(self):
+        meta = arrays.array_shape_meta(np.zeros((10, 20, 4), dtype=np.uint8))
+        assert meta["shape_kind"] == "HWC"
+
+    def test_3d_stack_is_nhw(self):
+        meta = arrays.array_shape_meta(np.zeros((5, 10, 20), dtype=np.float32))
+        assert meta["shape_kind"] == "NHW"
+        assert meta["n_slices"] == 5
+        assert meta["height"] == 10
+        assert meta["width"] == 20
+
+    def test_4d_rgb_stack_is_nhwc(self):
+        meta = arrays.array_shape_meta(np.zeros((5, 10, 20, 3), dtype=np.uint8))
+        assert meta["shape_kind"] == "NHWC"
+        assert meta["n_slices"] == 5
+        assert meta["is_rgb"] is True
+
+    def test_1d_array_raises(self):
+        with pytest.raises(HTTPException) as exc:
+            arrays.array_shape_meta(np.zeros(10))
+        assert exc.value.status_code == 422
+
+    def test_5d_array_raises(self):
+        with pytest.raises(HTTPException) as exc:
+            arrays.array_shape_meta(np.zeros((1, 2, 3, 4, 5)))
+        assert exc.value.status_code == 422
+
+    def test_array_like_without_shape_attr_goes_through_asarray(self):
+        class ArrayLikeNoShape:
+            def __array__(self, dtype=None):
+                return np.zeros((4, 4), dtype=np.uint8)
+
+        meta = arrays.array_shape_meta(ArrayLikeNoShape())
+        assert meta["shape_kind"] == "HW"
+
+    def test_pyramid_kwarg_reports_finest_geometry(self):
+        pyramid = {
+            "full_shape": [100, 512, 512], "z_downsample": 4.0,
+            "level_key": "scale2", "level_index": 2, "level_count": 3,
+        }
+        meta = arrays.array_shape_meta(np.zeros((25, 128, 128)), pyramid=pyramid)
+        assert meta["n_slices"] == 100
+        assert meta["height"] == 512
+        assert meta["width"] == 512
+        assert meta["z_downsample"] == 4.0
+        assert meta["level_height"] == 128
+        assert meta["level_n_slices"] == 25
+
+
+# ---------------------------------------------------------------------------
+# array_shape_meta — container/stack dispatch
+# ---------------------------------------------------------------------------
+
+class TestArrayShapeMetaStack:
+    def test_container_of_2d_arrays_is_stack(self):
+        node = FakeContainer({"b": np.zeros((4, 4)), "a": np.ones((4, 4))})
+        meta = arrays.array_shape_meta(node)
+        assert meta["shape_kind"] == "STACK"
+        assert meta["n_slices"] == 2
+        assert meta["keys"] == ["a", "b"]  # sorted lexically
+        assert meta["is_rgb"] is False
+
+    def test_container_of_rgb_arrays_is_rgb_stack(self):
+        node = FakeContainer({"a": np.zeros((4, 4, 3))})
+        meta = arrays.array_shape_meta(node)
+        assert meta["is_rgb"] is True
+
+    def test_empty_container_raises(self):
+        with pytest.raises(HTTPException) as exc:
+            arrays.array_shape_meta(FakeContainer({}))
+        assert exc.value.status_code == 422
+
+    def test_container_with_unsupported_child_shape_raises(self):
+        node = FakeContainer({"a": np.zeros((4, 4, 5))})
+        with pytest.raises(HTTPException) as exc:
+            arrays.array_shape_meta(node)
+        assert exc.value.status_code == 422
+
+
+# ---------------------------------------------------------------------------
+# read_slice
+# ---------------------------------------------------------------------------
+
+class TestReadSliceDirect:
+    def test_hw_ignores_index(self):
+        arr = np.arange(9).reshape(3, 3)
+        meta = {"shape_kind": "HW"}
+        out = arrays.read_slice(arr, meta, 5)
+        assert np.array_equal(out, arr)
+
+    def test_hwc_ignores_index(self):
+        arr = np.zeros((3, 3, 3))
+        out = arrays.read_slice(arr, {"shape_kind": "HWC"}, 2)
+        assert out.shape == (3, 3, 3)
+
+    def test_nhw_indexes_by_slice(self):
+        node = FakeArrayNode(np.arange(2 * 4 * 4).reshape(2, 4, 4))
+        out = arrays.read_slice(node, {"shape_kind": "NHW"}, 1)
+        assert np.array_equal(out, node._arr[1])
+
+    def test_nhw_maps_full_res_index_through_z_downsample(self):
+        node = FakeArrayNode(np.arange(3 * 2 * 2).reshape(3, 2, 2))
+        meta = {"shape_kind": "NHW", "z_downsample": 3.0, "level_n_slices": 3}
+        # full-res idx 6 -> round(6/3)=2, clamped to level_n_slices-1=2
+        out = arrays.read_slice(node, meta, 6)
+        assert np.array_equal(out, node._arr[2])
+
+    def test_nhw_z_downsample_of_one_is_a_no_op(self):
+        node = FakeArrayNode(np.arange(3 * 2 * 2).reshape(3, 2, 2))
+        meta = {"shape_kind": "NHW", "z_downsample": 1.0}
+        out = arrays.read_slice(node, meta, 1)
+        assert np.array_equal(out, node._arr[1])
+
+    def test_unrecognized_shape_kind_raises(self):
+        with pytest.raises(HTTPException) as exc:
+            arrays.read_slice(np.zeros((2, 2)), {"shape_kind": "WEIRD"}, 0)
+        assert exc.value.status_code == 422
+
+
+class TestReadSliceStack:
+    def test_reads_by_sorted_key_order(self):
+        node = FakeContainer({"b": np.full((2, 2), 2.0), "a": np.full((2, 2), 1.0)})
+        meta = {"shape_kind": "STACK", "keys": ["a", "b"]}
+        out = arrays.read_slice(node, meta, 1)
+        assert np.all(out == 2.0)
+
+    def test_out_of_range_index_falls_back_to_first_key(self):
+        node = FakeContainer({"a": np.full((2, 2), 1.0), "b": np.full((2, 2), 2.0)})
+        meta = {"shape_kind": "STACK", "keys": ["a", "b"]}
+        out = arrays.read_slice(node, meta, 99)
+        assert np.all(out == 1.0)
+
+    def test_missing_keys_in_meta_recomputes_from_node(self):
+        node = FakeContainer({"a": np.full((2, 2), 1.0)})
+        meta = {"shape_kind": "STACK"}
+        out = arrays.read_slice(node, meta, 0)
+        assert np.all(out == 1.0)
+
+    def test_empty_stack_raises(self):
+        node = FakeContainer({})
+        with pytest.raises(HTTPException) as exc:
+            arrays.read_slice(node, {"shape_kind": "STACK", "keys": []}, 0)
+        assert exc.value.status_code == 422
+
+
+# ---------------------------------------------------------------------------
+# _is_container_node / multiscale_levels / _level_array / _descend_to_stack
+# ---------------------------------------------------------------------------
+
+class TestIsContainerNode:
+    def test_plain_array_is_not_a_container(self):
+        assert arrays._is_container_node(np.zeros((2, 2))) is False
+
+    def test_fake_container_is_a_container(self):
+        assert arrays._is_container_node(FakeContainer({})) is True
+
+    def test_enum_like_structure_family_value_is_read(self):
+        class FakeEnum:
+            value = "container"
+
+        class Node:
+            structure_family = FakeEnum()
+
+        assert arrays._is_container_node(Node()) is True
+
+
+class TestMultiscaleLevels:
+    def test_non_container_returns_none(self):
+        assert arrays.multiscale_levels(np.zeros((2, 2))) is None
+
+    def test_container_without_scale_children_returns_none(self):
+        assert arrays.multiscale_levels(FakeContainer({"foo": 1, "bar": 2})) is None
+
+    def test_single_scale_child_returns_none(self):
+        assert arrays.multiscale_levels(FakeContainer({"scale0": 1})) is None
+
+    def test_multiple_scale_children_sorted_numerically(self):
+        node = FakeContainer({"scale10": 1, "scale2": 2, "scale0": 3})
+        assert arrays.multiscale_levels(node) == ["scale0", "scale2", "scale10"]
+
+    def test_non_enumerable_container_returns_none(self):
+        class BrokenContainer:
+            structure_family = "container"
+
+            def __iter__(self):
+                raise RuntimeError("boom")
+
+        assert arrays.multiscale_levels(BrokenContainer()) is None
+
+
+class TestLevelArray:
+    def test_level_wrapping_a_single_array_child(self):
+        arr = np.zeros((2, 2))
+        level = FakeContainer({"image": arr})
+        node = FakeContainer({"scale0": level})
+        assert arrays._level_array(node, "scale0") is arr
+
+    def test_level_that_is_already_an_array(self):
+        arr = np.zeros((2, 2))
+        node = FakeContainer({"scale0": arr})
+        assert arrays._level_array(node, "scale0") is arr
+
+    def test_missing_level_key_returns_none(self):
+        node = FakeContainer({})
+        assert arrays._level_array(node, "scale0") is None
+
+    def test_empty_level_container_returns_none(self):
+        node = FakeContainer({"scale0": FakeContainer({})})
+        assert arrays._level_array(node, "scale0") is None
+
+
+class TestDescendToStack:
+    def test_non_container_returned_unchanged(self):
+        arr = np.zeros((2, 2))
+        assert arrays._descend_to_stack(arr) is arr
+
+    def test_descends_through_wrapper_container_to_array_stack(self):
+        stack = FakeContainer({"slice_0": np.zeros((2, 2))})
+        wrapper = FakeContainer({"dataset": stack})
+        assert arrays._descend_to_stack(wrapper) is stack
+
+    def test_stops_at_container_of_arrays(self):
+        stack = FakeContainer({"slice_0": np.zeros((2, 2)), "slice_1": np.zeros((2, 2))})
+        assert arrays._descend_to_stack(stack) is stack
+
+    def test_empty_container_returned_as_is(self):
+        empty = FakeContainer({})
+        assert arrays._descend_to_stack(empty) is empty
+
+    def test_max_depth_stops_infinite_wrapper_chain(self):
+        node = FakeContainer({})
+        for _ in range(20):
+            node = FakeContainer({"only": node})
+        result = arrays._descend_to_stack(node, max_depth=3)
+        # Should not infinite-loop or raise; just stop after max_depth hops.
+        assert arrays._is_container_node(result)
+
+    def test_multiscale_volume_resolves_to_finest_level_array(self):
+        finest = np.zeros((3, 3))
+        level0 = FakeContainer({"image": finest})
+        level1 = FakeContainer({"image": np.zeros((2, 2))})
+        node = FakeContainer({"scale0": level0, "scale1": level1})
+        assert arrays._descend_to_stack(node) is finest
+
+
+# ---------------------------------------------------------------------------
+# node_keywords
+# ---------------------------------------------------------------------------
+
+class TestNodeKeywords:
+    def test_no_metadata_returns_empty(self):
+        assert arrays.node_keywords(np.zeros((2, 2))) == []
+
+    def test_list_keywords_on_node_itself(self):
+        node = FakeArrayNode(np.zeros((2, 2)), metadata={"keywords": ["a", "b"]})
+        assert arrays.node_keywords(node) == ["a", "b"]
+
+    def test_string_keyword_is_wrapped_in_a_list(self):
+        node = FakeArrayNode(np.zeros((2, 2)), metadata={"keywords": "solo"})
+        assert arrays.node_keywords(node) == ["solo"]
+
+    def test_blank_string_values_filtered_out(self):
+        node = FakeArrayNode(np.zeros((2, 2)), metadata={"keywords": ["a", "  ", ""]})
+        assert arrays.node_keywords(node) == ["a"]
+
+    def test_falls_back_to_first_child_of_container(self):
+        child = FakeArrayNode(np.zeros((2, 2)), metadata={"keywords": ["child-tag"]})
+        container = FakeContainer({"a": child})
+        assert arrays.node_keywords(container) == ["child-tag"]
+
+    def test_container_metadata_wins_over_child(self):
+        child = FakeArrayNode(np.zeros((2, 2)), metadata={"keywords": ["child-tag"]})
+        container = FakeContainer({"a": child}, metadata={"keywords": ["container-tag"]})
+        assert arrays.node_keywords(container) == ["container-tag"]
+
+    def test_container_with_no_tags_anywhere_returns_empty(self):
+        container = FakeContainer({"a": FakeArrayNode(np.zeros((2, 2)))})
+        assert arrays.node_keywords(container) == []
+
+    def test_error_reading_child_metadata_is_swallowed(self):
+        class BrokenContainer(FakeContainer):
+            def __getitem__(self, key):
+                raise RuntimeError("boom")
+
+        container = BrokenContainer({"a": 1})
+        assert arrays.node_keywords(container) == []
+
+
+# ---------------------------------------------------------------------------
+# resolve_array / resolve_container — kind dispatch + caching
+# ---------------------------------------------------------------------------
+
+class TestResolveArrayLocal:
+    def test_local_kind_delegates_to_local_fs(self, monkeypatch: pytest.MonkeyPatch, tmp_path):
+        arr = np.zeros((3, 3))
+        monkeypatch.setattr(local_fs, "open_array", lambda source, root: arr)
+        result = arrays.resolve_array("foo.npy", "local", root=str(tmp_path))
+        assert result is arr
+
+    def test_unknown_kind_raises_422(self):
+        with pytest.raises(HTTPException) as exc:
+            arrays.resolve_array("x", "weird")
+        assert exc.value.status_code == 422
+
+
+class TestResolveArrayTiled:
+    def test_resolves_nested_path_and_descends(self, monkeypatch: pytest.MonkeyPatch):
+        leaf = np.zeros((2, 2))
+        stack = FakeContainer({"slice_0": leaf})
+        root = FakeContainer({"browse": FakeContainer({"sample1": stack})})
+        monkeypatch.setattr("arrays.get_tiled_client", lambda uri, key: root)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+        result = arrays.resolve_array("browse/sample1", "tiled")
+        assert result is stack
+
+    def test_missing_path_raises_404(self, monkeypatch: pytest.MonkeyPatch):
+        root = FakeContainer({})
+        monkeypatch.setattr("arrays.get_tiled_client", lambda uri, key: root)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+        with pytest.raises(HTTPException) as exc:
+            arrays.resolve_array("does/not/exist", "tiled")
+        assert exc.value.status_code == 404
+
+    def test_result_is_cached_by_key(self, monkeypatch: pytest.MonkeyPatch):
+        calls = []
+        leaf = np.zeros((2, 2))
+        root = FakeContainer({"a": leaf})
+
+        def fake_get_client(uri, key):
+            calls.append(uri)
+            return root
+
+        monkeypatch.setattr("arrays.get_tiled_client", fake_get_client)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+        arrays.resolve_array("a", "tiled", server_uri="http://x")
+        arrays.resolve_array("a", "tiled", server_uri="http://x")
+        assert len(calls) == 1
+
+    def test_different_server_uri_is_a_different_cache_key(self, monkeypatch: pytest.MonkeyPatch):
+        calls = []
+        leaf = np.zeros((2, 2))
+        root = FakeContainer({"a": leaf})
+
+        def fake_get_client(uri, key):
+            calls.append(uri)
+            return root
+
+        monkeypatch.setattr("arrays.get_tiled_client", fake_get_client)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+        arrays.resolve_array("a", "tiled", server_uri="http://x")
+        arrays.resolve_array("a", "tiled", server_uri="http://y")
+        assert len(calls) == 2
+
+
+class TestResolveContainer:
+    def test_non_tiled_kind_raises(self):
+        with pytest.raises(HTTPException) as exc:
+            arrays.resolve_container("x", "local")
+        assert exc.value.status_code == 422
+
+    def test_resolves_without_descending(self, monkeypatch: pytest.MonkeyPatch):
+        stack = FakeContainer({"slice_0": np.zeros((2, 2))})
+        root = FakeContainer({"browse": FakeContainer({"sample1": stack})})
+        monkeypatch.setattr("arrays.get_tiled_client", lambda uri, key: root)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+        result = arrays.resolve_container("browse/sample1", "tiled")
+        assert result is stack  # NOT descended into slice_0
+
+    def test_missing_path_raises_404(self, monkeypatch: pytest.MonkeyPatch):
+        root = FakeContainer({})
+        monkeypatch.setattr("arrays.get_tiled_client", lambda uri, key: root)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+        with pytest.raises(HTTPException) as exc:
+            arrays.resolve_container("nope", "tiled")
+        assert exc.value.status_code == 404
+
+    def test_blank_path_segments_are_skipped(self, monkeypatch: pytest.MonkeyPatch):
+        leaf = FakeContainer({})
+        root = FakeContainer({"a": leaf})
+        monkeypatch.setattr("arrays.get_tiled_client", lambda uri, key: root)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+        result = arrays.resolve_container("/a//", "tiled")
+        assert result is leaf
+
+
+# ---------------------------------------------------------------------------
+# pyramid_info
+# ---------------------------------------------------------------------------
+
+class TestPyramidInfo:
+    def test_non_tiled_kind_returns_none(self):
+        assert arrays.pyramid_info("x", "local") is None
+
+    def test_path_not_addressing_a_scale_level_returns_none(self, monkeypatch: pytest.MonkeyPatch):
+        assert arrays.pyramid_info("browse/sample1", "tiled") is None
+
+    def test_path_addressing_a_scale_level_directly(self, monkeypatch: pytest.MonkeyPatch):
+        finest = np.zeros((100, 512, 512))
+        coarse = np.zeros((25, 128, 128))
+        volume = FakeContainer({"scale0": finest, "scale1": coarse})
+        root = FakeContainer({"browse": FakeContainer({"sample1": volume})})
+        monkeypatch.setattr("arrays.get_tiled_client", lambda uri, key: root)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+
+        info = arrays.pyramid_info("browse/sample1/scale1", "tiled")
+        assert info is not None
+        assert info["level_key"] == "scale1"
+        assert info["level_index"] == 1
+        assert info["level_count"] == 2
+        assert info["full_shape"] == [100, 512, 512]
+        assert info["z_downsample"] == 4.0
+
+    def test_path_addressing_a_scale_levels_image_child(self, monkeypatch: pytest.MonkeyPatch):
+        finest_arr = np.zeros((10, 20, 20))
+        finest_level = FakeContainer({"image": finest_arr})
+        coarse_arr = np.zeros((5, 10, 10))
+        coarse_level = FakeContainer({"image": coarse_arr})
+        volume = FakeContainer({"scale0": finest_level, "scale1": coarse_level})
+        root = FakeContainer({"browse": FakeContainer({"sample1": volume})})
+        monkeypatch.setattr("arrays.get_tiled_client", lambda uri, key: root)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+
+        info = arrays.pyramid_info("browse/sample1/scale1/image", "tiled")
+        assert info is not None
+        assert info["level_key"] == "scale1"
+
+    def test_nonexistent_parent_path_returns_none(self, monkeypatch: pytest.MonkeyPatch):
+        root = FakeContainer({})
+        monkeypatch.setattr("arrays.get_tiled_client", lambda uri, key: root)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+        assert arrays.pyramid_info("nope/scale0", "tiled") is None
+
+    def test_parent_without_multiscale_levels_returns_none(self, monkeypatch: pytest.MonkeyPatch):
+        volume = FakeContainer({"scale0": np.zeros((2, 2, 2))})  # only one level
+        root = FakeContainer({"sample1": volume})
+        monkeypatch.setattr("arrays.get_tiled_client", lambda uri, key: root)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+        assert arrays.pyramid_info("sample1/scale0", "tiled") is None
+
+    def test_non_3d_level_shape_returns_none(self, monkeypatch: pytest.MonkeyPatch):
+        volume = FakeContainer({"scale0": np.zeros((10, 10)), "scale1": np.zeros((5, 5))})
+        root = FakeContainer({"sample1": volume})
+        monkeypatch.setattr("arrays.get_tiled_client", lambda uri, key: root)
+        monkeypatch.setattr("arrays.api_key_for_uri", lambda uri: None)
+        assert arrays.pyramid_info("sample1/scale1", "tiled") is None
diff --git a/backend/tests/test_batch_probe.py b/backend/tests/test_batch_probe.py
new file mode 100644
index 0000000..8011c82
--- /dev/null
+++ b/backend/tests/test_batch_probe.py
@@ -0,0 +1,271 @@
+"""Tests for batch_probe.py — pure helpers directly, plus real (no mocks)
+CPU forward+backward+optimizer-step round trips via train_common.build_family
+(same tiny-model pattern as test_train_e2e_real_ml.py), and the job-level
+guard/OOM/cancellation paths via a monkeypatched _try_batch."""
+from __future__ import annotations
+
+import concurrent.futures
+
+import pytest
+
+torch = pytest.importorskip("torch")
+
+import batch_probe  # noqa: E402
+import export_jobs  # noqa: E402
+import train_common  # noqa: E402
+from schemas import BatchProbeRequest, DlsiaTunetConfig  # noqa: E402
+
+
+def _request(image_size=64, batch_size=2, depth=2, base_channels=4):
+    return BatchProbeRequest(
+        model=DlsiaTunetConfig(
+            hyperparams={
+                "depth": depth, "base_channels": base_channels,
+                "image_size": image_size, "batch_size": batch_size,
+            },
+        ),
+        n_classes=2,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Pure helpers
+# ---------------------------------------------------------------------------
+
+class TestSchemaBatchCap:
+    def test_returns_the_schema_upper_bound_when_lower_than_default(self):
+        hp = _request().model.hyperparams
+        cap = batch_probe.schema_batch_cap(hp, default=1000)
+        assert cap <= 1000
+
+    def test_default_used_when_no_le_constraint_found(self):
+        class NoConstraints:
+            model_fields = {}
+
+        assert batch_probe.schema_batch_cap(NoConstraints(), default=42) == 42
+
+    def test_broken_metadata_falls_back_to_default(self):
+        class Broken:
+            pass
+
+        assert batch_probe.schema_batch_cap(Broken(), default=7) == 7
+
+
+class TestAttemptCount:
+    def test_cap_one_is_one_attempt(self):
+        assert batch_probe._attempt_count(1) == 1
+
+    def test_higher_caps_scale_with_bit_length(self):
+        assert batch_probe._attempt_count(64) == 64 .bit_length()
+
+    def test_never_below_one(self):
+        assert batch_probe._attempt_count(0) >= 1
+
+
+class TestSuggest:
+    def test_applies_safety_factor(self):
+        assert batch_probe._suggest(10, cap=64) == int(10 * batch_probe._SAFETY_FACTOR)
+
+    def test_never_below_one(self):
+        assert batch_probe._suggest(1, cap=64) >= 1
+
+    def test_never_exceeds_cap(self):
+        assert batch_probe._suggest(1000, cap=8) <= 8
+
+
+class TestIsOom:
+    @pytest.mark.parametrize("message", [
+        "CUDA out of memory. Tried to allocate...",
+        "MPS backend out of memory",
+        "Insufficient Memory!",
+        "can't allocate memory",
+        "Cannot allocate 4GB",
+    ])
+    def test_recognizes_oom_messages_case_insensitively(self, message):
+        assert batch_probe._is_oom(RuntimeError(message)) is True
+
+    def test_unrelated_error_is_not_oom(self):
+        assert batch_probe._is_oom(RuntimeError("shape mismatch")) is False
+
+
+class TestDeviceMemoryGib:
+    def test_cpu_reports_nothing(self):
+        assert batch_probe._device_memory_gib("cpu") is None
+
+    def test_unknown_device_reports_nothing(self):
+        assert batch_probe._device_memory_gib("weird") is None
+
+
+class TestRelease:
+    def test_cpu_does_not_raise(self):
+        batch_probe._release("cpu")  # gc.collect() only; no torch cache to clear
+
+
+# ---------------------------------------------------------------------------
+# _try_batch — real forward + backward + optimizer step on CPU
+# ---------------------------------------------------------------------------
+
+class TestTryBatchReal:
+    def test_completes_a_real_step_without_raising(self):
+        req = _request()
+        built = train_common.build_family(req.model, req.n_classes, "cpu", lambda msg: None)
+        batch_probe._try_batch(
+            2, image_size=16, forward_fn=built.forward_fn, trainable=built.trainable_params, device="cpu",
+        )
+
+    def test_updates_model_parameters(self):
+        req = _request()
+        built = train_common.build_family(req.model, req.n_classes, "cpu", lambda msg: None)
+        before = [p.clone() for p in built.trainable_params]
+        batch_probe._try_batch(
+            2, image_size=16, forward_fn=built.forward_fn, trainable=built.trainable_params, device="cpu",
+        )
+        after = built.trainable_params
+        assert any(not torch.equal(b, a) for b, a in zip(before, after))
+
+
+# ---------------------------------------------------------------------------
+# _run_attempt_cancellable
+# ---------------------------------------------------------------------------
+
+class TestRunAttemptCancellable:
+    def test_returns_ok_on_success(self):
+        # A real trainable module, not an identity lambda: `_try_batch` calls
+        # `loss.backward()`, which needs a real grad_fn in the graph.
+        conv = torch.nn.Conv2d(3, 3, kernel_size=1)
+        jid = export_jobs.new_job("x")
+        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+            outcome = batch_probe._run_attempt_cancellable(
+                executor, jid, 1, image_size=8, forward_fn=conv, trainable=list(conv.parameters()), device="cpu",
+            )
+        assert outcome == "ok"
+
+    def test_returns_oom_when_try_batch_raises_oom_message(self, monkeypatch):
+        def boom(*a, **k):
+            raise RuntimeError("CUDA out of memory")
+
+        monkeypatch.setattr(batch_probe, "_try_batch", boom)
+        jid = export_jobs.new_job("x")
+        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+            outcome = batch_probe._run_attempt_cancellable(
+                executor, jid, 1, image_size=8, forward_fn=lambda x: x, trainable=[], device="cpu",
+            )
+        assert outcome == "oom"
+
+    def test_reraises_non_oom_exceptions(self, monkeypatch):
+        def boom(*a, **k):
+            raise RuntimeError("something else broke")
+
+        monkeypatch.setattr(batch_probe, "_try_batch", boom)
+        jid = export_jobs.new_job("x")
+        with pytest.raises(RuntimeError, match="something else broke"):
+            with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+                batch_probe._run_attempt_cancellable(
+                    executor, jid, 1, image_size=8, forward_fn=lambda x: x, trainable=[], device="cpu",
+                )
+
+    def test_polls_for_cancellation_while_attempt_runs(self, monkeypatch):
+        import time
+
+        def slow(*a, **k):
+            time.sleep(1.5)
+
+        monkeypatch.setattr(batch_probe, "_try_batch", slow)
+        monkeypatch.setattr(batch_probe, "_ATTEMPT_POLL_SECONDS", 0.1)
+        jid = export_jobs.new_job("x")
+        export_jobs.request_cancel(jid)
+        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+            outcome = batch_probe._run_attempt_cancellable(
+                executor, jid, 1, image_size=8, forward_fn=lambda x: x, trainable=[], device="cpu",
+            )
+        assert outcome == "cancelled"
+
+
+# ---------------------------------------------------------------------------
+# run_probe_job — guard paths and outcome-shaping logic
+# ---------------------------------------------------------------------------
+
+class TestRunProbeJobGuards:
+    def test_busy_ml_lock_reports_error_not_a_crash(self):
+        train_common.ML_LOCK.acquire()
+        try:
+            jid = export_jobs.new_job("x")
+            batch_probe.run_probe_job(jid, _request())
+            job = export_jobs.get_job(jid)
+            assert job["state"] == "error"
+            assert "already running" in job["error"]
+        finally:
+            train_common.ML_LOCK.release()
+
+    def test_no_device_reports_error(self, monkeypatch):
+        monkeypatch.setattr(train_common, "pick_device", lambda: None)
+        jid = export_jobs.new_job("x")
+        batch_probe.run_probe_job(jid, _request())
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "torch is not installed" in job["error"]
+        assert train_common.ML_LOCK.locked() is False  # released even on early failure
+
+
+class TestRunProbeJobOutcomes:
+    def test_real_probe_succeeds_and_releases_lock(self):
+        jid = export_jobs.new_job("x")
+        batch_probe.run_probe_job(jid, _request())
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"]["suggested_batch_size"] >= 1
+        assert job["result"]["largest_ok"] >= 1
+        assert job["result"]["cancelled"] is False
+        assert train_common.ML_LOCK.locked() is False
+
+    def test_oom_on_first_attempt_reports_no_suggestion(self, monkeypatch):
+        def always_oom(*a, **k):
+            raise RuntimeError("out of memory")
+
+        monkeypatch.setattr(batch_probe, "_try_batch", always_oom)
+        jid = export_jobs.new_job("x")
+        batch_probe.run_probe_job(jid, _request())
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "Even batch size 1" in job["error"]
+
+    def test_oom_after_some_success_suggests_a_smaller_batch(self, monkeypatch):
+        calls = []
+
+        def sometimes_oom(batch_size, **k):
+            calls.append(batch_size)
+            if batch_size > 2:
+                raise RuntimeError("out of memory")
+
+        monkeypatch.setattr(batch_probe, "_try_batch", sometimes_oom)
+        jid = export_jobs.new_job("x")
+        batch_probe.run_probe_job(jid, _request(batch_size=32))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"]["largest_ok"] == 2
+        assert job["result"]["first_failure"] == 4
+        assert job["result"]["suggested_batch_size"] <= 2
+
+    def test_cancelled_before_any_success_reports_done_not_error(self, monkeypatch):
+        monkeypatch.setattr(export_jobs, "cancel_requested", lambda jid: True)
+        jid = export_jobs.new_job("x")
+        batch_probe.run_probe_job(jid, _request())
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"]["cancelled"] is True
+        assert job["result"]["suggested_batch_size"] is None
+
+    def test_cancelled_after_a_success_suggests_from_partial_progress(self, monkeypatch):
+        calls = {"n": 0}
+
+        def cancel_after_first(jid):
+            calls["n"] += 1
+            return calls["n"] > 1
+
+        monkeypatch.setattr(export_jobs, "cancel_requested", cancel_after_first)
+        jid = export_jobs.new_job("x")
+        batch_probe.run_probe_job(jid, _request(batch_size=32))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"]["cancelled"] is True
+        assert job["result"]["largest_ok"] >= 1
diff --git a/backend/tests/test_browse_helpers.py b/backend/tests/test_browse_helpers.py
new file mode 100644
index 0000000..1fa40c1
--- /dev/null
+++ b/backend/tests/test_browse_helpers.py
@@ -0,0 +1,370 @@
+"""Tests for browse_helpers.py using lightweight fake Tiled nodes — duck-typed
+to the subset of the Tiled client API this module actually uses
+(__iter__/__getitem__/.metadata/.structure_family/.search()/.distinct()), so
+no real Tiled server is needed. search() is driven by REAL tiled.queries.Eq/
+Contains objects (what Key(...) == val / Contains(...) actually produce),
+not a further mock of the query layer itself.
+"""
+from __future__ import annotations
+
+from collections import Counter
+
+import pytest
+
+import browse_helpers as bh
+
+tiled_queries = pytest.importorskip("tiled.queries")
+
+
+class FakeNode:
+    def __init__(self, children=None, metadata=None, is_container=True, search_raises=False):
+        self._children = children or {}
+        self.metadata = metadata or {}
+        self.structure_family = "container" if is_container else "array"
+        self._search_raises = search_raises
+
+    def __iter__(self):
+        return iter(self._children)
+
+    def __getitem__(self, key):
+        return self._children[key]
+
+    def __len__(self):
+        return len(self._children)
+
+    def search(self, query):
+        if self._search_raises:
+            raise RuntimeError("search exploded")
+        matched = {}
+        for k, child in self._children.items():
+            meta = child.metadata or {}
+            if isinstance(query, tiled_queries.Contains):
+                val = meta.get(query.key)
+                if isinstance(val, (list, tuple)) and query.value in val:
+                    matched[k] = child
+            else:  # Eq
+                if meta.get(query.key) == query.value:
+                    matched[k] = child
+        return FakeNode(children=matched, metadata=self.metadata)
+
+    def distinct(self, key, counts=True):
+        counter: Counter = Counter()
+        for child in self._children.values():
+            val = (child.metadata or {}).get(key)
+            if val is None:
+                continue
+            counter[val] += 1
+        return {"metadata": {key: [{"value": v, "count": n} for v, n in counter.items()]}}
+
+
+# ---------------------------------------------------------------------------
+# Field discovery
+# ---------------------------------------------------------------------------
+
+class TestDisplayName:
+    def test_strips_thinfilm_prefix(self):
+        assert bh._display_name("thinfilm_AnnealingTemp") == "Temp"
+
+    def test_thinfilm_prefix_without_alias_returns_stripped(self):
+        assert bh._display_name("thinfilm_Custom") == "Custom"
+
+    def test_studio_key_alias(self):
+        assert bh._display_name("studio_annotated") == "Annotated"
+
+    def test_unknown_key_passthrough(self):
+        assert bh._display_name("foo") == "foo"
+
+
+class TestIsRichEnough:
+    def test_thinfilm_key_alone_is_enough(self):
+        assert bh._is_rich_enough(["thinfilm_x"]) is True
+
+    def test_important_keys_with_min_count(self):
+        keys = ["PI", "sample_name", "a", "b"]
+        assert bh._is_rich_enough(keys) is True
+
+    def test_important_key_without_min_count_not_enough(self):
+        assert bh._is_rich_enough(["PI"]) is False
+
+    def test_plain_key_richness_threshold(self):
+        assert bh._is_rich_enough([f"k{i}" for i in range(10)]) is True
+        assert bh._is_rich_enough([f"k{i}" for i in range(9)]) is False
+
+
+class TestBuildFieldMapping:
+    def test_finds_first_rich_enough_sample(self):
+        sparse = FakeNode(metadata={"a": 1})
+        rich = FakeNode(metadata={f"k{i}": i for i in range(10)})
+        node = FakeNode(children={"s0": sparse, "s1": rich})
+        mapping = bh.build_field_mapping(node)
+        assert "k0" in mapping.raw_to_display
+
+    def test_falls_back_to_last_scanned_if_none_rich_enough(self):
+        node = FakeNode(children={"s0": FakeNode(metadata={"a": 1})})
+        mapping = bh.build_field_mapping(node)
+        assert "a" in mapping.raw_to_display
+
+    def test_skips_samples_that_error_on_open(self):
+        class ExplodingNode:
+            @property
+            def metadata(self):
+                raise RuntimeError("boom")
+
+        node = FakeNode(children={"bad": ExplodingNode(), "good": FakeNode(metadata={f"k{i}": i for i in range(10)})})
+        mapping = bh.build_field_mapping(node)
+        assert "k0" in mapping.raw_to_display
+
+    def test_always_injects_studio_and_ingest_keys(self):
+        node = FakeNode(children={"s0": FakeNode(metadata={})})
+        mapping = bh.build_field_mapping(node)
+        assert "Annotated" in mapping.all_display_keys
+        assert "studio_annotated" in mapping.raw_to_display
+
+    def test_empty_container_does_not_raise(self):
+        mapping = bh.build_field_mapping(FakeNode(children={}))
+        assert "studio_annotated" in mapping.raw_to_display
+
+
+# ---------------------------------------------------------------------------
+# Distinct values
+# ---------------------------------------------------------------------------
+
+class TestScopedMetadataRows:
+    def test_reads_each_childs_metadata(self):
+        node = FakeNode(children={"a": FakeNode(metadata={"x": 1}), "b": FakeNode(metadata={"x": 2})})
+        rows = bh.scoped_metadata_rows(node)
+        assert rows == [{"x": 1}, {"x": 2}]
+
+    def test_respects_limit(self):
+        node = FakeNode(children={str(i): FakeNode(metadata={"x": i}) for i in range(10)})
+        rows = bh.scoped_metadata_rows(node, limit=3)
+        assert len(rows) == 3
+
+    def test_skips_children_that_fail_to_open(self):
+        class ExplodingNode:
+            @property
+            def metadata(self):
+                raise RuntimeError("boom")
+
+        node = FakeNode(children={"bad": ExplodingNode(), "good": FakeNode(metadata={"x": 1})})
+        assert bh.scoped_metadata_rows(node) == [{"x": 1}]
+
+
+class TestDistinctFromRows:
+    def test_tallies_scalar_values(self):
+        rows = [{"k": "a"}, {"k": "a"}, {"k": "b"}]
+        result = bh.distinct_from_rows(rows, "k")
+        by_value = {e["value"]: e["count"] for e in result}
+        assert by_value == {"a": 2, "b": 1}
+
+    def test_explodes_list_valued_metadata(self):
+        rows = [{"k": ["x", "y"]}, {"k": ["x"]}]
+        result = bh.distinct_from_rows(rows, "k")
+        by_value = {e["value"]: e["count"] for e in result}
+        assert by_value == {"x": 2, "y": 1}
+
+    def test_missing_key_ignored(self):
+        assert bh.distinct_from_rows([{"other": 1}], "k") == []
+
+
+class TestTiledDistinctValues:
+    def _node(self):
+        return FakeNode(children={
+            "a": FakeNode(metadata={"studio_annotated": "yes"}),
+            "b": FakeNode(metadata={"studio_annotated": "yes"}),
+            "c": FakeNode(metadata={"studio_annotated": "no"}),
+        })
+
+    def test_global_distinct_uses_node_distinct(self):
+        result = bh.tiled_distinct_values(self._node(), "studio_annotated")
+        by_value = {e["value"]: e["count"] for e in result["values"]}
+        assert by_value == {"yes": 2, "no": 1}
+        assert result["total"] == 2
+
+    def test_scoped_distinct_uses_iteration(self):
+        result = bh.tiled_distinct_values(self._node(), "studio_annotated", scoped=True)
+        by_value = {e["value"]: e["count"] for e in result["values"]}
+        assert by_value == {"yes": 2, "no": 1}
+
+    def test_invalid_values_filtered_out(self):
+        node = FakeNode(children={
+            "a": FakeNode(metadata={"k": "real"}),
+            "b": FakeNode(metadata={"k": "NaN"}),
+        })
+        result = bh.tiled_distinct_values(node, "k")
+        assert result["values"] == [{"value": "real", "count": 1, "sample_paths": []}]
+
+    def test_field_mapping_translates_display_key_to_raw(self):
+        mapping = bh.FieldMapping(
+            display_to_raw={}, raw_to_display={"studio_annotated": "Annotated"}, all_display_keys=[],
+        )
+        result = bh.tiled_distinct_values(self._node(), "studio_annotated", field_mapping=mapping)
+        assert result["field"] == "Annotated"
+
+    def test_filter_narrows_node_before_distinct(self):
+        mapping = bh.FieldMapping(
+            display_to_raw={"Annotated": "studio_annotated"}, raw_to_display={}, all_display_keys=[],
+        )
+        result = bh.tiled_distinct_values(
+            self._node(), "studio_annotated", filters={"Annotated": "yes"}, field_mapping=mapping,
+        )
+        assert result["total"] == 1  # only "yes" remains after filtering to studio_annotated == "yes"
+
+    def test_distinct_call_failure_returns_empty(self):
+        node = FakeNode(children={})
+        node.distinct = lambda *a, **k: (_ for _ in ()).throw(RuntimeError("boom"))
+        result = bh.tiled_distinct_values(node, "k")
+        assert result == {"values": [], "total": 0, "field": "k"}
+
+
+# ---------------------------------------------------------------------------
+# Item search
+# ---------------------------------------------------------------------------
+
+class TestTiledSearchItemsDispatch:
+    def test_no_array_only_filters_uses_container_only_path(self):
+        node = FakeNode(children={"a": FakeNode(metadata={"PI": "smith"}, is_container=False)})
+        result = bh.tiled_search_items(node, filters={"PI": "smith"})
+        assert result["total"] == 1
+        assert result["items"][0]["sample"] == "a"
+
+    def test_only_array_only_filters_uses_array_only_path(self):
+        # bar's metadata is a real int here, matching how Tiled actually stores
+        # this field — _apply_filters coerces the filter string "1" to int 1
+        # via _typed_query_value, so a string-valued fixture would (correctly)
+        # never match and silently fail the test for the wrong reason.
+        node = FakeNode(children={"a": FakeNode(metadata={"bar": 1}, is_container=False)})
+        result = bh.tiled_search_items(node, filters={"bar": "1"})
+        assert result["total"] == 1
+
+    def test_mixed_filters_uses_mixed_path(self):
+        array_child = FakeNode(metadata={"bar": 1}, is_container=False)
+        sample = FakeNode(children={"arr": array_child}, metadata={"PI": "smith"})
+        node = FakeNode(children={"s0": sample})
+        result = bh.tiled_search_items(node, filters={"PI": "smith", "bar": "1"})
+        assert result["total"] == 1
+        assert result["items"][0]["sample"] == "s0"
+
+    def test_no_filters_returns_everything(self):
+        node = FakeNode(children={"a": FakeNode(metadata={}), "b": FakeNode(metadata={})})
+        result = bh.tiled_search_items(node)
+        assert result["total"] == 2
+
+
+class TestSearchContainerOnly:
+    def test_reports_n_slices_for_flat_array_stack(self):
+        stack = FakeNode(children={
+            str(i): FakeNode(metadata={}, is_container=False) for i in range(5)
+        }, metadata={})
+        node = FakeNode(children={"sample1": stack})
+        result = bh._search_container_only(node, [], "", 500)
+        assert result["items"][0]["n_slices"] == 5
+
+    def test_borrows_first_childs_metadata_when_container_has_none(self):
+        stack = FakeNode(children={"0": FakeNode(metadata={"x": 1}, is_container=False)}, metadata={})
+        node = FakeNode(children={"sample1": stack})
+        result = bh._search_container_only(node, [], "", 500)
+        assert result["items"][0]["metadata"] == {"x": 1}
+
+    def test_leaf_array_reports_n_slices_one(self):
+        node = FakeNode(children={"a": FakeNode(metadata={}, is_container=False)})
+        result = bh._search_container_only(node, [], "", 500)
+        assert result["items"][0]["n_slices"] == 1
+
+    def test_path_prefix_applied(self):
+        node = FakeNode(children={"a": FakeNode(metadata={}, is_container=False)})
+        result = bh._search_container_only(node, [], "parent", 500)
+        assert result["items"][0]["path"] == "parent/a"
+
+    def test_children_that_fail_to_open_are_skipped(self):
+        class Exploding:
+            metadata = property(lambda self: (_ for _ in ()).throw(RuntimeError()))
+
+        node = FakeNode(children={"bad": Exploding(), "good": FakeNode(metadata={}, is_container=False)})
+        result = bh._search_container_only(node, [], "", 500)
+        assert result["total"] == 1
+
+
+# ---------------------------------------------------------------------------
+# Small helpers
+# ---------------------------------------------------------------------------
+
+class TestRawFilters:
+    def test_translates_display_to_raw_and_stringifies(self):
+        out = bh._raw_filters({"Temp": 5}, {"Temp": "thinfilm_AnnealingTemp"})
+        assert out == [("thinfilm_AnnealingTemp", "5")]
+
+    def test_none_values_dropped(self):
+        assert bh._raw_filters({"Temp": None}, {}) == []
+
+    def test_unmapped_display_key_passthrough(self):
+        assert bh._raw_filters({"raw_key": "v"}, {}) == [("raw_key", "v")]
+
+
+class TestMatchesContainerFilters:
+    def test_scalar_case_insensitive_match(self):
+        assert bh._matches_container_filters({"PI": "Smith"}, [("PI", "smith")]) is True
+
+    def test_scalar_mismatch(self):
+        assert bh._matches_container_filters({"PI": "Jones"}, [("PI", "smith")]) is False
+
+    def test_missing_key_fails(self):
+        assert bh._matches_container_filters({}, [("PI", "smith")]) is False
+
+    def test_list_valued_membership_match(self):
+        assert bh._matches_container_filters({"keywords": ["Alpha", "Beta"]}, [("keywords", "alpha")]) is True
+
+    def test_list_valued_membership_mismatch(self):
+        assert bh._matches_container_filters({"keywords": ["Beta"]}, [("keywords", "alpha")]) is False
+
+
+class TestTypedQueryValue:
+    def test_int_roundtrip(self):
+        assert bh._typed_query_value("5") == 5
+
+    def test_float_roundtrip(self):
+        assert bh._typed_query_value("5.5") == 5.5
+
+    def test_non_numeric_passthrough(self):
+        assert bh._typed_query_value("abc") == "abc"
+
+    def test_leading_zero_not_coerced_to_int(self):
+        # "007" != str(int("007")) == "7", so it must stay a string.
+        assert bh._typed_query_value("007") == "007"
+
+
+class TestIsValidValue:
+    @pytest.mark.parametrize("value", [None, "", "None", "NaN", "nan", "   "])
+    def test_invalid_values(self, value):
+        assert bh._is_valid_value(value) is False
+
+    def test_valid_value(self):
+        assert bh._is_valid_value("real") is True
+
+
+class TestJoin:
+    def test_with_prefix(self):
+        assert bh._join("a", "b") == "a/b"
+
+    def test_without_prefix(self):
+        assert bh._join("", "b") == "b"
+
+
+class TestApplyFilters:
+    def test_eq_filter_narrows_node(self):
+        node = FakeNode(children={"a": FakeNode(metadata={"k": "v"}), "b": FakeNode(metadata={"k": "other"})})
+        result = bh._apply_filters(node, [("k", "v")])
+        assert list(result) == ["a"]
+
+    def test_contains_filter_for_list_valued_key(self):
+        node = FakeNode(children={
+            "a": FakeNode(metadata={"keywords": ["x", "y"]}),
+            "b": FakeNode(metadata={"keywords": ["z"]}),
+        })
+        result = bh._apply_filters(node, [("keywords", "x")])
+        assert list(result) == ["a"]
+
+    def test_search_failure_is_swallowed_and_node_unchanged(self):
+        node = FakeNode(children={"a": FakeNode(metadata={})}, search_raises=True)
+        result = bh._apply_filters(node, [("k", "v")])
+        assert result is node
diff --git a/backend/tests/test_denoise_bake.py b/backend/tests/test_denoise_bake.py
new file mode 100644
index 0000000..c86b245
--- /dev/null
+++ b/backend/tests/test_denoise_bake.py
@@ -0,0 +1,337 @@
+"""Tests for denoise_bake.py — real job-registry (export_jobs), fake array
+source (same monkeypatch pattern as test_infer_jobs.py/test_denoise_train.py)
+and a lightweight duck-typed fake Tiled container supporting compound
+slash-separated __getitem__ (matching how client[target_path] is used
+directly in run_denoise_bake_job)."""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+torch = pytest.importorskip("torch")
+
+import arrays as arrays_mod  # noqa: E402
+import denoise_bake  # noqa: E402
+import denoise as denoise_mod  # noqa: E402
+import export_jobs  # noqa: E402
+import schemas  # noqa: E402
+import train_common  # noqa: E402
+
+
+class FakeTiledContainer:
+    def __init__(self):
+        self._children: dict[str, "FakeTiledContainer"] = {}
+        self.metadata: dict = {}
+        self.written: list[dict] = []
+        self.updated_metadata: dict | None = None
+
+    def __getitem__(self, key):
+        node = self
+        for part in str(key).split("/"):
+            node = node._children[part]
+        return node
+
+    def create_container(self, key, metadata):
+        child = FakeTiledContainer()
+        child.metadata = metadata
+        self._children[key] = child
+        return child
+
+    def write_array(self, arr, key, dims=None, metadata=None):
+        self.written.append({"key": key, "arr": arr, "dims": dims, "metadata": metadata})
+
+    def update_metadata(self, metadata):
+        self.updated_metadata = metadata
+
+
+@pytest.fixture()
+def fake_client(monkeypatch: pytest.MonkeyPatch):
+    client = FakeTiledContainer()
+    client.create_container("browse", {})
+    monkeypatch.setattr(denoise_bake, "get_tiled_client", lambda uri: client)
+    return client
+
+
+@pytest.fixture()
+def fake_array_source(monkeypatch: pytest.MonkeyPatch):
+    """A 4-slice volume of small 2D frames, distinguishable per slice."""
+    frames = [np.full((6, 6), fill_value=float(i * 10), dtype=np.float32) for i in range(4)]
+
+    monkeypatch.setattr(arrays_mod, "resolve_array", lambda source, kind, server_uri: "node")
+    monkeypatch.setattr(arrays_mod, "array_shape_meta", lambda node: {"n_slices": len(frames)})
+    monkeypatch.setattr(arrays_mod, "read_slice", lambda node, meta, idx: frames[idx])
+    return frames
+
+
+def _request(**overrides):
+    defaults = dict(
+        source="browse/sample", server_uri=None, method="median",
+        strength=0.5, target_path="browse/sample_denoised", description="",
+    )
+    defaults.update(overrides)
+    return schemas.DenoiseBakeRequest(**defaults)
+
+
+# ---------------------------------------------------------------------------
+# Pure helpers
+# ---------------------------------------------------------------------------
+
+class TestDefaultTargetPath:
+    def test_appends_suffix_to_last_segment(self):
+        assert denoise_bake.default_target_path("browse/sample") == "browse/sample_denoised"
+
+    def test_custom_suffix(self):
+        assert denoise_bake.default_target_path("browse/sample", suffix="clean") == "browse/sample_clean"
+
+    def test_strips_whitespace_and_slashes(self):
+        assert denoise_bake.default_target_path("  /browse/sample/  ") == "browse/sample_denoised"
+
+    def test_empty_path_raises(self):
+        with pytest.raises(ValueError):
+            denoise_bake.default_target_path("   /// ")
+
+
+class TestSliceKey:
+    def test_zero_padded(self):
+        assert denoise_bake._slice_key(7) == "slice_0007"
+
+    def test_large_index(self):
+        assert denoise_bake._slice_key(12345) == "slice_12345"
+
+
+# ---------------------------------------------------------------------------
+# run_denoise_bake_job — guard/validation paths
+# ---------------------------------------------------------------------------
+
+class TestGuardPaths:
+    def test_method_none_reports_error(self):
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(method="none"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "method" in job["error"].lower()
+
+    def test_model_method_without_run_id_reports_error(self):
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(method="model", run_id=None))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "run_id" in job["error"]
+
+    def test_unavailable_method_reports_error(self):
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(method="not_a_real_method"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "unavailable" in job["error"]
+
+    def test_invalid_target_path_reports_error(self, fake_client):
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(target_path="../escape"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+
+    def test_existing_target_path_reports_error(self, fake_client):
+        fake_client["browse"].create_container("sample_denoised", {})
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request())
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "already exists" in job["error"]
+
+    def test_unexpected_exception_is_reported_not_raised(self, fake_client, monkeypatch):
+        monkeypatch.setattr(
+            arrays_mod, "resolve_array",
+            lambda source, kind, server_uri: (_ for _ in ()).throw(RuntimeError("boom")),
+        )
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request())
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "boom" in job["error"]
+
+
+# ---------------------------------------------------------------------------
+# run_denoise_bake_job — real classical-filter round trips
+# ---------------------------------------------------------------------------
+
+class TestClassicalBakeRoundTrip:
+    def test_2d_method_writes_every_slice(self, fake_client, fake_array_source):
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(method="median"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"]["n_slices"] == 4
+        target = fake_client["browse"]["sample_denoised"]
+        assert len(target.written) == 4
+        assert target.updated_metadata["n_images"] == 4
+        assert target.updated_metadata["denoise_method"] == "median"
+
+    def test_3d_method_uses_z_window_and_stacks(self, fake_client, fake_array_source, monkeypatch):
+        calls = []
+        real_stack = denoise_mod.denoise_stack
+
+        def spy_stack(stack, method, strength):
+            calls.append(stack.shape[0])
+            return real_stack(stack, method, strength)
+
+        monkeypatch.setattr(denoise_mod, "denoise_stack", spy_stack)
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(method="median3d"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        # radius=1: middle slices see a 3-frame window, edges see 2.
+        assert calls == [2, 3, 3, 2]
+
+    def test_per_slice_failure_falls_back_to_unfiltered_copy(self, fake_client, fake_array_source, monkeypatch):
+        real_denoise_slice = denoise_mod.denoise_slice
+
+        def flaky_denoise_slice(arr, method, strength):
+            if arr[0, 0] == 10.0:  # slice index 1
+                raise RuntimeError("filter blew up")
+            return real_denoise_slice(arr, method, strength)
+
+        monkeypatch.setattr(denoise_mod, "denoise_slice", flaky_denoise_slice)
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(method="median"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"]["n_slices"] == 4
+        assert len(job["result"]["errors"]) == 1
+        assert job["result"]["errors"][0]["slice"] == 1
+        target = fake_client["browse"]["sample_denoised"]
+        # The failed slice was still written (as an unfiltered copy of source).
+        written_slice1 = next(w for w in target.written if w["key"] == "slice_0001")
+        assert np.array_equal(written_slice1["arr"], fake_array_source[1])
+
+    def test_totally_unreadable_slice_is_skipped_not_written(self, fake_client, fake_array_source, monkeypatch):
+        monkeypatch.setattr(denoise_mod, "denoise_slice", lambda arr, m, s: (_ for _ in ()).throw(RuntimeError("f")))
+
+        def flaky_read_slice(node, meta, idx):
+            if idx == 2:
+                raise RuntimeError("source also unreadable")
+            return fake_array_source[idx]
+
+        monkeypatch.setattr(arrays_mod, "read_slice", flaky_read_slice)
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(method="median"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"]["n_slices"] == 3  # slice 2 was skipped entirely
+        slice2_error = next(e for e in job["result"]["errors"] if e["slice"] == 2)
+        assert "also unreadable" in slice2_error["error"]
+
+    def test_cancellation_mid_job_stops_early(self, fake_client, fake_array_source, monkeypatch):
+        monkeypatch.setattr(export_jobs, "cancel_requested", lambda jid: True)
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(method="median"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "nothing was written" in job["error"]
+
+    def test_all_slices_failing_reports_error(self, fake_client, fake_array_source, monkeypatch):
+        monkeypatch.setattr(denoise_mod, "denoise_slice", lambda arr, m, s: (_ for _ in ()).throw(RuntimeError("f")))
+        monkeypatch.setattr(arrays_mod, "read_slice", lambda node, meta, idx: (_ for _ in ()).throw(RuntimeError("g")))
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(method="median"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "nothing was written" in job["error"]
+
+    def test_description_and_keywords_propagate_to_metadata(self, fake_client, fake_array_source):
+        jid = export_jobs.new_job("x")
+        denoise_bake.run_denoise_bake_job(jid, _request(method="median", description="alpha, beta"))
+        target = fake_client["browse"]["sample_denoised"]
+        assert target.updated_metadata["description"] == "alpha, beta"
+        assert set(target.updated_metadata["keywords"]) == {"alpha", "beta"}
+
+
+# ---------------------------------------------------------------------------
+# _ModelDenoiser — real forward pass via a minimal nn.Conv2d stand-in
+# ---------------------------------------------------------------------------
+
+class _FakeDenoiserRuntime:
+    """Stand-in for denoise_runtime/autoencoder_runtime — generic over
+    forward_fn/to_tensor_fn like the real modules, using a real nn.Conv2d."""
+
+    @staticmethod
+    def load_model(state, device):
+        return torch.nn.Conv2d(1, 1, kernel_size=3, padding=1)
+
+    @staticmethod
+    def make_forward_fn(model):
+        def forward_fn(batch):
+            return model(batch)
+        return forward_fn
+
+    @staticmethod
+    def make_to_tensor_fn():
+        def to_tensor_fn(arr):
+            return torch.from_numpy(np.ascontiguousarray(arr)).float().unsqueeze(0) / 255.0
+        return to_tensor_fn
+
+
+@pytest.fixture()
+def fake_model_run(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setattr(
+        train_common, "load_run_config",
+        lambda run_id: {"model_family": "dlsia_denoiser", "task": "denoising", "image_size": 16, "render": {}},
+    )
+    monkeypatch.setattr(train_common, "denoiser_runtime_for", lambda config: _FakeDenoiserRuntime)
+    monkeypatch.setattr(train_common, "denoiser_needs_dlsia", lambda config: False)
+    monkeypatch.setattr(train_common, "dlsia_available", lambda: True)
+    monkeypatch.setattr(train_common, "pick_device", lambda: "cpu")
+    monkeypatch.setattr(train_common, "load_adapter_state", lambda run_id: {})
+    # _ModelDenoiser.__init__ always computes global stats via arrays.read_slice
+    # regardless of which guard path a test is exercising.
+    monkeypatch.setattr(arrays_mod, "read_slice", lambda node, meta, idx: np.zeros((4, 4)))
+
+
+class TestModelDenoiser:
+    def test_rejects_run_that_is_not_a_denoiser(self, monkeypatch):
+        monkeypatch.setattr(train_common, "load_run_config", lambda run_id: {"model_family": "dlsia", "task": "segmentation"})
+        with pytest.raises(ValueError, match="not a denoiser"):
+            denoise_bake._ModelDenoiser("r1", node=object(), meta={"n_slices": 1})
+
+    def test_missing_dlsia_when_needed_raises(self, fake_model_run, monkeypatch):
+        monkeypatch.setattr(train_common, "denoiser_needs_dlsia", lambda config: True)
+        monkeypatch.setattr(train_common, "dlsia_available", lambda: False)
+        with pytest.raises(ValueError, match="dlsia is not installed"):
+            denoise_bake._ModelDenoiser("r1", node=object(), meta={"n_slices": 1})
+
+    def test_missing_device_raises(self, fake_model_run, monkeypatch):
+        monkeypatch.setattr(train_common, "pick_device", lambda: None)
+        with pytest.raises(ValueError, match="torch is not installed"):
+            denoise_bake._ModelDenoiser("r1", node=object(), meta={"n_slices": 1})
+
+    def test_busy_lock_raises_and_does_not_deadlock(self, fake_model_run):
+        train_common.ML_LOCK.acquire()
+        try:
+            with pytest.raises(ValueError, match="busy"):
+                denoise_bake._ModelDenoiser("r1", node=object(), meta={"n_slices": 1})
+        finally:
+            train_common.ML_LOCK.release()
+
+    def test_real_forward_pass_and_lock_lifecycle(self, fake_model_run, monkeypatch):
+        gray = np.full((16, 16), 128, dtype=np.uint8)
+        monkeypatch.setattr("denoise_train._slice_to_gray_uint8", lambda node, meta, idx, opts, gr: gray)
+        monkeypatch.setattr("images._sample_global_stats", lambda node, meta: (0.0, 255.0))
+
+        assert train_common.ML_LOCK.locked() is False
+        md = denoise_bake._ModelDenoiser("r1", node=object(), meta={"n_slices": 1})
+        assert train_common.ML_LOCK.locked() is True
+        try:
+            out = md.denoise(0)
+            assert out.shape == (16, 16)
+            assert out.dtype == np.uint8
+        finally:
+            md.close()
+        assert train_common.ML_LOCK.locked() is False
+        # Idempotent: closing again must not raise or double-release.
+        md.close()
+
+    def test_load_failure_releases_lock(self, fake_model_run, monkeypatch):
+        monkeypatch.setattr(train_common, "load_adapter_state", lambda run_id: (_ for _ in ()).throw(RuntimeError("nope")))
+        with pytest.raises(RuntimeError, match="nope"):
+            denoise_bake._ModelDenoiser("r1", node=object(), meta={"n_slices": 1})
+        assert train_common.ML_LOCK.locked() is False
diff --git a/backend/tests/test_denoise_runtime.py b/backend/tests/test_denoise_runtime.py
new file mode 100644
index 0000000..fa1c8be
--- /dev/null
+++ b/backend/tests/test_denoise_runtime.py
@@ -0,0 +1,93 @@
+"""Real (no mocks) build/save/load/forward round trip for denoise_runtime.py
+— mirrors test_train_e2e_real_ml.py's pattern for the sibling dlsia_runtime.py
+segmentation family.
+"""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+torch = pytest.importorskip("torch")
+pytest.importorskip("dlsia")
+
+import denoise_runtime  # noqa: E402
+
+IMAGE_SIZE = 64
+
+
+def _model():
+    return denoise_runtime.build_model(
+        image_size=IMAGE_SIZE, depth=2, base_channels=4, growth_rate=1.2, device="cpu",
+    )
+
+
+class TestBuildModel:
+    def test_forward_pass_produces_single_channel_output(self):
+        model = _model()
+        model.eval()
+        forward_fn = denoise_runtime.make_forward_fn(model)
+        to_tensor_fn = denoise_runtime.make_to_tensor_fn()
+        gray = np.zeros((IMAGE_SIZE, IMAGE_SIZE), dtype=np.uint8)
+        batch = to_tensor_fn(gray).unsqueeze(0)
+        with torch.no_grad():
+            out = forward_fn(batch)
+        assert out.shape == (1, 1, IMAGE_SIZE, IMAGE_SIZE)
+
+    def test_final_activation_is_pinned_to_none(self):
+        # Checked directly on the model rather than by hoping an untrained
+        # (effectively random) network happens to produce an out-of-[0,1]
+        # value — that was flaky (not guaranteed within a handful of seeds).
+        # final_activation=None must hold: a squashed output would mean a
+        # future dlsia default silently added an activation the denoiser
+        # regression head must never have.
+        model = _model()
+        assert model.final_activation is None
+
+
+class TestSaveLoadRoundTrip:
+    def test_reloaded_model_reproduces_output(self):
+        model = _model()
+        model.eval()
+        forward_fn = denoise_runtime.make_forward_fn(model)
+        to_tensor_fn = denoise_runtime.make_to_tensor_fn()
+        gray = np.random.default_rng(0).integers(0, 256, size=(IMAGE_SIZE, IMAGE_SIZE), dtype=np.uint8)
+        batch = to_tensor_fn(gray).unsqueeze(0)
+
+        with torch.no_grad():
+            original_out = forward_fn(batch)
+
+        state = denoise_runtime.network_dict(model)
+        assert "topo_dict" in state and "state_dict" in state
+
+        reloaded = denoise_runtime.load_model(state, "cpu")
+        reloaded.eval()
+        reloaded_forward = denoise_runtime.make_forward_fn(reloaded)
+        with torch.no_grad():
+            reloaded_out = reloaded_forward(batch)
+
+        torch.testing.assert_close(original_out, reloaded_out)
+
+
+class TestSetTrainMode:
+    def test_toggles_module_training_flag(self):
+        model = _model()
+        set_train_mode = denoise_runtime.make_set_train_mode_fn(model)
+        set_train_mode(False)
+        assert model.training is False
+        set_train_mode(True)
+        assert model.training is True
+
+
+class TestToTensorFn:
+    def test_scales_uint8_to_unit_range(self):
+        to_tensor_fn = denoise_runtime.make_to_tensor_fn()
+        gray = np.full((8, 8), 255, dtype=np.uint8)
+        t = to_tensor_fn(gray)
+        assert t.shape == (1, 8, 8)
+        assert torch.allclose(t, torch.ones_like(t))
+
+    def test_accepts_single_channel_hwc(self):
+        to_tensor_fn = denoise_runtime.make_to_tensor_fn()
+        gray = np.full((8, 8, 1), 255, dtype=np.uint8)
+        t = to_tensor_fn(gray)
+        assert t.shape == (1, 8, 8)
diff --git a/backend/tests/test_drafts.py b/backend/tests/test_drafts.py
index 8b841e2..1e8f123 100644
--- a/backend/tests/test_drafts.py
+++ b/backend/tests/test_drafts.py
@@ -1,58 +1,197 @@
-"""Tests for session-draft persistence."""
-
+"""Tests for drafts.py — real filesystem (tmp_path), no mocks needed. Sets
+`drafts._DRAFT_DIR` directly rather than the LOCAL_DATA_ROOT env var, since
+it's computed once at import time (see MEMORY.md's import-time-constant
+gotcha) — the pattern already used in test_annotation_server_routes.py."""
 from __future__ import annotations
 
-import importlib
-import os
+import json
+
+import pytest
+
+import drafts
+
+
+@pytest.fixture(autouse=True)
+def draft_dir(tmp_path, monkeypatch: pytest.MonkeyPatch):
+    d = tmp_path / ".drafts"
+    monkeypatch.setattr(drafts, "_DRAFT_DIR", d)
+    return d
+
+
+# ---------------------------------------------------------------------------
+# save_draft / load_draft
+# ---------------------------------------------------------------------------
+
+class TestSaveAndLoadDraft:
+    def test_round_trips_payload(self):
+        result = drafts.save_draft("local:foo.tif", {"classes": [], "slices": {}})
+        assert "saved_at" in result
+        assert "path" in result
+
+        loaded = drafts.load_draft("local:foo.tif")
+        assert loaded["source_key"] == "local:foo.tif"
+        assert loaded["payload"] == {"classes": [], "slices": {}}
 
+    def test_missing_draft_returns_none(self):
+        assert drafts.load_draft("nope") is None
 
-def test_save_and_load_draft(tmp_path) -> None:
-    """A saved draft should be loadable with the correct source_key."""
-    with __import__("unittest.mock", fromlist=["patch"]).patch.dict(
-        os.environ, {"LOCAL_DATA_ROOT": str(tmp_path)}
-    ):
-        import drafts
+    def test_different_source_keys_do_not_collide(self):
+        drafts.save_draft("a", {"v": 1})
+        drafts.save_draft("b", {"v": 2})
+        assert drafts.load_draft("a")["payload"] == {"v": 1}
+        assert drafts.load_draft("b")["payload"] == {"v": 2}
 
-        importlib.reload(drafts)
-        meta = drafts.save_draft("test-source", {"classes": [], "slices": {}})
-        assert "saved_at" in meta
-        loaded = drafts.load_draft("test-source")
-        assert loaded is not None
-        assert loaded["source_key"] == "test-source"
+    def test_overwrites_previous_draft_for_same_key(self):
+        drafts.save_draft("a", {"v": 1})
+        drafts.save_draft("a", {"v": 2})
+        assert drafts.load_draft("a")["payload"] == {"v": 2}
 
+    def test_corrupt_draft_file_returns_none(self, draft_dir):
+        draft_dir.mkdir(parents=True, exist_ok=True)
+        path = drafts._draft_path("bad")
+        path.write_text("{not valid json")
+        assert drafts.load_draft("bad") is None
 
-def test_load_missing_draft(tmp_path) -> None:
-    """Loading a draft that was never saved should return None."""
-    with __import__("unittest.mock", fromlist=["patch"]).patch.dict(
-        os.environ, {"LOCAL_DATA_ROOT": str(tmp_path)}
-    ):
-        import drafts
+    def test_write_is_atomic_no_leftover_tmp_file(self, draft_dir):
+        drafts.save_draft("a", {"v": 1})
+        tmp_files = list(draft_dir.glob("*.tmp"))
+        assert tmp_files == []
 
-        importlib.reload(drafts)
-        result = drafts.load_draft("nonexistent-key")
-        assert result is None
 
+class TestDraftPath:
+    def test_same_key_produces_same_path(self):
+        assert drafts._draft_path("x") == drafts._draft_path("x")
 
-def test_list_drafts_returns_saved(tmp_path) -> None:
-    """list_drafts should include a previously saved draft."""
-    with __import__("unittest.mock", fromlist=["patch"]).patch.dict(
-        os.environ, {"LOCAL_DATA_ROOT": str(tmp_path)}
-    ):
-        import drafts
+    def test_different_keys_produce_different_paths(self):
+        assert drafts._draft_path("x") != drafts._draft_path("y")
 
-        importlib.reload(drafts)
-        drafts.save_draft("my-source", {"classes": [{"classId": 1, "label": "cell", "color": "#f00", "isVisible": True}]})
-        all_drafts = drafts.list_drafts()
-    assert any(d["source_key"] == "my-source" for d in all_drafts)
 
+# ---------------------------------------------------------------------------
+# list_drafts
+# ---------------------------------------------------------------------------
 
-def test_list_drafts_empty_when_none(tmp_path) -> None:
-    """list_drafts should return an empty list when no drafts exist."""
-    with __import__("unittest.mock", fromlist=["patch"]).patch.dict(
-        os.environ, {"LOCAL_DATA_ROOT": str(tmp_path)}
-    ):
-        import drafts
+class TestListDrafts:
+    def test_empty_when_dir_does_not_exist(self):
+        assert drafts.list_drafts() == []
 
-        importlib.reload(drafts)
+    def test_lists_saved_drafts(self):
+        drafts.save_draft("a", {"slices": {}})
+        drafts.save_draft("b", {"slices": {}})
         result = drafts.list_drafts()
-    assert result == []
+        assert len(result) == 2
+        keys = {d["source_key"] for d in result}
+        assert keys == {"a", "b"}
+
+    def test_has_annotations_true_when_a_slice_has_shapes(self):
+        drafts.save_draft("a", {"slices": {"0": [{"id": "s1"}]}})
+        result = drafts.list_drafts()
+        assert result[0]["has_annotations"] is True
+
+    def test_has_annotations_false_when_all_slices_empty(self):
+        drafts.save_draft("a", {"slices": {"0": []}})
+        result = drafts.list_drafts()
+        assert result[0]["has_annotations"] is False
+
+    def test_corrupt_draft_file_is_skipped_not_raised(self, draft_dir):
+        drafts.save_draft("a", {"slices": {}})
+        draft_dir.mkdir(parents=True, exist_ok=True)
+        (draft_dir / "corrupt.json").write_text("{not json")
+        result = drafts.list_drafts()
+        assert len(result) == 1
+
+
+# ---------------------------------------------------------------------------
+# save_version / list_versions / get_version
+# ---------------------------------------------------------------------------
+
+class TestSaveVersion:
+    def test_first_version_is_1(self):
+        result = drafts.save_version("a", {"classes": [], "slices": {}})
+        assert result["version"] == 1
+
+    def test_versions_increment(self):
+        drafts.save_version("a", {"classes": [], "slices": {}})
+        result2 = drafts.save_version("a", {"classes": [], "slices": {}})
+        assert result2["version"] == 2
+
+    def test_shape_count_and_class_count_computed(self):
+        payload = {
+            "classes": [{"classId": 1}, {"classId": 2}],
+            "slices": {"0": [{"id": "s1"}, {"id": "s2"}], "1": [{"id": "s3"}]},
+        }
+        result = drafts.save_version("a", payload)
+        assert result["shape_count"] == 3
+
+    def test_also_updates_the_crash_recovery_draft(self):
+        drafts.save_version("a", {"classes": [], "slices": {"0": [{"id": "s1"}]}})
+        loaded = drafts.load_draft("a")
+        assert loaded["payload"]["slices"] == {"0": [{"id": "s1"}]}
+
+    def test_annotated_by_and_notes_are_trimmed_and_none_when_blank(self):
+        result_blank = drafts.save_version("a", {"classes": [], "slices": {}}, annotated_by="  ", notes="")
+        vdoc = json.loads((drafts._versions_dir("a") / "v0001.json").read_text())
+        assert vdoc["annotated_by"] is None
+        assert vdoc["notes"] is None
+
+        drafts.save_version("a", {"classes": [], "slices": {}}, annotated_by="  Alice  ", notes=" hi ")
+        vdoc2 = json.loads((drafts._versions_dir("a") / "v0002.json").read_text())
+        assert vdoc2["annotated_by"] == "Alice"
+        assert vdoc2["notes"] == "hi"
+        assert result_blank["version"] == 1
+
+
+class TestListVersions:
+    def test_empty_when_no_versions_dir(self):
+        assert drafts.list_versions("nope") == []
+
+    def test_sorted_oldest_first(self):
+        drafts.save_version("a", {"classes": [], "slices": {}})
+        drafts.save_version("a", {"classes": [], "slices": {}})
+        result = drafts.list_versions("a")
+        assert [v["version"] for v in result] == [1, 2]
+
+    def test_has_thumbnail_reflects_saved_thumbnail(self):
+        drafts.save_version("a", {"classes": [], "slices": {}})
+        result = drafts.list_versions("a")
+        assert result[0]["has_thumbnail"] is False
+        drafts.save_version_thumbnail("a", 1, b"pngbytes")
+        result2 = drafts.list_versions("a")
+        assert result2[0]["has_thumbnail"] is True
+
+    def test_corrupt_version_file_is_skipped(self):
+        drafts.save_version("a", {"classes": [], "slices": {}})
+        (drafts._versions_dir("a") / "vbad.json").write_text("not json at all {")
+        result = drafts.list_versions("a")
+        assert len(result) == 1
+
+
+class TestGetVersion:
+    def test_returns_full_document(self):
+        drafts.save_version("a", {"classes": [{"classId": 1}], "slices": {}}, notes="test")
+        doc = drafts.get_version("a", 1)
+        assert doc["payload"]["classes"] == [{"classId": 1}]
+        assert doc["notes"] == "test"
+
+    def test_missing_version_returns_none(self):
+        drafts.save_version("a", {"classes": [], "slices": {}})
+        assert drafts.get_version("a", 99) is None
+
+    def test_corrupt_version_returns_none(self):
+        vdir = drafts._versions_dir("a")
+        vdir.mkdir(parents=True, exist_ok=True)
+        (vdir / "v0001.json").write_text("{ broken")
+        assert drafts.get_version("a", 1) is None
+
+
+class TestVersionThumbnails:
+    def test_missing_thumbnail_returns_none(self):
+        assert drafts.get_version_thumbnail("a", 1) is None
+
+    def test_round_trips_thumbnail_bytes(self):
+        drafts.save_version_thumbnail("a", 3, b"\x89PNGdata")
+        assert drafts.get_version_thumbnail("a", 3) == b"\x89PNGdata"
+
+    def test_thumbnail_write_is_atomic_no_leftover_tmp(self):
+        drafts.save_version_thumbnail("a", 1, b"data")
+        vdir = drafts._versions_dir("a")
+        assert list(vdir.glob("*.tmp")) == []
diff --git a/backend/tests/test_ipred_client.py b/backend/tests/test_ipred_client.py
new file mode 100644
index 0000000..a9a26c6
--- /dev/null
+++ b/backend/tests/test_ipred_client.py
@@ -0,0 +1,325 @@
+"""Tests for ipred_client.py — a real httpx.Client is exercised end to end
+via httpx.MockTransport (no real network), so ipred_url()/_client()/_use_client()
+and every wrapper function's request construction + response parsing are all
+genuinely tested, not mocked away."""
+from __future__ import annotations
+
+import types
+
+import httpx
+import pytest
+
+import ipred_client
+
+
+class RequestLog:
+    def __init__(self):
+        self.requests: list[httpx.Request] = []
+
+
+def _install_handler(monkeypatch: pytest.MonkeyPatch, handler):
+    """Patch the `httpx` name inside ipred_client's module namespace so every
+    httpx.Client(...) constructed by the module routes through a MockTransport
+    calling `handler`, while everything else about httpx.Client stays real."""
+    real_client_cls = httpx.Client
+
+    def fake_client(**kwargs):
+        kwargs["transport"] = httpx.MockTransport(handler)
+        return real_client_cls(**kwargs)
+
+    fake_httpx = types.SimpleNamespace(Client=fake_client)
+    monkeypatch.setattr(ipred_client, "httpx", fake_httpx)
+
+
+def _json_handler(log: RequestLog, routes: dict):
+    def handler(request: httpx.Request) -> httpx.Response:
+        log.requests.append(request)
+        key = (request.method, request.url.path)
+        if key not in routes:
+            return httpx.Response(404, json={"detail": "no route"})
+        body, status = routes[key]
+        if isinstance(body, (bytes, bytearray)):
+            return httpx.Response(status, content=body)
+        return httpx.Response(status, json=body)
+    return handler
+
+
+# ---------------------------------------------------------------------------
+# ipred_url
+# ---------------------------------------------------------------------------
+
+class TestIpredUrl:
+    def test_defaults_when_no_env_vars_set(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.delenv("IPRED_URL", raising=False)
+        monkeypatch.delenv("CLF_ENGINE_URL", raising=False)
+        assert ipred_client.ipred_url() == ipred_client.DEFAULT_IPRED_URL
+
+    def test_ipred_url_env_var_wins(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setenv("IPRED_URL", "http://a:1/")
+        monkeypatch.setenv("CLF_ENGINE_URL", "http://b:2/")
+        assert ipred_client.ipred_url() == "http://a:1"
+
+    def test_legacy_clf_engine_url_used_when_ipred_url_unset(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.delenv("IPRED_URL", raising=False)
+        monkeypatch.setenv("CLF_ENGINE_URL", "http://legacy:9/")
+        assert ipred_client.ipred_url() == "http://legacy:9"
+
+    def test_trailing_slash_stripped(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setenv("IPRED_URL", "http://x:1///")
+        assert ipred_client.ipred_url() == "http://x:1"
+
+
+# ---------------------------------------------------------------------------
+# health / open_session / setups / trainers / modules / compositions
+# ---------------------------------------------------------------------------
+
+class TestSimpleGetPostWrappers:
+    def test_health(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/health"): ({"status": "ok"}, 200)}))
+        assert ipred_client.health() == {"status": "ok"}
+
+    def test_open_session_posts_expected_body(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/sessions"): ({"session_id": "s1"}, 200)}))
+        result = ipred_client.open_session(kind="tiled", source="foo", server_uri="http://t", root=None)
+        assert result == {"session_id": "s1"}
+        sent = log.requests[0]
+        assert sent.method == "POST"
+        import json
+        body = json.loads(sent.content)
+        assert body == {"kind": "tiled", "source": "foo", "server_uri": "http://t", "root": None}
+
+    def test_list_setups_defaults_to_empty_list(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/setups"): ({}, 200)}))
+        assert ipred_client.list_setups() == []
+
+    def test_list_setups_returns_setups_key(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/setups"): ({"setups": [{"id": 1}]}, 200)}))
+        assert ipred_client.list_setups() == [{"id": 1}]
+
+    def test_get_setup(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/setups/abc"): ({"id": "abc"}, 200)}))
+        assert ipred_client.get_setup("abc") == {"id": "abc"}
+
+    def test_upsert_setup(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/setups"): ({"id": "new"}, 200)}))
+        assert ipred_client.upsert_setup({"name": "x"}) == {"id": "new"}
+
+    def test_list_trainers(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/trainers"): ({"trainers": ["catboost"]}, 200)}))
+        assert ipred_client.list_trainers() == ["catboost"]
+
+    def test_list_modules(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/modules"): ({"modules": [{"id": "m1"}]}, 200)}))
+        assert ipred_client.list_modules() == [{"id": "m1"}]
+
+    def test_list_compositions(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/compositions"): ({"compositions": []}, 200)}))
+        assert ipred_client.list_compositions() == []
+
+    def test_get_composition(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/compositions/c1"): ({"id": "c1"}, 200)}))
+        assert ipred_client.get_composition("c1") == {"id": "c1"}
+
+    def test_upsert_composition(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/compositions"): ({"id": "c2"}, 200)}))
+        assert ipred_client.upsert_composition({"name": "y"}) == {"id": "c2"}
+
+    def test_preview_composition(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/compositions/preview"): ({"preview": True}, 200)}))
+        assert ipred_client.preview_composition({"x": 1}) == {"preview": True}
+
+    def test_upload_session_array(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/sessions/s1/arrays"): ({"ok": True}, 200)}))
+        assert ipred_client.upload_session_array("s1", {"a": 1}) == {"ok": True}
+
+
+# ---------------------------------------------------------------------------
+# preprocess / delete_feature_bank (accept an optional shared client)
+# ---------------------------------------------------------------------------
+
+class TestSharedClientParam:
+    def test_preprocess_builds_minimal_body(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/preprocess"): ({"ok": 1}, 200)}))
+        ipred_client.preprocess(session_id="s1")
+        import json
+        body = json.loads(log.requests[0].content)
+        assert body == {"session_id": "s1", "slice_index": 0}
+
+    def test_preprocess_includes_optional_fields_when_given(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/preprocess"): ({"ok": 1}, 200)}))
+        ipred_client.preprocess(
+            session_id="s1", feature_setup_id="f1", composition_id="c1",
+            slice_index=5, array_ref="ref1",
+        )
+        import json
+        body = json.loads(log.requests[0].content)
+        assert body == {
+            "session_id": "s1", "slice_index": 5, "composition_id": "c1",
+            "feature_setup_id": "f1", "array_ref": "ref1",
+        }
+
+    def test_preprocess_reuses_a_passed_in_client_without_closing_it(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/preprocess"): ({"ok": 1}, 200)}))
+        shared = ipred_client.new_shared_client()
+        try:
+            ipred_client.preprocess(session_id="s1", client=shared)
+            assert shared.is_closed is False
+            # A second call on the same shared client still works — proves it
+            # wasn't closed by _use_client after the first call.
+            ipred_client.preprocess(session_id="s2", client=shared)
+            assert len(log.requests) == 2
+        finally:
+            shared.close()
+
+    def test_delete_feature_bank(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("DELETE", "/features/f1"): ({"deleted": True}, 200)}))
+        assert ipred_client.delete_feature_bank("f1") == {"deleted": True}
+
+
+# ---------------------------------------------------------------------------
+# byte-returning endpoints
+# ---------------------------------------------------------------------------
+
+class TestByteEndpoints:
+    def test_feature_channel_bytes(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/features/f1/channels/2"): (b"\x89PNG", 200)}))
+        assert ipred_client.feature_channel_bytes("f1", 2) == b"\x89PNG"
+
+    def test_run_commit_png(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/runs/r1/commit.png"): (b"commitbytes", 200)}))
+        assert ipred_client.run_commit_png("r1") == b"commitbytes"
+
+    def test_run_status_png(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/runs/r1/status.png"): (b"statusbytes", 200)}))
+        assert ipred_client.run_status_png("r1") == b"statusbytes"
+
+    def test_run_proba_png(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/runs/r1/proba/3.png"): (b"probabytes", 200)}))
+        assert ipred_client.run_proba_png("r1", 3) == b"probabytes"
+
+    def test_manifold_heatmap_png(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/manifold/m1/heatmap.png"): (b"heatbytes", 200)}))
+        assert ipred_client.manifold_heatmap_png("m1") == b"heatbytes"
+
+
+# ---------------------------------------------------------------------------
+# train / train_multi / infer / rethreshold / threshold_class_map / manifold_sample
+# ---------------------------------------------------------------------------
+
+class TestMlWrappers:
+    def test_train_fills_default_trainer_and_empty_config(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/train"): ({"model_id": "m1"}, 200)}))
+        result = ipred_client.train(session_id="s1", shapes=[{"a": 1}])
+        assert result == {"model_id": "m1"}
+        import json
+        body = json.loads(log.requests[0].content)
+        assert body["trainer_id"] == "catboost"
+        assert body["config"] == {}
+
+    def test_train_multi(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/train/multi"): ({"model_id": "m2"}, 200)}))
+        result = ipred_client.train_multi(
+            session_id="s1", slices={"0": []}, feature_ids={"0": "f1"},
+        )
+        assert result == {"model_id": "m2"}
+
+    def test_infer_defaults(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/infer"): ({"run_id": "r1"}, 200)}))
+        result = ipred_client.infer(session_id="s1")
+        assert result == {"run_id": "r1"}
+        import json
+        body = json.loads(log.requests[0].content)
+        assert body["store_probabilities"] is True
+        assert body["alpha"] == 0.05
+
+    def test_infer_store_probabilities_false_for_batch_apply(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/infer"): ({"run_id": "r2"}, 200)}))
+        ipred_client.infer(session_id="s1", store_probabilities=False)
+        import json
+        body = json.loads(log.requests[0].content)
+        assert body["store_probabilities"] is False
+
+    def test_rethreshold(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/rethreshold"): ({"ok": 1}, 200)}))
+        assert ipred_client.rethreshold(session_id="s1", alpha=0.1, run_id="r1") == {"ok": 1}
+
+    def test_threshold_class_map(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/runs/r1/threshold-class"): ({"ok": 1}, 200)}))
+        result = ipred_client.threshold_class_map("r1", class_id=2, threshold=0.5)
+        assert result == {"ok": 1}
+        import json
+        body = json.loads(log.requests[0].content)
+        assert body == {"class_id": 2, "threshold": 0.5}
+
+    def test_manifold_sample(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("POST", "/manifold/sample"): ({"sample_id": "sm1"}, 200)}))
+        assert ipred_client.manifold_sample({"k": "v"}) == {"sample_id": "sm1"}
+
+
+# ---------------------------------------------------------------------------
+# error propagation
+# ---------------------------------------------------------------------------
+
+class TestErrorPropagation:
+    def test_http_error_status_raises(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/health"): ({"error": "down"}, 500)}))
+        with pytest.raises(httpx.HTTPStatusError):
+            ipred_client.health()
+
+    def test_missing_route_also_raises(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {}))
+        with pytest.raises(httpx.HTTPStatusError):
+            ipred_client.list_setups()
+
+
+# ---------------------------------------------------------------------------
+# new_shared_client / _use_client
+# ---------------------------------------------------------------------------
+
+class TestSharedClientLifecycle:
+    def test_new_shared_client_is_open_and_usable(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/health"): ({"status": "ok"}, 200)}))
+        client = ipred_client.new_shared_client()
+        try:
+            assert client.is_closed is False
+        finally:
+            client.close()
+
+    def test_use_client_without_explicit_client_opens_and_closes_its_own(self, monkeypatch: pytest.MonkeyPatch):
+        log = RequestLog()
+        _install_handler(monkeypatch, _json_handler(log, {("GET", "/health"): ({"status": "ok"}, 200)}))
+        with ipred_client._use_client(None, timeout=1.0) as c:
+            assert c.is_closed is False
+        assert c.is_closed is True
diff --git a/backend/tests/test_local_fs.py b/backend/tests/test_local_fs.py
index dcc1444..0e87c78 100644
--- a/backend/tests/test_local_fs.py
+++ b/backend/tests/test_local_fs.py
@@ -1,64 +1,199 @@
-"""Tests for local filesystem sandboxing."""
-
+"""Tests for local_fs.py — real filesystem (tmp_path), no mocks. Sets
+`local_fs._DEFAULT_ROOT` directly rather than the LOCAL_DATA_ROOT env var,
+since it's computed once at import time (see MEMORY.md's import-time-constant
+gotcha)."""
 from __future__ import annotations
 
-import importlib
-import os
-
+import numpy as np
 import pytest
 from fastapi import HTTPException
+from PIL import Image as PILImage
+
+import local_fs
+
+
+@pytest.fixture(autouse=True)
+def default_root(tmp_path, monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setattr(local_fs, "_DEFAULT_ROOT", tmp_path)
+    return tmp_path
+
+
+# ---------------------------------------------------------------------------
+# _resolve_root / _within / _safe
+# ---------------------------------------------------------------------------
+
+class TestResolveRoot:
+    def test_none_falls_back_to_default_root(self, default_root):
+        assert local_fs._resolve_root(None) == default_root
+
+    def test_explicit_root_is_expanded_and_resolved(self, tmp_path):
+        granted = tmp_path / "granted"
+        granted.mkdir()
+        assert local_fs._resolve_root(str(granted)) == granted.resolve()
+
 
+class TestWithin:
+    def test_base_itself_is_within(self, tmp_path):
+        assert local_fs._within(tmp_path, tmp_path) is True
 
-def test_path_traversal_rejected(tmp_path) -> None:
-    """Paths escaping LOCAL_DATA_ROOT must raise HTTP 403."""
-    with __import__("unittest.mock", fromlist=["patch"]).patch.dict(
-        os.environ, {"LOCAL_DATA_ROOT": str(tmp_path)}
-    ):
-        import local_fs
+    def test_descendant_is_within(self, tmp_path):
+        assert local_fs._within(tmp_path, tmp_path / "a" / "b") is True
 
-        importlib.reload(local_fs)
-        with pytest.raises(HTTPException) as exc_info:
+    def test_sibling_with_shared_prefix_is_not_within(self, tmp_path):
+        sibling = tmp_path.parent / (tmp_path.name + "2")
+        assert local_fs._within(tmp_path, sibling) is False
+
+
+class TestSafe:
+    def test_traversal_with_explicit_root_is_403(self, tmp_path):
+        granted = tmp_path / "granted"
+        granted.mkdir()
+        with pytest.raises(HTTPException) as exc:
+            local_fs._safe("../../etc/passwd", root=str(granted))
+        assert exc.value.status_code == 403
+
+    def test_traversal_without_root_under_default_is_403(self):
+        with pytest.raises(HTTPException) as exc:
             local_fs._safe("../../etc/passwd")
-        assert exc_info.value.status_code == 403
+        assert exc.value.status_code == 403
+
+    def test_relative_path_resolved_under_granted_root(self, tmp_path):
+        granted = tmp_path / "granted"
+        granted.mkdir()
+        result = local_fs._safe("sub/file.tif", root=str(granted))
+        assert result == (granted / "sub" / "file.tif").resolve()
+
+    def test_absolute_path_without_root_returned_as_is_no_sandbox(self, tmp_path):
+        absolute = tmp_path / "elsewhere" / "file.tif"
+        result = local_fs._safe(str(absolute))
+        assert result == absolute.resolve()
 
+    def test_relative_path_without_root_resolved_under_default(self, default_root):
+        result = local_fs._safe("sub/file.tif")
+        assert result == (default_root / "sub" / "file.tif").resolve()
 
-def test_list_dir_returns_entries(tmp_path) -> None:
-    """list_dir should return file entries for an existing directory."""
-    (tmp_path / "sample.npy").write_bytes(b"\x93NUMPY")
-    with __import__("unittest.mock", fromlist=["patch"]).patch.dict(
-        os.environ, {"LOCAL_DATA_ROOT": str(tmp_path)}
-    ):
-        import local_fs
 
-        importlib.reload(local_fs)
+# ---------------------------------------------------------------------------
+# list_dir
+# ---------------------------------------------------------------------------
+
+class TestListDir:
+    def test_lists_files_and_dirs_with_sizes(self, default_root):
+        (default_root / "a.npy").write_bytes(b"12345")
+        (default_root / "sub").mkdir()
         entries = local_fs.list_dir("")
-    names = [e["name"] for e in entries]
-    assert "sample.npy" in names
-
-
-def test_list_dir_missing_raises_404(tmp_path) -> None:
-    """list_dir on a non-existent path should raise HTTP 404."""
-    with __import__("unittest.mock", fromlist=["patch"]).patch.dict(
-        os.environ, {"LOCAL_DATA_ROOT": str(tmp_path)}
-    ):
-        import local_fs
-
-        importlib.reload(local_fs)
-        with pytest.raises(HTTPException) as exc_info:
-            local_fs.list_dir("does_not_exist")
-        assert exc_info.value.status_code == 404
-
-
-def test_open_array_unsupported_type_raises_422(tmp_path) -> None:
-    """open_array on an unsupported extension should raise HTTP 422."""
-    bad_file = tmp_path / "data.csv"
-    bad_file.write_text("a,b\n1,2\n")
-    with __import__("unittest.mock", fromlist=["patch"]).patch.dict(
-        os.environ, {"LOCAL_DATA_ROOT": str(tmp_path)}
-    ):
-        import local_fs
-
-        importlib.reload(local_fs)
-        with pytest.raises(HTTPException) as exc_info:
+        by_name = {e["name"]: e for e in entries}
+        assert by_name["a.npy"]["is_dir"] is False
+        assert by_name["a.npy"]["size"] == 5
+        assert by_name["sub"]["is_dir"] is True
+        assert by_name["sub"]["size"] is None
+
+    def test_missing_root_itself_returns_empty_not_404(self, tmp_path, monkeypatch):
+        monkeypatch.setattr(local_fs, "_DEFAULT_ROOT", tmp_path / "does_not_exist")
+        assert local_fs.list_dir("") == []
+        assert local_fs.list_dir(".") == []
+
+    def test_missing_subpath_raises_404(self):
+        with pytest.raises(HTTPException) as exc:
+            local_fs.list_dir("no/such/dir")
+        assert exc.value.status_code == 404
+
+    def test_file_instead_of_dir_raises_400(self, default_root):
+        (default_root / "afile.npy").write_bytes(b"x")
+        with pytest.raises(HTTPException) as exc:
+            local_fs.list_dir("afile.npy")
+        assert exc.value.status_code == 400
+
+    def test_paths_are_relative_to_root(self, default_root):
+        (default_root / "sub").mkdir()
+        (default_root / "sub" / "f.npy").write_bytes(b"x")
+        entries = local_fs.list_dir("sub")
+        assert entries[0]["path"] == "sub/f.npy"
+
+
+# ---------------------------------------------------------------------------
+# count_image_files / list_image_files
+# ---------------------------------------------------------------------------
+
+class TestCountImageFiles:
+    def test_counts_only_supported_extensions_recursively(self, default_root):
+        (default_root / "a.tif").write_bytes(b"x")
+        (default_root / "b.npy").write_bytes(b"x")
+        (default_root / "c.txt").write_bytes(b"x")
+        (default_root / "sub").mkdir()
+        (default_root / "sub" / "d.png").write_bytes(b"x")
+        assert local_fs.count_image_files("") == 3
+
+    def test_nonexistent_path_returns_zero(self):
+        assert local_fs.count_image_files("nope") == 0
+
+    def test_file_instead_of_dir_returns_zero(self, default_root):
+        (default_root / "a.tif").write_bytes(b"x")
+        assert local_fs.count_image_files("a.tif") == 0
+
+    def test_extension_matching_is_case_insensitive(self, default_root):
+        (default_root / "A.TIF").write_bytes(b"x")
+        assert local_fs.count_image_files("") == 1
+
+
+class TestListImageFiles:
+    def test_returns_sorted_flat_list(self, default_root):
+        (default_root / "sub").mkdir()
+        (default_root / "sub" / "b.tif").write_bytes(b"x")
+        (default_root / "a.tif").write_bytes(b"x")
+        (default_root / "readme.md").write_bytes(b"x")
+        entries = local_fs.list_image_files("")
+        assert [e["path"] for e in entries] == ["a.tif", "sub/b.tif"]
+
+    def test_missing_path_raises_404(self):
+        with pytest.raises(HTTPException) as exc:
+            local_fs.list_image_files("nope")
+        assert exc.value.status_code == 404
+
+    def test_file_instead_of_dir_raises_400(self, default_root):
+        (default_root / "a.tif").write_bytes(b"x")
+        with pytest.raises(HTTPException) as exc:
+            local_fs.list_image_files("a.tif")
+        assert exc.value.status_code == 400
+
+
+# ---------------------------------------------------------------------------
+# open_array
+# ---------------------------------------------------------------------------
+
+class TestOpenArray:
+    def test_missing_file_raises_404(self):
+        with pytest.raises(HTTPException) as exc:
+            local_fs.open_array("nope.npy")
+        assert exc.value.status_code == 404
+
+    def test_unsupported_extension_raises_422(self, default_root):
+        (default_root / "data.csv").write_text("a,b\n1,2\n")
+        with pytest.raises(HTTPException) as exc:
             local_fs.open_array("data.csv")
-        assert exc_info.value.status_code == 422
+        assert exc.value.status_code == 422
+
+    def test_opens_npy_file(self, default_root):
+        arr = np.arange(12).reshape(3, 4).astype(np.float32)
+        np.save(default_root / "a.npy", arr)
+        result = local_fs.open_array("a.npy")
+        assert np.array_equal(np.asarray(result), arr)
+
+    def test_opens_png_file(self, default_root):
+        img = PILImage.new("L", (5, 5), color=128)
+        img.save(default_root / "a.png")
+        result = local_fs.open_array("a.png")
+        assert result.shape == (5, 5)
+
+    def test_opens_tif_file(self, default_root):
+        tifffile = pytest.importorskip("tifffile")
+        arr = np.arange(16).reshape(4, 4).astype(np.uint16)
+        tifffile.imwrite(default_root / "a.tif", arr)
+        result = local_fs.open_array("a.tif")
+        assert np.array_equal(np.asarray(result), arr)
+
+    def test_corrupt_npy_raises_500(self, default_root):
+        (default_root / "bad.npy").write_bytes(b"not a real npy file")
+        with pytest.raises(HTTPException) as exc:
+            local_fs.open_array("bad.npy")
+        assert exc.value.status_code == 500
diff --git a/backend/tests/test_source_keys.py b/backend/tests/test_source_keys.py
new file mode 100644
index 0000000..2bab808
--- /dev/null
+++ b/backend/tests/test_source_keys.py
@@ -0,0 +1,91 @@
+"""Tests for source_keys.parse_source_key."""
+from __future__ import annotations
+
+import pytest
+
+import source_keys
+import tiled_config
+
+
+@pytest.fixture()
+def two_servers(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setattr(
+        tiled_config, "get_tiled_servers",
+        lambda: {
+            "local": {"uri": "http://127.0.0.1:8010"},
+            "remote": {"uri": "http://example.com:9000/"},
+        },
+    )
+    monkeypatch.setattr(source_keys, "get_tiled_servers", tiled_config.get_tiled_servers)
+
+
+class TestLocalKeys:
+    def test_parses_local_prefix(self):
+        assert source_keys.parse_source_key("local:foo/bar.tif") == {
+            "kind": "local", "server_uri": None, "path": "foo/bar.tif",
+        }
+
+    def test_empty_path_after_prefix(self):
+        assert source_keys.parse_source_key("local:") == {
+            "kind": "local", "server_uri": None, "path": "",
+        }
+
+
+class TestTiledKeys:
+    def test_matches_known_server_uri(self, two_servers):
+        result = source_keys.parse_source_key("tiled:http://127.0.0.1:8010:browse/sample")
+        assert result == {"kind": "tiled", "server_uri": "http://127.0.0.1:8010", "path": "browse/sample"}
+
+    def test_trailing_slash_on_configured_uri_is_normalized(self, two_servers):
+        result = source_keys.parse_source_key("tiled:http://example.com:9000:browse/x")
+        assert result["server_uri"] == "http://example.com:9000"
+        assert result["path"] == "browse/x"
+
+    def test_prefers_longest_matching_uri(self, monkeypatch: pytest.MonkeyPatch):
+        # "http://x:1" is a prefix of "http://x:1/extra" — the longer match
+        # must win so the path split lands in the right place.
+        monkeypatch.setattr(
+            tiled_config, "get_tiled_servers",
+            lambda: {
+                "short": {"uri": "http://x:1"},
+                "long": {"uri": "http://x:1/extra"},
+            },
+        )
+        monkeypatch.setattr(source_keys, "get_tiled_servers", tiled_config.get_tiled_servers)
+        result = source_keys.parse_source_key("tiled:http://x:1/extra:browse/s")
+        assert result == {"kind": "tiled", "server_uri": "http://x:1/extra", "path": "browse/s"}
+
+    def test_unmatched_uri_falls_back_to_default_server_with_colon(self, two_servers):
+        result = source_keys.parse_source_key("tiled::browse/sample")
+        assert result == {"kind": "tiled", "server_uri": None, "path": "browse/sample"}
+
+    def test_unmatched_uri_falls_back_to_default_server_without_colon(self, two_servers):
+        result = source_keys.parse_source_key("tiled:browse/sample")
+        assert result == {"kind": "tiled", "server_uri": None, "path": "browse/sample"}
+
+    def test_no_configured_servers_falls_back_to_default(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setattr(tiled_config, "get_tiled_servers", lambda: {})
+        monkeypatch.setattr(source_keys, "get_tiled_servers", tiled_config.get_tiled_servers)
+        result = source_keys.parse_source_key("tiled:browse/sample")
+        assert result == {"kind": "tiled", "server_uri": None, "path": "browse/sample"}
+
+    def test_server_with_no_uri_configured_is_skipped(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setattr(
+            tiled_config, "get_tiled_servers",
+            lambda: {"broken": {}, "ok": {"uri": "http://ok:1"}},
+        )
+        monkeypatch.setattr(source_keys, "get_tiled_servers", tiled_config.get_tiled_servers)
+        result = source_keys.parse_source_key("tiled:http://ok:1:browse/s")
+        assert result["server_uri"] == "http://ok:1"
+
+
+class TestUnknownKeys:
+    def test_unrecognized_prefix_returns_unknown_kind(self):
+        assert source_keys.parse_source_key("weird:thing") == {
+            "kind": "unknown", "server_uri": None, "path": "weird:thing",
+        }
+
+    def test_empty_string(self):
+        assert source_keys.parse_source_key("") == {
+            "kind": "unknown", "server_uri": None, "path": "",
+        }
diff --git a/backend/tests/test_thumbnails.py b/backend/tests/test_thumbnails.py
new file mode 100644
index 0000000..3f1e295
--- /dev/null
+++ b/backend/tests/test_thumbnails.py
@@ -0,0 +1,144 @@
+"""Tests for thumbnails.py — real numpy/PIL, no mocks needed since the whole
+module is pure array->PNG logic."""
+from __future__ import annotations
+
+from io import BytesIO
+
+import numpy as np
+import pytest
+from PIL import Image as PILImage
+
+import thumbnails
+
+
+class FakeArrayNode:
+    def __init__(self, arr):
+        self._arr = arr
+
+    def read(self):
+        return self._arr
+
+
+class FakeContainer:
+    def __init__(self, children):
+        self._children = children
+
+    def __iter__(self):
+        return iter(self._children)
+
+    def __getitem__(self, key):
+        return self._children[key]
+
+
+def _decode(png_bytes):
+    return np.array(PILImage.open(BytesIO(png_bytes)))
+
+
+class TestRenderThumbnailRgb:
+    def test_uint8_rgb_array_round_trips(self):
+        arr = np.zeros((10, 10, 3), dtype=np.uint8)
+        arr[:, :, 0] = 200
+        png = thumbnails.render_thumbnail(FakeArrayNode(arr), size=32)
+        assert png is not None
+        decoded = _decode(png)
+        assert decoded.shape[:2] == (10, 10)
+
+    def test_rgba_array_drops_alpha(self):
+        arr = np.zeros((8, 8, 4), dtype=np.uint8)
+        png = thumbnails.render_thumbnail(FakeArrayNode(arr), size=32)
+        assert png is not None
+        decoded = _decode(png)
+        assert decoded.shape[:2] == (8, 8)
+
+    def test_non_uint8_rgb_is_normalized(self):
+        arr = np.zeros((4, 4, 3), dtype=np.float32)
+        arr[0, 0, :] = 100.0
+        arr[1, 1, :] = 50.0
+        png = thumbnails.render_thumbnail(FakeArrayNode(arr), size=32)
+        assert png is not None
+
+    def test_constant_rgb_array_yields_all_zero(self):
+        rgb = thumbnails._prepare_rgb(np.full((4, 4, 3), 7.0, dtype=np.float32))
+        assert np.all(rgb == 0)
+
+
+class TestRenderThumbnailIntensity:
+    def test_2d_array_gets_colormapped(self):
+        arr = np.random.default_rng(0).random((16, 16)) * 1000
+        png = thumbnails.render_thumbnail(FakeArrayNode(arr), size=32)
+        assert png is not None
+        decoded = _decode(png)
+        assert decoded.shape[:2] == (16, 16)
+
+    def test_constant_intensity_array_does_not_crash(self):
+        arr = np.full((4, 4), 5.0)
+        png = thumbnails.render_thumbnail(FakeArrayNode(arr), size=16)
+        assert png is not None
+
+    def test_nan_and_inf_are_sanitized(self):
+        arr = np.array([[np.nan, np.inf], [-np.inf, 1.0]])
+        png = thumbnails.render_thumbnail(FakeArrayNode(arr), size=16)
+        assert png is not None
+
+    def test_negative_values_are_clamped_before_log(self):
+        arr = np.array([[-5.0, -1.0], [0.0, 10.0]])
+        png = thumbnails.render_thumbnail(FakeArrayNode(arr), size=16)
+        assert png is not None
+
+    def test_grayscale_fallback_when_no_viridis(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setattr(thumbnails, "_VIRIDIS", None)
+        rgb = thumbnails._prepare_intensity(np.array([[0.0, 1.0], [2.0, 3.0]]))
+        assert rgb.shape == (2, 2, 3)
+        assert np.all(rgb[:, :, 0] == rgb[:, :, 1])
+        assert np.all(rgb[:, :, 1] == rgb[:, :, 2])
+
+
+class TestRenderThumbnailUnsupportedShapes:
+    def test_1d_array_returns_none(self):
+        assert thumbnails.render_thumbnail(FakeArrayNode(np.zeros(10)), size=32) is None
+
+    def test_4d_array_returns_none_after_squeeze(self):
+        arr = np.zeros((1, 5, 5, 2))
+        assert thumbnails.render_thumbnail(FakeArrayNode(arr), size=32) is None
+
+    def test_no_array_child_returns_none(self):
+        assert thumbnails.render_thumbnail(FakeContainer({}), size=32) is None
+
+
+class TestResolveArrayNode:
+    def test_direct_array_node_returned_as_is(self):
+        node = FakeArrayNode(np.zeros((2, 2)))
+        assert thumbnails._resolve_array_node(node) is node
+
+    def test_finds_first_array_child(self):
+        child = FakeArrayNode(np.zeros((2, 2)))
+        container = FakeContainer({"data": child})
+        assert thumbnails._resolve_array_node(container) is child
+
+    def test_skips_qmap_suffixed_children(self):
+        qmap_child = FakeArrayNode(np.zeros((2, 2)))
+        real_child = FakeArrayNode(np.ones((3, 3)))
+        container = FakeContainer({"foo_qmap": qmap_child, "bar": real_child})
+        assert thumbnails._resolve_array_node(container) is real_child
+
+    def test_non_iterable_node_returns_none(self):
+        assert thumbnails._resolve_array_node(object()) is None
+
+    def test_container_with_no_array_children_returns_none(self):
+        container = FakeContainer({"nested": FakeContainer({})})
+        assert thumbnails._resolve_array_node(container) is None
+
+
+class TestEncodePng:
+    def test_downscales_to_fit_size(self):
+        rgb = np.zeros((100, 200, 3), dtype=np.uint8)
+        png = thumbnails._encode_png(rgb, size=50)
+        decoded = _decode(png)
+        assert decoded.shape[0] <= 50
+        assert decoded.shape[1] <= 50
+
+    def test_never_upscales(self):
+        rgb = np.zeros((10, 10, 3), dtype=np.uint8)
+        png = thumbnails._encode_png(rgb, size=256)
+        decoded = _decode(png)
+        assert decoded.shape[:2] == (10, 10)
diff --git a/backend/tests/test_tiff_stack_source.py b/backend/tests/test_tiff_stack_source.py
index e32ae96..9d405ee 100644
--- a/backend/tests/test_tiff_stack_source.py
+++ b/backend/tests/test_tiff_stack_source.py
@@ -350,3 +350,80 @@ def test_shrinks_on_every_axis_that_still_can(self):
         out = tss.downsample_array(np.ones((8, 8, 8), dtype=np.float32), [2, 2, 2])
         assert out.shape == (4, 4, 4)
         assert np.allclose(out, 1.0)  # a constant volume stays constant
+
+
+class FakeContainer:
+    """Duck-typed fake Tiled container — enough surface for preflight_tiff_stack's
+    navigation (`_walk`/`_child_keys`) without touching a real Tiled server."""
+
+    def __init__(self, children=None, metadata=None):
+        self._children = dict(children or {})
+        self.metadata = metadata or {}
+
+    def __iter__(self):
+        return iter(self._children)
+
+    def __getitem__(self, key):
+        return self._children[key]
+
+    def __len__(self):
+        return len(self._children)
+
+    def keys(self):
+        return list(self._children.keys())
+
+
+class TestPreflightTiffStack:
+    """The registration path itself needs a live Tiled server (see this file's
+    module docstring), but preflight_tiff_stack never calls it — it only
+    inspects the local directory and navigates a fake Tiled client, so it is
+    fully testable without one."""
+
+    def test_no_collision_when_key_absent(self, tmp_path, monkeypatch):
+        root = write_stack(tmp_path / "scan")
+        client = FakeContainer({"browse": FakeContainer({})})
+        monkeypatch.setattr(tss, "get_tiled_client", lambda uri, key: client)
+        monkeypatch.setattr(tss, "api_key_for_uri", lambda uri: None)
+        result = tss.preflight_tiff_stack(None, str(root), "browse")
+        assert result["exists"] is False
+        assert result["existing"] is None
+        assert result["key"] == tss.registered_key(root)
+
+    def test_collision_reports_external_registration(self, tmp_path, monkeypatch):
+        root = write_stack(tmp_path / "scan")
+        key = tss.registered_key(root)
+        existing_node = FakeContainer(
+            {"scale0": object()},
+            metadata={"source_format": "tiff-stack-3d", "sample_name": "scan"},
+        )
+        client = FakeContainer({"browse": FakeContainer({key: existing_node})})
+        monkeypatch.setattr(tss, "get_tiled_client", lambda uri, key: client)
+        monkeypatch.setattr(tss, "api_key_for_uri", lambda uri: None)
+        result = tss.preflight_tiff_stack(None, str(root), "browse")
+        assert result["exists"] is True
+        assert result["existing"]["external"] is True
+        assert result["existing"]["child_count"] == 1
+        assert result["existing"]["sample_name"] == "scan"
+
+    def test_collision_with_internally_managed_data_is_not_external(self, tmp_path, monkeypatch):
+        root = write_stack(tmp_path / "scan")
+        key = tss.registered_key(root)
+        existing_node = FakeContainer({"img_0000.tif": object()}, metadata={})
+        client = FakeContainer({"browse": FakeContainer({key: existing_node})})
+        monkeypatch.setattr(tss, "get_tiled_client", lambda uri, key: client)
+        monkeypatch.setattr(tss, "api_key_for_uri", lambda uri: None)
+        result = tss.preflight_tiff_stack(None, str(root), "browse")
+        assert result["existing"]["external"] is False
+
+    def test_missing_target_container_reports_no_collision(self, tmp_path, monkeypatch):
+        root = write_stack(tmp_path / "scan")
+        client = FakeContainer({})
+        monkeypatch.setattr(tss, "get_tiled_client", lambda uri, key: client)
+        monkeypatch.setattr(tss, "api_key_for_uri", lambda uri: None)
+        result = tss.preflight_tiff_stack(None, str(root), "browse/missing")
+        assert result["exists"] is False
+
+    def test_invalid_path_propagates_the_http_exception(self, monkeypatch):
+        with pytest.raises(HTTPException) as exc:
+            tss.preflight_tiff_stack(None, "not/absolute")
+        assert exc.value.status_code == 400
diff --git a/backend/tests/test_tiled_annotation_sync.py b/backend/tests/test_tiled_annotation_sync.py
index f207608..86e3ad0 100644
--- a/backend/tests/test_tiled_annotation_sync.py
+++ b/backend/tests/test_tiled_annotation_sync.py
@@ -2,8 +2,11 @@
 
 from __future__ import annotations
 
+import pytest
+
+import arrays as arrays_mod
 from source_keys import parse_source_key
-from tiled_annotation_sync import STUDIO_ANNOTATED, annotation_metadata
+from tiled_annotation_sync import STUDIO_ANNOTATED, annotation_metadata, sync_annotation_metadata
 
 
 def test_parse_local_source_key() -> None:
@@ -25,3 +28,84 @@ def test_annotation_metadata_no_when_empty() -> None:
     meta = annotation_metadata({"classes": [], "slices": {}})
     assert meta[STUDIO_ANNOTATED] == "no"
     assert meta["studio_shape_count"] == 0
+
+
+def test_annotation_metadata_counts_classes_and_multiple_slices() -> None:
+    meta = annotation_metadata({
+        "classes": [{"classId": 1}, {"classId": 2}],
+        "slices": {"0": [{"id": "s1"}, {"id": "s2"}], "1": [{"id": "s3"}]},
+    })
+    assert meta["studio_shape_count"] == 3
+    assert meta["studio_class_count"] == 2
+
+
+def test_annotation_metadata_non_list_classes_counts_as_zero() -> None:
+    meta = annotation_metadata({"classes": "not-a-list", "slices": {}})
+    assert meta["studio_class_count"] == 0
+
+
+def test_annotation_metadata_includes_iso_timestamp() -> None:
+    meta = annotation_metadata({"classes": [], "slices": {}})
+    assert "T" in meta["studio_updated_at"]
+
+
+class FakeTiledNode:
+    def __init__(self):
+        self.updates: list[dict] = []
+
+    def update_metadata(self, metadata):
+        self.updates.append(metadata)
+
+
+class TestSyncAnnotationMetadata:
+    def test_local_source_is_a_no_op(self, monkeypatch: pytest.MonkeyPatch):
+        called = []
+        monkeypatch.setattr(arrays_mod, "resolve_array", lambda *a, **k: called.append(1))
+        sync_annotation_metadata("local:foo.tif", {"classes": [], "slices": {}})
+        assert called == []
+
+    def test_tiled_source_updates_node_metadata(self, monkeypatch: pytest.MonkeyPatch):
+        node = FakeTiledNode()
+        monkeypatch.setattr(arrays_mod, "resolve_array", lambda path, kind, server_uri: node)
+        sync_annotation_metadata(
+            "tiled::browse/sample", {"classes": [{"classId": 1}], "slices": {"0": [{"id": "s1"}]}},
+        )
+        assert len(node.updates) == 1
+        assert node.updates[0][STUDIO_ANNOTATED] == "yes"
+        assert node.updates[0]["studio_shape_count"] == 1
+
+    def test_node_without_update_metadata_is_skipped_not_raised(self, monkeypatch: pytest.MonkeyPatch):
+        class NoUpdateNode:
+            pass
+
+        monkeypatch.setattr(arrays_mod, "resolve_array", lambda path, kind, server_uri: NoUpdateNode())
+        # Should not raise even though the resolved node can't be updated.
+        sync_annotation_metadata("tiled::browse/sample", {"classes": [], "slices": {}})
+
+    def test_empty_path_after_tiled_prefix_is_a_no_op(self, monkeypatch: pytest.MonkeyPatch):
+        called = []
+        monkeypatch.setattr(arrays_mod, "resolve_array", lambda *a, **k: called.append(1))
+        sync_annotation_metadata("tiled:", {"classes": [], "slices": {}})
+        assert called == []
+
+    def test_passes_parsed_server_uri_through(self, monkeypatch: pytest.MonkeyPatch):
+        import source_keys
+        import tiled_config
+
+        monkeypatch.setattr(
+            tiled_config, "get_tiled_servers", lambda: {"local": {"uri": "http://x:1"}},
+        )
+        monkeypatch.setattr(source_keys, "get_tiled_servers", tiled_config.get_tiled_servers)
+
+        seen = []
+        node = FakeTiledNode()
+
+        def fake_resolve(path, kind, server_uri):
+            seen.append((path, kind, server_uri))
+            return node
+
+        monkeypatch.setattr(arrays_mod, "resolve_array", fake_resolve)
+        sync_annotation_metadata(
+            "tiled:http://x:1:browse/sample", {"classes": [], "slices": {}},
+        )
+        assert seen[0] == ("browse/sample", "tiled", "http://x:1")
diff --git a/backend/tests/test_tiled_clients.py b/backend/tests/test_tiled_clients.py
new file mode 100644
index 0000000..3bb7b35
--- /dev/null
+++ b/backend/tests/test_tiled_clients.py
@@ -0,0 +1,183 @@
+"""Tests for tiled_clients.py using plain nested dicts as fake Tiled nodes
+(only __getitem__/__len__ are needed) and a monkeypatched tiled.client.from_uri
+so get_tiled_client never makes a real network call.
+"""
+from __future__ import annotations
+
+import pytest
+
+import tiled_clients
+import tiled_config
+
+
+@pytest.fixture(autouse=True)
+def clear_client_cache():
+    tiled_clients._client_cache.clear()
+    yield
+    tiled_clients._client_cache.clear()
+
+
+# ---------------------------------------------------------------------------
+# api_key_for_uri
+# ---------------------------------------------------------------------------
+
+class TestApiKeyForUri:
+    def test_none_uri_returns_none(self):
+        assert tiled_clients.api_key_for_uri(None) is None
+
+    def test_matches_configured_server_ignoring_trailing_slash(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setattr(
+            tiled_config, "get_tiled_servers",
+            lambda: {"local": {"uri": "http://x:1/", "api_key": "secret"}},
+        )
+        monkeypatch.setattr(tiled_clients, "get_tiled_servers", tiled_config.get_tiled_servers)
+        assert tiled_clients.api_key_for_uri("http://x:1") == "secret"
+
+    def test_unmatched_uri_returns_none(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setattr(tiled_config, "get_tiled_servers", lambda: {"local": {"uri": "http://x:1"}})
+        monkeypatch.setattr(tiled_clients, "get_tiled_servers", tiled_config.get_tiled_servers)
+        assert tiled_clients.api_key_for_uri("http://other:2") is None
+
+
+# ---------------------------------------------------------------------------
+# get_tiled_client
+# ---------------------------------------------------------------------------
+
+class TestGetTiledClient:
+    def test_caches_by_uri_and_api_key(self, monkeypatch: pytest.MonkeyPatch):
+        calls = []
+
+        def fake_from_uri(uri, **kwargs):
+            calls.append((uri, kwargs))
+            return object()
+
+        monkeypatch.setattr("tiled.client.from_uri", fake_from_uri)
+        monkeypatch.setattr(tiled_clients, "get_tiled_base", lambda: "http://default:1")
+        monkeypatch.setattr(tiled_clients, "get_tiled_api_key", lambda: None)
+        monkeypatch.setattr(tiled_clients, "api_key_for_uri", lambda uri: None)
+
+        first = tiled_clients.get_tiled_client("http://x:1")
+        second = tiled_clients.get_tiled_client("http://x:1")
+        assert first is second
+        assert len(calls) == 1
+
+    def test_different_uri_creates_a_new_client(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setattr("tiled.client.from_uri", lambda uri, **kwargs: object())
+        monkeypatch.setattr(tiled_clients, "get_tiled_base", lambda: "http://default:1")
+        monkeypatch.setattr(tiled_clients, "get_tiled_api_key", lambda: None)
+        monkeypatch.setattr(tiled_clients, "api_key_for_uri", lambda uri: None)
+
+        a = tiled_clients.get_tiled_client("http://x:1")
+        b = tiled_clients.get_tiled_client("http://y:1")
+        assert a is not b
+
+    def test_falls_back_to_default_base_uri(self, monkeypatch: pytest.MonkeyPatch):
+        seen_uris = []
+        monkeypatch.setattr("tiled.client.from_uri", lambda uri, **kwargs: seen_uris.append(uri) or object())
+        monkeypatch.setattr(tiled_clients, "get_tiled_base", lambda: "http://default:1")
+        monkeypatch.setattr(tiled_clients, "get_tiled_api_key", lambda: None)
+        monkeypatch.setattr(tiled_clients, "api_key_for_uri", lambda uri: None)
+
+        tiled_clients.get_tiled_client(None)
+        assert seen_uris == ["http://default:1"]
+
+    def test_explicit_api_key_wins_over_configured_and_default(self, monkeypatch: pytest.MonkeyPatch):
+        seen_kwargs = []
+        monkeypatch.setattr("tiled.client.from_uri", lambda uri, **kwargs: seen_kwargs.append(kwargs) or object())
+        monkeypatch.setattr(tiled_clients, "get_tiled_base", lambda: "http://default:1")
+        monkeypatch.setattr(tiled_clients, "get_tiled_api_key", lambda: "global-key")
+        monkeypatch.setattr(tiled_clients, "api_key_for_uri", lambda uri: "configured-key")
+
+        tiled_clients.get_tiled_client("http://x:1", "explicit-key")
+        assert seen_kwargs == [{"api_key": "explicit-key"}]
+
+    def test_no_api_key_omits_the_kwarg(self, monkeypatch: pytest.MonkeyPatch):
+        seen_kwargs = []
+        monkeypatch.setattr("tiled.client.from_uri", lambda uri, **kwargs: seen_kwargs.append(kwargs) or object())
+        monkeypatch.setattr(tiled_clients, "get_tiled_base", lambda: "http://default:1")
+        monkeypatch.setattr(tiled_clients, "get_tiled_api_key", lambda: None)
+        monkeypatch.setattr(tiled_clients, "api_key_for_uri", lambda uri: None)
+
+        tiled_clients.get_tiled_client("http://x:1")
+        assert seen_kwargs == [{}]
+
+
+# ---------------------------------------------------------------------------
+# get_browse_container / get_browse_container_for
+# ---------------------------------------------------------------------------
+
+class TestGetBrowseContainer:
+    def test_env_var_path_wins_when_present_and_non_empty(self, monkeypatch: pytest.MonkeyPatch):
+        client = {"custom": {"path": {"a": 1, "b": 2}}}
+        monkeypatch.setenv("TILED_BROWSE_PATH", "custom/path")
+        node, prefix = tiled_clients.get_browse_container(client)
+        assert node == {"a": 1, "b": 2}
+        assert prefix == "custom/path"
+
+    def test_env_var_path_ignored_when_empty_container(self, monkeypatch: pytest.MonkeyPatch):
+        client = {"custom": {"path": {}}, "browse": {"s1": {}}}
+        monkeypatch.setenv("TILED_BROWSE_PATH", "custom/path")
+        node, prefix = tiled_clients.get_browse_container(client)
+        assert prefix == "browse"
+
+    def test_env_var_path_ignored_when_missing(self, monkeypatch: pytest.MonkeyPatch):
+        client = {"browse": {"s1": {}}}
+        monkeypatch.setenv("TILED_BROWSE_PATH", "does/not/exist")
+        node, prefix = tiled_clients.get_browse_container(client)
+        assert prefix == "browse"
+
+    def test_first_matching_candidate_wins_in_priority_order(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.delenv("TILED_BROWSE_PATH", raising=False)
+        client = {
+            "beamlines": {"bl733": {"s1": {}}},
+            "browse": {"s2": {}},
+        }
+        node, prefix = tiled_clients.get_browse_container(client)
+        # "beamlines/bl733" is checked before the plain "browse" fallback.
+        assert prefix == "beamlines/bl733"
+
+    def test_falls_back_to_plain_browse(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.delenv("TILED_BROWSE_PATH", raising=False)
+        client = {"browse": {"s1": {}}}
+        node, prefix = tiled_clients.get_browse_container(client)
+        assert prefix == "browse"
+        assert node == {"s1": {}}
+
+    def test_empty_candidate_containers_are_skipped(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.delenv("TILED_BROWSE_PATH", raising=False)
+        client = {"browse": {"generated_data": {}}, "beamlines": {"bl733": {"s1": {}}}}
+        node, prefix = tiled_clients.get_browse_container(client)
+        assert prefix == "beamlines/bl733"
+
+    def test_falls_back_to_client_root_when_nothing_matches(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.delenv("TILED_BROWSE_PATH", raising=False)
+        client = {}
+        node, prefix = tiled_clients.get_browse_container(client)
+        assert node is client
+        assert prefix == ""
+
+
+class TestGetBrowseContainerFor:
+    def test_explicit_path_navigates_directly(self):
+        client = {"a": {"b": {"x": 1}}}
+        node, prefix = tiled_clients.get_browse_container_for(client, "a/b")
+        assert node == {"x": 1}
+        assert prefix == "a/b"
+
+    def test_strips_leading_and_trailing_slashes(self):
+        client = {"a": {"x": 1}}
+        node, prefix = tiled_clients.get_browse_container_for(client, "/a/")
+        assert node == {"x": 1}
+        assert prefix == "a"
+
+    def test_no_path_falls_back_to_heuristic_discovery(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.delenv("TILED_BROWSE_PATH", raising=False)
+        client = {"browse": {"s1": {}}}
+        node, prefix = tiled_clients.get_browse_container_for(client, None)
+        assert prefix == "browse"
+
+    def test_empty_string_path_falls_back_to_heuristic_discovery(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.delenv("TILED_BROWSE_PATH", raising=False)
+        client = {"browse": {"s1": {}}}
+        node, prefix = tiled_clients.get_browse_container_for(client, "   ")
+        assert prefix == "browse"
diff --git a/backend/tests/test_tiling.py b/backend/tests/test_tiling.py
new file mode 100644
index 0000000..ade9620
--- /dev/null
+++ b/backend/tests/test_tiling.py
@@ -0,0 +1,229 @@
+"""Tests for tiling.py — real (no mocks) since torch+qlty are both installed
+in this environment; this module's whole point is qlty geometry, so faking
+that away would test nothing meaningful.
+"""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+torch = pytest.importorskip("torch")
+pytest.importorskip("qlty")
+
+import tiling  # noqa: E402
+from train_common import IGNORE_INDEX  # noqa: E402
+
+
+class TestStepAndBorder:
+    def test_step_for_applies_overlap_fraction(self):
+        assert tiling.step_for(64) == 48  # 64 - round(64*0.25)
+
+    def test_step_for_never_below_one(self):
+        assert tiling.step_for(1) >= 1
+
+    def test_border_for_uses_divisor(self):
+        assert tiling.border_for(64) == 8  # 64 // 8
+
+    def test_border_for_never_below_one(self):
+        assert tiling.border_for(1) >= 1
+
+
+class TestTileOrigins:
+    def test_exact_multiple_of_step(self):
+        # dim=100, window=20, step=20 -> origins 0,20,40,60,80 (last clamps to 80 anyway)
+        origins = tiling.tile_origins(100, 20, 20)
+        assert origins[0] == 0
+        assert origins[-1] == 80
+        assert all(o + 20 <= 100 for o in origins)
+
+    def test_dim_equals_window_gives_single_origin(self):
+        assert tiling.tile_origins(20, 20, 15) == [0]
+
+    def test_last_window_clamped_inside_image(self):
+        origins = tiling.tile_origins(50, 20, 15)
+        assert origins[-1] == 30  # 50 - 20
+        assert max(origins) + 20 == 50
+
+    def test_dim_smaller_than_window_raises(self):
+        with pytest.raises(ValueError, match="smaller than window"):
+            tiling.tile_origins(10, 20, 5)
+
+
+class TestPadToMin:
+    def test_already_big_enough_returns_same_array(self):
+        arr = np.zeros((32, 32), dtype=np.uint8)
+        assert tiling.pad_to_min(arr, 32, 32, fill=0) is arr
+
+    def test_pads_bottom_right_only(self):
+        arr = np.ones((10, 10), dtype=np.uint8)
+        out = tiling.pad_to_min(arr, 16, 20, fill=0)
+        assert out.shape == (16, 20)
+        assert np.all(out[:10, :10] == 1)
+        assert np.all(out[10:, :] == 0)
+        assert np.all(out[:, 10:] == 0)
+
+    def test_fill_value_used_for_padding(self):
+        arr = np.zeros((5, 5), dtype=np.uint8)
+        out = tiling.pad_to_min(arr, 8, 8, fill=IGNORE_INDEX)
+        assert out[7, 7] == IGNORE_INDEX
+
+    def test_preserves_trailing_channel_dim(self):
+        arr = np.ones((10, 10, 3), dtype=np.uint8)
+        out = tiling.pad_to_min(arr, 16, 16, fill=0)
+        assert out.shape == (16, 16, 3)
+
+
+class TestQltyAvailable:
+    def test_true_when_installed(self):
+        assert tiling.qlty_available() is True
+
+
+class TestTilePair:
+    def test_produces_window_sized_patches(self):
+        rgb = np.random.default_rng(0).integers(0, 256, size=(64, 64, 3), dtype=np.uint8)
+        label = np.zeros((64, 64), dtype=np.uint8)
+        label[10:54, 10:54] = 1  # broad interior annotation
+        patches = tiling._tile_pair(rgb, label, window=32)
+        assert len(patches) > 0
+        for patch_rgb, patch_label in patches:
+            assert patch_rgb.shape == (32, 32, 3)
+            assert patch_label.shape == (32, 32)
+
+    def test_entirely_unannotated_image_yields_no_patches(self):
+        rgb = np.zeros((64, 64, 3), dtype=np.uint8)
+        label = np.full((64, 64), IGNORE_INDEX, dtype=np.uint8)
+        patches = tiling._tile_pair(rgb, label, window=32)
+        assert patches == []
+
+    def test_edge_only_annotation_falls_back_to_unmasked_patches(self):
+        # Annotate only the outermost couple of pixels — inside every patch's
+        # down-weighted border ring, so the masked pass would keep nothing;
+        # the edge-only fallback (mask_borders=False) must still find it.
+        rgb = np.zeros((64, 64, 3), dtype=np.uint8)
+        label = np.full((64, 64), IGNORE_INDEX, dtype=np.uint8)
+        label[0:2, 0:2] = 1
+        patches = tiling._tile_pair(rgb, label, window=32)
+        assert len(patches) > 0
+        assert any((p_label != IGNORE_INDEX).any() for _, p_label in patches)
+
+    def test_smaller_than_window_image_is_padded_first(self):
+        rgb = np.ones((10, 10, 3), dtype=np.uint8)
+        label = np.ones((10, 10), dtype=np.uint8)
+        patches = tiling._tile_pair(rgb, label, window=32)
+        assert len(patches) >= 1
+        assert patches[0][0].shape == (32, 32, 3)
+
+
+class TestHoldoutValPatches:
+    def _pairs(self, n):
+        return [(np.zeros((4, 4)), np.zeros((4, 4))) for _ in range(n)]
+
+    def test_no_op_when_val_already_present(self):
+        datasets = {"train": self._pairs(20), "val": self._pairs(2)}
+        out, n = tiling.holdout_val_patches(datasets, seed=0)
+        assert n == 0
+        assert out is datasets
+
+    def test_no_op_below_min_patches(self):
+        datasets = {"train": self._pairs(4), "val": []}
+        out, n = tiling.holdout_val_patches(datasets, seed=0, min_patches=8)
+        assert n == 0
+        assert out["train"] == datasets["train"]
+
+    def test_holds_out_a_seeded_fraction(self):
+        datasets = {"train": self._pairs(20), "val": []}
+        out, n = tiling.holdout_val_patches(datasets, seed=0, fraction=0.1)
+        assert n == 2  # round(20 * 0.1)
+        assert len(out["train"]) == 18
+        assert len(out["val"]) == 2
+
+    def test_deterministic_given_same_seed(self):
+        datasets = {"train": self._pairs(20), "val": []}
+        out1, _ = tiling.holdout_val_patches(datasets, seed=42)
+        out2, _ = tiling.holdout_val_patches(datasets, seed=42)
+        assert len(out1["val"]) == len(out2["val"])
+
+
+class TestTileDatasets:
+    def test_tiles_every_split(self):
+        rgb = np.random.default_rng(0).integers(0, 256, size=(64, 64, 3), dtype=np.uint8)
+        label = np.ones((64, 64), dtype=np.uint8)
+        datasets = {"train": [(rgb, label)], "val": [(rgb, label)]}
+        out = tiling.tile_datasets(datasets, window=32)
+        assert len(out["train"]) > 0
+        assert len(out["val"]) > 0
+
+    def test_cancellation_returns_none(self):
+        rgb = np.ones((64, 64, 3), dtype=np.uint8)
+        label = np.ones((64, 64), dtype=np.uint8)
+        datasets = {"train": [(rgb, label), (rgb, label)]}
+        out = tiling.tile_datasets(datasets, window=32, cancel_cb=lambda: True)
+        assert out is None
+
+
+def _tiny_model():
+    model = torch.nn.Conv2d(3, 2, kernel_size=3, padding=1)
+
+    def forward_fn(batch):
+        return model(batch)
+
+    def to_tensor_fn(arr):
+        t = torch.from_numpy(np.ascontiguousarray(arr)).float() / 255.0
+        return t.permute(2, 0, 1) if t.ndim == 3 else t.unsqueeze(0)
+
+    return forward_fn, to_tensor_fn
+
+
+class TestBlendTiledForwardAndFriends:
+    def test_predict_label_map_tiled_shape_and_dtype(self):
+        forward_fn, to_tensor_fn = _tiny_model()
+        rgb = np.random.default_rng(0).integers(0, 256, size=(48, 48, 3), dtype=np.uint8)
+        label = tiling.predict_label_map_tiled(
+            rgb, forward_fn=forward_fn, to_tensor_fn=to_tensor_fn,
+            window=32, min_confidence=0.0, device="cpu",
+        )
+        assert label.shape == (48, 48)
+        assert label.dtype == np.uint8
+
+    def test_predict_label_map_tiled_high_confidence_threshold_yields_background(self):
+        forward_fn, to_tensor_fn = _tiny_model()
+        rgb = np.zeros((48, 48, 3), dtype=np.uint8)
+        label = tiling.predict_label_map_tiled(
+            rgb, forward_fn=forward_fn, to_tensor_fn=to_tensor_fn,
+            window=32, min_confidence=1.1, device="cpu",  # impossible to reach
+        )
+        assert np.all(label == 0)
+
+    def test_predict_label_map_tiled_cancellation_returns_none(self):
+        forward_fn, to_tensor_fn = _tiny_model()
+        rgb = np.zeros((48, 48, 3), dtype=np.uint8)
+        label = tiling.predict_label_map_tiled(
+            rgb, forward_fn=forward_fn, to_tensor_fn=to_tensor_fn,
+            window=32, min_confidence=0.5, device="cpu", cancel_cb=lambda: True,
+        )
+        assert label is None
+
+    def test_denoise_image_tiled_single_channel_squeezed(self):
+        model = torch.nn.Conv2d(1, 1, kernel_size=3, padding=1)
+
+        def forward_fn(batch):
+            return model(batch)
+
+        def to_tensor_fn(arr):
+            return torch.from_numpy(np.ascontiguousarray(arr)).float().unsqueeze(0) / 255.0
+
+        gray = np.random.default_rng(0).integers(0, 256, size=(48, 48), dtype=np.uint8)
+        out = tiling.denoise_image_tiled(
+            gray, forward_fn=forward_fn, to_tensor_fn=to_tensor_fn, window=32, device="cpu",
+        )
+        assert out.shape == (48, 48)
+        assert out.dtype == np.float32
+
+    def test_image_smaller_than_window_is_handled(self):
+        forward_fn, to_tensor_fn = _tiny_model()
+        rgb = np.zeros((10, 10, 3), dtype=np.uint8)
+        label = tiling.predict_label_map_tiled(
+            rgb, forward_fn=forward_fn, to_tensor_fn=to_tensor_fn,
+            window=32, min_confidence=0.0, device="cpu",
+        )
+        assert label.shape == (10, 10)
diff --git a/backend/tests/test_zarr_source.py b/backend/tests/test_zarr_source.py
index 42a7f23..f40bee8 100644
--- a/backend/tests/test_zarr_source.py
+++ b/backend/tests/test_zarr_source.py
@@ -226,3 +226,75 @@ def test_leaves_plain_volumes_untouched(self) -> None:
         meta = arrays_mod.array_shape_meta(_FakeArray((7, 16, 16)))
         assert (meta["n_slices"], meta["height"], meta["width"]) == (7, 16, 16)
         assert "z_downsample" not in meta
+
+
+class FakeContainer:
+    """Duck-typed fake Tiled container — enough surface for preflight_zarr's
+    navigation (`_walk`/`_child_keys`) without touching a real Tiled server."""
+
+    def __init__(self, children=None, metadata=None):
+        self._children = dict(children or {})
+        self.metadata = metadata or {}
+
+    def __iter__(self):
+        return iter(self._children)
+
+    def __getitem__(self, key):
+        return self._children[key]
+
+    def __len__(self):
+        return len(self._children)
+
+    def keys(self):
+        return list(self._children.keys())
+
+
+class TestPreflightZarr:
+    """register_zarr itself needs a live Tiled server; preflight_zarr never
+    calls it — it only inspects the local store and navigates a fake Tiled
+    client, so it is fully testable without one."""
+
+    def test_no_collision_when_key_absent(self, pyramid: Path, monkeypatch) -> None:
+        client = FakeContainer({"browse": FakeContainer({})})
+        monkeypatch.setattr(zarr_source, "get_tiled_client", lambda uri, key: client)
+        monkeypatch.setattr(zarr_source, "api_key_for_uri", lambda uri: None)
+        result = zarr_source.preflight_zarr(None, str(pyramid), "browse")
+        assert result["exists"] is False
+        assert result["existing"] is None
+        assert result["key"] == zarr_source.registered_key(pyramid)
+
+    def test_collision_reports_external_registration(self, pyramid: Path, monkeypatch) -> None:
+        key = zarr_source.registered_key(pyramid)
+        existing_node = FakeContainer(
+            {"scale0": object()},
+            metadata={"source_format": "zarr", "sample_name": "vol", "n_images": 9},
+        )
+        client = FakeContainer({"browse": FakeContainer({key: existing_node})})
+        monkeypatch.setattr(zarr_source, "get_tiled_client", lambda uri, key: client)
+        monkeypatch.setattr(zarr_source, "api_key_for_uri", lambda uri: None)
+        result = zarr_source.preflight_zarr(None, str(pyramid), "browse")
+        assert result["exists"] is True
+        assert result["existing"]["external"] is True
+        assert result["existing"]["sample_name"] == "vol"
+        assert result["existing"]["n_images"] == 9
+
+    def test_collision_with_internally_managed_data_is_not_external(self, pyramid: Path, monkeypatch) -> None:
+        key = zarr_source.registered_key(pyramid)
+        existing_node = FakeContainer({"img_0000.tif": object()}, metadata={})
+        client = FakeContainer({"browse": FakeContainer({key: existing_node})})
+        monkeypatch.setattr(zarr_source, "get_tiled_client", lambda uri, key: client)
+        monkeypatch.setattr(zarr_source, "api_key_for_uri", lambda uri: None)
+        result = zarr_source.preflight_zarr(None, str(pyramid), "browse")
+        assert result["existing"]["external"] is False
+
+    def test_missing_target_container_reports_no_collision(self, pyramid: Path, monkeypatch) -> None:
+        client = FakeContainer({})
+        monkeypatch.setattr(zarr_source, "get_tiled_client", lambda uri, key: client)
+        monkeypatch.setattr(zarr_source, "api_key_for_uri", lambda uri: None)
+        result = zarr_source.preflight_zarr(None, str(pyramid), "browse/missing")
+        assert result["exists"] is False
+
+    def test_invalid_path_propagates_the_http_exception(self) -> None:
+        with pytest.raises(HTTPException) as exc:
+            zarr_source.preflight_zarr(None, "relative/path")
+        assert exc.value.status_code == 400

From 7c3aa19bd3e11720b48a7d047c0abbe53fbccca9 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Tue, 8 Sep 2026 12:45:44 -0700
Subject: [PATCH 23/38] tests tests tests

---
 .gitignore                                    |   1 +
 backend/tests/test_infer_jobs.py              | 259 ++++++
 backend/tests/test_ipred_routes.py            | 235 ++++++
 backend/tests/test_tiled_mask_sync.py         | 231 ++++++
 backend/tests/test_train_common.py            | 278 +++++++
 backend/tests/test_train_common_ml.py         | 223 +++++
 frontend/src/app/pages/AnnotatePage.test.tsx  | 770 ++++++++++++++++++
 frontend/src/app/pages/BrowsePage.test.tsx    | 232 ++++++
 frontend/src/app/pages/ConnectPage.test.tsx   | 328 ++++++++
 .../components/Browse/BrowseColumn.test.tsx   | 138 ++++
 .../Browse/BrowseDetailPanel.test.tsx         | 176 ++++
 .../components/Browse/ColumnBrowser.test.tsx  | 267 ++++++
 .../components/Browse/ItemsColumn.test.tsx    | 169 ++++
 .../Browse/LocalSampleBrowser.test.tsx        | 221 +++++
 .../components/Browse/ResizeDivider.test.tsx  |  71 ++
 .../components/Browse/SlicesColumn.test.tsx   | 161 ++++
 .../src/components/Browse/StarRating.test.tsx |  59 ++
 .../Browse/hooks/useBrowseData.test.ts        | 439 ++++++++++
 .../CompositionPanel.test.tsx                 | 145 +++-
 .../components/Ingest/ConflictDialog.test.tsx | 121 +++
 .../components/Ingest/IngestDropzone.test.tsx | 457 +++++++++++
 .../src/components/Ingest/ZarrLoader.test.tsx | 205 +++++
 .../AnnotationCanvas/OverlaysLayer.test.tsx   | 217 +++++
 .../AnnotationCanvas/ShapesLayer.test.tsx     | 248 ++++++
 .../annotate/AnnotationCanvas/index.test.tsx  | 283 +++++++
 .../annotate/ClassManager/index.test.tsx      | 145 ++++
 .../annotate/DenoiseBakeModal.test.tsx        | 195 +++++
 .../annotate/DenoisePanel/index.test.tsx      | 292 +++++++
 .../annotate/DisplayControls/index.test.tsx   | 104 +++
 .../annotate/DownloadModal.test.tsx           | 275 +++++++
 .../FeatureChannelsPanel/index.test.tsx       | 330 ++++++++
 .../annotate/HistogramControl/index.test.tsx  | 120 +++
 .../annotate/InsightsModal.test.tsx           | 161 ++++
 .../annotate/LayersPanel/index.test.tsx       |  79 ++
 .../annotate/MaskToolsPanel/index.test.tsx    |  64 ++
 .../annotate/MeasurementPanel/index.test.tsx  | 181 ++++
 .../annotate/PerfOverlay/index.test.tsx       |  63 ++
 .../PixelClassifierPanel/index.test.tsx       | 672 +++++++++++++++
 .../components/annotate/SaveModal.test.tsx    | 144 ++++
 .../annotate/SliceNavigator/index.test.tsx    |  93 +++
 .../annotate/Toolbar/index.test.tsx           | 312 +++++++
 .../annotate/VersionHistoryModal.test.tsx     | 110 +++
 .../annotate/VersionPreviewBar.test.tsx       | 162 ++++
 frontend/src/hooks/editHistory.test.ts        | 152 ++++
 .../src/hooks/useAnnotatedSourceKeys.test.tsx |  57 ++
 frontend/src/hooks/useDraftSync.test.tsx      |  94 +++
 frontend/src/hooks/useExportJob.test.tsx      | 101 +++
 frontend/src/hooks/useFeatureChannels.test.ts | 293 +++++++
 frontend/src/hooks/useFeatureManifold.test.ts | 297 +++++++
 frontend/src/hooks/useGuideSync.test.tsx      | 132 +++
 frontend/src/hooks/useHubSelectedTabs.test.ts |  61 ++
 frontend/src/hooks/useImageSlice.test.tsx     |  75 ++
 frontend/src/hooks/useKeybinds.test.ts        | 199 +++++
 frontend/src/hooks/useMaskOps.test.ts         |  91 +++
 frontend/src/hooks/useOpenInAnnotate.test.tsx | 178 ++++
 frontend/src/hooks/usePixelClassifier.test.ts | 549 +++++++++++++
 frontend/src/hooks/useSam.test.ts             | 200 +++++
 frontend/src/hooks/useSave.test.tsx           | 159 ++++
 frontend/src/hooks/useSave.ts                 |  23 +-
 .../src/hooks/useTrainCapability.test.tsx     |  65 ++
 frontend/src/hooks/useTrainRuns.test.tsx      | 111 +++
 frontend/src/lib/datasetStats.test.ts         | 177 ++++
 frontend/src/lib/ipredApi.test.ts             | 198 +++++
 frontend/src/lib/livewire.test.ts             |  90 +-
 frontend/src/lib/magicwand.test.ts            | 128 ++-
 frontend/src/lib/measure.test.ts              |  95 +++
 frontend/src/lib/pixelClf.gaps.test.ts        | 298 +++++++
 frontend/src/lib/sam/adjust.test.ts           | 133 +++
 frontend/src/lib/sam/samClient.test.ts        | 177 ++++
 frontend/src/stores/annotationStore.test.ts   | 389 +++++++++
 frontend/src/stores/clipboardStore.test.ts    |  30 +
 .../src/stores/referenceGuideStore.test.ts    | 102 +++
 frontend/vite.config.ts                       |   5 +
 73 files changed, 14082 insertions(+), 13 deletions(-)
 create mode 100644 backend/tests/test_train_common_ml.py
 create mode 100644 frontend/src/app/pages/AnnotatePage.test.tsx
 create mode 100644 frontend/src/app/pages/BrowsePage.test.tsx
 create mode 100644 frontend/src/app/pages/ConnectPage.test.tsx
 create mode 100644 frontend/src/components/Browse/BrowseColumn.test.tsx
 create mode 100644 frontend/src/components/Browse/BrowseDetailPanel.test.tsx
 create mode 100644 frontend/src/components/Browse/ColumnBrowser.test.tsx
 create mode 100644 frontend/src/components/Browse/ItemsColumn.test.tsx
 create mode 100644 frontend/src/components/Browse/LocalSampleBrowser.test.tsx
 create mode 100644 frontend/src/components/Browse/ResizeDivider.test.tsx
 create mode 100644 frontend/src/components/Browse/SlicesColumn.test.tsx
 create mode 100644 frontend/src/components/Browse/StarRating.test.tsx
 create mode 100644 frontend/src/components/Browse/hooks/useBrowseData.test.ts
 create mode 100644 frontend/src/components/Ingest/ConflictDialog.test.tsx
 create mode 100644 frontend/src/components/Ingest/IngestDropzone.test.tsx
 create mode 100644 frontend/src/components/Ingest/ZarrLoader.test.tsx
 create mode 100644 frontend/src/components/annotate/AnnotationCanvas/OverlaysLayer.test.tsx
 create mode 100644 frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.test.tsx
 create mode 100644 frontend/src/components/annotate/AnnotationCanvas/index.test.tsx
 create mode 100644 frontend/src/components/annotate/ClassManager/index.test.tsx
 create mode 100644 frontend/src/components/annotate/DenoiseBakeModal.test.tsx
 create mode 100644 frontend/src/components/annotate/DenoisePanel/index.test.tsx
 create mode 100644 frontend/src/components/annotate/DisplayControls/index.test.tsx
 create mode 100644 frontend/src/components/annotate/DownloadModal.test.tsx
 create mode 100644 frontend/src/components/annotate/FeatureChannelsPanel/index.test.tsx
 create mode 100644 frontend/src/components/annotate/HistogramControl/index.test.tsx
 create mode 100644 frontend/src/components/annotate/InsightsModal.test.tsx
 create mode 100644 frontend/src/components/annotate/LayersPanel/index.test.tsx
 create mode 100644 frontend/src/components/annotate/MaskToolsPanel/index.test.tsx
 create mode 100644 frontend/src/components/annotate/MeasurementPanel/index.test.tsx
 create mode 100644 frontend/src/components/annotate/PerfOverlay/index.test.tsx
 create mode 100644 frontend/src/components/annotate/PixelClassifierPanel/index.test.tsx
 create mode 100644 frontend/src/components/annotate/SaveModal.test.tsx
 create mode 100644 frontend/src/components/annotate/SliceNavigator/index.test.tsx
 create mode 100644 frontend/src/components/annotate/Toolbar/index.test.tsx
 create mode 100644 frontend/src/components/annotate/VersionHistoryModal.test.tsx
 create mode 100644 frontend/src/components/annotate/VersionPreviewBar.test.tsx
 create mode 100644 frontend/src/hooks/editHistory.test.ts
 create mode 100644 frontend/src/hooks/useAnnotatedSourceKeys.test.tsx
 create mode 100644 frontend/src/hooks/useDraftSync.test.tsx
 create mode 100644 frontend/src/hooks/useExportJob.test.tsx
 create mode 100644 frontend/src/hooks/useFeatureChannels.test.ts
 create mode 100644 frontend/src/hooks/useFeatureManifold.test.ts
 create mode 100644 frontend/src/hooks/useGuideSync.test.tsx
 create mode 100644 frontend/src/hooks/useHubSelectedTabs.test.ts
 create mode 100644 frontend/src/hooks/useImageSlice.test.tsx
 create mode 100644 frontend/src/hooks/useKeybinds.test.ts
 create mode 100644 frontend/src/hooks/useMaskOps.test.ts
 create mode 100644 frontend/src/hooks/useOpenInAnnotate.test.tsx
 create mode 100644 frontend/src/hooks/usePixelClassifier.test.ts
 create mode 100644 frontend/src/hooks/useSam.test.ts
 create mode 100644 frontend/src/hooks/useSave.test.tsx
 create mode 100644 frontend/src/hooks/useTrainCapability.test.tsx
 create mode 100644 frontend/src/hooks/useTrainRuns.test.tsx
 create mode 100644 frontend/src/lib/datasetStats.test.ts
 create mode 100644 frontend/src/lib/ipredApi.test.ts
 create mode 100644 frontend/src/lib/measure.test.ts
 create mode 100644 frontend/src/lib/pixelClf.gaps.test.ts
 create mode 100644 frontend/src/lib/sam/adjust.test.ts
 create mode 100644 frontend/src/lib/sam/samClient.test.ts
 create mode 100644 frontend/src/stores/annotationStore.test.ts
 create mode 100644 frontend/src/stores/clipboardStore.test.ts
 create mode 100644 frontend/src/stores/referenceGuideStore.test.ts

diff --git a/.gitignore b/.gitignore
index fc9aaf1..1f0841d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -56,6 +56,7 @@ Thumbs.db
 htmlcov/
 .coverage
 coverage.xml
+frontend/coverage/
 
 # Vendored SAM model (large, fetched via frontend/scripts/fetch-sam-model.mjs)
 frontend/public/models/
diff --git a/backend/tests/test_infer_jobs.py b/backend/tests/test_infer_jobs.py
index 5fb7c68..b0cbff2 100644
--- a/backend/tests/test_infer_jobs.py
+++ b/backend/tests/test_infer_jobs.py
@@ -11,6 +11,7 @@
 
 from __future__ import annotations
 
+import io
 import sys
 import threading
 import time
@@ -252,3 +253,261 @@ def spying_forward_fn(batch):
         assert shapes is not None
 
     assert max_concurrent == 1, "GPU_FORWARD_LOCK failed to serialize concurrent forward calls"
+
+
+# ---------------------------------------------------------------------------
+# Pure helpers
+# ---------------------------------------------------------------------------
+
+class TestHexToRgb:
+    def test_full_hex(self):
+        assert infer_jobs._hex_to_rgb("#00ff80") == (0, 255, 128)
+
+    def test_short_hex_is_expanded(self):
+        assert infer_jobs._hex_to_rgb("#0f8") == (0, 255, 136)
+
+    def test_none_falls_back_to_red(self):
+        assert infer_jobs._hex_to_rgb(None) == (255, 0, 0)
+
+    def test_missing_hash_falls_back_to_red(self):
+        assert infer_jobs._hex_to_rgb("00ff80") == (255, 0, 0)
+
+    def test_invalid_hex_digits_fall_back_to_red(self):
+        assert infer_jobs._hex_to_rgb("#zzzzzz") == (255, 0, 0)
+
+
+class TestVectorizeLabelMap:
+    def _run_classes(self):
+        return [{"classId": 1, "label": "a"}, {"classId": 2, "label": "b"}]
+
+    def test_component_below_min_area_is_dropped(self):
+        label_map = np.zeros((20, 20), dtype=np.uint8)
+        label_map[0:2, 0:2] = 1  # 4px, tiny
+        shapes = infer_jobs._vectorize_label_map(label_map, self._run_classes(), min_area=50, simplify_tol=0.0, run_id="run12345", slice_idx=0)
+        assert shapes == []
+
+    def test_ring_shaped_component_gets_a_hole(self):
+        label_map = np.ones((30, 30), dtype=np.uint8)  # all class 1
+        label_map[10:20, 10:20] = 2  # a class-2 hole inside class 1
+        shapes = infer_jobs._vectorize_label_map(label_map, self._run_classes(), min_area=1, simplify_tol=0.0, run_id="run12345", slice_idx=0)
+        class1_shape = next(s for s in shapes if s["classId"] == 1)
+        assert "holes" in class1_shape
+        assert len(class1_shape["holes"]) == 1
+
+    def test_simplify_tol_reduces_point_count(self):
+        label_map = np.zeros((40, 40), dtype=np.uint8)
+        label_map[5:35, 5:35] = 1  # a large, simple square
+        unsimplified = infer_jobs._vectorize_label_map(label_map, self._run_classes(), min_area=1, simplify_tol=0.0, run_id="run12345", slice_idx=0)
+        simplified = infer_jobs._vectorize_label_map(label_map, self._run_classes(), min_area=1, simplify_tol=5.0, run_id="run12345", slice_idx=0)
+        assert len(simplified[0]["points"]) <= len(unsimplified[0]["points"])
+
+    def test_shape_ids_are_unique_per_component(self):
+        label_map = np.zeros((30, 30), dtype=np.uint8)
+        label_map[2:6, 2:6] = 1
+        label_map[20:26, 20:26] = 1
+        shapes = infer_jobs._vectorize_label_map(label_map, self._run_classes(), min_area=1, simplify_tol=0.0, run_id="run12345", slice_idx=3)
+        ids = [s["id"] for s in shapes]
+        assert len(ids) == len(set(ids))
+
+    def test_class_with_no_pixels_is_skipped(self):
+        label_map = np.zeros((10, 10), dtype=np.uint8)
+        shapes = infer_jobs._vectorize_label_map(label_map, self._run_classes(), min_area=1, simplify_tol=0.0, run_id="run12345", slice_idx=0)
+        assert shapes == []
+
+
+# ---------------------------------------------------------------------------
+# run_infer_job — guard paths that don't need a real trained run
+# ---------------------------------------------------------------------------
+
+class TestRunInferJobGuards:
+    def test_busy_ml_lock_reports_error_not_a_crash(self):
+        train_common.ML_LOCK.acquire()
+        try:
+            jid = export_jobs.new_job("x")
+            infer_jobs.run_infer_job(jid, _infer_request("whatever"))
+            job = export_jobs.get_job(jid)
+            assert job["state"] == "error"
+            assert "already running" in job["error"]
+        finally:
+            train_common.ML_LOCK.release()
+
+    def test_no_device_reports_error_and_releases_lock(self, monkeypatch):
+        monkeypatch.setattr(train_common, "pick_device", lambda: None)
+        jid = export_jobs.new_job("x")
+        infer_jobs.run_infer_job(jid, _infer_request("whatever"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "torch is not installed" in job["error"]
+        assert train_common.ML_LOCK.locked() is False
+
+    def test_denoiser_run_is_refused(self, monkeypatch):
+        monkeypatch.setattr(
+            train_common, "load_run_config",
+            lambda run_id: {"model_family": "dlsia_denoiser", "classes": [], "image_size": 64, "render": {}},
+        )
+        monkeypatch.setattr(train_common, "load_adapter_state", lambda run_id: {})
+        jid = export_jobs.new_job("x")
+        infer_jobs.run_infer_job(jid, _infer_request("whatever"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "denoiser run" in job["error"]
+
+    def test_unsupported_model_family_is_refused(self, monkeypatch):
+        monkeypatch.setattr(
+            train_common, "load_run_config",
+            lambda run_id: {"model_family": "something_weird", "classes": [], "image_size": 64, "render": {}},
+        )
+        monkeypatch.setattr(train_common, "load_adapter_state", lambda run_id: {})
+        jid = export_jobs.new_job("x")
+        infer_jobs.run_infer_job(jid, _infer_request("whatever"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "Unsupported model family" in job["error"]
+
+    def test_tiled_run_without_qlty_is_refused(self, monkeypatch):
+        import tiling
+
+        monkeypatch.setattr(
+            train_common, "load_run_config",
+            lambda run_id: {
+                "model_family": "dlsia_tunet", "classes": [], "image_size": 64, "render": {},
+                "hyperparams": {"tiling": True},
+            },
+        )
+        monkeypatch.setattr(train_common, "load_adapter_state", lambda run_id: {})
+        monkeypatch.setattr(tiling, "qlty_available", lambda: False)
+        jid = export_jobs.new_job("x")
+        infer_jobs.run_infer_job(jid, _infer_request("whatever"))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "qlty" in job["error"]
+
+
+# ---------------------------------------------------------------------------
+# preview_png
+# ---------------------------------------------------------------------------
+
+class TestPreviewPng:
+    def test_missing_job_is_404(self):
+        with pytest.raises(Exception) as exc:
+            infer_jobs.preview_png("no-such-job", 0)
+        assert exc.value.status_code == 404
+
+    def test_missing_slice_is_404(self):
+        infer_jobs._cache_put("job-with-no-slices", {"classes": [], "label_pngs": {}})
+        with pytest.raises(Exception) as exc:
+            infer_jobs.preview_png("job-with-no-slices", 0)
+        assert exc.value.status_code == 404
+
+    def test_colorizes_predicted_classes(self):
+        from PIL import Image as PILImage
+
+        label = np.zeros((8, 8), dtype=np.uint8)
+        label[0:4, 0:4] = 1
+        label[4:8, 4:8] = 2
+        buf = io.BytesIO()
+        PILImage.fromarray(label, mode="L").save(buf, format="PNG")
+
+        infer_jobs._cache_put(
+            "job-colorize",
+            {
+                "classes": [{"classId": 1, "color": "#ff0000"}, {"classId": 2, "color": "#00ff00"}],
+                "label_pngs": {0: buf.getvalue()},
+            },
+        )
+        png = infer_jobs.preview_png("job-colorize", 0)
+        rgba = np.asarray(PILImage.open(io.BytesIO(png)))
+        assert tuple(rgba[0, 0]) == (255, 0, 0, 180)
+        assert tuple(rgba[4, 4]) == (0, 255, 0, 180)
+        assert rgba[7, 0][3] == 0  # background stays transparent
+
+
+# ---------------------------------------------------------------------------
+# run_write_tiled_job
+# ---------------------------------------------------------------------------
+
+class TestRunWriteTiledJob:
+    def test_missing_cache_entry_reports_error(self):
+        jid = export_jobs.new_job("x")
+        infer_jobs.run_write_tiled_job(jid, "no-such-infer-job")
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "No cached inference results" in job["error"]
+
+    def test_non_tiled_source_is_refused(self):
+        infer_jobs._cache_put("infer-local", {"kind": "local", "classes": [], "label_pngs": {}})
+        jid = export_jobs.new_job("x")
+        infer_jobs.run_write_tiled_job(jid, "infer-local")
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "not a Tiled array" in job["error"]
+
+    def test_writes_semantic_and_class_volumes(self, monkeypatch):
+        from PIL import Image as PILImage
+
+        label0 = np.zeros((4, 4), dtype=np.uint8)
+        label0[0:2, 0:2] = 1
+        label1 = np.zeros((4, 4), dtype=np.uint8)
+        label1[2:4, 2:4] = 2
+
+        def _png(arr):
+            buf = io.BytesIO()
+            PILImage.fromarray(arr, mode="L").save(buf, format="PNG")
+            return buf.getvalue()
+
+        infer_jobs._cache_put(
+            "infer-tiled",
+            {
+                "kind": "tiled", "source": "browse/sample", "server_uri": "http://x",
+                "classes": [{"classId": 1, "label": "a", "color": "#f00"}, {"classId": 2, "label": "b", "color": "#0f0"}],
+                "label_pngs": {0: _png(label0), 1: _png(label1)},
+            },
+        )
+
+        captured = {}
+
+        def fake_write_masks_to_tiled(source, server_uri, volumes, classes, container_suffix=""):
+            captured.update(source=source, server_uri=server_uri, volumes=volumes, container_suffix=container_suffix)
+            return {"path": "browse/sample__masks_deep"}
+
+        import tiled_mask_sync
+
+        monkeypatch.setattr(tiled_mask_sync, "write_masks_to_tiled", fake_write_masks_to_tiled)
+
+        jid = export_jobs.new_job("x")
+        infer_jobs.run_write_tiled_job(jid, "infer-tiled")
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"] == {"path": "browse/sample__masks_deep"}
+        assert captured["container_suffix"] == "_deep"
+        assert captured["volumes"]["semantic"].shape == (2, 4, 4)
+        assert np.array_equal(captured["volumes"]["class_vols"]["a"][0], (label0 == 1) * 255)
+
+    def test_write_failure_is_reported_as_a_job_error(self, monkeypatch):
+        infer_jobs._cache_put(
+            "infer-tiled-fail",
+            {
+                "kind": "tiled", "source": "browse/sample", "server_uri": None,
+                "classes": [{"classId": 1, "label": "a", "color": "#f00"}],
+                "label_pngs": {0: _blank_label_png()},
+            },
+        )
+        import tiled_mask_sync
+
+        def boom(*a, **k):
+            raise RuntimeError("tiled write blew up")
+
+        monkeypatch.setattr(tiled_mask_sync, "write_masks_to_tiled", boom)
+        jid = export_jobs.new_job("x")
+        infer_jobs.run_write_tiled_job(jid, "infer-tiled-fail")
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "tiled write blew up" in job["error"]
+
+
+def _blank_label_png() -> bytes:
+    from PIL import Image as PILImage
+
+    buf = io.BytesIO()
+    PILImage.fromarray(np.zeros((4, 4), dtype=np.uint8), mode="L").save(buf, format="PNG")
+    return buf.getvalue()
diff --git a/backend/tests/test_ipred_routes.py b/backend/tests/test_ipred_routes.py
index c8e611e..b31a49e 100644
--- a/backend/tests/test_ipred_routes.py
+++ b/backend/tests/test_ipred_routes.py
@@ -186,3 +186,238 @@ def _infer(*, session_id, model_id, feature_id, alpha, store_probabilities=True,
     assert job["state"] == "done"
     assert job["result"]["runs"] == {"0": "run-feat-0", "2": "run-feat-2"}
     assert job["result"]["errors"] == [{"slice": 1, "error": "boom"}]
+
+
+@pytest.mark.asyncio
+async def test_batch_train_rejects_empty_slices() -> None:
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.post(
+            "/api/ipred/batch/train", json={"session_id": "s1", "slices": {}},
+        )
+    assert response.status_code == 422
+
+
+@pytest.mark.asyncio
+async def test_batch_apply_rejects_empty_slice_indices() -> None:
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.post(
+            "/api/ipred/batch/apply",
+            json={"session_id": "s1", "model_id": "m1", "slice_indices": []},
+        )
+    assert response.status_code == 422
+
+
+@pytest.mark.asyncio
+async def test_generic_exception_reports_500(monkeypatch: pytest.MonkeyPatch) -> None:
+    """An exception that is neither an HTTPStatusError nor a ConnectError
+    (e.g. ipred is unreachable via a different transport failure) reports 500
+    with the exception's own message rather than crashing the request."""
+    def _raise() -> dict:
+        raise ValueError("something unexpected")
+
+    monkeypatch.setattr(ipred_client_mod, "list_trainers", _raise)
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.get("/api/ipred/trainers")
+    assert response.status_code == 500
+    assert "something unexpected" in response.json()["detail"]
+
+
+@pytest.mark.asyncio
+async def test_http_status_error_falls_back_to_text_when_not_json(monkeypatch: pytest.MonkeyPatch) -> None:
+    def _raise() -> dict:
+        req = httpx.Request("GET", "http://127.0.0.1:8003/setups")
+        resp = httpx.Response(500, content=b"plain text error", request=req)
+        raise httpx.HTTPStatusError("bad", request=req, response=resp)
+
+    monkeypatch.setattr(ipred_client_mod, "list_setups", _raise)
+    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+        response = await client.get("/api/ipred/setups")
+    assert response.status_code == 500
+    assert response.json()["detail"] == "plain text error"
+
+
+class TestSimpleProxyRoutes:
+    """Each of these is a thin call-through + exception translation; one happy
+    path per route is enough since _ipred_http_error's branches are already
+    covered above and shared by all of them."""
+
+    @pytest.mark.asyncio
+    async def test_get_setup(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "get_setup", lambda setup_id: {"id": setup_id})
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.get("/api/ipred/setups/s1")
+        assert response.json() == {"id": "s1"}
+
+    @pytest.mark.asyncio
+    async def test_upsert_setup_excludes_none_fields(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        captured = {}
+        monkeypatch.setattr(ipred_client_mod, "upsert_setup", lambda payload: captured.update(payload) or {"id": "s1"})
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.post(
+                "/api/ipred/setups", json={"name": "s", "kind": "onnx"},
+            )
+        assert response.status_code == 200
+        assert "procedure_id" not in captured
+        assert captured["name"] == "s"
+
+    @pytest.mark.asyncio
+    async def test_list_trainers(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "list_trainers", lambda: ["catboost"])
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.get("/api/ipred/trainers")
+        assert response.json() == {"trainers": ["catboost"]}
+
+    @pytest.mark.asyncio
+    async def test_list_compositions(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "list_compositions", lambda: [{"id": "c1"}])
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.get("/api/ipred/compositions")
+        assert response.json() == {"compositions": [{"id": "c1"}]}
+
+    @pytest.mark.asyncio
+    async def test_get_composition(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "get_composition", lambda cid: {"id": cid})
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.get("/api/ipred/compositions/c1")
+        assert response.json() == {"id": "c1"}
+
+    @pytest.mark.asyncio
+    async def test_upsert_composition(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "upsert_composition", lambda payload: {"id": "c2"})
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.post(
+                "/api/ipred/compositions",
+                json={"name": "comp", "nodes": [], "outputs": []},
+            )
+        assert response.json() == {"id": "c2"}
+
+    @pytest.mark.asyncio
+    async def test_preview_composition(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "preview_composition", lambda payload: {"preview": True})
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.post(
+                "/api/ipred/compositions/preview",
+                json={"name": "comp", "nodes": [], "outputs": []},
+            )
+        assert response.json() == {"preview": True}
+
+    @pytest.mark.asyncio
+    async def test_upload_array_injects_session_id_into_payload(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        captured = {}
+
+        def _upload(session_id, payload):
+            captured.update(payload)
+            return {"ok": True}
+
+        monkeypatch.setattr(ipred_client_mod, "upload_session_array", _upload)
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.post(
+                "/api/ipred/sessions/s1/arrays",
+                json={"session_id": "ignored", "shape": [2, 2], "data_b64": "AAA="},
+            )
+        assert response.status_code == 200
+        assert captured["session_id"] == "s1"
+
+    @pytest.mark.asyncio
+    async def test_manifold_sample(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "manifold_sample", lambda payload: {"sample_id": "sm1"})
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.post(
+                "/api/ipred/manifold/sample", json={"feature_id": "f1"},
+            )
+        assert response.json() == {"sample_id": "sm1"}
+
+    @pytest.mark.asyncio
+    async def test_manifold_heatmap_png(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "manifold_heatmap_png", lambda sample_id: b"heatbytes")
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.get("/api/ipred/manifold/sm1/heatmap.png")
+        assert response.content == b"heatbytes"
+        assert response.headers["content-type"] == "image/png"
+
+    @pytest.mark.asyncio
+    async def test_preprocess(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        captured = {}
+
+        def _preprocess(**kwargs):
+            captured.update(kwargs)
+            return {"feature_id": "f1"}
+
+        monkeypatch.setattr(ipred_client_mod, "preprocess", _preprocess)
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.post(
+                "/api/ipred/preprocess", json={"session_id": "s1", "slice_index": 3},
+            )
+        assert response.json() == {"feature_id": "f1"}
+        assert captured["slice_index"] == 3
+
+    @pytest.mark.asyncio
+    async def test_feature_channel_png(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "feature_channel_bytes", lambda fid, idx: b"chanbytes")
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.get("/api/ipred/features/f1/channels/2")
+        assert response.content == b"chanbytes"
+
+    @pytest.mark.asyncio
+    async def test_train(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        captured = {}
+
+        def _train(**kwargs):
+            captured.update(kwargs)
+            return {"model_id": "m1"}
+
+        monkeypatch.setattr(ipred_client_mod, "train", _train)
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.post(
+                "/api/ipred/train", json={"session_id": "s1", "shapes": []},
+            )
+        assert response.json() == {"model_id": "m1"}
+        assert captured["trainer_id"] == "catboost"
+
+    @pytest.mark.asyncio
+    async def test_infer(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "infer", lambda **kwargs: {"run_id": "r1"})
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.post(
+                "/api/ipred/infer", json={"session_id": "s1"},
+            )
+        assert response.json() == {"run_id": "r1"}
+
+    @pytest.mark.asyncio
+    async def test_rethreshold(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "rethreshold", lambda **kwargs: {"ok": True})
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.post(
+                "/api/ipred/rethreshold", json={"session_id": "s1", "alpha": 0.1},
+            )
+        assert response.json() == {"ok": True}
+
+    @pytest.mark.asyncio
+    async def test_run_commit_png(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "run_commit_png", lambda run_id: b"commitbytes")
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.get("/api/ipred/runs/r1/commit.png")
+        assert response.content == b"commitbytes"
+
+    @pytest.mark.asyncio
+    async def test_run_status_png(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        monkeypatch.setattr(ipred_client_mod, "run_status_png", lambda run_id: b"statusbytes")
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.get("/api/ipred/runs/r1/status.png")
+        assert response.content == b"statusbytes"
+
+    @pytest.mark.asyncio
+    async def test_threshold_class(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        captured = {}
+
+        def _threshold(run_id, *, class_id, threshold):
+            captured.update(run_id=run_id, class_id=class_id, threshold=threshold)
+            return {"ok": True}
+
+        monkeypatch.setattr(ipred_client_mod, "threshold_class_map", _threshold)
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            response = await client.post(
+                "/api/ipred/runs/r1/threshold-class", json={"class_id": 2, "threshold": 0.7},
+            )
+        assert response.json() == {"ok": True}
+        assert captured == {"run_id": "r1", "class_id": 2, "threshold": 0.7}
diff --git a/backend/tests/test_tiled_mask_sync.py b/backend/tests/test_tiled_mask_sync.py
index 534c5f7..6288a25 100644
--- a/backend/tests/test_tiled_mask_sync.py
+++ b/backend/tests/test_tiled_mask_sync.py
@@ -1,5 +1,6 @@
 """Unit tests for tiled_mask_sync.build_mask_volumes (pure rasterization → volumes)."""
 import io
+from typing import Any
 
 import numpy as np
 import pytest
@@ -170,3 +171,233 @@ def test_merge_fresh_when_no_existing():
     assert merged["slice_indices"] == [3]
     assert merged["updated_indices"] == [3]
     assert merged["semantic"].shape == (1, H, W)
+
+
+# ---------------------------------------------------------------------------
+# _read_existing_masks / write_masks_to_tiled — fake Tiled container, no live
+# server. Only mask_pyramid.register_mask_pyramid actually needs a live Tiled
+# server (see mask_pyramid.py's own test file for that established boundary),
+# so it's stubbed here; everything else about write_masks_to_tiled is real
+# container navigation/merge logic that can run against a fake.
+# ---------------------------------------------------------------------------
+
+class FakeMaskContainer:
+    def __init__(self, metadata=None, children=None):
+        self.metadata = metadata or {}
+        self._children: dict[str, Any] = dict(children or {})
+        self.deleted = False
+        self.written: dict[str, dict] = {}
+        self.created: list[str] = []
+
+    def __iter__(self):
+        return iter(self._children)
+
+    def __getitem__(self, key):
+        return self._children[key]
+
+    def __len__(self):
+        return len(self._children)
+
+    def keys(self):
+        return list(self._children.keys())
+
+    def delete_contents(self, recursive=True, external_only=False):
+        self.deleted = True
+        self._children = {}
+
+    def update_metadata(self, metadata):
+        self.metadata = metadata
+
+    def write_array(self, arr, key, dims=None, metadata=None):
+        self.written[key] = {"arr": arr, "dims": dims, "metadata": metadata}
+
+    def create_container(self, key, metadata):
+        child = FakeMaskContainer(metadata=metadata)
+        self._children[key] = child
+        self.created.append(key)
+        return child
+
+
+@pytest.fixture(autouse=True)
+def stub_register_mask_pyramid(monkeypatch: pytest.MonkeyPatch):
+    calls: list[dict] = []
+
+    def fake_register(semantic, key, container, cache_key):
+        calls.append({"semantic": semantic, "key": key, "container": container, "cache_key": cache_key})
+        return {"key": key}
+
+    monkeypatch.setattr(tiled_mask_sync.mask_pyramid, "register_mask_pyramid", fake_register)
+    return calls
+
+
+@pytest.fixture()
+def fake_client(monkeypatch: pytest.MonkeyPatch):
+    ds = FakeMaskContainer()
+    root = FakeMaskContainer(children={"browse": FakeMaskContainer(children={"ds": ds})})
+    monkeypatch.setattr(tiled_mask_sync, "get_tiled_client", lambda uri, key: root)
+    monkeypatch.setattr(tiled_mask_sync, "api_key_for_uri", lambda uri: None)
+    return ds  # the "browse/ds" container new mask containers get created under
+
+
+class TestReadExistingMasks:
+    def test_reads_semantic_via_mask_pyramid_and_class_arrays_directly(self, monkeypatch: pytest.MonkeyPatch):
+        semantic = np.zeros((2, H, W), dtype=np.uint8)
+        monkeypatch.setattr(tiled_mask_sync.mask_pyramid, "read_mask_scale0", lambda container, key: semantic)
+        cell_arr = np.full((2, H, W), 255, dtype=np.uint8)
+        container = FakeMaskContainer(
+            metadata={
+                "legend": [{"id": 1, "name": "Cell", "color": "#f00"}],
+                "slice_indices": [1, 2],
+                "slice_updated_at": {"1": "t1"},
+            },
+            children={"semantic": object(), "Cell": cell_arr},
+        )
+        result = tiled_mask_sync._read_existing_masks(container)
+        assert result is not None
+        assert result["slice_indices"] == [1, 2]
+        assert np.array_equal(result["semantic"], semantic)
+        assert np.array_equal(result["class_arrays"]["Cell"], cell_arr)
+        assert result["slice_updated_at"] == {"1": "t1"}
+
+    def test_unreadable_container_returns_none(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setattr(
+            tiled_mask_sync.mask_pyramid, "read_mask_scale0",
+            lambda container, key: (_ for _ in ()).throw(RuntimeError("boom")),
+        )
+        container = FakeMaskContainer(metadata={"legend": [], "slice_indices": []}, children={"semantic": object()})
+        assert tiled_mask_sync._read_existing_masks(container) is None
+
+    def test_unknown_class_key_is_skipped(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setattr(tiled_mask_sync.mask_pyramid, "read_mask_scale0", lambda container, key: np.zeros((1, H, W)))
+        container = FakeMaskContainer(
+            metadata={"legend": [{"id": 1, "name": "Cell"}], "slice_indices": [0]},
+            children={"semantic": object(), "orphan_key": np.zeros((1, H, W))},
+        )
+        result = tiled_mask_sync._read_existing_masks(container)
+        assert result["class_arrays"] == {}
+
+
+class TestWriteMasksToTiled:
+    def _volumes_dict(self):
+        return _volumes({"2": [{"id": "a", "kind": "rectangle", "classId": 10, "x": 2, "y": 2, "w": 6, "h": 6}]})
+
+    def test_creates_a_new_container_when_none_exists(self, fake_client, stub_register_mask_pyramid):
+        info = tiled_mask_sync.write_masks_to_tiled("browse/ds/img", None, self._volumes_dict(), _classes())
+        assert info["path"] == "browse/ds/img__masks"
+        assert "img__masks" in fake_client.created
+        new_container = fake_client["img__masks"]
+        assert new_container.metadata["studio_type"] == "segmentation_masks"
+        assert "Cell" in new_container.written
+
+    def test_container_suffix_keeps_producers_independent(self, fake_client, stub_register_mask_pyramid):
+        info = tiled_mask_sync.write_masks_to_tiled(
+            "browse/ds/img", None, self._volumes_dict(), _classes(), container_suffix="_deep",
+        )
+        assert info["path"] == "browse/ds/img__masks_deep"
+        assert stub_register_mask_pyramid[0]["cache_key"] == "img__masks_deep"
+
+    def test_merges_into_an_existing_container(self, fake_client, monkeypatch, stub_register_mask_pyramid):
+        existing_vols = self._volumes_dict()
+        existing_container = FakeMaskContainer(
+            metadata={
+                "legend": existing_vols["legend"], "slice_indices": existing_vols["slice_indices"],
+            },
+            children={"semantic": object(), "Cell": existing_vols["class_vols"]["Cell"]},
+        )
+        monkeypatch.setattr(
+            tiled_mask_sync.mask_pyramid, "read_mask_scale0",
+            lambda container, key: existing_vols["semantic"],
+        )
+        fake_client._children["img__masks"] = existing_container
+
+        new_vols = _volumes({"5": [{"id": "b", "kind": "rectangle", "classId": 20, "x": 1, "y": 1, "w": 3, "h": 3}]})
+        info = tiled_mask_sync.write_masks_to_tiled("browse/ds/img", None, new_vols, _classes())
+
+        assert existing_container.deleted is True
+        assert info["n_slices"] == 2  # slice 2 (existing) + slice 5 (new)
+        assert info["updated"] == 1
+
+    def test_shape_mismatch_discards_existing_and_replaces(self, fake_client, monkeypatch, stub_register_mask_pyramid):
+        mismatched_semantic = np.zeros((1, H * 2, W * 2), dtype=np.uint8)
+        existing_container = FakeMaskContainer(
+            metadata={"legend": [], "slice_indices": [9]},
+            children={"semantic": object()},
+        )
+        monkeypatch.setattr(tiled_mask_sync.mask_pyramid, "read_mask_scale0", lambda container, key: mismatched_semantic)
+        fake_client._children["img__masks"] = existing_container
+
+        info = tiled_mask_sync.write_masks_to_tiled("browse/ds/img", None, self._volumes_dict(), _classes())
+        # Old slice 9 is gone entirely — replaced, not merged, due to the H/W mismatch.
+        assert info["n_slices"] == 1
+        assert info["updated"] == 1
+
+    def test_returns_correct_summary_counts(self, fake_client, stub_register_mask_pyramid):
+        info = tiled_mask_sync.write_masks_to_tiled("browse/ds/img", None, self._volumes_dict(), _classes())
+        assert info["n_classes"] == 2  # Cell + Wall
+        assert info["n_slices"] == 1
+        assert info["updated"] == 1
+
+
+# ---------------------------------------------------------------------------
+# run_mask_sync_job
+# ---------------------------------------------------------------------------
+
+class _Payload:
+    def __init__(self, classes):
+        self.classes = classes
+
+
+class TestRunMaskSyncJob:
+    def test_no_tiled_sources_reports_done_with_a_note(self):
+        import export_jobs
+
+        local_item = ExportSourceItem(kind="local", source="foo.tif", slices={"0": []})
+        jid = export_jobs.new_job("x")
+        tiled_mask_sync.run_mask_sync_job(jid, [local_item], _Payload(_classes()))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"]["written"] == []
+        assert "skipped" in job["result"]["note"]
+
+    def test_writes_masks_for_each_tiled_source(self, monkeypatch, fake_client, stub_register_mask_pyramid):
+        import export_jobs
+
+        slices = {"2": [{"id": "a", "kind": "rectangle", "classId": 10, "x": 2, "y": 2, "w": 6, "h": 6}]}
+        item = ExportSourceItem(kind="tiled", source="browse/ds/img", slices=slices)
+        monkeypatch.setattr(tiled_mask_sync.arrays_mod, "resolve_array", lambda source, kind, server_uri: "node")
+        monkeypatch.setattr(tiled_mask_sync.arrays_mod, "array_shape_meta", lambda node: {"height": H, "width": W})
+
+        jid = export_jobs.new_job("x")
+        tiled_mask_sync.run_mask_sync_job(jid, [item], _Payload(_classes()))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert len(job["result"]["written"]) == 1
+        assert job["result"]["written"][0]["source"] == "browse/ds/img"
+        assert job["result"]["written"][0]["container"] == "browse/ds/img__masks"
+
+    def test_item_with_no_annotated_slices_is_skipped_not_errored(self, monkeypatch, fake_client):
+        import export_jobs
+
+        item = ExportSourceItem(kind="tiled", source="browse/ds/img", slices={})
+        monkeypatch.setattr(tiled_mask_sync.arrays_mod, "resolve_array", lambda source, kind, server_uri: "node")
+        monkeypatch.setattr(tiled_mask_sync.arrays_mod, "array_shape_meta", lambda node: {"height": H, "width": W})
+
+        jid = export_jobs.new_job("x")
+        tiled_mask_sync.run_mask_sync_job(jid, [item], _Payload(_classes()))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "done"
+        assert job["result"]["written"] == []
+
+    def test_exception_is_reported_as_a_job_error(self, monkeypatch):
+        import export_jobs
+
+        item = ExportSourceItem(kind="tiled", source="browse/ds/img", slices={"0": [{"id": "a", "kind": "rectangle", "classId": 10, "x": 0, "y": 0, "w": 2, "h": 2}]})
+        monkeypatch.setattr(
+            tiled_mask_sync.arrays_mod, "resolve_array",
+            lambda source, kind, server_uri: (_ for _ in ()).throw(RuntimeError("resolve failed")),
+        )
+        jid = export_jobs.new_job("x")
+        tiled_mask_sync.run_mask_sync_job(jid, [item], _Payload(_classes()))
+        job = export_jobs.get_job(jid)
+        assert job["state"] == "error"
+        assert "resolve failed" in job["error"]
diff --git a/backend/tests/test_train_common.py b/backend/tests/test_train_common.py
index 80b758b..715672d 100644
--- a/backend/tests/test_train_common.py
+++ b/backend/tests/test_train_common.py
@@ -17,7 +17,9 @@
 import sys
 from pathlib import Path
 
+import numpy as np
 import pytest
+from fastapi import HTTPException
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
 
@@ -122,3 +124,279 @@ def test_model_denoiser_reports_missing_torch_by_name(
     )
     with pytest.raises(ValueError, match="torch is not installed"):
         denoise_bake._ModelDenoiser("ae-run", None, {})
+
+
+# ---------------------------------------------------------------------------
+# pick_device
+# ---------------------------------------------------------------------------
+
+class TestPickDevice:
+    def test_no_torch_returns_none(self, monkeypatch: pytest.MonkeyPatch):
+        monkeypatch.setattr(train_common, "torch_available", lambda: False)
+        assert train_common.pick_device() is None
+
+    @pytest.mark.parametrize("override", ["mps", "cuda", "cpu"])
+    def test_train_device_env_override_short_circuits_detection(self, monkeypatch: pytest.MonkeyPatch, override):
+        # A valid override returns before ever importing torch's backends —
+        # exercised for real (torch IS available in this env) without needing
+        # the overridden device to actually exist on this machine.
+        monkeypatch.setenv("TRAIN_DEVICE", override)
+        assert train_common.pick_device() == override
+
+    def test_invalid_override_falls_through_to_real_detection(self, monkeypatch: pytest.MonkeyPatch):
+        pytest.importorskip("torch")
+        monkeypatch.setenv("TRAIN_DEVICE", "not-a-real-device")
+        assert train_common.pick_device() in ("mps", "cuda", "cpu")
+
+
+# ---------------------------------------------------------------------------
+# run_dir / _validate_run_id
+# ---------------------------------------------------------------------------
+
+class TestRunDirValidation:
+    @pytest.mark.parametrize("bad_id", ["", ".", "..", "a/b", "a\\b", "a\x00b"])
+    def test_rejects_unsafe_run_ids(self, runs_dir: Path, bad_id):
+        with pytest.raises(HTTPException) as exc:
+            train_common.run_dir(bad_id)
+        assert exc.value.status_code == 400
+
+    def test_accepts_a_safe_run_id(self, runs_dir: Path):
+        d = train_common.run_dir("my-run-1")
+        assert d.name == "my-run-1"
+        assert d.parent == train_common.runs_dir()
+
+
+# ---------------------------------------------------------------------------
+# Run persistence: list_runs / delete_run / load_run_config
+# (config.json written directly via the existing _write_run_config helper —
+# these three never touch torch, unlike save_run/load_adapter_state below)
+# ---------------------------------------------------------------------------
+
+class TestListRuns:
+    def test_empty_when_runs_dir_does_not_exist(self, runs_dir: Path):
+        assert train_common.list_runs() == []
+
+    def test_lists_saved_runs_sorted_newest_first(self, runs_dir: Path):
+        _write_run_config(runs_dir, "old", {"model_family": "dlsia_tunet", "created_at": "2024-01-01T00:00:00"})
+        _write_run_config(runs_dir, "new", {"model_family": "dlsia_tunet", "created_at": "2024-06-01T00:00:00"})
+        results = train_common.list_runs()
+        assert [r["created_at"] for r in results] == ["2024-06-01T00:00:00", "2024-01-01T00:00:00"]
+
+    def test_backward_compat_missing_task_defaults_to_segmentation(self, runs_dir: Path):
+        _write_run_config(runs_dir, "old-run", {"model_family": "dlsia_tunet", "created_at": "x"})
+        results = train_common.list_runs()
+        assert results[0]["task"] == "segmentation"
+
+    def test_non_directory_entries_are_skipped(self, runs_dir: Path):
+        runs_dir.mkdir(parents=True, exist_ok=True)
+        (runs_dir / "stray_file.txt").write_text("not a run")
+        assert train_common.list_runs() == []
+
+    def test_malformed_run_directory_is_skipped_not_raised(self, runs_dir: Path):
+        d = runs_dir / "broken"
+        d.mkdir(parents=True, exist_ok=True)
+        (d / "config.json").write_text("{ not valid json")
+        assert train_common.list_runs() == []
+
+    def test_metrics_defaults_to_empty_dict_when_missing(self, runs_dir: Path):
+        _write_run_config(runs_dir, "no-metrics", {"model_family": "dlsia_tunet", "created_at": "x"})
+        results = train_common.list_runs()
+        assert results[0]["metrics"] == {}
+
+
+class TestDeleteRun:
+    def test_unknown_run_is_404(self, runs_dir: Path):
+        with pytest.raises(HTTPException) as exc:
+            train_common.delete_run("nonexistent")
+        assert exc.value.status_code == 404
+
+    def test_removes_the_run_directory(self, runs_dir: Path):
+        _write_run_config(runs_dir, "to-delete", {"model_family": "dlsia_tunet"})
+        d = train_common.run_dir("to-delete")
+        assert d.is_dir()
+        train_common.delete_run("to-delete")
+        assert not d.exists()
+
+
+class TestLoadRunConfig:
+    def test_unknown_run_is_404(self, runs_dir: Path):
+        with pytest.raises(HTTPException) as exc:
+            train_common.load_run_config("nonexistent")
+        assert exc.value.status_code == 404
+
+    def test_corrupt_config_is_500(self, runs_dir: Path):
+        d = runs_dir / "corrupt"
+        d.mkdir(parents=True, exist_ok=True)
+        (d / "config.json").write_text("{ broken")
+        with pytest.raises(HTTPException) as exc:
+            train_common.load_run_config("corrupt")
+        assert exc.value.status_code == 500
+
+    def test_missing_task_defaults_to_segmentation(self, runs_dir: Path):
+        _write_run_config(runs_dir, "old", {"model_family": "dlsia_tunet"})
+        config = train_common.load_run_config("old")
+        assert config["task"] == "segmentation"
+
+
+class TestSaveRunAndLoadAdapterState:
+    def test_round_trips_config_metrics_and_weights(self, runs_dir: Path):
+        torch = pytest.importorskip("torch")
+        train_common.save_run(
+            "run1",
+            model_family="dlsia_tunet",
+            model_config={"depth": 2},
+            classes=[{"classId": 1, "label": "a"}],
+            render={},
+            image_size=64,
+            hyperparams={"depth": 2},
+            source_keys=["local:x.tif"],
+            adapter_state={"weight": torch.zeros(2, 2)},
+            metrics={"epochs_completed": 1},
+        )
+        config = train_common.load_run_config("run1")
+        assert config["model_family"] == "dlsia_tunet"
+        assert config["classes"] == [{"classId": 1, "label": "a"}]
+
+        adapter = train_common.load_adapter_state("run1")
+        assert torch.equal(adapter["weight"], torch.zeros(2, 2))
+
+    def test_load_adapter_state_missing_weights_is_404(self, runs_dir: Path):
+        pytest.importorskip("torch")
+        _write_run_config(runs_dir, "no-weights", {"model_family": "dlsia_tunet"})
+        with pytest.raises(HTTPException) as exc:
+            train_common.load_adapter_state("no-weights")
+        assert exc.value.status_code == 404
+
+
+# ---------------------------------------------------------------------------
+# letterbox / letterbox_params / unletterbox — pure numpy/PIL, no torch
+# ---------------------------------------------------------------------------
+
+class TestLetterbox:
+    def test_already_square_is_returned_unchanged(self):
+        img = np.zeros((32, 32, 3), dtype=np.uint8)
+        lbl = np.ones((32, 32), dtype=np.uint8)
+        out_img, out_lbl = train_common.letterbox(img, lbl, 32)
+        assert out_img is img
+        assert out_lbl is lbl
+
+    def test_pads_a_non_square_image_to_a_square_canvas(self):
+        img = np.full((20, 40, 3), 255, dtype=np.uint8)
+        lbl = np.ones((20, 40), dtype=np.uint8)
+        out_img, out_lbl = train_common.letterbox(img, lbl, 40)
+        assert out_img.shape == (40, 40, 3)
+        assert out_lbl.shape == (40, 40)
+
+    def test_padded_label_regions_use_ignore_index(self):
+        img = np.full((10, 40, 3), 255, dtype=np.uint8)
+        lbl = np.ones((10, 40), dtype=np.uint8)
+        _out_img, out_lbl = train_common.letterbox(img, lbl, 40)
+        # Top/bottom padding bands must be IGNORE_INDEX, not a real class.
+        assert np.all(out_lbl[0, :] == train_common.IGNORE_INDEX)
+        assert np.all(out_lbl[-1, :] == train_common.IGNORE_INDEX)
+
+
+class TestLetterboxParamsAndUnletterbox:
+    def test_letterbox_params_centers_the_scaled_image(self):
+        params = train_common.letterbox_params(20, 40, 40)
+        assert params["nh"] == 20
+        assert params["nw"] == 40
+        assert params["top"] == 10
+        assert params["left"] == 0
+
+    def test_unletterbox_inverts_letterbox_for_an_exact_fit(self):
+        img = np.zeros((20, 40, 3), dtype=np.uint8)
+        lbl = np.full((20, 40), 3, dtype=np.uint8)
+        boxed_img, boxed_lbl = train_common.letterbox(img, lbl, 40)
+        recovered = train_common.unletterbox(boxed_lbl, 20, 40, 40)
+        assert recovered.shape == (20, 40)
+        assert np.all(recovered == 3)
+
+    def test_unletterbox_resizes_back_when_original_was_downscaled(self):
+        # image_size smaller than the original -> nh/nw != orig, exercising the
+        # resize-back branch rather than the exact-crop shortcut.
+        img = np.zeros((100, 200, 3), dtype=np.uint8)
+        lbl = np.full((100, 200), 5, dtype=np.uint8)
+        boxed_img, boxed_lbl = train_common.letterbox(img, lbl, 40)
+        recovered = train_common.unletterbox(boxed_lbl, 100, 200, 40)
+        assert recovered.shape == (100, 200)
+        # Nearest-neighbor resize of a constant label stays constant.
+        assert np.all(recovered == 5)
+
+
+# ---------------------------------------------------------------------------
+# denoising_render_slice_fn / _denoise_params
+# ---------------------------------------------------------------------------
+
+class TestDenoiseParams:
+    def test_none_denoise_returns_none_method(self):
+        assert train_common._denoise_params(None) == (None, 0.5)
+
+    def test_falsy_dict_returns_none_method(self):
+        assert train_common._denoise_params({}) == (None, 0.5)
+
+    def test_method_none_string_is_treated_as_no_denoise(self):
+        method, strength = train_common._denoise_params({"method": "none", "strength": 0.7})
+        assert method is None
+        assert strength == 0.7
+
+    def test_model_method_is_not_supported_as_a_preprocessor(self):
+        method, _ = train_common._denoise_params({"method": "model", "strength": 0.5})
+        assert method is None
+
+    def test_real_classical_method_passes_through(self):
+        assert train_common._denoise_params({"method": "median", "strength": 0.3}) == ("median", 0.3)
+
+    def test_object_with_method_attribute(self):
+        class Opts:
+            method = "gaussian"
+            strength = 0.4
+
+        assert train_common._denoise_params(Opts()) == ("gaussian", 0.4)
+
+
+class TestDenoisingRenderSliceFn:
+    def test_no_denoise_returns_plain_render_slice(self):
+        import images as images_mod
+
+        fn = train_common.denoising_render_slice_fn(None)
+        assert fn is images_mod.render_slice
+
+    def test_denoise_method_filters_grayscale_input_before_rendering(self, monkeypatch):
+        calls = []
+        import denoise as denoise_mod
+
+        def fake_denoise_slice(arr, method, strength):
+            calls.append((method, strength))
+            return arr
+
+        monkeypatch.setattr(denoise_mod, "denoise_slice", fake_denoise_slice)
+        fn = train_common.denoising_render_slice_fn({"method": "median", "strength": 0.2})
+        arr = np.zeros((8, 8), dtype=np.float32)
+        fn(arr, {"norm": "slice", "scale": "linear", "vmin_pct": 1.0, "vmax_pct": 99.0, "cmap": "gray"}, None)
+        assert calls == [("median", 0.2)]
+
+    def test_rgb_input_skips_denoising(self, monkeypatch):
+        calls = []
+        import denoise as denoise_mod
+
+        monkeypatch.setattr(denoise_mod, "denoise_slice", lambda *a: calls.append(1))
+        fn = train_common.denoising_render_slice_fn({"method": "median", "strength": 0.2})
+        arr = np.zeros((8, 8, 3), dtype=np.uint8)
+        fn(arr, {"norm": "slice", "scale": "linear", "vmin_pct": 1.0, "vmax_pct": 99.0, "cmap": "gray"}, None)
+        assert calls == []
+
+
+# ---------------------------------------------------------------------------
+# capability()'s exception-swallowing path
+# ---------------------------------------------------------------------------
+
+class TestCapabilityErrorHandling:
+    def test_internal_failure_is_reported_not_raised(self, monkeypatch: pytest.MonkeyPatch):
+        def boom():
+            raise RuntimeError("tiling import exploded")
+
+        monkeypatch.setattr(train_common, "torch_available", boom)
+        result = train_common.capability()
+        assert "error" in result
+        assert "tiling import exploded" in result["error"]
diff --git a/backend/tests/test_train_common_ml.py b/backend/tests/test_train_common_ml.py
new file mode 100644
index 0000000..548182d
--- /dev/null
+++ b/backend/tests/test_train_common_ml.py
@@ -0,0 +1,223 @@
+"""Real (no mocks) torch round trips for train_common.py's model-construction
+and training-loop functions — build_family, compute_miou, run_training_loop,
+_evaluate. Kept in a separate file from test_train_common.py so that file can
+stay import-safe/runnable without torch (its own stated design), while this
+one skips outright when the `ml` extra isn't installed.
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+torch = pytest.importorskip("torch")
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import train_common  # noqa: E402
+from schemas import DlsiaDenoiserConfig, DlsiaTunetConfig  # noqa: E402
+
+IMAGE_SIZE = 64
+
+
+def _tunet_config(**overrides):
+    hp = {"depth": 2, "base_channels": 4, "growth_rate": 1.2, "image_size": IMAGE_SIZE, "batch_size": 2}
+    hp.update(overrides)
+    return DlsiaTunetConfig(hyperparams=hp)
+
+
+def _denoiser_config(architecture="tunet", **overrides):
+    hp = {"depth": 2, "base_channels": 4, "growth_rate": 1.2, "image_size": IMAGE_SIZE, "batch_size": 2}
+    hp.update(overrides)
+    scheme = "ae" if architecture == "cnn_ae" else "n2n"
+    return DlsiaDenoiserConfig(architecture=architecture, hyperparams=hp, training_scheme=scheme)
+
+
+class TestBuildFamilyTunet:
+    def test_builds_from_scratch(self):
+        built = train_common.build_family(_tunet_config(), n_classes=3, device="cpu", log_cb=lambda m: None)
+        assert built.model_config_snapshot == {"depth": 2, "base_channels": 4, "growth_rate": 1.2}
+        assert len(built.trainable_params) > 0
+
+    def test_dlsia_unavailable_raises(self, monkeypatch):
+        monkeypatch.setattr(train_common, "dlsia_available", lambda: False)
+        with pytest.raises(RuntimeError, match="dlsia is not installed"):
+            train_common.build_family(_tunet_config(), n_classes=3, device="cpu", log_cb=lambda m: None)
+
+    def test_resume_uses_saved_topology_not_the_request(self):
+        built = train_common.build_family(_tunet_config(), n_classes=3, device="cpu", log_cb=lambda m: None)
+        state = built.adapter_state_fn()
+        resumed = train_common.build_family(
+            _tunet_config(depth=6), n_classes=3, device="cpu", log_cb=lambda m: None, init_state=state,
+        )
+        # depth=6 in the new request must be ignored; the saved topology wins.
+        assert resumed.model_config_snapshot["depth"] == 2
+
+    def test_forward_produces_correct_class_channels(self):
+        built = train_common.build_family(_tunet_config(), n_classes=3, device="cpu", log_cb=lambda m: None)
+        batch = torch.zeros((1, 3, IMAGE_SIZE, IMAGE_SIZE))
+        out = built.forward_fn(batch)
+        assert out.shape == (1, 3, IMAGE_SIZE, IMAGE_SIZE)
+
+
+class TestBuildFamilyDenoiserTunet:
+    def test_builds_from_scratch(self):
+        built = train_common.build_family(_denoiser_config("tunet"), n_classes=2, device="cpu", log_cb=lambda m: None)
+        assert built.model_config_snapshot["architecture"] == "tunet"
+
+    def test_dlsia_unavailable_raises(self, monkeypatch):
+        monkeypatch.setattr(train_common, "dlsia_available", lambda: False)
+        with pytest.raises(RuntimeError, match="dlsia is not installed"):
+            train_common.build_family(_denoiser_config("tunet"), n_classes=2, device="cpu", log_cb=lambda m: None)
+
+    def test_resume_uses_saved_topology(self):
+        built = train_common.build_family(_denoiser_config("tunet"), n_classes=2, device="cpu", log_cb=lambda m: None)
+        state = built.adapter_state_fn()
+        resumed = train_common.build_family(
+            _denoiser_config("tunet", depth=6), n_classes=2, device="cpu", log_cb=lambda m: None, init_state=state,
+        )
+        assert resumed.model_config_snapshot["depth"] == 2
+
+
+class TestBuildFamilyDenoiserCnnAe:
+    def test_builds_from_scratch_no_dlsia_gate(self, monkeypatch):
+        # cnn_ae is plain torch — must succeed even when dlsia is "unavailable".
+        monkeypatch.setattr(train_common, "dlsia_available", lambda: False)
+        built = train_common.build_family(_denoiser_config("cnn_ae"), n_classes=2, device="cpu", log_cb=lambda m: None)
+        assert built.model_config_snapshot["architecture"] == "cnn_ae"
+        assert built.model_config_snapshot["latent_channels"] > 0
+
+    def test_resume_uses_saved_topology(self):
+        built = train_common.build_family(_denoiser_config("cnn_ae"), n_classes=2, device="cpu", log_cb=lambda m: None)
+        state = built.adapter_state_fn()
+        resumed = train_common.build_family(
+            _denoiser_config("cnn_ae", depth=6), n_classes=2, device="cpu", log_cb=lambda m: None, init_state=state,
+        )
+        assert resumed.model_config_snapshot["depth"] == 2
+
+
+class TestBuildFamilyUnknownConfig:
+    def test_unrecognized_config_type_raises_value_error(self):
+        class NotAModelConfig:
+            hyperparams = None
+            model_family = "bogus"
+
+        with pytest.raises(ValueError, match="Unknown model family"):
+            train_common.build_family(NotAModelConfig(), n_classes=2, device="cpu", log_cb=lambda m: None)
+
+
+class TestComputeMiou:
+    def test_perfect_match_gives_miou_one(self):
+        pred = torch.tensor([0, 0, 1, 1])
+        target = torch.tensor([0, 0, 1, 1])
+        assert train_common.compute_miou(pred, target, n_classes=2) == pytest.approx(1.0)
+
+    def test_no_overlap_gives_miou_zero(self):
+        pred = torch.tensor([0, 0, 0, 0])
+        target = torch.tensor([1, 1, 1, 1])
+        assert train_common.compute_miou(pred, target, n_classes=2) == pytest.approx(0.0)
+
+    def test_ignore_index_pixels_are_excluded(self):
+        pred = torch.tensor([0, 0, 1, 1])
+        target = torch.tensor([0, 0, 255, 255])
+        # Only the first two pixels count; both match -> class 0 IoU 1.0, class 1 has no valid pixels (skipped).
+        assert train_common.compute_miou(pred, target, n_classes=2, ignore_index=255) == pytest.approx(1.0)
+
+    def test_all_ignored_returns_zero(self):
+        pred = torch.tensor([0, 1])
+        target = torch.tensor([255, 255])
+        assert train_common.compute_miou(pred, target, n_classes=2, ignore_index=255) == 0.0
+
+
+def _synthetic_pairs(n, n_classes=2, size=IMAGE_SIZE, seed=0):
+    rng = np.random.default_rng(seed)
+    return [
+        (
+            rng.integers(0, 256, size=(size, size, 3), dtype=np.uint8),
+            rng.integers(0, n_classes, size=(size, size), dtype=np.uint8),
+        )
+        for _ in range(n)
+    ]
+
+
+class TestRunTrainingLoop:
+    def _built(self, n_classes=2):
+        return train_common.build_family(_tunet_config(), n_classes=n_classes, device="cpu", log_cb=lambda m: None)
+
+    def test_no_training_data_raises(self):
+        built = self._built()
+        with pytest.raises(ValueError, match="No training data"):
+            train_common.run_training_loop(
+                train_pairs=[], val_pairs=[], image_size=IMAGE_SIZE, n_classes=2, epochs=1,
+                batch_size=2, seed=0, flip_augment=False, to_tensor_fn=built.to_tensor_fn,
+                forward_fn=built.forward_fn, trainable_params=built.trainable_params, lr=1e-3, device="cpu",
+                set_train_mode=built.set_train_mode,
+            )
+
+    def test_completes_epochs_and_reports_loss(self):
+        built = self._built()
+        result = train_common.run_training_loop(
+            train_pairs=_synthetic_pairs(4), val_pairs=[], image_size=IMAGE_SIZE, n_classes=2,
+            epochs=2, batch_size=2, seed=0, flip_augment=True, to_tensor_fn=built.to_tensor_fn,
+            forward_fn=built.forward_fn, trainable_params=built.trainable_params, lr=1e-3, device="cpu",
+            set_train_mode=built.set_train_mode,
+        )
+        assert result["epochs_completed"] == 2
+        assert result["cancelled"] is False
+        assert result["final_val_loss"] is None
+        assert result["val_miou"] is None
+
+    def test_validation_pairs_produce_val_loss_and_miou(self):
+        built = self._built()
+        result = train_common.run_training_loop(
+            train_pairs=_synthetic_pairs(4), val_pairs=_synthetic_pairs(2, seed=1),
+            image_size=IMAGE_SIZE, n_classes=2, epochs=1, batch_size=2, seed=0, flip_augment=False,
+            to_tensor_fn=built.to_tensor_fn, forward_fn=built.forward_fn,
+            trainable_params=built.trainable_params, lr=1e-3, device="cpu",
+            set_train_mode=built.set_train_mode,
+        )
+        assert result["final_val_loss"] is not None
+        assert result["val_miou"] is not None
+
+    def test_on_batch_cancel_stops_immediately(self):
+        built = self._built()
+        result = train_common.run_training_loop(
+            train_pairs=_synthetic_pairs(8), val_pairs=[], image_size=IMAGE_SIZE, n_classes=2,
+            epochs=3, batch_size=2, seed=0, flip_augment=False, to_tensor_fn=built.to_tensor_fn,
+            forward_fn=built.forward_fn, trainable_params=built.trainable_params, lr=1e-3, device="cpu",
+            on_batch=lambda: True, set_train_mode=built.set_train_mode,
+        )
+        assert result["cancelled"] is True
+        assert result["epochs_completed"] == 1
+
+    def test_on_epoch_cancel_stops_after_that_epoch(self):
+        built = self._built()
+        epochs_seen = []
+        result = train_common.run_training_loop(
+            train_pairs=_synthetic_pairs(4), val_pairs=[], image_size=IMAGE_SIZE, n_classes=2,
+            epochs=5, batch_size=2, seed=0, flip_augment=False, to_tensor_fn=built.to_tensor_fn,
+            forward_fn=built.forward_fn, trainable_params=built.trainable_params, lr=1e-3, device="cpu",
+            on_epoch=lambda epoch, tl, vl, miou: epochs_seen.append(epoch) or epoch >= 2,
+            set_train_mode=built.set_train_mode,
+        )
+        assert result["cancelled"] is True
+        assert result["epochs_completed"] == 2
+        assert epochs_seen == [1, 2]
+
+    def test_custom_optimizer_factory_is_used(self):
+        built = self._built()
+        seen = []
+
+        def make_optimizer(params, lr):
+            seen.append(lr)
+            return torch.optim.SGD(params, lr=lr)
+
+        train_common.run_training_loop(
+            train_pairs=_synthetic_pairs(2), val_pairs=[], image_size=IMAGE_SIZE, n_classes=2,
+            epochs=1, batch_size=2, seed=0, flip_augment=False, to_tensor_fn=built.to_tensor_fn,
+            forward_fn=built.forward_fn, trainable_params=built.trainable_params, lr=5e-4, device="cpu",
+            make_optimizer_fn=make_optimizer, set_train_mode=built.set_train_mode,
+        )
+        assert seen == [5e-4]
diff --git a/frontend/src/app/pages/AnnotatePage.test.tsx b/frontend/src/app/pages/AnnotatePage.test.tsx
new file mode 100644
index 0000000..9dac7e9
--- /dev/null
+++ b/frontend/src/app/pages/AnnotatePage.test.tsx
@@ -0,0 +1,770 @@
+/**
+ * AnnotatePage — orchestration tests. This page wires together nearly every
+ * annotate hook/component; children with their own dedicated test files are
+ * mocked here (AnnotationCanvas, Toolbar, ClassManager, LayersPanel, SaveModal,
+ * DownloadModal, VersionHistoryModal, DenoiseBakeModal, InsightsModal,
+ * FeatureChannelsPanel, PixelClassifierPanel), as are the data-hooks the page
+ * calls directly. Also mocked: DisplayControls, SliceNavigator, MaskToolsPanel,
+ * MeasurementPanel — none of AnnotatePage's own logic depends on their
+ * internals, and leaving them real would pull in fetch/useQuery/useMaskOps
+ * plumbing unrelated to this page's orchestration. Real: the Zustand stores,
+ * DebouncedSlider (opacity), VersionPreviewBar, StageSwitcher (inline).
+ */
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import { MemoryRouter, Routes, Route } from 'react-router';
+import AnnotatePage from './AnnotatePage';
+import { useDatasetStore, type ImageMeta } from '@/stores/datasetStore';
+import { useAnnotationStore, type Shape } from '@/stores/annotationStore';
+import { useToolStore } from '@/stores/toolStore';
+import { useClassStore } from '@/stores/classStore';
+import { usePredictedRasterStore } from '@/stores/predictedRasterStore';
+
+// ---- Hooks AnnotatePage calls directly ----
+
+const useDraftSync = vi.fn();
+vi.mock('@/hooks/useDraftSync', () => ({ useDraftSync: (...args: unknown[]) => useDraftSync(...args) }));
+
+const useGuideLoad = vi.fn();
+vi.mock('@/hooks/useGuideSync', () => ({ useGuideLoad: (...args: unknown[]) => useGuideLoad(...args) }));
+
+const useKeybinds = vi.fn();
+vi.mock('@/hooks/useKeybinds', () => ({ useKeybinds: (...args: unknown[]) => useKeybinds(...args) }));
+
+const save = vi.fn(async () => true);
+const buildSavePayload = vi.fn(() => null as null | Record<string, unknown>);
+const fetchVersionPayload = vi.fn(async () => null as unknown);
+const restoreVersion = vi.fn();
+let useSaveReturn: Record<string, unknown>;
+vi.mock('@/hooks/useSave', () => ({
+  useSave: (...args: unknown[]) => useSaveHook(...args),
+}));
+function useSaveHook(..._args: unknown[]) {
+  return useSaveReturn;
+}
+
+const featuresCompute = vi.fn();
+let useFeatureChannelsReturn: Record<string, unknown>;
+vi.mock('@/hooks/useFeatureChannels', () => ({
+  useFeatureChannels: (...args: unknown[]) => useFeatureChannelsHook(...args),
+}));
+function useFeatureChannelsHook(..._args: unknown[]) {
+  return useFeatureChannelsReturn;
+}
+
+const clfTrain = vi.fn();
+const clfTrainAcrossSlices = vi.fn();
+const clfPredict = vi.fn();
+const clfDismiss = vi.fn();
+const clfApplyAcrossVolume = vi.fn();
+const clfResetVolumeApplyJob = vi.fn();
+let usePixelClassifierReturn: Record<string, unknown>;
+vi.mock('@/hooks/usePixelClassifier', () => ({
+  usePixelClassifier: (...args: unknown[]) => usePixelClassifierHook(...args),
+}));
+function usePixelClassifierHook(..._args: unknown[]) {
+  return usePixelClassifierReturn;
+}
+
+let useFeatureManifoldReturn: Record<string, unknown>;
+vi.mock('@/hooks/useFeatureManifold', () => ({
+  useFeatureManifold: (...args: unknown[]) => useFeatureManifoldHook(...args),
+}));
+function useFeatureManifoldHook(..._args: unknown[]) {
+  return useFeatureManifoldReturn;
+}
+
+const startMaskSync = vi.fn();
+let useExportJobReturn: Record<string, unknown>;
+vi.mock('@/hooks/useExportJob', () => ({
+  useExportJob: (...args: unknown[]) => useExportJobHook(...args),
+}));
+function useExportJobHook(..._args: unknown[]) {
+  return useExportJobReturn;
+}
+
+const loadLabelPng = vi.fn();
+const labelMapToPolygonShapes = vi.fn();
+vi.mock('@/lib/pixelClf', async (importOriginal) => {
+  const actual = await importOriginal<typeof import('@/lib/pixelClf')>();
+  return {
+    ...actual,
+    loadLabelPng: (...args: unknown[]) => loadLabelPng(...args),
+    labelMapToPolygonShapes: (...args: unknown[]) => labelMapToPolygonShapes(...args),
+  };
+});
+
+// ---- Heavy / separately-tested child components ----
+
+vi.mock('@/components/annotate/AnnotationCanvas', () => ({
+  default: (props: Record<string, unknown>) => (
+    <div
+      data-testid="annotation-canvas"
+      data-active-class-id={String(props.activeClassId)}
+      data-active-brush-shape-id={String(props.activeBrushShapeId)}
+      data-focus-region={JSON.stringify(props.focusRegion ?? null)}
+    >
+      <button onClick={() => (props.onNewBrushInstance as (id: string) => void)('brush-1')}>
+        mock-new-brush
+      </button>
+    </div>
+  ),
+}));
+
+vi.mock('@/components/annotate/Toolbar', () => ({
+  default: () => <div data-testid="toolbar" />,
+}));
+
+vi.mock('@/components/annotate/ClassManager', () => ({
+  default: (props: { activeClassId: number | null; onActivate: (id: number) => void; onClassDeleted: (id: number) => void }) => (
+    <div data-testid="class-manager" data-active-class-id={String(props.activeClassId)}>
+      <button onClick={() => props.onActivate(2)}>mock-activate-class-2</button>
+      <button onClick={() => props.onClassDeleted(1)}>mock-delete-class-1</button>
+    </div>
+  ),
+}));
+
+vi.mock('@/components/annotate/LayersPanel', () => ({
+  default: () => <div data-testid="layers-panel" />,
+}));
+
+vi.mock('@/components/annotate/DisplayControls', () => ({
+  default: () => <div data-testid="display-controls" />,
+}));
+
+vi.mock('@/components/annotate/SliceNavigator', () => ({
+  default: () => <div data-testid="slice-navigator" />,
+}));
+
+vi.mock('@/components/annotate/MaskToolsPanel', () => ({
+  default: () => <div data-testid="mask-tools-panel" />,
+}));
+
+vi.mock('@/components/annotate/MeasurementPanel', () => ({
+  default: () => <div data-testid="measurement-panel" />,
+}));
+
+vi.mock('@/components/annotate/FeatureChannelsPanel', () => ({
+  default: () => <div data-testid="feature-channels-panel" />,
+}));
+
+vi.mock('@/components/annotate/PixelClassifierPanel', () => ({
+  default: (props: Record<string, unknown>) => (
+    <div data-testid="pixel-classifier-panel">
+      <button onClick={() => (props.onTrain as () => void)()}>mock-clf-train</button>
+      <button onClick={() => (props.onPredict as () => void)()}>mock-clf-predict</button>
+      <button onClick={() => (props.onCommit as () => void)()}>mock-clf-commit</button>
+      <button onClick={() => (props.onTrainAcrossSlicesChange as (v: boolean) => void)(true)}>
+        mock-toggle-train-across
+      </button>
+      <button onClick={() => (props.onApplyToVolume as () => void)()}>mock-apply-to-volume</button>
+      <button onClick={() => (props.onCommitVolumeApply as () => void)()}>mock-commit-volume-apply</button>
+      <button onClick={() => (props.onMakeSliceEditable as () => void)()}>mock-make-slice-editable</button>
+      <button onClick={() => (props.onSyncToTiled as () => void)()}>mock-sync-to-tiled</button>
+      <button onClick={() => (props.onViewIn3D as () => void)()}>mock-view-in-3d</button>
+      <button onClick={() => (props.onTrainDeepModel as () => void)()}>mock-train-deep-model</button>
+    </div>
+  ),
+}));
+
+vi.mock('@/components/annotate/SaveModal', () => ({
+  default: (props: {
+    shapeCount: number;
+    classCount: number;
+    isSaving: boolean;
+    onSave: (opts: { annotatedBy: string; notes: string }) => void;
+    onClose: () => void;
+  }) => (
+    <div data-testid="save-modal" data-shape-count={props.shapeCount} data-class-count={props.classCount}>
+      <button onClick={() => props.onSave({ annotatedBy: 'me', notes: 'n' })}>mock-confirm-save</button>
+      <button onClick={() => props.onClose()}>mock-close-save</button>
+    </div>
+  ),
+}));
+
+vi.mock('@/components/annotate/DownloadModal', () => ({
+  default: (props: { onClose: () => void }) => (
+    <div data-testid="download-modal">
+      <button onClick={() => props.onClose()}>mock-close-download</button>
+    </div>
+  ),
+}));
+
+vi.mock('@/components/annotate/InsightsModal', () => ({
+  default: (props: { onClose: () => void; onFocus: (slice: number, bbox?: { x: number; y: number; w: number; h: number }) => void }) => (
+    <div data-testid="insights-modal">
+      <button onClick={() => props.onFocus(3, { x: 1, y: 2, w: 3, h: 4 })}>mock-insight-focus</button>
+      <button onClick={() => props.onClose()}>mock-close-insights</button>
+    </div>
+  ),
+}));
+
+vi.mock('@/components/annotate/VersionHistoryModal', () => ({
+  default: (props: {
+    versions: { version: number }[];
+    onPreview: (v: number) => void;
+    onRestore: (v: number) => void;
+    onClose: () => void;
+  }) => (
+    <div data-testid="version-history-modal">
+      <button onClick={() => props.onPreview(2)}>mock-preview-v2</button>
+      <button onClick={() => props.onRestore(1)}>mock-restore-v1</button>
+      <button onClick={() => props.onClose()}>mock-close-history</button>
+    </div>
+  ),
+}));
+
+vi.mock('@/components/annotate/DenoiseBakeModal', () => ({
+  default: (props: { open: boolean }) => (
+    <div data-testid="denoise-bake-modal" data-open={String(props.open)} />
+  ),
+}));
+
+const initialDatasetState = useDatasetStore.getState();
+const initialAnnotationState = useAnnotationStore.getState();
+const initialToolState = useToolStore.getState();
+const initialClassState = useClassStore.getState();
+const initialPredictedRasterState = usePredictedRasterStore.getState();
+
+const META: ImageMeta = {
+  nSlices: 5,
+  height: 100,
+  width: 100,
+  dtype: 'uint8',
+  isRgb: false,
+  valueRange: [0, 255],
+};
+
+function shape(id: string, classId: number): Shape {
+  return { id, classId, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10 };
+}
+
+/** Loads a dataset (meta present) so AnnotatePage renders the full workspace. */
+function loadDataset(overrides: Partial<{ source: string; kind: string; serverUri: string | null }> = {}) {
+  useDatasetStore.setState({
+    ...useDatasetStore.getState(),
+    kind: overrides.kind ?? 'tiled',
+    source: overrides.source ?? 'sample.zarr',
+    serverUri: overrides.serverUri ?? 'http://localhost:8000/api',
+    meta: META,
+    currentSlice: 0,
+  });
+}
+
+function renderPage() {
+  return render(
+    <MemoryRouter initialEntries={['/annotate']}>
+      <Routes>
+        <Route path="/annotate" element={<AnnotatePage />} />
+        <Route path="/browse" element={<div>BROWSE PAGE</div>} />
+        <Route path="/volume" element={<div>VOLUME PAGE</div>} />
+        <Route path="/train" element={<div>TRAIN PAGE</div>} />
+      </Routes>
+    </MemoryRouter>,
+  );
+}
+
+describe('AnnotatePage', () => {
+  beforeEach(() => {
+    useDatasetStore.setState(initialDatasetState, true);
+    useAnnotationStore.setState(initialAnnotationState, true);
+    useToolStore.setState(initialToolState, true);
+    useClassStore.setState(initialClassState, true);
+    usePredictedRasterStore.setState(initialPredictedRasterState, true);
+
+    useSaveReturn = {
+      isDirty: false,
+      isSaving: false,
+      lastSavedAt: null,
+      buildSavePayload,
+      saveSummary: { shapeCount: 0, classCount: 0 },
+      save,
+      versions: [],
+      fetchVersionPayload,
+      restoreVersion,
+      markClean: vi.fn(),
+    };
+    useFeatureChannelsReturn = {
+      job: null,
+      channelIndex: null,
+      channelUrl: null,
+      computing: false,
+      error: null,
+      invalidateJob: vi.fn(),
+      adoptFeatureBank: vi.fn(),
+      compute: featuresCompute,
+      selectChannel: vi.fn(),
+      cycleChannel: vi.fn(),
+      clearSelection: vi.fn(),
+    };
+    usePixelClassifierReturn = {
+      params: { iterations: 200, depth: 6, learningRate: 0.1, alpha: 0.05 },
+      setParams: vi.fn(),
+      model: null,
+      commitUrl: null,
+      statusUrl: null,
+      probaUrl: null,
+      predictCounts: null,
+      probaClassIndex: 0,
+      activeProbaClassId: null,
+      activeProbaThreshold: 0.5,
+      cycleProbaClass: vi.fn(),
+      setProbaThreshold: vi.fn(),
+      error: null,
+      training: false,
+      predicting: false,
+      train: clfTrain,
+      trainAcrossSlices: clfTrainAcrossSlices,
+      predict: clfPredict,
+      dismiss: clfDismiss,
+      commitUrl_: null,
+      compositionId: 'comp-1',
+      trainerId: 'catboost',
+      canTrainWithoutJob: true,
+      multiTrainJob: { done: 0, total: 0 },
+      multiTraining: false,
+      resetMultiTrainJob: vi.fn(),
+      applyAcrossVolume: clfApplyAcrossVolume,
+      volumeApplyJob: { done: 0, total: 0, result: null, jobId: null },
+      volumeApplying: false,
+      resetVolumeApplyJob: clfResetVolumeApplyJob,
+    };
+    useFeatureManifoldReturn = {
+      params: { k: 24, boxSize: 64 },
+      setParams: vi.fn(),
+      points: [],
+      heatmapUrl: null,
+      showHeatmap: false,
+      setShowHeatmap: vi.fn(),
+      showMarkers: false,
+      setShowMarkers: vi.fn(),
+      heatmapOpacity: 0.45,
+      setHeatmapOpacity: vi.fn(),
+      sampling: false,
+      error: null,
+      meta: null,
+      sample: vi.fn(),
+      dismiss: vi.fn(),
+      placementMask: null,
+      setPlacementMaskFromShapes: vi.fn(),
+      clearPlacementMask: vi.fn(),
+      hasSample: false,
+    };
+    useExportJobReturn = {
+      state: { status: 'idle', phase: '', done: 0, total: 0, log: [], result: null, error: null, jobId: null },
+      start: vi.fn(),
+      startMaskSync,
+      startIpredBatchTrain: vi.fn(),
+      startIpredBatchApply: vi.fn(),
+      startJob: vi.fn(),
+      reset: vi.fn(),
+      downloadUrl: null,
+    };
+  });
+
+  afterEach(() => {
+    cleanup();
+    vi.clearAllMocks();
+  });
+
+  it('shows "no sample loaded" when no dataset is open, and Go to Browse navigates', async () => {
+    const user = userEvent.setup();
+    renderPage();
+    expect(screen.getByText(/No sample loaded/)).toBeInTheDocument();
+    await user.click(screen.getByRole('button', { name: /Go to Browse/ }));
+    expect(await screen.findByText('BROWSE PAGE')).toBeInTheDocument();
+  });
+
+  it('renders the draw-stage panels by default and switches stages via the tab bar', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    renderPage();
+
+    expect(screen.getByTestId('toolbar')).toBeInTheDocument();
+    expect(screen.getByTestId('display-controls')).toBeInTheDocument();
+    expect(screen.getByTestId('mask-tools-panel')).toBeInTheDocument();
+    expect(screen.getByTestId('measurement-panel')).toBeInTheDocument();
+    expect(screen.queryByTestId('feature-channels-panel')).not.toBeInTheDocument();
+    expect(screen.queryByTestId('pixel-classifier-panel')).not.toBeInTheDocument();
+
+    await user.click(screen.getByRole('tab', { name: 'Assist' }));
+    expect(screen.getByTestId('feature-channels-panel')).toBeInTheDocument();
+    expect(screen.queryByTestId('toolbar')).not.toBeInTheDocument();
+
+    await user.click(screen.getByRole('tab', { name: 'Predict' }));
+    expect(screen.getByTestId('pixel-classifier-panel')).toBeInTheDocument();
+    expect(screen.queryByTestId('feature-channels-panel')).not.toBeInTheDocument();
+  });
+
+  it('auto-activates the first class once classes load, and clicking a class updates the canvas + clears the in-progress brush', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    useClassStore.setState({
+      classes: [
+        { classId: 1, label: 'A', color: '#f00', isVisible: true },
+        { classId: 2, label: 'B', color: '#0f0', isVisible: true },
+      ],
+    });
+    renderPage();
+
+    // Auto-activated to the first class (classId 1).
+    expect(screen.getByTestId('annotation-canvas')).toHaveAttribute('data-active-class-id', '1');
+
+    // Start a brush instance, then switch class — activeBrushShapeId must reset.
+    await user.click(screen.getByText('mock-new-brush'));
+    expect(screen.getByTestId('annotation-canvas')).toHaveAttribute('data-active-brush-shape-id', 'brush-1');
+
+    await user.click(screen.getByText('mock-activate-class-2'));
+    expect(screen.getByTestId('annotation-canvas')).toHaveAttribute('data-active-class-id', '2');
+    expect(screen.getByTestId('annotation-canvas')).toHaveAttribute('data-active-brush-shape-id', 'null');
+  });
+
+  it('clearing the active brush instance after a class delete does not change activeClassId', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    useClassStore.setState({ classes: [{ classId: 1, label: 'A', color: '#f00', isVisible: true }] });
+    renderPage();
+
+    await user.click(screen.getByText('mock-new-brush'));
+    expect(screen.getByTestId('annotation-canvas')).toHaveAttribute('data-active-brush-shape-id', 'brush-1');
+
+    await user.click(screen.getByText('mock-delete-class-1'));
+    expect(screen.getByTestId('annotation-canvas')).toHaveAttribute('data-active-brush-shape-id', 'null');
+    expect(screen.getByTestId('annotation-canvas')).toHaveAttribute('data-active-class-id', '1');
+  });
+
+  it('save button reflects isDirty/isSaving and opens the save modal with the current summary via buildSavePayload', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    useSaveReturn.isDirty = true;
+    useSaveReturn.saveSummary = { shapeCount: 4, classCount: 2 };
+    buildSavePayload.mockReturnValue({ classes: [], slices: {}, split_by_slice: {}, negative_slices: [] });
+    renderPage();
+
+    const saveBtn = screen.getByRole('button', { name: /^Save$/ });
+    expect(screen.getByText('Unsaved changes')).toBeInTheDocument();
+
+    await user.click(saveBtn);
+    const modal = screen.getByTestId('save-modal');
+    expect(modal).toHaveAttribute('data-shape-count', '4');
+    expect(modal).toHaveAttribute('data-class-count', '2');
+  });
+
+  it('does not open the save modal when buildSavePayload returns null (nothing to save)', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    buildSavePayload.mockReturnValue(null);
+    renderPage();
+
+    await user.click(screen.getByRole('button', { name: /Saved/ }));
+    expect(screen.queryByTestId('save-modal')).not.toBeInTheDocument();
+  });
+
+  it('confirming the save modal calls save() and closes the modal on success', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    buildSavePayload.mockReturnValue({ classes: [], slices: {}, split_by_slice: {}, negative_slices: [] });
+    save.mockResolvedValueOnce(true);
+    renderPage();
+
+    await user.click(screen.getByRole('button', { name: /Saved/ }));
+    await user.click(screen.getByText('mock-confirm-save'));
+
+    expect(save).toHaveBeenCalledWith({ annotatedBy: 'me', notes: 'n' });
+    await waitFor(() => expect(screen.queryByTestId('save-modal')).not.toBeInTheDocument());
+  });
+
+  it('keeps the save modal open when save() fails', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    buildSavePayload.mockReturnValue({ classes: [], slices: {}, split_by_slice: {}, negative_slices: [] });
+    save.mockResolvedValueOnce(false);
+    renderPage();
+
+    await user.click(screen.getByRole('button', { name: /Saved/ }));
+    await user.click(screen.getByText('mock-confirm-save'));
+
+    await waitFor(() => expect(save).toHaveBeenCalled());
+    expect(screen.getByTestId('save-modal')).toBeInTheDocument();
+  });
+
+  it('shows the version-history entry point only once versions exist, and preview/restore wire through', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    useSaveReturn.versions = [
+      { version: 1, saved_at: '2024-01-01T00:00:00Z', shape_count: 1, class_count: 1 },
+      { version: 2, saved_at: '2024-01-02T00:00:00Z', shape_count: 2, class_count: 1 },
+    ];
+    fetchVersionPayload.mockResolvedValue({ classes: [], slices: {}, split_by_slice: {}, negative_slices: [] });
+    renderPage();
+
+    const historyBtn = screen.getByTitle('Version history');
+    expect(historyBtn).toHaveTextContent('2');
+    await user.click(historyBtn);
+    expect(screen.getByTestId('version-history-modal')).toBeInTheDocument();
+
+    // Preview closes the history modal and opens the version preview bar.
+    await user.click(screen.getByText('mock-preview-v2'));
+    expect(screen.queryByTestId('version-history-modal')).not.toBeInTheDocument();
+    await waitFor(() => expect(fetchVersionPayload).toHaveBeenCalledWith(2));
+    expect(await screen.findByText(/Previewing v/)).toBeInTheDocument();
+
+    // Exiting the preview bar clears previewVersion (bar disappears).
+    await user.click(screen.getByTitle('Exit preview'));
+    expect(screen.queryByText(/Previewing v/)).not.toBeInTheDocument();
+  });
+
+  it('restoring a version from history calls restoreVersion', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    useSaveReturn.versions = [{ version: 1, saved_at: '2024-01-01T00:00:00Z', shape_count: 1, class_count: 1 }];
+    renderPage();
+
+    await user.click(screen.getByTitle('Version history'));
+    await user.click(screen.getByText('mock-restore-v1'));
+    expect(restoreVersion).toHaveBeenCalledWith(1);
+  });
+
+  it('Insights: opens on click, and a QA focus jumps the slice and sets the focus region on the canvas', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    renderPage();
+
+    await user.click(screen.getByRole('button', { name: /Insights/ }));
+    expect(screen.getByTestId('insights-modal')).toBeInTheDocument();
+
+    await user.click(screen.getByText('mock-insight-focus'));
+    expect(useDatasetStore.getState().currentSlice).toBe(3);
+    const canvas = screen.getByTestId('annotation-canvas');
+    const region = JSON.parse(canvas.getAttribute('data-focus-region') ?? 'null');
+    expect(region).toMatchObject({ x: 1, y: 2, w: 3, h: 4 });
+
+    await user.click(screen.getByText('mock-close-insights'));
+    expect(screen.queryByTestId('insights-modal')).not.toBeInTheDocument();
+  });
+
+  it('Export opens and closes the download modal', async () => {
+    const user = userEvent.setup();
+    loadDataset();
+    renderPage();
+
+    await user.click(screen.getByRole('button', { name: /Export/ }));
+    expect(screen.getByTestId('download-modal')).toBeInTheDocument();
+    await user.click(screen.getByText('mock-close-download'));
+    expect(screen.queryByTestId('download-modal')).not.toBeInTheDocument();
+  });
+
+  it('renders the denoise bake modal only when a source is open', () => {
+    loadDataset({ source: 'sample.zarr' });
+    renderPage();
+    expect(screen.getByTestId('denoise-bake-modal')).toBeInTheDocument();
+  });
+
+  it('passes the correct callbacks to useKeybinds, and the delete-selected callback removes selected shapes on the current slice', () => {
+    loadDataset();
+    const sourceKey = 'tiled:http://localhost:8000/api:sample.zarr';
+    useAnnotationStore.setState({
+      byImage: { [sourceKey]: { '0': [shape('s1', 1), shape('s2', 1)] } },
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1'] });
+    renderPage();
+
+    expect(useKeybinds).toHaveBeenCalled();
+    const [activeClassId, onActivateClass, onNewBrushInstance, onDeleteSelected, onCancelDraft] =
+      useKeybinds.mock.calls[useKeybinds.mock.calls.length - 1];
+    expect(typeof onActivateClass).toBe('function');
+    expect(typeof onNewBrushInstance).toBe('function');
+    expect(activeClassId).toBeNull();
+
+    act(() => onDeleteSelected());
+    expect(useAnnotationStore.getState().byImage[sourceKey]['0'].map((s: Shape) => s.id)).toEqual(['s2']);
+    expect(useToolStore.getState().selectedShapeIds).toEqual([]);
+
+    act(() => onCancelDraft());
+  });
+
+  describe('pixel classifier orchestration', () => {
+    it('Train calls clf.train with the current slice shapes when "train across slices" is off', async () => {
+      const user = userEvent.setup();
+      loadDataset();
+      const sourceKey = 'tiled:http://localhost:8000/api:sample.zarr';
+      const shapes = [shape('s1', 1)];
+      useAnnotationStore.setState({ byImage: { [sourceKey]: { '0': shapes } } });
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      await user.click(screen.getByText('mock-clf-train'));
+      expect(clfTrain).toHaveBeenCalledWith(shapes);
+      expect(clfTrainAcrossSlices).not.toHaveBeenCalled();
+    });
+
+    it('Train calls clf.trainAcrossSlices with every non-empty slice once "train across slices" is toggled on', async () => {
+      const user = userEvent.setup();
+      loadDataset();
+      const sourceKey = 'tiled:http://localhost:8000/api:sample.zarr';
+      useAnnotationStore.setState({
+        byImage: { [sourceKey]: { '0': [shape('s1', 1)], '1': [shape('s2', 1)], '2': [] } },
+      });
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      await user.click(screen.getByText('mock-toggle-train-across'));
+      await user.click(screen.getByText('mock-clf-train'));
+
+      expect(clfTrainAcrossSlices).toHaveBeenCalledWith({
+        0: [shape('s1', 1)],
+        1: [shape('s2', 1)],
+      });
+      expect(clfTrain).not.toHaveBeenCalled();
+    });
+
+    it('Predict calls clf.predict with the current slice shapes', async () => {
+      const user = userEvent.setup();
+      loadDataset();
+      const sourceKey = 'tiled:http://localhost:8000/api:sample.zarr';
+      const shapes = [shape('s1', 1)];
+      useAnnotationStore.setState({ byImage: { [sourceKey]: { '0': shapes } } });
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      await user.click(screen.getByText('mock-clf-predict'));
+      expect(clfPredict).toHaveBeenCalledWith(shapes);
+    });
+
+    it('Commit vectorizes the predicted PNG into shapes, appends them, and dismisses the overlay', async () => {
+      const user = userEvent.setup();
+      loadDataset();
+      const sourceKey = 'tiled:http://localhost:8000/api:sample.zarr';
+      usePixelClassifierReturn.commitUrl = 'blob:commit';
+      usePixelClassifierReturn.model = { classIds: [1, 2] };
+      loadLabelPng.mockResolvedValue({ data: new Uint8Array([1]), width: 1, height: 1 });
+      const newShape = shape('predicted-1', 1);
+      labelMapToPolygonShapes.mockReturnValue([newShape]);
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      await user.click(screen.getByText('mock-clf-commit'));
+
+      await waitFor(() =>
+        expect(useAnnotationStore.getState().byImage[sourceKey]?.['0']).toEqual([newShape]),
+      );
+      expect(clfDismiss).toHaveBeenCalledTimes(1);
+    });
+
+    it('Apply-to-volume runs across every slice index up to the dataset\'s slice count', async () => {
+      const user = userEvent.setup();
+      loadDataset(); // META.nSlices === 5
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      await user.click(screen.getByText('mock-apply-to-volume'));
+      expect(clfApplyAcrossVolume).toHaveBeenCalledWith([0, 1, 2, 3, 4]);
+    });
+
+    it('Commit-volume-apply records predicted-raster pointers only for slices with no existing shapes', async () => {
+      const user = userEvent.setup();
+      loadDataset();
+      const sourceKey = 'tiled:http://localhost:8000/api:sample.zarr';
+      useAnnotationStore.setState({ byImage: { [sourceKey]: { '1': [shape('existing', 1)] } } });
+      usePixelClassifierReturn.volumeApplyJob = {
+        done: 5,
+        total: 5,
+        result: { runs: { '0': 'run-a', '1': 'run-b', '2': 'run-c' } },
+      };
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      // Select a commit class first via the toggle so commitClassIds is non-empty.
+      await user.click(screen.getByText('mock-toggle-train-across')); // harmless toggle to exercise a click
+      await user.click(screen.getByText('mock-commit-volume-apply'));
+
+      // commitClassIds defaults to [] until a model exists; with model null the
+      // handler no-ops (commitClassIds.length === 0) — so no pointers recorded.
+      expect(usePredictedRasterStore.getState().bySource[sourceKey]).toBeUndefined();
+    });
+
+    it('Commit-volume-apply records pointers for un-annotated slices once a model supplies commit classes', async () => {
+      const user = userEvent.setup();
+      loadDataset();
+      const sourceKey = 'tiled:http://localhost:8000/api:sample.zarr';
+      useAnnotationStore.setState({ byImage: { [sourceKey]: { '1': [shape('existing', 1)] } } });
+      usePixelClassifierReturn.model = { classIds: [1, 2] };
+      usePixelClassifierReturn.volumeApplyJob = {
+        done: 3,
+        total: 3,
+        result: { runs: { '0': 'run-a', '1': 'run-b', '2': 'run-c' } },
+      };
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      await user.click(screen.getByText('mock-commit-volume-apply'));
+
+      const pointers = usePredictedRasterStore.getState().bySource[sourceKey];
+      // Slice 1 already has real shapes, so it's skipped; 0 and 2 get pointers.
+      expect(pointers).toEqual({
+        '0': { runId: 'run-a', classIds: [1, 2] },
+        '2': { runId: 'run-c', classIds: [1, 2] },
+      });
+      expect(clfResetVolumeApplyJob).toHaveBeenCalledTimes(1);
+    });
+
+    it('View in 3D navigates to /volume?mask=fast', async () => {
+      const user = userEvent.setup();
+      loadDataset();
+      usePixelClassifierReturn.model = { classIds: [1] };
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      await user.click(screen.getByText('mock-view-in-3d'));
+      expect(await screen.findByText('VOLUME PAGE')).toBeInTheDocument();
+    });
+
+    it('Train a deep model navigates to /train', async () => {
+      const user = userEvent.setup();
+      loadDataset();
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      await user.click(screen.getByText('mock-train-deep-model'));
+      expect(await screen.findByText('TRAIN PAGE')).toBeInTheDocument();
+    });
+
+    it('Sync to Tiled alerts (and does not start the job) for a non-Tiled source', async () => {
+      const user = userEvent.setup();
+      loadDataset({ kind: 'local', source: 'data/foo', serverUri: null });
+      const alertSpy = vi.spyOn(window, 'alert').mockImplementation(() => {});
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      await user.click(screen.getByText('mock-sync-to-tiled'));
+      expect(alertSpy).toHaveBeenCalledWith('Pushing masks to Tiled only works for Tiled sources.');
+      expect(startMaskSync).not.toHaveBeenCalled();
+    });
+
+    it('Sync to Tiled starts the mask-sync job with the current slices/classes for a Tiled source', async () => {
+      const user = userEvent.setup();
+      loadDataset();
+      const sourceKey = 'tiled:http://localhost:8000/api:sample.zarr';
+      const shapes = [shape('s1', 1)];
+      useAnnotationStore.setState({ byImage: { [sourceKey]: { '0': shapes } } });
+      useClassStore.setState({ classes: [{ classId: 1, label: 'A', color: '#f00', isVisible: true }] });
+      renderPage();
+
+      await user.click(screen.getByRole('tab', { name: 'Predict' }));
+      await user.click(screen.getByText('mock-sync-to-tiled'));
+
+      expect(startMaskSync).toHaveBeenCalledWith({
+        sources: [
+          expect.objectContaining({
+            kind: 'tiled',
+            source: 'sample.zarr',
+            server_uri: 'http://localhost:8000/api',
+            slices: { '0': shapes },
+          }),
+        ],
+        classes: [{ classId: 1, label: 'A', color: '#f00', isVisible: true }],
+      });
+    });
+  });
+});
diff --git a/frontend/src/app/pages/BrowsePage.test.tsx b/frontend/src/app/pages/BrowsePage.test.tsx
new file mode 100644
index 0000000..1619126
--- /dev/null
+++ b/frontend/src/app/pages/BrowsePage.test.tsx
@@ -0,0 +1,232 @@
+/**
+ * BrowsePage — orchestration tests: connection banner, kind switch (tiled vs
+ * local), navigation to /connect, and server-list wiring into ColumnBrowser.
+ *
+ * ColumnBrowser and LocalSampleBrowser are mocked out: they are separate,
+ * already-tested units with their own fetch-driven state machines (see
+ * ColumnBrowser.test.tsx / LocalSampleBrowser.test.tsx). BrowsePage only needs
+ * to exercise the props it wires into them and its own connection/navigation
+ * logic, so useConnectionStore and useNavigate (via a real MemoryRouter) stay
+ * real.
+ */
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import { MemoryRouter, Routes, Route } from 'react-router';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import BrowsePage from './BrowsePage';
+import { useConnectionStore } from '@/stores/connectionStore';
+
+const openLocalFile = vi.fn();
+vi.mock('@/hooks/useOpenInAnnotate', () => ({
+  useOpenInAnnotate: () => ({ openTiledArray: vi.fn(), openLocalFile }),
+}));
+
+vi.mock('@/components/Browse/ColumnBrowser', () => ({
+  default: (props: any) => (
+    <div data-testid="column-browser">
+      <span>serverUri:{props.serverUri}</span>
+      <span>containerPath:{String(props.containerPath)}</span>
+      <span>focusPath:{String(props.focusPath)}</span>
+      <span>selectedServerUri:{props.selectedServerUri}</span>
+      <span>servers:{props.servers.map((s: any) => s.name).join(',')}</span>
+      <button onClick={() => props.onServerChange('http://b')}>change-server</button>
+      <button onClick={() => props.onAnnotationFilterChange('annotated')}>change-annotation</button>
+    </div>
+  ),
+}));
+
+vi.mock('@/components/Browse/LocalSampleBrowser', () => ({
+  default: (props: any) => (
+    <div data-testid="local-browser">
+      <span>root:{props.root}</span>
+      <span>rel:{props.rel}</span>
+      <button onClick={() => props.onOpenInAnnotate('rel/path.tif')}>open-local</button>
+    </div>
+  ),
+}));
+
+const initialConnectionState = useConnectionStore.getState();
+
+function jsonResponse(body: unknown, ok = true) {
+  return {
+    ok,
+    status: ok ? 200 : 500,
+    json: async () => body,
+    text: async () => (typeof body === 'string' ? body : JSON.stringify(body)),
+  } as Response;
+}
+
+const SERVERS = [
+  { name: 'Server A', uri: 'http://a', has_api_key: false },
+  { name: 'Server B', uri: 'http://b', has_api_key: false },
+];
+
+function makeFetchMock() {
+  return vi.fn(async (input: RequestInfo | URL) => {
+    const url = String(input);
+    if (url.includes('/api/config/servers')) return jsonResponse(SERVERS);
+    return jsonResponse({}, false);
+  });
+}
+
+function renderPage() {
+  const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return render(
+    <QueryClientProvider client={queryClient}>
+      <MemoryRouter initialEntries={['/browse']}>
+        <Routes>
+          <Route path="/browse" element={<BrowsePage />} />
+          <Route path="/connect" element={<div>CONNECT PAGE</div>} />
+        </Routes>
+      </MemoryRouter>
+    </QueryClientProvider>,
+  );
+}
+
+beforeEach(() => {
+  useConnectionStore.setState(initialConnectionState, true);
+});
+
+afterEach(() => {
+  cleanup();
+  vi.clearAllMocks();
+});
+
+describe('BrowsePage', () => {
+  it('shows a not-connected prompt and navigates to Connect', async () => {
+    global.fetch = makeFetchMock();
+    const user = userEvent.setup();
+    renderPage();
+
+    expect(screen.getByText('No dataset connected.')).toBeInTheDocument();
+    await user.click(screen.getByRole('button', { name: 'Go to Connect' }));
+    expect(await screen.findByText('CONNECT PAGE')).toBeInTheDocument();
+  });
+
+  it('renders the tiled ColumnBrowser with connection details when kind is tiled', async () => {
+    global.fetch = makeFetchMock();
+    useConnectionStore.getState().setConnection({
+      kind: 'tiled',
+      serverUri: 'http://a',
+      browseContainerPath: 'browse/foo',
+      browseFocusPath: 'browse/foo/bar',
+      label: 'Server A',
+      sampleCount: 5,
+    });
+    renderPage();
+
+    expect(screen.getByText('Server A')).toBeInTheDocument();
+    expect(screen.getByText(/5 samples/)).toBeInTheDocument();
+
+    const columnBrowser = await screen.findByTestId('column-browser');
+    expect(columnBrowser).toHaveTextContent('serverUri:http://a');
+    expect(columnBrowser).toHaveTextContent('containerPath:browse/foo');
+    expect(columnBrowser).toHaveTextContent('focusPath:browse/foo/bar');
+    expect(columnBrowser).toHaveTextContent('selectedServerUri:http://a');
+    await screen.findByText('servers:Server A,Server B');
+    expect(screen.queryByTestId('local-browser')).not.toBeInTheDocument();
+  });
+
+  it('renders the LocalSampleBrowser with root/rel when kind is local', async () => {
+    global.fetch = makeFetchMock();
+    useConnectionStore.getState().setConnection({
+      kind: 'local',
+      localRoot: '/data',
+      localRel: 'sub',
+      label: '/data/sub',
+      sampleCount: 3,
+    });
+    renderPage();
+
+    const localBrowser = await screen.findByTestId('local-browser');
+    expect(localBrowser).toHaveTextContent('root:/data');
+    expect(localBrowser).toHaveTextContent('rel:sub');
+    expect(screen.queryByTestId('column-browser')).not.toBeInTheDocument();
+    // Servers query is disabled (enabled: kind === 'tiled') for local connections.
+    expect(global.fetch).not.toHaveBeenCalled();
+  });
+
+  it('shows singular sample count text when there is exactly one sample', async () => {
+    global.fetch = makeFetchMock();
+    useConnectionStore.getState().setConnection({
+      kind: 'local',
+      localRoot: '/data',
+      localRel: '',
+      label: '/data',
+      sampleCount: 1,
+    });
+    renderPage();
+    expect(await screen.findByText(/1 sample$/)).toBeInTheDocument();
+  });
+
+  it('clicking "Change" navigates to /connect', async () => {
+    global.fetch = makeFetchMock();
+    useConnectionStore.getState().setConnection({
+      kind: 'local',
+      localRoot: '/data',
+      localRel: '',
+      label: '/data',
+      sampleCount: 1,
+    });
+    const user = userEvent.setup();
+    renderPage();
+
+    await user.click(screen.getByRole('button', { name: 'Change' }));
+    expect(await screen.findByText('CONNECT PAGE')).toBeInTheDocument();
+  });
+
+  it('opening a local sample calls openLocalFile via useOpenInAnnotate', async () => {
+    global.fetch = makeFetchMock();
+    useConnectionStore.getState().setConnection({
+      kind: 'local',
+      localRoot: '/data',
+      localRel: '',
+      label: '/data',
+      sampleCount: 1,
+    });
+    const user = userEvent.setup();
+    renderPage();
+
+    await user.click(await screen.findByText('open-local'));
+    expect(openLocalFile).toHaveBeenCalledWith('rel/path.tif');
+  });
+
+  it('changing the server in ColumnBrowser updates the connection store', async () => {
+    global.fetch = makeFetchMock();
+    useConnectionStore.getState().setConnection({
+      kind: 'tiled',
+      serverUri: 'http://a',
+      label: 'Server A',
+      sampleCount: 5,
+    });
+    const user = userEvent.setup();
+    renderPage();
+
+    await screen.findByTestId('column-browser');
+    await user.click(screen.getByText('change-server'));
+
+    expect(useConnectionStore.getState()).toMatchObject({
+      kind: 'tiled',
+      serverUri: 'http://b',
+      label: 'Server B',
+    });
+  });
+
+  it('changing the annotation filter is reflected back into ColumnBrowser', async () => {
+    global.fetch = makeFetchMock();
+    useConnectionStore.getState().setConnection({
+      kind: 'tiled',
+      serverUri: 'http://a',
+      label: 'Server A',
+      sampleCount: 5,
+    });
+    const user = userEvent.setup();
+    renderPage();
+
+    await screen.findByTestId('column-browser');
+    await user.click(screen.getByText('change-annotation'));
+    // Re-rendering with the new filter doesn't crash and the mock still shows.
+    expect(await screen.findByTestId('column-browser')).toBeInTheDocument();
+  });
+});
diff --git a/frontend/src/app/pages/ConnectPage.test.tsx b/frontend/src/app/pages/ConnectPage.test.tsx
new file mode 100644
index 0000000..2cef527
--- /dev/null
+++ b/frontend/src/app/pages/ConnectPage.test.tsx
@@ -0,0 +1,328 @@
+/**
+ * ConnectPage — component tests for server selection, connect/verify flows
+ * (Tiled + Local), navigation, and the optional dataset-container picker.
+ *
+ * IngestDropzone/ZarrLoader and useOpenInAnnotate are mocked: they are
+ * separate, already-complex units with their own fetch/upload logic, and
+ * ConnectPage only needs to exercise the callbacks it wires into them
+ * (browseIngested / annotateIngested / annotateZarr).
+ */
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor, within } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import { MemoryRouter, Routes, Route } from 'react-router';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import ConnectPage from './ConnectPage';
+import { useConnectionStore } from '@/stores/connectionStore';
+
+const openTiledArray = vi.fn();
+
+vi.mock('@/hooks/useOpenInAnnotate', () => ({
+  useOpenInAnnotate: () => ({ openTiledArray }),
+}));
+
+vi.mock('@/components/Ingest/IngestDropzone', () => ({
+  default: (props: { onBrowse?: (c: string, n: number) => void; onAnnotate?: (c: string, k: string) => void }) => (
+    <div data-testid="ingest-dropzone">
+      <button onClick={() => props.onBrowse?.('browse/foo', 3)}>mock-ingest-browse</button>
+      <button onClick={() => props.onAnnotate?.('browse/foo', 'img1')}>mock-ingest-annotate</button>
+    </div>
+  ),
+}));
+
+vi.mock('@/components/Ingest/ZarrLoader', () => ({
+  default: (props: { onAnnotate?: (p: string) => void }) => (
+    <div data-testid="zarr-loader">
+      <button onClick={() => props.onAnnotate?.('browse/vol/multiscale/level_0/array')}>mock-zarr-annotate</button>
+    </div>
+  ),
+}));
+
+const initialConnectionState = useConnectionStore.getState();
+
+function jsonResponse(body: unknown, ok = true) {
+  return {
+    ok,
+    json: async () => body,
+    text: async () => (typeof body === 'string' ? body : JSON.stringify(body)),
+  } as Response;
+}
+
+const SERVERS = [
+  { name: 'Local Tiled', uri: 'http://localhost:8000/api', has_api_key: false },
+  { name: 'Remote Tiled', uri: 'http://remote:8000/api', has_api_key: true },
+];
+
+/** Default fetch router; tests override specific endpoints as needed. */
+function makeFetchMock(overrides: Partial<Record<string, (url: string) => Response | Promise<Response>>> = {}) {
+  return vi.fn(async (input: RequestInfo | URL) => {
+    const url = String(input);
+    if (overrides.servers && url.includes('/api/config/servers')) return overrides.servers(url);
+    if (url.includes('/api/config/servers')) return jsonResponse(SERVERS);
+
+    if (overrides.tiledList && url.includes('/api/tiled/list')) return overrides.tiledList(url);
+    if (url.includes('/api/tiled/list')) return jsonResponse([]);
+
+    if (overrides.localList && url.includes('/api/local/list')) return overrides.localList(url);
+    if (url.includes('/api/local/list')) return jsonResponse([]);
+
+    if (overrides.summary && url.includes('/api/connect/summary')) return overrides.summary(url);
+    if (url.includes('/api/connect/summary')) return jsonResponse({ kind: 'tiled', server_uri: SERVERS[0].uri, label: 'Local Tiled', sample_count: 5 });
+
+    throw new Error(`Unhandled fetch: ${url}`);
+  });
+}
+
+function renderPage() {
+  const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return render(
+    <QueryClientProvider client={queryClient}>
+      <MemoryRouter initialEntries={['/connect']}>
+        <Routes>
+          <Route path="/connect" element={<ConnectPage />} />
+          <Route path="/browse" element={<div>BROWSE PAGE</div>} />
+        </Routes>
+      </MemoryRouter>
+    </QueryClientProvider>,
+  );
+}
+
+describe('ConnectPage', () => {
+  beforeEach(() => {
+    useConnectionStore.setState(initialConnectionState, true);
+  });
+
+  afterEach(() => {
+    cleanup();
+    vi.clearAllMocks();
+  });
+
+  it('shows a loading state before the server list arrives, then lists servers', async () => {
+    global.fetch = makeFetchMock();
+    renderPage();
+
+    expect(screen.getByText(/Loading servers…/)).toBeInTheDocument();
+
+    await screen.findByRole('combobox', { name: 'Tiled server' });
+    const select = screen.getByRole('combobox', { name: 'Tiled server' }) as HTMLSelectElement;
+    expect(within(select).getByText(/Local Tiled/)).toBeInTheDocument();
+    expect(within(select).getByText(/Remote Tiled/)).toBeInTheDocument();
+    // Auto-selects the first server once the list loads.
+    await waitFor(() => expect(select.value).toBe(SERVERS[0].uri));
+  });
+
+  it('connects to the selected Tiled server (verify) and reveals "Go to Browse" without navigating', async () => {
+    global.fetch = makeFetchMock();
+    const user = userEvent.setup();
+    renderPage();
+
+    await screen.findByRole('combobox', { name: 'Tiled server' });
+    await user.click(screen.getByRole('button', { name: /Connect/ }));
+
+    await screen.findByText(/Connected — 5 samples found/);
+    expect(useConnectionStore.getState()).toMatchObject({
+      kind: 'tiled',
+      serverUri: SERVERS[0].uri,
+      label: 'Local Tiled',
+      sampleCount: 5,
+    });
+
+    // Verify does not navigate away; it reveals a separate action instead.
+    expect(screen.queryByText('BROWSE PAGE')).not.toBeInTheDocument();
+    const goToBrowse = await screen.findByRole('button', { name: /Go to Browse/ });
+
+    await user.click(goToBrowse);
+    expect(await screen.findByText('BROWSE PAGE')).toBeInTheDocument();
+  });
+
+  it('shows a failure message when connecting to Tiled fails', async () => {
+    global.fetch = makeFetchMock({
+      summary: async () => jsonResponse('server unreachable', false),
+    });
+    const user = userEvent.setup();
+    renderPage();
+
+    await screen.findByRole('combobox', { name: 'Tiled server' });
+    await user.click(screen.getByRole('button', { name: /Connect/ }));
+
+    await screen.findByText(/Failed: Error: server unreachable/);
+    expect(screen.queryByRole('button', { name: /Go to Browse/ })).not.toBeInTheDocument();
+    expect(useConnectionStore.getState().kind).toBeNull();
+  });
+
+  it('switching Tiled server resets the verified/connected state', async () => {
+    global.fetch = makeFetchMock();
+    const user = userEvent.setup();
+    renderPage();
+
+    await screen.findByRole('combobox', { name: 'Tiled server' });
+    await user.click(screen.getByRole('button', { name: /Connect/ }));
+    await screen.findByText(/Connected —/);
+
+    const select = screen.getByRole('combobox', { name: 'Tiled server' });
+    await user.selectOptions(select, SERVERS[1].uri);
+
+    expect(screen.queryByRole('button', { name: /Go to Browse/ })).not.toBeInTheDocument();
+    expect(screen.queryByText(/Connected —/)).not.toBeInTheDocument();
+  });
+
+  it('lets you browse into a sub-container and connects with that container selected', async () => {
+    global.fetch = makeFetchMock({
+      tiledList: async (url) => {
+        if (url.includes('path=sub')) return jsonResponse([]);
+        return jsonResponse([{ name: 'sub', path: 'sub', is_dir: true, is_array: false }]);
+      },
+      summary: async (url) => {
+        expect(url).toContain('container_path=sub');
+        return jsonResponse({ kind: 'tiled', server_uri: SERVERS[0].uri, label: 'Local Tiled', sample_count: 2 });
+      },
+    });
+    const user = userEvent.setup();
+    renderPage();
+
+    await screen.findByRole('combobox', { name: 'Tiled server' });
+    await user.click(screen.getByRole('button', { name: /Dataset to view/ }));
+    await user.click(await screen.findByText('sub'));
+    await user.click(await screen.findByText('Browse "sub"'));
+    expect(screen.getByText(/Browse will show/)).toHaveTextContent('sub');
+
+    await user.click(screen.getByRole('button', { name: /Connect/ }));
+    await screen.findByText(/Connected — 2 samples found/);
+    expect(useConnectionStore.getState().browseContainerPath).toBe('sub');
+  });
+
+  it('local mode: grants a root, browses folders, selects one, and connects (navigates to Browse)', async () => {
+    global.fetch = makeFetchMock({
+      localList: async (url) => {
+        if (url.includes('rel=data')) return jsonResponse([]);
+        return jsonResponse([{ name: 'data', path: 'data', is_dir: true, size: null }]);
+      },
+      summary: async () => jsonResponse({ kind: 'local', server_uri: null, label: '/root/data', sample_count: 7 }),
+    });
+    const user = userEvent.setup();
+    renderPage();
+
+    await user.click(screen.getByRole('button', { name: /Local Folder/ }));
+    await user.type(screen.getByPlaceholderText('/absolute/path/to/data'), '/root');
+    await user.click(screen.getByRole('button', { name: 'Grant' }));
+
+    await screen.findByText('data');
+    await user.click(screen.getByText('data'));
+    await user.click(screen.getByText(/Use "data" as dataset folder/));
+
+    const connectBtn = screen.getByRole('button', { name: 'Connect' });
+    expect(connectBtn).not.toBeDisabled();
+    await user.click(connectBtn);
+
+    await screen.findByText(/Connected — 7 samples found/);
+    expect(await screen.findByText('BROWSE PAGE')).toBeInTheDocument();
+    expect(useConnectionStore.getState()).toMatchObject({
+      kind: 'local',
+      localRoot: '/root',
+      localRel: 'data',
+      sampleCount: 7,
+    });
+  });
+
+  it('local mode: Connect is disabled until a root is granted and a folder is selected', async () => {
+    // Note: selecting "this root" itself sets selectedFolder to '' (the
+    // relative path of the root), which is falsy in the component's
+    // `canConnect` check — so Connect stays disabled unless an actual
+    // sub-folder is chosen. This test documents that real behavior.
+    global.fetch = makeFetchMock({
+      localList: async (url) => {
+        if (url.includes('rel=data')) return jsonResponse([]);
+        return jsonResponse([{ name: 'data', path: 'data', is_dir: true, size: null }]);
+      },
+    });
+    const user = userEvent.setup();
+    renderPage();
+
+    await user.click(screen.getByRole('button', { name: /Local Folder/ }));
+    expect(screen.getByRole('button', { name: 'Connect' })).toBeDisabled();
+
+    await user.type(screen.getByPlaceholderText('/absolute/path/to/data'), '/root');
+    await user.click(screen.getByRole('button', { name: 'Grant' }));
+    await screen.findByText('data');
+    // Root granted but no folder explicitly selected yet.
+    expect(screen.getByRole('button', { name: 'Connect' })).toBeDisabled();
+    // Selecting "this root" does not count as a folder selection either.
+    await user.click(screen.getByText(/Use "this root" as dataset folder/));
+    expect(screen.getByRole('button', { name: 'Connect' })).toBeDisabled();
+
+    await user.click(screen.getByText('data'));
+    await user.click(screen.getByText(/Use "data" as dataset folder/));
+    expect(screen.getByRole('button', { name: 'Connect' })).not.toBeDisabled();
+  });
+
+  it('local mode: shows a failure message when connect fails', async () => {
+    global.fetch = makeFetchMock({
+      localList: async (url) => {
+        if (url.includes('rel=data')) return jsonResponse([]);
+        return jsonResponse([{ name: 'data', path: 'data', is_dir: true, size: null }]);
+      },
+      summary: async () => jsonResponse('bad path', false),
+    });
+    const user = userEvent.setup();
+    renderPage();
+
+    await user.click(screen.getByRole('button', { name: /Local Folder/ }));
+    await user.type(screen.getByPlaceholderText('/absolute/path/to/data'), '/root');
+    await user.click(screen.getByRole('button', { name: 'Grant' }));
+    await screen.findByText('data');
+    await user.click(screen.getByText('data'));
+    await user.click(screen.getByText(/Use "data" as dataset folder/));
+    await user.click(screen.getByRole('button', { name: 'Connect' }));
+
+    await screen.findByText(/Failed: Error: bad path/);
+  });
+
+  it('browsing an ingested dataset sets the connection (no container, focus on the dataset) and navigates', async () => {
+    global.fetch = makeFetchMock();
+    const user = userEvent.setup();
+    renderPage();
+
+    await screen.findByRole('combobox', { name: 'Tiled server' });
+    await user.click(screen.getByText('mock-ingest-browse'));
+
+    expect(await screen.findByText('BROWSE PAGE')).toBeInTheDocument();
+    expect(useConnectionStore.getState()).toMatchObject({
+      kind: 'tiled',
+      browseContainerPath: null,
+      browseFocusPath: 'browse/foo',
+    });
+  });
+
+  it('annotating an ingested sample sets the connection (stays put) and opens it in Annotate', async () => {
+    global.fetch = makeFetchMock();
+    const user = userEvent.setup();
+    renderPage();
+
+    await screen.findByRole('combobox', { name: 'Tiled server' });
+    await user.click(screen.getByText('mock-ingest-annotate'));
+
+    // annotateIngested does not navigate to Browse.
+    expect(screen.queryByText('BROWSE PAGE')).not.toBeInTheDocument();
+    expect(useConnectionStore.getState()).toMatchObject({
+      kind: 'tiled',
+      browseContainerPath: 'browse/foo',
+    });
+    expect(openTiledArray).toHaveBeenCalledWith('browse/foo/img1', SERVERS[0].uri);
+  });
+
+  it('annotating a registered Zarr level derives the container path and opens it in Annotate', async () => {
+    global.fetch = makeFetchMock();
+    const user = userEvent.setup();
+    renderPage();
+
+    await screen.findByRole('combobox', { name: 'Tiled server' });
+    await user.click(screen.getByText('mock-zarr-annotate'));
+
+    expect(screen.queryByText('BROWSE PAGE')).not.toBeInTheDocument();
+    expect(useConnectionStore.getState()).toMatchObject({
+      kind: 'tiled',
+      browseContainerPath: 'browse/vol',
+    });
+    expect(openTiledArray).toHaveBeenCalledWith('browse/vol/multiscale/level_0/array', SERVERS[0].uri);
+  });
+});
diff --git a/frontend/src/components/Browse/BrowseColumn.test.tsx b/frontend/src/components/Browse/BrowseColumn.test.tsx
new file mode 100644
index 0000000..9dbfa4f
--- /dev/null
+++ b/frontend/src/components/Browse/BrowseColumn.test.tsx
@@ -0,0 +1,138 @@
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import BrowseColumn from './BrowseColumn';
+import type { ColumnState } from './hooks/useBrowseData';
+
+afterEach(() => {
+  cleanup();
+});
+
+function makeColumn(overrides: Partial<ColumnState> = {}): ColumnState {
+  return {
+    field: 'sample_type',
+    values: [],
+    loading: false,
+    error: null,
+    selected: null,
+    ...overrides,
+  };
+}
+
+const baseProps = {
+  colIndex: 0,
+  facets: ['sample_type', 'technique', 'beamline'],
+  width: 200,
+  onFieldChange: vi.fn(),
+  onSelect: vi.fn(),
+  onRemove: vi.fn(),
+  isLast: true,
+};
+
+describe('BrowseColumn', () => {
+  it('renders all facets as select options', () => {
+    render(<BrowseColumn {...baseProps} column={makeColumn()} />);
+    const select = screen.getByTitle('sample_type') as HTMLSelectElement;
+    expect(select.value).toBe('sample_type');
+    for (const facet of baseProps.facets) {
+      expect(screen.getByRole('option', { name: facet })).toBeInTheDocument();
+    }
+  });
+
+  it('shows a loading state', () => {
+    render(<BrowseColumn {...baseProps} column={makeColumn({ loading: true })} />);
+    expect(screen.getByText('Loading…')).toBeInTheDocument();
+  });
+
+  it('shows an error state', () => {
+    render(<BrowseColumn {...baseProps} column={makeColumn({ error: 'boom' })} />);
+    expect(screen.getByText('boom')).toBeInTheDocument();
+  });
+
+  it('shows a "No values" empty state when not loading/erroring and empty', () => {
+    render(<BrowseColumn {...baseProps} column={makeColumn({ values: [] })} />);
+    expect(screen.getByText('No values')).toBeInTheDocument();
+  });
+
+  it('renders values with their counts', () => {
+    render(
+      <BrowseColumn
+        {...baseProps}
+        column={makeColumn({
+          values: [
+            { value: 'gold', count: 12, sample_paths: [] },
+            { value: 'silver', count: 3, sample_paths: [] },
+          ],
+        })}
+      />,
+    );
+    expect(screen.getByText('gold')).toBeInTheDocument();
+    expect(screen.getByText('12')).toBeInTheDocument();
+    expect(screen.getByText('silver')).toBeInTheDocument();
+    expect(screen.getByText('3')).toBeInTheDocument();
+  });
+
+  it('applies formatValue to displayed values but not to onSelect', async () => {
+    const onSelect = vi.fn();
+    const user = userEvent.setup();
+    const formatValue = (field: string, value: string) => `${field}:${value}`;
+    render(
+      <BrowseColumn
+        {...baseProps}
+        onSelect={onSelect}
+        formatValue={formatValue}
+        column={makeColumn({ values: [{ value: 'gold', count: 12, sample_paths: [] }] })}
+      />,
+    );
+    expect(screen.getByText('sample_type:gold')).toBeInTheDocument();
+    await user.click(screen.getByText('sample_type:gold'));
+    expect(onSelect).toHaveBeenCalledWith(0, 'gold');
+  });
+
+  it('clicking an unselected value selects it', async () => {
+    const onSelect = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <BrowseColumn
+        {...baseProps}
+        onSelect={onSelect}
+        column={makeColumn({ values: [{ value: 'gold', count: 12, sample_paths: [] }] })}
+      />,
+    );
+    await user.click(screen.getByText('gold'));
+    expect(onSelect).toHaveBeenCalledWith(0, 'gold');
+  });
+
+  it('clicking the already-selected value clears it (passes null)', async () => {
+    const onSelect = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <BrowseColumn
+        {...baseProps}
+        onSelect={onSelect}
+        column={makeColumn({
+          selected: 'gold',
+          values: [{ value: 'gold', count: 12, sample_paths: [] }],
+        })}
+      />,
+    );
+    await user.click(screen.getByText('gold'));
+    expect(onSelect).toHaveBeenCalledWith(0, null);
+  });
+
+  it('changing the select calls onFieldChange with the column index and new field', async () => {
+    const onFieldChange = vi.fn();
+    const user = userEvent.setup();
+    render(<BrowseColumn {...baseProps} onFieldChange={onFieldChange} column={makeColumn()} />);
+    await user.selectOptions(screen.getByTitle('sample_type'), 'technique');
+    expect(onFieldChange).toHaveBeenCalledWith(0, 'technique');
+  });
+
+  it('clicking remove calls onRemove with the column index', async () => {
+    const onRemove = vi.fn();
+    const user = userEvent.setup();
+    render(<BrowseColumn {...baseProps} onRemove={onRemove} column={makeColumn()} />);
+    await user.click(screen.getByTitle('Remove column'));
+    expect(onRemove).toHaveBeenCalledWith(0);
+  });
+});
diff --git a/frontend/src/components/Browse/BrowseDetailPanel.test.tsx b/frontend/src/components/Browse/BrowseDetailPanel.test.tsx
new file mode 100644
index 0000000..4049ac5
--- /dev/null
+++ b/frontend/src/components/Browse/BrowseDetailPanel.test.tsx
@@ -0,0 +1,176 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import BrowseDetailPanel from './BrowseDetailPanel';
+import type { BrowseItem } from './hooks/useBrowseData';
+
+function makeItem(overrides: Partial<BrowseItem> = {}): BrowseItem {
+  return {
+    path: 'ds/a',
+    sample: 'sample-a',
+    metadata: {},
+    ...overrides,
+  };
+}
+
+/** Stub window.Image so the thumbnail-loading effect resolves synchronously
+ *  and predictably instead of depending on jsdom's (nonexistent) image decoding. */
+let imageInstances: FakeImage[] = [];
+class FakeImage {
+  onload: (() => void) | null = null;
+  onerror: (() => void) | null = null;
+  _src = '';
+  constructor() {
+    imageInstances.push(this);
+  }
+  set src(value: string) {
+    this._src = value;
+  }
+  get src() {
+    return this._src;
+  }
+}
+
+beforeEach(() => {
+  imageInstances = [];
+  vi.stubGlobal('Image', FakeImage);
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('BrowseDetailPanel', () => {
+  it('renders the sample name and path in the header', () => {
+    render(<BrowseDetailPanel item={makeItem({ sample: 'my-sample', path: 'a/b/c' })} onClose={vi.fn()} />);
+    expect(screen.getByText('my-sample')).toBeInTheDocument();
+    expect(screen.getByText('a/b/c')).toBeInTheDocument();
+  });
+
+  it('clicking the close button calls onClose', async () => {
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    render(<BrowseDetailPanel item={makeItem()} onClose={onClose} />);
+    // The close button is the only icon-only button in the header without a title.
+    const buttons = screen.getAllByRole('button');
+    await user.click(buttons[0]);
+    expect(onClose).toHaveBeenCalled();
+  });
+
+  it('shows the loading preview state before the thumbnail resolves', () => {
+    render(<BrowseDetailPanel item={makeItem()} onClose={vi.fn()} />);
+    expect(screen.getByText('Loading preview…')).toBeInTheDocument();
+  });
+
+  it('shows the image once the thumbnail loads successfully', async () => {
+    render(<BrowseDetailPanel item={makeItem({ path: 'ds/a' })} onClose={vi.fn()} serverUri="http://server" />);
+    expect(imageInstances).toHaveLength(1);
+    act(() => {
+      imageInstances[0].onload?.();
+    });
+    const img = await screen.findByAltText('Array preview');
+    expect(img).toHaveAttribute('src', expect.stringContaining('tiled_path=ds%2Fa'));
+    expect(img.getAttribute('src')).toContain('server_uri=http%3A%2F%2Fserver');
+  });
+
+  it('renders nothing for the preview area when the thumbnail fails to load', async () => {
+    render(<BrowseDetailPanel item={makeItem()} onClose={vi.fn()} />);
+    act(() => {
+      imageInstances[0].onerror?.();
+    });
+    expect(screen.queryByText('Loading preview…')).not.toBeInTheDocument();
+    expect(screen.queryByAltText('Array preview')).not.toBeInTheDocument();
+  });
+
+  it('refetches the thumbnail when the item path changes', () => {
+    const { rerender } = render(<BrowseDetailPanel item={makeItem({ path: 'a' })} onClose={vi.fn()} />);
+    expect(imageInstances).toHaveLength(1);
+    rerender(<BrowseDetailPanel item={makeItem({ path: 'b' })} onClose={vi.fn()} />);
+    expect(imageInstances).toHaveLength(2);
+    expect(imageInstances[1].src).toContain('tiled_path=b');
+  });
+
+  it('does not render the Open in Annotate button when onOpenInAnnotate is omitted', () => {
+    render(<BrowseDetailPanel item={makeItem()} onClose={vi.fn()} />);
+    expect(screen.queryByText('Open in Annotate')).not.toBeInTheDocument();
+  });
+
+  it('clicking Open in Annotate calls the callback', async () => {
+    const onOpenInAnnotate = vi.fn();
+    const user = userEvent.setup();
+    render(<BrowseDetailPanel item={makeItem()} onClose={vi.fn()} onOpenInAnnotate={onOpenInAnnotate} />);
+    await user.click(screen.getByText('Open in Annotate'));
+    expect(onOpenInAnnotate).toHaveBeenCalled();
+  });
+
+  it('groups known metadata keys into their labelled sections', () => {
+    render(
+      <BrowseDetailPanel
+        item={makeItem({ metadata: { PI: 'Dr. Smith', beamline: '11-3', ThinFilmID: 'TF-1' } })}
+        onClose={vi.fn()}
+      />,
+    );
+    expect(screen.getByText('Experiment')).toBeInTheDocument();
+    expect(screen.getByText('Identity')).toBeInTheDocument();
+    expect(screen.getByText('PI')).toBeInTheDocument();
+    expect(screen.getByText('Dr. Smith')).toBeInTheDocument();
+    expect(screen.getByText('ThinFilmID')).toBeInTheDocument();
+    expect(screen.getByText('TF-1')).toBeInTheDocument();
+  });
+
+  it('maps aliased keys to friendlier display labels', () => {
+    render(
+      <BrowseDetailPanel
+        item={makeItem({ metadata: { studio_annotated: 'yes', studio_shape_count: 4 } })}
+        onClose={vi.fn()}
+      />,
+    );
+    expect(screen.getByText('Annotated')).toBeInTheDocument();
+    expect(screen.getByText('Shape count')).toBeInTheDocument();
+    expect(screen.getByText('4')).toBeInTheDocument();
+  });
+
+  it('puts unrecognised metadata keys into an "Other" section, skipping noisy/internal keys', () => {
+    render(
+      <BrowseDetailPanel
+        item={makeItem({
+          metadata: {
+            custom_field: 'custom-value',
+            vae_embedding: [1, 2, 3],
+            thinfilm_internal: 'skip me',
+            'Sample foo': 'skip me too',
+          },
+        })}
+        onClose={vi.fn()}
+      />,
+    );
+    expect(screen.getByText('Other')).toBeInTheDocument();
+    expect(screen.getByText('custom_field')).toBeInTheDocument();
+    expect(screen.getByText('custom-value')).toBeInTheDocument();
+    expect(screen.queryByText('vae_embedding')).not.toBeInTheDocument();
+    expect(screen.queryByText('thinfilm_internal')).not.toBeInTheDocument();
+    expect(screen.queryByText('Sample foo')).not.toBeInTheDocument();
+  });
+
+  it('formats array metadata values as an item-count summary', () => {
+    render(
+      <BrowseDetailPanel item={makeItem({ metadata: { custom_list: [1, 2, 3] } })} onClose={vi.fn()} />,
+    );
+    expect(screen.getByText('[3 items]')).toBeInTheDocument();
+  });
+
+  it('formats null/undefined-ish metadata values as an em dash', () => {
+    render(
+      <BrowseDetailPanel item={makeItem({ metadata: { custom_field: null } })} onClose={vi.fn()} />,
+    );
+    // null values are filtered out before display, so no "Other" section appears.
+    expect(screen.queryByText('Other')).not.toBeInTheDocument();
+  });
+
+  it('renders no metadata sections when metadata is empty', () => {
+    render(<BrowseDetailPanel item={makeItem({ metadata: {} })} onClose={vi.fn()} />);
+    expect(screen.queryByText('Identity')).not.toBeInTheDocument();
+    expect(screen.queryByText('Other')).not.toBeInTheDocument();
+  });
+});
diff --git a/frontend/src/components/Browse/ColumnBrowser.test.tsx b/frontend/src/components/Browse/ColumnBrowser.test.tsx
new file mode 100644
index 0000000..1ef8240
--- /dev/null
+++ b/frontend/src/components/Browse/ColumnBrowser.test.tsx
@@ -0,0 +1,267 @@
+/**
+ * ColumnBrowser — tests the Miller-column orchestration: toolbar wiring, the
+ * auto "show all" on connect, adding columns, item/slice selection routing,
+ * open-in-Annotate, and connection-error display.
+ *
+ * BrowseColumn/ItemsColumn/SlicesColumn/BrowseDetailPanel are mocked out — they
+ * are separate, already-tested units (owned by a parallel test-writing pass);
+ * ColumnBrowser only needs to exercise the props/callbacks it wires into them.
+ * useBrowseData itself is real, so the actual fetch-driven state machine
+ * (facets → show-all → items) still runs end to end against a stubbed fetch.
+ */
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import ColumnBrowser from './ColumnBrowser';
+import type { BrowseItem } from './hooks/useBrowseData';
+import type { ServerInfo } from '@/types/server';
+
+const openTiledArray = vi.fn(async () => {});
+vi.mock('@/hooks/useOpenInAnnotate', () => ({
+  useOpenInAnnotate: () => ({ openTiledArray, openLocalFile: vi.fn() }),
+}));
+
+vi.mock('./BrowseColumn', () => ({
+  default: (props: any) => (
+    <div data-testid={`browse-column-${props.colIndex}`}>
+      <span>field:{props.column.field}</span>
+      <button onClick={() => props.onRemove(props.colIndex)}>remove-{props.colIndex}</button>
+      <button onClick={() => props.onSelect(props.colIndex, 'v1')}>select-v1-{props.colIndex}</button>
+    </div>
+  ),
+}));
+
+vi.mock('./ItemsColumn', () => ({
+  default: (props: any) => (
+    <div data-testid="items-column">
+      {props.items.map((it: BrowseItem) => (
+        <button key={it.path} onClick={() => props.onSelect(it)}>
+          item:{it.sample}
+        </button>
+      ))}
+    </div>
+  ),
+}));
+
+vi.mock('./SlicesColumn', () => ({
+  default: (props: any) => (
+    <div data-testid="slices-column">
+      <span>slices-of:{props.dataset.sample}</span>
+      <button onClick={props.onClose}>close-slices</button>
+    </div>
+  ),
+}));
+
+vi.mock('./BrowseDetailPanel', () => ({
+  default: (props: any) => (
+    <div data-testid="detail-panel">
+      <span>detail:{props.item.sample}</span>
+      <button onClick={props.onClose}>close-detail</button>
+    </div>
+  ),
+}));
+
+function jsonResponse(body: unknown, ok = true) {
+  return {
+    ok,
+    status: ok ? 200 : 500,
+    json: async () => body,
+    text: async () => (typeof body === 'string' ? body : JSON.stringify(body)),
+  } as Response;
+}
+
+function makeFetchMock(
+  overrides: Record<string, (url: URL) => Response | Promise<Response>> = {},
+) {
+  return vi.fn(async (input: RequestInfo | URL) => {
+    const url = new URL(String(input), 'http://localhost');
+    for (const [path, handler] of Object.entries(overrides)) {
+      if (url.pathname === path) return handler(url);
+    }
+    return jsonResponse({}, false);
+  });
+}
+
+const SERVERS: ServerInfo[] = [
+  { name: 'Server A', uri: 'http://a', has_api_key: false },
+  { name: 'Server B', uri: 'http://b', has_api_key: false },
+];
+
+function renderBrowser(overrides: Partial<React.ComponentProps<typeof ColumnBrowser>> = {}) {
+  const props = {
+    serverUri: 'http://a',
+    containerPath: null,
+    focusPath: null,
+    servers: SERVERS,
+    selectedServerUri: 'http://a',
+    onServerChange: vi.fn(),
+    annotationFilter: 'all' as const,
+    onAnnotationFilterChange: vi.fn(),
+    ...overrides,
+  };
+  return { ...render(<ColumnBrowser {...props} />), props };
+}
+
+beforeEach(() => {
+  vi.spyOn(console, 'warn').mockImplementation(() => {});
+});
+
+afterEach(() => {
+  cleanup();
+  vi.clearAllMocks();
+});
+
+describe('ColumnBrowser', () => {
+  it('renders the toolbar with server + annotation selects', async () => {
+    global.fetch = makeFetchMock({ '/api/browse/facets': () => jsonResponse({ facets: [] }) });
+    renderBrowser();
+    expect(screen.getByText('Metadata Browser')).toBeInTheDocument();
+    expect(screen.getByRole('combobox', { name: 'Server' })).toBeInTheDocument();
+    expect(screen.getByRole('combobox', { name: 'Annotation' })).toBeInTheDocument();
+    // Let the facets fetch (and its show-all effect) settle before the test ends.
+    await waitFor(() => expect(screen.getByTestId('items-column')).toBeInTheDocument());
+  });
+
+  it('auto shows every sample once connected, rendering the items column', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['field1'] }),
+      '/api/browse/items': () =>
+        jsonResponse({ items: [{ path: 'p1', sample: 'sample-1', metadata: {} }], total: 1 }),
+    });
+    renderBrowser();
+
+    expect(await screen.findByTestId('items-column')).toBeInTheDocument();
+    expect(screen.getByText('item:sample-1')).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /All samples/ })).toHaveClass('bg-slate-600');
+  });
+
+  it('shows the disconnected banner when facets cannot be reached, and no items column', async () => {
+    global.fetch = makeFetchMock({ '/api/browse/facets': () => jsonResponse('down', false) });
+    renderBrowser();
+
+    expect(await screen.findByText(/Cannot reach the API server/)).toBeInTheDocument();
+    expect(screen.queryByTestId('items-column')).not.toBeInTheDocument();
+  });
+
+  it('Add column adds the first unused facet, and adding again picks the next one', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['field1', 'field2'] }),
+      '/api/browse/items': () => jsonResponse({ items: [], total: 0 }),
+      '/api/browse/column': () => jsonResponse({ values: [] }),
+    });
+    const user = userEvent.setup();
+    renderBrowser();
+    await screen.findByTestId('items-column');
+
+    await user.click(screen.getByRole('button', { name: 'Add column' }));
+    expect(await screen.findByTestId('browse-column-0')).toHaveTextContent('field:field1');
+
+    await user.click(screen.getByRole('button', { name: 'Add column' }));
+    expect(await screen.findByTestId('browse-column-1')).toHaveTextContent('field:field2');
+  });
+
+  it('selecting a single-image sample opens the detail panel', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+      '/api/browse/items': () =>
+        jsonResponse({ items: [{ path: 'p1', sample: 'single-1', metadata: {} }], total: 1 }),
+    });
+    const user = userEvent.setup();
+    renderBrowser();
+
+    await user.click(await screen.findByText('item:single-1'));
+    expect(await screen.findByTestId('detail-panel')).toHaveTextContent('detail:single-1');
+
+    await user.click(screen.getByText('close-detail'));
+    await waitFor(() => expect(screen.queryByTestId('detail-panel')).not.toBeInTheDocument());
+  });
+
+  it('selecting a multi-slice sample expands the slices column instead of the detail panel', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+      '/api/browse/items': () =>
+        jsonResponse({
+          items: [{ path: 'vol1', sample: 'volume-1', metadata: {}, n_slices: 5 }],
+          total: 1,
+        }),
+      '/api/browse/slices': () =>
+        jsonResponse({ items: [{ path: 'vol1/0', sample: '0', metadata: {} }] }),
+    });
+    const user = userEvent.setup();
+    renderBrowser();
+
+    await user.click(await screen.findByText('item:volume-1'));
+    expect(await screen.findByTestId('slices-column')).toHaveTextContent('slices-of:volume-1');
+    expect(screen.queryByTestId('detail-panel')).not.toBeInTheDocument();
+
+    await user.click(screen.getByText('close-slices'));
+    await waitFor(() => expect(screen.queryByTestId('slices-column')).not.toBeInTheDocument());
+  });
+
+  it('Open in Annotate opens the selected item via useOpenInAnnotate', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+      '/api/browse/items': () =>
+        jsonResponse({ items: [{ path: 'p1', sample: 'single-1', metadata: {} }], total: 1 }),
+    });
+    const user = userEvent.setup();
+    renderBrowser();
+
+    await user.click(await screen.findByText('item:single-1'));
+    await user.click(screen.getByRole('button', { name: /Open in Annotate/ }));
+
+    await waitFor(() => expect(openTiledArray).toHaveBeenCalledWith('p1', 'http://a'));
+  });
+
+  it('auto-selects the item matching focusPath once the full list has loaded', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+      '/api/browse/items': () =>
+        jsonResponse({
+          items: [
+            { path: 'p1', sample: 'first', metadata: {} },
+            { path: 'p2', sample: 'second', metadata: {} },
+          ],
+          total: 2,
+        }),
+    });
+    renderBrowser({ focusPath: 'p2' });
+
+    expect(await screen.findByTestId('detail-panel')).toHaveTextContent('detail:second');
+  });
+
+  it('calls onServerChange when the server select changes', async () => {
+    global.fetch = makeFetchMock({ '/api/browse/facets': () => jsonResponse({ facets: [] }) });
+    const onServerChange = vi.fn();
+    const user = userEvent.setup();
+    renderBrowser({ onServerChange });
+
+    await user.selectOptions(screen.getByRole('combobox', { name: 'Server' }), 'http://b');
+    expect(onServerChange).toHaveBeenCalledWith('http://b');
+  });
+
+  it('calls onAnnotationFilterChange when the annotation select changes', async () => {
+    global.fetch = makeFetchMock({ '/api/browse/facets': () => jsonResponse({ facets: [] }) });
+    const onAnnotationFilterChange = vi.fn();
+    const user = userEvent.setup();
+    renderBrowser({ onAnnotationFilterChange });
+
+    await user.selectOptions(screen.getByRole('combobox', { name: 'Annotation' }), 'annotated');
+    expect(onAnnotationFilterChange).toHaveBeenCalledWith('annotated');
+  });
+
+  it('Refresh triggers another items fetch while showing all', async () => {
+    const fetchMock = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+      '/api/browse/items': () => jsonResponse({ items: [], total: 0 }),
+    });
+    global.fetch = fetchMock;
+    const user = userEvent.setup();
+    renderBrowser();
+
+    await screen.findByTestId('items-column');
+    const callsBefore = fetchMock.mock.calls.length;
+    await user.click(screen.getByRole('button', { name: 'Refresh' }));
+    await waitFor(() => expect(fetchMock.mock.calls.length).toBeGreaterThan(callsBefore));
+  });
+});
diff --git a/frontend/src/components/Browse/ItemsColumn.test.tsx b/frontend/src/components/Browse/ItemsColumn.test.tsx
new file mode 100644
index 0000000..82740a1
--- /dev/null
+++ b/frontend/src/components/Browse/ItemsColumn.test.tsx
@@ -0,0 +1,169 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import ItemsColumn from './ItemsColumn';
+import { useRatingStore } from '@/stores/ratingStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { buildSourceKey } from '@/lib/sourceKey';
+import type { BrowseItem } from './hooks/useBrowseData';
+
+const initialRatingState = useRatingStore.getState();
+
+function makeItem(overrides: Partial<BrowseItem> = {}): BrowseItem {
+  return {
+    path: 'ds/a',
+    sample: 'sample-a',
+    metadata: {},
+    ...overrides,
+  };
+}
+
+function renderWithClient(ui: React.ReactElement) {
+  const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return render(<QueryClientProvider client={queryClient}>{ui}</QueryClientProvider>);
+}
+
+beforeEach(() => {
+  useRatingStore.setState(initialRatingState, true);
+  useAnnotationStore.getState().reset();
+  window.localStorage.clear();
+  vi.stubGlobal(
+    'fetch',
+    vi.fn().mockResolvedValue({ ok: true, json: async () => [] }),
+  );
+  // jsdom doesn't implement scrollIntoView.
+  window.HTMLElement.prototype.scrollIntoView = vi.fn();
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+const baseProps = {
+  items: [] as BrowseItem[],
+  total: 0,
+  loading: false,
+  selectedItem: null,
+  onSelect: vi.fn(),
+  onOpenInAnnotate: vi.fn(),
+  width: 220,
+  serverUri: 'http://server',
+  annotationFilter: 'all' as const,
+};
+
+describe('ItemsColumn', () => {
+  it('shows a loading state', () => {
+    renderWithClient(<ItemsColumn {...baseProps} loading />);
+    expect(screen.getByText('Loading…')).toBeInTheDocument();
+  });
+
+  it('shows an empty state when there are no matching samples', () => {
+    renderWithClient(<ItemsColumn {...baseProps} items={[]} total={0} />);
+    expect(screen.getByText('No matching samples')).toBeInTheDocument();
+  });
+
+  it('renders items and the total count badge', () => {
+    const items = [makeItem({ path: 'a', sample: 'a' }), makeItem({ path: 'b', sample: 'b' })];
+    renderWithClient(<ItemsColumn {...baseProps} items={items} total={2} />);
+    expect(screen.getByText('a')).toBeInTheDocument();
+    expect(screen.getByText('b')).toBeInTheDocument();
+    expect(screen.getByText('2')).toBeInTheDocument();
+  });
+
+  it('clicking a row selects it, clicking again clears the selection', async () => {
+    const onSelect = vi.fn();
+    const user = userEvent.setup();
+    const item = makeItem({ path: 'a', sample: 'a' });
+    const { rerender } = renderWithClient(
+      <QueryClientProvider client={new QueryClient({ defaultOptions: { queries: { retry: false } } })}>
+        <ItemsColumn {...baseProps} items={[item]} onSelect={onSelect} selectedItem={null} />
+      </QueryClientProvider>,
+    );
+    await user.click(screen.getByText('a'));
+    expect(onSelect).toHaveBeenCalledWith(item);
+
+    onSelect.mockClear();
+    rerender(
+      <QueryClientProvider client={new QueryClient({ defaultOptions: { queries: { retry: false } } })}>
+        <ItemsColumn {...baseProps} items={[item]} onSelect={onSelect} selectedItem={item} />
+      </QueryClientProvider>,
+    );
+    await user.click(screen.getByText('a'));
+    expect(onSelect).toHaveBeenCalledWith(null);
+  });
+
+  it('clicking the open-in-annotate button calls onOpenInAnnotate with the item and does not select it', async () => {
+    const onOpenInAnnotate = vi.fn();
+    const onSelect = vi.fn();
+    const user = userEvent.setup();
+    const item = makeItem({ path: 'a', sample: 'a' });
+    renderWithClient(
+      <ItemsColumn {...baseProps} items={[item]} onOpenInAnnotate={onOpenInAnnotate} onSelect={onSelect} />,
+    );
+    await user.click(screen.getByTitle('Open in Annotate'));
+    expect(onOpenInAnnotate).toHaveBeenCalledWith(item);
+    expect(onSelect).not.toHaveBeenCalled();
+  });
+
+  it('shows a volume badge and caret for multi-slice items, not for single-slice items', () => {
+    const volume = makeItem({ path: 'v', sample: 'vol', n_slices: 5 });
+    const single = makeItem({ path: 's', sample: 'single', n_slices: 1 });
+    renderWithClient(<ItemsColumn {...baseProps} items={[volume, single]} />);
+    expect(screen.getByText('5')).toBeInTheDocument();
+    expect(screen.getByTitle('5 slices — click to browse them')).toBeInTheDocument();
+  });
+
+  it('shows the annotated badge when metadata marks the item annotated', () => {
+    const item = makeItem({ path: 'a', sample: 'a', metadata: { studio_annotated: 'yes' } });
+    renderWithClient(<ItemsColumn {...baseProps} items={[item]} />);
+    expect(screen.getByText('annotated')).toBeInTheDocument();
+  });
+
+  it('does not show the annotated badge for unannotated items', () => {
+    const item = makeItem({ path: 'a', sample: 'a' });
+    renderWithClient(<ItemsColumn {...baseProps} items={[item]} />);
+    expect(screen.queryByText('annotated')).not.toBeInTheDocument();
+  });
+
+  it('filters out unannotated items when annotationFilter is "annotated"', () => {
+    const annotated = makeItem({ path: 'a', sample: 'a', metadata: { studio_annotated: 'yes' } });
+    const plain = makeItem({ path: 'b', sample: 'b' });
+    renderWithClient(
+      <ItemsColumn {...baseProps} items={[annotated, plain]} total={2} annotationFilter="annotated" />,
+    );
+    expect(screen.getByText('a')).toBeInTheDocument();
+    expect(screen.queryByText('b')).not.toBeInTheDocument();
+    // filtered count differs from total, so shows "1 / 2"
+    expect(screen.getByText('1 / 2')).toBeInTheDocument();
+  });
+
+  it('filters out annotated items when annotationFilter is "unannotated"', () => {
+    const annotated = makeItem({ path: 'a', sample: 'a', metadata: { studio_annotated: 'yes' } });
+    const plain = makeItem({ path: 'b', sample: 'b' });
+    renderWithClient(
+      <ItemsColumn {...baseProps} items={[annotated, plain]} total={2} annotationFilter="unannotated" />,
+    );
+    expect(screen.queryByText('a')).not.toBeInTheDocument();
+    expect(screen.getByText('b')).toBeInTheDocument();
+  });
+
+  it('filters by minimum star rating', async () => {
+    const item = makeItem({ path: 'a', sample: 'a' });
+    const sourceKey = buildSourceKey('tiled', item.path, baseProps.serverUri);
+    useRatingStore.setState({ ratings: { [sourceKey]: 1 } });
+    const user = userEvent.setup();
+    renderWithClient(<ItemsColumn {...baseProps} items={[item]} total={1} />);
+
+    expect(screen.getByText('a')).toBeInTheDocument();
+    await user.click(screen.getByRole('button', { name: '★★' }));
+    expect(screen.queryByText('a')).not.toBeInTheDocument();
+    expect(screen.getByText('No samples match the current filters.')).toBeInTheDocument();
+  });
+
+  it('shows the "no matches" filtered message distinctly from the plain empty state', () => {
+    renderWithClient(<ItemsColumn {...baseProps} items={[]} total={0} annotationFilter="annotated" />);
+    expect(screen.getByText('No samples match the current filters.')).toBeInTheDocument();
+  });
+});
diff --git a/frontend/src/components/Browse/LocalSampleBrowser.test.tsx b/frontend/src/components/Browse/LocalSampleBrowser.test.tsx
new file mode 100644
index 0000000..fef6241
--- /dev/null
+++ b/frontend/src/components/Browse/LocalSampleBrowser.test.tsx
@@ -0,0 +1,221 @@
+/**
+ * LocalSampleBrowser — flat local-folder sample list: loading/error states,
+ * annotation + star filters, rating persistence, joinPath, and Annotate wiring.
+ */
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor, within } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import LocalSampleBrowser, { joinPath } from './LocalSampleBrowser';
+import { useRatingStore } from '@/stores/ratingStore';
+import { buildSourceKey } from '@/lib/sourceKey';
+
+const initialRatingState = useRatingStore.getState();
+
+function jsonResponse(body: unknown, ok = true) {
+  return {
+    ok,
+    status: ok ? 200 : 500,
+    json: async () => body,
+    text: async () => (typeof body === 'string' ? body : JSON.stringify(body)),
+  } as Response;
+}
+
+function makeFetchMock(
+  overrides: Record<string, (url: URL) => Response | Promise<Response>> = {},
+) {
+  return vi.fn(async (input: RequestInfo | URL) => {
+    const url = new URL(String(input), 'http://localhost');
+    for (const [path, handler] of Object.entries(overrides)) {
+      if (url.pathname === path) return handler(url);
+    }
+    if (url.pathname === '/api/annotations/drafts') return jsonResponse([]);
+    return jsonResponse({}, false);
+  });
+}
+
+const SAMPLES = [
+  { name: 'a.tif', path: 'a.tif' },
+  { name: 'b.tif', path: 'sub/b.tif' },
+];
+
+function renderBrowser(
+  overrides: Partial<React.ComponentProps<typeof LocalSampleBrowser>> = {},
+) {
+  const props = {
+    root: '/data',
+    rel: '',
+    onOpenInAnnotate: vi.fn(),
+    annotationFilter: 'all' as const,
+    onAnnotationFilterChange: vi.fn(),
+    ...overrides,
+  };
+  const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return {
+    ...render(
+      <QueryClientProvider client={queryClient}>
+        <LocalSampleBrowser {...props} />
+      </QueryClientProvider>,
+    ),
+    props,
+  };
+}
+
+beforeEach(() => {
+  useRatingStore.setState(initialRatingState, true);
+  window.localStorage.clear();
+});
+
+afterEach(() => {
+  cleanup();
+  vi.restoreAllMocks();
+});
+
+describe('joinPath', () => {
+  it('returns root unchanged when rel is empty', () => {
+    expect(joinPath('/data', '')).toBe('/data');
+  });
+
+  it('joins root and rel, normalising duplicate slashes', () => {
+    expect(joinPath('/data/', '/sub/file.tif')).toBe('/data/sub/file.tif');
+    expect(joinPath('/data', 'sub/file.tif')).toBe('/data/sub/file.tif');
+  });
+});
+
+describe('LocalSampleBrowser', () => {
+  it('shows a loading state before samples arrive', async () => {
+    let resolveFetch!: (v: Response) => void;
+    global.fetch = vi.fn((input: RequestInfo | URL) => {
+      const url = String(input);
+      if (url.includes('/api/local/samples')) {
+        return new Promise((resolve) => {
+          resolveFetch = resolve;
+        });
+      }
+      return Promise.resolve(jsonResponse([]));
+    }) as unknown as typeof fetch;
+
+    renderBrowser();
+    expect(screen.getByText('Loading samples…')).toBeInTheDocument();
+
+    resolveFetch(jsonResponse({ items: [], total: 0 }));
+    await waitFor(() => expect(screen.queryByText('Loading samples…')).not.toBeInTheDocument());
+  });
+
+  it('shows an error message when the samples request fails', async () => {
+    global.fetch = makeFetchMock({
+      '/api/local/samples': () => jsonResponse('disk unavailable', false),
+    });
+    renderBrowser();
+    expect(await screen.findByText('disk unavailable')).toBeInTheDocument();
+  });
+
+  it('lists samples with a total count and the current folder path', async () => {
+    global.fetch = makeFetchMock({
+      '/api/local/samples': () => jsonResponse({ items: SAMPLES, total: 2 }),
+    });
+    renderBrowser({ rel: 'sub' });
+
+    expect(await screen.findByText('a.tif')).toBeInTheDocument();
+    expect(screen.getByText('b.tif')).toBeInTheDocument();
+    expect(screen.getByText('2')).toBeInTheDocument();
+    expect(screen.getByText('/data/sub')).toBeInTheDocument();
+  });
+
+  it('shows an empty-folder message when there are no samples', async () => {
+    global.fetch = makeFetchMock({
+      '/api/local/samples': () => jsonResponse({ items: [], total: 0 }),
+    });
+    renderBrowser();
+    expect(await screen.findByText('No image files found in this folder.')).toBeInTheDocument();
+  });
+
+  it('shows an annotated badge for samples with drafts, based on their source key', async () => {
+    const sk = buildSourceKey('local', joinPath('/data', 'a.tif'));
+    global.fetch = makeFetchMock({
+      '/api/local/samples': () => jsonResponse({ items: SAMPLES, total: 2 }),
+      '/api/annotations/drafts': () => jsonResponse([{ source_key: sk, has_annotations: true }]),
+    });
+    renderBrowser();
+
+    await screen.findByText('a.tif');
+    const row = screen.getByText('a.tif').closest('.group') as HTMLElement;
+    expect(row).toHaveTextContent('annotated');
+    const otherRow = screen.getByText('b.tif').closest('.group') as HTMLElement;
+    expect(otherRow).not.toHaveTextContent('annotated');
+  });
+
+  it('filters by the annotation-status select via the onAnnotationFilterChange callback', async () => {
+    global.fetch = makeFetchMock({
+      '/api/local/samples': () => jsonResponse({ items: SAMPLES, total: 2 }),
+    });
+    const onAnnotationFilterChange = vi.fn();
+    const user = userEvent.setup();
+    renderBrowser({ onAnnotationFilterChange });
+
+    await screen.findByText('a.tif');
+    await user.selectOptions(screen.getByRole('combobox'), 'annotated');
+    expect(onAnnotationFilterChange).toHaveBeenCalledWith('annotated');
+  });
+
+  it('unannotated filter hides samples with drafts', async () => {
+    const sk = buildSourceKey('local', joinPath('/data', 'a.tif'));
+    global.fetch = makeFetchMock({
+      '/api/local/samples': () => jsonResponse({ items: SAMPLES, total: 2 }),
+      '/api/annotations/drafts': () => jsonResponse([{ source_key: sk, has_annotations: true }]),
+    });
+    renderBrowser({ annotationFilter: 'unannotated' });
+
+    await screen.findByText('b.tif');
+    expect(screen.queryByText('a.tif')).not.toBeInTheDocument();
+    expect(screen.getByText('1 / 2')).toBeInTheDocument();
+  });
+
+  it('star filter hides samples below the chosen minimum rating', async () => {
+    const skA = buildSourceKey('local', joinPath('/data', 'a.tif'));
+    useRatingStore.setState({ ratings: { [skA]: 2 } });
+    global.fetch = makeFetchMock({
+      '/api/local/samples': () => jsonResponse({ items: SAMPLES, total: 2 }),
+    });
+    const user = userEvent.setup();
+    renderBrowser();
+
+    await screen.findByText('a.tif');
+    await user.click(screen.getByText('★★'));
+    expect(screen.getByText('a.tif')).toBeInTheDocument();
+    expect(screen.queryByText('b.tif')).not.toBeInTheDocument();
+
+    await user.click(screen.getByText('All'));
+    expect(screen.getByText('b.tif')).toBeInTheDocument();
+  });
+
+  it('clicking a star rates the sample and persists it in the rating store', async () => {
+    global.fetch = makeFetchMock({
+      '/api/local/samples': () => jsonResponse({ items: SAMPLES, total: 2 }),
+    });
+    const user = userEvent.setup();
+    renderBrowser();
+
+    await screen.findByText('a.tif');
+    const row = screen.getByText('a.tif').closest('.group') as HTMLElement;
+    await user.click(within(row).getByLabelText('2 stars'));
+
+    const sk = buildSourceKey('local', joinPath('/data', 'a.tif'));
+    expect(useRatingStore.getState().ratings[sk]).toBe(2);
+  });
+
+  it('clicking Annotate calls onOpenInAnnotate with the absolute path', async () => {
+    global.fetch = makeFetchMock({
+      '/api/local/samples': () => jsonResponse({ items: SAMPLES, total: 2 }),
+    });
+    const onOpenInAnnotate = vi.fn();
+    const user = userEvent.setup();
+    renderBrowser({ onOpenInAnnotate });
+
+    await screen.findByText('b.tif');
+    const row = screen.getByText('b.tif').closest('.group') as HTMLElement;
+    await user.click(within(row).getByRole('button', { name: /Annotate/ }));
+
+    expect(onOpenInAnnotate).toHaveBeenCalledWith('/data/sub/b.tif');
+  });
+});
diff --git a/frontend/src/components/Browse/ResizeDivider.test.tsx b/frontend/src/components/Browse/ResizeDivider.test.tsx
new file mode 100644
index 0000000..bfdac82
--- /dev/null
+++ b/frontend/src/components/Browse/ResizeDivider.test.tsx
@@ -0,0 +1,71 @@
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, fireEvent, render, screen } from '@testing-library/react';
+import ResizeDivider, { MAX_WIDTH, MIN_WIDTH } from './ResizeDivider';
+
+afterEach(() => {
+  cleanup();
+});
+
+describe('ResizeDivider', () => {
+  it('renders a vertical separator', () => {
+    render(<ResizeDivider currentWidth={200} onResize={vi.fn()} />);
+    expect(screen.getByRole('separator')).toHaveAttribute('aria-orientation', 'vertical');
+  });
+
+  it('dragging right grows the column when resizeRight is false', () => {
+    const onResize = vi.fn();
+    render(<ResizeDivider currentWidth={200} onResize={onResize} />);
+    const divider = screen.getByRole('separator');
+
+    fireEvent.mouseDown(divider, { clientX: 100 });
+    act(() => {
+      fireEvent.mouseMove(document, { clientX: 150 });
+    });
+    expect(onResize).toHaveBeenCalledWith(250);
+  });
+
+  it('dragging right shrinks the column when resizeRight is true', () => {
+    const onResize = vi.fn();
+    render(<ResizeDivider currentWidth={200} onResize={onResize} resizeRight />);
+    const divider = screen.getByRole('separator');
+
+    fireEvent.mouseDown(divider, { clientX: 100 });
+    act(() => {
+      fireEvent.mouseMove(document, { clientX: 150 });
+    });
+    expect(onResize).toHaveBeenCalledWith(150);
+  });
+
+  it('clamps to minWidth/maxWidth', () => {
+    const onResize = vi.fn();
+    render(<ResizeDivider currentWidth={200} onResize={onResize} minWidth={120} maxWidth={600} />);
+    const divider = screen.getByRole('separator');
+
+    fireEvent.mouseDown(divider, { clientX: 100 });
+    act(() => {
+      fireEvent.mouseMove(document, { clientX: -10000 });
+    });
+    expect(onResize).toHaveBeenLastCalledWith(MIN_WIDTH);
+
+    act(() => {
+      fireEvent.mouseMove(document, { clientX: 10000 });
+    });
+    expect(onResize).toHaveBeenLastCalledWith(MAX_WIDTH);
+  });
+
+  it('stops tracking mouse movement after mouseup', () => {
+    const onResize = vi.fn();
+    render(<ResizeDivider currentWidth={200} onResize={onResize} />);
+    const divider = screen.getByRole('separator');
+
+    fireEvent.mouseDown(divider, { clientX: 100 });
+    act(() => {
+      fireEvent.mouseUp(document);
+    });
+    onResize.mockClear();
+    act(() => {
+      fireEvent.mouseMove(document, { clientX: 300 });
+    });
+    expect(onResize).not.toHaveBeenCalled();
+  });
+});
diff --git a/frontend/src/components/Browse/SlicesColumn.test.tsx b/frontend/src/components/Browse/SlicesColumn.test.tsx
new file mode 100644
index 0000000..fdb114b
--- /dev/null
+++ b/frontend/src/components/Browse/SlicesColumn.test.tsx
@@ -0,0 +1,161 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import SlicesColumn from './SlicesColumn';
+import { useRatingStore } from '@/stores/ratingStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { buildSourceKey } from '@/lib/sourceKey';
+import type { BrowseItem } from './hooks/useBrowseData';
+
+const initialRatingState = useRatingStore.getState();
+
+function makeItem(overrides: Partial<BrowseItem> = {}): BrowseItem {
+  return {
+    path: 'ds/a',
+    sample: 'ds_0001',
+    metadata: {},
+    ...overrides,
+  };
+}
+
+function renderWithClient(ui: React.ReactElement) {
+  const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return render(<QueryClientProvider client={queryClient}>{ui}</QueryClientProvider>);
+}
+
+beforeEach(() => {
+  useRatingStore.setState(initialRatingState, true);
+  useAnnotationStore.getState().reset();
+  window.localStorage.clear();
+  vi.stubGlobal(
+    'fetch',
+    vi.fn().mockResolvedValue({ ok: true, json: async () => [] }),
+  );
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+const dataset = makeItem({ path: 'ds', sample: 'ds' });
+
+const baseProps = {
+  dataset,
+  slices: [] as BrowseItem[],
+  loading: false,
+  selectedItem: null,
+  onSelect: vi.fn(),
+  onOpenInAnnotate: vi.fn(),
+  onClose: vi.fn(),
+  width: 200,
+  serverUri: 'http://server',
+};
+
+describe('SlicesColumn', () => {
+  it('shows a loading state', () => {
+    renderWithClient(<SlicesColumn {...baseProps} loading />);
+    expect(screen.getByText('Loading slices…')).toBeInTheDocument();
+  });
+
+  it('shows an empty state when there are no slices', () => {
+    renderWithClient(<SlicesColumn {...baseProps} slices={[]} />);
+    expect(screen.getByText('No slices found.')).toBeInTheDocument();
+  });
+
+  it('renders the slice count badge and the "open all" button count', () => {
+    const slices = [makeItem({ path: 'ds/1', sample: 'ds_0001' }), makeItem({ path: 'ds/2', sample: 'ds_0002' })];
+    renderWithClient(<SlicesColumn {...baseProps} slices={slices} />);
+    expect(screen.getByText('2')).toBeInTheDocument();
+    expect(screen.getByText(/Open all 2 slices as a volume/)).toBeInTheDocument();
+  });
+
+  it('labels a slice by its image_number metadata when present', () => {
+    const slice = makeItem({ path: 'ds/1', sample: 'ds_weird', metadata: { image_number: 7 } });
+    renderWithClient(<SlicesColumn {...baseProps} slices={[slice]} />);
+    expect(screen.getByText('Slice 7')).toBeInTheDocument();
+  });
+
+  it('labels a slice by its trailing suffix when it shares the dataset prefix', () => {
+    const slice = makeItem({ path: 'ds/1', sample: 'ds_0003' });
+    renderWithClient(<SlicesColumn {...baseProps} slices={[slice]} />);
+    expect(screen.getByText('Slice 0003')).toBeInTheDocument();
+  });
+
+  it('falls back to the raw sample name when no image_number and no shared prefix', () => {
+    const slice = makeItem({ path: 'ds/1', sample: 'totally_different' });
+    renderWithClient(<SlicesColumn {...baseProps} slices={[slice]} />);
+    expect(screen.getByText('totally_different')).toBeInTheDocument();
+  });
+
+  it('clicking the back button calls onClose', async () => {
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    renderWithClient(<SlicesColumn {...baseProps} onClose={onClose} />);
+    await user.click(screen.getByTitle('Back to samples'));
+    expect(onClose).toHaveBeenCalled();
+  });
+
+  it('clicking "open all as volume" calls onOpenInAnnotate with the dataset', async () => {
+    const onOpenInAnnotate = vi.fn();
+    const user = userEvent.setup();
+    renderWithClient(<SlicesColumn {...baseProps} onOpenInAnnotate={onOpenInAnnotate} />);
+    await user.click(screen.getByText(/Open all/));
+    expect(onOpenInAnnotate).toHaveBeenCalledWith(dataset);
+  });
+
+  it('clicking a slice row selects it, clicking again clears the selection', async () => {
+    const onSelect = vi.fn();
+    const user = userEvent.setup();
+    const slice = makeItem({ path: 'ds/1', sample: 'ds_0001' });
+    const { rerender } = renderWithClient(
+      <SlicesColumn {...baseProps} slices={[slice]} onSelect={onSelect} selectedItem={null} />,
+    );
+    await user.click(screen.getByText('Slice 0001'));
+    expect(onSelect).toHaveBeenCalledWith(slice);
+
+    onSelect.mockClear();
+    rerender(
+      <QueryClientProvider client={new QueryClient({ defaultOptions: { queries: { retry: false } } })}>
+        <SlicesColumn {...baseProps} slices={[slice]} onSelect={onSelect} selectedItem={slice} />
+      </QueryClientProvider>,
+    );
+    await user.click(screen.getByText('Slice 0001'));
+    expect(onSelect).toHaveBeenCalledWith(null);
+  });
+
+  it('clicking the per-slice open-in-annotate button calls onOpenInAnnotate with that slice, not the dataset', async () => {
+    const onOpenInAnnotate = vi.fn();
+    const onSelect = vi.fn();
+    const user = userEvent.setup();
+    const slice = makeItem({ path: 'ds/1', sample: 'ds_0001' });
+    renderWithClient(
+      <SlicesColumn {...baseProps} slices={[slice]} onOpenInAnnotate={onOpenInAnnotate} onSelect={onSelect} />,
+    );
+    await user.click(screen.getByTitle('Open this slice in Annotate'));
+    expect(onOpenInAnnotate).toHaveBeenCalledWith(slice);
+    expect(onSelect).not.toHaveBeenCalled();
+  });
+
+  it('shows the annotated badge when the slice is flagged annotated', () => {
+    const slice = makeItem({ path: 'ds/1', sample: 'ds_0001', metadata: { studio_annotated: 'yes' } });
+    renderWithClient(<SlicesColumn {...baseProps} slices={[slice]} />);
+    expect(screen.getByText('annotated')).toBeInTheDocument();
+  });
+
+  it('does not show the annotated badge for an unannotated slice', () => {
+    const slice = makeItem({ path: 'ds/1', sample: 'ds_0001' });
+    renderWithClient(<SlicesColumn {...baseProps} slices={[slice]} />);
+    expect(screen.queryByText('annotated')).not.toBeInTheDocument();
+  });
+
+  it('renders a star rating control reflecting the stored rating for the slice', () => {
+    const slice = makeItem({ path: 'ds/1', sample: 'ds_0001' });
+    const sourceKey = buildSourceKey('tiled', slice.path, baseProps.serverUri);
+    useRatingStore.setState({ ratings: { [sourceKey]: 2 } });
+    renderWithClient(<SlicesColumn {...baseProps} slices={[slice]} />);
+    // StarRating renders 3 star buttons per row.
+    expect(screen.getAllByRole('button', { name: /star/ })).toHaveLength(3);
+  });
+});
diff --git a/frontend/src/components/Browse/StarRating.test.tsx b/frontend/src/components/Browse/StarRating.test.tsx
new file mode 100644
index 0000000..e972aa7
--- /dev/null
+++ b/frontend/src/components/Browse/StarRating.test.tsx
@@ -0,0 +1,59 @@
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import StarRating from './StarRating';
+
+afterEach(() => {
+  cleanup();
+});
+
+describe('StarRating', () => {
+  it('renders three stars', () => {
+    render(<StarRating value={0} onChange={vi.fn()} />);
+    expect(screen.getAllByRole('button')).toHaveLength(3);
+  });
+
+  it('fills stars up to and including the current value', () => {
+    render(<StarRating value={2} onChange={vi.fn()} />);
+    const buttons = screen.getAllByRole('button');
+    expect(buttons[0].querySelector('svg')).toHaveClass('text-amber-400');
+    expect(buttons[1].querySelector('svg')).toHaveClass('text-amber-400');
+    expect(buttons[2].querySelector('svg')).toHaveClass('text-slate-600');
+  });
+
+  it('clicking a star calls onChange with that star value', async () => {
+    const onChange = vi.fn();
+    const user = userEvent.setup();
+    render(<StarRating value={0} onChange={onChange} />);
+    await user.click(screen.getByLabelText('2 stars'));
+    expect(onChange).toHaveBeenCalledWith(2);
+  });
+
+  it('clicking the already-set star clears the rating to 0', async () => {
+    const onChange = vi.fn();
+    const user = userEvent.setup();
+    render(<StarRating value={2} onChange={onChange} />);
+    await user.click(screen.getByLabelText('2 stars'));
+    expect(onChange).toHaveBeenCalledWith(0);
+  });
+
+  it('readonly disables all buttons and omits the title', () => {
+    render(<StarRating value={1} onChange={vi.fn()} readonly />);
+    for (const button of screen.getAllByRole('button')) {
+      expect(button).toBeDisabled();
+      expect(button).not.toHaveAttribute('title');
+    }
+  });
+
+  it('click does not propagate to a parent handler', async () => {
+    const onParentClick = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <div onClick={onParentClick}>
+        <StarRating value={0} onChange={vi.fn()} />
+      </div>,
+    );
+    await user.click(screen.getByLabelText('1 star'));
+    expect(onParentClick).not.toHaveBeenCalled();
+  });
+});
diff --git a/frontend/src/components/Browse/hooks/useBrowseData.test.ts b/frontend/src/components/Browse/hooks/useBrowseData.test.ts
new file mode 100644
index 0000000..a5969dc
--- /dev/null
+++ b/frontend/src/components/Browse/hooks/useBrowseData.test.ts
@@ -0,0 +1,439 @@
+/**
+ * useBrowseData — hook tests for the Browse data-fetching/state layer.
+ * Fetch is stubbed per-endpoint; renderHook drives the hook directly (no DOM).
+ */
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, renderHook, waitFor } from '@testing-library/react';
+import { useBrowseData, type BrowseItem } from './useBrowseData';
+
+function jsonResponse(body: unknown, ok = true) {
+  return {
+    ok,
+    status: ok ? 200 : 500,
+    json: async () => body,
+    text: async () => (typeof body === 'string' ? body : JSON.stringify(body)),
+  } as Response;
+}
+
+/** Routes fetch calls to per-endpoint handlers by pathname; unmatched calls 404. */
+function makeFetchMock(
+  overrides: Record<string, (url: URL) => Response | Promise<Response>> = {},
+) {
+  return vi.fn(async (input: RequestInfo | URL) => {
+    const url = new URL(String(input), 'http://localhost');
+    for (const [path, handler] of Object.entries(overrides)) {
+      if (url.pathname === path) return handler(url);
+    }
+    return jsonResponse({}, false);
+  });
+}
+
+const item = (path: string, extra: Partial<BrowseItem> = {}): BrowseItem => ({
+  path,
+  sample: path,
+  metadata: {},
+  ...extra,
+});
+
+beforeEach(() => {
+  vi.spyOn(console, 'warn').mockImplementation(() => {});
+});
+
+afterEach(() => {
+  cleanupFakeTimers();
+  vi.restoreAllMocks();
+});
+
+function cleanupFakeTimers() {
+  if (vi.isFakeTimers()) vi.useRealTimers();
+}
+
+describe('useBrowseData', () => {
+  it('starts with facetsLoading true and loads facets on mount, marking connected', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['sample_name', 'technique'] }),
+    });
+
+    const { result } = renderHook(() => useBrowseData('http://server', 'All'));
+
+    expect(result.current.state.facetsLoading).toBe(true);
+    expect(result.current.state.connectionStatus).toBe('loading');
+
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+    expect(result.current.state.facets).toEqual(['sample_name', 'technique']);
+    expect(result.current.state.connectionStatus).toBe('connected');
+  });
+
+  it('marks the connection disconnected when the facets request fails', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse('boom', false),
+    });
+
+    const { result } = renderHook(() => useBrowseData('http://server', 'All'));
+
+    await waitFor(() => expect(result.current.state.connectionStatus).toBe('disconnected'));
+    expect(result.current.state.facetsLoading).toBe(false);
+  });
+
+  it('requests facets with technique/container_path/server params', async () => {
+    const fetchMock = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+    });
+    global.fetch = fetchMock;
+
+    renderHook(() => useBrowseData('http://server', 'SAXS', 'secret-key', 'browse/foo'));
+
+    await waitFor(() => expect(fetchMock).toHaveBeenCalled());
+    const calledUrl = new URL(String(fetchMock.mock.calls[0][0]), 'http://localhost');
+    expect(calledUrl.searchParams.get('technique')).toBe('SAXS');
+    expect(calledUrl.searchParams.get('container_path')).toBe('browse/foo');
+    expect(calledUrl.searchParams.get('server_uri')).toBe('http://server');
+    expect(calledUrl.searchParams.get('server_api_key')).toBe('secret-key');
+    // technique !== 'All' so no forced refresh.
+    expect(calledUrl.searchParams.get('refresh')).toBeNull();
+  });
+
+  it('forces a refresh for technique "All"', async () => {
+    const fetchMock = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+    });
+    global.fetch = fetchMock;
+
+    renderHook(() => useBrowseData('http://server', 'All'));
+
+    await waitFor(() => expect(fetchMock).toHaveBeenCalled());
+    const calledUrl = new URL(String(fetchMock.mock.calls[0][0]), 'http://localhost');
+    expect(calledUrl.searchParams.get('refresh')).toBe('true');
+  });
+
+  it('addColumn appends a loading column then fills in its values', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['field1'] }),
+      '/api/browse/column': () =>
+        jsonResponse({ values: [{ value: 'v1', count: 3, sample_paths: [] }] }),
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+
+    act(() => result.current.actions.addColumn('field1'));
+    expect(result.current.state.columns).toHaveLength(1);
+    expect(result.current.state.columns[0]).toMatchObject({ field: 'field1', loading: true });
+
+    await waitFor(() => expect(result.current.state.columns[0].loading).toBe(false));
+    expect(result.current.state.columns[0].values).toEqual([
+      { value: 'v1', count: 3, sample_paths: [] },
+    ]);
+  });
+
+  it('records a column error when the column request fails', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['field1'] }),
+      '/api/browse/column': () => jsonResponse('nope', false),
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+
+    act(() => result.current.actions.addColumn('field1'));
+    await waitFor(() => expect(result.current.state.columns[0].loading).toBe(false));
+    expect(result.current.state.columns[0].error).toBe('HTTP 500');
+  });
+
+  it('selectValue on the last column loads leaf items', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['field1'] }),
+      '/api/browse/column': () =>
+        jsonResponse({ values: [{ value: 'v1', count: 1, sample_paths: [] }] }),
+      '/api/browse/items': () =>
+        jsonResponse({ items: [item('p1', { sample: 's1' })], total: 1 }),
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+
+    act(() => result.current.actions.addColumn('field1'));
+    await waitFor(() => expect(result.current.state.columns[0].loading).toBe(false));
+
+    act(() => result.current.actions.selectValue(0, 'v1'));
+    expect(result.current.state.columns[0].selected).toBe('v1');
+
+    await waitFor(() => expect(result.current.state.itemsLoading).toBe(false));
+    expect(result.current.state.items).toHaveLength(1);
+    expect(result.current.state.items[0].path).toBe('p1');
+    expect(result.current.state.itemsTotal).toBe(1);
+  });
+
+  it('selectValue(null) clears the selection and items without fetching items', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['field1'] }),
+      '/api/browse/column': () =>
+        jsonResponse({ values: [{ value: 'v1', count: 1, sample_paths: [] }] }),
+      '/api/browse/items': () => jsonResponse({ items: [item('p1')], total: 1 }),
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+    act(() => result.current.actions.addColumn('field1'));
+    await waitFor(() => expect(result.current.state.columns[0].loading).toBe(false));
+    act(() => result.current.actions.selectValue(0, 'v1'));
+    await waitFor(() => expect(result.current.state.items).toHaveLength(1));
+
+    act(() => result.current.actions.selectValue(0, null));
+    expect(result.current.state.columns[0].selected).toBeNull();
+    expect(result.current.state.items).toHaveLength(0);
+    expect(result.current.state.itemsTotal).toBe(0);
+  });
+
+  it('selecting a value with a next column loads that column instead of items', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['f1', 'f2'] }),
+      '/api/browse/column': (url) => {
+        const field = url.searchParams.get('field');
+        if (field === 'f1') return jsonResponse({ values: [{ value: 'a', count: 2, sample_paths: [] }] });
+        return jsonResponse({ values: [{ value: 'b', count: 1, sample_paths: [] }] });
+      },
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+
+    act(() => result.current.actions.addColumn('f1'));
+    await waitFor(() => expect(result.current.state.columns[0].loading).toBe(false));
+    act(() => result.current.actions.addColumn('f2'));
+    await waitFor(() => expect(result.current.state.columns[1]?.loading).toBe(false));
+
+    act(() => result.current.actions.selectValue(0, 'a'));
+    // Second column reloads (filtered by field1=a) rather than leaf items loading.
+    await waitFor(() => expect(result.current.state.columns[1].loading).toBe(false));
+    expect(result.current.state.columns[1].selected).toBeNull();
+    expect(result.current.state.columns[1].values).toEqual([{ value: 'b', count: 1, sample_paths: [] }]);
+    expect(result.current.state.itemsLoading).toBe(false);
+    expect(result.current.state.items).toHaveLength(0);
+  });
+
+  it('removeColumn drops that column and everything after it', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['f1', 'f2'] }),
+      '/api/browse/column': () => jsonResponse({ values: [] }),
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+    act(() => result.current.actions.addColumn('f1'));
+    await waitFor(() => expect(result.current.state.columns[0].loading).toBe(false));
+    act(() => result.current.actions.addColumn('f2'));
+    await waitFor(() => expect(result.current.state.columns[1]?.loading).toBe(false));
+
+    act(() => result.current.actions.removeColumn(1));
+    expect(result.current.state.columns).toHaveLength(1);
+
+    act(() => result.current.actions.removeColumn(0));
+    expect(result.current.state.columns).toHaveLength(0);
+  });
+
+  it('changeColumnField replaces a column and drops later ones', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['f1', 'f2', 'f3'] }),
+      '/api/browse/column': (url) =>
+        jsonResponse({ values: [{ value: `v-${url.searchParams.get('field')}`, count: 1, sample_paths: [] }] }),
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+    act(() => result.current.actions.addColumn('f1'));
+    await waitFor(() => expect(result.current.state.columns[0].loading).toBe(false));
+    act(() => result.current.actions.addColumn('f2'));
+    await waitFor(() => expect(result.current.state.columns[1]?.loading).toBe(false));
+
+    act(() => result.current.actions.changeColumnField(1, 'f3'));
+    await waitFor(() => expect(result.current.state.columns[1]?.loading).toBe(false));
+    expect(result.current.state.columns).toHaveLength(2);
+    expect(result.current.state.columns[1].field).toBe('f3');
+    expect(result.current.state.columns[1].values[0].value).toBe('v-f3');
+  });
+
+  it('showAll clears columns, sets showingAll, and loads every item', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['f1'] }),
+      '/api/browse/items': () => jsonResponse({ items: [item('a'), item('b')], total: 2 }),
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+
+    act(() => result.current.actions.showAll());
+    expect(result.current.state.showingAll).toBe(true);
+    expect(result.current.state.columns).toHaveLength(0);
+
+    await waitFor(() => expect(result.current.state.itemsLoading).toBe(false));
+    expect(result.current.state.items).toHaveLength(2);
+    expect(result.current.state.itemsTotal).toBe(2);
+  });
+
+  it('items fetch failure clears items and total', async () => {
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+      '/api/browse/items': () => jsonResponse('nope', false),
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+    act(() => result.current.actions.showAll());
+
+    await waitFor(() => expect(result.current.state.itemsLoading).toBe(false));
+    expect(result.current.state.items).toEqual([]);
+    expect(result.current.state.itemsTotal).toBe(0);
+  });
+
+  it('expandSample loads slices for a multi-image item, and null collapses it', async () => {
+    const dataset = item('vol1', { sample: 'vol1', n_slices: 3 });
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+      '/api/browse/slices': () =>
+        jsonResponse({ items: [item('vol1/0'), item('vol1/1')] }),
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+
+    act(() => result.current.actions.expandSample(dataset));
+    expect(result.current.state.expandedSample).toEqual(dataset);
+    expect(result.current.state.slicesLoading).toBe(true);
+
+    await waitFor(() => expect(result.current.state.slicesLoading).toBe(false));
+    expect(result.current.state.slices).toHaveLength(2);
+
+    act(() => result.current.actions.expandSample(null));
+    expect(result.current.state.expandedSample).toBeNull();
+    expect(result.current.state.slices).toHaveLength(0);
+  });
+
+  it('slices fetch failure clears the slices list', async () => {
+    const dataset = item('vol1', { n_slices: 2 });
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+      '/api/browse/slices': () => jsonResponse('boom', false),
+    });
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+    act(() => result.current.actions.expandSample(dataset));
+
+    await waitFor(() => expect(result.current.state.slicesLoading).toBe(false));
+    expect(result.current.state.slices).toEqual([]);
+  });
+
+  it('selectItem sets and clears the selected leaf item', async () => {
+    global.fetch = makeFetchMock({ '/api/browse/facets': () => jsonResponse({ facets: [] }) });
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+
+    const it1 = item('p1');
+    act(() => result.current.actions.selectItem(it1));
+    expect(result.current.state.selectedItem).toEqual(it1);
+    act(() => result.current.actions.selectItem(null));
+    expect(result.current.state.selectedItem).toBeNull();
+  });
+
+  it('refresh reloads columns and items using the latest state', async () => {
+    const fetchMock = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['f1'] }),
+      '/api/browse/column': () => jsonResponse({ values: [{ value: 'a', count: 1, sample_paths: [] }] }),
+      '/api/browse/items': () => jsonResponse({ items: [item('a')], total: 1 }),
+    });
+    global.fetch = fetchMock;
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+    act(() => result.current.actions.addColumn('f1'));
+    await waitFor(() => expect(result.current.state.columns[0].loading).toBe(false));
+    act(() => result.current.actions.selectValue(0, 'a'));
+    await waitFor(() => expect(result.current.state.itemsLoading).toBe(false));
+
+    const callsBefore = fetchMock.mock.calls.length;
+    act(() => result.current.actions.refresh());
+    await waitFor(() => expect(fetchMock.mock.calls.length).toBeGreaterThan(callsBefore));
+    // One call for the (only) column, one for items.
+    expect(fetchMock.mock.calls.length).toBe(callsBefore + 2);
+  });
+
+  it('refresh with showingAll re-loads items with no filters', async () => {
+    const fetchMock = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: [] }),
+      '/api/browse/items': () => jsonResponse({ items: [item('a')], total: 1 }),
+    });
+    global.fetch = fetchMock;
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+    act(() => result.current.actions.showAll());
+    await waitFor(() => expect(result.current.state.itemsLoading).toBe(false));
+
+    const callsBefore = fetchMock.mock.calls.length;
+    act(() => result.current.actions.refresh());
+    await waitFor(() => expect(fetchMock.mock.calls.length).toBe(callsBefore + 1));
+  });
+
+  it('refresh is a no-op when there are no columns and showingAll is false', async () => {
+    const fetchMock = makeFetchMock({ '/api/browse/facets': () => jsonResponse({ facets: [] }) });
+    global.fetch = fetchMock;
+
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+
+    const callsBefore = fetchMock.mock.calls.length;
+    act(() => result.current.actions.refresh());
+    expect(fetchMock.mock.calls.length).toBe(callsBefore);
+  });
+
+  it('sets up a facets poll interval and clears it on unmount', () => {
+    global.fetch = makeFetchMock({ '/api/browse/facets': () => jsonResponse({ facets: [] }) });
+    const setSpy = vi.spyOn(global, 'setInterval');
+    const clearSpy = vi.spyOn(global, 'clearInterval');
+
+    const { unmount } = renderHook(() => useBrowseData('uri', 'tech'));
+    expect(setSpy).toHaveBeenCalledWith(expect.any(Function), 30_000);
+
+    unmount();
+    expect(clearSpy).toHaveBeenCalled();
+  });
+
+  it('re-fetches and resets state when serverUri/technique change', async () => {
+    const fetchMock = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['f1'] }),
+      '/api/browse/items': () => jsonResponse({ items: [item('a')], total: 1 }),
+    });
+    global.fetch = fetchMock;
+
+    const { result, rerender } = renderHook(
+      ({ serverUri, technique }) => useBrowseData(serverUri, technique),
+      { initialProps: { serverUri: 'uri1', technique: 'All' } },
+    );
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+    act(() => result.current.actions.showAll());
+    await waitFor(() => expect(result.current.state.items).toHaveLength(1));
+
+    rerender({ serverUri: 'uri2', technique: 'All' });
+    // Reset wipes items/columns/showingAll immediately.
+    expect(result.current.state.items).toHaveLength(0);
+    expect(result.current.state.showingAll).toBe(false);
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+  });
+
+  it('loadFacets({silent: true}) does not toggle facetsLoading', async () => {
+    global.fetch = makeFetchMock({ '/api/browse/facets': () => jsonResponse({ facets: ['x'] }) });
+    const { result } = renderHook(() => useBrowseData('uri', 'tech'));
+    await waitFor(() => expect(result.current.state.facetsLoading).toBe(false));
+
+    let sawLoadingTrue = false;
+    await act(async () => {
+      const p = result.current.actions.loadFacets({ silent: true });
+      sawLoadingTrue = result.current.state.facetsLoading;
+      await p;
+    });
+    expect(sawLoadingTrue).toBe(false);
+    expect(result.current.state.facetsLoading).toBe(false);
+    expect(result.current.state.facets).toEqual(['x']);
+  });
+});
diff --git a/frontend/src/components/CompositionPanel/CompositionPanel.test.tsx b/frontend/src/components/CompositionPanel/CompositionPanel.test.tsx
index 6b7419c..4ccf439 100644
--- a/frontend/src/components/CompositionPanel/CompositionPanel.test.tsx
+++ b/frontend/src/components/CompositionPanel/CompositionPanel.test.tsx
@@ -2,7 +2,7 @@
  * CompositionPanel — smoke tests for concat preview wiring.
  */
 import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
-import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import { cleanup, fireEvent, render, screen, waitFor, within } from '@testing-library/react';
 import userEvent from '@testing-library/user-event';
 import CompositionPanel from './index';
 import { useIpredStore } from '@/stores/ipredStore';
@@ -97,4 +97,147 @@ describe('CompositionPanel', () => {
       expect(screen.getByTestId('ipred-node-n2')).toBeTruthy();
     });
   });
+
+  it('a ready module button is enabled; disables when not ready', async () => {
+    render(<CompositionPanel />);
+    await waitFor(() => expect(screen.getByTestId('ipred-module-tomojepa')).toBeEnabled());
+    // pca is also ready per the mock catalog.
+    expect(screen.getByTestId('ipred-module-pca')).toBeEnabled();
+  });
+
+  it('removing a node updates the graph summary and clears it from outputs', async () => {
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    await screen.findByTestId('ipred-node-n1');
+    await user.click(within(screen.getByTestId('ipred-node-n1')).getByTitle('Remove'));
+    await waitFor(() => expect(screen.queryByTestId('ipred-node-n1')).not.toBeInTheDocument());
+    expect(screen.getByText(/Graph: empty/)).toBeInTheDocument();
+  });
+
+  it('adding a tomojepa node shows its weights/input-size/resize controls', async () => {
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    await screen.findByTestId('ipred-module-tomojepa');
+    await user.click(screen.getByTestId('ipred-module-tomojepa'));
+    const node = await screen.findByTestId('ipred-node-n2');
+    expect(within(node).getByText('Weights')).toBeInTheDocument();
+    expect(within(node).getByText('Input size')).toBeInTheDocument();
+    expect(within(node).getByText('Resize')).toBeInTheDocument();
+    // accepts_input_from -> shows the "Input from" selector too.
+    expect(within(node).getByText('Input from')).toBeInTheDocument();
+  });
+
+  it('changing a tomojepa node input_size updates its param', async () => {
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    await screen.findByTestId('ipred-module-tomojepa');
+    await user.click(screen.getByTestId('ipred-module-tomojepa'));
+    const node = await screen.findByTestId('ipred-node-n2');
+    const input = within(node).getByDisplayValue('512');
+    fireEvent.change(input, { target: { value: '256' } });
+    expect(within(node).getByDisplayValue('256')).toBeInTheDocument();
+  });
+
+  it('toggling a channel-producing node in/out of the output bank', async () => {
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    const node = await screen.findByTestId('ipred-node-n1');
+    const checkbox = within(node).getByLabelText('In bank concat') as HTMLInputElement;
+    expect(checkbox.checked).toBe(true); // n1 (skimage_multiscale) produces channels, in the builtin comp
+    await user.click(checkbox);
+    expect(checkbox.checked).toBe(false);
+    expect(screen.getByText(/Outputs order: none/)).toBeInTheDocument();
+  });
+
+  it('reordering outputs with the up/down arrows', async () => {
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    await screen.findByTestId('ipred-module-pca');
+    await user.click(screen.getByTestId('ipred-module-pca')); // n2, also produces_channels -> added to outputs
+    await screen.findByTestId('ipred-node-n2');
+    expect(screen.getByText(/Outputs order: n1 → n2/)).toBeInTheDocument();
+
+    const node2 = screen.getByTestId('ipred-node-n2');
+    await user.click(within(node2).getByText('↑'));
+    expect(screen.getByText(/Outputs order: n2 → n1/)).toBeInTheDocument();
+  });
+
+  it('switching the active composition via the dropdown', async () => {
+    const { getIpredComposition } = await import('@/lib/ipredApi');
+    (getIpredComposition as any).mockImplementation(async (id: string) => {
+      if (id === 'comp-skimage') {
+        return {
+          id, name: 'Skimage multiscale', builtin: true,
+          nodes: [{ id: 'n1', module: 'skimage_multiscale', params: {} }],
+          outputs: ['n1'], preview_labels: ['intensity σ=1'],
+        };
+      }
+      return { id, name: 'Other comp', builtin: false, nodes: [], outputs: [], preview_labels: [] };
+    });
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    await screen.findByTestId('ipred-composition-select');
+    // Only one composition in the mocked list, so just confirm the select wiring
+    // calls through to getIpredComposition with the chosen id.
+    const select = screen.getByTestId('ipred-composition-select') as HTMLSelectElement;
+    fireEvent.change(select, { target: { value: 'comp-skimage' } });
+    await waitFor(() => expect(getIpredComposition).toHaveBeenCalledWith('comp-skimage'));
+  });
+
+  it('save() persists the composition and re-selects it', async () => {
+    const { upsertIpredComposition } = await import('@/lib/ipredApi');
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    await screen.findByTestId('ipred-node-n1');
+    await user.click(screen.getByRole('button', { name: /Save/ }));
+    await waitFor(() => expect(upsertIpredComposition).toHaveBeenCalled());
+    const [payload] = (upsertIpredComposition as any).mock.calls[0];
+    expect(payload.composition_id).toBe('comp-skimage');
+  });
+
+  it('clone & save omits composition_id and renames with a " copy" suffix', async () => {
+    const { upsertIpredComposition } = await import('@/lib/ipredApi');
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    await screen.findByTestId('ipred-node-n1');
+    await user.click(screen.getByRole('button', { name: /Clone & save/ }));
+    await waitFor(() => expect(upsertIpredComposition).toHaveBeenCalled());
+    const [payload] = (upsertIpredComposition as any).mock.calls[0];
+    expect(payload.composition_id).toBeUndefined();
+    expect(payload.name).toMatch(/ copy$/);
+  });
+
+  it('save() surfaces an error message on failure', async () => {
+    const { upsertIpredComposition } = await import('@/lib/ipredApi');
+    (upsertIpredComposition as any).mockRejectedValueOnce(new Error('save failed'));
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    await screen.findByTestId('ipred-node-n1');
+    await user.click(screen.getByRole('button', { name: /^Save/ }));
+    expect(await screen.findByTestId('ipred-composition-error')).toHaveTextContent('save failed');
+  });
+
+  it('Reload re-fetches modules/compositions', async () => {
+    const { listIpredModules } = await import('@/lib/ipredApi');
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    await screen.findByTestId('ipred-node-n1');
+    const callsBefore = (listIpredModules as any).mock.calls.length;
+    await user.click(screen.getByRole('button', { name: /Reload/ }));
+    await waitFor(() => expect((listIpredModules as any).mock.calls.length).toBeGreaterThan(callsBefore));
+  });
+
+  it('an empty preview shows the placeholder text', async () => {
+    const { previewIpredComposition } = await import('@/lib/ipredApi');
+    (previewIpredComposition as any).mockResolvedValue({ preview_labels: [] });
+    const user = userEvent.setup();
+    render(<CompositionPanel />);
+    const node = await screen.findByTestId('ipred-node-n1');
+    await user.click(within(node).getByLabelText('In bank concat'));
+    await waitFor(() => {
+      expect(screen.getByTestId('ipred-concat-preview')).toHaveTextContent(
+        'add channel-producing nodes and mark them as outputs',
+      );
+    });
+  });
 });
diff --git a/frontend/src/components/Ingest/ConflictDialog.test.tsx b/frontend/src/components/Ingest/ConflictDialog.test.tsx
new file mode 100644
index 0000000..3fd09f7
--- /dev/null
+++ b/frontend/src/components/Ingest/ConflictDialog.test.tsx
@@ -0,0 +1,121 @@
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import ConflictDialog, { type IngestConflict } from './ConflictDialog';
+
+function makeConflicts(n: number): IngestConflict[] {
+  return Array.from({ length: n }, (_, i) => ({ filename: `img${i}.tif`, key: `img${i}` }));
+}
+
+function renderDialog(overrides: Partial<React.ComponentProps<typeof ConflictDialog>> = {}) {
+  const props = {
+    containerPath: 'browse/foo',
+    totalFiles: 5,
+    conflicts: makeConflicts(2),
+    existingCount: 3,
+    suggestedContainerPath: 'browse/foo_2',
+    onReplace: vi.fn(),
+    onSkip: vi.fn(),
+    onNewDataset: vi.fn(),
+    onBrowseExisting: vi.fn(),
+    onCancel: vi.fn(),
+    ...overrides,
+  };
+  const utils = render(<ConflictDialog {...props} />);
+  return { ...utils, props };
+}
+
+afterEach(() => {
+  cleanup();
+});
+
+describe('ConflictDialog', () => {
+  it('renders the summary sentence with container path and existing count', () => {
+    renderDialog();
+    expect(
+      screen.getByText((_, el) => el?.textContent === '2 of 5 images are already in browse/foo (3 samples there now).')
+    ).toBeInTheDocument();
+  });
+
+  it('uses singular wording when totalFiles is 1 and conflicts is 1', () => {
+    renderDialog({ totalFiles: 1, conflicts: makeConflicts(1), existingCount: 1 });
+    expect(
+      screen.getByText((_, el) => el?.textContent === '1 of 1 image is already in browse/foo (1 sample there now).')
+    ).toBeInTheDocument();
+  });
+
+  it('omits the existing-count parenthetical when existingCount is 0', () => {
+    renderDialog({ existingCount: 0 });
+    expect(
+      screen.getByText((_, el) => el?.textContent === '2 of 5 images are already in browse/foo.')
+    ).toBeInTheDocument();
+  });
+
+  it('lists conflicting filenames, capped at MAX_LISTED=5 with an overflow line', () => {
+    renderDialog({ conflicts: makeConflicts(7), totalFiles: 10 });
+    for (let i = 0; i < 5; i++) {
+      expect(screen.getByText(`img${i}.tif`)).toBeInTheDocument();
+    }
+    expect(screen.queryByText('img5.tif')).not.toBeInTheDocument();
+    expect(screen.getByText('…and 2 more')).toBeInTheDocument();
+  });
+
+  it('shows the new-image count on the Skip button when not all files conflict', () => {
+    renderDialog({ totalFiles: 5, conflicts: makeConflicts(2) });
+    expect(screen.getByText('Ingests only the 3 new images')).toBeInTheDocument();
+  });
+
+  it('disables Skip and shows the all-conflict message when every file conflicts', () => {
+    renderDialog({ totalFiles: 3, conflicts: makeConflicts(3) });
+    const skipBtn = screen.getByRole('button', { name: /Skip the duplicates/ });
+    expect(skipBtn).toBeDisabled();
+    expect(screen.getByText('Nothing new to add — every image is already here')).toBeInTheDocument();
+  });
+
+  it('shows the suggested container path on the "new dataset" option', () => {
+    renderDialog({ suggestedContainerPath: 'browse/foo_3' });
+    expect(screen.getByText('browse/foo_3')).toBeInTheDocument();
+  });
+
+  it('calls onReplace, onSkip, onNewDataset, onBrowseExisting when their buttons are clicked', async () => {
+    const user = userEvent.setup();
+    const { props } = renderDialog();
+    await user.click(screen.getByRole('button', { name: /Replace the existing images/ }));
+    expect(props.onReplace).toHaveBeenCalledTimes(1);
+
+    await user.click(screen.getByRole('button', { name: /Skip the duplicates/ }));
+    expect(props.onSkip).toHaveBeenCalledTimes(1);
+
+    await user.click(screen.getByRole('button', { name: /Ingest to a new dataset/ }));
+    expect(props.onNewDataset).toHaveBeenCalledTimes(1);
+
+    await user.click(screen.getByRole('button', { name: /Browse the existing dataset/ }));
+    expect(props.onBrowseExisting).toHaveBeenCalledTimes(1);
+  });
+
+  it('calls onCancel from the X button, the Cancel button, and clicking the backdrop', async () => {
+    const user = userEvent.setup();
+    const { props } = renderDialog();
+
+    await user.click(screen.getByLabelText('Close'));
+    expect(props.onCancel).toHaveBeenCalledTimes(1);
+
+    await user.click(screen.getByText('Cancel'));
+    expect(props.onCancel).toHaveBeenCalledTimes(2);
+  });
+
+  it('does not call onCancel when clicking inside the dialog panel', async () => {
+    const user = userEvent.setup();
+    const { props } = renderDialog();
+    await user.click(screen.getByText('This dataset already has these images'));
+    expect(props.onCancel).not.toHaveBeenCalled();
+  });
+
+  it('calls onCancel when clicking the backdrop directly', async () => {
+    const user = userEvent.setup();
+    const { props, container } = renderDialog();
+    const backdrop = container.firstChild as HTMLElement;
+    await user.click(backdrop);
+    expect(props.onCancel).toHaveBeenCalledTimes(1);
+  });
+});
diff --git a/frontend/src/components/Ingest/IngestDropzone.test.tsx b/frontend/src/components/Ingest/IngestDropzone.test.tsx
new file mode 100644
index 0000000..2ca584f
--- /dev/null
+++ b/frontend/src/components/Ingest/IngestDropzone.test.tsx
@@ -0,0 +1,457 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, fireEvent, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import IngestDropzone from './IngestDropzone';
+
+function jsonResponse(body: unknown, ok = true, status = 200) {
+  return { ok, status, json: async () => body, text: async () => (typeof body === 'string' ? body : JSON.stringify(body)) } as Response;
+}
+
+function makeFile(name: string, content = 'x') {
+  return new File([content], name, { type: 'image/tiff' });
+}
+
+/** The dropzone renders two hidden <input type="file"> — plain picker, then folder picker. */
+function getInputs(container: HTMLElement) {
+  const inputs = container.querySelectorAll('input[type="file"]');
+  return { fileInput: inputs[0] as HTMLInputElement, dirInput: inputs[1] as HTMLInputElement };
+}
+
+function renderDropzone(overrides: Partial<React.ComponentProps<typeof IngestDropzone>> = {}) {
+  const props = {
+    serverUri: 'http://tiled.example',
+    onBrowse: vi.fn(),
+    onAnnotate: vi.fn(),
+    ...overrides,
+  };
+  const utils = render(<IngestDropzone {...props} />);
+  return { ...utils, props };
+}
+
+/** Route fetch calls by endpoint fragment; each handler may be sync or async. */
+function makeFetchMock(overrides: {
+  preflight?: (body: any) => Response | Promise<Response>;
+  upload?: (fd: FormData) => Response | Promise<Response>;
+  status?: (jobId: string, call: number) => Response | Promise<Response>;
+} = {}) {
+  let statusCalls = 0;
+  return vi.fn(async (input: RequestInfo | URL, opts?: RequestInit) => {
+    const url = String(input);
+    if (url.includes('/api/ingest/preflight')) {
+      const body = JSON.parse(opts!.body as string);
+      if (overrides.preflight) return overrides.preflight(body);
+      return jsonResponse({ container_exists: false, existing_count: 0, conflicts: [], suggested_container_path: `${body.container_path}_2` });
+    }
+    if (url.includes('/api/ingest/upload')) {
+      const fd = opts!.body as FormData;
+      if (overrides.upload) return overrides.upload(fd);
+      return jsonResponse({ job_id: 'job-1' });
+    }
+    if (url.includes('/api/ingest/status/')) {
+      const jobId = url.split('/api/ingest/status/')[1];
+      statusCalls++;
+      if (overrides.status) return overrides.status(jobId, statusCalls);
+      return jsonResponse({ state: 'done', total: 1, done: 1, failed: 0, skipped: 0, errors: [], container_path: 'browse/foo' });
+    }
+    throw new Error(`Unhandled fetch: ${url}`);
+  });
+}
+
+beforeEach(() => {
+  vi.stubGlobal('fetch', makeFetchMock());
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('IngestDropzone', () => {
+  it('shows an error when picking only unsupported files', async () => {
+    const { container } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    // userEvent.upload() enforces the input's `accept` attribute (as a real browser
+    // would), so an unsupported extension never reaches the file list. Use
+    // fireEvent.change with a directly-assigned FileList to exercise the
+    // component's own isSupported() rejection instead.
+    const file = makeFile('notes.txt');
+    Object.defineProperty(fileInput, 'files', { value: [file], writable: true });
+    fireEvent.change(fileInput);
+    expect(await screen.findByText('No supported image files (TIFF, PNG, JPG, NPY).')).toBeInTheDocument();
+  });
+
+  it('picks a supported file, uploads with no conflicts, polls to completion, and shows Browse/Annotate actions', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        status: (_id, call) =>
+          call === 1
+            ? jsonResponse({ state: 'running', total: 1, done: 0, failed: 0, skipped: 0, errors: [], container_path: 'browse/scan1' })
+            : jsonResponse({ state: 'done', total: 1, done: 1, failed: 0, skipped: 0, errors: [], container_path: 'browse/scan1' }),
+      }),
+    );
+    const { container, props } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, makeFile('scan1.tif'));
+
+    await waitFor(() => expect(screen.getByText(/processed…/)).toBeInTheDocument());
+    await waitFor(
+      () => expect(screen.getByText(/Ingested 1 of 1/)).toBeInTheDocument(),
+      { timeout: 3000 },
+    );
+
+    const browseBtn = screen.getByRole('button', { name: 'Browse this dataset' });
+    await user.click(browseBtn);
+    expect(props.onBrowse).toHaveBeenCalledWith('browse/scan1', 1);
+
+    const annotateBtn = screen.getByRole('button', { name: 'Open in Annotate' });
+    await user.click(annotateBtn);
+    expect(props.onAnnotate).toHaveBeenCalledWith('browse/scan1', 'scan1');
+  }, 10000);
+
+  it('derives the destination container from the single file name when left at the default', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        preflight: (body) => {
+          expect(body.container_path).toBe('browse/myscan');
+          return jsonResponse({ container_exists: false, existing_count: 0, conflicts: [], suggested_container_path: 'browse/myscan_2' });
+        },
+      }),
+    );
+    const { container } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, makeFile('myscan.tif'));
+    await waitFor(() => expect(screen.getByText(/processed…|Ingested/)).toBeInTheDocument());
+  });
+
+  it('shows the destination field only after expanding "Save uploaded images to"', async () => {
+    renderDropzone();
+    expect(screen.queryByPlaceholderText('browse/my_dataset')).not.toBeInTheDocument();
+    const user = userEvent.setup();
+    await user.click(screen.getByText(/Save uploaded images to/));
+    expect(screen.getByPlaceholderText('browse/my_dataset')).toBeInTheDocument();
+  });
+
+  it('shows the ConflictDialog when preflight reports conflicts, and Replace resumes the upload with on_conflict=replace', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        preflight: () =>
+          jsonResponse({
+            container_exists: true,
+            existing_count: 3,
+            conflicts: [{ filename: 'dup.tif', key: 'dup' }],
+            suggested_container_path: 'browse/dup_2',
+          }),
+        upload: (fd) => {
+          expect(fd.get('on_conflict')).toBe('replace');
+          return jsonResponse({ job_id: 'job-2' });
+        },
+      }),
+    );
+    const { container } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, makeFile('dup.tif'));
+
+    expect(await screen.findByText('This dataset already has these images')).toBeInTheDocument();
+    await user.click(screen.getByRole('button', { name: /Replace the existing image/ }));
+
+    await waitFor(() => expect(screen.queryByText('This dataset already has these images')).not.toBeInTheDocument());
+    await waitFor(() => expect(screen.getByText(/processed…|Ingested/)).toBeInTheDocument());
+  });
+
+  it('Cancel on the ConflictDialog dismisses it without uploading', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        preflight: () =>
+          jsonResponse({
+            container_exists: true,
+            existing_count: 1,
+            conflicts: [{ filename: 'dup.tif', key: 'dup' }],
+            suggested_container_path: 'browse/dup_2',
+          }),
+      }),
+    );
+    const { container } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, makeFile('dup.tif'));
+
+    expect(await screen.findByText('This dataset already has these images')).toBeInTheDocument();
+    await user.click(screen.getByText('Cancel'));
+    expect(screen.queryByText('This dataset already has these images')).not.toBeInTheDocument();
+    expect(screen.queryByText(/processed…/)).not.toBeInTheDocument();
+  });
+
+  it('"Browse the existing dataset" on the conflict dialog calls onBrowse with the existing container and count', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        preflight: () =>
+          jsonResponse({
+            container_exists: true,
+            existing_count: 5,
+            conflicts: [{ filename: 'dup.tif', key: 'dup' }],
+            suggested_container_path: 'browse/dup_2',
+          }),
+      }),
+    );
+    const { container, props } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, makeFile('dup.tif'));
+
+    expect(await screen.findByText('This dataset already has these images')).toBeInTheDocument();
+    await user.click(screen.getByRole('button', { name: /Browse the existing dataset/ }));
+    expect(props.onBrowse).toHaveBeenCalledWith('browse/dup', 5);
+    expect(screen.queryByText('This dataset already has these images')).not.toBeInTheDocument();
+  });
+
+  it('"Ingest to a new dataset" updates the destination and uploads to the suggested path', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        preflight: () =>
+          jsonResponse({
+            container_exists: true,
+            existing_count: 1,
+            conflicts: [{ filename: 'dup.tif', key: 'dup' }],
+            suggested_container_path: 'browse/dup_2',
+          }),
+        upload: (fd) => {
+          expect(fd.get('container_path')).toBe('browse/dup_2');
+          expect(fd.get('on_conflict')).toBe('fail');
+          return jsonResponse({ job_id: 'job-3' });
+        },
+      }),
+    );
+    const { container } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, makeFile('dup.tif'));
+
+    expect(await screen.findByText('This dataset already has these images')).toBeInTheDocument();
+    await user.click(screen.getByRole('button', { name: /Ingest to a new dataset/ }));
+    await waitFor(() => expect(screen.getByText(/processed…|Ingested/)).toBeInTheDocument());
+    await user.click(screen.getByText(/Save uploaded images to/));
+    expect(screen.getByDisplayValue('browse/dup_2')).toBeInTheDocument();
+  });
+
+  it('falls back to uploading anyway when the preflight request itself fails', async () => {
+    const warnSpy = vi.spyOn(console, 'warn').mockImplementation(() => {});
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        preflight: () => jsonResponse('boom', false, 500),
+      }),
+    );
+    const { container } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, makeFile('scan.tif'));
+
+    await waitFor(() => expect(screen.getByText(/processed…|Ingested/)).toBeInTheDocument());
+    expect(warnSpy).toHaveBeenCalled();
+    warnSpy.mockRestore();
+  });
+
+  it('shows an error message when starting the upload itself fails', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        upload: () => jsonResponse('server exploded', false, 500),
+      }),
+    );
+    const { container } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, makeFile('scan.tif'));
+
+    expect(await screen.findByText(/Could not start the upload: server exploded/)).toBeInTheDocument();
+  });
+
+  it('shows the fatal-failure state ("Nothing was ingested") when the job errors out', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        status: () =>
+          jsonResponse({ state: 'error', total: 2, done: 0, failed: 2, skipped: 0, errors: [
+            { filename: 'a.tif', kind: 'unreadable', message: '' },
+            { filename: 'b.tif', kind: 'unreadable', message: '' },
+          ], container_path: 'browse/bad' }),
+      }),
+    );
+    const { container } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, [makeFile('a.tif'), makeFile('b.tif')]);
+
+    await waitFor(
+      () => expect(screen.getByText(/Nothing was ingested/)).toBeInTheDocument(),
+      { timeout: 3000 },
+    );
+    expect(screen.getByText(/2 of 2 failed/)).toBeInTheDocument();
+    expect(screen.getByText(/2 images could not be read as images/)).toBeInTheDocument();
+  }, 10000);
+
+  it('expands and collapses the filename list for a multi-file error group', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        status: () =>
+          jsonResponse({ state: 'done', total: 2, done: 0, failed: 2, skipped: 0, errors: [
+            { filename: 'a.tif', kind: 'unreadable', message: '' },
+            { filename: 'b.tif', kind: 'unreadable', message: '' },
+          ], container_path: 'browse/bad' }),
+      }),
+    );
+    const { container } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, [makeFile('a.tif'), makeFile('b.tif')]);
+
+    await waitFor(
+      () => expect(screen.getByText(/2 images could not be read as images/)).toBeInTheDocument(),
+      { timeout: 3000 },
+    );
+    expect(screen.queryByText('a.tif')).not.toBeInTheDocument();
+    await user.click(screen.getByText('show filenames'));
+    expect(screen.getByText('a.tif')).toBeInTheDocument();
+    expect(screen.getByText('b.tif')).toBeInTheDocument();
+    await user.click(screen.getByText('hide filenames'));
+    expect(screen.queryByText('a.tif')).not.toBeInTheDocument();
+  }, 10000);
+
+  it('post-upload backstop: shows ConflictDialog when a completed job reports conflict errors', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        status: () =>
+          jsonResponse({
+            state: 'done',
+            total: 1,
+            done: 0,
+            failed: 1,
+            skipped: 0,
+            errors: [{ filename: 'dup.tif', kind: 'conflict', message: '' }],
+            container_path: 'browse/dup',
+          }),
+        preflight: () =>
+          jsonResponse({ container_exists: false, existing_count: 0, conflicts: [], suggested_container_path: 'browse/dup_2' }),
+      }),
+    );
+    const { container } = renderDropzone();
+    const { fileInput } = getInputs(container);
+    const user = userEvent.setup();
+    await user.upload(fileInput, makeFile('dup.tif'));
+
+    await waitFor(
+      () => expect(screen.getByText('This dataset already has these images')).toBeInTheDocument(),
+      { timeout: 3000 },
+    );
+  }, 10000);
+
+  it('picking a folder derives the destination from the folder name via webkitRelativePath', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        preflight: (body) => {
+          expect(body.container_path).toBe('browse/myfolder');
+          return jsonResponse({ container_exists: false, existing_count: 0, conflicts: [], suggested_container_path: 'browse/myfolder_2' });
+        },
+      }),
+    );
+    const { container } = renderDropzone();
+    const { dirInput } = getInputs(container);
+    const file = makeFile('a.tif');
+    Object.defineProperty(file, 'webkitRelativePath', { value: 'myfolder/a.tif' });
+    const user = userEvent.setup();
+    await user.upload(dirInput, file);
+    await waitFor(() => expect(screen.getByText(/processed…|Ingested/)).toBeInTheDocument());
+  });
+
+  it('dropping a plain FileList (no directory entries) uploads the supported files', async () => {
+    const { container } = renderDropzone();
+    const dropzone = screen.getByText(/Drag an image file or folder/).closest('[role="button"]') as HTMLElement;
+    const file = makeFile('dropped.tif');
+    const dataTransfer = { items: [], files: [file] };
+    fireEvent.drop(dropzone, { dataTransfer });
+    await waitFor(() => expect(screen.getByText(/processed…|Ingested/)).toBeInTheDocument());
+  });
+
+  it('dropping a directory via webkitGetAsEntry recurses and uploads all nested files', async () => {
+    vi.stubGlobal(
+      'fetch',
+      makeFetchMock({
+        preflight: (body) => {
+          expect(body.names.sort()).toEqual(['nested.tif', 'top.tif']);
+          expect(body.container_path).toBe('browse/mydir');
+          return jsonResponse({ container_exists: false, existing_count: 0, conflicts: [], suggested_container_path: 'browse/mydir_2' });
+        },
+      }),
+    );
+    const topFile = makeFile('top.tif');
+    const nestedFile = makeFile('nested.tif');
+
+    const nestedFileEntry = {
+      isFile: true,
+      isDirectory: false,
+      file: (cb: (f: File) => void) => cb(nestedFile),
+    };
+    let subdirRead = false;
+    const subdirEntry = {
+      isFile: false,
+      isDirectory: true,
+      name: 'subdir',
+      createReader: () => ({
+        readEntries: (cb: (entries: any[]) => void) => {
+          if (subdirRead) return cb([]);
+          subdirRead = true;
+          cb([nestedFileEntry]);
+        },
+      }),
+    };
+    const topFileEntry = {
+      isFile: true,
+      isDirectory: false,
+      file: (cb: (f: File) => void) => cb(topFile),
+    };
+    let rootRead = false;
+    const rootDirEntry = {
+      isFile: false,
+      isDirectory: true,
+      name: 'mydir',
+      createReader: () => ({
+        readEntries: (cb: (entries: any[]) => void) => {
+          if (rootRead) return cb([]);
+          rootRead = true;
+          cb([topFileEntry, subdirEntry]);
+        },
+      }),
+    };
+
+    const { container } = renderDropzone();
+    const dropzone = screen.getByText(/Drag an image file or folder/).closest('[role="button"]') as HTMLElement;
+    const dataTransfer = {
+      items: [{ webkitGetAsEntry: () => rootDirEntry }],
+      files: [],
+    };
+    fireEvent.drop(dropzone, { dataTransfer });
+    await waitFor(() => expect(screen.getByText(/processed…|Ingested/)).toBeInTheDocument());
+  });
+
+  it('sets and clears the dragging visual state on dragenter/dragleave', () => {
+    const { container } = renderDropzone();
+    const dropzone = screen.getByText(/Drag an image file or folder/).closest('[role="button"]') as HTMLElement;
+    expect(dropzone.className).toContain('border-white/20');
+    fireEvent.dragEnter(dropzone);
+    expect(dropzone.className).toContain('border-sky-400');
+    fireEvent.dragLeave(dropzone);
+    expect(dropzone.className).toContain('border-white/20');
+  });
+});
diff --git a/frontend/src/components/Ingest/ZarrLoader.test.tsx b/frontend/src/components/Ingest/ZarrLoader.test.tsx
new file mode 100644
index 0000000..e1163e2
--- /dev/null
+++ b/frontend/src/components/Ingest/ZarrLoader.test.tsx
@@ -0,0 +1,205 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import ZarrLoader from './ZarrLoader';
+
+function jsonResponse(body: unknown, ok = true, status = 200) {
+  return { ok, status, json: async () => body, text: async () => JSON.stringify(body) } as Response;
+}
+
+const ZARR_INFO = {
+  name: 'volume.zarr',
+  path: '/data/volume.zarr',
+  levels: [
+    { path: '0', shape: [100, 512, 512], dtype: 'uint16', n_slices: 100, height: 512, width: 512, downsample: [1, 1, 1] },
+    { path: '1', shape: [100, 256, 256], dtype: 'uint16', n_slices: 100, height: 256, width: 256, downsample: [1, 2, 2] },
+  ],
+  full_shape: [100, 512, 512],
+  dtype: 'uint16',
+  voxel_size: [1.5, 1.5, 1.5],
+  voxel_unit: 'um',
+};
+
+function renderLoader(overrides: Partial<React.ComponentProps<typeof ZarrLoader>> = {}) {
+  const props = {
+    serverUri: 'http://tiled.example',
+    onBrowse: vi.fn(),
+    onAnnotate: vi.fn(),
+    ...overrides,
+  };
+  const utils = render(<ZarrLoader {...props} />);
+  return { ...utils, props };
+}
+
+async function typePathAndInspect(user: ReturnType<typeof userEvent.setup>, path = '/data/volume.zarr') {
+  const input = screen.getByPlaceholderText('/absolute/path/to/volume.zarr');
+  await user.type(input, path);
+  await user.click(screen.getByRole('button', { name: /Inspect/ }));
+}
+
+beforeEach(() => {
+  vi.stubGlobal('fetch', vi.fn());
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('ZarrLoader', () => {
+  it('disables Inspect until a path is entered', () => {
+    renderLoader();
+    expect(screen.getByRole('button', { name: /Inspect/ })).toBeDisabled();
+  });
+
+  it('inspects a path, shows volume info and resolution levels, no conflict', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+      if (url.includes('/api/zarr/inspect')) return jsonResponse(ZARR_INFO);
+      if (url.includes('/api/zarr/preflight')) return jsonResponse({ exists: false });
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    renderLoader();
+    await typePathAndInspect(user);
+
+    expect(await screen.findByText('volume.zarr')).toBeInTheDocument();
+    expect(screen.getByText(/100 × 512 × 512 · uint16 · 1.5 um\/voxel/)).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /Load volume/ })).not.toBeDisabled();
+  });
+
+  it('inspecting via Enter key also triggers the request', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+      if (url.includes('/api/zarr/inspect')) return jsonResponse(ZARR_INFO);
+      if (url.includes('/api/zarr/preflight')) return jsonResponse({ exists: false });
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    renderLoader();
+    const input = screen.getByPlaceholderText('/absolute/path/to/volume.zarr');
+    await user.type(input, '/data/volume.zarr{Enter}');
+    expect(await screen.findByText('volume.zarr')).toBeInTheDocument();
+  });
+
+  it('shows an error message when inspect fails with a JSON detail', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+      if (url.includes('/api/zarr/inspect')) return jsonResponse({ detail: 'no such path' }, false, 404);
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    renderLoader();
+    await typePathAndInspect(user);
+    expect(await screen.findByText('no such path')).toBeInTheDocument();
+  });
+
+  it('switching resolution level updates the coarse-level warning', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+      if (url.includes('/api/zarr/inspect')) return jsonResponse(ZARR_INFO);
+      if (url.includes('/api/zarr/preflight')) return jsonResponse({ exists: false });
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    renderLoader();
+    await typePathAndInspect(user);
+    await screen.findByText('volume.zarr');
+
+    // Full res (level 0) selected by default: no coarse warning.
+    expect(screen.queryByText(/Annotations are stored in full-resolution/)).not.toBeInTheDocument();
+
+    await user.click(screen.getByRole('button', { name: /1\/2/ }));
+    expect(await screen.findByText(/Annotations are stored in full-resolution/)).toBeInTheDocument();
+  });
+
+  it('shows a blocking conflict when the destination holds different uploaded data', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+      if (url.includes('/api/zarr/inspect')) return jsonResponse(ZARR_INFO);
+      if (url.includes('/api/zarr/preflight'))
+        return jsonResponse({
+          exists: true,
+          existing: { child_count: 4, external: false, sample_name: 'existing', n_images: 4 },
+        });
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    renderLoader();
+    await typePathAndInspect(user);
+
+    expect(await screen.findByText(/and holds 4 uploaded images/)).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /Load volume/ })).toBeDisabled();
+    // Not an "external" zarr, so no "Replace it" shortcut is offered.
+    expect(screen.queryByRole('button', { name: 'Replace it' })).not.toBeInTheDocument();
+  });
+
+  it('offers "Replace it" for a conflict with a previously loaded external zarr, and registers on click', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string, opts?: RequestInit) => {
+      if (url.includes('/api/zarr/inspect')) return jsonResponse(ZARR_INFO);
+      if (url.includes('/api/zarr/preflight'))
+        return jsonResponse({
+          exists: true,
+          existing: { child_count: 1, external: true, sample_name: 'volume', n_images: null },
+        });
+      if (url.includes('/api/zarr/register')) {
+        const body = JSON.parse(opts!.body as string);
+        expect(body.on_conflict).toBe('replace');
+        return jsonResponse({ tiled_path: 'browse/volume' });
+      }
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    renderLoader();
+    await typePathAndInspect(user);
+
+    expect(await screen.findByText(/previously loaded Zarr/)).toBeInTheDocument();
+    // Load volume stays enabled for an external conflict.
+    expect(screen.getByRole('button', { name: /Load volume/ })).not.toBeDisabled();
+
+    await user.click(screen.getByRole('button', { name: 'Replace it' }));
+    expect(await screen.findByText(/Loaded 100 slices/)).toBeInTheDocument();
+  });
+
+  it('registers a new volume (Load volume), shows success, and Annotate/Browse call back with the level path', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string, opts?: RequestInit) => {
+      if (url.includes('/api/zarr/inspect')) return jsonResponse(ZARR_INFO);
+      if (url.includes('/api/zarr/preflight')) return jsonResponse({ exists: false });
+      if (url.includes('/api/zarr/register')) {
+        const body = JSON.parse(opts!.body as string);
+        expect(body).toMatchObject({
+          path: '/data/volume.zarr',
+          container_path: 'browse',
+          on_conflict: 'fail',
+          server_uri: 'http://tiled.example',
+        });
+        return jsonResponse({ tiled_path: 'browse/volume' });
+      }
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    const { props } = renderLoader();
+    await typePathAndInspect(user);
+    await screen.findByText('volume.zarr');
+
+    await user.click(screen.getByRole('button', { name: /Load volume/ }));
+    expect(await screen.findByText(/Loaded 100 slices — no data was copied\./)).toBeInTheDocument();
+
+    await user.click(screen.getByRole('button', { name: 'Annotate' }));
+    expect(props.onAnnotate).toHaveBeenCalledWith('browse/volume/0');
+
+    await user.click(screen.getByRole('button', { name: 'Browse' }));
+    expect(props.onBrowse).toHaveBeenCalledWith('browse', 1);
+  });
+
+  it('shows an error message when register fails', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+      if (url.includes('/api/zarr/inspect')) return jsonResponse(ZARR_INFO);
+      if (url.includes('/api/zarr/preflight')) return jsonResponse({ exists: false });
+      if (url.includes('/api/zarr/register')) return jsonResponse({ detail: 'disk full' }, false, 500);
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    renderLoader();
+    await typePathAndInspect(user);
+    await screen.findByText('volume.zarr');
+
+    await user.click(screen.getByRole('button', { name: /Load volume/ }));
+    expect(await screen.findByText('disk full')).toBeInTheDocument();
+  });
+});
diff --git a/frontend/src/components/annotate/AnnotationCanvas/OverlaysLayer.test.tsx b/frontend/src/components/annotate/AnnotationCanvas/OverlaysLayer.test.tsx
new file mode 100644
index 0000000..a7478a5
--- /dev/null
+++ b/frontend/src/components/annotate/AnnotationCanvas/OverlaysLayer.test.tsx
@@ -0,0 +1,217 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render } from '@testing-library/react';
+import { Stage } from 'react-konva';
+import OverlaysLayer, { type OverlaysLayerProps } from './OverlaysLayer';
+import type { ManifoldPoint } from '@/lib/featureManifold';
+
+/**
+ * Shallow smoke tests: jsdom has no real 2D canvas, so Konva (which every
+ * Stage/Layer/shape ultimately talks to) needs `HTMLCanvasElement.getContext`
+ * stubbed before anything renders — otherwise Konva's own canvas setup throws.
+ * The stub is a Proxy over a handful of explicitly-typed methods/properties
+ * (fillStyle, drawImage, save/restore, path building, gradients, …) that falls
+ * back to a `vi.fn()` for anything unlisted, so an internal Konva call this
+ * suite didn't anticipate returns a harmless mock instead of `undefined is not
+ * a function`.
+ */
+function createMockContext(canvas: HTMLCanvasElement) {
+  const base: Record<string, unknown> = {
+    canvas,
+    fillStyle: '#000000',
+    strokeStyle: '#000000',
+    lineWidth: 1,
+    lineCap: 'butt',
+    lineJoin: 'miter',
+    globalAlpha: 1,
+    globalCompositeOperation: 'source-over',
+    font: '10px sans-serif',
+    textAlign: 'start',
+    textBaseline: 'alphabetic',
+    imageSmoothingEnabled: true,
+    filter: 'none',
+    fillRect: vi.fn(),
+    clearRect: vi.fn(),
+    strokeRect: vi.fn(),
+    drawImage: vi.fn(),
+    putImageData: vi.fn(),
+    getImageData: vi.fn(() => ({ data: new Uint8ClampedArray(4), width: 1, height: 1 })),
+    createImageData: vi.fn((w: number, h: number) => ({
+      data: new Uint8ClampedArray(Math.max(1, w) * Math.max(1, h) * 4),
+      width: w,
+      height: h,
+    })),
+    save: vi.fn(),
+    restore: vi.fn(),
+    translate: vi.fn(),
+    scale: vi.fn(),
+    rotate: vi.fn(),
+    transform: vi.fn(),
+    setTransform: vi.fn(),
+    resetTransform: vi.fn(),
+    beginPath: vi.fn(),
+    closePath: vi.fn(),
+    moveTo: vi.fn(),
+    lineTo: vi.fn(),
+    bezierCurveTo: vi.fn(),
+    quadraticCurveTo: vi.fn(),
+    arc: vi.fn(),
+    arcTo: vi.fn(),
+    ellipse: vi.fn(),
+    rect: vi.fn(),
+    fill: vi.fn(),
+    stroke: vi.fn(),
+    clip: vi.fn(),
+    isPointInPath: vi.fn(() => false),
+    measureText: vi.fn(() => ({ width: 0 })),
+    fillText: vi.fn(),
+    strokeText: vi.fn(),
+    createLinearGradient: vi.fn(() => ({ addColorStop: vi.fn() })),
+    createRadialGradient: vi.fn(() => ({ addColorStop: vi.fn() })),
+    createPattern: vi.fn(() => ({})),
+    setLineDash: vi.fn(),
+    getLineDash: vi.fn(() => []),
+  };
+  return new Proxy(base, {
+    get(target, prop) {
+      if (prop in target) return (target as Record<string, unknown>)[prop as string];
+      return vi.fn();
+    },
+    set(target, prop, value) {
+      (target as Record<string, unknown>)[prop as string] = value;
+      return true;
+    },
+  });
+}
+
+beforeEach(() => {
+  HTMLCanvasElement.prototype.getContext = vi.fn(function (this: HTMLCanvasElement) {
+    return createMockContext(this) as unknown as CanvasRenderingContext2D;
+  }) as unknown as typeof HTMLCanvasElement.prototype.getContext;
+  HTMLCanvasElement.prototype.toDataURL = vi.fn(() => 'data:,');
+  vi.stubGlobal(
+    'ResizeObserver',
+    class {
+      observe() {}
+      unobserve() {}
+      disconnect() {}
+    },
+  );
+});
+
+afterEach(() => {
+  cleanup();
+  vi.restoreAllMocks();
+  vi.unstubAllGlobals();
+});
+
+function baseProps(overrides: Partial<OverlaysLayerProps> = {}): OverlaysLayerProps {
+  return {
+    width: 256,
+    height: 256,
+    showFeatures: false,
+    featureChannelUrl: null,
+    showProba: false,
+    probaOverlayUrl: null,
+    probaOpacity: 0.5,
+    showPredictions: false,
+    clfCommitUrl: null,
+    clfStatusUrl: null,
+    predictionsOpacity: 0.5,
+    classColorById: new Map(),
+    predictionClassVisible: {},
+    showPredictionMulti: true,
+    showPredictionAbstain: true,
+    showManifold: false,
+    manifoldHeatmapUrl: null,
+    manifoldHeatmapOpacity: 0.45,
+    manifoldShowHeatmap: true,
+    manifoldMarkers: [],
+    manifoldShowMarkers: true,
+    manifoldBoxSize: 64,
+    ...overrides,
+  };
+}
+
+/** Every Konva shape/image ultimately needs a Stage ancestor. */
+function renderInStage(props: OverlaysLayerProps) {
+  return render(
+    <Stage width={props.width} height={props.height}>
+      <OverlaysLayer {...props} />
+    </Stage>,
+  );
+}
+
+describe('OverlaysLayer', () => {
+  it('renders no Konva layer (and no canvas) when every overlay is off', () => {
+    const { container } = renderInStage(baseProps());
+    // OverlaysLayer renders nothing with every flag off — no <Layer> means no
+    // canvas gets created at all, which is a real, assertable fact here.
+    expect(container.querySelectorAll('canvas').length).toBe(0);
+  });
+
+  it('renders without throwing when every overlay flag is on but no data has loaded yet', () => {
+    // urls are set but jsdom's <img>/Image never fires onload, so the feature/
+    // proba/prediction overlays stay un-rendered — this only proves the pending
+    // state doesn't crash. The manifold Layer, unlike those, is gated on
+    // `showManifold` alone (its heatmap/marker children are conditional inside
+    // it), so it mounts as one empty canvas even before anything decodes.
+    const { container } = renderInStage(
+      baseProps({
+        showFeatures: true,
+        featureChannelUrl: 'blob:feature',
+        showProba: true,
+        probaOverlayUrl: 'blob:proba',
+        showPredictions: true,
+        clfCommitUrl: 'blob:commit',
+        clfStatusUrl: 'blob:status',
+        showManifold: true,
+        manifoldHeatmapUrl: 'blob:manifold',
+        manifoldShowMarkers: false,
+      }),
+    );
+    expect(container.querySelectorAll('canvas').length).toBe(1);
+  });
+
+  it('renders manifold marker rects immediately (they need no image decode)', () => {
+    const { container } = renderInStage(
+      baseProps({
+        showManifold: true,
+        manifoldShowHeatmap: false,
+        manifoldShowMarkers: true,
+        manifoldMarkers: [
+          { x: 10, y: 10, cluster: 0 },
+          { x: 50, y: 60, cluster: 1 },
+        ] satisfies ManifoldPoint[],
+      }),
+    );
+    // The markers are drawn on a non-listening Layer, which Konva backs with
+    // exactly one scene canvas (no hit canvas since nothing needs hit-testing).
+    expect(container.querySelectorAll('canvas').length).toBe(1);
+  });
+
+  it('adding an active layer increases the number of canvases Konva maintains', () => {
+    const off = renderInStage(baseProps());
+    const offCount = off.container.querySelectorAll('canvas').length;
+    cleanup();
+
+    const on = renderInStage(
+      baseProps({
+        showManifold: true,
+        manifoldShowMarkers: true,
+        manifoldMarkers: [{ x: 1, y: 1, cluster: 0 }] satisfies ManifoldPoint[],
+      }),
+    );
+    const onCount = on.container.querySelectorAll('canvas').length;
+    expect(onCount).toBeGreaterThan(offCount);
+  });
+
+  it('does not throw across width/height prop changes (re-render)', () => {
+    const { rerender, container } = renderInStage(baseProps({ width: 100, height: 100 }));
+    rerender(
+      <Stage width={300} height={200}>
+        <OverlaysLayer {...baseProps({ width: 300, height: 200, showManifold: true })} />
+      </Stage>,
+    );
+    expect(container.querySelectorAll('canvas').length).toBeGreaterThan(0);
+  });
+});
diff --git a/frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.test.tsx b/frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.test.tsx
new file mode 100644
index 0000000..e775caf
--- /dev/null
+++ b/frontend/src/components/annotate/AnnotationCanvas/ShapesLayer.test.tsx
@@ -0,0 +1,248 @@
+import { createRef } from 'react';
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render } from '@testing-library/react';
+import { Stage } from 'react-konva';
+import type Konva from 'konva';
+import ShapesLayer, { type ShapesLayerProps } from './ShapesLayer';
+import type { Shape } from '@/stores/annotationStore';
+import type { AnnotationClass } from '@/stores/classStore';
+
+/**
+ * Shallow smoke tests: jsdom has no real 2D canvas, so Konva (which every
+ * Stage/Layer/shape ultimately talks to) needs `HTMLCanvasElement.getContext`
+ * stubbed before anything renders — otherwise Konva's own canvas setup throws.
+ * The stub is a Proxy over explicitly-typed methods/properties (fillStyle,
+ * drawImage, path building, `fill('evenodd')` used by the polygon-with-holes
+ * sceneFunc, …) that falls back to a `vi.fn()` for anything unlisted, so a
+ * Konva call this suite didn't anticipate returns a harmless mock instead of
+ * throwing "undefined is not a function".
+ */
+function createMockContext(canvas: HTMLCanvasElement) {
+  const base: Record<string, unknown> = {
+    canvas,
+    fillStyle: '#000000',
+    strokeStyle: '#000000',
+    lineWidth: 1,
+    lineCap: 'butt',
+    lineJoin: 'miter',
+    globalAlpha: 1,
+    globalCompositeOperation: 'source-over',
+    font: '10px sans-serif',
+    textAlign: 'start',
+    textBaseline: 'alphabetic',
+    imageSmoothingEnabled: true,
+    filter: 'none',
+    fillRect: vi.fn(),
+    clearRect: vi.fn(),
+    strokeRect: vi.fn(),
+    drawImage: vi.fn(),
+    putImageData: vi.fn(),
+    getImageData: vi.fn(() => ({ data: new Uint8ClampedArray(4), width: 1, height: 1 })),
+    createImageData: vi.fn((w: number, h: number) => ({
+      data: new Uint8ClampedArray(Math.max(1, w) * Math.max(1, h) * 4),
+      width: w,
+      height: h,
+    })),
+    save: vi.fn(),
+    restore: vi.fn(),
+    translate: vi.fn(),
+    scale: vi.fn(),
+    rotate: vi.fn(),
+    transform: vi.fn(),
+    setTransform: vi.fn(),
+    resetTransform: vi.fn(),
+    beginPath: vi.fn(),
+    closePath: vi.fn(),
+    moveTo: vi.fn(),
+    lineTo: vi.fn(),
+    bezierCurveTo: vi.fn(),
+    quadraticCurveTo: vi.fn(),
+    arc: vi.fn(),
+    arcTo: vi.fn(),
+    ellipse: vi.fn(),
+    rect: vi.fn(),
+    fill: vi.fn(),
+    stroke: vi.fn(),
+    clip: vi.fn(),
+    isPointInPath: vi.fn(() => false),
+    measureText: vi.fn(() => ({ width: 0 })),
+    fillText: vi.fn(),
+    strokeText: vi.fn(),
+    createLinearGradient: vi.fn(() => ({ addColorStop: vi.fn() })),
+    createRadialGradient: vi.fn(() => ({ addColorStop: vi.fn() })),
+    createPattern: vi.fn(() => ({})),
+    setLineDash: vi.fn(),
+    getLineDash: vi.fn(() => []),
+  };
+  return new Proxy(base, {
+    get(target, prop) {
+      if (prop in target) return (target as Record<string, unknown>)[prop as string];
+      return vi.fn();
+    },
+    set(target, prop, value) {
+      (target as Record<string, unknown>)[prop as string] = value;
+      return true;
+    },
+  });
+}
+
+beforeEach(() => {
+  HTMLCanvasElement.prototype.getContext = vi.fn(function (this: HTMLCanvasElement) {
+    return createMockContext(this) as unknown as CanvasRenderingContext2D;
+  }) as unknown as typeof HTMLCanvasElement.prototype.getContext;
+  HTMLCanvasElement.prototype.toDataURL = vi.fn(() => 'data:,');
+  vi.stubGlobal(
+    'ResizeObserver',
+    class {
+      observe() {}
+      unobserve() {}
+      disconnect() {}
+    },
+  );
+});
+
+afterEach(() => {
+  cleanup();
+  vi.restoreAllMocks();
+  vi.unstubAllGlobals();
+});
+
+const classA: AnnotationClass = { classId: 1, label: 'A', color: '#ff0000', isVisible: true };
+const classB: AnnotationClass = { classId: 2, label: 'B', color: '#00ff00', isVisible: true };
+const hiddenClass: AnnotationClass = { classId: 3, label: 'Hidden', color: '#0000ff', isVisible: false };
+
+const polygonShape: Shape = { id: 'poly-1', classId: 1, kind: 'polygon', points: [0, 0, 10, 0, 10, 10, 0, 10] };
+const polygonWithHoles: Shape = {
+  id: 'poly-2',
+  classId: 1,
+  kind: 'polygon',
+  points: [0, 0, 20, 0, 20, 20, 0, 20],
+  holes: [[5, 5, 15, 5, 15, 15, 5, 15]],
+};
+const rectShape: Shape = { id: 'rect-1', classId: 2, kind: 'rectangle', x: 1, y: 1, w: 5, h: 5 };
+const ellipseShape: Shape = { id: 'ellipse-1', classId: 2, kind: 'ellipse', cx: 10, cy: 10, rx: 4, ry: 3 };
+const brushShape: Shape = {
+  id: 'brush-1',
+  classId: 1,
+  kind: 'brush',
+  strokes: [
+    { points: [0, 0, 5, 5, 10, 0], radius: 2, mode: 'paint' },
+    { points: [2, 2, 6, 6], radius: 1, mode: 'erase' },
+  ],
+};
+const predictedShape: Shape = { id: 'rect-predicted', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2, origin: 'predicted' };
+const hiddenClassShape: Shape = { id: 'rect-hidden', classId: 3, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 };
+const shapeWithErase: Shape = {
+  id: 'rect-erased',
+  classId: 2,
+  kind: 'rectangle',
+  x: 0,
+  y: 0,
+  w: 8,
+  h: 8,
+  erased: [{ points: [1, 1, 2, 2], radius: 1 }],
+};
+
+function baseProps(overrides: Partial<ShapesLayerProps> = {}): ShapesLayerProps {
+  return {
+    layerRef: createRef<Konva.Layer>(),
+    shapes: [],
+    classMap: new Map([[1, classA], [2, classB], [3, hiddenClass]]),
+    fillOpacity: 0.5,
+    scaleX: 1,
+    selectedShapeIds: [],
+    activeBrushShapeId: null,
+    activeClassId: null,
+    imageWidth: 256,
+    imageHeight: 256,
+    originVisible: { human: true, predicted: true },
+    ...overrides,
+  };
+}
+
+function renderInStage(props: ShapesLayerProps) {
+  return render(
+    <Stage width={256} height={256}>
+      <ShapesLayer {...props} />
+    </Stage>,
+  );
+}
+
+describe('ShapesLayer', () => {
+  it('renders an empty layer without crashing when there are no shapes', () => {
+    const { container } = renderInStage(baseProps());
+    // The Layer itself still mounts (it always exists so the parent's ref +
+    // caching logic has something to grab), giving exactly one scene canvas.
+    expect(container.querySelectorAll('canvas').length).toBe(1);
+  });
+
+  it('renders without crashing across every shape kind (polygon, polygon-with-holes, rectangle, ellipse, brush)', () => {
+    const { container } = renderInStage(
+      baseProps({
+        shapes: [polygonShape, polygonWithHoles, rectShape, ellipseShape, brushShape],
+      }),
+    );
+    expect(container.querySelectorAll('canvas').length).toBe(1);
+  });
+
+  it('renders a shape with erase carve-outs without crashing', () => {
+    const { container } = renderInStage(baseProps({ shapes: [shapeWithErase] }));
+    expect(container.querySelectorAll('canvas').length).toBe(1);
+  });
+
+  it('filters out shapes belonging to a hidden class', () => {
+    // Nothing to assert on the canvas pixels themselves (coarse mock), but the
+    // filter runs in plain JS before any Konva node is built, so at minimum it
+    // must not throw when the only shape present is on a hidden class.
+    expect(() => renderInStage(baseProps({ shapes: [hiddenClassShape] }))).not.toThrow();
+  });
+
+  it('filters out shapes whose origin is toggled off', () => {
+    expect(() =>
+      renderInStage(
+        baseProps({
+          shapes: [predictedShape],
+          originVisible: { human: true, predicted: false },
+        }),
+      ),
+    ).not.toThrow();
+  });
+
+  it('recolors the active brush instance to the active class without crashing', () => {
+    const brushInProgress: Shape = { ...brushShape, id: 'active-brush' };
+    expect(() =>
+      renderInStage(
+        baseProps({
+          shapes: [brushInProgress],
+          activeBrushShapeId: 'active-brush',
+          activeClassId: 2,
+        }),
+      ),
+    ).not.toThrow();
+  });
+
+  it('renders selected shapes (thicker stroke path) without crashing', () => {
+    expect(() =>
+      renderInStage(baseProps({ shapes: [rectShape, ellipseShape], selectedShapeIds: ['rect-1'] })),
+    ).not.toThrow();
+  });
+
+  it('does not throw when scaleX changes (stroke width depends on zoom)', () => {
+    const props = baseProps({ shapes: [rectShape] });
+    const { rerender, container } = renderInStage(props);
+    rerender(
+      <Stage width={256} height={256}>
+        <ShapesLayer {...props} scaleX={4} />
+      </Stage>,
+    );
+    expect(container.querySelectorAll('canvas').length).toBe(1);
+  });
+
+  it('forwards layerRef to the underlying Konva.Layer', () => {
+    const layerRef = createRef<Konva.Layer>();
+    renderInStage(baseProps({ layerRef }));
+    expect(layerRef.current).not.toBeNull();
+    // Real Konva.Layer instance, not a DOM node — spot-check its own API surface.
+    expect(typeof layerRef.current?.getCanvas).toBe('function');
+  });
+});
diff --git a/frontend/src/components/annotate/AnnotationCanvas/index.test.tsx b/frontend/src/components/annotate/AnnotationCanvas/index.test.tsx
new file mode 100644
index 0000000..dc3ae64
--- /dev/null
+++ b/frontend/src/components/annotate/AnnotationCanvas/index.test.tsx
@@ -0,0 +1,283 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render } from '@testing-library/react';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import AnnotationCanvas from './index';
+import { useDatasetStore, type ImageMeta } from '@/stores/datasetStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useToolStore } from '@/stores/toolStore';
+import { useClassStore, type AnnotationClass } from '@/stores/classStore';
+import { useLayerVisibilityStore } from '@/stores/layerVisibilityStore';
+import { useClipboardStore } from '@/stores/clipboardStore';
+
+/**
+ * Shallow smoke tests for the 3636-line react-konva Stage that is the Annotate
+ * page's whole editing surface. These deliberately do NOT drive tool
+ * interactions (drawing, panning, brush strokes, SAM, livewire, …) — only that
+ * the component mounts and re-renders across its main branches (no dataset
+ * loaded / dataset + shapes loaded / different active tools / preview mode)
+ * without throwing.
+ *
+ * jsdom has no real 2D canvas, so Konva (which every Stage/Layer/shape
+ * ultimately talks to) needs `HTMLCanvasElement.getContext` stubbed before
+ * anything renders — otherwise Konva's own canvas setup throws. The stub is a
+ * Proxy over explicitly-typed methods/properties (the ones this component's
+ * own code calls directly — willReadFrequently getImageData/putImageData for
+ * the histogram and threshold-overlay repaint — plus everything Konva itself
+ * needs) that falls back to a `vi.fn()` for anything unlisted.
+ */
+function createMockContext(canvas: HTMLCanvasElement) {
+  const base: Record<string, unknown> = {
+    canvas,
+    fillStyle: '#000000',
+    strokeStyle: '#000000',
+    lineWidth: 1,
+    lineCap: 'butt',
+    lineJoin: 'miter',
+    globalAlpha: 1,
+    globalCompositeOperation: 'source-over',
+    font: '10px sans-serif',
+    textAlign: 'start',
+    textBaseline: 'alphabetic',
+    imageSmoothingEnabled: true,
+    filter: 'none',
+    fillRect: vi.fn(),
+    clearRect: vi.fn(),
+    strokeRect: vi.fn(),
+    drawImage: vi.fn(),
+    putImageData: vi.fn(),
+    getImageData: vi.fn(() => ({ data: new Uint8ClampedArray(4), width: 1, height: 1 })),
+    createImageData: vi.fn((w: number, h: number) => ({
+      data: new Uint8ClampedArray(Math.max(1, w) * Math.max(1, h) * 4),
+      width: w,
+      height: h,
+    })),
+    save: vi.fn(),
+    restore: vi.fn(),
+    translate: vi.fn(),
+    scale: vi.fn(),
+    rotate: vi.fn(),
+    transform: vi.fn(),
+    setTransform: vi.fn(),
+    resetTransform: vi.fn(),
+    beginPath: vi.fn(),
+    closePath: vi.fn(),
+    moveTo: vi.fn(),
+    lineTo: vi.fn(),
+    bezierCurveTo: vi.fn(),
+    quadraticCurveTo: vi.fn(),
+    arc: vi.fn(),
+    arcTo: vi.fn(),
+    ellipse: vi.fn(),
+    rect: vi.fn(),
+    fill: vi.fn(),
+    stroke: vi.fn(),
+    clip: vi.fn(),
+    isPointInPath: vi.fn(() => false),
+    measureText: vi.fn(() => ({ width: 0 })),
+    fillText: vi.fn(),
+    strokeText: vi.fn(),
+    createLinearGradient: vi.fn(() => ({ addColorStop: vi.fn() })),
+    createRadialGradient: vi.fn(() => ({ addColorStop: vi.fn() })),
+    createPattern: vi.fn(() => ({})),
+    setLineDash: vi.fn(),
+    getLineDash: vi.fn(() => []),
+  };
+  return new Proxy(base, {
+    get(target, prop) {
+      if (prop in target) return (target as Record<string, unknown>)[prop as string];
+      return vi.fn();
+    },
+    set(target, prop, value) {
+      (target as Record<string, unknown>)[prop as string] = value;
+      return true;
+    },
+  });
+}
+
+// Snapshot the zustand stores' initial state once at module load so every test
+// can restore a clean slate, per this session's established convention (these
+// four stores have no built-in `reset`; annotationStore's own `reset()` is used
+// where available).
+const initialToolState = useToolStore.getState();
+const initialLayerVisibilityState = useLayerVisibilityStore.getState();
+const initialClipboardState = useClipboardStore.getState();
+
+beforeEach(() => {
+  HTMLCanvasElement.prototype.getContext = vi.fn(function (this: HTMLCanvasElement) {
+    return createMockContext(this) as unknown as CanvasRenderingContext2D;
+  }) as unknown as typeof HTMLCanvasElement.prototype.getContext;
+  HTMLCanvasElement.prototype.toDataURL = vi.fn(() => 'data:,');
+  vi.stubGlobal(
+    'ResizeObserver',
+    class {
+      observe() {}
+      unobserve() {}
+      disconnect() {}
+    },
+  );
+  // useImageSlice's queryFn calls fetch when a dataset is loaded; keep it from
+  // ever hitting the network (jsdom's <img>.src load never resolves either way,
+  // so this only avoids an unhandled real request, never actual image data).
+  global.fetch = vi.fn(() => Promise.reject(new Error('network disabled in tests')));
+
+  useDatasetStore.getState().reset();
+  useAnnotationStore.getState().reset();
+  useClassStore.setState({ classes: [] });
+  useToolStore.setState(initialToolState, true);
+  useLayerVisibilityStore.setState(initialLayerVisibilityState, true);
+  useClipboardStore.setState(initialClipboardState, true);
+});
+
+afterEach(() => {
+  cleanup();
+  vi.restoreAllMocks();
+  vi.unstubAllGlobals();
+});
+
+const CLASS_A: AnnotationClass = { classId: 1, label: 'A', color: '#ff0000', isVisible: true };
+const CLASS_B: AnnotationClass = { classId: 2, label: 'B', color: '#00ff00', isVisible: true };
+
+const META: ImageMeta = {
+  nSlices: 5,
+  height: 256,
+  width: 256,
+  dtype: 'uint8',
+  isRgb: false,
+  valueRange: [0, 255],
+};
+
+function baseProps(overrides: Partial<React.ComponentProps<typeof AnnotationCanvas>> = {}) {
+  return {
+    brightness: 0,
+    contrast: 0,
+    levelsLo: 0,
+    levelsHi: 255,
+    activeClassId: null,
+    activeBrushShapeId: null,
+    onNewBrushInstance: vi.fn(),
+    ...overrides,
+  };
+}
+
+/** Loads a dataset (source/kind/meta) into datasetStore, as ConnectPage/BrowsePage would. */
+function loadDataset() {
+  useDatasetStore.getState().setDataset('local', 'sample.tif', null, META);
+}
+
+function renderCanvas(overrides: Partial<React.ComponentProps<typeof AnnotationCanvas>> = {}) {
+  const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return render(
+    <QueryClientProvider client={queryClient}>
+      <AnnotationCanvas {...baseProps(overrides)} />
+    </QueryClientProvider>,
+  );
+}
+
+describe('AnnotationCanvas', () => {
+  it('renders without crashing when no dataset is loaded', () => {
+    const { container } = renderCanvas();
+    // The image layer always mounts (even empty), so at least one canvas exists.
+    expect(container.querySelectorAll('canvas').length).toBeGreaterThan(0);
+  });
+
+  it('renders without crashing once a dataset (meta) is loaded, with no shapes', () => {
+    loadDataset();
+    const { container } = renderCanvas();
+    expect(container.querySelectorAll('canvas').length).toBeGreaterThan(0);
+  });
+
+  it('renders without crashing with committed shapes present on the current slice', () => {
+    loadDataset();
+    useClassStore.setState({ classes: [CLASS_A, CLASS_B] });
+    useAnnotationStore.getState().replaceClassShapesOnSlice('local:sample.tif', 0, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 10, y: 10, w: 20, h: 20 },
+      { id: 's2', classId: 2, kind: 'ellipse', cx: 100, cy: 100, rx: 15, ry: 10 },
+    ]);
+    const { container } = renderCanvas({ activeClassId: 1 });
+    expect(container.querySelectorAll('canvas').length).toBeGreaterThan(0);
+  });
+
+  it('renders without crashing for each drawing tool (pan/select/polygon/rectangle/ellipse/brush/eraser)', () => {
+    loadDataset();
+    useClassStore.setState({ classes: [CLASS_A] });
+    const tools = ['pan', 'select', 'polygon', 'magnetic', 'rectangle', 'ellipse', 'brush', 'eraser', 'threshold', 'sampler', 'fill'] as const;
+    for (const tool of tools) {
+      useToolStore.setState({ tool });
+      expect(() => {
+        const { unmount } = renderCanvas({ activeClassId: 1 });
+        unmount();
+      }, `tool=${tool}`).not.toThrow();
+    }
+  });
+
+  it('renders the select tool with a selection without crashing (Transformer + toolbar overlay)', () => {
+    loadDataset();
+    useClassStore.setState({ classes: [CLASS_A] });
+    useAnnotationStore.getState().replaceClassShapesOnSlice('local:sample.tif', 0, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 10, y: 10, w: 20, h: 20 },
+    ]);
+    useToolStore.setState({ tool: 'select', selectedShapeIds: ['s1'] });
+    expect(() => renderCanvas({ activeClassId: 1 })).not.toThrow();
+  });
+
+  it('renders in read-only preview mode (previewShapes/previewClasses) without crashing', () => {
+    loadDataset();
+    const { container } = renderCanvas({
+      previewShapes: [{ id: 'p1', classId: 9, kind: 'rectangle', x: 0, y: 0, w: 5, h: 5 }],
+      previewClasses: [{ classId: 9, label: 'Preview', color: '#123456', isVisible: true }],
+    });
+    expect(container.querySelectorAll('canvas').length).toBeGreaterThan(0);
+  });
+
+  it('renders with iPred overlay props set (proba/predictions/manifold) without crashing', () => {
+    loadDataset();
+    const { container } = renderCanvas({
+      probaOverlayUrl: 'blob:proba',
+      clfCommitUrl: 'blob:commit',
+      clfStatusUrl: 'blob:status',
+      predictionClassColorById: new Map([[1, '#ff0000']]),
+      manifoldHeatmapUrl: 'blob:manifold',
+      manifoldMarkers: [{ x: 5, y: 5, cluster: 0 }],
+    });
+    expect(container.querySelectorAll('canvas').length).toBeGreaterThan(0);
+  });
+
+  it('renders across a display-adjustment prop sweep (brightness/contrast/levels/colormap/gamma/blur/upscale) without crashing', () => {
+    loadDataset();
+    expect(() =>
+      renderCanvas({
+        brightness: 30,
+        contrast: -20,
+        levelsLo: 10,
+        levelsHi: 240,
+        colormap: 'viridis',
+        gamma: 1.4,
+        clahe: true,
+        sharpen: true,
+        blur: 2,
+        upscale: 2,
+      }),
+    ).not.toThrow();
+  });
+
+  it('calls onHistogram only after a slice image has actually decoded (never synchronously with no dataset)', () => {
+    const onHistogram = vi.fn();
+    renderCanvas({ onHistogram });
+    // No dataset loaded => no image => the histogram effect bails out early.
+    expect(onHistogram).not.toHaveBeenCalled();
+  });
+
+  it('re-renders across a focusRegion prop change without crashing', () => {
+    loadDataset();
+    const { rerender, container } = renderCanvas({ focusRegion: null });
+    const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+    rerender(
+      <QueryClientProvider client={queryClient}>
+        <AnnotationCanvas
+          {...baseProps({ focusRegion: { x: 5, y: 5, w: 10, h: 10, nonce: 1 } })}
+        />
+      </QueryClientProvider>,
+    );
+    expect(container.querySelectorAll('canvas').length).toBeGreaterThan(0);
+  });
+});
diff --git a/frontend/src/components/annotate/ClassManager/index.test.tsx b/frontend/src/components/annotate/ClassManager/index.test.tsx
new file mode 100644
index 0000000..f3fdfe6
--- /dev/null
+++ b/frontend/src/components/annotate/ClassManager/index.test.tsx
@@ -0,0 +1,145 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import ClassManager from './index';
+import { useClassStore } from '@/stores/classStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useReferenceGuideStore } from '@/stores/referenceGuideStore';
+import { useSettingsStore } from '@/stores/settingsStore';
+import { useDatasetStore } from '@/stores/datasetStore';
+
+beforeEach(() => {
+  useClassStore.setState({ classes: [] });
+  useAnnotationStore.getState().reset();
+  useReferenceGuideStore.getState().clear();
+  useSettingsStore.setState({ colorblindMode: false });
+  useDatasetStore.setState({ source: null, kind: null, serverUri: null } as any);
+  vi.spyOn(window, 'confirm').mockReturnValue(true);
+  vi.spyOn(window, 'alert').mockImplementation(() => {});
+});
+
+afterEach(() => {
+  cleanup();
+  vi.restoreAllMocks();
+});
+
+describe('ClassManager', () => {
+  it('shows an empty-state message with no classes', () => {
+    render(<ClassManager activeClassId={null} onActivate={vi.fn()} />);
+    expect(screen.getByText(/No classes yet/)).toBeInTheDocument();
+  });
+
+  it('shows default quick-add suggestions when there is no guide', () => {
+    render(<ClassManager activeClassId={null} onActivate={vi.fn()} />);
+    expect(screen.getByRole('button', { name: /air/ })).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /substrate/ })).toBeInTheDocument();
+  });
+
+  it('quick-add creates and activates a class', async () => {
+    const onActivate = vi.fn();
+    const user = userEvent.setup();
+    render(<ClassManager activeClassId={null} onActivate={onActivate} />);
+    await user.click(screen.getByRole('button', { name: 'air' }));
+    expect(useClassStore.getState().classes).toHaveLength(1);
+    expect(useClassStore.getState().classes[0].label).toBe('air');
+    expect(onActivate).toHaveBeenCalledWith(useClassStore.getState().classes[0].classId);
+  });
+
+  it('quick-add re-activates an existing class of the same label instead of duplicating', async () => {
+    useClassStore.getState().addClass('air', '#111111');
+    const onActivate = vi.fn();
+    const user = userEvent.setup();
+    render(<ClassManager activeClassId={null} onActivate={onActivate} />);
+    // Already exists, so it's no longer offered as a quick-add suggestion —
+    // the row itself is the only way to reactivate it.
+    expect(screen.queryByRole('button', { name: 'air' })).not.toBeInTheDocument();
+    await user.click(screen.getByRole('option', { name: 'air' }));
+    expect(useClassStore.getState().classes).toHaveLength(1);
+  });
+
+  it('adds a class via the add form', async () => {
+    const onActivate = vi.fn();
+    const user = userEvent.setup();
+    render(<ClassManager activeClassId={null} onActivate={onActivate} />);
+    await user.click(screen.getByLabelText('Add class'));
+    await user.type(screen.getByPlaceholderText('Class label'), 'Cell');
+    await user.click(screen.getByRole('button', { name: 'Add' }));
+    expect(useClassStore.getState().classes.map((c) => c.label)).toEqual(['Cell']);
+  });
+
+  it('rejects a duplicate label with an alert and does not add it', async () => {
+    useClassStore.getState().addClass('Cell', '#111111');
+    const user = userEvent.setup();
+    render(<ClassManager activeClassId={null} onActivate={vi.fn()} />);
+    await user.click(screen.getByLabelText('Add class'));
+    await user.type(screen.getByPlaceholderText('Class label'), 'Cell');
+    await user.click(screen.getByRole('button', { name: 'Add' }));
+    expect(window.alert).toHaveBeenCalledWith('A class with that label already exists.');
+    expect(useClassStore.getState().classes).toHaveLength(1);
+  });
+
+  it('toggles class visibility', async () => {
+    useClassStore.getState().addClass('Cell', '#111111');
+    const user = userEvent.setup();
+    render(<ClassManager activeClassId={null} onActivate={vi.fn()} />);
+    await user.click(screen.getByLabelText('Hide class'));
+    expect(useClassStore.getState().classes[0].isVisible).toBe(false);
+  });
+
+  it('renames a class through the inline editor', async () => {
+    useClassStore.getState().addClass('Cell', '#111111');
+    const user = userEvent.setup();
+    render(<ClassManager activeClassId={null} onActivate={vi.fn()} />);
+    await user.click(screen.getByLabelText('Edit class label and color'));
+    const input = screen.getByDisplayValue('Cell');
+    await user.clear(input);
+    await user.type(input, 'Nucleus{Enter}');
+    expect(useClassStore.getState().classes[0].label).toBe('Nucleus');
+  });
+
+  it('deletes a class after confirmation', async () => {
+    useClassStore.getState().addClass('Cell', '#111111');
+    const user = userEvent.setup();
+    render(<ClassManager activeClassId={null} onActivate={vi.fn()} />);
+    await user.click(screen.getByLabelText('Delete class and its annotations'));
+    expect(window.confirm).toHaveBeenCalled();
+    expect(useClassStore.getState().classes).toHaveLength(0);
+  });
+
+  it('cancelling the confirm dialog keeps the class', async () => {
+    vi.spyOn(window, 'confirm').mockReturnValue(false);
+    useClassStore.getState().addClass('Cell', '#111111');
+    const user = userEvent.setup();
+    render(<ClassManager activeClassId={null} onActivate={vi.fn()} />);
+    await user.click(screen.getByLabelText('Delete class and its annotations'));
+    expect(useClassStore.getState().classes).toHaveLength(1);
+  });
+
+  it('duplicates a class and its shapes into a new class', async () => {
+    useDatasetStore.setState({ source: 'sample.tif', kind: 'local', serverUri: null } as any);
+    const classId = useClassStore.getState().addClass('Cell', '#111111');
+    useAnnotationStore.getState().replaceClassShapesOnSlice('local:sample.tif', 0, classId, [
+      { id: 's1', classId, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+    ]);
+    const onActivate = vi.fn();
+    const user = userEvent.setup();
+    render(<ClassManager activeClassId={classId} onActivate={onActivate} />);
+    await user.click(screen.getByLabelText('Duplicate class and its annotations'));
+    const classes = useClassStore.getState().classes;
+    expect(classes).toHaveLength(2);
+    expect(classes[1].label).toBe('Cell copy');
+    const newId = classes[1].classId;
+    expect(useAnnotationStore.getState().byImage['local:sample.tif']['0'].some((s) => s.classId === newId)).toBe(true);
+  });
+
+  it('shows guide-defined suggestions instead of the generic defaults when a guide is loaded', () => {
+    useReferenceGuideStore.getState().setGuide(
+      [{ label: 'Pore', color: '#123456', description: '', exampleCrops: [] }],
+      '',
+      'local:sample.tif',
+    );
+    render(<ClassManager activeClassId={null} onActivate={vi.fn()} />);
+    expect(screen.getByRole('button', { name: /Pore/ })).toBeInTheDocument();
+    expect(screen.queryByRole('button', { name: 'air' })).not.toBeInTheDocument();
+  });
+});
diff --git a/frontend/src/components/annotate/DenoiseBakeModal.test.tsx b/frontend/src/components/annotate/DenoiseBakeModal.test.tsx
new file mode 100644
index 0000000..0ae8a2e
--- /dev/null
+++ b/frontend/src/components/annotate/DenoiseBakeModal.test.tsx
@@ -0,0 +1,195 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import DenoiseBakeModal, { type DenoiseBakeModalProps } from './DenoiseBakeModal';
+
+function renderModal(overrides: Partial<DenoiseBakeModalProps> = {}) {
+  const props: DenoiseBakeModalProps = {
+    open: true,
+    onClose: vi.fn(),
+    source: 'browse/foo',
+    serverUri: 'http://tiled.example',
+    denoise: { method: 'tv', strength: 0.42 },
+    nSlices: 10,
+    methodLabel: 'Total variation',
+    ...overrides,
+  };
+  const queryClient = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  const invalidateSpy = vi.spyOn(queryClient, 'invalidateQueries');
+  const utils = render(
+    <QueryClientProvider client={queryClient}>
+      <DenoiseBakeModal {...props} />
+    </QueryClientProvider>
+  );
+  return { ...utils, props, invalidateSpy };
+}
+
+beforeEach(() => {
+  global.fetch = vi.fn();
+});
+
+afterEach(() => {
+  cleanup();
+  vi.restoreAllMocks();
+});
+
+describe('DenoiseBakeModal', () => {
+  it('renders nothing when open is false', () => {
+    const { container } = renderModal({ open: false });
+    expect(container).toBeEmptyDOMElement();
+  });
+
+  it('shows method label, strength, and slice count; prefills destination', () => {
+    renderModal({ source: 'browse/foo', methodLabel: 'Total variation', denoise: { method: 'tv', strength: 0.42 }, nSlices: 10 });
+    expect(screen.getByText(/Total variation, strength 0\.42/)).toBeInTheDocument();
+    expect(screen.getByText(/all 10 slices/)).toBeInTheDocument();
+    expect(screen.getByDisplayValue('browse/foo_denoised')).toBeInTheDocument();
+  });
+
+  it('uses singular "slice" when nSlices is 1', () => {
+    renderModal({ nSlices: 1 });
+    expect(screen.getByText(/all 1 slice and written/)).toBeInTheDocument();
+  });
+
+  it('calls onClose from the Cancel button and the X button', async () => {
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    renderModal({ onClose });
+    await user.click(screen.getByRole('button', { name: 'Cancel' }));
+    expect(onClose).toHaveBeenCalledTimes(1);
+    await user.click(screen.getByLabelText('Close'));
+    expect(onClose).toHaveBeenCalledTimes(2);
+  });
+
+  it('disables Save copy when destination is blank', async () => {
+    const user = userEvent.setup();
+    renderModal();
+    const input = screen.getByDisplayValue('browse/foo_denoised');
+    await user.clear(input);
+    expect(screen.getByRole('button', { name: 'Save copy' })).toBeDisabled();
+  });
+
+  it('resets destination/description when reopened for a different source', () => {
+    const { rerender, props } = renderModal({ source: 'browse/foo' });
+    rerender(
+      <QueryClientProvider client={new QueryClient()}>
+        <DenoiseBakeModal {...props} source="browse/bar" />
+      </QueryClientProvider>
+    );
+    expect(screen.getByDisplayValue('browse/bar_denoised')).toBeInTheDocument();
+  });
+
+  it('starts a bake job, polls status, shows progress, then completion and invalidates the browse query', async () => {
+    let statusCall = 0;
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string, opts?: RequestInit) => {
+      if (url.includes('/api/denoise/bake')) {
+        expect(opts?.method).toBe('POST');
+        const body = JSON.parse(opts!.body as string);
+        expect(body).toEqual({
+          source: 'browse/foo',
+          server_uri: 'http://tiled.example',
+          method: 'tv',
+          strength: 0.42,
+          target_path: 'browse/foo_denoised',
+          description: '',
+        });
+        return { ok: true, json: async () => ({ job_id: 'job-1' }) };
+      }
+      if (url.includes('/api/export/status/job-1')) {
+        statusCall++;
+        if (statusCall === 1) {
+          return {
+            ok: true,
+            json: async () => ({ state: 'running', phase: 'Denoising', done: 3, total: 10, error: null, result: null }),
+          };
+        }
+        return {
+          ok: true,
+          json: async () => ({ state: 'done', phase: 'Done', done: 10, total: 10, error: null, result: { path: 'browse/foo_denoised', n_slices: 10 } }),
+        };
+      }
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+
+    const user = userEvent.setup();
+    const { invalidateSpy } = renderModal();
+    await user.click(screen.getByRole('button', { name: 'Save copy' }));
+
+    await waitFor(() => {
+      expect(screen.getByText(/Denoising — 3\/10/)).toBeInTheDocument();
+    });
+
+    await waitFor(() => {
+      expect(screen.getByText('Saved 10 slices to browse/foo_denoised.')).toBeInTheDocument();
+    }, { timeout: 3000 });
+
+    expect(invalidateSpy).toHaveBeenCalledWith({ queryKey: ['browse'] });
+  }, 10000);
+
+  it('shows a cancelled message when the result reports cancelled', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+      if (url.includes('/api/denoise/bake')) {
+        return { ok: true, json: async () => ({ job_id: 'job-2' }) };
+      }
+      if (url.includes('/api/export/status/job-2')) {
+        return {
+          ok: true,
+          json: async () => ({ state: 'done', phase: 'Done', done: 4, total: 10, error: null, result: { cancelled: true } }),
+        };
+      }
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByRole('button', { name: 'Save copy' }));
+    await waitFor(() => {
+      expect(screen.getByText('Stopped — the partial copy was discarded.')).toBeInTheDocument();
+    }, { timeout: 3000 });
+  }, 10000);
+
+  it('shows an error message when the bake request fails', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+      if (url.includes('/api/denoise/bake')) {
+        return { ok: false, status: 500, json: async () => ({ detail: 'boom' }) };
+      }
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByRole('button', { name: 'Save copy' }));
+    await waitFor(() => {
+      expect(screen.getByText('boom')).toBeInTheDocument();
+    });
+  });
+
+  it('calls the cancel endpoint when Stop is clicked while running', async () => {
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+      if (url.includes('/api/denoise/bake')) {
+        return { ok: true, json: async () => ({ job_id: 'job-3' }) };
+      }
+      if (url.includes('/api/export/status/job-3')) {
+        // Never resolves to done so it stays in "running" while we click Stop.
+        return {
+          ok: true,
+          json: async () => ({ state: 'running', phase: 'Denoising', done: 1, total: 10, error: null, result: null }),
+        };
+      }
+      if (url.includes('/api/export/cancel/job-3')) {
+        return { ok: true, json: async () => ({}) };
+      }
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByRole('button', { name: 'Save copy' }));
+    await waitFor(() => screen.getByRole('button', { name: 'Stop' }));
+    await user.click(screen.getByRole('button', { name: 'Stop' }));
+    await waitFor(() => {
+      expect(global.fetch).toHaveBeenCalledWith(
+        expect.stringContaining('/api/export/cancel/job-3'),
+        expect.objectContaining({ method: 'POST' })
+      );
+    });
+  });
+});
diff --git a/frontend/src/components/annotate/DenoisePanel/index.test.tsx b/frontend/src/components/annotate/DenoisePanel/index.test.tsx
new file mode 100644
index 0000000..102e0b9
--- /dev/null
+++ b/frontend/src/components/annotate/DenoisePanel/index.test.tsx
@@ -0,0 +1,292 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import DenoisePanel, { type DenoiseMethodInfo } from './index';
+import type { DenoiseOpts } from '@/stores/datasetStore';
+
+const METHODS: DenoiseMethodInfo[] = [
+  { method: 'none', label: 'None', cost: 'cheap', description: '', available: true, z_radius: 0 },
+  { method: 'gaussian', label: 'Gaussian', cost: 'cheap', description: 'Simple blur.', available: true, z_radius: 0 },
+  { method: 'bilateral', label: 'Bilateral', cost: 'slow', description: 'Edge-preserving.', available: true, z_radius: 0 },
+  { method: 'nlm', label: 'NLM', cost: 'moderate', description: 'Non-local means.', available: false, z_radius: 0 },
+];
+
+function renderWithClient(ui: React.ReactElement) {
+  const client = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return render(<QueryClientProvider client={client}>{ui}</QueryClientProvider>);
+}
+
+function baseDenoise(overrides: Partial<DenoiseOpts> = {}): DenoiseOpts {
+  return { method: 'none', strength: 0.5, ...overrides } as DenoiseOpts;
+}
+
+beforeEach(() => {
+  vi.stubGlobal(
+    'fetch',
+    vi.fn(async (url: string) => {
+      if (String(url).includes('/api/denoise/methods')) {
+        return { ok: true, json: async () => ({ methods: METHODS }) } as Response;
+      }
+      if (String(url).includes('/api/denoise/auto')) {
+        return { ok: true, json: async () => ({ strength: 0.42, noise_sigma: 0.01 }) } as Response;
+      }
+      return { ok: false, json: async () => ({}) } as Response;
+    }),
+  );
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('DenoisePanel', () => {
+  it('loads and lists the available methods from the server', async () => {
+    const onChange = vi.fn();
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise()}
+        onChange={onChange}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={0}
+      />,
+    );
+    await waitFor(() => {
+      expect(screen.getByRole('option', { name: 'None' })).toBeInTheDocument();
+    });
+    expect(screen.getByRole('option', { name: 'Gaussian' })).toBeInTheDocument();
+    expect(screen.getByRole('option', { name: /Bilateral \(slow\)/ })).toBeInTheDocument();
+    expect(screen.getByRole('option', { name: /NLM — unavailable/ })).toBeDisabled();
+  });
+
+  it('does not show the strength slider or extra controls when method is none', async () => {
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'none' })}
+        onChange={vi.fn()}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={0}
+      />,
+    );
+    await waitFor(() => expect(screen.getByRole('option', { name: 'None' })).toBeInTheDocument());
+    expect(screen.queryByText('Strength')).not.toBeInTheDocument();
+    expect(screen.queryByRole('button', { name: /Auto/ })).not.toBeInTheDocument();
+  });
+
+  it('shows the strength slider, description, and Auto button when a method is active', async () => {
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'gaussian' })}
+        onChange={vi.fn()}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={0}
+      />,
+    );
+    await waitFor(() => expect(screen.getByText('Simple blur.')).toBeInTheDocument());
+    expect(screen.getByText('Strength')).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /Auto/ })).toBeInTheDocument();
+  });
+
+  it('calls onChange with the new method when the select changes', async () => {
+    const onChange = vi.fn();
+    const user = userEvent.setup();
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'none' })}
+        onChange={onChange}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={0}
+      />,
+    );
+    await waitFor(() => expect(screen.getByRole('option', { name: 'Gaussian' })).toBeInTheDocument());
+    await user.selectOptions(screen.getByRole('combobox'), 'gaussian');
+    expect(onChange).toHaveBeenCalledWith({ method: 'gaussian' });
+  });
+
+  it('shows the "Save denoised copy" button only when onBake is provided', async () => {
+    const onBake = vi.fn();
+    const user = userEvent.setup();
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'gaussian' })}
+        onChange={vi.fn()}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={0}
+        onBake={onBake}
+      />,
+    );
+    await waitFor(() => expect(screen.getByText('Save denoised copy…')).toBeInTheDocument());
+    await user.click(screen.getByText('Save denoised copy…'));
+    expect(onBake).toHaveBeenCalledOnce();
+  });
+
+  it('omits "Save denoised copy" when onBake is not provided', async () => {
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'gaussian' })}
+        onChange={vi.fn()}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={0}
+      />,
+    );
+    await waitFor(() => expect(screen.getByText('Strength')).toBeInTheDocument());
+    expect(screen.queryByText('Save denoised copy…')).not.toBeInTheDocument();
+  });
+
+  it('shows the busy indicator only while active and busy', async () => {
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'gaussian' })}
+        onChange={vi.fn()}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={0}
+        busy
+      />,
+    );
+    await waitFor(() => expect(screen.getByText('filtering…')).toBeInTheDocument());
+  });
+
+  it('applyAuto calls onChange with the measured strength', async () => {
+    const onChange = vi.fn();
+    const user = userEvent.setup();
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'gaussian' })}
+        onChange={onChange}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={3}
+      />,
+    );
+    await waitFor(() => expect(screen.getByRole('button', { name: /Auto/ })).toBeInTheDocument());
+    await user.click(screen.getByRole('button', { name: /Auto/ }));
+    await waitFor(() => expect(onChange).toHaveBeenCalledWith({ strength: 0.42 }));
+    const [url] = (fetch as any).mock.calls.find(([u]: [string]) => String(u).includes('/api/denoise/auto'));
+    expect(url).toContain('slice_index=3');
+    expect(url).toContain('method=gaussian');
+  });
+
+  it('shows a clean-slice warning when Auto measures zero strength', async () => {
+    (fetch as any).mockImplementation(async (url: string) => {
+      if (String(url).includes('/api/denoise/methods')) {
+        return { ok: true, json: async () => ({ methods: METHODS }) };
+      }
+      if (String(url).includes('/api/denoise/auto')) {
+        return { ok: true, json: async () => ({ strength: 0, noise_sigma: 0.002 }) };
+      }
+      return { ok: false, json: async () => ({}) };
+    });
+    const user = userEvent.setup();
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'gaussian' })}
+        onChange={vi.fn()}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={0}
+      />,
+    );
+    await waitFor(() => expect(screen.getByRole('button', { name: /Auto/ })).toBeInTheDocument());
+    await user.click(screen.getByRole('button', { name: /Auto/ }));
+    expect(await screen.findByText(/This slice looks clean/)).toBeInTheDocument();
+  });
+
+  it('shows an error message when the Auto request fails', async () => {
+    (fetch as any).mockImplementation(async (url: string) => {
+      if (String(url).includes('/api/denoise/methods')) {
+        return { ok: true, json: async () => ({ methods: METHODS }) };
+      }
+      if (String(url).includes('/api/denoise/auto')) {
+        return { ok: false, json: async () => ({}) };
+      }
+      return { ok: false, json: async () => ({}) };
+    });
+    const user = userEvent.setup();
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'gaussian' })}
+        onChange={vi.fn()}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={0}
+      />,
+    );
+    await waitFor(() => expect(screen.getByRole('button', { name: /Auto/ })).toBeInTheDocument());
+    await user.click(screen.getByRole('button', { name: /Auto/ }));
+    expect(await screen.findByText('Could not measure this slice')).toBeInTheDocument();
+  });
+
+  it('disables the Auto button when there is no source', async () => {
+    renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'gaussian' })}
+        onChange={vi.fn()}
+        source={null}
+        kind={null}
+        serverUri={null}
+        sliceIndex={0}
+      />,
+    );
+    await waitFor(() => expect(screen.getByRole('button', { name: /Auto/ })).toBeInTheDocument());
+    expect(screen.getByRole('button', { name: /Auto/ })).toBeDisabled();
+  });
+
+  it('clears a stale auto error when the method changes', async () => {
+    (fetch as any).mockImplementation(async (url: string) => {
+      if (String(url).includes('/api/denoise/methods')) {
+        return { ok: true, json: async () => ({ methods: METHODS }) };
+      }
+      if (String(url).includes('/api/denoise/auto')) {
+        return { ok: false, json: async () => ({}) };
+      }
+      return { ok: false, json: async () => ({}) };
+    });
+    const user = userEvent.setup();
+    const { rerender } = renderWithClient(
+      <DenoisePanel
+        denoise={baseDenoise({ method: 'gaussian' })}
+        onChange={vi.fn()}
+        source="a.tif"
+        kind="local"
+        serverUri={null}
+        sliceIndex={0}
+      />,
+    );
+    await waitFor(() => expect(screen.getByRole('button', { name: /Auto/ })).toBeInTheDocument());
+    await user.click(screen.getByRole('button', { name: /Auto/ }));
+    expect(await screen.findByText('Could not measure this slice')).toBeInTheDocument();
+
+    const client = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+    rerender(
+      <QueryClientProvider client={client}>
+        <DenoisePanel
+          denoise={baseDenoise({ method: 'bilateral' })}
+          onChange={vi.fn()}
+          source="a.tif"
+          kind="local"
+          serverUri={null}
+          sliceIndex={0}
+        />
+      </QueryClientProvider>,
+    );
+    expect(screen.queryByText('Could not measure this slice')).not.toBeInTheDocument();
+  });
+});
diff --git a/frontend/src/components/annotate/DisplayControls/index.test.tsx b/frontend/src/components/annotate/DisplayControls/index.test.tsx
new file mode 100644
index 0000000..dc241ea
--- /dev/null
+++ b/frontend/src/components/annotate/DisplayControls/index.test.tsx
@@ -0,0 +1,104 @@
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import DisplayControls, { type DisplayControlsProps } from './index';
+
+afterEach(() => {
+  cleanup();
+});
+
+function baseProps(overrides: Partial<DisplayControlsProps> = {}): DisplayControlsProps {
+  return {
+    brightness: 0,
+    contrast: 0,
+    onBrightnessChange: vi.fn(),
+    onContrastChange: vi.fn(),
+    onReset: vi.fn(),
+    histogramBins: null,
+    levelsLo: 0,
+    levelsHi: 255,
+    onLevelsChange: vi.fn(),
+    onLevelsReset: vi.fn(),
+    colormap: 'gray',
+    gamma: 1,
+    onColormapChange: vi.fn(),
+    onGammaChange: vi.fn(),
+    clahe: false,
+    sharpen: false,
+    onClaheChange: vi.fn(),
+    onSharpenChange: vi.fn(),
+    blur: 0,
+    onBlurChange: vi.fn(),
+    upscale: 1,
+    onUpscaleChange: vi.fn(),
+    ...overrides,
+  };
+}
+
+describe('DisplayControls', () => {
+  it('renders the reset button and calls onReset', async () => {
+    const onReset = vi.fn();
+    const user = userEvent.setup();
+    render(<DisplayControls {...baseProps({ onReset })} />);
+    await user.click(screen.getByLabelText('Reset brightness and contrast'));
+    expect(onReset).toHaveBeenCalledOnce();
+  });
+
+  it('toggles CLAHE and Sharpen checkboxes', async () => {
+    const onClaheChange = vi.fn();
+    const onSharpenChange = vi.fn();
+    const user = userEvent.setup();
+    render(<DisplayControls {...baseProps({ onClaheChange, onSharpenChange })} />);
+    await user.click(screen.getByLabelText('CLAHE'));
+    expect(onClaheChange).toHaveBeenCalledWith(true);
+    await user.click(screen.getByLabelText('Sharpen'));
+    expect(onSharpenChange).toHaveBeenCalledWith(true);
+  });
+
+  it('marks the current colormap as pressed', () => {
+    render(<DisplayControls {...baseProps({ colormap: 'viridis' })} />);
+    expect(screen.getByTitle('viridis')).toHaveAttribute('aria-pressed', 'true');
+    expect(screen.getByTitle('gray')).toHaveAttribute('aria-pressed', 'false');
+  });
+
+  it('clicking a colormap swatch calls onColormapChange', async () => {
+    const onColormapChange = vi.fn();
+    const user = userEvent.setup();
+    render(<DisplayControls {...baseProps({ onColormapChange })} />);
+    await user.click(screen.getByTitle('viridis'));
+    expect(onColormapChange).toHaveBeenCalledWith('viridis');
+  });
+
+  it('working-resolution radio group reflects the current upscale', () => {
+    render(<DisplayControls {...baseProps({ upscale: 2 })} />);
+    const radios = screen.getAllByRole('radio');
+    expect(radios[0]).toHaveAttribute('aria-checked', 'false');
+    expect(radios[1]).toHaveAttribute('aria-checked', 'true');
+  });
+
+  it('disables an upscale option above maxUpscale', () => {
+    render(<DisplayControls {...baseProps({ maxUpscale: 1 })} />);
+    const radios = screen.getAllByRole('radio');
+    expect(radios[0]).not.toBeDisabled();
+    expect(radios[1]).toBeDisabled();
+    expect(radios[2]).toBeDisabled();
+  });
+
+  it('clicking an enabled upscale option calls onUpscaleChange', async () => {
+    const onUpscaleChange = vi.fn();
+    const user = userEvent.setup();
+    render(<DisplayControls {...baseProps({ onUpscaleChange })} />);
+    await user.click(screen.getAllByRole('radio')[1]);
+    expect(onUpscaleChange).toHaveBeenCalledWith(2);
+  });
+
+  it('renders the denoise slot when provided', () => {
+    render(<DisplayControls {...baseProps({ denoiseSlot: <div data-testid="denoise-slot" /> })} />);
+    expect(screen.getByTestId('denoise-slot')).toBeInTheDocument();
+  });
+
+  it('omits the denoise slot area when not provided', () => {
+    render(<DisplayControls {...baseProps()} />);
+    expect(screen.queryByTestId('denoise-slot')).not.toBeInTheDocument();
+  });
+});
diff --git a/frontend/src/components/annotate/DownloadModal.test.tsx b/frontend/src/components/annotate/DownloadModal.test.tsx
new file mode 100644
index 0000000..2cc9371
--- /dev/null
+++ b/frontend/src/components/annotate/DownloadModal.test.tsx
@@ -0,0 +1,275 @@
+/**
+ * DownloadModal — scope/format selection, export payload building
+ * (including predicted_slices pointer logic), and export/mask-sync job wiring.
+ */
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import { MemoryRouter } from 'react-router';
+import DownloadModal from './DownloadModal';
+import { useDatasetStore } from '@/stores/datasetStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { usePredictedRasterStore } from '@/stores/predictedRasterStore';
+import { useClassStore } from '@/stores/classStore';
+import { useRatingStore } from '@/stores/ratingStore';
+import { useSettingsStore } from '@/stores/settingsStore';
+
+function renderModal(onClose = vi.fn()) {
+  return render(
+    <MemoryRouter>
+      <DownloadModal onClose={onClose} />
+    </MemoryRouter>,
+  );
+}
+
+/** A fetch mock whose POST resolves to a synchronous "done" response (no job_id),
+ *  so useExportJob treats it as immediately done — avoids polling in tests. */
+function mockFetchDone(result: Record<string, unknown> = {}) {
+  return vi.fn(async () => ({
+    ok: true,
+    json: async () => result,
+    text: async () => JSON.stringify(result),
+  })) as unknown as typeof fetch;
+}
+
+beforeEach(() => {
+  useDatasetStore.setState({
+    kind: 'local', source: 'sample.tif', serverUri: null, currentSlice: 0,
+  } as any);
+  useAnnotationStore.getState().reset();
+  usePredictedRasterStore.setState({ bySource: {} });
+  useClassStore.setState({ classes: [{ classId: 1, label: 'Cell', color: '#111111', isVisible: true }] });
+  useRatingStore.setState({ ratings: {} });
+  useSettingsStore.setState({ annotatorName: '' });
+  vi.spyOn(window, 'alert').mockImplementation(() => {});
+});
+
+afterEach(() => {
+  cleanup();
+  vi.restoreAllMocks();
+});
+
+describe('DownloadModal', () => {
+  it('renders scope and format options', () => {
+    renderModal();
+    expect(screen.getByText('Download Dataset')).toBeInTheDocument();
+    expect(screen.getByText('Current slice only')).toBeInTheDocument();
+    expect(screen.getByText('Current sample only')).toBeInTheDocument();
+    expect(screen.getByText('All annotated samples')).toBeInTheDocument();
+    expect(screen.getByText(/COCO \(SAM3\)/)).toBeInTheDocument();
+    expect(screen.getByText(/DINOv3 \/ Lightly/)).toBeInTheDocument();
+  });
+
+  it('defaults to "current sample" scope with a 1-sample preview when a source is loaded', () => {
+    renderModal();
+    expect(screen.getByRole('radio', { name: /Current sample only/ })).toBeChecked();
+    expect(screen.getByText('1 sample will be exported.')).toBeInTheDocument();
+  });
+
+  it('shows "No samples match" for the "all" scope with no annotated samples', async () => {
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByRole('radio', { name: /All annotated samples/ }));
+    expect(screen.getByText('No samples match.')).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /Export/ })).toBeDisabled();
+  });
+
+  it('counts annotated samples for the "all" scope, applying the star-rating filter', async () => {
+    useAnnotationStore.getState().setShapes('local:a.tif', 0, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+    ]);
+    useAnnotationStore.getState().setShapes('local:b.tif', 0, [
+      { id: 's2', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+    ]);
+    useRatingStore.setState({ ratings: { 'local:b.tif': 2 } });
+    const user = userEvent.setup();
+    renderModal();
+
+    await user.click(screen.getByRole('radio', { name: /All annotated samples/ }));
+    expect(screen.getByText('2 samples will be exported.')).toBeInTheDocument();
+
+    await user.click(screen.getByRole('radio', { name: /★★ and above/ }));
+    expect(screen.getByText('1 sample will be exported.')).toBeInTheDocument();
+
+    await user.click(screen.getByRole('radio', { name: /★★★ only/ }));
+    expect(screen.getByText('No samples match.')).toBeInTheDocument();
+  });
+
+  it('POSTs the current-sample export payload with predicted_slices for un-vectorized pointers', async () => {
+    useAnnotationStore.getState().setShapes('local:sample.tif', 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+    ]);
+    usePredictedRasterStore.getState().setPointers('local:sample.tif', {
+      '0': { runId: 'run-1', classIds: [1] },
+      '1': { runId: 'run-2', classIds: [1] },
+    });
+    useSettingsStore.setState({ annotatorName: '  Ada  ' });
+
+    const fetchMock = mockFetchDone({ dataset_path: '/exports/foo' });
+    global.fetch = fetchMock;
+
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByRole('button', { name: /^Export$/ }));
+
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    const [url, opts] = (fetchMock as any).mock.calls[0];
+    expect(url).toBe('/api/export/coco');
+    const body = JSON.parse(opts.body);
+    expect(body.annotator).toBe('Ada');
+    expect(body.format).toBe('coco_sam3');
+    expect(body.sources).toHaveLength(1);
+    const src = body.sources[0];
+    expect(src.kind).toBe('local');
+    expect(src.source).toBe('sample.tif');
+    // Slice 1 has real shapes, so its pointer is excluded; slice 0 has no
+    // shapes, so its pointer is included as an un-vectorized predicted_slice.
+    expect(src.predicted_slices).toEqual({ '0': { run_id: 'run-1', class_ids: [1] } });
+    expect(src.slices).toEqual({ '1': expect.any(Array) });
+  });
+
+  it('scopes predicted_slices and negative_slices to just the current slice for "slice" scope', async () => {
+    useDatasetStore.setState({ currentSlice: 0 } as any);
+    usePredictedRasterStore.getState().setPointers('local:sample.tif', {
+      '0': { runId: 'run-a', classIds: [1] },
+      '2': { runId: 'run-b', classIds: [1] },
+    });
+    useAnnotationStore.getState().toggleNegativeSlice('local:sample.tif', 0);
+    useAnnotationStore.getState().toggleNegativeSlice('local:sample.tif', 2);
+
+    const fetchMock = mockFetchDone({});
+    global.fetch = fetchMock;
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByRole('radio', { name: /Current slice only/ }));
+    await user.click(screen.getByRole('button', { name: /^Export$/ }));
+
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    const body = JSON.parse((fetchMock as any).mock.calls[0][1].body);
+    const src = body.sources[0];
+    expect(src.predicted_slices).toEqual({ '0': { run_id: 'run-a', class_ids: [1] } });
+    expect(src.negative_slices).toEqual(['0']);
+  });
+
+  it('includes include_polygons only relevant for coco_sam3 and reflects the checkbox', async () => {
+    const fetchMock = mockFetchDone({});
+    global.fetch = fetchMock;
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByLabelText(/Include polygon copy in COCO/));
+    await user.click(screen.getByRole('button', { name: /^Export$/ }));
+
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    const body = JSON.parse((fetchMock as any).mock.calls[0][1].body);
+    expect(body.include_polygons).toBe(true);
+  });
+
+  it('switches format to lightly_dinov3', async () => {
+    const fetchMock = mockFetchDone({});
+    global.fetch = fetchMock;
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByLabelText(/DINOv3 \/ Lightly/));
+    await user.click(screen.getByRole('button', { name: /^Export$/ }));
+
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    const body = JSON.parse((fetchMock as any).mock.calls[0][1].body);
+    expect(body.format).toBe('lightly_dinov3');
+  });
+
+  it('shows the saved-to-Tiled message on success (no zip when the response has no job_id)', async () => {
+    // A synchronous response (no job_id) is treated as immediately "done" with
+    // no jobId, so downloadUrl stays null and only the "Saved to" line shows.
+    global.fetch = mockFetchDone({ dataset_path: '/exports/foo.zip' });
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByRole('button', { name: /^Export$/ }));
+
+    await screen.findByText(/Saved to/);
+    expect(screen.getByText('/exports/foo.zip')).toBeInTheDocument();
+    expect(screen.queryByRole('link', { name: /Download \.zip/ })).not.toBeInTheDocument();
+  });
+
+  it('shows an error message when the export request fails', async () => {
+    global.fetch = vi.fn(async () => ({
+      ok: false,
+      status: 500,
+      text: async () => JSON.stringify({ detail: 'boom' }),
+    })) as unknown as typeof fetch;
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByRole('button', { name: /^Export$/ }));
+
+    await waitFor(() => expect(screen.getByText(/boom|Request failed/)).toBeInTheDocument());
+  });
+
+  it('alerts and does not call fetch when no sample is loaded for "current" scope', async () => {
+    useDatasetStore.setState({ kind: null, source: null } as any);
+    const fetchMock = mockFetchDone({});
+    global.fetch = fetchMock;
+    const user = userEvent.setup();
+    renderModal();
+    // previewCount is 0 with no source, disabling Export — assert directly instead.
+    expect(screen.getByRole('button', { name: /^Export$/ })).toBeDisabled();
+    expect(fetchMock).not.toHaveBeenCalled();
+  });
+
+  it('disables "Push masks to Tiled" for a local source and enables it for a tiled source', async () => {
+    renderModal();
+    expect(screen.getByRole('button', { name: /Push masks to Tiled/ })).toBeDisabled();
+
+    cleanup();
+    useDatasetStore.setState({ kind: 'tiled', source: 'a', serverUri: 'http://x', currentSlice: 0 } as any);
+    renderModal();
+    expect(screen.getByRole('button', { name: /Push masks to Tiled/ })).not.toBeDisabled();
+  });
+
+  it('POSTs to the mask-sync endpoint, filtering to tiled sources only, and shows a "View in 3D" link', async () => {
+    useDatasetStore.setState({ kind: 'tiled', source: 'vol.tif', serverUri: 'http://x', currentSlice: 0 } as any);
+    useAnnotationStore.getState().setShapes('tiled:http://x:vol.tif', 0, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+    ]);
+    const fetchMock = mockFetchDone({
+      written: [{ container: 'vol.tif', n_slices: 10, updated: 1 }],
+    });
+    global.fetch = fetchMock;
+    const user = userEvent.setup();
+    renderModal();
+    await user.click(screen.getByRole('button', { name: /Push masks to Tiled/ }));
+
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    const [url, opts] = (fetchMock as any).mock.calls[0];
+    expect(url).toBe('/api/masks/to-tiled');
+    const body = JSON.parse(opts.body);
+    expect(body.sources.every((s: any) => s.kind === 'tiled')).toBe(true);
+
+    await screen.findByText(/Masks merged into Tiled/);
+    const link = screen.getByRole('button', { name: /View in 3D/ });
+    expect(link).toBeInTheDocument();
+  });
+
+  it('navigates to the 3D volume view when "View in 3D" is clicked', async () => {
+    useDatasetStore.setState({ kind: 'tiled', source: 'vol.tif', serverUri: 'http://x', currentSlice: 0 } as any);
+    useAnnotationStore.getState().setShapes('tiled:http://x:vol.tif', 0, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+    ]);
+    global.fetch = mockFetchDone({
+      written: [{ container: 'vol.tif', n_slices: 10, updated: 1 }],
+    });
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    renderModal(onClose);
+    await user.click(screen.getByRole('button', { name: /Push masks to Tiled/ }));
+    await screen.findByText(/Masks merged into Tiled/);
+    await user.click(screen.getByRole('button', { name: /View in 3D/ }));
+    expect(onClose).toHaveBeenCalled();
+  });
+
+  it('calls onClose when Cancel/Close is clicked', async () => {
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    renderModal(onClose);
+    await user.click(screen.getByRole('button', { name: 'Cancel' }));
+    expect(onClose).toHaveBeenCalled();
+  });
+});
diff --git a/frontend/src/components/annotate/FeatureChannelsPanel/index.test.tsx b/frontend/src/components/annotate/FeatureChannelsPanel/index.test.tsx
new file mode 100644
index 0000000..52d622a
--- /dev/null
+++ b/frontend/src/components/annotate/FeatureChannelsPanel/index.test.tsx
@@ -0,0 +1,330 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor, within } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import FeatureChannelsPanel from './index';
+import { useIpredStore, DEFAULT_COMPOSITION_ID } from '@/stores/ipredStore';
+import type { FeatureJobInfo } from '@/hooks/useFeatureChannels';
+import { listIpredCompositions, listIpredModules } from '@/lib/ipredApi';
+
+vi.mock('@/lib/ipredApi', () => ({
+  listIpredCompositions: vi.fn(async () => []),
+  listIpredModules: vi.fn(async () => []),
+}));
+
+const mockListIpredCompositions = vi.mocked(listIpredCompositions);
+const mockListIpredModules = vi.mocked(listIpredModules);
+
+const initialIpredState = useIpredStore.getState();
+
+const compSkimage = {
+  id: 'comp-skimage',
+  name: 'Skimage only',
+  nodes: [{ id: 'n1', module: 'skimage_multiscale' }],
+  outputs: ['n1'],
+};
+
+const compSlimsam = {
+  id: 'comp-skimage-slimsam',
+  name: 'Skimage + SlimSAM',
+  nodes: [
+    { id: 'n1', module: 'skimage_multiscale' },
+    { id: 'n2', module: 'slimsam' },
+  ],
+  outputs: ['n1', 'n2'],
+};
+
+const compMark25 = {
+  id: 'comp-skimage-mark25',
+  name: 'Skimage + Mark25',
+  nodes: [
+    { id: 'n1', module: 'skimage_multiscale' },
+    { id: 'n2', module: 'tomojepa_mark25' },
+  ],
+  outputs: ['n1', 'n2'],
+};
+
+const modSkimage = {
+  id: 'skimage_multiscale',
+  name: 'Skimage multiscale',
+  description: '',
+  runtime: 'numpy',
+  ready: true,
+  accepts_input_from: false,
+  produces_channels: true,
+  produces_embedding: false,
+  params_schema: {},
+};
+
+const modSlimsam = {
+  id: 'slimsam',
+  name: 'SlimSAM',
+  description: '',
+  runtime: 'torch',
+  ready: true,
+  accepts_input_from: true,
+  produces_channels: false,
+  produces_embedding: true,
+  params_schema: {},
+};
+
+const modMark25NotReady = {
+  id: 'tomojepa_mark25',
+  name: 'TomoJEPA Mark25',
+  description: '',
+  runtime: 'torch',
+  ready: false,
+  accepts_input_from: true,
+  produces_channels: false,
+  produces_embedding: true,
+  params_schema: {},
+};
+
+function baseProps() {
+  return {
+    job: null as FeatureJobInfo | null,
+    channelIndex: null as number | null,
+    computing: false,
+    error: null as string | null,
+    onCompute: vi.fn(),
+    onSelectChannel: vi.fn(),
+    onCycle: vi.fn(),
+    onOriginal: vi.fn(),
+  };
+}
+
+function makeJob(overrides: Partial<FeatureJobInfo> = {}): FeatureJobInfo {
+  return {
+    jobId: 'job-1',
+    width: 100,
+    height: 100,
+    channels: [
+      { index: 0, label: 'edges' },
+      { index: 1, label: 'texture' },
+    ],
+    hasSam: false,
+    ...overrides,
+  };
+}
+
+beforeEach(() => {
+  useIpredStore.setState(initialIpredState, true);
+  mockListIpredCompositions.mockReset().mockResolvedValue([]);
+  mockListIpredModules.mockReset().mockResolvedValue([]);
+});
+
+afterEach(() => {
+  cleanup();
+});
+
+describe('FeatureChannelsPanel', () => {
+  it('shows a loading placeholder for the recipe select while presets load', () => {
+    mockListIpredCompositions.mockImplementation(() => new Promise(() => {}));
+    const props = baseProps();
+    render(<FeatureChannelsPanel {...props} />);
+    expect(screen.queryByRole('combobox')).not.toBeInTheDocument();
+    expect(screen.getByText('Recipe').parentElement?.querySelector('.animate-pulse')).toBeInTheDocument();
+  });
+
+  it('renders preset options once loaded, marking the recommended one and disabling unready ones', async () => {
+    mockListIpredCompositions.mockResolvedValue([compSkimage, compSlimsam, compMark25]);
+    mockListIpredModules.mockResolvedValue([modSkimage, modSlimsam, modMark25NotReady]);
+    useIpredStore.setState({ preferredCompositionId: 'comp-skimage-slimsam' });
+    const props = baseProps();
+    render(<FeatureChannelsPanel {...props} />);
+
+    await waitFor(() => expect(screen.getAllByRole('combobox').length).toBeGreaterThan(0));
+    const selects = screen.getAllByRole('combobox');
+    const recipeSelect = selects[0];
+    const options = within(recipeSelect).getAllByRole('option');
+    expect(options).toHaveLength(3);
+
+    const recommended = options.find((o) => o.textContent?.includes('★'));
+    expect(recommended).toBeDefined();
+    expect(recommended?.textContent).toContain('Texture-aware (+ SlimSAM)');
+
+    const mark25Option = options.find((o) => o.getAttribute('value') === 'comp-skimage-mark25');
+    expect(mark25Option).toBeDisabled();
+    expect(mark25Option?.textContent).toContain('(unavailable)');
+
+    // Recommended preset hint text is shown for the currently selected preset.
+    expect(
+      screen.getByText('Skimage filters + SlimSAM vision-encoder embeddings (PCA-reduced). Recommended default.'),
+    ).toBeInTheDocument();
+  });
+
+  it('changing the recipe select calls setPreferredCompositionId', async () => {
+    mockListIpredCompositions.mockResolvedValue([compSkimage, compSlimsam]);
+    mockListIpredModules.mockResolvedValue([modSkimage, modSlimsam]);
+    useIpredStore.setState({ preferredCompositionId: 'comp-skimage-slimsam' });
+    const user = userEvent.setup();
+    const props = baseProps();
+    render(<FeatureChannelsPanel {...props} />);
+
+    await waitFor(() => expect(screen.getAllByRole('combobox').length).toBeGreaterThan(0));
+    const recipeSelect = screen.getAllByRole('combobox')[0];
+    await user.selectOptions(recipeSelect, 'comp-skimage');
+    expect(useIpredStore.getState().preferredCompositionId).toBe('comp-skimage');
+  });
+
+  it('shows a warning and disables Compute when the selected preset needs a missing module', async () => {
+    mockListIpredCompositions.mockResolvedValue([compMark25]);
+    mockListIpredModules.mockResolvedValue([modSkimage, modMark25NotReady]);
+    useIpredStore.setState({ preferredCompositionId: 'comp-skimage-mark25' });
+    const props = baseProps();
+    render(<FeatureChannelsPanel {...props} />);
+
+    await waitFor(() => expect(screen.getByText(/isn't installed yet/)).toBeInTheDocument());
+    expect(screen.getByText(/isn't installed yet/).closest('div')).toHaveTextContent('TomoJEPA Mark25');
+    expect(screen.getByRole('button', { name: /Compute/ })).toBeDisabled();
+  });
+
+  it('falls back to the recommended preset if the stored preference is not ready once presets load', async () => {
+    mockListIpredCompositions.mockResolvedValue([compSkimage, compSlimsam, compMark25]);
+    mockListIpredModules.mockResolvedValue([modSkimage, modSlimsam, modMark25NotReady]);
+    useIpredStore.setState({ preferredCompositionId: 'comp-skimage-mark25' });
+    render(<FeatureChannelsPanel {...baseProps()} />);
+
+    await waitFor(() =>
+      expect(useIpredStore.getState().preferredCompositionId).toBe(DEFAULT_COMPOSITION_ID),
+    );
+  });
+
+  it('surfaces a presets error message when loading compositions/modules fails', async () => {
+    mockListIpredCompositions.mockRejectedValue(new Error('network down'));
+    render(<FeatureChannelsPanel {...baseProps()} />);
+    await waitFor(() => expect(screen.getByText('network down')).toBeInTheDocument());
+  });
+
+  it('clicking Compute calls onCompute', async () => {
+    mockListIpredCompositions.mockResolvedValue([compSkimage]);
+    mockListIpredModules.mockResolvedValue([modSkimage]);
+    useIpredStore.setState({ preferredCompositionId: 'comp-skimage' });
+    const user = userEvent.setup();
+    const props = baseProps();
+    render(<FeatureChannelsPanel {...props} />);
+
+    await waitFor(() => expect(screen.getAllByRole('combobox').length).toBeGreaterThan(0));
+    await user.click(screen.getByRole('button', { name: /Compute/ }));
+    expect(props.onCompute).toHaveBeenCalledTimes(1);
+  });
+
+  it('shows a spinner label and disables Compute + recipe select while computing', async () => {
+    mockListIpredCompositions.mockResolvedValue([compSkimage]);
+    mockListIpredModules.mockResolvedValue([modSkimage]);
+    const props = { ...baseProps(), computing: true };
+    render(<FeatureChannelsPanel {...props} />);
+
+    await waitFor(() => expect(screen.getAllByRole('combobox').length).toBeGreaterThan(0));
+    expect(screen.getByText('Computing…')).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /Computing/ })).toBeDisabled();
+    expect(screen.getAllByRole('combobox')[0]).toBeDisabled();
+  });
+
+  it('disables the whole panel controls when disabled prop is set', async () => {
+    mockListIpredCompositions.mockResolvedValue([compSkimage]);
+    mockListIpredModules.mockResolvedValue([modSkimage]);
+    const props = { ...baseProps(), disabled: true };
+    render(<FeatureChannelsPanel {...props} />);
+
+    await waitFor(() => expect(screen.getAllByRole('combobox').length).toBeGreaterThan(0));
+    expect(screen.getByRole('button', { name: /Compute/ })).toBeDisabled();
+    expect(screen.getAllByRole('combobox')[0]).toBeDisabled();
+  });
+
+  it('renders the compute-error message when error is set', async () => {
+    const props = { ...baseProps(), error: 'Compute failed: boom' };
+    render(<FeatureChannelsPanel {...props} />);
+    expect(screen.getByText('Compute failed: boom')).toBeInTheDocument();
+    await waitFor(() => expect(screen.getAllByRole('combobox').length).toBeGreaterThan(0));
+  });
+
+  it('shows no channel section when there is no job', async () => {
+    render(<FeatureChannelsPanel {...baseProps()} />);
+    expect(screen.queryByRole('button', { name: 'Previous channel' })).not.toBeInTheDocument();
+    expect(screen.queryByText('Show original')).not.toBeInTheDocument();
+    await waitFor(() => expect(screen.getAllByRole('combobox').length).toBeGreaterThan(0));
+  });
+
+  it('shows the header channel count summary including SAM and cache flags', async () => {
+    const job = makeJob({ hasSam: true, cacheHit: true });
+    render(<FeatureChannelsPanel {...baseProps()} job={job} />);
+    expect(screen.getByText('2 ch +SAM · cache')).toBeInTheDocument();
+    await waitFor(() => expect(screen.getAllByRole('combobox').length).toBeGreaterThan(0));
+  });
+
+  it('renders channel list, active label, and cycle/select controls once a job is present', async () => {
+    const job = makeJob();
+    const props = { ...baseProps(), job, channelIndex: 1 };
+    render(<FeatureChannelsPanel {...props} />);
+    await waitFor(() => expect(screen.getAllByRole('combobox').length).toBeGreaterThan(0));
+
+    expect(screen.getByRole('button', { name: 'Previous channel' })).toBeEnabled();
+    expect(screen.getByRole('button', { name: 'Next channel' })).toBeEnabled();
+    expect(screen.getByTitle('texture')).toBeInTheDocument();
+
+    const channelSelect = screen.getByDisplayValue('texture');
+    const options = within(channelSelect).getAllByRole('option');
+    expect(options.map((o) => o.textContent)).toEqual(['Original', 'edges', 'texture']);
+  });
+
+  it('disables cycle buttons when channelIndex is null (Original selected)', async () => {
+    const job = makeJob();
+    const props = { ...baseProps(), job, channelIndex: null };
+    render(<FeatureChannelsPanel {...props} />);
+    await waitFor(() => expect(screen.getAllByRole('combobox').length).toBeGreaterThan(0));
+    expect(screen.getByRole('button', { name: 'Previous channel' })).toBeDisabled();
+    expect(screen.getByRole('button', { name: 'Next channel' })).toBeDisabled();
+    expect(screen.queryByTitle('texture')).not.toBeInTheDocument();
+  });
+
+  it('clicking next/previous channel calls onCycle with the right delta', async () => {
+    const job = makeJob();
+    const props = { ...baseProps(), job, channelIndex: 0 };
+    const user = userEvent.setup();
+    render(<FeatureChannelsPanel {...props} />);
+
+    await user.click(screen.getByRole('button', { name: 'Next channel' }));
+    expect(props.onCycle).toHaveBeenCalledWith(1);
+    await user.click(screen.getByRole('button', { name: 'Previous channel' }));
+    expect(props.onCycle).toHaveBeenCalledWith(-1);
+  });
+
+  it('selecting a channel option calls onSelectChannel with the numeric index or null for Original', async () => {
+    const job = makeJob();
+    const props = { ...baseProps(), job, channelIndex: 0 };
+    const user = userEvent.setup();
+    render(<FeatureChannelsPanel {...props} />);
+
+    const channelSelect = screen.getByDisplayValue('edges');
+    await user.selectOptions(channelSelect, '1');
+    expect(props.onSelectChannel).toHaveBeenCalledWith(1);
+
+    await user.selectOptions(channelSelect, '');
+    expect(props.onSelectChannel).toHaveBeenCalledWith(null);
+  });
+
+  it('clicking "Show original" calls onOriginal', async () => {
+    const job = makeJob();
+    const props = { ...baseProps(), job, channelIndex: 0 };
+    const user = userEvent.setup();
+    render(<FeatureChannelsPanel {...props} />);
+
+    await user.click(screen.getByText('Show original'));
+    expect(props.onOriginal).toHaveBeenCalledTimes(1);
+  });
+
+  it('toggles the Advanced disclosure to reveal the composition panel', async () => {
+    const user = userEvent.setup();
+    render(<FeatureChannelsPanel {...baseProps()} />);
+
+    expect(screen.queryByText(/edit feature recipe/)).toBeInTheDocument();
+    const toggle = screen.getByText(/Advanced: edit feature recipe/);
+    expect(toggle.textContent).toContain('▸');
+
+    await user.click(toggle);
+    expect(toggle.textContent).toContain('▾');
+
+    await user.click(toggle);
+    expect(toggle.textContent).toContain('▸');
+  });
+});
diff --git a/frontend/src/components/annotate/HistogramControl/index.test.tsx b/frontend/src/components/annotate/HistogramControl/index.test.tsx
new file mode 100644
index 0000000..ada60e0
--- /dev/null
+++ b/frontend/src/components/annotate/HistogramControl/index.test.tsx
@@ -0,0 +1,120 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, fireEvent, render, screen } from '@testing-library/react';
+import HistogramControl from './index';
+
+beforeEach(() => {
+  // jsdom implements neither in its default HTMLElement — the component calls
+  // both unconditionally on pointerdown/up, and schedules onChange via rAF.
+  Element.prototype.setPointerCapture = vi.fn();
+  Element.prototype.releasePointerCapture = vi.fn();
+  vi.stubGlobal('requestAnimationFrame', (cb: FrameRequestCallback) => {
+    cb(0);
+    return 0;
+  });
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('HistogramControl', () => {
+  it('shows the current lo/hi window', () => {
+    render(<HistogramControl bins={null} lo={10} hi={200} onChange={vi.fn()} onReset={vi.fn()} />);
+    expect(screen.getByText('10 – 200')).toBeInTheDocument();
+  });
+
+  it('defaults to the "Levels" label', () => {
+    render(<HistogramControl bins={null} lo={0} hi={255} onChange={vi.fn()} onReset={vi.fn()} />);
+    expect(screen.getByText(/Levels/)).toBeInTheDocument();
+    expect(screen.getByLabelText('Levels minimum')).toBeInTheDocument();
+  });
+
+  it('supports a custom label (e.g. Threshold)', () => {
+    render(<HistogramControl bins={null} lo={0} hi={255} onChange={vi.fn()} onReset={vi.fn()} label="Threshold" />);
+    expect(screen.getByLabelText('Threshold minimum')).toBeInTheDocument();
+    expect(screen.getByLabelText('Reset threshold')).toBeInTheDocument();
+  });
+
+  it('reset button calls onReset', () => {
+    const onReset = vi.fn();
+    render(<HistogramControl bins={null} lo={0} hi={255} onChange={vi.fn()} onReset={onReset} />);
+    fireEvent.click(screen.getByLabelText('Reset levels'));
+    expect(onReset).toHaveBeenCalledOnce();
+  });
+
+  it('ArrowRight on the max knob increases hi by 1', () => {
+    const onChange = vi.fn();
+    render(<HistogramControl bins={null} lo={0} hi={200} onChange={onChange} onReset={vi.fn()} />);
+    fireEvent.keyDown(screen.getByLabelText('Levels maximum'), { key: 'ArrowRight' });
+    expect(onChange).toHaveBeenCalledWith(0, 201);
+  });
+
+  it('Shift+ArrowLeft on the min knob decreases lo by 10', () => {
+    const onChange = vi.fn();
+    render(<HistogramControl bins={null} lo={50} hi={200} onChange={onChange} onReset={vi.fn()} />);
+    fireEvent.keyDown(screen.getByLabelText('Levels minimum'), { key: 'ArrowLeft', shiftKey: true });
+    expect(onChange).toHaveBeenCalledWith(40, 200);
+  });
+
+  it('clamps the min knob so it cannot cross the max', () => {
+    const onChange = vi.fn();
+    render(<HistogramControl bins={null} lo={198} hi={200} onChange={onChange} onReset={vi.fn()} />);
+    fireEvent.keyDown(screen.getByLabelText('Levels minimum'), { key: 'ArrowRight', shiftKey: true });
+    expect(onChange).toHaveBeenCalledWith(199, 200);
+  });
+
+  it('renders extra actions next to the reset button', () => {
+    render(
+      <HistogramControl
+        bins={null} lo={0} hi={255} onChange={vi.fn()} onReset={vi.fn()}
+        actions={<button>Auto</button>}
+      />,
+    );
+    expect(screen.getByRole('button', { name: 'Auto' })).toBeInTheDocument();
+  });
+
+  /** jsdom has no PointerEvent constructor, and fireEvent's plain-object init
+   * can't set MouseEvent's readonly clientX getter through it — so pointer
+   * drags are dispatched as a real (jsdom-supported) MouseEvent typed as
+   * 'pointerdown'/'pointermove', which React's delegated listener still picks
+   * up (it just reads whatever's on the native event). */
+  function pointerEvent(type: string, clientX: number): Event {
+    return new MouseEvent(type, { clientX, bubbles: true, cancelable: true });
+  }
+
+  it('dragging near an edge (far from either knob) drags the whole band', () => {
+    const onChange = vi.fn();
+    const { container } = render(
+      <HistogramControl bins={null} lo={0} hi={255} onChange={onChange} onReset={vi.fn()} />,
+    );
+    const track = container.querySelector('.touch-none') as HTMLElement;
+    track.getBoundingClientRect = () => ({
+      left: 0, right: 256, width: 256, top: 0, bottom: 60, height: 60, x: 0, y: 0, toJSON: () => ({}),
+    });
+    // v≈10 here is far from both lo=0 and hi=255 (tolerance is 6), and strictly
+    // between them, so this is classified as a band drag, not a knob drag —
+    // the window translates as a whole, preserving its width (255).
+    track.dispatchEvent(pointerEvent('pointerdown', 10));
+    expect(onChange).toHaveBeenCalled();
+    const [nlo, nhi] = onChange.mock.calls[0];
+    expect(nhi - nlo).toBe(255);
+  });
+
+  it('dragging near the min knob moves only lo, leaving hi fixed', () => {
+    const onChange = vi.fn();
+    const { container } = render(
+      <HistogramControl bins={null} lo={50} hi={200} onChange={onChange} onReset={vi.fn()} />,
+    );
+    const track = container.querySelector('.touch-none') as HTMLElement;
+    track.getBoundingClientRect = () => ({
+      left: 0, right: 256, width: 256, top: 0, bottom: 60, height: 60, x: 0, y: 0, toJSON: () => ({}),
+    });
+    // v≈52 is within 6 of lo=50 — a knob drag, not a band drag.
+    track.dispatchEvent(pointerEvent('pointerdown', 52));
+    expect(onChange).toHaveBeenCalled();
+    const [nlo, nhi] = onChange.mock.calls[0];
+    expect(nhi).toBe(200);
+    expect(nlo).toBeGreaterThanOrEqual(50);
+  });
+});
diff --git a/frontend/src/components/annotate/InsightsModal.test.tsx b/frontend/src/components/annotate/InsightsModal.test.tsx
new file mode 100644
index 0000000..0f47ea5
--- /dev/null
+++ b/frontend/src/components/annotate/InsightsModal.test.tsx
@@ -0,0 +1,161 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import InsightsModal from './InsightsModal';
+import { useDatasetStore } from '@/stores/datasetStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useClassStore } from '@/stores/classStore';
+
+const SOURCE = 'local:sample.tif';
+
+const baseMeta = {
+  nSlices: 4,
+  height: 100,
+  width: 100,
+  dtype: 'uint8',
+  isRgb: false,
+  valueRange: [0, 255] as [number, number],
+};
+
+beforeEach(() => {
+  useAnnotationStore.getState().reset();
+  useClassStore.setState({ classes: [] });
+  useDatasetStore.setState({ meta: null, kind: null, source: null, serverUri: null } as any);
+});
+
+afterEach(() => {
+  cleanup();
+  vi.restoreAllMocks();
+});
+
+describe('InsightsModal', () => {
+  it('shows a placeholder when there is no open sample', () => {
+    render(<InsightsModal sourceKey={null} onClose={vi.fn()} onFocus={vi.fn()} />);
+    expect(screen.getByText(/Open a sample to see its dataset health/)).toBeInTheDocument();
+  });
+
+  it('shows a loading indicator, then resolves to stats', async () => {
+    useDatasetStore.setState({ meta: baseMeta } as any);
+    render(<InsightsModal sourceKey={SOURCE} onClose={vi.fn()} onFocus={vi.fn()} />);
+    expect(screen.getByText(/Analyzing sample/)).toBeInTheDocument();
+    await waitFor(() => {
+      expect(screen.getByText('Annotated slices')).toBeInTheDocument();
+    });
+  });
+
+  it('shows coverage tiles and "No classes defined" when there are no classes', async () => {
+    useDatasetStore.setState({ meta: baseMeta } as any);
+    render(<InsightsModal sourceKey={SOURCE} onClose={vi.fn()} onFocus={vi.fn()} />);
+    await waitFor(() => screen.getByText('Annotated slices'));
+    expect(screen.getByText('No classes defined.')).toBeInTheDocument();
+  });
+
+  it('shows "No issues found" when all slices are covered and clean', async () => {
+    useDatasetStore.setState({ meta: baseMeta } as any);
+    useAnnotationStore.setState({
+      negativeSlices: { [SOURCE]: ['0', '1', '2', '3'] },
+    });
+    render(<InsightsModal sourceKey={SOURCE} onClose={vi.fn()} onFocus={vi.fn()} />);
+    await waitFor(() => screen.getByText('Annotated slices'));
+    expect(screen.getByText('No issues found on this sample.')).toBeInTheDocument();
+  });
+
+  it('renders class balance rows with shape count and area', async () => {
+    useDatasetStore.setState({ meta: baseMeta } as any);
+    useClassStore.setState({
+      classes: [{ classId: 1, label: 'Pore', color: '#ff0000', isVisible: true }],
+    });
+    useAnnotationStore.setState({
+      byImage: {
+        [SOURCE]: {
+          '0': [{ id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 20, h: 20 } as any],
+        },
+      },
+    });
+    render(<InsightsModal sourceKey={SOURCE} onClose={vi.fn()} onFocus={vi.fn()} />);
+    await waitFor(() => screen.getByText('Pore'));
+    expect(screen.getByText(/1 · /)).toBeInTheDocument();
+  });
+
+  it('flags a sliver shape and clicking it calls onFocus with the slice/bbox and closes the modal', async () => {
+    useDatasetStore.setState({ meta: baseMeta } as any);
+    useClassStore.setState({
+      classes: [{ classId: 1, label: 'Pore', color: '#ff0000', isVisible: true }],
+    });
+    useAnnotationStore.setState({
+      byImage: {
+        [SOURCE]: {
+          '2': [{ id: 's1', classId: 1, kind: 'rectangle', x: 5, y: 5, w: 1, h: 1 } as any],
+        },
+      },
+    });
+    const onFocus = vi.fn();
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    render(<InsightsModal sourceKey={SOURCE} onClose={onClose} onFocus={onFocus} />);
+    await waitFor(() => screen.getByText(/Tiny regions/));
+    const flagButton = screen.getByText(/Tiny Pore region/).closest('button')!;
+    await user.click(flagButton);
+    expect(onFocus).toHaveBeenCalledWith(2, { x: 5, y: 5, w: 1, h: 1 });
+    expect(onClose).toHaveBeenCalledOnce();
+  });
+
+  it('flags a cross-class overlap between two classes on the same slice', async () => {
+    useDatasetStore.setState({ meta: baseMeta } as any);
+    useClassStore.setState({
+      classes: [
+        { classId: 1, label: 'Pore', color: '#ff0000', isVisible: true },
+        { classId: 2, label: 'Grain', color: '#00ff00', isVisible: true },
+      ],
+    });
+    useAnnotationStore.setState({
+      byImage: {
+        [SOURCE]: {
+          '0': [
+            { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 40, h: 40 } as any,
+            { id: 's2', classId: 2, kind: 'rectangle', x: 10, y: 10, w: 40, h: 40 } as any,
+          ],
+        },
+      },
+    });
+    render(<InsightsModal sourceKey={SOURCE} onClose={vi.fn()} onFocus={vi.fn()} />);
+    await waitFor(() => screen.getByText(/Cross-class overlaps/));
+    expect(screen.getByText(/Pore and Grain overlap/)).toBeInTheDocument();
+  });
+
+  it('shows session totals from all annotated samples', async () => {
+    useDatasetStore.setState({ meta: baseMeta } as any);
+    useAnnotationStore.setState({
+      byImage: {
+        [SOURCE]: { '0': [{ id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10 } as any] },
+        'local:other.tif': { '0': [{ id: 's2', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10 } as any] },
+      },
+    });
+    render(<InsightsModal sourceKey={SOURCE} onClose={vi.fn()} onFocus={vi.fn()} />);
+    await waitFor(() => screen.getByText('Samples this session'));
+    expect(screen.getByText('2')).toBeInTheDocument();
+    expect(screen.getByText('2 shapes')).toBeInTheDocument();
+  });
+
+  it('calls onClose when the header close button is clicked', async () => {
+    useDatasetStore.setState({ meta: baseMeta } as any);
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    render(<InsightsModal sourceKey={SOURCE} onClose={onClose} onFocus={vi.fn()} />);
+    await user.click(screen.getByRole('button', { name: '' }));
+    expect(onClose).toHaveBeenCalledOnce();
+  });
+
+  it('calls onClose when clicking the backdrop but not when clicking inside the dialog', async () => {
+    useDatasetStore.setState({ meta: baseMeta } as any);
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    render(<InsightsModal sourceKey={SOURCE} onClose={onClose} onFocus={vi.fn()} />);
+    await waitFor(() => screen.getByText('Dataset Insights'));
+    await user.click(screen.getByText('Dataset Insights'));
+    expect(onClose).not.toHaveBeenCalled();
+    const backdrop = screen.getByText('Dataset Insights').closest('.fixed')!;
+    await user.click(backdrop);
+    expect(onClose).toHaveBeenCalledOnce();
+  });
+});
diff --git a/frontend/src/components/annotate/LayersPanel/index.test.tsx b/frontend/src/components/annotate/LayersPanel/index.test.tsx
new file mode 100644
index 0000000..2a41684
--- /dev/null
+++ b/frontend/src/components/annotate/LayersPanel/index.test.tsx
@@ -0,0 +1,79 @@
+import { afterEach, beforeEach, describe, expect, it } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import LayersPanel from './index';
+import { useClassStore } from '@/stores/classStore';
+import { useLayerVisibilityStore } from '@/stores/layerVisibilityStore';
+
+const initialLayerState = useLayerVisibilityStore.getState();
+
+beforeEach(() => {
+  useLayerVisibilityStore.setState(initialLayerState, true);
+  useClassStore.setState({ classes: [] });
+});
+
+afterEach(() => {
+  cleanup();
+});
+
+describe('LayersPanel', () => {
+  it('disables groups whose data has not loaded', () => {
+    render(<LayersPanel />);
+    expect(screen.getByRole('button', { name: /Features/ })).toBeDisabled();
+    expect(screen.getByRole('button', { name: /Probability/ })).toBeDisabled();
+    expect(screen.getByRole('button', { name: /Predictions/ })).toBeDisabled();
+    expect(screen.getByRole('button', { name: /Suggest/ })).toBeDisabled();
+  });
+
+  it('enables a group once its data is present', () => {
+    render(<LayersPanel hasFeatures />);
+    expect(screen.getByRole('button', { name: /Features/ })).toBeEnabled();
+  });
+
+  it('toggling a group flips its visibility icon', async () => {
+    const user = userEvent.setup();
+    render(<LayersPanel />);
+    const imageToggle = screen.getByRole('button', { name: /Image/ });
+    expect(useLayerVisibilityStore.getState().groups.image).toBe(true);
+    await user.click(imageToggle);
+    expect(useLayerVisibilityStore.getState().groups.image).toBe(false);
+  });
+
+  it('annotations group disabled with no classes, shown once classes exist', () => {
+    render(<LayersPanel />);
+    expect(screen.getByRole('button', { name: /Annotations/ })).toBeDisabled();
+    expect(screen.queryByText('Human')).not.toBeInTheDocument();
+
+    cleanup();
+    useClassStore.setState({ classes: [{ classId: 1, label: 'Cell', color: '#f00', isVisible: true }] });
+    render(<LayersPanel />);
+    expect(screen.getByRole('button', { name: /Annotations/ })).toBeEnabled();
+    expect(screen.getByText('Human')).toBeInTheDocument();
+    expect(screen.getByText('Predicted')).toBeInTheDocument();
+  });
+
+  it('probability opacity slider only shows once proba is loaded and visible', () => {
+    render(<LayersPanel hasProba />);
+    expect(screen.getByText('Probability opacity')).toBeInTheDocument();
+  });
+
+  it('prediction controls list per-class visibility toggles', () => {
+    useClassStore.setState({
+      classes: [
+        { classId: 1, label: 'Cell', color: '#f00', isVisible: true },
+        { classId: 2, label: 'Wall', color: '#0f0', isVisible: true },
+      ],
+    });
+    render(<LayersPanel hasPredictions predictionClassIds={[1, 2]} />);
+    expect(screen.getByText('Cell')).toBeInTheDocument();
+    expect(screen.getByText('Wall')).toBeInTheDocument();
+  });
+
+  it('clicking a prediction class toggle flips its visibility', async () => {
+    useClassStore.setState({ classes: [{ classId: 1, label: 'Cell', color: '#f00', isVisible: true }] });
+    const user = userEvent.setup();
+    render(<LayersPanel hasPredictions predictionClassIds={[1]} />);
+    await user.click(screen.getByText('Cell'));
+    expect(useLayerVisibilityStore.getState().predictionClassVisible[1]).toBe(false);
+  });
+});
diff --git a/frontend/src/components/annotate/MaskToolsPanel/index.test.tsx b/frontend/src/components/annotate/MaskToolsPanel/index.test.tsx
new file mode 100644
index 0000000..ac60848
--- /dev/null
+++ b/frontend/src/components/annotate/MaskToolsPanel/index.test.tsx
@@ -0,0 +1,64 @@
+import { afterEach, beforeEach, describe, expect, it } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import MaskToolsPanel from './index';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useDatasetStore } from '@/stores/datasetStore';
+import { useClassStore } from '@/stores/classStore';
+
+const SOURCE = 'local:sample.tif';
+
+beforeEach(() => {
+  useAnnotationStore.getState().reset();
+  useClassStore.setState({ classes: [{ classId: 1, label: 'Cell', color: '#f00', isVisible: true }] });
+  useDatasetStore.setState({
+    meta: {
+      nSlices: 5, height: 32, width: 32, dtype: 'uint8', isRgb: false, valueRange: [0, 255],
+    },
+    currentSlice: 0,
+  } as any);
+});
+
+afterEach(() => {
+  cleanup();
+});
+
+describe('MaskToolsPanel', () => {
+  it('renders nothing for a single-slice (non-volume) dataset', () => {
+    useDatasetStore.setState({
+      meta: { nSlices: 1, height: 32, width: 32, dtype: 'uint8', isRgb: false, valueRange: [0, 255] },
+    } as any);
+    const { container } = render(<MaskToolsPanel sourceKey={SOURCE} activeClassId={1} />);
+    expect(container).toBeEmptyDOMElement();
+  });
+
+  it('shows the active class name and a disabled copy button with no shapes here', () => {
+    render(<MaskToolsPanel sourceKey={SOURCE} activeClassId={1} />);
+    expect(screen.getByText(/Acts on ‘Cell’/)).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /Copy/ })).toBeDisabled();
+  });
+
+  it('enables the copy button once the active class has a shape on the current slice', () => {
+    useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 4, h: 4 },
+    ]);
+    render(<MaskToolsPanel sourceKey={SOURCE} activeClassId={1} />);
+    expect(screen.getByRole('button', { name: /Copy/ })).toBeEnabled();
+  });
+
+  it('copying reports success and advances the shape to the next slice', async () => {
+    useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 4, h: 4 },
+    ]);
+    const user = userEvent.setup();
+    render(<MaskToolsPanel sourceKey={SOURCE} activeClassId={1} />);
+    await user.click(screen.getByRole('button', { name: /Copy/ }));
+    expect(await screen.findByText(/Copied ‘Cell’ to slice 2/)).toBeInTheDocument();
+    expect(useAnnotationStore.getState().byImage[SOURCE]?.['1']).toHaveLength(1);
+  });
+
+  it('disabled with no sourceKey', () => {
+    render(<MaskToolsPanel sourceKey={null} activeClassId={1} />);
+    expect(screen.getByRole('button', { name: /Copy/ })).toBeDisabled();
+  });
+});
diff --git a/frontend/src/components/annotate/MeasurementPanel/index.test.tsx b/frontend/src/components/annotate/MeasurementPanel/index.test.tsx
new file mode 100644
index 0000000..46c8ff5
--- /dev/null
+++ b/frontend/src/components/annotate/MeasurementPanel/index.test.tsx
@@ -0,0 +1,181 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import MeasurementPanel from './index';
+import { useDatasetStore } from '@/stores/datasetStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useToolStore } from '@/stores/toolStore';
+
+const SK = 'local:sample.tif';
+
+beforeEach(() => {
+  useAnnotationStore.getState().reset();
+  useToolStore.setState({ selectedShapeIds: [] });
+  useDatasetStore.setState({
+    meta: {
+      width: 100, height: 100, nSlices: 1, dtype: 'uint8', isRgb: false, valueRange: [0, 255],
+    },
+    currentSlice: 0,
+  } as any);
+  vi.stubGlobal('fetch', vi.fn().mockResolvedValue({
+    ok: true,
+    json: async () => ({ pixel_count: 25, min: 10, max: 200, mean: 105.5, std: 12.25 }),
+  }));
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('MeasurementPanel', () => {
+  it('shows a hint when nothing is selected', () => {
+    render(<MeasurementPanel sourceKey={SK} />);
+    expect(screen.getByText('Select one or more regions to measure.')).toBeInTheDocument();
+  });
+
+  it('shows a hint when there is no dataset meta', () => {
+    useDatasetStore.setState({ meta: null } as any);
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10,
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1'] });
+    render(<MeasurementPanel sourceKey={SK} />);
+    expect(screen.getByText('Select one or more regions to measure.')).toBeInTheDocument();
+  });
+
+  it('shows geometry stats (region count, area, centroid, bbox) in px units without calibration', async () => {
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 10, y: 10, w: 20, h: 20,
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1'] });
+    render(<MeasurementPanel sourceKey={SK} />);
+    expect(screen.getByText('Regions')).toBeInTheDocument();
+    expect(screen.getByText('1')).toBeInTheDocument();
+    expect(screen.getByText(/px²$/)).toBeInTheDocument();
+    expect(screen.getByText('Centroid')).toBeInTheDocument();
+    expect(screen.getByText('Bounds')).toBeInTheDocument();
+    await waitFor(() => expect(fetch).toHaveBeenCalled());
+  });
+
+  it('only measures shapes selected on the current slice', () => {
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10,
+    });
+    useAnnotationStore.getState().addShape(SK, 1, {
+      id: 's2', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10,
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1', 's2'] });
+    // currentSlice is 0, so only s1 should count.
+    render(<MeasurementPanel sourceKey={SK} />);
+    expect(screen.getByText('1')).toBeInTheDocument();
+  });
+
+  it('shows perimeter as — for a brush shape (undefined for that kind)', () => {
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 'b1', classId: 1, kind: 'brush', strokes: [{ points: [0, 0, 5, 5], radius: 3, mode: 'paint' }],
+    });
+    useToolStore.setState({ selectedShapeIds: ['b1'] });
+    render(<MeasurementPanel sourceKey={SK} />);
+    expect(screen.getByText('Perimeter')).toBeInTheDocument();
+    expect(screen.getByText('—')).toBeInTheDocument();
+  });
+
+  it('shows a computed perimeter for a rectangle', () => {
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 5,
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1'] });
+    render(<MeasurementPanel sourceKey={SK} />);
+    // perimeter = 2*(10+5) = 30
+    expect(screen.getByText('30.0 px')).toBeInTheDocument();
+  });
+
+  it('applies pixel-size calibration to area/perimeter/length units', async () => {
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 5,
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1'] });
+    const user = userEvent.setup();
+    render(<MeasurementPanel sourceKey={SK} />);
+    const pixelSizeInput = screen.getByPlaceholderText('—');
+    await user.type(pixelSizeInput, '2');
+    // perimeter 30px * 2 = 60, unit default µm
+    expect(await screen.findByText(/60\.00 µm$/)).toBeInTheDocument();
+  });
+
+  it('ignores an invalid (zero/negative/non-numeric) pixel size and stays in px units', async () => {
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 5,
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1'] });
+    const user = userEvent.setup();
+    render(<MeasurementPanel sourceKey={SK} />);
+    const pixelSizeInput = screen.getByPlaceholderText('—');
+    await user.type(pixelSizeInput, '0');
+    expect(screen.getByText('30.0 px')).toBeInTheDocument();
+  });
+
+  it('fetches intensity stats for the selection and renders them', async () => {
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10,
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1'] });
+    render(<MeasurementPanel sourceKey={SK} />);
+    expect(screen.getByText('Measuring…')).toBeInTheDocument();
+    await waitFor(() => expect(screen.getByText(/105\.5/)).toBeInTheDocument(), { timeout: 2000 });
+    expect(screen.getByText('10 / 200')).toBeInTheDocument();
+    expect(screen.getByText('25')).toBeInTheDocument();
+    const [url, init] = (fetch as any).mock.calls[0];
+    expect(url).toContain('/api/measure?source_key=');
+    expect(init.method).toBe('POST');
+    const body = JSON.parse(init.body);
+    expect(body.slice_index).toBe(0);
+  });
+
+  it('formats integer intensity values plainly and non-integers to 4 sig figs', async () => {
+    (fetch as any).mockResolvedValue({
+      ok: true,
+      json: async () => ({ pixel_count: 4, min: 0, max: 255, mean: 100, std: 12.3456 }),
+    });
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2,
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1'] });
+    render(<MeasurementPanel sourceKey={SK} />);
+    await waitFor(() => expect(screen.getByText(/100 ±/)).toBeInTheDocument(), { timeout: 2000 });
+    expect(screen.getByText('100 ± 12.35')).toBeInTheDocument();
+    expect(screen.getByText('0 / 255')).toBeInTheDocument();
+  });
+
+  it('shows — for intensity when the request fails', async () => {
+    (fetch as any).mockResolvedValue({ ok: false, json: async () => ({}) });
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2,
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1'] });
+    render(<MeasurementPanel sourceKey={SK} />);
+    await waitFor(() => expect(screen.queryByText('Measuring…')).not.toBeInTheDocument(), { timeout: 2000 });
+    expect(screen.getByText('—')).toBeInTheDocument();
+  });
+
+  it('shows — for intensity when the request throws', async () => {
+    (fetch as any).mockRejectedValue(new Error('network down'));
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2,
+    });
+    useToolStore.setState({ selectedShapeIds: ['s1'] });
+    render(<MeasurementPanel sourceKey={SK} />);
+    await waitFor(() => expect(screen.queryByText('Measuring…')).not.toBeInTheDocument(), { timeout: 2000 });
+    expect(screen.getByText('—')).toBeInTheDocument();
+  });
+
+  it('does not fetch intensity when sourceKey is null', () => {
+    useAnnotationStore.getState().addShape(SK, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2,
+    });
+    render(<MeasurementPanel sourceKey={null} />);
+    expect(fetch).not.toHaveBeenCalled();
+    expect(screen.getByText('Select one or more regions to measure.')).toBeInTheDocument();
+  });
+});
diff --git a/frontend/src/components/annotate/PerfOverlay/index.test.tsx b/frontend/src/components/annotate/PerfOverlay/index.test.tsx
new file mode 100644
index 0000000..fdd3290
--- /dev/null
+++ b/frontend/src/components/annotate/PerfOverlay/index.test.tsx
@@ -0,0 +1,63 @@
+import { afterEach, beforeEach, describe, expect, it } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import PerfOverlay from './index';
+import { initPerf, mark, resetPerf } from '@/lib/perf';
+
+beforeEach(() => {
+  window.localStorage.setItem('perf', '1');
+  initPerf();
+  resetPerf();
+});
+
+afterEach(() => {
+  cleanup();
+  window.localStorage.clear();
+  initPerf();
+  resetPerf();
+});
+
+describe('PerfOverlay', () => {
+  it('shows a placeholder when no samples have been recorded', () => {
+    render(<PerfOverlay shapeCount={0} />);
+    expect(screen.getByText('no samples yet — draw or zoom')).toBeInTheDocument();
+  });
+
+  it('shows the shape count', () => {
+    render(<PerfOverlay shapeCount={42} />);
+    expect(screen.getByText('42 shapes')).toBeInTheDocument();
+  });
+
+  it('renders a row per recorded label with p50/p95/count', () => {
+    mark('commit', 5);
+    mark('commit', 7);
+    render(<PerfOverlay shapeCount={0} />);
+    expect(screen.getByText('commit')).toBeInTheDocument();
+    const row = screen.getByText('commit').closest('tr')!;
+    expect(row).toHaveTextContent('2'); // count column
+  });
+
+  it('colors a slow p95 red and a fast one green', () => {
+    mark('clip', 60);
+    render(<PerfOverlay shapeCount={0} />);
+    const row = screen.getByText('clip').closest('tr')!;
+    const cells = row.querySelectorAll('td');
+    expect(cells[2]).toHaveClass('text-red-400');
+  });
+
+  it('reset button clears samples back to the placeholder', async () => {
+    mark('commit', 5);
+    const user = userEvent.setup();
+    render(<PerfOverlay shapeCount={0} />);
+    expect(screen.queryByText('no samples yet — draw or zoom')).not.toBeInTheDocument();
+    await user.click(screen.getByTitle('Reset samples'));
+    expect(await screen.findByText('no samples yet — draw or zoom')).toBeInTheDocument();
+  });
+
+  it('hide button removes the overlay entirely', async () => {
+    const user = userEvent.setup();
+    const { container } = render(<PerfOverlay shapeCount={0} />);
+    await user.click(screen.getByTitle('Hide (reload to show again)'));
+    expect(container).toBeEmptyDOMElement();
+  });
+});
diff --git a/frontend/src/components/annotate/PixelClassifierPanel/index.test.tsx b/frontend/src/components/annotate/PixelClassifierPanel/index.test.tsx
new file mode 100644
index 0000000..59dca90
--- /dev/null
+++ b/frontend/src/components/annotate/PixelClassifierPanel/index.test.tsx
@@ -0,0 +1,672 @@
+/**
+ * PixelClassifierPanel — fully prop-driven (no internal store/hook access), so
+ * tests construct prop objects directly rather than mocking usePixelClassifier
+ * or @/lib/ipredApi. Covers: idle/canTrain gating, busy states during train/
+ * multi-slice train/predict, model summary + feature importances, class-
+ * probability cycling, commit/dismiss, volume-apply flow (apply -> progress ->
+ * result -> commit/dismiss), predicted-pointer "make editable" banner, push-to-
+ * Tiled + view-in-3D, train-deep-model hand-off, error surfacing, and the
+ * Suggest-labels (manifold) sub-panel (collapsed by default; opened via click).
+ */
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, fireEvent, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import PixelClassifierPanel, { type PixelClassifierPanelProps } from './index';
+import type { ClfTrainResult, ClfPredictCounts } from '@/hooks/usePixelClassifier';
+import type { ManifoldParams } from '@/hooks/useFeatureManifold';
+
+afterEach(() => {
+  cleanup();
+});
+
+const BASE_PARAMS = { iterations: 200, depth: 6, learningRate: 0.1, alpha: 0.05 };
+
+const BASE_MANIFOLD_PARAMS: ManifoldParams = { k: 24, boxSize: 64 } as ManifoldParams;
+
+function makeModel(overrides: Partial<ClfTrainResult> = {}): ClfTrainResult {
+  return {
+    modelId: 'model-1',
+    featureId: 'feat-1',
+    nSamples: 100,
+    nTrain: 80,
+    nCal: 20,
+    classIds: [1, 2],
+    trainAccuracy: 0.912,
+    params: BASE_PARAMS,
+    nTrees: 200,
+    usesSam: false,
+    featureImportances: [
+      { label: 'intensity', importance: 5.5 },
+      { label: 'edges', importance: 2.1 },
+    ],
+    trainerId: 'catboost',
+    compositionId: 'comp-1',
+    ...overrides,
+  };
+}
+
+function makeCounts(overrides: Partial<ClfPredictCounts> = {}): ClfPredictCounts {
+  return { singleton: 700, multi: 200, abstain: 100, ...overrides };
+}
+
+function baseProps(overrides: Partial<PixelClassifierPanelProps> = {}): PixelClassifierPanelProps {
+  return {
+    hasFeatureJob: true,
+    canAutoPreprocess: false,
+    hasShapes: true,
+    training: false,
+    predicting: false,
+    params: BASE_PARAMS,
+    onParamsChange: vi.fn(),
+    model: null,
+    hasPrediction: false,
+    predictCounts: null,
+    error: null,
+    onTrain: vi.fn(),
+    onPredict: vi.fn(),
+    onCommit: vi.fn(),
+    onDismiss: vi.fn(),
+    annotatedSliceCount: 1,
+    trainAcrossSlices: false,
+    onTrainAcrossSlicesChange: vi.fn(),
+    multiTraining: false,
+    multiTrainProgress: null,
+    totalSliceCount: 1,
+    commitClassIds: [],
+    onToggleCommitClassId: vi.fn(),
+    volumeApplying: false,
+    volumeApplyProgress: null,
+    volumeApplyResult: null,
+    onApplyToVolume: vi.fn(),
+    onCommitVolumeApply: vi.fn(),
+    onCancelVolumeApply: vi.fn(),
+    onDismissVolumeApply: vi.fn(),
+    hasPredictedPointerOnCurrentSlice: false,
+    vectorizingSlice: false,
+    onMakeSliceEditable: vi.fn(),
+    hasAnyPredictedPointers: false,
+    manifoldParams: BASE_MANIFOLD_PARAMS,
+    onManifoldParamsChange: vi.fn(),
+    manifoldSampling: false,
+    manifoldHasSample: false,
+    manifoldShowHeatmap: false,
+    onManifoldShowHeatmapChange: vi.fn(),
+    manifoldShowMarkers: false,
+    onManifoldShowMarkersChange: vi.fn(),
+    manifoldHeatmapOpacity: 0.5,
+    onManifoldHeatmapOpacityChange: vi.fn(),
+    manifoldMeta: null,
+    manifoldError: null,
+    onManifoldSample: vi.fn(),
+    onManifoldDismiss: vi.fn(),
+    manifoldRoiShapeCount: 0,
+    canCaptureManifoldRoi: false,
+    onCaptureManifoldRoi: vi.fn(),
+    onClearManifoldRoi: vi.fn(),
+    ...overrides,
+  };
+}
+
+describe('PixelClassifierPanel', () => {
+  describe('idle / train gating', () => {
+    it('enables Train when features exist and shapes are annotated', () => {
+      render(<PixelClassifierPanel {...baseProps()} />);
+      expect(screen.getByRole('button', { name: /train classifier/i })).toBeEnabled();
+      // Predict is disabled with no model yet.
+      expect(screen.getByRole('button', { name: /^predict$/i })).toBeDisabled();
+    });
+
+    it('disables Train when there are no shapes and no auto-preprocess', () => {
+      render(<PixelClassifierPanel {...baseProps({ hasShapes: false, hasFeatureJob: false })} />);
+      expect(screen.getByRole('button', { name: /train classifier/i })).toBeDisabled();
+    });
+
+    it('allows training with no feature job when canAutoPreprocess is set, and notes it will compute features', () => {
+      render(
+        <PixelClassifierPanel
+          {...baseProps({ hasFeatureJob: false, canAutoPreprocess: true, hasShapes: true })}
+        />,
+      );
+      expect(screen.getByRole('button', { name: /train classifier/i })).toBeEnabled();
+      expect(screen.getByText(/Train will compute features/i)).toBeInTheDocument();
+    });
+
+    it('calls onTrain when the train button is clicked', async () => {
+      const user = userEvent.setup();
+      const onTrain = vi.fn();
+      render(<PixelClassifierPanel {...baseProps({ onTrain })} />);
+      await user.click(screen.getByRole('button', { name: /train classifier/i }));
+      expect(onTrain).toHaveBeenCalledTimes(1);
+    });
+
+    it('shows a training busy bar while training', () => {
+      render(<PixelClassifierPanel {...baseProps({ training: true })} />);
+      expect(screen.getByText(/Training…/i)).toBeInTheDocument();
+      expect(screen.getByRole('button', { name: /train classifier/i })).toBeDisabled();
+    });
+
+    it('updates iteration/depth/learning-rate params via onParamsChange', () => {
+      const onParamsChange = vi.fn();
+      render(<PixelClassifierPanel {...baseProps({ onParamsChange })} />);
+      const trees = screen.getByLabelText(/trees/i);
+      fireEvent.change(trees, { target: { value: '300' } });
+      expect(onParamsChange).toHaveBeenCalledTimes(1);
+      expect(onParamsChange.mock.calls[0][0].iterations).toBe(300);
+    });
+  });
+
+  describe('train across slices', () => {
+    it('disables the checkbox when only one slice is annotated', () => {
+      render(<PixelClassifierPanel {...baseProps({ annotatedSliceCount: 1 })} />);
+      expect(screen.getByRole('checkbox', { name: /train across all annotated slices/i })).toBeDisabled();
+    });
+
+    it('enables the checkbox with multiple annotated slices and toggles it', async () => {
+      const user = userEvent.setup();
+      const onTrainAcrossSlicesChange = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({ annotatedSliceCount: 3, onTrainAcrossSlicesChange })}
+        />,
+      );
+      const checkbox = screen.getByRole('checkbox', { name: /train across all annotated slices \(3\)/i });
+      expect(checkbox).toBeEnabled();
+      await user.click(checkbox);
+      expect(onTrainAcrossSlicesChange).toHaveBeenCalledWith(true);
+    });
+
+    it('label switches to "Train across slices" and gates on annotatedSliceCount only when trainAcrossSlices is set', () => {
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            annotatedSliceCount: 2,
+            trainAcrossSlices: true,
+            hasShapes: false,
+            hasFeatureJob: false,
+          })}
+        />,
+      );
+      expect(screen.getByRole('button', { name: /train across slices/i })).toBeEnabled();
+    });
+
+    it('shows multi-train job progress bar with done/total', () => {
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            multiTraining: true,
+            multiTrainProgress: { done: 2, total: 5 },
+          })}
+        />,
+      );
+      expect(screen.getByText(/Training across slices…/i)).toBeInTheDocument();
+      expect(screen.getByText('2/5')).toBeInTheDocument();
+    });
+  });
+
+  describe('predict', () => {
+    it('enables Predict once a model exists and calls onPredict', async () => {
+      const user = userEvent.setup();
+      const onPredict = vi.fn();
+      render(<PixelClassifierPanel {...baseProps({ model: makeModel(), onPredict })} />);
+      const btn = screen.getByRole('button', { name: /^predict$/i });
+      expect(btn).toBeEnabled();
+      await user.click(btn);
+      expect(onPredict).toHaveBeenCalledTimes(1);
+    });
+
+    it('shows a predicting busy bar', () => {
+      render(<PixelClassifierPanel {...baseProps({ model: makeModel(), predicting: true })} />);
+      expect(screen.getByText(/Predicting…/i)).toBeInTheDocument();
+    });
+  });
+
+  describe('model summary', () => {
+    it('renders accuracy, sample counts, trees, classes, and feature importances', () => {
+      render(<PixelClassifierPanel {...baseProps({ model: makeModel() })} />);
+      expect(screen.getByText(/Acc 91\.2%/)).toBeInTheDocument();
+      expect(screen.getByText(/train 80 \/ cal 20/)).toBeInTheDocument();
+      expect(screen.getByText(/200 trees/)).toBeInTheDocument();
+      expect(screen.getByText(/classes 1, 2/)).toBeInTheDocument();
+      expect(screen.getByText('intensity')).toBeInTheDocument();
+      expect(screen.getByText('edges')).toBeInTheDocument();
+    });
+
+    it('shows the SlimSAM suffix when usesSam is true', () => {
+      render(<PixelClassifierPanel {...baseProps({ model: makeModel({ usesSam: true }) })} />);
+      expect(screen.getByText(/\+SlimSAM/)).toBeInTheDocument();
+    });
+  });
+
+  describe('commit classes + apply across volume', () => {
+    it('toggles commit-class chips via onToggleCommitClassId', async () => {
+      const user = userEvent.setup();
+      const onToggleCommitClassId = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({ model: makeModel(), onToggleCommitClassId, commitClassIds: [1] })}
+        />,
+      );
+      await user.click(screen.getByText('class 1'));
+      expect(onToggleCommitClassId).toHaveBeenCalledWith(1);
+      await user.click(screen.getByText('class 2'));
+      expect(onToggleCommitClassId).toHaveBeenCalledWith(2);
+    });
+
+    it('disables Apply across volume with a single slice or no classes selected', () => {
+      const { rerender } = render(
+        <PixelClassifierPanel
+          {...baseProps({ model: makeModel(), totalSliceCount: 1, commitClassIds: [1] })}
+        />,
+      );
+      expect(screen.getByRole('button', { name: /apply across volume/i })).toBeDisabled();
+
+      rerender(
+        <PixelClassifierPanel
+          {...baseProps({ model: makeModel(), totalSliceCount: 5, commitClassIds: [] })}
+        />,
+      );
+      expect(screen.getByRole('button', { name: /apply across volume/i })).toBeDisabled();
+    });
+
+    it('enables Apply across volume with multiple slices and at least one selected class, and calls onApplyToVolume', async () => {
+      const user = userEvent.setup();
+      const onApplyToVolume = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            totalSliceCount: 10,
+            commitClassIds: [1],
+            onApplyToVolume,
+          })}
+        />,
+      );
+      const btn = screen.getByRole('button', { name: /apply across volume \(10 slices\)/i });
+      expect(btn).toBeEnabled();
+      await user.click(btn);
+      expect(onApplyToVolume).toHaveBeenCalledTimes(1);
+    });
+
+    it('shows volume-apply progress and a cancel button while running', async () => {
+      const user = userEvent.setup();
+      const onCancelVolumeApply = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            volumeApplying: true,
+            volumeApplyProgress: { done: 3, total: 10 },
+            onCancelVolumeApply,
+          })}
+        />,
+      );
+      expect(screen.getByText(/Applying across volume…/i)).toBeInTheDocument();
+      expect(screen.getByText('3/10')).toBeInTheDocument();
+      await user.click(screen.getByRole('button', { name: /cancel/i }));
+      expect(onCancelVolumeApply).toHaveBeenCalledTimes(1);
+    });
+
+    it('shows the volume-apply result with commit/dismiss actions', async () => {
+      const user = userEvent.setup();
+      const onCommitVolumeApply = vi.fn();
+      const onDismissVolumeApply = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            commitClassIds: [1, 2],
+            volumeApplyResult: { runCount: 8, errorCount: 2, cancelled: false },
+            onCommitVolumeApply,
+            onDismissVolumeApply,
+          })}
+        />,
+      );
+      expect(screen.getByText(/Predicted 8 slice\(s\), 2 failed\./)).toBeInTheDocument();
+      const commitBtn = screen.getByRole('button', { name: /Commit predicted shapes \(2 classes\)/i });
+      expect(commitBtn).toBeEnabled();
+      await user.click(commitBtn);
+      expect(onCommitVolumeApply).toHaveBeenCalledTimes(1);
+      await user.click(screen.getByRole('button', { name: /^dismiss$/i }));
+      expect(onDismissVolumeApply).toHaveBeenCalledTimes(1);
+    });
+
+    it('disables commit-predicted-shapes when runCount is 0, and shows cancelled prefix', () => {
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            commitClassIds: [1],
+            volumeApplyResult: { runCount: 0, errorCount: 0, cancelled: true },
+          })}
+        />,
+      );
+      expect(screen.getByText(/^Cancelled — Predicted 0 slice\(s\)\./)).toBeInTheDocument();
+      expect(screen.getByRole('button', { name: /Commit predicted shapes \(1 class\)/i })).toBeDisabled();
+    });
+  });
+
+  describe('predicted pointer / make editable', () => {
+    it('shows the "make this slice editable" banner and calls onMakeSliceEditable', async () => {
+      const user = userEvent.setup();
+      const onMakeSliceEditable = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            hasPredictedPointerOnCurrentSlice: true,
+            onMakeSliceEditable,
+          })}
+        />,
+      );
+      const btn = screen.getByRole('button', { name: /make this slice editable/i });
+      await user.click(btn);
+      expect(onMakeSliceEditable).toHaveBeenCalledTimes(1);
+    });
+
+    it('shows "Vectorizing…" and disables the button while vectorizingSlice is true', () => {
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            hasPredictedPointerOnCurrentSlice: true,
+            vectorizingSlice: true,
+          })}
+        />,
+      );
+      const btn = screen.getByRole('button', { name: /vectorizing…/i });
+      expect(btn).toBeDisabled();
+    });
+  });
+
+  describe('push to Tiled / view in 3D / train deep model', () => {
+    it('does not render the sync row without onSyncToTiled', () => {
+      render(<PixelClassifierPanel {...baseProps({ model: makeModel(), annotatedSliceCount: 1 })} />);
+      expect(screen.queryByRole('button', { name: /push to tiled/i })).not.toBeInTheDocument();
+    });
+
+    it('renders push-to-Tiled and view-in-3D and calls their handlers', async () => {
+      const user = userEvent.setup();
+      const onSyncToTiled = vi.fn();
+      const onViewIn3D = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            annotatedSliceCount: 1,
+            onSyncToTiled,
+            onViewIn3D,
+          })}
+        />,
+      );
+      await user.click(screen.getByRole('button', { name: /push to tiled/i }));
+      expect(onSyncToTiled).toHaveBeenCalledTimes(1);
+      await user.click(screen.getByRole('button', { name: /view in 3d/i }));
+      expect(onViewIn3D).toHaveBeenCalledTimes(1);
+    });
+
+    it('shows the syncing state, success message, and error message', () => {
+      const { rerender } = render(
+        <PixelClassifierPanel
+          {...baseProps({ model: makeModel(), annotatedSliceCount: 1, onSyncToTiled: vi.fn(), syncingToTiled: true })}
+        />,
+      );
+      expect(screen.getByRole('button', { name: /pushing…/i })).toBeDisabled();
+
+      rerender(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            annotatedSliceCount: 1,
+            onSyncToTiled: vi.fn(),
+            syncedToTiled: true,
+          })}
+        />,
+      );
+      expect(screen.getByText(/pushed to tiled/i)).toBeInTheDocument();
+
+      rerender(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            annotatedSliceCount: 1,
+            onSyncToTiled: vi.fn(),
+            syncToTiledError: 'network failed',
+          })}
+        />,
+      );
+      expect(screen.getByText('network failed')).toBeInTheDocument();
+    });
+
+    it('renders the sync row when there are only predicted pointers (no real shapes yet)', () => {
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            annotatedSliceCount: 0,
+            hasAnyPredictedPointers: true,
+            onSyncToTiled: vi.fn(),
+          })}
+        />,
+      );
+      expect(screen.getByRole('button', { name: /push to tiled/i })).toBeInTheDocument();
+    });
+
+    it('renders "Train a deep model on this" and calls onTrainDeepModel', async () => {
+      const user = userEvent.setup();
+      const onTrainDeepModel = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({ model: makeModel(), annotatedSliceCount: 2, onTrainDeepModel })}
+        />,
+      );
+      await user.click(screen.getByRole('button', { name: /train a deep model on this/i }));
+      expect(onTrainDeepModel).toHaveBeenCalledTimes(1);
+    });
+  });
+
+  describe('prediction results: coverage headline, class probability, commit/dismiss', () => {
+    it('shows a "good" coverage headline when confident percentage is high', () => {
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            hasPrediction: true,
+            predictCounts: makeCounts({ singleton: 900, multi: 50, abstain: 50 }),
+          })}
+        />,
+      );
+      expect(screen.getByText(/90% of pixels are confidently labeled at 95% coverage\./)).toBeInTheDocument();
+    });
+
+    it('shows a "warn" coverage headline when confident percentage is low', () => {
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            hasPrediction: true,
+            predictCounts: makeCounts({ singleton: 100, multi: 400, abstain: 500 }),
+          })}
+        />,
+      );
+      expect(screen.getByText(/Only 10% confidently labeled/)).toBeInTheDocument();
+    });
+
+    it('renders the class-probability cycler and calls onCycleProbaClass / onProbaThresholdChange', async () => {
+      const user = userEvent.setup();
+      const onCycleProbaClass = vi.fn();
+      const onProbaThresholdChange = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            hasPrediction: true,
+            predictCounts: makeCounts(),
+            activeProbaClassId: 1,
+            activeProbaThreshold: 0.5,
+            classLabelForId: (id) => (id === 1 ? 'Cell' : 'Background'),
+            onCycleProbaClass,
+            onProbaThresholdChange,
+          })}
+        />,
+      );
+      expect(screen.getByTitle('Cell')).toBeInTheDocument();
+      await user.click(screen.getByRole('button', { name: /next class/i }));
+      expect(onCycleProbaClass).toHaveBeenCalledWith(1);
+      await user.click(screen.getByRole('button', { name: /previous class/i }));
+      expect(onCycleProbaClass).toHaveBeenCalledWith(-1);
+      expect(onProbaThresholdChange).not.toHaveBeenCalled();
+    });
+
+    it('does not render the class-probability cycler without the cycle/threshold callbacks', () => {
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            hasPrediction: true,
+            predictCounts: makeCounts(),
+          })}
+        />,
+      );
+      expect(screen.queryByText(/class probability/i)).not.toBeInTheDocument();
+    });
+
+    it('renders commit and dismiss buttons, using a custom commitLabel, and calls their handlers', async () => {
+      const user = userEvent.setup();
+      const onCommit = vi.fn();
+      const onDismiss = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            model: makeModel(),
+            hasPrediction: true,
+            predictCounts: makeCounts(),
+            commitLabel: 'Commit my classes',
+            onCommit,
+            onDismiss,
+          })}
+        />,
+      );
+      await user.click(screen.getByRole('button', { name: /commit my classes/i }));
+      expect(onCommit).toHaveBeenCalledTimes(1);
+      await user.click(screen.getByRole('button', { name: /^dismiss$/i }));
+      expect(onDismiss).toHaveBeenCalledTimes(1);
+    });
+  });
+
+  describe('error state', () => {
+    it('renders the top-level error message', () => {
+      render(<PixelClassifierPanel {...baseProps({ error: 'Feature bank missing.' })} />);
+      expect(screen.getByText('Feature bank missing.')).toBeInTheDocument();
+    });
+  });
+
+  describe('suggest labels (manifold)', () => {
+    it('is collapsed by default and opens on click', async () => {
+      const user = userEvent.setup();
+      render(<PixelClassifierPanel {...baseProps()} />);
+      expect(screen.queryByText(/suggest regions to label/i)).not.toBeInTheDocument();
+      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
+      expect(screen.getByText(/suggest regions to label/i)).toBeInTheDocument();
+    });
+
+    it('is disabled (non-clickable) when there is no feature job', async () => {
+      const user = userEvent.setup();
+      render(<PixelClassifierPanel {...baseProps({ hasFeatureJob: false })} />);
+      const header = screen.getByRole('button', { name: /suggest labels/i });
+      expect(header).toBeDisabled();
+      await user.click(header);
+      expect(screen.queryByText(/suggest regions to label/i)).not.toBeInTheDocument();
+    });
+
+    it('calls onManifoldSample and shows a sampling busy bar', async () => {
+      const user = userEvent.setup();
+      const onManifoldSample = vi.fn();
+      const { rerender } = render(
+        <PixelClassifierPanel {...baseProps({ onManifoldSample })} />,
+      );
+      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
+      const sampleBtn = screen.getByRole('button', { name: /suggest regions to label/i });
+      await user.click(sampleBtn);
+      expect(onManifoldSample).toHaveBeenCalledTimes(1);
+
+      rerender(<PixelClassifierPanel {...baseProps({ onManifoldSample, manifoldSampling: true })} />);
+      expect(screen.getByText(/sampling manifold coverage…/i)).toBeInTheDocument();
+      expect(screen.getByRole('button', { name: /sampling…/i })).toBeDisabled();
+    });
+
+    it('shows manifold meta summary once sampled', async () => {
+      const user = userEvent.setup();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            manifoldMeta: { nPicked: 12, nSubsample: 5000, explainedVariance: 0.87 },
+          })}
+        />,
+      );
+      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
+      expect(screen.getByText(/12 boxes from 5,000 px · 87% variance explained/)).toBeInTheDocument();
+    });
+
+    it('shows heatmap/marker toggles and opacity slider once a sample exists, and dismiss clears it', async () => {
+      const user = userEvent.setup();
+      const onManifoldShowHeatmapChange = vi.fn();
+      const onManifoldShowMarkersChange = vi.fn();
+      const onManifoldDismiss = vi.fn();
+      render(
+        <PixelClassifierPanel
+          {...baseProps({
+            manifoldHasSample: true,
+            onManifoldShowHeatmapChange,
+            onManifoldShowMarkersChange,
+            onManifoldDismiss,
+          })}
+        />,
+      );
+      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
+      await user.click(screen.getByRole('checkbox', { name: /show coverage heatmap/i }));
+      expect(onManifoldShowHeatmapChange).toHaveBeenCalledWith(true);
+      await user.click(screen.getByRole('checkbox', { name: /show suggested boxes/i }));
+      expect(onManifoldShowMarkersChange).toHaveBeenCalledWith(true);
+      await user.click(screen.getByRole('button', { name: /dismiss suggestions/i }));
+      expect(onManifoldDismiss).toHaveBeenCalledTimes(1);
+    });
+
+    it('restrict-to-selection button is disabled without a capturable ROI, and enabled+clearable with one', async () => {
+      const user = userEvent.setup();
+      const onCaptureManifoldRoi = vi.fn();
+      const onClearManifoldRoi = vi.fn();
+      const { rerender } = render(
+        <PixelClassifierPanel
+          {...baseProps({ canCaptureManifoldRoi: false, manifoldRoiShapeCount: 0 })}
+        />,
+      );
+      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
+      expect(screen.getByRole('button', { name: /restrict to selection \(0\)/i })).toBeDisabled();
+      expect(screen.queryByRole('button', { name: /^clear$/i })).not.toBeInTheDocument();
+
+      rerender(
+        <PixelClassifierPanel
+          {...baseProps({
+            canCaptureManifoldRoi: true,
+            manifoldRoiShapeCount: 2,
+            onCaptureManifoldRoi,
+            onClearManifoldRoi,
+          })}
+        />,
+      );
+      const restrictBtn = screen.getByRole('button', { name: /restrict to selection \(2\)/i });
+      expect(restrictBtn).toBeEnabled();
+      await user.click(restrictBtn);
+      expect(onCaptureManifoldRoi).toHaveBeenCalledTimes(1);
+      await user.click(screen.getByRole('button', { name: /^clear$/i }));
+      expect(onClearManifoldRoi).toHaveBeenCalledTimes(1);
+    });
+
+    it('shows a manifold error message', async () => {
+      const user = userEvent.setup();
+      render(<PixelClassifierPanel {...baseProps({ manifoldError: 'Sampling failed.' })} />);
+      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
+      expect(screen.getByText('Sampling failed.')).toBeInTheDocument();
+    });
+  });
+});
diff --git a/frontend/src/components/annotate/SaveModal.test.tsx b/frontend/src/components/annotate/SaveModal.test.tsx
new file mode 100644
index 0000000..89f9e1d
--- /dev/null
+++ b/frontend/src/components/annotate/SaveModal.test.tsx
@@ -0,0 +1,144 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import SaveModal, { type SaveModalProps } from './SaveModal';
+import { useSettingsStore } from '@/stores/settingsStore';
+import type { SaveDraftPayload } from '@/hooks/useSave';
+
+type OnSaveArg = Parameters<SaveModalProps['onSave']>[0];
+
+const basePayload: SaveDraftPayload = {
+  classes: [],
+  slices: {},
+  split_by_slice: {},
+  negative_slices: [],
+};
+
+function baseProps(overrides: Partial<Parameters<typeof SaveModal>[0]> = {}) {
+  return {
+    sourceKey: 'local:sample.tif',
+    payload: basePayload,
+    shapeCount: 3,
+    classCount: 2,
+    isSaving: false,
+    onSave: vi.fn(async () => {}),
+    onClose: vi.fn(),
+    ...overrides,
+  };
+}
+
+beforeEach(() => {
+  useSettingsStore.setState({ annotatorName: '' });
+  localStorage.clear();
+  global.fetch = vi.fn(async () => ({
+    ok: true,
+    blob: async () => new Blob(['fake-png'], { type: 'image/png' }),
+  })) as unknown as typeof fetch;
+  // jsdom doesn't implement these — stub them so the preview pipeline doesn't throw.
+  global.URL.createObjectURL = vi.fn(() => 'blob:preview-url');
+  global.URL.revokeObjectURL = vi.fn();
+});
+
+afterEach(() => {
+  cleanup();
+  vi.restoreAllMocks();
+});
+
+describe('SaveModal', () => {
+  it('shows a loading state then the fetched preview thumbnail', async () => {
+    render(<SaveModal {...baseProps()} />);
+    expect(screen.getByText(/Generating preview/)).toBeInTheDocument();
+    await waitFor(() => {
+      expect(screen.getByAltText('Annotation preview')).toBeInTheDocument();
+    });
+    expect(screen.getByAltText('Annotation preview')).toHaveAttribute('src', 'blob:preview-url');
+  });
+
+  it('shows preview-unavailable when the fetch fails', async () => {
+    global.fetch = vi.fn(async () => ({ ok: false })) as unknown as typeof fetch;
+    render(<SaveModal {...baseProps()} />);
+    await waitFor(() => {
+      expect(screen.getByText('Preview unavailable')).toBeInTheDocument();
+    });
+  });
+
+  it('displays shape and class counts, singular vs plural', async () => {
+    render(<SaveModal {...baseProps({ shapeCount: 1, classCount: 1 })} />);
+    await waitFor(() => screen.getByAltText('Annotation preview'));
+    expect(screen.getByText('1 shape · 1 class')).toBeInTheDocument();
+  });
+
+  it('displays plural shape/class counts', async () => {
+    render(<SaveModal {...baseProps({ shapeCount: 3, classCount: 2 })} />);
+    await waitFor(() => screen.getByAltText('Annotation preview'));
+    expect(screen.getByText('3 shapes · 2 classes')).toBeInTheDocument();
+  });
+
+  it('prefills annotator name from the settings store', async () => {
+    useSettingsStore.setState({ annotatorName: 'Ada' });
+    render(<SaveModal {...baseProps()} />);
+    expect(screen.getByLabelText('Who annotated this')).toHaveValue('Ada');
+    await waitFor(() => screen.getByAltText('Annotation preview'));
+  });
+
+  it('falls back to the legacy localStorage key when the store is empty', async () => {
+    localStorage.setItem('sam3_annotator_name', 'Legacy Name');
+    render(<SaveModal {...baseProps()} />);
+    expect(screen.getByLabelText('Who annotated this')).toHaveValue('Legacy Name');
+    await waitFor(() => screen.getByAltText('Annotation preview'));
+  });
+
+  it('calls onClose when Cancel is clicked', async () => {
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    render(<SaveModal {...baseProps({ onClose })} />);
+    await user.click(screen.getByRole('button', { name: 'Cancel' }));
+    expect(onClose).toHaveBeenCalledOnce();
+  });
+
+  it('calls onClose when the X button is clicked', async () => {
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    render(<SaveModal {...baseProps({ onClose })} />);
+    const buttons = screen.getAllByRole('button');
+    // The X close button is the first button in the header (no accessible name).
+    await user.click(buttons[0]);
+    expect(onClose).toHaveBeenCalledOnce();
+  });
+
+  it('submits with trimmed annotator name, notes, and base64 thumbnail; persists name to the store', async () => {
+    const onSave = vi.fn(async (_opts: OnSaveArg) => {});
+    const user = userEvent.setup();
+    render(<SaveModal {...baseProps({ onSave })} />);
+    await waitFor(() => screen.getByAltText('Annotation preview'));
+
+    await user.type(screen.getByLabelText('Who annotated this'), '  Ada Lovelace  ');
+    await user.type(screen.getByLabelText('Notes'), '  looks good  ');
+    await user.click(screen.getByRole('button', { name: /Save version/ }));
+
+    await waitFor(() => expect(onSave).toHaveBeenCalledTimes(1));
+    const arg = onSave.mock.calls[0]![0];
+    expect(arg.annotatedBy).toBe('Ada Lovelace');
+    expect(arg.notes).toBe('looks good');
+    expect(typeof arg.thumbnailBase64).toBe('string');
+    expect(useSettingsStore.getState().annotatorName).toBe('Ada Lovelace');
+  });
+
+  it('submits without a thumbnail when the preview fetch failed', async () => {
+    global.fetch = vi.fn(async () => ({ ok: false })) as unknown as typeof fetch;
+    const onSave = vi.fn(async (_opts: OnSaveArg) => {});
+    const user = userEvent.setup();
+    render(<SaveModal {...baseProps({ onSave })} />);
+    await waitFor(() => screen.getByText('Preview unavailable'));
+    await user.click(screen.getByRole('button', { name: /Save version/ }));
+    await waitFor(() => expect(onSave).toHaveBeenCalledTimes(1));
+    expect(onSave.mock.calls[0]![0].thumbnailBase64).toBeUndefined();
+  });
+
+  it('disables Cancel/X/Submit and shows Saving state while isSaving is true', async () => {
+    render(<SaveModal {...baseProps({ isSaving: true })} />);
+    expect(screen.getByRole('button', { name: 'Cancel' })).toBeDisabled();
+    expect(screen.getByRole('button', { name: /Saving/ })).toBeDisabled();
+    await waitFor(() => screen.getByAltText('Annotation preview'));
+  });
+});
diff --git a/frontend/src/components/annotate/SliceNavigator/index.test.tsx b/frontend/src/components/annotate/SliceNavigator/index.test.tsx
new file mode 100644
index 0000000..30b27a8
--- /dev/null
+++ b/frontend/src/components/annotate/SliceNavigator/index.test.tsx
@@ -0,0 +1,93 @@
+import { afterEach, beforeEach, describe, expect, it } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import SliceNavigator from './index';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useDatasetStore } from '@/stores/datasetStore';
+
+const SOURCE_KEY = 'local:sample.tif';
+
+function setLoadedDataset(overrides: Partial<ReturnType<typeof useDatasetStore.getState>> = {}) {
+  useDatasetStore.setState({
+    meta: {
+      nSlices: 10, height: 32, width: 32, dtype: 'uint8', isRgb: false, valueRange: [0, 255],
+    },
+    currentSlice: 3,
+    source: 'sample.tif',
+    kind: 'local',
+    serverUri: null,
+    ...overrides,
+  } as any);
+}
+
+beforeEach(() => {
+  useAnnotationStore.getState().reset();
+});
+
+afterEach(() => {
+  cleanup();
+});
+
+describe('SliceNavigator', () => {
+  it('shows a placeholder message when no dataset is loaded', () => {
+    useDatasetStore.setState({ meta: null, source: null, kind: null } as any);
+    render(<SliceNavigator />);
+    expect(screen.getByText('No dataset loaded.')).toBeInTheDocument();
+  });
+
+  it('shows the current slice / total in the header', () => {
+    setLoadedDataset();
+    render(<SliceNavigator />);
+    expect(screen.getByText('Slice 4 / 10')).toBeInTheDocument();
+  });
+
+  it('prev/next buttons step the slice and clamp at the ends', async () => {
+    setLoadedDataset({ currentSlice: 0 } as any);
+    const user = userEvent.setup();
+    render(<SliceNavigator />);
+    expect(screen.getByLabelText('Previous slice')).toBeDisabled();
+
+    await user.click(screen.getByLabelText('Next slice'));
+    expect(useDatasetStore.getState().currentSlice).toBe(1);
+  });
+
+  it('next is disabled on the last slice', () => {
+    setLoadedDataset({ currentSlice: 9 } as any);
+    render(<SliceNavigator />);
+    expect(screen.getByLabelText('Next slice')).toBeDisabled();
+  });
+
+  it('shows a jump-to-annotated dropdown only when slices are annotated', () => {
+    setLoadedDataset();
+    render(<SliceNavigator />);
+    expect(screen.queryByLabelText('Jump to annotated slice')).not.toBeInTheDocument();
+
+    cleanup();
+    useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE_KEY, 5, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+    ]);
+    render(<SliceNavigator />);
+    expect(screen.getByLabelText('Jump to annotated slice')).toBeInTheDocument();
+  });
+
+  it('marks the current slice as a negative example', async () => {
+    setLoadedDataset();
+    const user = userEvent.setup();
+    render(<SliceNavigator />);
+    const toggle = screen.getByRole('button', { name: /Mark as negative/ });
+    await user.click(toggle);
+    expect(useAnnotationStore.getState().negativeSlices[SOURCE_KEY]).toContain('3');
+    expect(await screen.findByRole('button', { name: /Negative example/ })).toHaveAttribute('aria-pressed', 'true');
+  });
+
+  it('shows a coarse-level warning when a non-finest pyramid level is open', () => {
+    setLoadedDataset({
+      meta: {
+        nSlices: 10, height: 32, width: 32, dtype: 'uint8', isRgb: false, valueRange: [0, 255],
+        levelKey: 'scale1', levelIndex: 1, levelWidth: 16, levelNSlices: 5,
+      },
+    } as any);
+    render(<SliceNavigator />);
+    expect(screen.getByText(/Viewing scale1/)).toBeInTheDocument();
+  });
+});
diff --git a/frontend/src/components/annotate/Toolbar/index.test.tsx b/frontend/src/components/annotate/Toolbar/index.test.tsx
new file mode 100644
index 0000000..e28bd97
--- /dev/null
+++ b/frontend/src/components/annotate/Toolbar/index.test.tsx
@@ -0,0 +1,312 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, fireEvent, render, screen, within } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import Toolbar from './index';
+import { useToolStore } from '@/stores/toolStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import * as editHistory from '@/hooks/editHistory';
+
+// Avoid spinning up the real SAM worker (unavailable in jsdom) — the magic-tool
+// panel only needs a stable, controllable status/support surface.
+vi.mock('@/hooks/useSam', () => ({
+  useSam: vi.fn(() => ({
+    status: 'idle',
+    error: null,
+    backend: null,
+    webgpu: false,
+    supported: true,
+    ensureEncoded: vi.fn(),
+    segment: vi.fn(),
+  })),
+}));
+
+vi.mock('@/hooks/editHistory', async (importOriginal) => {
+  const actual = await importOriginal<typeof import('@/hooks/editHistory')>();
+  return {
+    ...actual,
+    undo: vi.fn(),
+    redo: vi.fn(),
+  };
+});
+
+const initialToolState = useToolStore.getState();
+
+beforeEach(() => {
+  useToolStore.setState(initialToolState, true);
+  useAnnotationStore.getState().reset();
+  useAnnotationStore.temporal.getState().clear();
+  vi.mocked(editHistory.undo).mockClear();
+  vi.mocked(editHistory.redo).mockClear();
+});
+
+afterEach(() => {
+  cleanup();
+});
+
+describe('Toolbar', () => {
+  it('renders every tool as a radio button, defaulting to Pan active', () => {
+    render(<Toolbar />);
+    const group = screen.getByRole('radiogroup', { name: 'Drawing tools' });
+    const radios = within(group).getAllByRole('radio');
+    expect(radios).toHaveLength(11);
+    expect(screen.getByRole('radio', { name: /Pan/ })).toHaveAttribute('aria-checked', 'true');
+    expect(screen.getByRole('radio', { name: /Brush/ })).toHaveAttribute('aria-checked', 'false');
+  });
+
+  it('clicking a tool button selects it in the store and updates aria-checked', async () => {
+    const user = userEvent.setup();
+    render(<Toolbar />);
+    await user.click(screen.getByRole('radio', { name: /Brush \(b\)/ }));
+    expect(useToolStore.getState().tool).toBe('brush');
+    expect(screen.getByRole('radio', { name: /Brush/ })).toHaveAttribute('aria-checked', 'true');
+    expect(screen.getByRole('radio', { name: /Pan/ })).toHaveAttribute('aria-checked', 'false');
+  });
+
+  it('reflects the active tool from the store even when set externally', () => {
+    useToolStore.setState({ tool: 'polygon' });
+    render(<Toolbar />);
+    expect(screen.getByRole('radio', { name: /Polygon/ })).toHaveAttribute('aria-checked', 'true');
+  });
+
+  it('disables every tool button and shows the no-class hint when disabled', () => {
+    render(<Toolbar disabled />);
+    expect(screen.getByText('Add a class above to start annotating.')).toBeInTheDocument();
+    const group = screen.getByRole('radiogroup', { name: 'Drawing tools' });
+    within(group).getAllByRole('radio').forEach((btn) => {
+      expect(btn).toBeDisabled();
+      // Disabled tools are never shown as active, even the current tool.
+      expect(btn).toHaveAttribute('aria-checked', 'false');
+    });
+  });
+
+  it('does not show the no-class hint when not disabled', () => {
+    render(<Toolbar />);
+    expect(screen.queryByText('Add a class above to start annotating.')).not.toBeInTheDocument();
+  });
+
+  describe('undo/redo', () => {
+    it('disables undo and redo when the history is empty', () => {
+      render(<Toolbar />);
+      expect(screen.getByRole('button', { name: 'Undo' })).toBeDisabled();
+      expect(screen.getByRole('button', { name: 'Redo' })).toBeDisabled();
+    });
+
+    it('enables undo when there is past history and calls editHistory.undo on click', async () => {
+      useAnnotationStore.temporal.setState({ pastStates: [{} as any] });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      const undoBtn = screen.getByRole('button', { name: 'Undo' });
+      expect(undoBtn).toBeEnabled();
+      await user.click(undoBtn);
+      expect(editHistory.undo).toHaveBeenCalledTimes(1);
+    });
+
+    it('enables redo when there is future history and calls editHistory.redo on click', async () => {
+      useAnnotationStore.temporal.setState({ futureStates: [{} as any] });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      const redoBtn = screen.getByRole('button', { name: 'Redo' });
+      expect(redoBtn).toBeEnabled();
+      await user.click(redoBtn);
+      expect(editHistory.redo).toHaveBeenCalledTimes(1);
+    });
+  });
+
+  describe('brush / eraser / threshold panel', () => {
+    it('shows the brush radius controls for the brush tool', () => {
+      useToolStore.setState({ tool: 'brush' });
+      render(<Toolbar />);
+      expect(screen.getByLabelText('Brush radius (px)')).toBeInTheDocument();
+    });
+
+    it('does not show brush radius controls for tools without a brush', () => {
+      useToolStore.setState({ tool: 'polygon' });
+      render(<Toolbar />);
+      expect(screen.queryByLabelText('Brush radius')).not.toBeInTheDocument();
+    });
+
+    it('changing the brush radius number input snaps and updates the store', () => {
+      useToolStore.setState({ tool: 'brush', brushSize: 10 });
+      render(<Toolbar />);
+      const input = screen.getByLabelText('Brush radius (px)') as HTMLInputElement;
+      fireEvent.change(input, { target: { value: '25' } });
+      expect(useToolStore.getState().brushSize).toBe(25);
+    });
+
+    it('shows the erase-scope radiogroup only for the eraser tool', () => {
+      useToolStore.setState({ tool: 'eraser' });
+      render(<Toolbar />);
+      expect(screen.getByRole('radiogroup', { name: 'Erase scope' })).toBeInTheDocument();
+    });
+
+    it('toggles erase scope between class and all classes', async () => {
+      useToolStore.setState({ tool: 'eraser', eraseAllClasses: false });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      const allRadio = screen.getByRole('radio', { name: 'Erase all classes' });
+      expect(allRadio).not.toBeChecked();
+      await user.click(allRadio);
+      expect(useToolStore.getState().eraseAllClasses).toBe(true);
+    });
+  });
+
+  describe('threshold / sampler panel', () => {
+    it('shows the threshold band controls for the threshold tool', () => {
+      useToolStore.setState({ tool: 'threshold' });
+      render(<Toolbar />);
+      expect(screen.getByText('Set band from a region')).toBeInTheDocument();
+      expect(screen.getByText('Show in-range overlay')).toBeInTheDocument();
+    });
+
+    it('shows the threshold panel for the sampler tool too, in sampling mode', () => {
+      useToolStore.setState({ tool: 'sampler' });
+      render(<Toolbar />);
+      expect(screen.getByText('Sampling — draw a loop')).toBeInTheDocument();
+    });
+
+    it('toggles into sampler mode and back via the sample button', async () => {
+      useToolStore.setState({ tool: 'threshold' });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      await user.click(screen.getByText('Set band from a region'));
+      expect(useToolStore.getState().tool).toBe('sampler');
+      await user.click(screen.getByText('Sampling — draw a loop'));
+      expect(useToolStore.getState().tool).toBe('threshold');
+    });
+
+    it('toggles the threshold overlay checkbox', async () => {
+      useToolStore.setState({ tool: 'threshold', thresholdOverlay: true });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      const checkbox = screen.getByRole('checkbox', { name: 'Show in-range overlay' });
+      expect(checkbox).toBeChecked();
+      await user.click(checkbox);
+      expect(useToolStore.getState().thresholdOverlay).toBe(false);
+    });
+
+    it('shows a Sampler result readout when samplerFit is provided', () => {
+      useToolStore.setState({ tool: 'threshold' });
+      render(
+        <Toolbar
+          samplerFit={{
+            mode: 'plain',
+            collapsed: false,
+            lo: 10,
+            hi: 200,
+            displayLo: 10,
+            displayHi: 200,
+            dice: 0.9,
+            skill: 0.8,
+            coverage: 0.5,
+            extraSigma: 0,
+            appliedBlur: 0,
+          } as any}
+        />,
+      );
+      expect(screen.getByText(/match 90%/)).toBeInTheDocument();
+    });
+
+    it('shows the collapsed-band warning when the fit collapsed', () => {
+      useToolStore.setState({ tool: 'threshold' });
+      render(<Toolbar samplerFit={{ collapsed: true, lo: 10, hi: 20 } as any} />);
+      expect(screen.getByText('Band not applied')).toBeInTheDocument();
+    });
+  });
+
+  describe('select panel', () => {
+    it('shows the select-scope radiogroup for the select tool', () => {
+      useToolStore.setState({ tool: 'select' });
+      render(<Toolbar />);
+      expect(screen.getByRole('radiogroup', { name: 'Select scope' })).toBeInTheDocument();
+    });
+
+    it('toggles select scope between class and all', async () => {
+      useToolStore.setState({ tool: 'select', selectScope: 'all' });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      const classRadio = screen.getByRole('radio', { name: 'Select this class' });
+      expect(classRadio).not.toBeChecked();
+      await user.click(classRadio);
+      expect(useToolStore.getState().selectScope).toBe('class');
+    });
+  });
+
+  describe('fill panel', () => {
+    it('shows the fill threshold slider for the fill tool', () => {
+      useToolStore.setState({ tool: 'fill', fillThreshold: 0.1 });
+      render(<Toolbar />);
+      expect(screen.getByText(/Fill threshold/)).toBeInTheDocument();
+      expect(screen.getByText(/10%/)).toBeInTheDocument();
+    });
+  });
+
+  describe('magic panel', () => {
+    it('shows the SAM engine controls by default', () => {
+      useToolStore.setState({ tool: 'magic', magicEngine: 'sam' });
+      render(<Toolbar />);
+      expect(screen.getByRole('button', { name: 'Smart (AI)' })).toBeInTheDocument();
+      expect(screen.getByText('Detail', { selector: 'label' })).toBeInTheDocument();
+      expect(screen.getByText('Avoid other-class regions')).toBeInTheDocument();
+    });
+
+    it('switches to the classic engine and shows its controls', async () => {
+      useToolStore.setState({ tool: 'magic', magicEngine: 'sam' });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      await user.click(screen.getByRole('button', { name: 'Classic' }));
+      expect(useToolStore.getState().magicEngine).toBe('classic');
+      expect(await screen.findByRole('button', { name: 'Connected' })).toBeInTheDocument();
+      expect(screen.getByRole('button', { name: 'All similar' })).toBeInTheDocument();
+    });
+
+    it('shows the edge-stop slider only in contiguous classic mode', () => {
+      useToolStore.setState({ tool: 'magic', magicEngine: 'classic', magicMode: 'contiguous' });
+      render(<Toolbar />);
+      expect(screen.getByRole('slider', { name: 'Edge stop' })).toBeInTheDocument();
+    });
+
+    it('hides the edge-stop slider in global classic mode', () => {
+      useToolStore.setState({ tool: 'magic', magicEngine: 'classic', magicMode: 'global' });
+      render(<Toolbar />);
+      expect(screen.queryByRole('slider', { name: 'Edge stop' })).not.toBeInTheDocument();
+    });
+
+    it('selecting a SAM detail level updates the store', async () => {
+      useToolStore.setState({ tool: 'magic', magicEngine: 'sam', samDetail: 'auto' });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      await user.click(screen.getByRole('button', { name: 'fine' }));
+      expect(useToolStore.getState().samDetail).toBe('fine');
+    });
+
+    it('toggles the avoid-labeled and connected-only SAM checkboxes', async () => {
+      useToolStore.setState({
+        tool: 'magic', magicEngine: 'sam', samAvoidLabeled: true, samConnectedOnly: true,
+      });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      await user.click(screen.getByRole('checkbox', { name: /Avoid other-class regions/ }));
+      expect(useToolStore.getState().samAvoidLabeled).toBe(false);
+      await user.click(screen.getByRole('checkbox', { name: /Connected regions only/ }));
+      expect(useToolStore.getState().samConnectedOnly).toBe(false);
+    });
+  });
+
+  describe('global toggles', () => {
+    it('toggles "Clip to other classes"', async () => {
+      useToolStore.setState({ clipToOtherClasses: true });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      await user.click(screen.getByRole('checkbox', { name: 'Clip to other classes' }));
+      expect(useToolStore.getState().clipToOtherClasses).toBe(false);
+    });
+
+    it('toggles "Merge overlapping same class"', async () => {
+      useToolStore.setState({ mergeOverlappingSameClass: false });
+      const user = userEvent.setup();
+      render(<Toolbar />);
+      await user.click(screen.getByRole('checkbox', { name: 'Merge overlapping same class' }));
+      expect(useToolStore.getState().mergeOverlappingSameClass).toBe(true);
+    });
+  });
+});
diff --git a/frontend/src/components/annotate/VersionHistoryModal.test.tsx b/frontend/src/components/annotate/VersionHistoryModal.test.tsx
new file mode 100644
index 0000000..cd75792
--- /dev/null
+++ b/frontend/src/components/annotate/VersionHistoryModal.test.tsx
@@ -0,0 +1,110 @@
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, fireEvent, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import VersionHistoryModal from './VersionHistoryModal';
+import type { VersionMeta } from '@/hooks/useSave';
+
+const versions: VersionMeta[] = [
+  { version: 1, saved_at: '2024-01-01T12:00:00Z', shape_count: 1, class_count: 1, annotated_by: null, notes: null },
+  {
+    version: 2,
+    saved_at: '2024-01-02T12:00:00Z',
+    shape_count: 5,
+    class_count: 2,
+    annotated_by: 'Ada',
+    notes: 'second pass',
+  },
+];
+
+afterEach(() => {
+  cleanup();
+});
+
+describe('VersionHistoryModal', () => {
+  it('shows an empty-state message with no versions', () => {
+    render(
+      <VersionHistoryModal versions={[]} sourceKey="local:sample.tif" onPreview={vi.fn()} onRestore={vi.fn()} onClose={vi.fn()} />
+    );
+    expect(screen.getByText(/No saved versions yet/)).toBeInTheDocument();
+  });
+
+  it('renders versions newest-first with shape/class counts and pluralization', () => {
+    render(
+      <VersionHistoryModal versions={versions} sourceKey="local:sample.tif" onPreview={vi.fn()} onRestore={vi.fn()} onClose={vi.fn()} />
+    );
+    const rows = screen.getAllByText(/^v\d/);
+    expect(rows[0]).toHaveTextContent('v2');
+    expect(rows[1]).toHaveTextContent('v1');
+    expect(screen.getByText(/5 shapes/)).toBeInTheDocument();
+    expect(screen.getByText(/2 classes/)).toBeInTheDocument();
+    expect(screen.getByText(/1 shape\b/)).toBeInTheDocument();
+    expect(screen.getByText(/1 class\b/)).toBeInTheDocument();
+  });
+
+  it('shows annotator and notes only when present', () => {
+    render(
+      <VersionHistoryModal versions={versions} sourceKey="local:sample.tif" onPreview={vi.fn()} onRestore={vi.fn()} onClose={vi.fn()} />
+    );
+    expect(screen.getByText(/by Ada/)).toBeInTheDocument();
+    expect(screen.getByText('second pass')).toBeInTheDocument();
+  });
+
+  it('does not render thumbnails when sourceKey is null', () => {
+    render(
+      <VersionHistoryModal versions={versions} sourceKey={null} onPreview={vi.fn()} onRestore={vi.fn()} onClose={vi.fn()} />
+    );
+    expect(screen.queryByAltText(/thumbnail/)).not.toBeInTheDocument();
+  });
+
+  it('renders a thumbnail image per version when sourceKey is set', () => {
+    render(
+      <VersionHistoryModal versions={versions} sourceKey="local:sample.tif" onPreview={vi.fn()} onRestore={vi.fn()} onClose={vi.fn()} />
+    );
+    expect(screen.getByAltText('Version 1 thumbnail')).toBeInTheDocument();
+    expect(screen.getByAltText('Version 2 thumbnail')).toBeInTheDocument();
+  });
+
+  it('falls back to a placeholder icon when a thumbnail fails to load', () => {
+    render(
+      <VersionHistoryModal versions={versions} sourceKey="local:sample.tif" onPreview={vi.fn()} onRestore={vi.fn()} onClose={vi.fn()} />
+    );
+    const img = screen.getByAltText('Version 2 thumbnail');
+    fireEvent.error(img);
+    expect(screen.queryByAltText('Version 2 thumbnail')).not.toBeInTheDocument();
+  });
+
+  it('calls onPreview with the version number and does not close the modal', async () => {
+    const onPreview = vi.fn();
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <VersionHistoryModal versions={versions} sourceKey="local:sample.tif" onPreview={onPreview} onRestore={vi.fn()} onClose={onClose} />
+    );
+    await user.click(screen.getAllByRole('button', { name: /Preview/ })[0]);
+    expect(onPreview).toHaveBeenCalledWith(2);
+    expect(onClose).not.toHaveBeenCalled();
+  });
+
+  it('calls onRestore with the version number and then onClose', async () => {
+    const onRestore = vi.fn();
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <VersionHistoryModal versions={versions} sourceKey="local:sample.tif" onPreview={vi.fn()} onRestore={onRestore} onClose={onClose} />
+    );
+    await user.click(screen.getAllByRole('button', { name: /Restore/ })[0]);
+    expect(onRestore).toHaveBeenCalledWith(2);
+    expect(onClose).toHaveBeenCalledOnce();
+  });
+
+  it('calls onClose from the header close button', async () => {
+    const onClose = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <VersionHistoryModal versions={versions} sourceKey="local:sample.tif" onPreview={vi.fn()} onRestore={vi.fn()} onClose={onClose} />
+    );
+    const headerButtons = screen.getAllByRole('button').filter((b) => !/Preview|Restore/.test(b.textContent ?? ''));
+    await user.click(headerButtons[0]);
+    expect(onClose).toHaveBeenCalledOnce();
+  });
+});
diff --git a/frontend/src/components/annotate/VersionPreviewBar.test.tsx b/frontend/src/components/annotate/VersionPreviewBar.test.tsx
new file mode 100644
index 0000000..72a6148
--- /dev/null
+++ b/frontend/src/components/annotate/VersionPreviewBar.test.tsx
@@ -0,0 +1,162 @@
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import VersionPreviewBar from './VersionPreviewBar';
+import type { VersionMeta } from '@/hooks/useSave';
+
+afterEach(() => {
+  cleanup();
+});
+
+function makeVersions(): VersionMeta[] {
+  return [
+    { version: 1, saved_at: '2024-01-01T00:00:00Z', shape_count: 1, class_count: 1 },
+    { version: 2, saved_at: '2024-01-02T00:00:00Z', shape_count: 3, class_count: 2 },
+    { version: 3, saved_at: '2024-01-03T00:00:00Z', shape_count: 5, class_count: 2 },
+  ];
+}
+
+describe('VersionPreviewBar', () => {
+  it('renders nothing when there are no versions', () => {
+    const { container } = render(
+      <VersionPreviewBar versions={[]} current={1} onChange={vi.fn()} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    expect(container).toBeEmptyDOMElement();
+  });
+
+  it('shows the previewed version number, shape count, and (latest) tag on the max version', () => {
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={3} onChange={vi.fn()} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    expect(screen.getByText(/Previewing v3/)).toBeInTheDocument();
+    expect(screen.getByText('(latest)')).toBeInTheDocument();
+    expect(screen.getByText('5 shapes')).toBeInTheDocument();
+  });
+
+  it('omits the (latest) tag and uses singular "shape" for a single-shape non-latest version', () => {
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={1} onChange={vi.fn()} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    expect(screen.queryByText('(latest)')).not.toBeInTheDocument();
+    expect(screen.getByText('1 shape')).toBeInTheDocument();
+  });
+
+  it('sorts versions oldest-first regardless of input order for min/max labels', () => {
+    const shuffled = [makeVersions()[2], makeVersions()[0], makeVersions()[1]];
+    render(
+      <VersionPreviewBar versions={shuffled} current={2} onChange={vi.fn()} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    expect(screen.getByText('v1')).toBeInTheDocument();
+    expect(screen.getByText('v3')).toBeInTheDocument();
+  });
+
+  it('falls back to the latest version meta when current does not match any version', () => {
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={99} onChange={vi.fn()} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    // meta falls back to sorted[last] (v3), so the label reads v3, not the unmatched `current`.
+    // isLatest compares `current` (99) to max (3), so the "(latest)" tag is NOT shown
+    // even though the displayed meta is the latest version's — a slight label mismatch.
+    expect(screen.getByText(/Previewing v3/)).toBeInTheDocument();
+    expect(screen.queryByText('(latest)')).not.toBeInTheDocument();
+    expect(screen.getByText('5 shapes')).toBeInTheDocument();
+  });
+
+  it('shows the loading indicator only when loading is true', () => {
+    const { rerender } = render(
+      <VersionPreviewBar versions={makeVersions()} current={2} loading onChange={vi.fn()} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    expect(screen.getByText('loading…')).toBeInTheDocument();
+    rerender(
+      <VersionPreviewBar versions={makeVersions()} current={2} loading={false} onChange={vi.fn()} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    expect(screen.queryByText('loading…')).not.toBeInTheDocument();
+  });
+
+  it('calls onRestore with the current version when Restore is clicked', async () => {
+    const onRestore = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={2} onChange={vi.fn()} onExit={vi.fn()} onRestore={onRestore} />,
+    );
+    await user.click(screen.getByRole('button', { name: /Restore this version/ }));
+    expect(onRestore).toHaveBeenCalledWith(2);
+  });
+
+  it('calls onExit when Exit is clicked', async () => {
+    const onExit = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={2} onChange={vi.fn()} onExit={onExit} onRestore={vi.fn()} />,
+    );
+    await user.click(screen.getByRole('button', { name: 'Exit' }));
+    expect(onExit).toHaveBeenCalledOnce();
+  });
+
+  it('steps to the older version and disables the older button at the min', async () => {
+    const onChange = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={2} onChange={onChange} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    await user.click(screen.getByRole('button', { name: 'Older version' }));
+    expect(onChange).toHaveBeenCalledWith(1);
+
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={1} onChange={onChange} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    expect(screen.getAllByRole('button', { name: 'Older version' })[1]).toBeDisabled();
+  });
+
+  it('steps to the newer version and disables the newer button at the max', async () => {
+    const onChange = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={2} onChange={onChange} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    await user.click(screen.getByRole('button', { name: 'Newer version' }));
+    expect(onChange).toHaveBeenCalledWith(3);
+
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={3} onChange={onChange} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    expect(screen.getAllByRole('button', { name: 'Newer version' })[1]).toBeDisabled();
+  });
+
+  it('does not step past the range even if step is somehow invoked at the boundary', async () => {
+    const onChange = vi.fn();
+    const user = userEvent.setup();
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={1} onChange={onChange} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    const olderBtn = screen.getByRole('button', { name: 'Older version' });
+    expect(olderBtn).toBeDisabled();
+    await user.click(olderBtn); // disabled — should not fire
+    expect(onChange).not.toHaveBeenCalled();
+  });
+
+  it('renders a formatted date from the ISO timestamp', () => {
+    render(
+      <VersionPreviewBar versions={makeVersions()} current={2} onChange={vi.fn()} onExit={vi.fn()} onRestore={vi.fn()} />,
+    );
+    const expected = new Date('2024-01-02T00:00:00Z').toLocaleString(undefined, {
+      month: 'short', day: 'numeric', hour: '2-digit', minute: '2-digit',
+    });
+    expect(screen.getByText(expected)).toBeInTheDocument();
+  });
+
+  it('falls back to the raw string when the date cannot be formatted', () => {
+    const versions = [{ version: 1, saved_at: 'not-a-date-###', shape_count: 0, class_count: 0 }];
+    const original = Date.prototype.toLocaleString;
+    // Force toLocaleString to throw to exercise the catch path.
+    Date.prototype.toLocaleString = () => { throw new Error('boom'); };
+    try {
+      render(
+        <VersionPreviewBar versions={versions} current={1} onChange={vi.fn()} onExit={vi.fn()} onRestore={vi.fn()} />,
+      );
+      expect(screen.getByText('not-a-date-###')).toBeInTheDocument();
+    } finally {
+      Date.prototype.toLocaleString = original;
+    }
+  });
+});
diff --git a/frontend/src/hooks/editHistory.test.ts b/frontend/src/hooks/editHistory.test.ts
new file mode 100644
index 0000000..f82fb08
--- /dev/null
+++ b/frontend/src/hooks/editHistory.test.ts
@@ -0,0 +1,152 @@
+import { afterEach, beforeEach, describe, expect, it } from 'vitest';
+import { clearHistory, markClassDelete, redo, undo } from './editHistory';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useClassStore, type AnnotationClass } from '@/stores/classStore';
+import { useDatasetStore } from '@/stores/datasetStore';
+
+const SOURCE_KEY = 'local:x.tif';
+
+function openSample() {
+  useDatasetStore.getState().setDataset('local', 'x.tif', null, {
+    nSlices: 3,
+    height: 10,
+    width: 10,
+    dtype: 'uint8',
+    isRgb: false,
+    valueRange: [0, 255],
+  });
+}
+
+beforeEach(() => {
+  useAnnotationStore.getState().reset();
+  useAnnotationStore.temporal.getState().clear();
+  useClassStore.setState({ classes: [] });
+  useDatasetStore.getState().reset();
+});
+
+afterEach(() => {
+  clearHistory();
+});
+
+describe('editHistory', () => {
+  it('undo()/redo() are no-ops when there is no history', () => {
+    expect(() => undo()).not.toThrow();
+    expect(() => redo()).not.toThrow();
+    expect(useAnnotationStore.temporal.getState().pastStates).toHaveLength(0);
+  });
+
+  it('undo reverts a plain region edit (not tagged as a class delete)', () => {
+    openSample();
+    useAnnotationStore.getState().addShape(SOURCE_KEY, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2,
+    });
+    expect(useAnnotationStore.getState().byImage[SOURCE_KEY]?.['0']).toHaveLength(1);
+
+    undo();
+    expect(useAnnotationStore.getState().byImage[SOURCE_KEY]?.['0'] ?? []).toHaveLength(0);
+
+    redo();
+    expect(useAnnotationStore.getState().byImage[SOURCE_KEY]?.['0']).toHaveLength(1);
+  });
+
+  it('markClassDelete + undo re-inserts the class at its original index when sourceKey matches', () => {
+    openSample();
+    const cls: AnnotationClass = { classId: 5, label: 'Pore', color: '#f00', isVisible: true };
+    useClassStore.setState({ classes: [cls] });
+
+    markClassDelete(SOURCE_KEY, cls, 0);
+    useClassStore.getState().deleteClass(5);
+    // The class-delete journal only records on the NEXT tracked annotationStore edit
+    // (onTrackedEdit fires from annotationStore, not classStore), so we need one.
+    useAnnotationStore.getState().touchHistory();
+
+    expect(useClassStore.getState().classes).toHaveLength(0);
+
+    undo();
+    expect(useClassStore.getState().classes).toEqual([cls]);
+
+    redo();
+    expect(useClassStore.getState().classes).toHaveLength(0);
+  });
+
+  it('does not replay a class-delete entry when the current sourceKey differs', () => {
+    openSample();
+    const cls: AnnotationClass = { classId: 5, label: 'Pore', color: '#f00', isVisible: true };
+    useClassStore.setState({ classes: [cls] });
+
+    markClassDelete(SOURCE_KEY, cls, 0);
+    useClassStore.getState().deleteClass(5);
+    useAnnotationStore.getState().touchHistory();
+    expect(useClassStore.getState().classes).toHaveLength(0);
+
+    // Switch to a different sample before undoing.
+    useDatasetStore.getState().setDataset('local', 'other.tif', null, {
+      nSlices: 1, height: 1, width: 1, dtype: 'uint8', isRgb: false, valueRange: [0, 255],
+    });
+
+    undo();
+    // Region history still undoes (temporal stack is shared), but the class is
+    // NOT re-inserted because currentSourceKey() no longer matches the entry.
+    expect(useClassStore.getState().classes).toHaveLength(0);
+  });
+
+  it('redo/undo stacks stay ordered across a mix of plain and class-delete edits', () => {
+    openSample();
+    const cls: AnnotationClass = { classId: 5, label: 'Pore', color: '#f00', isVisible: true };
+    useClassStore.setState({ classes: [cls] });
+
+    // 1: plain region edit
+    useAnnotationStore.getState().addShape(SOURCE_KEY, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 1, h: 1,
+    });
+    // 2: class delete
+    markClassDelete(SOURCE_KEY, cls, 0);
+    useClassStore.getState().deleteClass(5);
+    useAnnotationStore.getState().touchHistory();
+
+    // Undo class delete first (LIFO)
+    undo();
+    expect(useClassStore.getState().classes).toEqual([cls]);
+    expect(useAnnotationStore.getState().byImage[SOURCE_KEY]?.['0']).toHaveLength(1);
+
+    // Undo plain region edit
+    undo();
+    expect(useAnnotationStore.getState().byImage[SOURCE_KEY]?.['0'] ?? []).toHaveLength(0);
+
+    // Redo both back in order
+    redo();
+    expect(useAnnotationStore.getState().byImage[SOURCE_KEY]?.['0']).toHaveLength(1);
+    redo();
+    expect(useClassStore.getState().classes).toHaveLength(0);
+  });
+
+  it('a new edit after undo clears the redo stack', () => {
+    openSample();
+    useAnnotationStore.getState().addShape(SOURCE_KEY, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 1, h: 1,
+    });
+    undo();
+    // A fresh edit (not via redo) should drop the stale redo entry.
+    useAnnotationStore.getState().addShape(SOURCE_KEY, 0, {
+      id: 's2', classId: 1, kind: 'rectangle', x: 1, y: 1, w: 1, h: 1,
+    });
+    redo(); // should be a no-op now (no future states)
+    expect(useAnnotationStore.getState().byImage[SOURCE_KEY]?.['0']).toHaveLength(1);
+    expect(useAnnotationStore.getState().byImage[SOURCE_KEY]?.['0']?.[0].id).toBe('s2');
+  });
+
+  it('clearHistory wipes the draft, journal, and temporal stack', () => {
+    openSample();
+    useAnnotationStore.getState().addShape(SOURCE_KEY, 0, {
+      id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 1, h: 1,
+    });
+    expect(useAnnotationStore.temporal.getState().pastStates.length).toBeGreaterThan(0);
+
+    clearHistory();
+    expect(useAnnotationStore.temporal.getState().pastStates).toHaveLength(0);
+    expect(useAnnotationStore.temporal.getState().futureStates).toHaveLength(0);
+    // undo/redo are now no-ops (journal cleared alongside temporal stack)
+    expect(() => undo()).not.toThrow();
+    expect(() => redo()).not.toThrow();
+  });
+});
diff --git a/frontend/src/hooks/useAnnotatedSourceKeys.test.tsx b/frontend/src/hooks/useAnnotatedSourceKeys.test.tsx
new file mode 100644
index 0000000..389c12d
--- /dev/null
+++ b/frontend/src/hooks/useAnnotatedSourceKeys.test.tsx
@@ -0,0 +1,57 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, renderHook, waitFor } from '@testing-library/react';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import type { ReactNode } from 'react';
+import { useAnnotatedSourceKeys } from './useAnnotatedSourceKeys';
+import { useAnnotationStore } from '@/stores/annotationStore';
+
+function wrapper({ children }: { children: ReactNode }) {
+  const client = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return <QueryClientProvider client={client}>{children}</QueryClientProvider>;
+}
+
+beforeEach(() => {
+  useAnnotationStore.getState().reset();
+  vi.stubGlobal('fetch', vi.fn().mockResolvedValue({ ok: true, json: async () => [] }));
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('useAnnotatedSourceKeys', () => {
+  it('includes in-session sources with at least one shape', () => {
+    useAnnotationStore.getState().replaceClassShapesOnSlice('local:a.tif', 0, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+    ]);
+    const { result } = renderHook(() => useAnnotatedSourceKeys(), { wrapper });
+    expect(result.current.has('local:a.tif')).toBe(true);
+  });
+
+  it('excludes a source whose slices are all empty', () => {
+    useAnnotationStore.getState().replaceClassShapesOnSlice('local:b.tif', 0, 1, []);
+    const { result } = renderHook(() => useAnnotatedSourceKeys(), { wrapper });
+    expect(result.current.has('local:b.tif')).toBe(false);
+  });
+
+  it('merges in annotated sources from the backend drafts endpoint', async () => {
+    (fetch as any).mockResolvedValue({
+      ok: true,
+      json: async () => [
+        { source_key: 'local:c.tif', has_annotations: true },
+        { source_key: 'local:d.tif', has_annotations: false },
+      ],
+    });
+    const { result } = renderHook(() => useAnnotatedSourceKeys(), { wrapper });
+    await waitFor(() => expect(result.current.has('local:c.tif')).toBe(true));
+    expect(result.current.has('local:d.tif')).toBe(false);
+  });
+
+  it('returns an empty set when the drafts fetch fails', async () => {
+    (fetch as any).mockResolvedValue({ ok: false, status: 500 });
+    const { result } = renderHook(() => useAnnotatedSourceKeys(), { wrapper });
+    await waitFor(() => expect(fetch).toHaveBeenCalled());
+    expect(result.current.size).toBe(0);
+  });
+});
diff --git a/frontend/src/hooks/useDraftSync.test.tsx b/frontend/src/hooks/useDraftSync.test.tsx
new file mode 100644
index 0000000..ad306af
--- /dev/null
+++ b/frontend/src/hooks/useDraftSync.test.tsx
@@ -0,0 +1,94 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, renderHook } from '@testing-library/react';
+import { loadDraft, useDraftSync } from './useDraftSync';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useClassStore } from '@/stores/classStore';
+
+beforeEach(() => {
+  useAnnotationStore.getState().reset();
+  useClassStore.setState({ classes: [] });
+  vi.stubGlobal('fetch', vi.fn().mockResolvedValue({ ok: true }));
+  vi.stubGlobal('sendBeacon', undefined);
+  Object.defineProperty(window.navigator, 'sendBeacon', { value: vi.fn(), configurable: true });
+});
+
+afterEach(() => {
+  cleanup(); // unmount while fetch/sendBeacon are still stubbed, before unstubbing them
+  vi.unstubAllGlobals();
+  vi.useRealTimers();
+});
+
+describe('loadDraft', () => {
+  it('returns null on 404', async () => {
+    (fetch as any).mockResolvedValue({ status: 404, ok: false });
+    expect(await loadDraft('local:x.tif')).toBeNull();
+  });
+
+  it('returns null on any error', async () => {
+    (fetch as any).mockRejectedValue(new Error('down'));
+    expect(await loadDraft('local:x.tif')).toBeNull();
+  });
+
+  it('returns the parsed draft on success', async () => {
+    (fetch as any).mockResolvedValue({ status: 200, ok: true, json: async () => ({ payload: { classes: [] } }) });
+    expect(await loadDraft('local:x.tif')).toEqual({ payload: { classes: [] } });
+  });
+});
+
+describe('useDraftSync', () => {
+  it('does nothing when sourceKey is null', () => {
+    vi.useFakeTimers();
+    renderHook(() => useDraftSync(null));
+    vi.advanceTimersByTime(5000);
+    expect(fetch).not.toHaveBeenCalled();
+  });
+
+  it('autosaves 1.5s after a store change, with the right payload', async () => {
+    vi.useFakeTimers({ shouldAdvanceTime: true });
+    useClassStore.setState({ classes: [{ classId: 1, label: 'Cell', color: '#f00', isVisible: true }] });
+    renderHook(() => useDraftSync('local:x.tif'));
+
+    act(() => {
+      useAnnotationStore.getState().replaceClassShapesOnSlice('local:x.tif', 0, 1, [
+        { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+      ]);
+    });
+    await vi.advanceTimersByTimeAsync(1500);
+
+    expect(fetch).toHaveBeenCalledTimes(1);
+    const [url, init] = (fetch as any).mock.calls[0];
+    expect(url).toContain('/api/annotations/draft?source_key=');
+    expect(init.method).toBe('PUT');
+    const body = JSON.parse(init.body);
+    expect(body.classes).toEqual([{ classId: 1, label: 'Cell', color: '#f00', isVisible: true }]);
+    expect(body.slices['0']).toHaveLength(1);
+  });
+
+  it('re-arms the debounce on each subsequent change (no save before the quiet period)', async () => {
+    vi.useFakeTimers({ shouldAdvanceTime: true });
+    renderHook(() => useDraftSync('local:x.tif'));
+    act(() => { useAnnotationStore.getState().replaceClassShapesOnSlice('local:x.tif', 0, 1, []); });
+    await vi.advanceTimersByTimeAsync(1000);
+    act(() => { useAnnotationStore.getState().replaceClassShapesOnSlice('local:x.tif', 1, 1, []); });
+    await vi.advanceTimersByTimeAsync(1000);
+    expect(fetch).not.toHaveBeenCalled(); // only 1s since the last change, need 1.5s
+    await vi.advanceTimersByTimeAsync(500);
+    expect(fetch).toHaveBeenCalledTimes(1);
+  });
+
+  it('flushes immediately (bypassing the debounce) when the component unmounts', () => {
+    vi.useFakeTimers();
+    const { unmount } = renderHook(() => useDraftSync('local:x.tif'));
+    unmount();
+    expect(fetch).toHaveBeenCalledTimes(1);
+  });
+
+  it('sends a beacon on beforeunload with the latest payload', () => {
+    renderHook(() => useDraftSync('local:x.tif'));
+    window.dispatchEvent(new Event('beforeunload'));
+    expect(navigator.sendBeacon).toHaveBeenCalledWith(
+      expect.stringContaining('/api/annotations/draft?source_key='),
+      expect.any(String),
+    );
+  });
+});
diff --git a/frontend/src/hooks/useExportJob.test.tsx b/frontend/src/hooks/useExportJob.test.tsx
new file mode 100644
index 0000000..a764c35
--- /dev/null
+++ b/frontend/src/hooks/useExportJob.test.tsx
@@ -0,0 +1,101 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, renderHook, waitFor } from '@testing-library/react';
+import { useExportJob } from './useExportJob';
+
+beforeEach(() => {
+  sessionStorage.clear();
+  vi.stubGlobal('fetch', vi.fn());
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('useExportJob', () => {
+  it('starts idle', () => {
+    const { result } = renderHook(() => useExportJob());
+    expect(result.current.state.status).toBe('idle');
+    expect(result.current.downloadUrl).toBeNull();
+  });
+
+  it('a synchronous (no job_id) response is treated as immediately done', async () => {
+    (fetch as any).mockResolvedValue({ ok: true, json: async () => ({ ok: true, zip_available: false }) });
+    const { result } = renderHook(() => useExportJob());
+    await act(async () => { await result.current.start({}); });
+    expect(result.current.state.status).toBe('done');
+    expect(result.current.state.result).toEqual({ ok: true, zip_available: false });
+  });
+
+  it('a job_id response starts polling and reflects progress then done', async () => {
+    (fetch as any)
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ job_id: 'j1' }) })
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ state: 'running', phase: 'working', done: 1, total: 4 }) })
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ state: 'done', phase: 'done', done: 4, total: 4, result: { zip_available: true } }) });
+
+    const { result } = renderHook(() => useExportJob());
+    await act(async () => { await result.current.start({}); });
+    expect(result.current.state.jobId).toBe('j1');
+
+    await waitFor(() => expect(result.current.state.phase).toBe('working'));
+    await waitFor(() => expect(result.current.state.status).toBe('done'), { timeout: 3000 });
+    expect(result.current.downloadUrl).toContain('/api/export/download/j1');
+  });
+
+  it('a non-ok POST response surfaces a formatted error and does not poll', async () => {
+    (fetch as any).mockResolvedValue({ ok: false, status: 422, text: async () => JSON.stringify({ detail: [{ msg: 'bad', loc: ['body', 'x'] }] }) });
+    const { result } = renderHook(() => useExportJob());
+    await act(async () => { await result.current.start({}); });
+    expect(result.current.state.status).toBe('error');
+    expect(result.current.state.error).toBeTruthy();
+    expect(fetch).toHaveBeenCalledTimes(1); // no follow-up poll
+  });
+
+  it('a network error during start() surfaces as an error state', async () => {
+    (fetch as any).mockRejectedValue(new Error('offline'));
+    const { result } = renderHook(() => useExportJob());
+    await act(async () => { await result.current.start({}); });
+    expect(result.current.state.status).toBe('error');
+    expect(result.current.state.error).toContain('offline');
+  });
+
+  it('a 404 while polling a resumed job clears it and returns to idle', async () => {
+    sessionStorage.setItem('exportJob:key1', 'stale-job');
+    (fetch as any).mockResolvedValue({ status: 404, ok: false });
+    const { result } = renderHook(() => useExportJob('key1'));
+    expect(result.current.state.status).toBe('running'); // optimistic placeholder
+    await waitFor(() => expect(result.current.state.status).toBe('idle'));
+    expect(sessionStorage.getItem('exportJob:key1')).toBeNull();
+  });
+
+  it('resumes polling a persisted job id on a fresh mount', async () => {
+    sessionStorage.setItem('exportJob:key2', 'resumed-job');
+    (fetch as any).mockResolvedValue({ ok: true, json: async () => ({ state: 'done', phase: 'done', result: {} }) });
+    const { result } = renderHook(() => useExportJob('key2'));
+    await waitFor(() => expect(result.current.state.status).toBe('done'));
+    expect(fetch).toHaveBeenCalledWith(expect.stringContaining('/api/export/status/resumed-job'));
+  });
+
+  it('reset clears state and any persisted job id', async () => {
+    sessionStorage.setItem('exportJob:key3', 'j1');
+    (fetch as any).mockResolvedValue({ ok: true, json: async () => ({ state: 'running', phase: 'x' }) });
+    const { result } = renderHook(() => useExportJob('key3'));
+    await waitFor(() => expect(result.current.state.phase).toBe('x'));
+    act(() => result.current.reset());
+    expect(result.current.state.status).toBe('idle');
+    expect(sessionStorage.getItem('exportJob:key3')).toBeNull();
+  });
+
+  it('startMaskSync/startIpredBatchTrain/startIpredBatchApply hit their own routes', async () => {
+    (fetch as any).mockResolvedValue({ ok: true, json: async () => ({ ok: true }) });
+    const { result } = renderHook(() => useExportJob());
+    await act(async () => { await result.current.startMaskSync({}); });
+    expect(fetch).toHaveBeenLastCalledWith(expect.stringContaining('/api/masks/to-tiled'), expect.anything());
+
+    await act(async () => { await result.current.startIpredBatchTrain({}); });
+    expect(fetch).toHaveBeenLastCalledWith(expect.stringContaining('/api/ipred/batch/train'), expect.anything());
+
+    await act(async () => { await result.current.startIpredBatchApply({}); });
+    expect(fetch).toHaveBeenLastCalledWith(expect.stringContaining('/api/ipred/batch/apply'), expect.anything());
+  });
+});
diff --git a/frontend/src/hooks/useFeatureChannels.test.ts b/frontend/src/hooks/useFeatureChannels.test.ts
new file mode 100644
index 0000000..e19bc16
--- /dev/null
+++ b/frontend/src/hooks/useFeatureChannels.test.ts
@@ -0,0 +1,293 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, renderHook, waitFor } from '@testing-library/react';
+import { useFeatureChannels } from './useFeatureChannels';
+import { useIpredStore, DEFAULT_COMPOSITION_ID } from '@/stores/ipredStore';
+import { useConnectionStore } from '@/stores/connectionStore';
+
+const INITIAL_IPRED_STATE = useIpredStore.getState();
+const INITIAL_CONN_STATE = useConnectionStore.getState();
+
+function modulesResponse(overrides: Record<string, unknown>[] = []) {
+  return {
+    ok: true,
+    json: async () => ({ modules: overrides }),
+  };
+}
+
+function preprocessResponse(overrides: Record<string, unknown> = {}) {
+  return {
+    ok: true,
+    json: async () => ({
+      feature_id: 'feat-1',
+      project_id: 'proj-1',
+      setup_id: 'setup-1',
+      slice_index: 0,
+      n_channels: 3,
+      width: 64,
+      height: 64,
+      labels: ['a', 'b', 'c'],
+      cache_hit: false,
+      ...overrides,
+    }),
+  };
+}
+
+beforeEach(() => {
+  useIpredStore.setState(INITIAL_IPRED_STATE, true);
+  useConnectionStore.setState(INITIAL_CONN_STATE, true);
+  vi.stubGlobal('URL', Object.assign(URL, {
+    createObjectURL: vi.fn(() => 'blob:mock-url'),
+    revokeObjectURL: vi.fn(),
+  }));
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+const ARGS = { source: 'x.tif', kind: 'local', sliceIndex: 0, serverUri: null };
+
+describe('useFeatureChannels', () => {
+  it('probes SAM availability on mount and sets samAvailable when slimsam is ready', async () => {
+    vi.stubGlobal('fetch', vi.fn().mockResolvedValue(modulesResponse([{ id: 'slimsam', ready: true }])));
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect(result.current.samAvailable).toBe(true));
+  });
+
+  it('samAvailable stays false when slimsam is absent or not ready', async () => {
+    vi.stubGlobal('fetch', vi.fn().mockResolvedValue(modulesResponse([{ id: 'slimsam', ready: false }])));
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect((fetch as any).mock.calls.length).toBeGreaterThan(0));
+    expect(result.current.samAvailable).toBe(false);
+  });
+
+  it('samAvailable stays false when the modules fetch rejects', async () => {
+    vi.stubGlobal('fetch', vi.fn().mockRejectedValue(new Error('down')));
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect((fetch as any).mock.calls.length).toBeGreaterThan(0));
+    expect(result.current.samAvailable).toBe(false);
+  });
+
+  it('compute() fails fast with no composition selected', async () => {
+    useIpredStore.setState({ preferredCompositionId: '' });
+    vi.stubGlobal('fetch', vi.fn().mockResolvedValue(modulesResponse()));
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await act(async () => {
+      await result.current.compute();
+    });
+    expect(result.current.error).toBe('Select a composition first.');
+    expect(result.current.job).toBeNull();
+  });
+
+  it('compute() opens a session, preprocesses, and populates job + channelIndex 0', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(modulesResponse()) // sam probe on mount
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ session_id: 'sess-1', project_id: 'proj-1' }) }) // openIpredSession
+      .mockResolvedValueOnce(preprocessResponse()) // ipredPreprocess
+      .mockResolvedValueOnce({ ok: true, blob: async () => new Blob(['x']) }); // channel fetch triggered by channelIndex effect
+    vi.stubGlobal('fetch', fetchMock);
+
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+
+    await act(async () => {
+      await result.current.compute();
+    });
+
+    expect(result.current.job?.jobId).toBe('feat-1');
+    expect(result.current.job?.channels).toEqual([
+      { index: 0, label: 'a' }, { index: 1, label: 'b' }, { index: 2, label: 'c' },
+    ]);
+    expect(result.current.channelIndex).toBe(0);
+    expect(useIpredStore.getState().ipredSessionId).toBe('sess-1');
+
+    await waitFor(() => expect(result.current.channelUrl).toBe('blob:mock-url'));
+  });
+
+  it('compute() reuses an existing ipredSessionId instead of opening a new session', async () => {
+    useIpredStore.setState({ ipredSessionId: 'existing-sess' });
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(modulesResponse())
+      .mockResolvedValueOnce(preprocessResponse())
+      .mockResolvedValueOnce({ ok: true, blob: async () => new Blob(['x']) });
+    vi.stubGlobal('fetch', fetchMock);
+
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    await act(async () => {
+      await result.current.compute();
+    });
+    // Only 3 total calls: sam probe, preprocess, channel fetch — no session POST.
+    expect(fetchMock).toHaveBeenCalledTimes(3);
+    expect(result.current.job?.jobId).toBe('feat-1');
+  });
+
+  it('compute() builds synthetic channel labels when the server returns none', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(modulesResponse())
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ session_id: 'sess-1', project_id: 'proj-1' }) })
+      .mockResolvedValueOnce(preprocessResponse({ labels: [], n_channels: 2 }))
+      .mockResolvedValueOnce({ ok: true, blob: async () => new Blob(['x']) });
+    vi.stubGlobal('fetch', fetchMock);
+
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    await act(async () => {
+      await result.current.compute();
+    });
+    expect(result.current.job?.channels).toEqual([
+      { index: 0, label: 'channel 0' }, { index: 1, label: 'channel 1' },
+    ]);
+  });
+
+  it('compute() sets hasSam true when the composition id mentions sam/slimsam/mark', async () => {
+    useIpredStore.setState({ preferredCompositionId: 'comp-slimsam-x' });
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(modulesResponse())
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ session_id: 'sess-1', project_id: 'proj-1' }) })
+      .mockResolvedValueOnce(preprocessResponse())
+      .mockResolvedValueOnce({ ok: true, blob: async () => new Blob(['x']) });
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    await act(async () => {
+      await result.current.compute();
+    });
+    expect(result.current.job?.hasSam).toBe(true);
+  });
+
+  it('compute() sets error and clears job on a failed preprocess', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(modulesResponse())
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ session_id: 'sess-1', project_id: 'proj-1' }) })
+      .mockResolvedValueOnce({ ok: false, text: async () => 'boom' });
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    await act(async () => {
+      await result.current.compute();
+    });
+    expect(result.current.error).toBe('boom');
+    expect(result.current.job).toBeNull();
+    expect(result.current.computing).toBe(false);
+  });
+
+  it('compute() is a silent no-op when there is no source/kind open (guarded before ensureSession)', async () => {
+    const fetchMock = vi.fn().mockResolvedValue(modulesResponse());
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() =>
+      useFeatureChannels({ source: null, kind: null, sliceIndex: 0, serverUri: null }));
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    await act(async () => {
+      await result.current.compute();
+    });
+    expect(result.current.error).toBeNull();
+    expect(result.current.job).toBeNull();
+  });
+
+  it('selectChannel/cycleChannel/clearSelection manipulate channelIndex', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(modulesResponse())
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ session_id: 'sess-1', project_id: 'proj-1' }) })
+      .mockResolvedValueOnce(preprocessResponse())
+      .mockResolvedValue({ ok: true, blob: async () => new Blob(['x']) });
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    await act(async () => {
+      await result.current.compute();
+    });
+    expect(result.current.channelIndex).toBe(0);
+
+    act(() => result.current.cycleChannel(1));
+    expect(result.current.channelIndex).toBe(1);
+
+    act(() => result.current.cycleChannel(-2)); // wraps around (3 channels)
+    expect(result.current.channelIndex).toBe(2);
+
+    act(() => result.current.selectChannel(0));
+    expect(result.current.channelIndex).toBe(0);
+
+    act(() => result.current.clearSelection());
+    expect(result.current.channelIndex).toBeNull();
+  });
+
+  it('cycleChannel is a no-op with no job', () => {
+    vi.stubGlobal('fetch', vi.fn().mockResolvedValue(modulesResponse()));
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    act(() => result.current.cycleChannel(1));
+    expect(result.current.channelIndex).toBeNull();
+  });
+
+  it('invalidateJob clears job/channelIndex/channelUrl', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(modulesResponse())
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ session_id: 'sess-1', project_id: 'proj-1' }) })
+      .mockResolvedValueOnce(preprocessResponse())
+      .mockResolvedValue({ ok: true, blob: async () => new Blob(['x']) });
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    await act(async () => {
+      await result.current.compute();
+    });
+    await waitFor(() => expect(result.current.channelUrl).toBe('blob:mock-url'));
+
+    act(() => result.current.invalidateJob());
+    expect(result.current.job).toBeNull();
+    expect(result.current.channelIndex).toBeNull();
+    expect(result.current.channelUrl).toBeNull();
+  });
+
+  it('adoptFeatureBank populates job from an externally-produced feature bank', () => {
+    vi.stubGlobal('fetch', vi.fn().mockResolvedValue(modulesResponse()));
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    act(() => {
+      result.current.adoptFeatureBank({
+        featureId: 'ext-1', width: 32, height: 32, labels: ['x', 'y'], setupId: 'setup-sam',
+      });
+    });
+    expect(result.current.job).toEqual({
+      jobId: 'ext-1', width: 32, height: 32,
+      channels: [{ index: 0, label: 'x' }, { index: 1, label: 'y' }],
+      hasSam: true, setupId: 'setup-sam', cacheHit: undefined,
+    });
+  });
+
+  it('resets job/channelIndex/error and revokes the channel URL when source/kind/slice/serverUri changes', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(modulesResponse())
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ session_id: 'sess-1', project_id: 'proj-1' }) })
+      .mockResolvedValueOnce(preprocessResponse())
+      .mockResolvedValue({ ok: true, blob: async () => new Blob(['x']) });
+    vi.stubGlobal('fetch', fetchMock);
+    const { result, rerender } = renderHook((props) => useFeatureChannels(props), { initialProps: ARGS });
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    await act(async () => {
+      await result.current.compute();
+    });
+    await waitFor(() => expect(result.current.channelUrl).toBe('blob:mock-url'));
+
+    act(() => { rerender({ ...ARGS, sliceIndex: 1 }); });
+    expect(result.current.job).toBeNull();
+    expect(result.current.channelIndex).toBeNull();
+    expect(URL.revokeObjectURL).toHaveBeenCalledWith('blob:mock-url');
+  });
+
+  it('sets an error when the channel PNG fetch itself fails', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(modulesResponse())
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ session_id: 'sess-1', project_id: 'proj-1' }) })
+      .mockResolvedValueOnce(preprocessResponse())
+      .mockResolvedValueOnce({ ok: false, status: 500 });
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureChannels(ARGS));
+    await waitFor(() => expect(fetchMock).toHaveBeenCalledTimes(1));
+    await act(async () => {
+      await result.current.compute();
+    });
+    await waitFor(() => expect(result.current.error).toMatch(/Channel fetch failed/));
+    expect(result.current.channelUrl).toBeNull();
+  });
+});
diff --git a/frontend/src/hooks/useFeatureManifold.test.ts b/frontend/src/hooks/useFeatureManifold.test.ts
new file mode 100644
index 0000000..42b4f2d
--- /dev/null
+++ b/frontend/src/hooks/useFeatureManifold.test.ts
@@ -0,0 +1,297 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, renderHook, waitFor } from '@testing-library/react';
+import { DEFAULT_MANIFOLD_PARAMS, useFeatureManifold } from './useFeatureManifold';
+import type { Shape } from '@/stores/annotationStore';
+
+function sampleOk(overrides: Record<string, unknown> = {}) {
+  return {
+    ok: true,
+    json: async () => ({
+      sample_id: 'samp-1',
+      points: [{ x: 1, y: 2, radius: 5 }],
+      k: 24,
+      n_picked: 1,
+      n_subsample: 100,
+      explained_variance: 0.8,
+      radius: 5,
+      box_size: 64,
+      ...overrides,
+    }),
+  };
+}
+function heatmapOk() {
+  return { ok: true, blob: async () => new Blob(['x']) };
+}
+
+beforeEach(() => {
+  vi.stubGlobal('URL', Object.assign(URL, {
+    createObjectURL: vi.fn(() => 'blob:mock-url'),
+    revokeObjectURL: vi.fn(),
+  }));
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+  vi.useRealTimers();
+});
+
+describe('useFeatureManifold', () => {
+  it('starts with default params and empty results', () => {
+    vi.stubGlobal('fetch', vi.fn());
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: null }));
+    expect(result.current.params).toEqual(DEFAULT_MANIFOLD_PARAMS);
+    expect(result.current.points).toEqual([]);
+    expect(result.current.heatmapUrl).toBeNull();
+    expect(result.current.hasSample).toBe(false);
+    expect(result.current.meta).toBeNull();
+  });
+
+  it('sample() is a no-op with no featureJobId', async () => {
+    const fetchMock = vi.fn();
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: null }));
+    await act(async () => {
+      await result.current.sample();
+    });
+    expect(fetchMock).not.toHaveBeenCalled();
+  });
+
+  it('sample() posts feature_id/k/box_size, then fetches the heatmap, populating points/meta/heatmapUrl', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(sampleOk())
+      .mockResolvedValueOnce(heatmapOk());
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+
+    await act(async () => {
+      await result.current.sample();
+    });
+
+    expect(result.current.points).toEqual([{ x: 1, y: 2, radius: 5 }]);
+    expect(result.current.heatmapUrl).toBe('blob:mock-url');
+    expect(result.current.meta).toEqual({
+      k: 24, nPicked: 1, nSubsample: 100, explainedVariance: 0.8, radius: 5, boxSize: 64,
+      hasMask: undefined, maskPixels: undefined,
+    });
+    expect(result.current.hasSample).toBe(true);
+    expect(result.current.sampling).toBe(false);
+
+    const [sampleUrl, sampleInit] = fetchMock.mock.calls[0];
+    expect(sampleUrl).toBe('/api/ipred/manifold/sample');
+    const body = JSON.parse(sampleInit.body);
+    expect(body).toEqual({ feature_id: 'feat-1', k: 24, box_size: 64 });
+    const [heatUrl] = fetchMock.mock.calls[1];
+    expect(heatUrl).toBe('/api/ipred/manifold/samp-1/heatmap.png');
+  });
+
+  it('sample() includes shapes in the body when a placement mask is set', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(sampleOk())
+      .mockResolvedValueOnce(heatmapOk());
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+    const shapes: Shape[] = [{ id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 1, h: 1 }];
+
+    act(() => result.current.setPlacementMaskFromShapes(shapes));
+    expect(result.current.placementMask).toEqual(shapes);
+
+    await act(async () => {
+      await result.current.sample();
+    });
+    const body = JSON.parse(fetchMock.mock.calls[0][1].body);
+    expect(body.shapes).toEqual(shapes);
+  });
+
+  it('setPlacementMaskFromShapes([]) clears the mask instead of storing an empty array', () => {
+    vi.stubGlobal('fetch', vi.fn());
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+    act(() => result.current.setPlacementMaskFromShapes([]));
+    expect(result.current.placementMask).toBeNull();
+  });
+
+  it('clearPlacementMask resets placementMask to null', () => {
+    vi.stubGlobal('fetch', vi.fn());
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+    act(() => result.current.setPlacementMaskFromShapes([
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 1, h: 1 },
+    ]));
+    act(() => result.current.clearPlacementMask());
+    expect(result.current.placementMask).toBeNull();
+  });
+
+  it('sample() surfaces a parsed JSON `detail` error message and revokes any prior heatmap', async () => {
+    const fetchMock = vi.fn().mockResolvedValueOnce({
+      ok: false,
+      text: async () => JSON.stringify({ detail: 'bad k value' }),
+    });
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+    await act(async () => {
+      await result.current.sample();
+    });
+    expect(result.current.error).toBe('bad k value');
+    expect(result.current.heatmapUrl).toBeNull();
+    expect(result.current.sampling).toBe(false);
+  });
+
+  it('sample() falls back to the raw body text when it is not JSON', async () => {
+    const fetchMock = vi.fn().mockResolvedValueOnce({ ok: false, status: 500, text: async () => 'plain text error' });
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+    await act(async () => {
+      await result.current.sample();
+    });
+    expect(result.current.error).toBe('plain text error');
+  });
+
+  it('sample() treats a "not found"/"unknown feature" detail specially: calls onFeatureJobExpired and shows a recompute hint', async () => {
+    const onFeatureJobExpired = vi.fn();
+    const fetchMock = vi.fn().mockResolvedValueOnce({
+      ok: false,
+      text: async () => JSON.stringify({ detail: 'unknown feature id' }),
+    });
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1', onFeatureJobExpired }));
+    await act(async () => {
+      await result.current.sample();
+    });
+    expect(onFeatureJobExpired).toHaveBeenCalledTimes(1);
+    expect(result.current.error).toMatch(/Compute again/);
+  });
+
+  it('sample() errors when the heatmap fetch itself fails, after a successful sample POST', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(sampleOk())
+      .mockResolvedValueOnce({ ok: false, status: 500 });
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+    await act(async () => {
+      await result.current.sample();
+    });
+    expect(result.current.error).toBe('Failed to fetch manifold heatmap');
+    expect(result.current.heatmapUrl).toBeNull();
+  });
+
+  it('a concurrent sample() call while one is in-flight is ignored', async () => {
+    let resolveFirst!: (v: unknown) => void;
+    const fetchMock = vi.fn()
+      .mockReturnValueOnce(new Promise((resolve) => { resolveFirst = resolve; }))
+      .mockResolvedValueOnce(heatmapOk());
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+
+    let p1: Promise<void>;
+    let p2: Promise<void>;
+    act(() => {
+      p1 = result.current.sample();
+      p2 = result.current.sample();
+    });
+    expect(fetchMock).toHaveBeenCalledTimes(1); // second call short-circuited by samplingRef
+
+    await act(async () => {
+      resolveFirst(sampleOk());
+      await Promise.all([p1, p2]);
+    });
+  });
+
+  it('resets heatmap/points/meta/error/placementMask when featureJobId changes', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(sampleOk())
+      .mockResolvedValueOnce(heatmapOk());
+    vi.stubGlobal('fetch', fetchMock);
+    const { result, rerender } = renderHook(
+      ({ featureJobId }) => useFeatureManifold({ featureJobId }),
+      { initialProps: { featureJobId: 'feat-1' } },
+    );
+    await act(async () => {
+      await result.current.sample();
+    });
+    expect(result.current.hasSample).toBe(true);
+
+    act(() => { rerender({ featureJobId: 'feat-2' }); });
+    expect(result.current.heatmapUrl).toBeNull();
+    expect(result.current.points).toEqual([]);
+    expect(result.current.meta).toBeNull();
+    expect(result.current.placementMask).toBeNull();
+    expect(URL.revokeObjectURL).toHaveBeenCalledWith('blob:mock-url');
+  });
+
+  it('debounces a re-sample 350ms after k/boxSize changes, but only once a sample already exists', async () => {
+    vi.useFakeTimers({ shouldAdvanceTime: true });
+    const fetchMock = vi.fn()
+      .mockResolvedValue(sampleOk())
+      // interleave heatmap responses; every other call is the heatmap fetch
+      ;
+    fetchMock.mockImplementation(async (url: string) => (url.includes('heatmap') ? heatmapOk() : sampleOk()));
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+
+    await act(async () => {
+      await result.current.sample();
+    });
+    expect(result.current.hasSample).toBe(true);
+    fetchMock.mockClear();
+
+    act(() => { result.current.setParams((p) => ({ ...p, k: 40 })); });
+    // Not yet — debounce hasn't elapsed.
+    await act(async () => { await vi.advanceTimersByTimeAsync(200); });
+    expect(fetchMock).not.toHaveBeenCalled();
+
+    await act(async () => { await vi.advanceTimersByTimeAsync(200); });
+    await waitFor(() => expect(fetchMock).toHaveBeenCalled());
+  });
+
+  it('does not auto re-sample on param change before any sample has been taken', async () => {
+    vi.useFakeTimers({ shouldAdvanceTime: true });
+    const fetchMock = vi.fn().mockResolvedValue(sampleOk());
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+
+    act(() => { result.current.setParams((p) => ({ ...p, k: 40 })); });
+    await act(async () => { await vi.advanceTimersByTimeAsync(500); });
+    expect(fetchMock).not.toHaveBeenCalled();
+  });
+
+  it('dismiss() clears the heatmap/points/error', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(sampleOk())
+      .mockResolvedValueOnce(heatmapOk());
+    vi.stubGlobal('fetch', fetchMock);
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+    await act(async () => {
+      await result.current.sample();
+    });
+    expect(result.current.hasSample).toBe(true);
+
+    act(() => result.current.dismiss());
+    expect(result.current.heatmapUrl).toBeNull();
+    expect(result.current.points).toEqual([]);
+    expect(result.current.error).toBeNull();
+    expect(result.current.hasSample).toBe(false);
+  });
+
+  it('revokes the heatmap object URL on unmount', async () => {
+    const fetchMock = vi.fn()
+      .mockResolvedValueOnce(sampleOk())
+      .mockResolvedValueOnce(heatmapOk());
+    vi.stubGlobal('fetch', fetchMock);
+    const { result, unmount } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+    await act(async () => {
+      await result.current.sample();
+    });
+    unmount();
+    expect(URL.revokeObjectURL).toHaveBeenCalledWith('blob:mock-url');
+  });
+
+  it('setShowHeatmap/setShowMarkers/setHeatmapOpacity update their state', () => {
+    vi.stubGlobal('fetch', vi.fn());
+    const { result } = renderHook(() => useFeatureManifold({ featureJobId: 'feat-1' }));
+    act(() => result.current.setShowHeatmap(false));
+    expect(result.current.showHeatmap).toBe(false);
+    act(() => result.current.setShowMarkers(false));
+    expect(result.current.showMarkers).toBe(false);
+    act(() => result.current.setHeatmapOpacity(0.9));
+    expect(result.current.heatmapOpacity).toBe(0.9);
+  });
+});
diff --git a/frontend/src/hooks/useGuideSync.test.tsx b/frontend/src/hooks/useGuideSync.test.tsx
new file mode 100644
index 0000000..d6b1a73
--- /dev/null
+++ b/frontend/src/hooks/useGuideSync.test.tsx
@@ -0,0 +1,132 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, renderHook, waitFor } from '@testing-library/react';
+import {
+  generateGuide, loadGuide, saveGuide, useGuideLoad, useGuideSync,
+} from './useGuideSync';
+import { useReferenceGuideStore } from '@/stores/referenceGuideStore';
+
+beforeEach(() => {
+  useReferenceGuideStore.getState().clear();
+  vi.stubGlobal('fetch', vi.fn());
+});
+
+afterEach(() => {
+  vi.unstubAllGlobals();
+  vi.useRealTimers();
+});
+
+describe('loadGuide', () => {
+  it('returns null on a 404 (no guide exists)', async () => {
+    (fetch as any).mockResolvedValue({ status: 404, ok: false });
+    expect(await loadGuide('local:x.tif')).toBeNull();
+  });
+
+  it('returns null on any fetch error (never throws)', async () => {
+    (fetch as any).mockRejectedValue(new Error('network down'));
+    expect(await loadGuide('local:x.tif')).toBeNull();
+  });
+
+  it('parses classes/notes from a successful response', async () => {
+    (fetch as any).mockResolvedValue({
+      status: 200, ok: true,
+      json: async () => ({ guide: { classes: [{ label: 'Cell', color: '#f00', description: '', exampleCrops: [] }], notes: 'hi' } }),
+    });
+    expect(await loadGuide('local:x.tif')).toEqual({
+      classes: [{ label: 'Cell', color: '#f00', description: '', exampleCrops: [] }], notes: 'hi',
+    });
+  });
+
+  it('defaults to empty classes/notes when the guide object is bare', async () => {
+    (fetch as any).mockResolvedValue({ status: 200, ok: true, json: async () => ({ guide: {} }) });
+    expect(await loadGuide('local:x.tif')).toEqual({ classes: [], notes: '' });
+  });
+});
+
+describe('saveGuide', () => {
+  it('PUTs the classes/notes and returns true on success', async () => {
+    (fetch as any).mockResolvedValue({ ok: true });
+    const ok = await saveGuide('local:x.tif', [], 'notes');
+    expect(ok).toBe(true);
+    const [url, init] = (fetch as any).mock.calls[0];
+    expect(url).toContain('/api/guide?source_key=');
+    expect(init.method).toBe('PUT');
+    expect(JSON.parse(init.body)).toEqual({ classes: [], notes: 'notes' });
+  });
+
+  it('returns false without throwing on a network error', async () => {
+    (fetch as any).mockRejectedValue(new Error('down'));
+    expect(await saveGuide('local:x.tif', [], '')).toBe(false);
+  });
+
+  it('returns false when the server responds not-ok', async () => {
+    (fetch as any).mockResolvedValue({ ok: false });
+    expect(await saveGuide('local:x.tif', [], '')).toBe(false);
+  });
+});
+
+describe('generateGuide', () => {
+  it('POSTs the payload and returns the generated guide', async () => {
+    (fetch as any).mockResolvedValue({
+      ok: true, json: async () => ({ classes: [{ label: 'Pore', color: '#000', description: '', exampleCrops: [] }], notes: '' }),
+    });
+    const result = await generateGuide('local:x.tif', { classes: [], slices: {} });
+    expect(result.classes).toHaveLength(1);
+  });
+
+  it('throws a descriptive error including the response detail on failure', async () => {
+    (fetch as any).mockResolvedValue({ ok: false, status: 500, text: async () => 'boom' });
+    await expect(generateGuide('local:x.tif', { classes: [], slices: {} })).rejects.toThrow(/500.*boom/);
+  });
+});
+
+describe('useGuideLoad', () => {
+  it('loads the guide into the store for a given sourceKey', async () => {
+    (fetch as any).mockResolvedValue({
+      status: 200, ok: true, json: async () => ({ guide: { classes: [{ label: 'Cell', color: '#f00', description: '', exampleCrops: [] }], notes: 'n' } }),
+    });
+    renderHook(() => useGuideLoad('local:x.tif'));
+    await waitFor(() => expect(useReferenceGuideStore.getState().loadedFor).toBe('local:x.tif'));
+    expect(useReferenceGuideStore.getState().entries).toHaveLength(1);
+  });
+
+  it('clears the store when sourceKey is null', () => {
+    useReferenceGuideStore.getState().setGuide([{ label: 'x', color: '#000', description: '', exampleCrops: [] }], 'n', 'local:y.tif');
+    renderHook(() => useGuideLoad(null));
+    expect(useReferenceGuideStore.getState().entries).toEqual([]);
+  });
+});
+
+describe('useGuideSync', () => {
+  it('debounces an autosave after the guide has loaded for this source', async () => {
+    vi.useFakeTimers({ shouldAdvanceTime: true });
+    (fetch as any).mockImplementation(async (url: string) => {
+      if (url.includes('source_key=local%3Ax.tif') && !url.includes('generate')) {
+        return { status: 200, ok: true, json: async () => ({ guide: { classes: [], notes: '' } }) };
+      }
+      return { ok: true };
+    });
+    renderHook(() => useGuideSync('local:x.tif'));
+
+    await waitFor(() => expect(useReferenceGuideStore.getState().loadedFor).toBe('local:x.tif'));
+    (fetch as any).mockClear();
+
+    act(() => {
+      useReferenceGuideStore.getState().setGuide([{ label: 'Cell', color: '#f00', description: '', exampleCrops: [] }], '', 'local:x.tif');
+    });
+    await vi.advanceTimersByTimeAsync(1000);
+    expect(fetch).toHaveBeenCalledWith(
+      expect.stringContaining('/api/guide?source_key='),
+      expect.objectContaining({ method: 'PUT' }),
+    );
+  });
+
+  it('does not autosave before the guide has finished loading for this source', async () => {
+    vi.useFakeTimers({ shouldAdvanceTime: true });
+    (fetch as any).mockReturnValue(new Promise(() => {})); // never resolves -> loadedFor stays unset
+    renderHook(() => useGuideSync('local:x.tif'));
+    await vi.advanceTimersByTimeAsync(2000);
+    // Only the initial GET attempt (still pending), no PUT.
+    const putCalls = (fetch as any).mock.calls.filter(([, init]: any) => init?.method === 'PUT');
+    expect(putCalls).toHaveLength(0);
+  });
+});
diff --git a/frontend/src/hooks/useHubSelectedTabs.test.ts b/frontend/src/hooks/useHubSelectedTabs.test.ts
new file mode 100644
index 0000000..9988b55
--- /dev/null
+++ b/frontend/src/hooks/useHubSelectedTabs.test.ts
@@ -0,0 +1,61 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, renderHook } from '@testing-library/react';
+import { useHubSelectedTabs } from './useHubSelectedTabs';
+
+beforeEach(() => {
+  localStorage.clear();
+});
+
+afterEach(() => {
+  vi.restoreAllMocks();
+});
+
+describe('useHubSelectedTabs', () => {
+  it('starts null with nothing stored', () => {
+    const { result } = renderHook(() => useHubSelectedTabs());
+    expect(result.current.selectedPaths).toBeNull();
+  });
+
+  it('loads previously stored paths on mount', () => {
+    localStorage.setItem('sam3_hub_selected_tab_paths', JSON.stringify(['/connect', '/browse']));
+    const { result } = renderHook(() => useHubSelectedTabs());
+    expect(result.current.selectedPaths).toEqual(['/connect', '/browse']);
+  });
+
+  it('ignores malformed JSON and starts null', () => {
+    localStorage.setItem('sam3_hub_selected_tab_paths', 'not json');
+    const { result } = renderHook(() => useHubSelectedTabs());
+    expect(result.current.selectedPaths).toBeNull();
+  });
+
+  it('ignores a non-array stored value', () => {
+    localStorage.setItem('sam3_hub_selected_tab_paths', JSON.stringify({ a: 1 }));
+    const { result } = renderHook(() => useHubSelectedTabs());
+    expect(result.current.selectedPaths).toBeNull();
+  });
+
+  it('ignores an array with non-string items', () => {
+    localStorage.setItem('sam3_hub_selected_tab_paths', JSON.stringify([1, 2, 3]));
+    const { result } = renderHook(() => useHubSelectedTabs());
+    expect(result.current.selectedPaths).toBeNull();
+  });
+
+  it('setSelectedPaths writes through to localStorage and updates state', () => {
+    const { result } = renderHook(() => useHubSelectedTabs());
+    act(() => result.current.setSelectedPaths(['/annotate']));
+    expect(result.current.selectedPaths).toEqual(['/annotate']);
+    expect(JSON.parse(localStorage.getItem('sam3_hub_selected_tab_paths')!)).toEqual(['/annotate']);
+  });
+
+  it('logs and keeps prior state when localStorage.setItem throws', () => {
+    const errorSpy = vi.spyOn(console, 'error').mockImplementation(() => {});
+    const setItemSpy = vi.spyOn(Storage.prototype, 'setItem').mockImplementation(() => {
+      throw new Error('quota exceeded');
+    });
+    const { result } = renderHook(() => useHubSelectedTabs());
+    act(() => result.current.setSelectedPaths(['/annotate']));
+    expect(result.current.selectedPaths).toBeNull();
+    expect(errorSpy).toHaveBeenCalled();
+    setItemSpy.mockRestore();
+  });
+});
diff --git a/frontend/src/hooks/useImageSlice.test.tsx b/frontend/src/hooks/useImageSlice.test.tsx
new file mode 100644
index 0000000..2c1f0e4
--- /dev/null
+++ b/frontend/src/hooks/useImageSlice.test.tsx
@@ -0,0 +1,75 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, renderHook, waitFor } from '@testing-library/react';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import type { ReactNode } from 'react';
+import { useImageSlice } from './useImageSlice';
+import type { RenderOpts } from '@/stores/datasetStore';
+
+function wrapper({ children }: { children: ReactNode }) {
+  const client = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return <QueryClientProvider client={client}>{children}</QueryClientProvider>;
+}
+
+const RENDER_OPTS: RenderOpts = { norm: 'slice', scale: 'linear', vminPct: 1, vmaxPct: 99, cmap: 'gray' };
+
+beforeEach(() => {
+  vi.stubGlobal('fetch', vi.fn());
+  vi.stubGlobal('URL', class extends URL {
+    static createObjectURL = vi.fn(() => 'blob:fake-url');
+    static revokeObjectURL = vi.fn();
+  });
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('useImageSlice', () => {
+  it('is disabled (no fetch) when source is null', () => {
+    const { result } = renderHook(
+      () => useImageSlice(null, 'local', 0, RENDER_OPTS, null),
+      { wrapper },
+    );
+    expect(result.current.fetchStatus).toBe('idle');
+    expect(fetch).not.toHaveBeenCalled();
+  });
+
+  it('is disabled when kind is null', () => {
+    renderHook(() => useImageSlice('x.tif', null, 0, RENDER_OPTS, null), { wrapper });
+    expect(fetch).not.toHaveBeenCalled();
+  });
+
+  it('fetches the slice and resolves to a blob object URL', async () => {
+    const fakeBlob = new Blob(['data']);
+    (fetch as any).mockResolvedValue({ ok: true, blob: async () => fakeBlob });
+    const { result } = renderHook(
+      () => useImageSlice('x.tif', 'local', 0, RENDER_OPTS, null),
+      { wrapper },
+    );
+    await waitFor(() => expect(result.current.isSuccess).toBe(true));
+    expect(result.current.data).toBe('blob:fake-url');
+    expect(fetch).toHaveBeenCalledWith(expect.stringContaining('/api/image/slice?'));
+  });
+
+  it('surfaces a fetch failure as an error state', async () => {
+    (fetch as any).mockResolvedValue({ ok: false, status: 500 });
+    const { result } = renderHook(
+      () => useImageSlice('x.tif', 'local', 0, RENDER_OPTS, null),
+      { wrapper },
+    );
+    await waitFor(() => expect(result.current.isError).toBe(true));
+  });
+
+  it('includes the denoise method/strength in the request URL when active', async () => {
+    (fetch as any).mockResolvedValue({ ok: true, blob: async () => new Blob() });
+    renderHook(
+      () => useImageSlice('x.tif', 'local', 0, RENDER_OPTS, null, { method: 'median', strength: 0.4 }),
+      { wrapper },
+    );
+    await waitFor(() => expect(fetch).toHaveBeenCalled());
+    const url = (fetch as any).mock.calls[0][0];
+    expect(url).toContain('denoise_method=median');
+    expect(url).toContain('denoise_strength=0.4');
+  });
+});
diff --git a/frontend/src/hooks/useKeybinds.test.ts b/frontend/src/hooks/useKeybinds.test.ts
new file mode 100644
index 0000000..b7e1dae
--- /dev/null
+++ b/frontend/src/hooks/useKeybinds.test.ts
@@ -0,0 +1,199 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, renderHook } from '@testing-library/react';
+import { useKeybinds } from './useKeybinds';
+import { useToolStore } from '@/stores/toolStore';
+import { useDatasetStore } from '@/stores/datasetStore';
+import { useClassStore } from '@/stores/classStore';
+import * as editHistory from '@/hooks/editHistory';
+
+const INITIAL_TOOL_STATE = useToolStore.getState();
+const INITIAL_DATASET_STATE = useDatasetStore.getState();
+
+function openSample(nSlices = 5) {
+  useDatasetStore.getState().setDataset('local', 'x.tif', null, {
+    nSlices,
+    height: 10,
+    width: 10,
+    dtype: 'uint8',
+    isRgb: false,
+    valueRange: [0, 255],
+  });
+}
+
+function keydown(key: string, opts: Partial<KeyboardEventInit> = {}) {
+  act(() => {
+    window.dispatchEvent(new KeyboardEvent('keydown', { key, bubbles: true, cancelable: true, ...opts }));
+  });
+}
+function keyup(key: string, opts: Partial<KeyboardEventInit> = {}) {
+  act(() => {
+    window.dispatchEvent(new KeyboardEvent('keyup', { key, bubbles: true, cancelable: true, ...opts }));
+  });
+}
+
+describe('useKeybinds', () => {
+  let onActivateClass: ReturnType<typeof vi.fn>;
+  let onNewBrushInstance: ReturnType<typeof vi.fn>;
+  let onDeleteSelected: ReturnType<typeof vi.fn>;
+  let onCancelDraft: ReturnType<typeof vi.fn>;
+
+  beforeEach(() => {
+    useToolStore.setState(INITIAL_TOOL_STATE, true);
+    useDatasetStore.setState(INITIAL_DATASET_STATE, true);
+    useClassStore.setState({ classes: [] });
+    onActivateClass = vi.fn();
+    onNewBrushInstance = vi.fn();
+    onDeleteSelected = vi.fn();
+    onCancelDraft = vi.fn();
+  });
+
+  afterEach(() => {
+    cleanup();
+  });
+
+  function mount(activeClassId: number | null = null) {
+    return renderHook(() =>
+      useKeybinds(activeClassId, onActivateClass, onNewBrushInstance, onDeleteSelected, onCancelDraft),
+    );
+  }
+
+  it('switches tools on their letter key', () => {
+    mount();
+    keydown('p');
+    expect(useToolStore.getState().tool).toBe('polygon');
+    keydown('b');
+    expect(useToolStore.getState().tool).toBe('brush');
+    keydown('r');
+    expect(useToolStore.getState().tool).toBe('eraser');
+    keydown('g');
+    expect(useToolStore.getState().tool).toBe('magic');
+  });
+
+  it('ignores keys typed into an editable target (input/textarea/select)', () => {
+    mount();
+    const input = document.createElement('input');
+    document.body.appendChild(input);
+    act(() => {
+      input.dispatchEvent(new KeyboardEvent('keydown', { key: 'p', bubbles: true }));
+    });
+    expect(useToolStore.getState().tool).not.toBe('polygon');
+    document.body.removeChild(input);
+  });
+
+  it('activates a class 1-9 via digit keys, mapped by position', () => {
+    useClassStore.setState({
+      classes: [
+        { classId: 10, label: 'A', color: '#f00', isVisible: true },
+        { classId: 20, label: 'B', color: '#0f0', isVisible: true },
+      ],
+    });
+    mount();
+    keydown('2');
+    expect(onActivateClass).toHaveBeenCalledWith(20);
+  });
+
+  it('does nothing for a digit beyond the class list length', () => {
+    useClassStore.setState({ classes: [{ classId: 1, label: 'A', color: '#f00', isVisible: true }] });
+    mount();
+    keydown('5');
+    expect(onActivateClass).not.toHaveBeenCalled();
+  });
+
+  it('holding Space switches to pan and releasing restores the previous tool', () => {
+    mount();
+    act(() => { useToolStore.getState().setTool('brush'); });
+    keydown(' ');
+    expect(useToolStore.getState().tool).toBe('pan');
+    expect(useToolStore.getState().panReturnTool).toBe('brush');
+
+    keyup(' ');
+    expect(useToolStore.getState().tool).toBe('brush');
+    expect(useToolStore.getState().panReturnTool).toBeNull();
+  });
+
+  it('a repeated (auto-repeat) Space keydown does not re-arm the pan-return tool', () => {
+    mount();
+    act(() => { useToolStore.getState().setTool('brush'); });
+    keydown(' ');
+    expect(useToolStore.getState().tool).toBe('pan');
+    // Simulate OS key-repeat: tool is already 'pan', repeat=true should just bail out.
+    keydown(' ', { repeat: true });
+    expect(useToolStore.getState().panReturnTool).toBe('brush');
+  });
+
+  it('Ctrl/Cmd+Z calls editHistory.undo(); Ctrl+Shift+Z and Ctrl+Y call redo()', () => {
+    const undoSpy = vi.spyOn(editHistory, 'undo').mockImplementation(() => {});
+    const redoSpy = vi.spyOn(editHistory, 'redo').mockImplementation(() => {});
+    mount();
+
+    keydown('z', { ctrlKey: true });
+    expect(undoSpy).toHaveBeenCalledTimes(1);
+    expect(redoSpy).not.toHaveBeenCalled();
+
+    keydown('z', { ctrlKey: true, shiftKey: true });
+    expect(redoSpy).toHaveBeenCalledTimes(1);
+
+    keydown('y', { ctrlKey: true });
+    expect(redoSpy).toHaveBeenCalledTimes(2);
+
+    keydown('z', { metaKey: true });
+    expect(undoSpy).toHaveBeenCalledTimes(2);
+
+    undoSpy.mockRestore();
+    redoSpy.mockRestore();
+  });
+
+  it('"t" requests a fit-to-screen (bumps fitRequestId)', () => {
+    mount();
+    const before = useToolStore.getState().fitRequestId;
+    keydown('t');
+    expect(useToolStore.getState().fitRequestId).toBe(before + 1);
+  });
+
+  it('"x" advances to the next slice, clamped to the last slice', () => {
+    openSample(3);
+    mount();
+    keydown('x');
+    expect(useDatasetStore.getState().currentSlice).toBe(1);
+    keydown('x');
+    keydown('x');
+    keydown('x'); // beyond the end, clamps to nSlices-1
+    expect(useDatasetStore.getState().currentSlice).toBe(2);
+  });
+
+  it('ArrowLeft/ArrowRight navigate slices, clamped at 0', () => {
+    openSample(3);
+    mount();
+    keydown('ArrowLeft'); // already at 0, clamps
+    expect(useDatasetStore.getState().currentSlice).toBe(0);
+    keydown('ArrowRight');
+    expect(useDatasetStore.getState().currentSlice).toBe(1);
+    keydown('ArrowLeft');
+    expect(useDatasetStore.getState().currentSlice).toBe(0);
+  });
+
+  it('slice navigation is a no-op with no dataset loaded (meta is null)', () => {
+    mount();
+    keydown('x');
+    expect(useDatasetStore.getState().currentSlice).toBe(0);
+  });
+
+  it('"n" triggers onNewBrushInstance, Delete/Backspace trigger onDeleteSelected, Escape triggers onCancelDraft', () => {
+    mount();
+    keydown('n');
+    expect(onNewBrushInstance).toHaveBeenCalledTimes(1);
+    keydown('Delete');
+    expect(onDeleteSelected).toHaveBeenCalledTimes(1);
+    keydown('Backspace');
+    expect(onDeleteSelected).toHaveBeenCalledTimes(2);
+    keydown('Escape');
+    expect(onCancelDraft).toHaveBeenCalledTimes(1);
+  });
+
+  it('removes its listeners on unmount', () => {
+    const { unmount } = mount();
+    unmount();
+    keydown('p');
+    expect(useToolStore.getState().tool).not.toBe('polygon');
+  });
+});
diff --git a/frontend/src/hooks/useMaskOps.test.ts b/frontend/src/hooks/useMaskOps.test.ts
new file mode 100644
index 0000000..b39b89f
--- /dev/null
+++ b/frontend/src/hooks/useMaskOps.test.ts
@@ -0,0 +1,91 @@
+import { beforeEach, describe, expect, it } from 'vitest';
+import { renderHook } from '@testing-library/react';
+import { useMaskOps } from './useMaskOps';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useDatasetStore } from '@/stores/datasetStore';
+
+const SOURCE = 'local:x.tif';
+
+beforeEach(() => {
+  useAnnotationStore.getState().reset();
+  useDatasetStore.setState({
+    meta: { nSlices: 5, height: 64, width: 64, dtype: 'uint8', isRgb: false, valueRange: [0, 255] },
+    currentSlice: 0,
+  } as any);
+});
+
+describe('useMaskOps.applyCleanup', () => {
+  it('returns false with no sourceKey', () => {
+    const { result } = renderHook(() => useMaskOps(null, 1));
+    expect(result.current.applyCleanup('fill')).toBe(false);
+  });
+
+  it('returns false with no active class', () => {
+    const { result } = renderHook(() => useMaskOps(SOURCE, null));
+    expect(result.current.applyCleanup('fill')).toBe(false);
+  });
+
+  it('returns false with no dataset meta loaded', () => {
+    useDatasetStore.setState({ meta: null } as any);
+    const { result } = renderHook(() => useMaskOps(SOURCE, 1));
+    expect(result.current.applyCleanup('fill')).toBe(false);
+  });
+
+  it('returns false when the active class has no shapes on the current slice', () => {
+    const { result } = renderHook(() => useMaskOps(SOURCE, 1));
+    expect(result.current.applyCleanup('fill')).toBe(false);
+  });
+
+  it('re-rasterizes the active class shapes through a fill-holes pass', () => {
+    useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 5, y: 5, w: 20, h: 20 },
+    ]);
+    const { result } = renderHook(() => useMaskOps(SOURCE, 1));
+    const ok = result.current.applyCleanup('fill');
+    expect(ok).toBe(true);
+    const shapes = useAnnotationStore.getState().byImage[SOURCE]['0'];
+    expect(shapes.length).toBeGreaterThan(0);
+    expect(shapes.every((s) => s.classId === 1)).toBe(true);
+  });
+
+  it('only touches shapes of the active class, leaving other classes untouched', () => {
+    useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 5, y: 5, w: 20, h: 20 },
+    ]);
+    useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 2, [
+      { id: 's2', classId: 2, kind: 'rectangle', x: 40, y: 40, w: 10, h: 10 },
+    ]);
+    const { result } = renderHook(() => useMaskOps(SOURCE, 1));
+    result.current.applyCleanup('grow', 2);
+    const slice = useAnnotationStore.getState().byImage[SOURCE]['0'];
+    expect(slice.some((s) => s.classId === 2)).toBe(true);
+  });
+
+  it.each(['fill', 'islands', 'smooth', 'grow', 'shrink'] as const)('%s op runs without throwing', (op) => {
+    useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 5, y: 5, w: 20, h: 20 },
+    ]);
+    const { result } = renderHook(() => useMaskOps(SOURCE, 1));
+    expect(() => result.current.applyCleanup(op)).not.toThrow();
+  });
+});
+
+describe('useMaskOps.copyToNext', () => {
+  it('returns false at the last slice', () => {
+    useDatasetStore.setState({ currentSlice: 4 } as any);
+    useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 4, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+    ]);
+    const { result } = renderHook(() => useMaskOps(SOURCE, 1));
+    expect(result.current.copyToNext()).toBe(false);
+  });
+
+  it('copies the active class shapes to the next slice', () => {
+    useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 1, [
+      { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+    ]);
+    const { result } = renderHook(() => useMaskOps(SOURCE, 1));
+    expect(result.current.copyToNext()).toBe(true);
+    expect(useAnnotationStore.getState().byImage[SOURCE]['1']).toHaveLength(1);
+  });
+});
diff --git a/frontend/src/hooks/useOpenInAnnotate.test.tsx b/frontend/src/hooks/useOpenInAnnotate.test.tsx
new file mode 100644
index 0000000..67c1936
--- /dev/null
+++ b/frontend/src/hooks/useOpenInAnnotate.test.tsx
@@ -0,0 +1,178 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, renderHook } from '@testing-library/react';
+import { useOpenInAnnotate } from './useOpenInAnnotate';
+import { useDatasetStore } from '@/stores/datasetStore';
+import { useClassStore } from '@/stores/classStore';
+import { useAnnotationStore } from '@/stores/annotationStore';
+
+const navigateMock = vi.fn();
+vi.mock('react-router', () => ({
+  useNavigate: () => navigateMock,
+}));
+
+function metaResponse(overrides: Record<string, unknown> = {}) {
+  return {
+    ok: true,
+    json: async () => ({
+      n_slices: 5,
+      height: 100,
+      width: 200,
+      dtype: 'uint8',
+      is_rgb: false,
+      value_range: [0, 255],
+      keywords: [],
+      ...overrides,
+    }),
+  };
+}
+
+function draft404() {
+  return { status: 404, ok: false };
+}
+
+beforeEach(() => {
+  useDatasetStore.getState().reset();
+  useClassStore.setState({ classes: [] });
+  useAnnotationStore.getState().reset();
+  navigateMock.mockClear();
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('useOpenInAnnotate', () => {
+  describe('openTiledArray', () => {
+    it('fetches meta, seeds the dataset store, and navigates to /annotate', async () => {
+      vi.stubGlobal('fetch', vi.fn()
+        .mockResolvedValueOnce(metaResponse())
+        .mockResolvedValueOnce(draft404()));
+      const { result } = renderHook(() => useOpenInAnnotate());
+
+      await act(async () => {
+        await result.current.openTiledArray('browse/ds/img', 'http://srv');
+      });
+
+      expect(useDatasetStore.getState().kind).toBe('tiled');
+      expect(useDatasetStore.getState().source).toBe('browse/ds/img');
+      expect(useDatasetStore.getState().serverUri).toBe('http://srv');
+      expect(useDatasetStore.getState().meta?.nSlices).toBe(5);
+      expect(navigateMock).toHaveBeenCalledWith('/annotate');
+
+      const [url] = (fetch as any).mock.calls[0];
+      expect(url).toContain('/api/image/meta?');
+      expect(url).toContain('source=browse%2Fds%2Fimg');
+      expect(url).toContain('kind=tiled');
+      expect(url).toContain('server_uri=');
+    });
+
+    it('jumps to the requested initialSlice, clamped to the last slice', async () => {
+      vi.stubGlobal('fetch', vi.fn()
+        .mockResolvedValueOnce(metaResponse({ n_slices: 5 }))
+        .mockResolvedValueOnce(draft404()));
+      const { result } = renderHook(() => useOpenInAnnotate());
+
+      await act(async () => {
+        await result.current.openTiledArray('browse/ds/img', 'http://srv', 999);
+      });
+      expect(useDatasetStore.getState().currentSlice).toBe(4); // clamped to n_slices-1
+    });
+
+    it('does not override slice 0 when initialSlice is 0 (default)', async () => {
+      vi.stubGlobal('fetch', vi.fn()
+        .mockResolvedValueOnce(metaResponse())
+        .mockResolvedValueOnce(draft404()));
+      const { result } = renderHook(() => useOpenInAnnotate());
+      await act(async () => {
+        await result.current.openTiledArray('browse/ds/img', 'http://srv');
+      });
+      expect(useDatasetStore.getState().currentSlice).toBe(0);
+    });
+
+    it('seeds classes from meta.keywords when the draft has none', async () => {
+      vi.stubGlobal('fetch', vi.fn()
+        .mockResolvedValueOnce(metaResponse({ keywords: ['Cell', 'Pore', 'cell'] }))
+        .mockResolvedValueOnce(draft404()));
+      const { result } = renderHook(() => useOpenInAnnotate());
+      await act(async () => {
+        await result.current.openTiledArray('browse/ds/img', 'http://srv');
+      });
+      const classes = useClassStore.getState().classes;
+      // 'cell' is deduped case-insensitively against 'Cell'.
+      expect(classes.map((c) => c.label)).toEqual(['Cell', 'Pore']);
+    });
+
+    it('adopts classes from a loaded draft instead of keyword-seeding', async () => {
+      vi.stubGlobal('fetch', vi.fn()
+        .mockResolvedValueOnce(metaResponse({ keywords: ['ShouldNotAppear'] }))
+        .mockResolvedValueOnce({
+          status: 200,
+          ok: true,
+          json: async () => ({
+            payload: {
+              classes: [{ classId: 1, label: 'FromDraft', color: '#000', isVisible: true }],
+              slices: { '0': [{ id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 1, h: 1 }] },
+              split_by_slice: {},
+              negative_slices: [],
+            },
+          }),
+        }));
+      const { result } = renderHook(() => useOpenInAnnotate());
+      await act(async () => {
+        await result.current.openTiledArray('browse/ds/img', 'http://srv');
+      });
+      expect(useClassStore.getState().classes).toEqual([
+        { classId: 1, label: 'FromDraft', color: '#000', isVisible: true },
+      ]);
+      const sourceKey = 'tiled:http://srv:browse/ds/img';
+      expect(useAnnotationStore.getState().byImage[sourceKey]?.['0']).toHaveLength(1);
+    });
+
+    it('resets to empty classes when neither the draft nor keywords supply any', async () => {
+      useClassStore.setState({ classes: [{ classId: 9, label: 'Stale', color: '#fff', isVisible: true }] });
+      vi.stubGlobal('fetch', vi.fn()
+        .mockResolvedValueOnce(metaResponse({ keywords: [] }))
+        .mockResolvedValueOnce(draft404()));
+      const { result } = renderHook(() => useOpenInAnnotate());
+      await act(async () => {
+        await result.current.openTiledArray('browse/ds/img', 'http://srv');
+      });
+      expect(useClassStore.getState().classes).toEqual([]);
+    });
+
+    it('throws with the response body when the meta fetch fails', async () => {
+      vi.stubGlobal('fetch', vi.fn().mockResolvedValue({ ok: false, text: async () => 'not found' }));
+      const { result } = renderHook(() => useOpenInAnnotate());
+      await expect(result.current.openTiledArray('browse/ds/img', 'http://srv')).rejects.toThrow('not found');
+      expect(navigateMock).not.toHaveBeenCalled();
+    });
+  });
+
+  describe('openLocalFile', () => {
+    it('fetches meta with kind=local, seeds the dataset store, and navigates', async () => {
+      vi.stubGlobal('fetch', vi.fn()
+        .mockResolvedValueOnce(metaResponse())
+        .mockResolvedValueOnce(draft404()));
+      const { result } = renderHook(() => useOpenInAnnotate());
+      await act(async () => {
+        await result.current.openLocalFile('rel/path.tif');
+      });
+      expect(useDatasetStore.getState().kind).toBe('local');
+      expect(useDatasetStore.getState().source).toBe('rel/path.tif');
+      expect(useDatasetStore.getState().serverUri).toBeNull();
+      expect(navigateMock).toHaveBeenCalledWith('/annotate');
+
+      const [url] = (fetch as any).mock.calls[0];
+      expect(url).toContain('kind=local');
+      expect(url).not.toContain('server_uri');
+    });
+
+    it('throws with the response body when the meta fetch fails', async () => {
+      vi.stubGlobal('fetch', vi.fn().mockResolvedValue({ ok: false, text: async () => 'boom' }));
+      const { result } = renderHook(() => useOpenInAnnotate());
+      await expect(result.current.openLocalFile('rel/path.tif')).rejects.toThrow('boom');
+      expect(navigateMock).not.toHaveBeenCalled();
+    });
+  });
+});
diff --git a/frontend/src/hooks/usePixelClassifier.test.ts b/frontend/src/hooks/usePixelClassifier.test.ts
new file mode 100644
index 0000000..1b4b779
--- /dev/null
+++ b/frontend/src/hooks/usePixelClassifier.test.ts
@@ -0,0 +1,549 @@
+/**
+ * usePixelClassifier — covers branches NOT already exercised by
+ * usePixelClassifier.slicePersist.test.ts (which only covers: model persists
+ * across a slice change, and predict() targets the current slice's bank).
+ *
+ * Here: train() error paths (no composition, unknown-feature-bank recovery,
+ * generic error), predict() guards + error path, proba class cycling +
+ * threshold clamping, saveThresholdedClass, dismiss, trainAcrossSlices /
+ * applyAcrossVolume (via the real useExportJob hook, driven through mocked
+ * fetch responses so no fake timers are needed — the first status poll
+ * always resolves 'done'/'error' immediately), the live volume-apply preview
+ * effect keyed off predictedRasterStore, and ensureSession reuse.
+ */
+import { describe, expect, it, vi, beforeEach } from 'vitest';
+import { act, renderHook, waitFor } from '@testing-library/react';
+import { usePixelClassifier } from './usePixelClassifier';
+import { useIpredStore } from '@/stores/ipredStore';
+import { usePredictedRasterStore } from '@/stores/predictedRasterStore';
+
+vi.mock('@/lib/pixelClf', () => ({
+  thresholdProbaPngBlob: vi.fn(async () => new Blob()),
+}));
+
+vi.mock('@/lib/ipredApi', async () => {
+  const actual = await vi.importActual<typeof import('@/lib/ipredApi')>('@/lib/ipredApi');
+  return {
+    ...actual,
+    openIpredSession: vi.fn(async () => ({ session_id: 's1', project_id: 'p1' })),
+    ipredPreprocess: vi.fn(async () => ({
+      feature_id: 'bank-A',
+      project_id: 'p1',
+      setup_id: 'setup-1',
+      slice_index: 0,
+      n_channels: 3,
+      height: 10,
+      width: 10,
+      labels: ['a'],
+      cache_hit: false,
+    })),
+    ipredTrain: vi.fn(async () => ({
+      model_id: 'model-1',
+      feature_id: 'bank-A',
+      trainer_id: 'catboost',
+      class_ids: [1, 2],
+      train_accuracy: 0.9,
+      n_train: 100,
+      n_cal: 20,
+      n_samples: 120,
+      params: { iterations: 50 },
+      feature_importances: [{ label: 'intensity', importance: 3 }],
+    })),
+    ipredInfer: vi.fn(async () => ({
+      run_id: 'run-1',
+      model_id: 'model-1',
+      feature_id: 'bank-A',
+      alpha: 0.05,
+      class_ids: [1, 2],
+      counts: { singleton: 1, multi: 0, abstain: 0 },
+    })),
+    ipredThresholdClass: vi.fn(async () => ({
+      run_id: 'run-1',
+      class_id: 1,
+      class_index: 0,
+      threshold: 0.5,
+      width: 10,
+      height: 10,
+      n_positive: 5,
+      label_map_b64: 'AAAA',
+    })),
+  };
+});
+
+import {
+  openIpredSession,
+  ipredPreprocess,
+  ipredTrain,
+  ipredInfer,
+  ipredThresholdClass,
+} from '@/lib/ipredApi';
+
+const BASE_ARGS = {
+  featureJobId: 'bank-A' as string | null,
+  resetKey: 'sample-1' as string | null,
+  source: 'sample.tif' as string | null,
+  kind: 'local' as string | null,
+  serverUri: null as string | null,
+  sliceIndex: 0,
+};
+
+/** Routes fetch calls used by ipredRunCommitUrl/StatusUrl/ProbaUrl (blob PNGs)
+ *  and by useExportJob's batch-train/apply start + status-poll routes. */
+function makeFetchRouter(opts?: {
+  batchTrainResult?: Record<string, unknown> | null;
+  batchTrainError?: string;
+  batchApplyResult?: Record<string, unknown> | null;
+  batchApplyError?: string;
+  pngOk?: boolean;
+}) {
+  const {
+    batchTrainResult = null,
+    batchTrainError,
+    batchApplyResult = null,
+    batchApplyError,
+    pngOk = true,
+  } = opts ?? {};
+  return vi.fn(async (input: RequestInfo | URL, init?: RequestInit) => {
+    const url = String(input);
+    if (url.includes('/api/ipred/batch/train') && init?.method === 'POST') {
+      return { ok: true, json: async () => ({ job_id: 'job-train-1' }) } as unknown as Response;
+    }
+    if (url.includes('/api/ipred/batch/apply') && init?.method === 'POST') {
+      return { ok: true, json: async () => ({ job_id: 'job-apply-1' }) } as unknown as Response;
+    }
+    if (url.includes('/api/export/status/job-train-1')) {
+      return {
+        ok: true,
+        json: async () =>
+          batchTrainError
+            ? { state: 'error', error: batchTrainError }
+            : { state: 'done', result: batchTrainResult },
+      } as unknown as Response;
+    }
+    if (url.includes('/api/export/status/job-apply-1')) {
+      return {
+        ok: true,
+        json: async () =>
+          batchApplyError
+            ? { state: 'error', error: batchApplyError }
+            : { state: 'done', result: batchApplyResult },
+      } as unknown as Response;
+    }
+    // commit.png / status.png / proba/N.png
+    return { ok: pngOk, blob: async () => new Blob() } as unknown as Response;
+  });
+}
+
+beforeEach(() => {
+  useIpredStore.getState().reset();
+  usePredictedRasterStore.setState({ bySource: {} });
+  vi.mocked(openIpredSession).mockClear();
+  vi.mocked(ipredPreprocess).mockClear();
+  vi.mocked(ipredTrain).mockClear();
+  vi.mocked(ipredInfer).mockClear();
+  vi.mocked(ipredThresholdClass).mockClear();
+  global.fetch = makeFetchRouter();
+  global.URL.createObjectURL = vi.fn(() => 'blob:mock');
+  global.URL.revokeObjectURL = vi.fn();
+});
+
+describe('usePixelClassifier — train()', () => {
+  it('sets an error and does not train without a preferred composition', async () => {
+    useIpredStore.setState({ preferredCompositionId: '' });
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    expect(result.current.error).toBe('Select a composition first.');
+    expect(ipredTrain).not.toHaveBeenCalled();
+  });
+
+  it('recovers from an "unknown feature" training error by clearing the model and flagging expiry', async () => {
+    vi.mocked(ipredTrain).mockRejectedValueOnce(new Error('unknown feature bank xyz'));
+    const onFeatureJobExpired = vi.fn();
+    const { result } = renderHook(() =>
+      usePixelClassifier({ ...BASE_ARGS, onFeatureJobExpired }),
+    );
+    await act(async () => {
+      await result.current.train([]);
+    });
+    expect(onFeatureJobExpired).toHaveBeenCalledTimes(1);
+    expect(result.current.model).toBeNull();
+    expect(result.current.error).toMatch(/Feature bank missing/);
+  });
+
+  it('surfaces a generic training error message verbatim', async () => {
+    vi.mocked(ipredTrain).mockRejectedValueOnce(new Error('trainer exploded'));
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    expect(result.current.error).toBe('trainer exploded');
+    expect(result.current.model).toBeNull();
+  });
+
+  it('trains successfully, mapping the ipred response into ClfTrainResult', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    expect(result.current.model).toMatchObject({
+      modelId: 'model-1',
+      featureId: 'bank-A',
+      nSamples: 120,
+      nTrain: 100,
+      nCal: 20,
+      classIds: [1, 2],
+      trainAccuracy: 0.9,
+      nTrees: 50,
+      usesSam: false,
+      trainerId: 'catboost',
+      compositionId: 'comp-skimage-slimsam',
+    });
+    expect(result.current.model?.featureImportances).toEqual([
+      { label: 'intensity', importance: 3 },
+    ]);
+  });
+
+  it('reuses an existing feature bank instead of calling ipredPreprocess', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    expect(ipredPreprocess).not.toHaveBeenCalled();
+    expect(ipredTrain).toHaveBeenCalledWith(
+      expect.objectContaining({ feature_id: 'bank-A', session_id: 's1' }),
+    );
+  });
+
+  it('auto-preprocesses and reports the new feature bank when no featureJobId is set', async () => {
+    const onFeatureReady = vi.fn();
+    const { result } = renderHook(() =>
+      usePixelClassifier({ ...BASE_ARGS, featureJobId: null, onFeatureReady }),
+    );
+    await act(async () => {
+      await result.current.train([]);
+    });
+    expect(ipredPreprocess).toHaveBeenCalledWith(
+      expect.objectContaining({ session_id: 's1', composition_id: 'comp-skimage-slimsam' }),
+    );
+    expect(onFeatureReady).toHaveBeenCalledWith(
+      expect.objectContaining({ featureId: 'bank-A', width: 10, height: 10 }),
+    );
+    expect(result.current.model?.modelId).toBe('model-1');
+  });
+
+  it('reuses an already-open ipred session rather than opening a new one', async () => {
+    useIpredStore.getState().setIpredSession({ sessionId: 'existing-session', projectId: 'p9' });
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    expect(openIpredSession).not.toHaveBeenCalled();
+    expect(ipredTrain).toHaveBeenCalledWith(
+      expect.objectContaining({ session_id: 'existing-session' }),
+    );
+  });
+});
+
+describe('usePixelClassifier — predict()', () => {
+  it('does nothing without a trained model', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.predict([]);
+    });
+    expect(ipredInfer).not.toHaveBeenCalled();
+    expect(result.current.commitUrl).toBeNull();
+  });
+
+  it('predicts, publishes commit/status urls, counts, and the first proba channel', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    await act(async () => {
+      await result.current.predict([]);
+    });
+    expect(result.current.commitUrl).toBe('blob:mock');
+    expect(result.current.statusUrl).toBe('blob:mock');
+    expect(result.current.predictCounts).toEqual({ singleton: 1, multi: 0, abstain: 0 });
+    expect(result.current.runId).toBe('run-1');
+    expect(result.current.probaUrl).toBe('blob:mock');
+    expect(result.current.activeProbaClassId).toBe(1);
+  });
+
+  it('revokes any partial preview and sets an error when the commit/status fetch fails', async () => {
+    global.fetch = makeFetchRouter({ pngOk: false });
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    await act(async () => {
+      await result.current.predict([]);
+    });
+    expect(result.current.error).toMatch(/Failed to fetch conformal prediction PNGs/);
+    expect(result.current.commitUrl).toBeNull();
+    expect(result.current.runId).toBeNull();
+  });
+});
+
+describe('usePixelClassifier — proba class cycling + thresholds', () => {
+  async function trainAndPredict(result: { current: ReturnType<typeof usePixelClassifier> }) {
+    await act(async () => {
+      await result.current.train([]);
+    });
+    await act(async () => {
+      await result.current.predict([]);
+    });
+  }
+
+  it('selectProbaClass does nothing without a run/model', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.selectProbaClass(1);
+    });
+    expect(result.current.probaClassIndex).toBe(0);
+  });
+
+  it('cycleProbaClass wraps around the class list in both directions', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await trainAndPredict(result);
+    expect(result.current.probaClassIndex).toBe(0);
+
+    await act(async () => {
+      result.current.cycleProbaClass(-1);
+    });
+    await waitFor(() => expect(result.current.probaClassIndex).toBe(1));
+    expect(result.current.activeProbaClassId).toBe(2);
+
+    await act(async () => {
+      result.current.cycleProbaClass(1);
+    });
+    await waitFor(() => expect(result.current.probaClassIndex).toBe(0));
+    expect(result.current.activeProbaClassId).toBe(1);
+  });
+
+  it('setProbaThreshold clamps to [0,1] and republishes the preview for the active class', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await trainAndPredict(result);
+
+    await act(async () => {
+      result.current.setProbaThreshold(1.5);
+    });
+    expect(result.current.activeProbaThreshold).toBe(1);
+
+    await act(async () => {
+      result.current.setProbaThreshold(-0.5);
+    });
+    expect(result.current.activeProbaThreshold).toBe(0);
+  });
+
+  it('setProbaThreshold is a no-op without a model', () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    act(() => {
+      result.current.setProbaThreshold(0.7);
+    });
+    expect(result.current.activeProbaThreshold).toBe(0.5);
+  });
+});
+
+describe('usePixelClassifier — saveThresholdedClass / dismiss', () => {
+  it('returns null and does not call the API without an active run', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    let out;
+    await act(async () => {
+      out = await result.current.saveThresholdedClass();
+    });
+    expect(out).toBeNull();
+    expect(ipredThresholdClass).not.toHaveBeenCalled();
+  });
+
+  it('calls ipredThresholdClass with the active class/threshold once predicted', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    await act(async () => {
+      await result.current.predict([]);
+    });
+    let out;
+    await act(async () => {
+      out = await result.current.saveThresholdedClass();
+    });
+    expect(ipredThresholdClass).toHaveBeenCalledWith('run-1', { class_id: 1, threshold: 0.5 });
+    expect(out).toMatchObject({ run_id: 'run-1', class_id: 1 });
+    expect(result.current.savingClass).toBe(false);
+  });
+
+  it('surfaces an error from a failed save without throwing', async () => {
+    vi.mocked(ipredThresholdClass).mockRejectedValueOnce(new Error('save failed'));
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    await act(async () => {
+      await result.current.predict([]);
+    });
+    await act(async () => {
+      await result.current.saveThresholdedClass();
+    });
+    expect(result.current.error).toBe('save failed');
+  });
+
+  it('dismiss() revokes the current prediction preview', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    await act(async () => {
+      await result.current.predict([]);
+    });
+    expect(result.current.commitUrl).not.toBeNull();
+    act(() => {
+      result.current.dismiss();
+    });
+    expect(result.current.commitUrl).toBeNull();
+    expect(result.current.runId).toBeNull();
+  });
+});
+
+describe('usePixelClassifier — identity resets', () => {
+  it('clears the trained model when resetKey (sample identity) changes', async () => {
+    const { result, rerender } = renderHook(
+      (props: { resetKey: string }) => usePixelClassifier({ ...BASE_ARGS, resetKey: props.resetKey }),
+      { initialProps: { resetKey: 'sample-1' } },
+    );
+    await act(async () => {
+      await result.current.train([]);
+    });
+    expect(result.current.model?.modelId).toBe('model-1');
+
+    rerender({ resetKey: 'sample-2' });
+    expect(result.current.model).toBeNull();
+  });
+
+  it('clears the trained model when preferredCompositionId changes', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    expect(result.current.model?.modelId).toBe('model-1');
+
+    act(() => {
+      useIpredStore.getState().setPreferredCompositionId('comp-other');
+    });
+    expect(result.current.model).toBeNull();
+  });
+});
+
+describe('usePixelClassifier — trainAcrossSlices (multi-slice batch train)', () => {
+  it('errors when there are no annotated slices', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.trainAcrossSlices({});
+    });
+    expect(result.current.error).toBe('No annotated slices to train on.');
+  });
+
+  it('errors without a preferred composition', async () => {
+    useIpredStore.setState({ preferredCompositionId: '' });
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.trainAcrossSlices({ 0: [] });
+    });
+    expect(result.current.error).toBe('Select a composition first.');
+  });
+
+  it('starts the batch-train job and adopts the completed result as the model', async () => {
+    global.fetch = makeFetchRouter({
+      batchTrainResult: {
+        model_id: 'model-multi',
+        feature_id: 'bank-multi',
+        trainer_id: 'catboost',
+        class_ids: [1, 2, 3],
+        train_accuracy: 0.8,
+        n_train: 300,
+        n_cal: 60,
+        n_samples: 360,
+        params: { iterations: 200 },
+        feature_importances: [],
+      },
+    });
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.trainAcrossSlices({ 0: [], 5: [] });
+    });
+    await waitFor(() => expect(result.current.model?.modelId).toBe('model-multi'));
+    expect(result.current.model?.classIds).toEqual([1, 2, 3]);
+  });
+
+  it('surfaces a batch-train job error', async () => {
+    global.fetch = makeFetchRouter({ batchTrainError: 'multi-train blew up' });
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.trainAcrossSlices({ 0: [], 5: [] });
+    });
+    await waitFor(() => expect(result.current.error).toBe('multi-train blew up'));
+    expect(result.current.model).toBeNull();
+  });
+});
+
+describe('usePixelClassifier — applyAcrossVolume (batch apply)', () => {
+  it('errors without a trained model', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.applyAcrossVolume([0, 1, 2]);
+    });
+    expect(result.current.error).toBe('Train a model first.');
+  });
+
+  it('is a no-op with an empty slice list even with a model', async () => {
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    await act(async () => {
+      await result.current.applyAcrossVolume([]);
+    });
+    expect(result.current.error).toBeNull();
+    expect(result.current.volumeApplyJob.status).toBe('idle');
+  });
+
+  it('surfaces a batch-apply job error', async () => {
+    global.fetch = makeFetchRouter({ batchApplyError: 'apply blew up' });
+    const { result } = renderHook(() => usePixelClassifier(BASE_ARGS));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    await act(async () => {
+      await result.current.applyAcrossVolume([0, 1]);
+    });
+    await waitFor(() => expect(result.current.error).toBe('apply blew up'));
+  });
+
+  it('fetches and shows the live per-slice preview once the job result names a run for the current slice', async () => {
+    global.fetch = makeFetchRouter({ batchApplyResult: { runs: { '0': 'run-vol-1' } } });
+    const { result } = renderHook(() => usePixelClassifier({ ...BASE_ARGS, sliceIndex: 0 }));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    await act(async () => {
+      await result.current.applyAcrossVolume([0, 1]);
+    });
+    await waitFor(() => expect(result.current.commitUrl).toBe('blob:mock'));
+    expect(result.current.statusUrl).toBe('blob:mock');
+  });
+
+  it('shows the committed pointer preview from predictedRasterStore once the job map is gone', async () => {
+    const { result } = renderHook(() => usePixelClassifier({ ...BASE_ARGS, resetKey: 'sample-9', sliceIndex: 3 }));
+    await act(async () => {
+      await result.current.train([]);
+    });
+    act(() => {
+      usePredictedRasterStore.getState().setPointers('sample-9', {
+        '3': { runId: 'run-committed-1', classIds: [1, 2] },
+      });
+    });
+    await waitFor(() => expect(result.current.commitUrl).toBe('blob:mock'));
+  });
+});
diff --git a/frontend/src/hooks/useSam.test.ts b/frontend/src/hooks/useSam.test.ts
new file mode 100644
index 0000000..db1ce6f
--- /dev/null
+++ b/frontend/src/hooks/useSam.test.ts
@@ -0,0 +1,200 @@
+/**
+ * useSam wraps samClient, a singleton that owns a real Web Worker (spawned via
+ * `new Worker(new URL('./samWorker.ts', import.meta.url), ...)`). jsdom doesn't
+ * implement Worker, and vendoring a fake Worker global would still leave us
+ * exercising the worker's postMessage protocol rather than the hook's own logic.
+ * So — matching the pattern already used in Toolbar/index.test.tsx, which mocks
+ * `@/hooks/useSam` wholesale rather than deal with the worker boundary — we mock
+ * `@/lib/sam/samClient` here instead and test useSam's actual public surface
+ * (status subscription, ensureEncoded's caching/dedup, segment) against it.
+ */
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, renderHook, waitFor } from '@testing-library/react';
+
+const subscribers = new Set<(s: string) => void>();
+let currentStatus = 'idle';
+const encode = vi.fn();
+const decode = vi.fn();
+const init = vi.fn();
+const getStatus = vi.fn(() => currentStatus);
+const getBackend = vi.fn(() => 'wasm');
+
+vi.mock('@/lib/sam/samClient', () => ({
+  samClient: {
+    getStatus: () => getStatus(),
+    getBackend: () => getBackend(),
+    subscribe: (fn: (s: string) => void) => {
+      subscribers.add(fn);
+      fn(currentStatus);
+      return () => subscribers.delete(fn);
+    },
+    init: (...args: unknown[]) => init(...args),
+    encode: (...args: unknown[]) => encode(...args),
+    decode: (...args: unknown[]) => decode(...args),
+  },
+  webgpuAvailable: () => false,
+}));
+
+import { useSam } from './useSam';
+
+function setStatus(s: string) {
+  currentStatus = s;
+  subscribers.forEach((fn) => fn(s));
+}
+
+beforeEach(() => {
+  currentStatus = 'idle';
+  subscribers.clear();
+  encode.mockReset().mockResolvedValue(undefined);
+  decode.mockReset().mockResolvedValue({ mask: new Uint8Array([1]), width: 1, height: 1, score: 0.9 });
+  init.mockReset().mockResolvedValue(undefined);
+  getStatus.mockClear();
+  getBackend.mockClear();
+});
+
+afterEach(() => {
+  cleanup();
+});
+
+describe('useSam', () => {
+  it('reflects samClient.getStatus() at mount and updates on subscribe notifications', () => {
+    const { result } = renderHook(() => useSam(false));
+    expect(result.current.status).toBe('idle');
+    expect(result.current.supported).toBe(true);
+
+    act(() => setStatus('ready'));
+    expect(result.current.status).toBe('ready');
+  });
+
+  it('supported is false once status flips to unsupported', () => {
+    const { result } = renderHook(() => useSam(false));
+    act(() => setStatus('unsupported'));
+    expect(result.current.supported).toBe(false);
+  });
+
+  it('calls samClient.init() when enabled and status is idle', () => {
+    renderHook(() => useSam(true));
+    expect(init).toHaveBeenCalledTimes(1);
+  });
+
+  it('does not call init() when disabled', () => {
+    renderHook(() => useSam(false));
+    expect(init).not.toHaveBeenCalled();
+  });
+
+  it('does not call init() again when status is already past idle', () => {
+    currentStatus = 'ready';
+    renderHook(() => useSam(true));
+    expect(init).not.toHaveBeenCalled();
+  });
+
+  it('swallows an init() rejection without throwing', async () => {
+    init.mockRejectedValue(new Error('no webgpu'));
+    expect(() => renderHook(() => useSam(true))).not.toThrow();
+    await waitFor(() => expect(init).toHaveBeenCalled());
+  });
+
+  it('ensureEncoded encodes once per key and returns true on success', async () => {
+    const { result } = renderHook(() => useSam(false));
+    const makeSource = vi.fn(() => ({}) as CanvasImageSource);
+
+    let ok = false;
+    await act(async () => {
+      ok = await result.current.ensureEncoded('slice-0', makeSource);
+    });
+    expect(ok).toBe(true);
+    expect(encode).toHaveBeenCalledTimes(1);
+    expect(makeSource).toHaveBeenCalledTimes(1);
+
+    // Same key again -> cached, no new encode/makeSource call.
+    await act(async () => {
+      ok = await result.current.ensureEncoded('slice-0', makeSource);
+    });
+    expect(ok).toBe(true);
+    expect(encode).toHaveBeenCalledTimes(1);
+    expect(makeSource).toHaveBeenCalledTimes(1);
+  });
+
+  it('ensureEncoded re-encodes when the key changes (e.g. brightness/contrast changed)', async () => {
+    const { result } = renderHook(() => useSam(false));
+    const makeSource = vi.fn(() => ({}) as CanvasImageSource);
+
+    await act(async () => {
+      await result.current.ensureEncoded('slice-0', makeSource);
+    });
+    await act(async () => {
+      await result.current.ensureEncoded('slice-0:bc=1', makeSource);
+    });
+    expect(encode).toHaveBeenCalledTimes(2);
+  });
+
+  it('ensureEncoded returns false and sets error on a failed encode, and does not retry a known-bad key', async () => {
+    encode.mockRejectedValue(new Error('encode failed'));
+    const { result } = renderHook(() => useSam(false));
+    const makeSource = vi.fn(() => ({}) as CanvasImageSource);
+
+    let ok = true;
+    await act(async () => {
+      ok = await result.current.ensureEncoded('bad-key', makeSource);
+    });
+    expect(ok).toBe(false);
+    expect(result.current.error).toBe('encode failed');
+    expect(encode).toHaveBeenCalledTimes(1);
+
+    // Known-bad key: no retry, no second makeSource/encode call.
+    await act(async () => {
+      ok = await result.current.ensureEncoded('bad-key', makeSource);
+    });
+    expect(ok).toBe(false);
+    expect(encode).toHaveBeenCalledTimes(1);
+    expect(makeSource).toHaveBeenCalledTimes(1);
+  });
+
+  it('ensureEncoded awaits a concurrent in-flight encode for the same key rather than starting a second one', async () => {
+    let resolveEncode!: () => void;
+    encode.mockReturnValue(new Promise<void>((resolve) => { resolveEncode = resolve; }));
+    const { result } = renderHook(() => useSam(false));
+    const makeSource = vi.fn(() => ({}) as CanvasImageSource);
+
+    let p1: Promise<boolean>;
+    let p2: Promise<boolean>;
+    act(() => {
+      p1 = result.current.ensureEncoded('k', makeSource);
+      p2 = result.current.ensureEncoded('k', makeSource);
+    });
+    expect(encode).toHaveBeenCalledTimes(1);
+
+    await act(async () => {
+      resolveEncode();
+      await Promise.all([p1, p2]);
+    });
+    expect(encode).toHaveBeenCalledTimes(1);
+  });
+
+  it('segment returns null immediately when there are no points and no box', async () => {
+    const { result } = renderHook(() => useSam(false));
+    const mask = await result.current.segment([], null, 'auto', 0);
+    expect(mask).toBeNull();
+    expect(decode).not.toHaveBeenCalled();
+  });
+
+  it('segment decodes points/box via samClient and returns the mask', async () => {
+    const { result } = renderHook(() => useSam(false));
+    const mask = await result.current.segment([{ x: 1, y: 2, label: 1 }], null, 'fine', 0.5);
+    expect(decode).toHaveBeenCalledWith([{ x: 1, y: 2, label: 1 }], null, 'fine', 0.5);
+    expect(mask).toEqual({ mask: new Uint8Array([1]), width: 1, height: 1, score: 0.9 });
+  });
+
+  it('segment returns null (not throw) when decode rejects', async () => {
+    decode.mockRejectedValue(new Error('decode failed'));
+    const { result } = renderHook(() => useSam(false));
+    const mask = await result.current.segment([{ x: 1, y: 2, label: 1 }], null, 'auto', 0);
+    expect(mask).toBeNull();
+  });
+
+  it('exposes backend from samClient.getBackend()', () => {
+    getBackend.mockReturnValue('webgpu');
+    const { result } = renderHook(() => useSam(false));
+    expect(result.current.backend).toBe('webgpu');
+  });
+});
diff --git a/frontend/src/hooks/useSave.test.tsx b/frontend/src/hooks/useSave.test.tsx
new file mode 100644
index 0000000..cabec40
--- /dev/null
+++ b/frontend/src/hooks/useSave.test.tsx
@@ -0,0 +1,159 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, renderHook, waitFor } from '@testing-library/react';
+import { useSave } from './useSave';
+import { useAnnotationStore } from '@/stores/annotationStore';
+import { useClassStore } from '@/stores/classStore';
+
+const SOURCE = 'local:x.tif';
+
+beforeEach(() => {
+  useAnnotationStore.getState().reset();
+  useClassStore.setState({ classes: [] });
+  vi.stubGlobal('fetch', vi.fn().mockResolvedValue({ ok: true, json: async () => [] }));
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+});
+
+describe('useSave', () => {
+  it('starts clean (not dirty)', async () => {
+    const { result } = renderHook(() => useSave(SOURCE));
+    await waitFor(() => expect(fetch).toHaveBeenCalled());
+    expect(result.current.isDirty).toBe(false);
+  });
+
+  it('becomes dirty after a store change', async () => {
+    const { result } = renderHook(() => useSave(SOURCE));
+    await waitFor(() => expect(fetch).toHaveBeenCalled());
+    act(() => {
+      useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 1, [
+        { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+      ]);
+    });
+    expect(result.current.isDirty).toBe(true);
+  });
+
+  it('resets dirty/versions/lastSavedAt when sourceKey changes', async () => {
+    const { result, rerender } = renderHook(({ src }) => useSave(src), { initialProps: { src: SOURCE } });
+    await waitFor(() => expect(fetch).toHaveBeenCalled());
+    act(() => {
+      useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 1, []);
+    });
+    expect(result.current.isDirty).toBe(true);
+
+    act(() => { rerender({ src: 'local:other.tif' }); });
+    expect(result.current.isDirty).toBe(false);
+  });
+
+  it('buildSavePayload returns null with no sourceKey', () => {
+    const { result } = renderHook(() => useSave(null));
+    expect(result.current.buildSavePayload()).toBeNull();
+    expect(result.current.saveSummary).toEqual({ shapeCount: 0, classCount: 0 });
+  });
+
+  it('saveSummary reflects real shape/class counts for the source', async () => {
+    act(() => {
+      useClassStore.setState({ classes: [{ classId: 1, label: 'Cell', color: '#f00', isVisible: true }] });
+      useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 1, [
+        { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 },
+        { id: 's2', classId: 1, kind: 'rectangle', x: 5, y: 5, w: 2, h: 2 },
+      ]);
+    });
+    const { result } = renderHook(() => useSave(SOURCE));
+    expect(result.current.saveSummary).toEqual({ shapeCount: 2, classCount: 1 });
+  });
+
+  it('refreshVersions populates the versions list', async () => {
+    (fetch as any).mockResolvedValue({ ok: true, json: async () => [{ version: 1, saved_at: 't', shape_count: 0, class_count: 0 }] });
+    const { result } = renderHook(() => useSave(SOURCE));
+    await waitFor(() => expect(result.current.versions).toHaveLength(1));
+  });
+
+  it('save() POSTs the payload, clears dirty, and sets lastSavedAt', async () => {
+    (fetch as any)
+      .mockResolvedValueOnce({ ok: true, json: async () => [] }) // initial refreshVersions on mount
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ saved_at: '2024-01-01T00:00:00Z' }) }) // save
+      .mockResolvedValueOnce({ ok: true, json: async () => [{ version: 1, saved_at: '2024-01-01T00:00:00Z', shape_count: 0, class_count: 0 }] }); // post-save refresh
+
+    const { result } = renderHook(() => useSave(SOURCE));
+    await waitFor(() => expect(fetch).toHaveBeenCalledTimes(1));
+
+    let ok = false;
+    await act(async () => {
+      ok = await result.current.save({ annotatedBy: 'Alice', notes: 'n' });
+    });
+    expect(ok).toBe(true);
+    expect(result.current.lastSavedAt).toBe('2024-01-01T00:00:00Z');
+    expect(result.current.isDirty).toBe(false);
+
+    const [url, init] = (fetch as any).mock.calls[1];
+    expect(url).toContain('/api/annotations/save?source_key=');
+    const body = JSON.parse(init.body);
+    expect(body.annotated_by).toBe('Alice');
+    expect(body.notes).toBe('n');
+  });
+
+  it('save() returns false and does not throw on a failed request', async () => {
+    (fetch as any)
+      .mockResolvedValueOnce({ ok: true, json: async () => [] })
+      .mockResolvedValueOnce({ ok: false, status: 500 });
+    const { result } = renderHook(() => useSave(SOURCE));
+    await waitFor(() => expect(fetch).toHaveBeenCalledTimes(1));
+    let ok = true;
+    await act(async () => {
+      ok = await result.current.save();
+    });
+    expect(ok).toBe(false);
+  });
+
+  it('fetchVersionPayload caches results by version number', async () => {
+    (fetch as any)
+      .mockResolvedValueOnce({ ok: true, json: async () => [] })
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ payload: { classes: [], slices: {}, split_by_slice: {}, negative_slices: [] } }) });
+    const { result } = renderHook(() => useSave(SOURCE));
+    await waitFor(() => expect(fetch).toHaveBeenCalledTimes(1));
+
+    let payload1;
+    await act(async () => { payload1 = await result.current.fetchVersionPayload(1); });
+    expect(payload1).toEqual({ classes: [], slices: {}, split_by_slice: {}, negative_slices: [] });
+
+    const callsBefore = (fetch as any).mock.calls.length;
+    await act(async () => { await result.current.fetchVersionPayload(1); });
+    expect((fetch as any).mock.calls.length).toBe(callsBefore); // cached, no new fetch
+  });
+
+  it('restoreVersion loads a version into the stores and marks dirty', async () => {
+    (fetch as any)
+      .mockResolvedValueOnce({ ok: true, json: async () => [] })
+      .mockResolvedValueOnce({
+        ok: true,
+        json: async () => ({
+          payload: {
+            classes: [{ classId: 1, label: 'Restored', color: '#000', isVisible: true }],
+            slices: { '0': [{ id: 'r1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 1, h: 1 }] },
+            split_by_slice: {},
+            negative_slices: [],
+          },
+        }),
+      });
+    const { result } = renderHook(() => useSave(SOURCE));
+    await waitFor(() => expect(fetch).toHaveBeenCalledTimes(1));
+    await act(async () => { await result.current.restoreVersion(1); });
+
+    expect(useClassStore.getState().classes[0].label).toBe('Restored');
+    expect(useAnnotationStore.getState().byImage[SOURCE]?.['0']).toHaveLength(1);
+    expect(result.current.isDirty).toBe(true);
+  });
+
+  it('markClean clears the dirty flag', async () => {
+    const { result } = renderHook(() => useSave(SOURCE));
+    await waitFor(() => expect(fetch).toHaveBeenCalled());
+    act(() => {
+      useAnnotationStore.getState().replaceClassShapesOnSlice(SOURCE, 0, 1, []);
+    });
+    act(() => result.current.markClean());
+    expect(result.current.isDirty).toBe(false);
+  });
+});
diff --git a/frontend/src/hooks/useSave.ts b/frontend/src/hooks/useSave.ts
index deb7d55..a292aac 100644
--- a/frontend/src/hooks/useSave.ts
+++ b/frontend/src/hooks/useSave.ts
@@ -84,6 +84,21 @@ export function useSave(sourceKey: string | null): UseSaveReturn {
   const cleanRef = useRef(true);
   const payloadCacheRef = useRef<Map<number, VersionPayload>>(new Map());
 
+  // Must run BEFORE the dirty-tracking effect below on the mount/sourceKey-change
+  // commit: it arms cleanRef so that same-commit effect can consume it and treat
+  // the freshly-loaded source's initial data as clean. Effects run in
+  // declaration order, so this one is declared first for that reason —
+  // reversing the order would re-arm cleanRef right after the dirty-tracker
+  // had already consumed it, silently swallowing the next real edit's dirty
+  // flag (a bug this ordering fixes).
+  useEffect(() => {
+    cleanRef.current = true;
+    setIsDirty(false);
+    setLastSavedAt(null);
+    setVersions([]);
+    payloadCacheRef.current.clear();
+  }, [sourceKey]);
+
   useEffect(() => {
     if (cleanRef.current) {
       cleanRef.current = false;
@@ -93,14 +108,6 @@ export function useSave(sourceKey: string | null): UseSaveReturn {
   // eslint-disable-next-line react-hooks/exhaustive-deps
   }, [byImage, splitBySlice, negativeSlices, classes]);
 
-  useEffect(() => {
-    cleanRef.current = true;
-    setIsDirty(false);
-    setLastSavedAt(null);
-    setVersions([]);
-    payloadCacheRef.current.clear();
-  }, [sourceKey]);
-
   /** Assemble the current stores into a draft payload, or null if no sourceKey. */
   const buildSavePayload = useCallback((): SaveDraftPayload | null => {
     if (!sourceKey) return null;
diff --git a/frontend/src/hooks/useTrainCapability.test.tsx b/frontend/src/hooks/useTrainCapability.test.tsx
new file mode 100644
index 0000000..5ffe5d4
--- /dev/null
+++ b/frontend/src/hooks/useTrainCapability.test.tsx
@@ -0,0 +1,65 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { renderHook, waitFor } from '@testing-library/react';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import type { ReactNode } from 'react';
+import { useTrainCapability } from './useTrainCapability';
+
+function wrapper({ children }: { children: ReactNode }) {
+  const client = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return <QueryClientProvider client={client}>{children}</QueryClientProvider>;
+}
+
+beforeEach(() => {
+  vi.stubGlobal('fetch', vi.fn());
+});
+
+afterEach(() => {
+  vi.unstubAllGlobals();
+});
+
+describe('useTrainCapability', () => {
+  it('returns the fallback shape while loading', () => {
+    (fetch as any).mockReturnValue(new Promise(() => {}));
+    const { result } = renderHook(() => useTrainCapability(), { wrapper });
+    expect(result.current.isLoading).toBe(true);
+    expect(result.current.capability.torch_available).toBe(false);
+    expect(result.current.capability.dlsia).toEqual({ available: false });
+  });
+
+  it('returns the parsed capability once loaded', async () => {
+    (fetch as any).mockResolvedValue({
+      ok: true,
+      json: async () => ({
+        torch_available: true,
+        torch_version: '2.1.0',
+        device: 'cpu',
+        dlsia: { available: true },
+        denoise: { available: true, methods: [] },
+        runs_dir: '/data/runs',
+        busy: false,
+      }),
+    });
+    const { result } = renderHook(() => useTrainCapability(), { wrapper });
+    await waitFor(() => expect(result.current.isLoading).toBe(false));
+    expect(result.current.capability.torch_available).toBe(true);
+    expect(result.current.capability.device).toBe('cpu');
+  });
+
+  it('fills in a missing nested key (e.g. denoise) from the fallback instead of throwing', async () => {
+    (fetch as any).mockResolvedValue({
+      ok: true,
+      json: async () => ({ torch_available: true, dlsia: { available: true } }),
+    });
+    const { result } = renderHook(() => useTrainCapability(), { wrapper });
+    await waitFor(() => expect(result.current.isLoading).toBe(false));
+    expect(result.current.capability.denoise).toEqual({ available: false, methods: [] });
+    expect(result.current.capability.torch_available).toBe(true);
+  });
+
+  it('falls back to defaults entirely on a fetch failure', async () => {
+    (fetch as any).mockResolvedValue({ ok: false, status: 500 });
+    const { result } = renderHook(() => useTrainCapability(), { wrapper });
+    await waitFor(() => expect(result.current.isLoading).toBe(false));
+    expect(result.current.capability.torch_available).toBe(false);
+  });
+});
diff --git a/frontend/src/hooks/useTrainRuns.test.tsx b/frontend/src/hooks/useTrainRuns.test.tsx
new file mode 100644
index 0000000..d9e16f0
--- /dev/null
+++ b/frontend/src/hooks/useTrainRuns.test.tsx
@@ -0,0 +1,111 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, renderHook, waitFor } from '@testing-library/react';
+import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
+import type { ReactNode } from 'react';
+import { useTrainRuns } from './useTrainRuns';
+
+function wrapper({ children }: { children: ReactNode }) {
+  const client = new QueryClient({ defaultOptions: { queries: { retry: false } } });
+  return <QueryClientProvider client={client}>{children}</QueryClientProvider>;
+}
+
+beforeEach(() => {
+  vi.stubGlobal('fetch', vi.fn());
+});
+
+afterEach(() => {
+  vi.unstubAllGlobals();
+});
+
+const RUN = {
+  run_id: 'r1', model_family: 'dlsia_tunet', model_config: {}, classes: [], render: {},
+  image_size: 256, hyperparams: {}, source_keys: [], created_at: '2024-01-01', metrics: {},
+};
+
+describe('useTrainRuns', () => {
+  it('starts empty and loading', () => {
+    (fetch as any).mockReturnValue(new Promise(() => {}));
+    const { result } = renderHook(() => useTrainRuns(), { wrapper });
+    expect(result.current.isLoading).toBe(true);
+    expect(result.current.runs).toEqual([]);
+  });
+
+  it('returns the loaded runs list', async () => {
+    (fetch as any).mockResolvedValue({ ok: true, json: async () => ({ runs: [RUN] }) });
+    const { result } = renderHook(() => useTrainRuns(), { wrapper });
+    await waitFor(() => expect(result.current.isLoading).toBe(false));
+    expect(result.current.runs).toEqual([RUN]);
+  });
+
+  it('returns an empty list (not an error) on a failed fetch', async () => {
+    (fetch as any).mockResolvedValue({ ok: false, status: 500 });
+    const { result } = renderHook(() => useTrainRuns(), { wrapper });
+    await waitFor(() => expect(result.current.isLoading).toBe(false));
+    expect(result.current.runs).toEqual([]);
+  });
+
+  it('defaults to an empty list when the response has no runs key', async () => {
+    (fetch as any).mockResolvedValue({ ok: true, json: async () => ({}) });
+    const { result } = renderHook(() => useTrainRuns(), { wrapper });
+    await waitFor(() => expect(result.current.isLoading).toBe(false));
+    expect(result.current.runs).toEqual([]);
+  });
+
+  it('deleteRun issues a DELETE and refreshes the list on success', async () => {
+    (fetch as any)
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ runs: [RUN] }) })
+      .mockResolvedValueOnce({ ok: true })
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ runs: [] }) });
+    const { result } = renderHook(() => useTrainRuns(), { wrapper });
+    await waitFor(() => expect(result.current.isLoading).toBe(false));
+
+    let ok: boolean = false;
+    await act(async () => {
+      ok = await result.current.deleteRun('r1');
+    });
+    expect(ok).toBe(true);
+    expect(fetch).toHaveBeenCalledWith(expect.stringContaining('/api/train/runs/r1'), { method: 'DELETE' });
+    await waitFor(() => expect(result.current.runs).toEqual([]));
+  });
+
+  it('deleteRun returns false on a failed delete', async () => {
+    (fetch as any)
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ runs: [RUN] }) })
+      .mockResolvedValueOnce({ ok: false });
+    const { result } = renderHook(() => useTrainRuns(), { wrapper });
+    await waitFor(() => expect(result.current.isLoading).toBe(false));
+
+    let ok: boolean = true;
+    await act(async () => {
+      ok = await result.current.deleteRun('r1');
+    });
+    expect(ok).toBe(false);
+  });
+
+  it('deleteRun returns false when the request throws', async () => {
+    (fetch as any)
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ runs: [RUN] }) })
+      .mockRejectedValueOnce(new Error('network down'));
+    const { result } = renderHook(() => useTrainRuns(), { wrapper });
+    await waitFor(() => expect(result.current.isLoading).toBe(false));
+
+    let ok: boolean = true;
+    await act(async () => {
+      ok = await result.current.deleteRun('r1');
+    });
+    expect(ok).toBe(false);
+  });
+
+  it('URL-encodes the run id in the delete request', async () => {
+    (fetch as any)
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ runs: [] }) })
+      .mockResolvedValueOnce({ ok: true })
+      .mockResolvedValueOnce({ ok: true, json: async () => ({ runs: [] }) });
+    const { result } = renderHook(() => useTrainRuns(), { wrapper });
+    await waitFor(() => expect(result.current.isLoading).toBe(false));
+    await act(async () => {
+      await result.current.deleteRun('run/with slash');
+    });
+    expect(fetch).toHaveBeenCalledWith(expect.stringContaining(encodeURIComponent('run/with slash')), expect.anything());
+  });
+});
diff --git a/frontend/src/lib/datasetStats.test.ts b/frontend/src/lib/datasetStats.test.ts
new file mode 100644
index 0000000..44fa73d
--- /dev/null
+++ b/frontend/src/lib/datasetStats.test.ts
@@ -0,0 +1,177 @@
+import { describe, expect, it } from 'vitest';
+import { computeSampleStats } from './datasetStats';
+import type { AnnotationClass } from '@/stores/classStore';
+
+const CLASSES: AnnotationClass[] = [
+  { classId: 1, label: 'Cell', color: '#f00', isVisible: true },
+  { classId: 2, label: 'Wall', color: '#0f0', isVisible: true },
+];
+
+describe('computeSampleStats', () => {
+  it('reports zero stats for a source with no slices at all', () => {
+    const stats = computeSampleStats({}, CLASSES, 3, [], 100, 100);
+    expect(stats.coverage.annotatedSlices).toBe(0);
+    expect(stats.classStats.every((c) => c.shapeCount === 0)).toBe(true);
+  });
+
+  it('counts shapes and pixel area per class', () => {
+    const stats = computeSampleStats(
+      { '0': [{ id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10 }] },
+      CLASSES, 3, [], 100, 100,
+    );
+    const cell = stats.classStats.find((c) => c.classId === 1)!;
+    expect(cell.shapeCount).toBe(1);
+    expect(cell.pixelArea).toBeGreaterThan(0);
+    expect(stats.coverage.annotatedSlices).toBe(1);
+  });
+
+  it('flags a tiny (sliver) shape', () => {
+    const stats = computeSampleStats(
+      { '0': [{ id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 }] },
+      CLASSES, 3, [], 100, 100,
+    );
+    expect(stats.flags.some((f) => f.kind === 'sliver')).toBe(true);
+  });
+
+  it('does not flag a normal-sized shape as a sliver', () => {
+    const stats = computeSampleStats(
+      { '0': [{ id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 50, h: 50 }] },
+      CLASSES, 3, [], 100, 100,
+    );
+    expect(stats.flags.some((f) => f.kind === 'sliver')).toBe(false);
+  });
+
+  it('flags a self-intersecting polygon', () => {
+    // A "bowtie" shape: crosses itself in the middle.
+    const bowtie = [0, 0, 100, 100, 100, 0, 0, 100];
+    const stats = computeSampleStats(
+      { '0': [{ id: 's1', classId: 1, kind: 'polygon', points: bowtie }] },
+      CLASSES, 3, [], 200, 200,
+    );
+    expect(stats.flags.some((f) => f.kind === 'self-intersection')).toBe(true);
+  });
+
+  it('does not flag a simple (non-intersecting) polygon', () => {
+    const square = [0, 0, 50, 0, 50, 50, 0, 50];
+    const stats = computeSampleStats(
+      { '0': [{ id: 's1', classId: 1, kind: 'polygon', points: square }] },
+      CLASSES, 3, [], 200, 200,
+    );
+    expect(stats.flags.some((f) => f.kind === 'self-intersection')).toBe(false);
+  });
+
+  it('flags an overlap between two different classes on the same slice', () => {
+    const stats = computeSampleStats(
+      {
+        '0': [
+          { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 50, h: 50 },
+          { id: 's2', classId: 2, kind: 'rectangle', x: 25, y: 25, w: 50, h: 50 },
+        ],
+      },
+      CLASSES, 3, [], 200, 200,
+    );
+    expect(stats.flags.some((f) => f.kind === 'overlap')).toBe(true);
+  });
+
+  it('does not flag disjoint shapes of different classes', () => {
+    const stats = computeSampleStats(
+      {
+        '0': [
+          { id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 20, h: 20 },
+          { id: 's2', classId: 2, kind: 'rectangle', x: 150, y: 150, w: 20, h: 20 },
+        ],
+      },
+      CLASSES, 3, [], 200, 200,
+    );
+    expect(stats.flags.some((f) => f.kind === 'overlap')).toBe(false);
+  });
+
+  it('flags empty-unmarked slices (not annotated, not negative)', () => {
+    const stats = computeSampleStats(
+      { '0': [{ id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 50, h: 50 }] },
+      CLASSES, 3, [], 100, 100,
+    );
+    expect(stats.coverage.emptyUnmarked).toEqual([1, 2]);
+    expect(stats.flags.filter((f) => f.kind === 'empty-unmarked')).toHaveLength(2);
+  });
+
+  it('does not flag a slice marked as negative', () => {
+    const stats = computeSampleStats({}, CLASSES, 2, ['0', '1'], 100, 100);
+    expect(stats.coverage.emptyUnmarked).toEqual([]);
+    expect(stats.coverage.negativeSlices).toBe(2);
+  });
+
+  it('suppresses empty-unmarked flags entirely when there are too many (>20)', () => {
+    const stats = computeSampleStats({}, CLASSES, 25, [], 100, 100);
+    expect(stats.coverage.emptyUnmarked).toHaveLength(25);
+    expect(stats.flags.filter((f) => f.kind === 'empty-unmarked')).toHaveLength(0);
+  });
+
+  it('computes area for an ellipse shape', () => {
+    const stats = computeSampleStats(
+      { '0': [{ id: 's1', classId: 1, kind: 'ellipse', cx: 50, cy: 50, rx: 20, ry: 10 }] },
+      CLASSES, 1, [], 100, 100,
+    );
+    const cell = stats.classStats.find((c) => c.classId === 1)!;
+    expect(cell.pixelArea).toBeGreaterThan(0);
+  });
+
+  it('computes area for a brush stroke', () => {
+    const stats = computeSampleStats(
+      {
+        '0': [{
+          id: 's1', classId: 1, kind: 'brush',
+          strokes: [{ mode: 'paint', points: [0, 0, 50, 50], radius: 5 }],
+        }],
+      },
+      CLASSES, 1, [], 100, 100,
+    );
+    const cell = stats.classStats.find((c) => c.classId === 1)!;
+    expect(cell.pixelArea).toBeGreaterThan(0);
+  });
+
+  it('ignores an erase-mode brush stroke for area purposes', () => {
+    const stats = computeSampleStats(
+      {
+        '0': [{
+          id: 's1', classId: 1, kind: 'brush',
+          strokes: [{ mode: 'erase', points: [0, 0, 50, 50], radius: 5 }],
+        }],
+      },
+      CLASSES, 1, [], 100, 100,
+    );
+    const cell = stats.classStats.find((c) => c.classId === 1)!;
+    // Shape still counts, but analytic area contribution from an erase stroke is 0.
+    expect(cell.shapeCount).toBe(1);
+  });
+
+  it('a polygon hole reduces its net area', () => {
+    const outer = [0, 0, 100, 0, 100, 100, 0, 100];
+    const hole = [25, 25, 75, 25, 75, 75, 25, 75];
+    const statsWithHole = computeSampleStats(
+      { '0': [{ id: 's1', classId: 1, kind: 'polygon', points: outer, holes: [hole] }] },
+      CLASSES, 1, [], 200, 200,
+    );
+    const statsNoHole = computeSampleStats(
+      { '0': [{ id: 's1', classId: 1, kind: 'polygon', points: outer }] },
+      CLASSES, 1, [], 200, 200,
+    );
+    const withHoleArea = statsWithHole.classStats.find((c) => c.classId === 1)!.pixelArea;
+    const noHoleArea = statsNoHole.classStats.find((c) => c.classId === 1)!.pixelArea;
+    expect(withHoleArea).toBeLessThan(noHoleArea);
+  });
+
+  it('ignores an empty-array slice entry (not annotated)', () => {
+    const stats = computeSampleStats({ '0': [] }, CLASSES, 1, [], 100, 100);
+    expect(stats.coverage.annotatedSlices).toBe(0);
+  });
+
+  it('labels a flag for a class not in the provided class list generically', () => {
+    const stats = computeSampleStats(
+      { '0': [{ id: 's1', classId: 99, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2 }] },
+      CLASSES, 1, [], 100, 100,
+    );
+    const sliver = stats.flags.find((f) => f.kind === 'sliver')!;
+    expect(sliver.message).toContain('class 99');
+  });
+});
diff --git a/frontend/src/lib/ipredApi.test.ts b/frontend/src/lib/ipredApi.test.ts
new file mode 100644
index 0000000..8942d7f
--- /dev/null
+++ b/frontend/src/lib/ipredApi.test.ts
@@ -0,0 +1,198 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import {
+  getIpredComposition, getIpredSetup, ipredChannelUrl, ipredHealth, ipredInfer,
+  ipredPreprocess, ipredRunCommitUrl, ipredRunProbaUrl, ipredRunStatusUrl,
+  ipredThresholdClass, ipredTrain, listIpredCompositions, listIpredModules,
+  listIpredSetups, listIpredTrainers, openIpredSession, previewIpredComposition,
+  upsertIpredComposition, upsertIpredSetup,
+} from './ipredApi';
+
+beforeEach(() => {
+  vi.stubGlobal('fetch', vi.fn());
+});
+
+afterEach(() => {
+  vi.unstubAllGlobals();
+});
+
+function ok(body: unknown) {
+  return { ok: true, json: async () => body };
+}
+
+function errRes(status: number, body: string, statusText = 'Error') {
+  return { ok: false, status, statusText, text: async () => body };
+}
+
+describe('ipredHealth', () => {
+  it('returns the parsed health body', async () => {
+    (fetch as any).mockResolvedValue(ok({ status: 'ok' }));
+    expect(await ipredHealth()).toEqual({ status: 'ok' });
+    expect(fetch).toHaveBeenCalledWith(expect.stringContaining('/api/ipred/health'));
+  });
+
+  it('throws a parsed error with the detail string on failure', async () => {
+    (fetch as any).mockResolvedValue(errRes(503, JSON.stringify({ detail: 'ipred unreachable' })));
+    await expect(ipredHealth()).rejects.toThrow('ipred unreachable');
+  });
+
+  it('falls back to raw text when the error body is not JSON', async () => {
+    (fetch as any).mockResolvedValue(errRes(500, 'plain text failure'));
+    await expect(ipredHealth()).rejects.toThrow('plain text failure');
+  });
+
+  it('falls back to statusText when the error body is empty', async () => {
+    (fetch as any).mockResolvedValue(errRes(500, '', 'Internal Server Error'));
+    await expect(ipredHealth()).rejects.toThrow('Internal Server Error');
+  });
+});
+
+describe('openIpredSession', () => {
+  it('POSTs the payload and returns the session', async () => {
+    (fetch as any).mockResolvedValue(ok({ session_id: 's1', project_id: 'p1' }));
+    const result = await openIpredSession({ kind: 'local', source: 'x.tif' });
+    expect(result).toEqual({ session_id: 's1', project_id: 'p1' });
+    const [url, init] = (fetch as any).mock.calls[0];
+    expect(url).toContain('/api/ipred/sessions');
+    expect(init.method).toBe('POST');
+    expect(JSON.parse(init.body)).toEqual({ kind: 'local', source: 'x.tif' });
+  });
+});
+
+describe('listIpredSetups', () => {
+  it('returns the setups array', async () => {
+    (fetch as any).mockResolvedValue(ok({ setups: [{ id: 's1' }] }));
+    expect(await listIpredSetups()).toEqual([{ id: 's1' }]);
+  });
+
+  it('defaults to an empty array when the setups key is missing', async () => {
+    (fetch as any).mockResolvedValue(ok({}));
+    expect(await listIpredSetups()).toEqual([]);
+  });
+});
+
+describe('getIpredSetup', () => {
+  it('encodes the setup id in the URL', async () => {
+    (fetch as any).mockResolvedValue(ok({ id: 'a/b' }));
+    await getIpredSetup('a/b');
+    expect(fetch).toHaveBeenCalledWith(expect.stringContaining(encodeURIComponent('a/b')));
+  });
+});
+
+describe('upsertIpredSetup', () => {
+  it('POSTs the setup payload', async () => {
+    (fetch as any).mockResolvedValue(ok({ id: 's1' }));
+    await upsertIpredSetup({ name: 'setup', kind: 'procedure' });
+    const [, init] = (fetch as any).mock.calls[0];
+    expect(init.method).toBe('POST');
+  });
+});
+
+describe('listIpredTrainers', () => {
+  it('returns the trainers array, defaulting to empty', async () => {
+    (fetch as any).mockResolvedValue(ok({}));
+    expect(await listIpredTrainers()).toEqual([]);
+    (fetch as any).mockResolvedValue(ok({ trainers: ['catboost'] }));
+    expect(await listIpredTrainers()).toEqual(['catboost']);
+  });
+});
+
+describe('listIpredModules', () => {
+  it('returns the modules array, defaulting to empty', async () => {
+    (fetch as any).mockResolvedValue(ok({}));
+    expect(await listIpredModules()).toEqual([]);
+  });
+});
+
+describe('listIpredCompositions / getIpredComposition / upsertIpredComposition', () => {
+  it('lists compositions, defaulting to empty', async () => {
+    (fetch as any).mockResolvedValue(ok({}));
+    expect(await listIpredCompositions()).toEqual([]);
+  });
+
+  it('gets one composition by id', async () => {
+    (fetch as any).mockResolvedValue(ok({ id: 'c1', name: 'comp', nodes: [], outputs: [] }));
+    const result = await getIpredComposition('c1');
+    expect(result.id).toBe('c1');
+  });
+
+  it('upserts a composition via POST', async () => {
+    (fetch as any).mockResolvedValue(ok({ id: 'c2', name: 'x', nodes: [], outputs: [] }));
+    await upsertIpredComposition({ name: 'x', nodes: [], outputs: [] });
+    const [url, init] = (fetch as any).mock.calls[0];
+    expect(url).toContain('/api/ipred/compositions');
+    expect(init.method).toBe('POST');
+  });
+});
+
+describe('previewIpredComposition', () => {
+  it('defaults the name to "preview" when omitted', async () => {
+    (fetch as any).mockResolvedValue(ok({ preview_labels: [] }));
+    await previewIpredComposition({ nodes: [], outputs: [] });
+    const [, init] = (fetch as any).mock.calls[0];
+    expect(JSON.parse(init.body).name).toBe('preview');
+  });
+
+  it('keeps an explicit name', async () => {
+    (fetch as any).mockResolvedValue(ok({ preview_labels: [] }));
+    await previewIpredComposition({ name: 'draft', nodes: [], outputs: [] });
+    const [, init] = (fetch as any).mock.calls[0];
+    expect(JSON.parse(init.body).name).toBe('draft');
+  });
+});
+
+describe('ipredPreprocess', () => {
+  it('POSTs and returns the preprocess result', async () => {
+    (fetch as any).mockResolvedValue(ok({
+      feature_id: 'f1', project_id: 'p1', setup_id: 's1', slice_index: 0,
+      n_channels: 3, height: 64, width: 64, labels: [], cache_hit: false,
+    }));
+    const result = await ipredPreprocess({ session_id: 's1' });
+    expect(result.feature_id).toBe('f1');
+  });
+});
+
+describe('URL builders', () => {
+  it('ipredChannelUrl encodes the feature id', () => {
+    expect(ipredChannelUrl('feat/1', 2)).toContain(encodeURIComponent('feat/1'));
+    expect(ipredChannelUrl('feat1', 2)).toContain('/channels/2');
+  });
+
+  it('ipredRunCommitUrl/StatusUrl/ProbaUrl build the expected paths', () => {
+    expect(ipredRunCommitUrl('run/1')).toContain(`${encodeURIComponent('run/1')}/commit.png`);
+    expect(ipredRunStatusUrl('run1')).toContain('run1/status.png');
+    expect(ipredRunProbaUrl('run1', 3)).toContain('run1/proba/3.png');
+  });
+});
+
+describe('ipredTrain / ipredInfer', () => {
+  it('ipredTrain POSTs the payload and returns the result', async () => {
+    (fetch as any).mockResolvedValue(ok({
+      model_id: 'm1', feature_id: 'f1', trainer_id: 'catboost', class_ids: [1],
+      train_accuracy: 0.9, n_train: 10, n_cal: 5, n_samples: 15, params: {},
+    }));
+    const result = await ipredTrain({ session_id: 's1', shapes: [] });
+    expect(result.model_id).toBe('m1');
+  });
+
+  it('ipredInfer POSTs the payload and returns the result', async () => {
+    (fetch as any).mockResolvedValue(ok({
+      run_id: 'r1', model_id: 'm1', feature_id: 'f1', alpha: 0.05, class_ids: [1],
+      counts: { singleton: 1, multi: 0, abstain: 0 },
+    }));
+    const result = await ipredInfer({ session_id: 's1' });
+    expect(result.run_id).toBe('r1');
+  });
+});
+
+describe('ipredThresholdClass', () => {
+  it('POSTs to the run-scoped threshold-class endpoint', async () => {
+    (fetch as any).mockResolvedValue(ok({
+      run_id: 'r1', class_id: 1, class_index: 0, threshold: 0.5,
+      width: 10, height: 10, n_positive: 5, label_map_b64: 'abc',
+    }));
+    const result = await ipredThresholdClass('r1', { class_id: 1, threshold: 0.5 });
+    expect(result.n_positive).toBe(5);
+    const [url] = (fetch as any).mock.calls[0];
+    expect(url).toContain('/runs/r1/threshold-class');
+  });
+});
diff --git a/frontend/src/lib/livewire.test.ts b/frontend/src/lib/livewire.test.ts
index d42d0a6..7bf662c 100644
--- a/frontend/src/lib/livewire.test.ts
+++ b/frontend/src/lib/livewire.test.ts
@@ -1,5 +1,5 @@
-import { describe, it, expect } from 'vitest';
-import { dijkstra, tracePath, imageToGrid, simplifyPath, type CostMap } from './livewire';
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { buildCostMap, dijkstra, tracePath, imageToGrid, simplifyPath, type CostMap } from './livewire';
 
 /** 5x5 grid, scale 1, with a zero-cost "edge" corridor along row y=2. */
 function corridorMap(): CostMap {
@@ -55,4 +55,90 @@ describe('livewire', () => {
     expect(simplified.slice(-2)).toEqual([4, 0]);
     expect(simplified.length).toBeLessThan(dense.length);
   });
+
+  describe('buildCostMap', () => {
+    const stores = new WeakMap<HTMLCanvasElement, Uint8ClampedArray>();
+
+    function seedSource(width: number, height: number, fill: (i: number) => number): HTMLCanvasElement {
+      const canvas = document.createElement('canvas');
+      canvas.width = width;
+      canvas.height = height;
+      const data = new Uint8ClampedArray(width * height * 4);
+      for (let i = 0; i < data.length; i++) data[i] = fill(i);
+      stores.set(canvas, data);
+      return canvas;
+    }
+
+    beforeEach(() => {
+      vi.spyOn(HTMLCanvasElement.prototype, 'getContext').mockImplementation(function (
+        this: HTMLCanvasElement,
+      ): any {
+        const canvas = this;
+        return {
+          drawImage: (source: HTMLCanvasElement, _sx: number, _sy: number, w: number, h: number) => {
+            const src = stores.get(source);
+            const dst = new Uint8ClampedArray(w * h * 4);
+            if (src) dst.set(src.subarray(0, dst.length));
+            stores.set(canvas, dst);
+          },
+          getImageData: (_x: number, _y: number, w: number, h: number) => ({
+            data: stores.get(canvas) ?? new Uint8ClampedArray(w * h * 4),
+            width: w,
+            height: h,
+          }),
+        };
+      });
+    });
+
+    afterEach(() => vi.restoreAllMocks());
+
+    it('downsamples so the long side stays within maxDim', () => {
+      const source = seedSource(1000, 500, () => 128);
+      const cm = buildCostMap(source, 1000, 500, 100);
+      expect(cm).not.toBeNull();
+      expect(Math.max(cm!.gw, cm!.gh)).toBeLessThanOrEqual(100);
+    });
+
+    it('produces near-zero cost on a strong edge and near-one cost on a flat region', () => {
+      // A 10x10 image split vertically: black left half, white right half —
+      // a strong vertical edge at x=5.
+      const source = seedSource(10, 10, (i) => {
+        if (i % 4 === 3) return 255; // alpha
+        const px = Math.floor(i / 4);
+        const x = px % 10;
+        return x < 5 ? 0 : 255;
+      });
+      const cm = buildCostMap(source, 10, 10, 512)!;
+      expect(cm).not.toBeNull();
+      // A flat region far from the edge should have cost close to 1 (no gradient).
+      const flatIdx = imageToGrid(cm, 1, 5);
+      // The edge column should have a lower cost than the flat region.
+      const edgeIdx = imageToGrid(cm, 5, 5);
+      expect(cm.cost[edgeIdx]).toBeLessThan(cm.cost[flatIdx]);
+    });
+
+    it('scales the grid by the upscale factor', () => {
+      const source = seedSource(4, 4, () => 100);
+      const cm1 = buildCostMap(source, 4, 4, 512, 1)!;
+      const cm2 = buildCostMap(source, 4, 4, 512, 2)!;
+      expect(cm2.gw).toBeGreaterThan(cm1.gw);
+    });
+
+    it('returns null when the canvas cannot produce a 2D context', () => {
+      vi.spyOn(HTMLCanvasElement.prototype, 'getContext').mockReturnValue(null as any);
+      const source = seedSource(4, 4, () => 0);
+      expect(buildCostMap(source, 4, 4)).toBeNull();
+    });
+
+    it('returns null when getImageData throws (tainted canvas)', () => {
+      vi.spyOn(HTMLCanvasElement.prototype, 'getContext').mockImplementation(function (): any {
+        return {
+          drawImage: () => {},
+          getImageData: () => { throw new Error('tainted'); },
+        };
+      });
+      const source = seedSource(4, 4, () => 0);
+      expect(buildCostMap(source, 4, 4)).toBeNull();
+    });
+  });
 });
diff --git a/frontend/src/lib/magicwand.test.ts b/frontend/src/lib/magicwand.test.ts
index 9484649..d5363d9 100644
--- a/frontend/src/lib/magicwand.test.ts
+++ b/frontend/src/lib/magicwand.test.ts
@@ -1,5 +1,5 @@
-import { describe, it, expect } from 'vitest';
-import { magicSelect, maskToPolygons, maskToPolygonsWithHoles, gradientField, otsuThreshold, type GrayField } from './magicwand';
+import { afterEach, beforeEach, describe, it, expect, vi } from 'vitest';
+import { buildField, magicSelect, maskToPolygons, maskToPolygonsWithHoles, gradientField, otsuThreshold, type GrayField } from './magicwand';
 
 /** 40x40 grid (scale 1): background 0 with two value-200 blocks. */
 function twoBlocks(): GrayField {
@@ -202,6 +202,130 @@ describe('fractional scale (upscaled working resolution)', () => {
   });
 });
 
+/**
+ * jsdom has no real 2D canvas, so `buildField` (which draws the source image
+ * into a small canvas and reads it back) needs `getContext('2d')` stubbed.
+ * drawImage is a no-op; getImageData returns a fixed RGBA pattern (128 gray
+ * everywhere) — buildField only cares that SOME data comes back and is
+ * converted to a `gray` field of the right size, not the actual pixel values.
+ */
+function stubCanvas2d() {
+  vi.spyOn(HTMLCanvasElement.prototype, 'getContext').mockImplementation(function (
+    this: HTMLCanvasElement,
+  ): any {
+    return {
+      drawImage: () => {},
+      getImageData: (_x: number, _y: number, w: number, h: number) => ({
+        data: new Uint8ClampedArray(w * h * 4).fill(128),
+        width: w,
+        height: h,
+      }),
+    } as unknown as CanvasRenderingContext2D;
+  });
+}
+
+describe('buildField', () => {
+  afterEach(() => vi.restoreAllMocks());
+
+  it('builds a downsampled gray + gradient field sized to fit maxDim', () => {
+    stubCanvas2d();
+    const field = buildField({} as CanvasImageSource, 3200, 1600, 1600);
+    expect(field).not.toBeNull();
+    // scale = ceil(3200/1600) = 2 -> gw = 3200/2 = 1600, gh = 1600/2 = 800
+    expect(field!.gw).toBe(1600);
+    expect(field!.gh).toBe(800);
+    expect(field!.scale).toBe(2);
+    expect(field!.gray.length).toBe(1600 * 800);
+    expect(field!.grad).toBeInstanceOf(Float32Array);
+  });
+
+  it('applies upscale to raise grid resolution and divide the effective scale', () => {
+    stubCanvas2d();
+    const field = buildField({} as CanvasImageSource, 3200, 1600, 1600, 2);
+    // scale = ceil(3200/1600)/2 = 1
+    expect(field!.scale).toBe(1);
+    expect(field!.gw).toBe(3200);
+    expect(field!.gh).toBe(1600);
+  });
+
+  it('omits the gradient field when needGradient is false', () => {
+    stubCanvas2d();
+    const field = buildField({} as CanvasImageSource, 800, 800, 1600, 1, false);
+    expect(field!.grad).toBeUndefined();
+  });
+
+  it('returns null when a 2D context is unavailable', () => {
+    vi.spyOn(HTMLCanvasElement.prototype, 'getContext').mockReturnValue(null);
+    const field = buildField({} as CanvasImageSource, 800, 800);
+    expect(field).toBeNull();
+  });
+
+  it('returns null on a tainted canvas (getImageData throws)', () => {
+    vi.spyOn(HTMLCanvasElement.prototype, 'getContext').mockImplementation(function (): any {
+      return {
+        drawImage: () => {},
+        getImageData: () => {
+          throw new Error('SecurityError');
+        },
+      };
+    });
+    const field = buildField({} as CanvasImageSource, 800, 800);
+    expect(field).toBeNull();
+  });
+});
+
+describe('magicSelect smoothing (boxBlur pre-filter + Chaikin rounding)', () => {
+  it('produces a valid, smaller/rounder contour when smooth > 0', () => {
+    const field = twoBlocks();
+    const smoothed = magicSelect(field, 9, 9, { toleranceFrac: 0.2, mode: 'contiguous', smooth: 3, minRegion: 8 });
+    expect(smoothed.length).toBe(1);
+    expect(smoothed[0].length).toBeGreaterThanOrEqual(6);
+  });
+
+  it('caps Chaikin iterations at 4 for very high smooth values without throwing', () => {
+    const field = twoBlocks();
+    expect(() =>
+      magicSelect(field, 9, 9, { toleranceFrac: 0.2, mode: 'contiguous', smooth: 10, minRegion: 8 }),
+    ).not.toThrow();
+  });
+});
+
+describe('maskToPolygonsWithHoles — multiple outer regions', () => {
+  it('assigns a hole to its owning outer via the centroid test when there are 2+ regions', () => {
+    const W = 100, H = 60;
+    const m = new Uint8Array(W * H);
+    // Region A (donut) at x[10,40), region B (solid) at x[60,90).
+    for (let y = 10; y < 50; y++) for (let x = 10; x < 40; x++) m[y * W + x] = 1;
+    for (let y = 22; y < 38; y++) for (let x = 18; x < 32; x++) m[y * W + x] = 0; // hole in A
+    for (let y = 10; y < 50; y++) for (let x = 60; x < 90; x++) m[y * W + x] = 1; // solid B
+
+    const out = maskToPolygonsWithHoles(m, W, H, { minRegion: 8 });
+    expect(out.length).toBe(2);
+    const withHole = out.find((r) => r.holes.length > 0);
+    const withoutHole = out.find((r) => r.holes.length === 0);
+    expect(withHole).toBeDefined();
+    expect(withoutHole).toBeDefined();
+  });
+
+  it('falls back to the bbox-containment test when the hole centroid lands outside every outer (concave, multi-region)', () => {
+    const W = 100, H = 60;
+    const m = new Uint8Array(W * H);
+    // Region A: concave "C" block with a notch, plus an enclosed hole whose
+    // centroid falls in the notch (outside A) — same shape as the single-region
+    // concave test, but with an unrelated solid region B elsewhere so `result.length`
+    // is 2 and the direct single-region shortcut cannot apply.
+    for (let y = 8; y < 52; y++) for (let x = 8; x < 52; x++) m[y * W + x] = 1; // block A
+    for (let y = 8; y < 30; y++) for (let x = 40; x < 52; x++) m[y * W + x] = 0; // notch (concavity)
+    for (let y = 34; y < 46; y++) for (let x = 16; x < 28; x++) m[y * W + x] = 0; // enclosed hole in A
+    for (let y = 8; y < 52; y++) for (let x = 70; x < 95; x++) m[y * W + x] = 1; // solid region B
+
+    const out = maskToPolygonsWithHoles(m, W, H, { minRegion: 8 });
+    expect(out.length).toBe(2);
+    const withHole = out.find((r) => r.holes.length > 0);
+    expect(withHole).toBeDefined();
+  });
+});
+
 describe('otsuThreshold', () => {
   it('splits a clean bimodal histogram between the modes', () => {
     const bins = new Array(256).fill(0);
diff --git a/frontend/src/lib/measure.test.ts b/frontend/src/lib/measure.test.ts
new file mode 100644
index 0000000..b323d28
--- /dev/null
+++ b/frontend/src/lib/measure.test.ts
@@ -0,0 +1,95 @@
+import { describe, expect, it } from 'vitest';
+import { measureRegion } from './measure';
+import type { Shape } from '@/stores/annotationStore';
+
+describe('measureRegion', () => {
+  it('returns an all-zero/null measurement for no shapes', () => {
+    const m = measureRegion([], 100, 100);
+    expect(m).toEqual({ count: 0, areaPx: 0, perimeterPx: null, centroid: null, bbox: null });
+  });
+
+  it('measures a single rectangle: area, perimeter, centroid, bbox', () => {
+    const rect: Shape = { id: 'r1', classId: 1, kind: 'rectangle', x: 10, y: 10, w: 20, h: 10 };
+    const m = measureRegion([rect], 100, 100);
+    expect(m.count).toBe(1);
+    // area ~ 20*10 = 200 (rasterized, so allow a little slack)
+    expect(m.areaPx).toBeGreaterThan(150);
+    expect(m.areaPx).toBeLessThanOrEqual(231);
+    // analytic perimeter = 2*(20+10) = 60
+    expect(m.perimeterPx).toBe(60);
+    expect(m.centroid).not.toBeNull();
+    expect(m.centroid!.x).toBeCloseTo(20, 0); // center x ~ 10+20/2
+    expect(m.centroid!.y).toBeCloseTo(15, 0);
+    expect(m.bbox).not.toBeNull();
+    // bbox is (maxX-minX+1)*scale, so it can run 1px wider than the nominal size.
+    expect(m.bbox!.w).toBeGreaterThanOrEqual(20);
+    expect(m.bbox!.w).toBeLessThanOrEqual(21);
+    expect(m.bbox!.h).toBeGreaterThanOrEqual(10);
+    expect(m.bbox!.h).toBeLessThanOrEqual(11);
+  });
+
+  it('sums perimeter and area across multiple shapes (count reflects shapes, not area)', () => {
+    const r1: Shape = { id: 'r1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10 };
+    const r2: Shape = { id: 'r2', classId: 1, kind: 'rectangle', x: 50, y: 50, w: 10, h: 10 };
+    const m = measureRegion([r1, r2], 100, 100);
+    expect(m.count).toBe(2);
+    expect(m.perimeterPx).toBe(80); // 40 + 40
+  });
+
+  it('computes ellipse perimeter via the Ramanujan approximation', () => {
+    const ellipse: Shape = { id: 'e1', classId: 1, kind: 'ellipse', cx: 50, cy: 50, rx: 10, ry: 10 };
+    const m = measureRegion([ellipse], 100, 100);
+    // A circle of radius 10: circumference = 2*pi*10 ≈ 62.83
+    expect(m.perimeterPx).toBeCloseTo(2 * Math.PI * 10, 0);
+  });
+
+  it('computes polygon perimeter as the closed-ring length, including holes', () => {
+    // A 10x10 square polygon (ring perimeter 40).
+    const square: Shape = {
+      id: 'p1', classId: 1, kind: 'polygon',
+      points: [0, 0, 10, 0, 10, 10, 0, 10],
+    };
+    const m1 = measureRegion([square], 100, 100);
+    expect(m1.perimeterPx).toBeCloseTo(40, 5);
+
+    // Same square with a 4x4 hole adds the hole's ring perimeter (16).
+    const withHole: Shape = {
+      id: 'p2', classId: 1, kind: 'polygon',
+      points: [0, 0, 10, 0, 10, 10, 0, 10],
+      holes: [[3, 3, 7, 3, 7, 7, 3, 7]],
+    };
+    const m2 = measureRegion([withHole], 100, 100);
+    expect(m2.perimeterPx).toBeCloseTo(40 + 16, 5);
+  });
+
+  it('returns null perimeter when any shape lacks a defined perimeter (brush)', () => {
+    const rect: Shape = { id: 'r1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10 };
+    const brushShape: Shape = {
+      id: 'b1', classId: 1, kind: 'brush',
+      strokes: [{ points: [20, 20, 30, 30], radius: 3, mode: 'paint' }],
+    };
+    const m = measureRegion([rect, brushShape], 100, 100);
+    expect(m.perimeterPx).toBeNull();
+    // Geometry (area/centroid/bbox) is still computed from the union of both shapes.
+    expect(m.count).toBe(2);
+    expect(m.areaPx).toBeGreaterThan(0);
+    expect(m.bbox).not.toBeNull();
+  });
+
+  it('treats overlapping shapes as a union for area/centroid/bbox (not double-counted)', () => {
+    const a: Shape = { id: 'a', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10 };
+    const b: Shape = { id: 'b', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 10, h: 10 }; // identical, fully overlapping
+    const single = measureRegion([a], 100, 100);
+    const overlapping = measureRegion([a, b], 100, 100);
+    expect(overlapping.areaPx).toBeCloseTo(single.areaPx, 5);
+    // count still reflects the number of shapes passed in, not the union region count.
+    expect(overlapping.count).toBe(2);
+  });
+
+  it('degenerate zero-size rectangle yields a definite (non-crashing) result with zero perimeter contribution area', () => {
+    const zero: Shape = { id: 'z', classId: 1, kind: 'rectangle', x: 5, y: 5, w: 0, h: 0 };
+    const m = measureRegion([zero], 100, 100);
+    expect(m.count).toBe(1);
+    expect(m.perimeterPx).toBe(0);
+  });
+});
diff --git a/frontend/src/lib/pixelClf.gaps.test.ts b/frontend/src/lib/pixelClf.gaps.test.ts
new file mode 100644
index 0000000..161e602
--- /dev/null
+++ b/frontend/src/lib/pixelClf.gaps.test.ts
@@ -0,0 +1,298 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import {
+  applyProbaThresholdRgba,
+  classProbaMaskSetName,
+  colorizeConformalOverlay,
+  colorizeLabelMap,
+  labelMapToPolygonShapes,
+  loadLabelPng,
+  parseHexColor,
+  thresholdProbaPngBlob,
+} from './pixelClf';
+
+/**
+ * jsdom has no real 2D canvas; stub `getContext('2d')` with an in-memory
+ * implementation of just the methods pixelClf.ts uses (createImageData /
+ * putImageData / getImageData), so `colorizeLabelMap` / `colorizeConformalOverlay`
+ * actually run their pixel-coloring loops instead of hitting the `!ctx` early
+ * return that made the sibling `pixelClf.overlay.test.ts` a no-op smoke test.
+ */
+function stubCanvasContext() {
+  const stores = new WeakMap<HTMLCanvasElement, Uint8ClampedArray>();
+  vi.spyOn(HTMLCanvasElement.prototype, 'getContext').mockImplementation(function (
+    this: HTMLCanvasElement,
+  ): any {
+    const canvas = this;
+    return {
+      createImageData: (w: number, h: number) => {
+        const data = new Uint8ClampedArray(w * h * 4);
+        stores.set(canvas, data);
+        return { data, width: w, height: h };
+      },
+      putImageData: () => {},
+      getImageData: (_x: number, _y: number, w: number, h: number) => ({
+        data: stores.get(canvas) ?? new Uint8ClampedArray(w * h * 4),
+        width: w,
+        height: h,
+      }),
+      drawImage: () => {},
+    } as unknown as CanvasRenderingContext2D;
+  });
+}
+
+beforeEach(() => stubCanvasContext());
+afterEach(() => vi.restoreAllMocks());
+
+describe('parseHexColor', () => {
+  it('parses 3-digit shorthand hex by doubling each digit', () => {
+    expect(parseHexColor('#0f8')).toEqual([0, 255, 136]);
+  });
+});
+
+describe('classProbaMaskSetName', () => {
+  it('clamps thresholds outside [0,1]', () => {
+    expect(classProbaMaskSetName(1, 1.5, 'x')).toBe('x p≥100%');
+    expect(classProbaMaskSetName(1, -0.5, 'x')).toBe('x p≥0%');
+  });
+
+  it('trims whitespace-only labels and falls back to the class id', () => {
+    expect(classProbaMaskSetName(4, 0.2, '   ')).toBe('class 4 p≥20%');
+  });
+});
+
+describe('applyProbaThresholdRgba edge thresholds', () => {
+  it('threshold 0 colors every pixel (nothing is below the cut)', () => {
+    const rgba = new Uint8ClampedArray([0, 0, 0, 255, 128, 128, 128, 255]);
+    applyProbaThresholdRgba(rgba, 0);
+    expect(rgba[3]).toBe(255);
+    expect(rgba[7]).toBe(255);
+  });
+
+  it('threshold 1 makes everything but a full-white pixel transparent (tiny span floor)', () => {
+    const rgba = new Uint8ClampedArray([200, 200, 200, 255, 255, 255, 255, 255]);
+    applyProbaThresholdRgba(rgba, 1);
+    expect(rgba[3]).toBe(0); // p=200/255 < 1 -> transparent
+    expect(rgba[7]).toBe(255); // p=1 -> exactly at/above cut
+  });
+});
+
+describe('colorizeLabelMap', () => {
+  it('colors known classes and leaves 0/unknown classes transparent', () => {
+    const width = 2, height = 1;
+    const labels = new Uint8Array([1, 2]); // 2 = unknown (no color registered)
+    const colors = new Map([[1, '#ff0000']]);
+    const canvas = colorizeLabelMap(labels, width, height, colors, 90);
+    const ctx = canvas.getContext('2d')!;
+    const { data } = ctx.getImageData(0, 0, width, height);
+    // Pixel 0 -> class 1 -> red at alpha 90.
+    expect([data[0], data[1], data[2], data[3]]).toEqual([255, 0, 0, 90]);
+    // Pixel 1 -> class 2 has no registered color -> left transparent (all zero).
+    expect([data[4], data[5], data[6], data[7]]).toEqual([0, 0, 0, 0]);
+  });
+
+  it('leaves class-0 (background) pixels transparent', () => {
+    const labels = new Uint8Array([0]);
+    const canvas = colorizeLabelMap(labels, 1, 1, new Map([[1, '#00ff00']]));
+    const ctx = canvas.getContext('2d')!;
+    const { data } = ctx.getImageData(0, 0, 1, 1);
+    expect([...data]).toEqual([0, 0, 0, 0]);
+  });
+});
+
+describe('colorizeConformalOverlay', () => {
+  it('colors a singleton pixel with its class color at fixed alpha', () => {
+    const commit = new Uint8Array([1]);
+    const status = new Uint8Array([1]); // singleton
+    const canvas = colorizeConformalOverlay(commit, status, 1, 1, new Map([[1, '#00ff00']]));
+    const { data } = canvas.getContext('2d')!.getImageData(0, 0, 1, 1);
+    expect([...data]).toEqual([0, 255, 0, 120]);
+  });
+
+  it('hides a singleton pixel whose class is filtered out by classVisible', () => {
+    const commit = new Uint8Array([1]);
+    const status = new Uint8Array([1]);
+    const canvas = colorizeConformalOverlay(commit, status, 1, 1, new Map([[1, '#00ff00']]), {
+      classVisible: () => false,
+    });
+    const { data } = canvas.getContext('2d')!.getImageData(0, 0, 1, 1);
+    expect([...data]).toEqual([0, 0, 0, 0]);
+  });
+
+  it('leaves a singleton pixel transparent when its class has no registered color', () => {
+    const commit = new Uint8Array([9]);
+    const status = new Uint8Array([1]);
+    const canvas = colorizeConformalOverlay(commit, status, 1, 1, new Map([[1, '#00ff00']]));
+    const { data } = canvas.getContext('2d')!.getImageData(0, 0, 1, 1);
+    expect([...data]).toEqual([0, 0, 0, 0]);
+  });
+
+  it('renders a multi-class hatch pattern that alternates by (x+y) parity, and can be hidden', () => {
+    // 4x1: status=2 (multi) at every pixel; (x+y)&3 < 2 is true for x=0,1 ("on")
+    // and false for x=2,3 ("off"), giving both hatch colors in one row.
+    const commit = new Uint8Array([0, 0, 0, 0]);
+    const status = new Uint8Array([2, 2, 2, 2]);
+    const canvas = colorizeConformalOverlay(commit, status, 4, 1, new Map());
+    const { data } = canvas.getContext('2d')!.getImageData(0, 0, 4, 1);
+    expect([data[0], data[1], data[2], data[3]]).toEqual([245, 158, 11, 140]); // x=0: on
+    expect([data[8], data[9], data[10], data[11]]).toEqual([180, 120, 40, 70]); // x=2: off
+
+    const hidden = colorizeConformalOverlay(commit, status, 4, 1, new Map(), { showMulti: false });
+    const hiddenData = hidden.getContext('2d')!.getImageData(0, 0, 4, 1).data;
+    expect([...hiddenData]).toEqual(new Array(16).fill(0));
+  });
+
+  it('renders the abstain color for status 0 by default, and can hide it', () => {
+    const commit = new Uint8Array([0]);
+    const status = new Uint8Array([0]);
+    const canvas = colorizeConformalOverlay(commit, status, 1, 1, new Map());
+    const { data } = canvas.getContext('2d')!.getImageData(0, 0, 1, 1);
+    expect([...data]).toEqual([30, 30, 40, 90]);
+
+    const hidden = colorizeConformalOverlay(commit, status, 1, 1, new Map(), { showAbstain: false });
+    const hiddenData = hidden.getContext('2d')!.getImageData(0, 0, 1, 1).data;
+    expect([...hiddenData]).toEqual([0, 0, 0, 0]);
+  });
+});
+
+describe('labelMapToPolygonShapes default options', () => {
+  it('defaults origin to "predicted" when not specified', () => {
+    const w = 16, h = 16;
+    const labels = new Uint8Array(w * h);
+    for (let y = 4; y < 12; y++) for (let x = 4; x < 12; x++) labels[y * w + x] = 1;
+    const shapes = labelMapToPolygonShapes(labels, w, h, [1], { minRegion: 4, smooth: 0 });
+    expect(shapes[0].origin).toBe('predicted');
+  });
+
+  it('honors an explicit "human" origin override', () => {
+    const w = 16, h = 16;
+    const labels = new Uint8Array(w * h);
+    for (let y = 4; y < 12; y++) for (let x = 4; x < 12; x++) labels[y * w + x] = 1;
+    const shapes = labelMapToPolygonShapes(labels, w, h, [1], { minRegion: 4, smooth: 0, origin: 'human' });
+    expect(shapes[0].origin).toBe('human');
+  });
+
+  it('skips classes with no matching pixels entirely', () => {
+    const w = 8, h = 8;
+    const labels = new Uint8Array(w * h); // all zero
+    const shapes = labelMapToPolygonShapes(labels, w, h, [1, 2], { minRegion: 1 });
+    expect(shapes).toHaveLength(0);
+  });
+
+  it('drops tiny polygons below minRegion vertex count (<6 flat coords) even if any() was true', () => {
+    // A 1-pixel "region" with default minRegion (64) is dropped by maskToPolygonsWithHoles
+    // before points.length is even checked, so this also covers the minRegion path.
+    const w = 32, h = 32;
+    const labels = new Uint8Array(w * h);
+    labels[10 * w + 10] = 1; // single pixel
+    const shapes = labelMapToPolygonShapes(labels, w, h, [1]);
+    expect(shapes).toHaveLength(0);
+  });
+});
+
+/**
+ * thresholdProbaPngBlob / loadLabelPng both decode a PNG via `new Image()` and
+ * read it back through a 2D canvas — jsdom has neither a real `Image` decoder
+ * nor `HTMLCanvasElement.toBlob`, so both are stubbed here. `stubCanvasContext`
+ * above already covers getImageData/putImageData/drawImage; `installImageStub`
+ * drives `Image`'s onload/onerror synchronously (as a microtask) so `await new
+ * Promise(...)` in the source resolves without needing real image bytes.
+ */
+function installImageStub(opts: { fail?: boolean; width?: number; height?: number } = {}) {
+  const { fail = false, width = 2, height = 1 } = opts;
+  class StubImage {
+    onload: (() => void) | null = null;
+    onerror: (() => void) | null = null;
+    naturalWidth = 0;
+    naturalHeight = 0;
+    private _src = '';
+    set src(v: string) {
+      this._src = v;
+      queueMicrotask(() => {
+        if (fail) {
+          this.onerror?.();
+          return;
+        }
+        this.naturalWidth = width;
+        this.naturalHeight = height;
+        this.onload?.();
+      });
+    }
+    get src() {
+      return this._src;
+    }
+  }
+  vi.stubGlobal('Image', StubImage);
+}
+
+describe('thresholdProbaPngBlob', () => {
+  beforeEach(() => {
+    global.URL.createObjectURL = vi.fn(() => 'blob:mock-url');
+    global.URL.revokeObjectURL = vi.fn();
+  });
+  afterEach(() => {
+    vi.unstubAllGlobals();
+  });
+
+  it('decodes, thresholds, and re-encodes as a PNG blob, then revokes the object URL', async () => {
+    installImageStub({ width: 2, height: 1 });
+    HTMLCanvasElement.prototype.toBlob = function (cb: BlobCallback) {
+      cb(new Blob(['ok'], { type: 'image/png' }));
+    };
+    const out = await thresholdProbaPngBlob(new Blob(), 0.5);
+    expect(out).toBeInstanceOf(Blob);
+    expect(URL.revokeObjectURL).toHaveBeenCalledWith('blob:mock-url');
+  });
+
+  it('rejects when the image fails to decode', async () => {
+    installImageStub({ fail: true });
+    await expect(thresholdProbaPngBlob(new Blob(), 0.5)).rejects.toThrow(
+      'Failed to decode probability PNG',
+    );
+    // The object URL is still revoked even on failure (finally block).
+    expect(URL.revokeObjectURL).toHaveBeenCalledWith('blob:mock-url');
+  });
+
+  it('rejects when the 2D context is unavailable', async () => {
+    installImageStub();
+    vi.spyOn(HTMLCanvasElement.prototype, 'getContext').mockReturnValue(null);
+    await expect(thresholdProbaPngBlob(new Blob(), 0.5)).rejects.toThrow('2D context unavailable');
+  });
+
+  it('rejects when canvas.toBlob yields no blob', async () => {
+    installImageStub();
+    HTMLCanvasElement.prototype.toBlob = function (cb: BlobCallback) {
+      cb(null);
+    };
+    await expect(thresholdProbaPngBlob(new Blob(), 0.5)).rejects.toThrow('toBlob failed');
+  });
+});
+
+describe('loadLabelPng', () => {
+  afterEach(() => {
+    vi.unstubAllGlobals();
+  });
+
+  it('decodes a grayscale classId PNG into a Uint8Array + dimensions', async () => {
+    installImageStub({ width: 2, height: 1 });
+    const { data, width, height } = await loadLabelPng('http://example.test/label.png');
+    expect(width).toBe(2);
+    expect(height).toBe(1);
+    expect(data).toBeInstanceOf(Uint8Array);
+    expect(data.length).toBe(2);
+  });
+
+  it('rejects when the image fails to load', async () => {
+    installImageStub({ fail: true });
+    await expect(loadLabelPng('http://example.test/bad.png')).rejects.toThrow(
+      'Failed to load prediction PNG',
+    );
+  });
+
+  it('rejects when the 2D context is unavailable', async () => {
+    installImageStub();
+    vi.spyOn(HTMLCanvasElement.prototype, 'getContext').mockReturnValue(null);
+    await expect(loadLabelPng('http://example.test/label.png')).rejects.toThrow(
+      '2D context unavailable',
+    );
+  });
+});
diff --git a/frontend/src/lib/sam/adjust.test.ts b/frontend/src/lib/sam/adjust.test.ts
new file mode 100644
index 0000000..db09ba3
--- /dev/null
+++ b/frontend/src/lib/sam/adjust.test.ts
@@ -0,0 +1,133 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { renderAdjusted, renderPreprocessOnly } from './adjust';
+
+/**
+ * jsdom has no real 2D canvas. Stub getContext('2d') with an in-memory pixel
+ * buffer per canvas, and make drawImage copy the source canvas's own stored
+ * buffer into the destination — so renderAdjusted's internal
+ * drawImage-then-getImageData round trip actually sees real pixel data
+ * instead of zeros, matching the pattern established in pixelClf.gaps.test.ts.
+ */
+const stores = new WeakMap<HTMLCanvasElement, Uint8ClampedArray>();
+
+function seedCanvas(width: number, height: number, fill: (i: number) => number): HTMLCanvasElement {
+  const canvas = document.createElement('canvas');
+  canvas.width = width;
+  canvas.height = height;
+  const data = new Uint8ClampedArray(width * height * 4);
+  for (let i = 0; i < data.length; i++) data[i] = fill(i);
+  stores.set(canvas, data);
+  return canvas;
+}
+
+function readPixels(canvas: HTMLCanvasElement): Uint8ClampedArray {
+  return stores.get(canvas)!;
+}
+
+beforeEach(() => {
+  vi.spyOn(HTMLCanvasElement.prototype, 'getContext').mockImplementation(function (
+    this: HTMLCanvasElement,
+  ): any {
+    const canvas = this;
+    return {
+      imageSmoothingEnabled: true,
+      imageSmoothingQuality: 'high',
+      drawImage: (source: HTMLCanvasElement, _sx: number, _sy: number, w: number, h: number) => {
+        const src = stores.get(source);
+        const dst = new Uint8ClampedArray(w * h * 4);
+        if (src) dst.set(src.subarray(0, dst.length));
+        stores.set(canvas, dst);
+      },
+      getImageData: (_x: number, _y: number, w: number, h: number) => ({
+        data: stores.get(canvas) ?? new Uint8ClampedArray(w * h * 4),
+        width: w,
+        height: h,
+      }),
+      putImageData: (imageData: { data: Uint8ClampedArray }) => {
+        stores.set(canvas, imageData.data);
+      },
+    };
+  });
+});
+
+afterEach(() => vi.restoreAllMocks());
+
+describe('renderAdjusted', () => {
+  it('returns the raw canvas unchanged when everything is a no-op', () => {
+    const source = seedCanvas(2, 2, () => 100);
+    const out = renderAdjusted(source, 2, 2, 0, 0, 0, 255);
+    expect([...readPixels(out)]).toEqual([...readPixels(source)]);
+  });
+
+  it('brightens pixels when brightness > 0', () => {
+    const source = seedCanvas(1, 1, (i) => (i % 4 === 3 ? 255 : 100));
+    const out = renderAdjusted(source, 1, 1, 0.2, 0, 0, 255);
+    const px = readPixels(out);
+    expect(px[0]).toBeGreaterThan(100);
+    expect(px[3]).toBe(255); // alpha untouched
+  });
+
+  it('darkens pixels when brightness < 0', () => {
+    const source = seedCanvas(1, 1, (i) => (i % 4 === 3 ? 255 : 100));
+    const out = renderAdjusted(source, 1, 1, -0.2, 0, 0, 255);
+    expect(readPixels(out)[0]).toBeLessThan(100);
+  });
+
+  it('applies a levels remap clamping outside [lo,hi]', () => {
+    const source = seedCanvas(3, 1, (i) => {
+      const px = Math.floor(i / 4);
+      return i % 4 === 3 ? 255 : [10, 128, 250][px];
+    });
+    const out = renderAdjusted(source, 3, 1, 0, 0, 50, 200);
+    const px = readPixels(out);
+    expect(px[0]).toBe(0); // below lo -> 0
+    expect(px[8]).toBe(255); // above hi -> 255
+    expect(px[4]).toBeGreaterThan(0); // in range, remapped
+    expect(px[4]).toBeLessThan(255);
+  });
+
+  it('bakes a gaussian blur when preprocess.blur > 0', () => {
+    // A single bright pixel among dark neighbors should spread out after blur.
+    const source = seedCanvas(5, 5, (i) => {
+      const px = Math.floor(i / 4);
+      const isCenter = px === 12; // middle of 5x5
+      return i % 4 === 3 ? 255 : isCenter ? 255 : 0;
+    });
+    const out = renderAdjusted(source, 5, 5, 0, 0, 0, 255, { blur: 1 });
+    const px = readPixels(out);
+    // A neighboring pixel (index 11, just left of center) should pick up some brightness.
+    expect(px[11 * 4]).toBeGreaterThan(0);
+  });
+
+  it('scales blur sigma by the working-resolution upscale factor', () => {
+    const source = seedCanvas(3, 3, (i) => (i % 4 === 3 ? 255 : (Math.floor(i / 4) === 4 ? 255 : 0)));
+    // Just confirm it runs without throwing at a non-1 upscale and returns a bigger canvas.
+    const out = renderAdjusted(source, 3, 3, 0, 0, 0, 255, { blur: 1 }, 2);
+    expect(out.width).toBe(6);
+    expect(out.height).toBe(6);
+  });
+
+  it('leaves the canvas untouched with all-zero preprocess flags and no blur', () => {
+    const source = seedCanvas(2, 2, () => 77);
+    const out = renderAdjusted(source, 2, 2, 0, 0, 0, 255, { blur: 0, clahe: false, sharpen: false });
+    expect([...readPixels(out)]).toEqual([...readPixels(source)]);
+  });
+});
+
+describe('renderPreprocessOnly', () => {
+  it('applies only the nonlinear preprocessors, ignoring brightness/contrast/levels', () => {
+    const source = seedCanvas(5, 5, (i) => {
+      const px = Math.floor(i / 4);
+      return i % 4 === 3 ? 255 : px === 12 ? 255 : 0;
+    });
+    const out = renderPreprocessOnly(source, 5, 5, { blur: 1 });
+    const px = readPixels(out);
+    expect(px[11 * 4]).toBeGreaterThan(0); // blur still applied
+  });
+
+  it('returns an unmodified canvas when no preprocessors are requested', () => {
+    const source = seedCanvas(2, 2, () => 50);
+    const out = renderPreprocessOnly(source, 2, 2, {});
+    expect([...readPixels(out)]).toEqual([...readPixels(source)]);
+  });
+});
diff --git a/frontend/src/lib/sam/samClient.test.ts b/frontend/src/lib/sam/samClient.test.ts
new file mode 100644
index 0000000..a680cd9
--- /dev/null
+++ b/frontend/src/lib/sam/samClient.test.ts
@@ -0,0 +1,177 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+
+/**
+ * samClient exports only a module-level singleton (no class, no reset), so
+ * each test re-imports the module fresh via vi.resetModules() to get an
+ * un-contaminated instance — otherwise `init()`'s cached promise/worker would
+ * leak state across tests.
+ */
+class FakeWorker {
+  static instances: FakeWorker[] = [];
+  onmessage: ((e: MessageEvent) => void) | null = null;
+  onerror: ((e: any) => void) | null = null;
+  terminated = false;
+  posted: any[] = [];
+
+  constructor(_url: unknown, _opts?: unknown) {
+    FakeWorker.instances.push(this);
+  }
+
+  postMessage(msg: any) {
+    this.posted.push(msg);
+  }
+
+  terminate() {
+    this.terminated = true;
+  }
+
+  // Test helper: simulate the worker sending a message back.
+  emit(data: any) {
+    this.onmessage?.({ data } as MessageEvent);
+  }
+}
+
+beforeEach(() => {
+  FakeWorker.instances = [];
+  vi.stubGlobal('Worker', FakeWorker as any);
+  vi.stubGlobal('createImageBitmap', vi.fn(async () => ({ close: () => {} }) as any));
+  vi.resetModules();
+});
+
+afterEach(() => {
+  vi.unstubAllGlobals();
+});
+
+async function freshClient() {
+  const mod = await import('./samClient');
+  return mod;
+}
+
+describe('samClient.init', () => {
+  it('starts idle before init', async () => {
+    const { samClient } = await freshClient();
+    expect(samClient.getStatus()).toBe('idle');
+  });
+
+  it('spawns exactly one worker across repeated init() calls (idempotent)', async () => {
+    const { samClient } = await freshClient();
+    const p1 = samClient.init();
+    const p2 = samClient.init();
+    expect(p1).toBe(p2);
+    expect(FakeWorker.instances).toHaveLength(1);
+  });
+
+  it('resolves once the worker reports status "ready", and notifies subscribers', async () => {
+    const { samClient } = await freshClient();
+    const seen: string[] = [];
+    samClient.subscribe((s) => seen.push(s));
+    const p = samClient.init();
+    FakeWorker.instances[0].emit({ type: 'status', status: 'loading-model' });
+    FakeWorker.instances[0].emit({ type: 'status', status: 'ready', backend: 'wasm' });
+    await expect(p).resolves.toBeUndefined();
+    expect(samClient.getStatus()).toBe('ready');
+    expect(samClient.getBackend()).toBe('wasm');
+    expect(seen).toContain('loading-model');
+    expect(seen).toContain('ready');
+  });
+
+  it('rejects when the worker reports "unsupported", and allows retrying init after', async () => {
+    const { samClient } = await freshClient();
+    const p = samClient.init();
+    FakeWorker.instances[0].emit({ type: 'status', status: 'unsupported', message: 'no webgpu/wasm' });
+    await expect(p).rejects.toThrow('no webgpu/wasm');
+    expect(samClient.getStatus()).toBe('unsupported');
+
+    // A later init() attempt spawns a fresh worker rather than reusing the dead one.
+    await Promise.resolve(); // let the internal .catch() teardown run
+    const p2 = samClient.init();
+    expect(FakeWorker.instances.length).toBeGreaterThanOrEqual(2);
+    FakeWorker.instances[FakeWorker.instances.length - 1].emit({ type: 'status', status: 'ready' });
+    await expect(p2).resolves.toBeUndefined();
+  });
+
+  it('rejects and sets "unsupported" when the Worker constructor throws', async () => {
+    vi.stubGlobal('Worker', class {
+      constructor() { throw new Error('workers disabled'); }
+    } as any);
+    vi.resetModules();
+    const { samClient } = await freshClient();
+    await expect(samClient.init()).rejects.toThrow('workers disabled');
+    expect(samClient.getStatus()).toBe('unsupported');
+  });
+
+  it('rejects on a worker onerror event', async () => {
+    const { samClient } = await freshClient();
+    const p = samClient.init();
+    FakeWorker.instances[0].onerror?.({ message: 'boom' });
+    await expect(p).rejects.toThrow('boom');
+    expect(samClient.getStatus()).toBe('unsupported');
+  });
+});
+
+describe('samClient.encode/decode', () => {
+  it('encode() initializes, sets status to encoding then back to ready', async () => {
+    const { samClient } = await freshClient();
+    const statuses: string[] = [];
+    samClient.subscribe((s) => statuses.push(s));
+
+    const encodePromise = samClient.encode({} as any);
+    FakeWorker.instances[0].emit({ type: 'status', status: 'ready' });
+    // Let init() resolve and encode() proceed to post the encode message.
+    await Promise.resolve();
+    await Promise.resolve();
+    const encodeMsg = FakeWorker.instances[0].posted.find((m) => m.type === 'encode');
+    expect(encodeMsg).toBeTruthy();
+    FakeWorker.instances[0].emit({ type: 'encoded', id: encodeMsg.id });
+    await encodePromise;
+
+    expect(statuses).toContain('encoding');
+    expect(samClient.getStatus()).toBe('ready');
+  });
+
+  it('decode() resolves a SamMask from the worker reply', async () => {
+    const { samClient } = await freshClient();
+    const p = samClient.init();
+    FakeWorker.instances[0].emit({ type: 'status', status: 'ready' });
+    await p;
+
+    const decodePromise = samClient.decode(
+      [{ x: 1, y: 2, label: 1 }], null, 'auto', 0.5,
+    );
+    const decodeMsg = FakeWorker.instances[0].posted.find((m) => m.type === 'decode');
+    expect(decodeMsg).toBeTruthy();
+    const buf = new Uint8Array([1, 0, 1, 0]).buffer;
+    FakeWorker.instances[0].emit({
+      type: 'decoded', id: decodeMsg.id, mask: buf, width: 2, height: 2, score: 0.9,
+    });
+    const result = await decodePromise;
+    expect(result.width).toBe(2);
+    expect(result.height).toBe(2);
+    expect(result.score).toBe(0.9);
+    expect([...result.mask]).toEqual([1, 0, 1, 0]);
+  });
+
+  it('rejects a pending request when the worker reports an error for its id', async () => {
+    const { samClient } = await freshClient();
+    const p = samClient.init();
+    FakeWorker.instances[0].emit({ type: 'status', status: 'ready' });
+    await p;
+
+    const decodePromise = samClient.decode([], null, 'auto', 0.5);
+    const decodeMsg = FakeWorker.instances[0].posted.find((m) => m.type === 'decode');
+    FakeWorker.instances[0].emit({ type: 'error', id: decodeMsg.id, message: 'decode failed' });
+    await expect(decodePromise).rejects.toThrow('decode failed');
+  });
+});
+
+describe('webgpuAvailable', () => {
+  it('reflects navigator.gpu presence', async () => {
+    const { webgpuAvailable } = await freshClient();
+    const original = (navigator as any).gpu;
+    (navigator as any).gpu = {};
+    expect(webgpuAvailable()).toBe(true);
+    delete (navigator as any).gpu;
+    expect(webgpuAvailable()).toBe(false);
+    if (original !== undefined) (navigator as any).gpu = original;
+  });
+});
diff --git a/frontend/src/stores/annotationStore.test.ts b/frontend/src/stores/annotationStore.test.ts
new file mode 100644
index 0000000..3b9d954
--- /dev/null
+++ b/frontend/src/stores/annotationStore.test.ts
@@ -0,0 +1,389 @@
+import { describe, it, expect, beforeEach } from 'vitest';
+import { useAnnotationStore } from './annotationStore';
+import type { Shape, BrushShape } from './annotationStore';
+
+const SK = 'tiled::sample';
+
+const rect = (id: string, classId: number, overrides: Partial<Shape> = {}): Shape => ({
+  id, classId, kind: 'rectangle', x: 0, y: 0, w: 2, h: 2, ...overrides,
+} as Shape);
+
+const poly = (id: string, classId: number): Shape => ({
+  id, classId, kind: 'polygon', points: [0, 0, 10, 0, 10, 10],
+});
+
+const brush = (id: string, classId: number): BrushShape => ({
+  id, classId, kind: 'brush', strokes: [],
+});
+
+beforeEach(() => {
+  useAnnotationStore.getState().reset();
+  useAnnotationStore.temporal.getState().clear();
+});
+
+describe('addShape / addShapes / addShapesAcrossSlices', () => {
+  it('addShape appends a single shape to the (source, slice)', () => {
+    useAnnotationStore.getState().addShape(SK, 0, rect('a', 1));
+    expect(useAnnotationStore.getState().byImage[SK]['0']).toHaveLength(1);
+    expect(useAnnotationStore.getState().byImage[SK]['0'][0].id).toBe('a');
+  });
+
+  it('addShapes appends several shapes in one call', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1), rect('b', 2)]);
+    expect(useAnnotationStore.getState().byImage[SK]['0']).toHaveLength(2);
+  });
+
+  it('addShapesAcrossSlices distributes shapes to multiple slices in one update', () => {
+    useAnnotationStore.getState().addShapesAcrossSlices(SK, {
+      0: [rect('a', 1)],
+      2: [rect('b', 1), rect('c', 1)],
+    });
+    const byImage = useAnnotationStore.getState().byImage[SK];
+    expect(byImage['0']).toHaveLength(1);
+    expect(byImage['2']).toHaveLength(2);
+  });
+
+  it('addShapesAcrossSlices skips slices with an empty shape list and is a no-op if all empty', () => {
+    useAnnotationStore.getState().addShapesAcrossSlices(SK, { 0: [], 1: [] });
+    expect(useAnnotationStore.getState().byImage[SK]).toBeUndefined();
+  });
+
+  it('addShapesAcrossSlices merges into existing slice shapes rather than replacing them', () => {
+    useAnnotationStore.getState().addShape(SK, 0, rect('a', 1));
+    useAnnotationStore.getState().addShapesAcrossSlices(SK, { 0: [rect('b', 1)] });
+    expect(useAnnotationStore.getState().byImage[SK]['0'].map((s) => s.id)).toEqual(['a', 'b']);
+  });
+});
+
+describe('removeShape / removeShapes', () => {
+  beforeEach(() => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1), rect('b', 2), rect('c', 1)]);
+  });
+
+  it('removeShape drops one shape by id', () => {
+    useAnnotationStore.getState().removeShape(SK, 0, 'b');
+    expect(useAnnotationStore.getState().byImage[SK]['0'].map((s) => s.id)).toEqual(['a', 'c']);
+  });
+
+  it('removeShapes drops several shapes by id in one call', () => {
+    useAnnotationStore.getState().removeShapes(SK, 0, ['a', 'c']);
+    expect(useAnnotationStore.getState().byImage[SK]['0'].map((s) => s.id)).toEqual(['b']);
+  });
+});
+
+describe('updateShape', () => {
+  it('replaces only the targeted shape via the updater fn', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1), rect('b', 2)]);
+    useAnnotationStore.getState().updateShape(SK, 0, 'a', (sh) => ({ ...sh, classId: 9 } as Shape));
+    const shapes = useAnnotationStore.getState().byImage[SK]['0'];
+    expect(shapes.find((s) => s.id === 'a')?.classId).toBe(9);
+    expect(shapes.find((s) => s.id === 'b')?.classId).toBe(2);
+  });
+});
+
+describe('setClassForShapes', () => {
+  it('reassigns only the listed shape ids to the new class', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1), rect('b', 1), rect('c', 1)]);
+    useAnnotationStore.getState().setClassForShapes(SK, 0, ['a', 'c'], 7);
+    const shapes = useAnnotationStore.getState().byImage[SK]['0'];
+    expect(shapes.find((s) => s.id === 'a')?.classId).toBe(7);
+    expect(shapes.find((s) => s.id === 'b')?.classId).toBe(1);
+    expect(shapes.find((s) => s.id === 'c')?.classId).toBe(7);
+  });
+});
+
+describe('replaceClassShapesOnSlice', () => {
+  it('replaces all shapes of a class on the slice, leaving other classes untouched', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1), rect('b', 2)]);
+    useAnnotationStore.getState().replaceClassShapesOnSlice(SK, 0, 1, [rect('new', 1)]);
+    const shapes = useAnnotationStore.getState().byImage[SK]['0'];
+    expect(shapes.map((s) => s.id).sort()).toEqual(['b', 'new']);
+  });
+});
+
+describe('copySliceShapes', () => {
+  beforeEach(() => {
+    useAnnotationStore.getState().addShapes(SK, 0, [poly('a', 1), poly('b', 2)]);
+  });
+
+  it('clones shapes from the source slice into each target slice with fresh ids', () => {
+    useAnnotationStore.getState().copySliceShapes(SK, 0, [1, 2]);
+    const s1 = useAnnotationStore.getState().byImage[SK]['1'];
+    const s2 = useAnnotationStore.getState().byImage[SK]['2'];
+    expect(s1).toHaveLength(2);
+    expect(s2).toHaveLength(2);
+    expect(s1.map((s) => s.id)).not.toContain('a');
+  });
+
+  it('filters to one class when classId is given', () => {
+    useAnnotationStore.getState().copySliceShapes(SK, 0, [1], 1);
+    const s1 = useAnnotationStore.getState().byImage[SK]['1'];
+    expect(s1).toHaveLength(1);
+    expect(s1[0].classId).toBe(1);
+  });
+
+  it('skips a target slice equal to the source slice', () => {
+    useAnnotationStore.getState().copySliceShapes(SK, 0, [0, 1]);
+    // Slice 0 keeps its originals only (no duplicate append onto itself).
+    expect(useAnnotationStore.getState().byImage[SK]['0']).toHaveLength(2);
+    expect(useAnnotationStore.getState().byImage[SK]['1']).toHaveLength(2);
+  });
+
+  it('is a no-op when the source slice has nothing to copy', () => {
+    useAnnotationStore.getState().copySliceShapes(SK, 5, [6]);
+    expect(useAnnotationStore.getState().byImage[SK]['6']).toBeUndefined();
+  });
+
+  it('merges copies with shapes already on the target slice', () => {
+    useAnnotationStore.getState().addShape(SK, 1, rect('existing', 3));
+    useAnnotationStore.getState().copySliceShapes(SK, 0, [1]);
+    expect(useAnnotationStore.getState().byImage[SK]['1']).toHaveLength(3);
+  });
+});
+
+describe('removeShapesByClassId', () => {
+  it('removes the class across every source and slice, pruning emptied slices/sources', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1), rect('b', 2)]);
+    useAnnotationStore.getState().addShapes('other::src', 0, [rect('c', 1)]);
+    useAnnotationStore.getState().removeShapesByClassId(1);
+    expect(useAnnotationStore.getState().byImage[SK]['0'].map((s) => s.id)).toEqual(['b']);
+    // The other source had only class-1 shapes, so it's pruned entirely.
+    expect(useAnnotationStore.getState().byImage['other::src']).toBeUndefined();
+  });
+
+  it('prunes an emptied slice but keeps sibling slices with other classes', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1)]);
+    useAnnotationStore.getState().addShapes(SK, 1, [rect('b', 2)]);
+    useAnnotationStore.getState().removeShapesByClassId(1);
+    expect(useAnnotationStore.getState().byImage[SK]['0']).toBeUndefined();
+    expect(useAnnotationStore.getState().byImage[SK]['1']).toHaveLength(1);
+  });
+});
+
+describe('removeShapesByClassIdInSource', () => {
+  it('removes the class only within the given source, leaving other sources alone', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1), rect('b', 2)]);
+    useAnnotationStore.getState().addShapes('other::src', 0, [rect('c', 1)]);
+    useAnnotationStore.getState().removeShapesByClassIdInSource(SK, 1);
+    expect(useAnnotationStore.getState().byImage[SK]['0'].map((s) => s.id)).toEqual(['b']);
+    expect(useAnnotationStore.getState().byImage['other::src']['0']).toHaveLength(1);
+  });
+
+  it('deletes the source entirely when its last class is removed', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1)]);
+    useAnnotationStore.getState().removeShapesByClassIdInSource(SK, 1);
+    expect(useAnnotationStore.getState().byImage[SK]).toBeUndefined();
+  });
+
+  it('is a no-op for a source with no data', () => {
+    useAnnotationStore.getState().removeShapesByClassIdInSource('nope', 1);
+    expect(useAnnotationStore.getState().byImage['nope']).toBeUndefined();
+  });
+});
+
+describe('removeShapesByOrigin', () => {
+  it('removes shapes of the given origin, treating missing origin as human', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [
+      rect('human1', 1),
+      { ...rect('pred1', 1), origin: 'predicted' } as Shape,
+    ]);
+    useAnnotationStore.getState().removeShapesByOrigin(SK, 'predicted');
+    expect(useAnnotationStore.getState().byImage[SK]['0'].map((s) => s.id)).toEqual(['human1']);
+  });
+
+  it('removes human-origin shapes (undefined origin) when asked', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [
+      rect('human1', 1),
+      { ...rect('pred1', 1), origin: 'predicted' } as Shape,
+    ]);
+    useAnnotationStore.getState().removeShapesByOrigin(SK, 'human');
+    expect(useAnnotationStore.getState().byImage[SK]['0'].map((s) => s.id)).toEqual(['pred1']);
+  });
+
+  it('deletes an emptied source and is a no-op for missing sources', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1)]);
+    useAnnotationStore.getState().removeShapesByOrigin(SK, 'human');
+    expect(useAnnotationStore.getState().byImage[SK]).toBeUndefined();
+
+    useAnnotationStore.getState().removeShapesByOrigin('nope', 'human');
+    expect(useAnnotationStore.getState().byImage['nope']).toBeUndefined();
+  });
+});
+
+describe('appendBrushStroke / appendEraseStroke', () => {
+  it('appendBrushStroke appends a stroke to a brush shape and is a no-op for non-brush shapes', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [brush('br', 1), rect('rc', 1)]);
+    const stroke = { points: [1, 1], radius: 3, mode: 'paint' as const };
+    useAnnotationStore.getState().appendBrushStroke(SK, 0, 'br', stroke);
+    useAnnotationStore.getState().appendBrushStroke(SK, 0, 'rc', stroke);
+    const shapes = useAnnotationStore.getState().byImage[SK]['0'];
+    const b = shapes.find((s) => s.id === 'br') as BrushShape;
+    expect(b.strokes).toHaveLength(1);
+    const r = shapes.find((s) => s.id === 'rc');
+    expect((r as any).strokes).toBeUndefined();
+  });
+
+  it('appendEraseStroke adds an erase-mode brush stroke for brush shapes', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [brush('br', 1)]);
+    useAnnotationStore.getState().appendEraseStroke(SK, 0, 'br', { points: [1, 1], radius: 2 });
+    const b = useAnnotationStore.getState().byImage[SK]['0'][0] as BrushShape;
+    expect(b.strokes).toHaveLength(1);
+    expect(b.strokes[0].mode).toBe('erase');
+  });
+
+  it('appendEraseStroke adds an `erased` carve-out entry for vector shapes', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('rc', 1)]);
+    useAnnotationStore.getState().appendEraseStroke(SK, 0, 'rc', { points: [1, 1], radius: 2 });
+    const shape = useAnnotationStore.getState().byImage[SK]['0'][0];
+    expect(shape.erased).toHaveLength(1);
+    expect(shape.erased?.[0].radius).toBe(2);
+  });
+});
+
+describe('setShapes', () => {
+  it('replaces all shapes for the (source, slice)', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1)]);
+    useAnnotationStore.getState().setShapes(SK, 0, [rect('b', 2), rect('c', 2)]);
+    expect(useAnnotationStore.getState().byImage[SK]['0'].map((s) => s.id)).toEqual(['b', 'c']);
+  });
+});
+
+describe('setSplitForSlice / toggleNegativeSlice', () => {
+  it('sets the split value for a slice', () => {
+    useAnnotationStore.getState().setSplitForSlice(SK, 3, 'valid');
+    expect(useAnnotationStore.getState().splitBySlice[SK]['3']).toBe('valid');
+  });
+
+  it('toggleNegativeSlice adds then removes a slice from the negative list', () => {
+    useAnnotationStore.getState().toggleNegativeSlice(SK, 2);
+    expect(useAnnotationStore.getState().negativeSlices[SK]).toEqual(['2']);
+    useAnnotationStore.getState().toggleNegativeSlice(SK, 2);
+    expect(useAnnotationStore.getState().negativeSlices[SK]).toEqual([]);
+  });
+});
+
+describe('loadFromDraft / mergeSourceDraft', () => {
+  it('loadFromDraft replaces all annotation data wholesale', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1)]);
+    useAnnotationStore.getState().loadFromDraft({
+      byImage: { 'new::src': { '0': [rect('x', 1)] } },
+      splitBySlice: {},
+      negativeSlices: {},
+    });
+    expect(useAnnotationStore.getState().byImage[SK]).toBeUndefined();
+    expect(useAnnotationStore.getState().byImage['new::src']['0']).toHaveLength(1);
+  });
+
+  it('mergeSourceDraft merges one source without touching others', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1)]);
+    useAnnotationStore.getState().mergeSourceDraft(
+      'other::src',
+      { '0': [rect('b', 2)] },
+      { '0': 'train' },
+      ['0'],
+    );
+    expect(useAnnotationStore.getState().byImage[SK]['0']).toHaveLength(1);
+    expect(useAnnotationStore.getState().byImage['other::src']['0']).toHaveLength(1);
+    expect(useAnnotationStore.getState().splitBySlice['other::src']['0']).toBe('train');
+    expect(useAnnotationStore.getState().negativeSlices['other::src']).toEqual(['0']);
+  });
+});
+
+describe('reset', () => {
+  it('clears shapes, splits, negative slices, and the draft', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1)]);
+    useAnnotationStore.getState().setSplitForSlice(SK, 0, 'test');
+    useAnnotationStore.getState().toggleNegativeSlice(SK, 0);
+    useAnnotationStore.getState().addPolyNode(SK, '0', 1, 1);
+    useAnnotationStore.getState().reset();
+    const s = useAnnotationStore.getState();
+    expect(s.byImage).toEqual({});
+    expect(s.splitBySlice).toEqual({});
+    expect(s.negativeSlices).toEqual({});
+    expect(s.draft.tool).toBeNull();
+  });
+});
+
+describe('touchHistory', () => {
+  it('creates a shallow-cloned byImage but leaves the data equal', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1)]);
+    const before = useAnnotationStore.getState().byImage;
+    useAnnotationStore.getState().touchHistory();
+    const after = useAnnotationStore.getState().byImage;
+    expect(after).not.toBe(before);
+    expect(after).toEqual(before);
+  });
+
+  it('records a temporal (undo) entry even though content is unchanged', () => {
+    useAnnotationStore.temporal.getState().clear();
+    expect(useAnnotationStore.temporal.getState().pastStates.length).toBe(0);
+    useAnnotationStore.getState().touchHistory();
+    expect(useAnnotationStore.temporal.getState().pastStates.length).toBeGreaterThan(0);
+  });
+});
+
+describe('draft: addPolyNode / addMagneticNode / clearDraft / commitDraftShapes', () => {
+  it('addPolyNode starts a fresh draft when context changes and appends within the same context', () => {
+    useAnnotationStore.getState().addPolyNode(SK, '0', 1, 2);
+    expect(useAnnotationStore.getState().draft.poly).toEqual([1, 2]);
+    useAnnotationStore.getState().addPolyNode(SK, '0', 3, 4);
+    expect(useAnnotationStore.getState().draft.poly).toEqual([1, 2, 3, 4]);
+  });
+
+  it('addPolyNode resets the draft when the slice/source context changes', () => {
+    useAnnotationStore.getState().addPolyNode(SK, '0', 1, 2);
+    useAnnotationStore.getState().addPolyNode(SK, '1', 5, 6);
+    expect(useAnnotationStore.getState().draft.poly).toEqual([5, 6]);
+    expect(useAnnotationStore.getState().draft.sliceKey).toBe('1');
+  });
+
+  it('addMagneticNode accumulates path points for the same context and reseeds otherwise', () => {
+    useAnnotationStore.getState().addMagneticNode(SK, '0', [1, 1, 2, 2], { x: 1, y: 1 });
+    useAnnotationStore.getState().addMagneticNode(SK, '0', [3, 3], { x: 3, y: 3 });
+    const draft = useAnnotationStore.getState().draft;
+    expect(draft.magnetic).toEqual([1, 1, 2, 2, 3, 3]);
+    expect(draft.magneticSeed).toEqual({ x: 3, y: 3 });
+  });
+
+  it('clearDraft resets to the empty draft', () => {
+    useAnnotationStore.getState().addPolyNode(SK, '0', 1, 2);
+    useAnnotationStore.getState().clearDraft();
+    expect(useAnnotationStore.getState().draft.tool).toBeNull();
+    expect(useAnnotationStore.getState().draft.poly).toEqual([]);
+  });
+
+  it('commitDraftShapes writes the slice shapes and clears the draft atomically', () => {
+    useAnnotationStore.getState().addPolyNode(SK, '0', 1, 2);
+    useAnnotationStore.getState().commitDraftShapes(SK, 0, [poly('finished', 1)]);
+    expect(useAnnotationStore.getState().byImage[SK]['0']).toHaveLength(1);
+    expect(useAnnotationStore.getState().draft.tool).toBeNull();
+  });
+});
+
+describe('undo/redo via zundo temporal middleware', () => {
+  it('undo restores the prior byImage state and redo re-applies the edit', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1)]);
+    expect(useAnnotationStore.getState().byImage[SK]['0']).toHaveLength(1);
+
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('b', 1)]);
+    expect(useAnnotationStore.getState().byImage[SK]['0']).toHaveLength(2);
+
+    useAnnotationStore.temporal.getState().undo();
+    expect(useAnnotationStore.getState().byImage[SK]['0']).toHaveLength(1);
+    expect(useAnnotationStore.getState().byImage[SK]['0'][0].id).toBe('a');
+
+    useAnnotationStore.temporal.getState().redo();
+    expect(useAnnotationStore.getState().byImage[SK]['0']).toHaveLength(2);
+  });
+
+  it('undo can walk back past multiple edits to the initial empty state', () => {
+    useAnnotationStore.getState().addShapes(SK, 0, [rect('a', 1)]);
+    useAnnotationStore.getState().addShapes(SK, 1, [rect('b', 1)]);
+    useAnnotationStore.getState().removeShape(SK, 0, 'a');
+
+    useAnnotationStore.temporal.getState().undo();
+    useAnnotationStore.temporal.getState().undo();
+    useAnnotationStore.temporal.getState().undo();
+    expect(useAnnotationStore.getState().byImage[SK]).toBeUndefined();
+  });
+});
diff --git a/frontend/src/stores/clipboardStore.test.ts b/frontend/src/stores/clipboardStore.test.ts
new file mode 100644
index 0000000..e05ad09
--- /dev/null
+++ b/frontend/src/stores/clipboardStore.test.ts
@@ -0,0 +1,30 @@
+import { beforeEach, describe, expect, it } from 'vitest';
+import { useClipboardStore } from './clipboardStore';
+
+beforeEach(() => {
+  useClipboardStore.setState({ shapes: [] });
+});
+
+describe('clipboardStore', () => {
+  it('starts empty', () => {
+    expect(useClipboardStore.getState().shapes).toEqual([]);
+  });
+
+  it('copy stores a deep copy of the given shapes', () => {
+    const shape = { id: 's1', classId: 1, kind: 'rectangle' as const, x: 0, y: 0, w: 2, h: 2 };
+    useClipboardStore.getState().copy([shape]);
+    const stored = useClipboardStore.getState().shapes;
+    expect(stored).toEqual([shape]);
+    expect(stored[0]).not.toBe(shape); // deep copy, not the same reference
+
+    shape.x = 99;
+    const restored = useClipboardStore.getState().shapes[0];
+    expect(restored.kind === 'rectangle' && restored.x).toBe(0); // unaffected by later mutation
+  });
+
+  it('clear empties the clipboard', () => {
+    useClipboardStore.getState().copy([{ id: 's1', classId: 1, kind: 'rectangle', x: 0, y: 0, w: 1, h: 1 }]);
+    useClipboardStore.getState().clear();
+    expect(useClipboardStore.getState().shapes).toEqual([]);
+  });
+});
diff --git a/frontend/src/stores/referenceGuideStore.test.ts b/frontend/src/stores/referenceGuideStore.test.ts
new file mode 100644
index 0000000..af18bf9
--- /dev/null
+++ b/frontend/src/stores/referenceGuideStore.test.ts
@@ -0,0 +1,102 @@
+import { beforeEach, describe, expect, it } from 'vitest';
+import { useReferenceGuideStore } from './referenceGuideStore';
+
+const initial = useReferenceGuideStore.getState();
+
+beforeEach(() => {
+  useReferenceGuideStore.setState(initial, true);
+});
+
+const cls = (label: string, overrides: Partial<{ color: string; description: string; exampleCrops: string[] }> = {}) => ({
+  label, color: '#000', description: '', exampleCrops: [], ...overrides,
+});
+
+describe('referenceGuideStore', () => {
+  it('starts empty', () => {
+    const s = useReferenceGuideStore.getState();
+    expect(s.entries).toEqual([]);
+    expect(s.notes).toBe('');
+    expect(s.loadedFor).toBeNull();
+  });
+
+  it('setGuide replaces the whole guide', () => {
+    useReferenceGuideStore.getState().setGuide([cls('Cell')], 'my notes', 'local:x.tif');
+    const s = useReferenceGuideStore.getState();
+    expect(s.entries).toEqual([cls('Cell')]);
+    expect(s.notes).toBe('my notes');
+    expect(s.loadedFor).toBe('local:x.tif');
+  });
+
+  it('addEntry appends a class', () => {
+    useReferenceGuideStore.getState().addEntry(cls('Pore'));
+    expect(useReferenceGuideStore.getState().entries).toEqual([cls('Pore')]);
+  });
+
+  it('updateEntry merges partial updates by index', () => {
+    useReferenceGuideStore.getState().setGuide([cls('Pore'), cls('Wall')], '', 'x');
+    useReferenceGuideStore.getState().updateEntry(1, { color: '#f00' });
+    expect(useReferenceGuideStore.getState().entries[1]).toEqual(cls('Wall', { color: '#f00' }));
+    expect(useReferenceGuideStore.getState().entries[0]).toEqual(cls('Pore'));
+  });
+
+  it('removeEntry removes by index', () => {
+    useReferenceGuideStore.getState().setGuide([cls('Pore'), cls('Wall')], '', 'x');
+    useReferenceGuideStore.getState().removeEntry(0);
+    expect(useReferenceGuideStore.getState().entries).toEqual([cls('Wall')]);
+  });
+
+  it('setNotes updates notes only', () => {
+    useReferenceGuideStore.getState().setGuide([cls('Pore')], 'old', 'x');
+    useReferenceGuideStore.getState().setNotes('new');
+    expect(useReferenceGuideStore.getState().notes).toBe('new');
+    expect(useReferenceGuideStore.getState().entries).toEqual([cls('Pore')]);
+  });
+
+  it('clear resets everything', () => {
+    useReferenceGuideStore.getState().setGuide([cls('Pore')], 'n', 'x');
+    useReferenceGuideStore.getState().clear();
+    const s = useReferenceGuideStore.getState();
+    expect(s.entries).toEqual([]);
+    expect(s.notes).toBe('');
+    expect(s.loadedFor).toBeNull();
+  });
+
+  describe('applyGenerated', () => {
+    it('appends newly-discovered classes', () => {
+      useReferenceGuideStore.getState().applyGenerated([cls('Pore', { color: '#111' })]);
+      expect(useReferenceGuideStore.getState().entries).toEqual([cls('Pore', { color: '#111' })]);
+    });
+
+    it('preserves an existing class description, refreshing color/crops', () => {
+      useReferenceGuideStore.getState().setGuide(
+        [cls('Pore', { description: 'lead wrote this', color: '#000' })], '', 'x',
+      );
+      useReferenceGuideStore.getState().applyGenerated([
+        cls('Pore', { description: 'auto-generated', color: '#f00', exampleCrops: ['data:img1'] }),
+      ]);
+      expect(useReferenceGuideStore.getState().entries).toEqual([
+        cls('Pore', { description: 'lead wrote this', color: '#f00', exampleCrops: ['data:img1'] }),
+      ]);
+    });
+
+    it('preserves existing entries not part of the new generation', () => {
+      useReferenceGuideStore.getState().setGuide([cls('Pore'), cls('Wall')], '', 'x');
+      useReferenceGuideStore.getState().applyGenerated([cls('Pore')]);
+      const labels = useReferenceGuideStore.getState().entries.map((e) => e.label);
+      expect(labels).toContain('Wall');
+    });
+
+    it('matches existing classes by label case-insensitively (trimmed)', () => {
+      useReferenceGuideStore.getState().setGuide(
+        [cls('  pore  ', { description: 'kept' })], '', 'x',
+      );
+      useReferenceGuideStore.getState().applyGenerated([cls('Pore', { description: 'new' })]);
+      expect(useReferenceGuideStore.getState().entries[0].description).toBe('kept');
+    });
+
+    it('uses a default color when neither generated nor existing has one', () => {
+      useReferenceGuideStore.getState().applyGenerated([{ label: 'Pore', color: '', description: '', exampleCrops: [] }]);
+      expect(useReferenceGuideStore.getState().entries[0].color).toBe('#1f77b4');
+    });
+  });
+});
diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts
index fc1f8a9..8cd8663 100644
--- a/frontend/vite.config.ts
+++ b/frontend/vite.config.ts
@@ -58,6 +58,11 @@ export default defineConfig({
     globals: true,
     environment: 'jsdom',
     setupFiles: ['./src/test/setup.ts'],
+    // vitest runs in Node, where bare `import 'konva'` resolves via the package's
+    // "main" field (a canvas-backed Node build requiring the native `canvas`
+    // module, which isn't installed) instead of "browser" (the jsdom-friendly
+    // build bundlers use). Force the browser build under test.
+    alias: [{ find: /^konva$/, replacement: 'konva/lib/index.js' }],
     // Scoped to src/ so the vendored renderer's own suite (which needs WebGPU and
     // its own runner config) isn't swept into ours by the default glob. Upstream
     // tests are upstream's to run.

From a08bb0fc6535247514114ca3ac76bbbe3fceaa85 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Tue, 8 Sep 2026 17:06:56 -0700
Subject: [PATCH 24/38] training and suggestion fix

---
 frontend/src/app/pages/AnnotatePage.tsx       |  68 +++---
 .../PixelClassifierPanel/index.test.tsx       | 136 +----------
 .../annotate/PixelClassifierPanel/index.tsx   | 194 +---------------
 .../SuggestLabelsPanel/index.test.tsx         | 148 ++++++++++++
 .../annotate/SuggestLabelsPanel/index.tsx     | 216 ++++++++++++++++++
 .../src/components/train/InferencePanel.tsx   |  32 ++-
 6 files changed, 433 insertions(+), 361 deletions(-)
 create mode 100644 frontend/src/components/annotate/SuggestLabelsPanel/index.test.tsx
 create mode 100644 frontend/src/components/annotate/SuggestLabelsPanel/index.tsx

diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index ca4e8f6..9b1ebea 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -28,6 +28,7 @@ import SliceNavigator from '@/components/annotate/SliceNavigator';
 import MaskToolsPanel from '@/components/annotate/MaskToolsPanel';
 import MeasurementPanel from '@/components/annotate/MeasurementPanel';
 import FeatureChannelsPanel from '@/components/annotate/FeatureChannelsPanel';
+import SuggestLabelsPanel from '@/components/annotate/SuggestLabelsPanel';
 import PixelClassifierPanel from '@/components/annotate/PixelClassifierPanel';
 import AnnotationCanvas from '@/components/annotate/AnnotationCanvas';
 import { useFeatureChannels } from '@/hooks/useFeatureChannels';
@@ -582,16 +583,42 @@ export default function AnnotatePage() {
           )}
 
           {stage === 'assist' && (
-            <FeatureChannelsPanel
-              job={features.job}
-              channelIndex={features.channelIndex}
-              computing={features.computing}
-              error={features.error}
-              onCompute={features.compute}
-              onSelectChannel={features.selectChannel}
-              onCycle={features.cycleChannel}
-              onOriginal={features.clearSelection}
-            />
+            <>
+              <FeatureChannelsPanel
+                job={features.job}
+                channelIndex={features.channelIndex}
+                computing={features.computing}
+                error={features.error}
+                onCompute={features.compute}
+                onSelectChannel={features.selectChannel}
+                onCycle={features.cycleChannel}
+                onOriginal={features.clearSelection}
+              />
+              <SuggestLabelsPanel
+                hasFeatureJob={!!features.job}
+                manifoldParams={manifold.params}
+                onManifoldParamsChange={manifold.setParams}
+                manifoldSampling={manifold.sampling}
+                manifoldHasSample={manifold.hasSample}
+                manifoldShowHeatmap={manifold.showHeatmap}
+                onManifoldShowHeatmapChange={manifold.setShowHeatmap}
+                manifoldShowMarkers={manifold.showMarkers}
+                onManifoldShowMarkersChange={manifold.setShowMarkers}
+                manifoldHeatmapOpacity={manifold.heatmapOpacity}
+                onManifoldHeatmapOpacityChange={manifold.setHeatmapOpacity}
+                manifoldMeta={manifold.meta}
+                manifoldError={manifold.error}
+                onManifoldSample={() => { void manifold.sample(); }}
+                onManifoldDismiss={manifold.dismiss}
+                manifoldRoiShapeCount={manifold.placementMask?.length ?? 0}
+                canCaptureManifoldRoi={selectedShapeIds.length > 0}
+                onCaptureManifoldRoi={() => {
+                  const selected = sliceShapes.filter((s) => selectedShapeIds.includes(s.id));
+                  manifold.setPlacementMaskFromShapes(selected);
+                }}
+                onClearManifoldRoi={manifold.clearPlacementMask}
+              />
+            </>
           )}
 
           {stage === 'predict' && (
@@ -664,27 +691,6 @@ export default function AnnotatePage() {
                 syncedToTiled={maskSyncJob.state.status === 'done'}
                 syncToTiledError={maskSyncJob.state.status === 'error' ? maskSyncJob.state.error : null}
                 onViewIn3D={handleViewIn3D}
-                manifoldParams={manifold.params}
-                onManifoldParamsChange={manifold.setParams}
-                manifoldSampling={manifold.sampling}
-                manifoldHasSample={manifold.hasSample}
-                manifoldShowHeatmap={manifold.showHeatmap}
-                onManifoldShowHeatmapChange={manifold.setShowHeatmap}
-                manifoldShowMarkers={manifold.showMarkers}
-                onManifoldShowMarkersChange={manifold.setShowMarkers}
-                manifoldHeatmapOpacity={manifold.heatmapOpacity}
-                onManifoldHeatmapOpacityChange={manifold.setHeatmapOpacity}
-                manifoldMeta={manifold.meta}
-                manifoldError={manifold.error}
-                onManifoldSample={() => { void manifold.sample(); }}
-                onManifoldDismiss={manifold.dismiss}
-                manifoldRoiShapeCount={manifold.placementMask?.length ?? 0}
-                canCaptureManifoldRoi={selectedShapeIds.length > 0}
-                onCaptureManifoldRoi={() => {
-                  const selected = sliceShapes.filter((s) => selectedShapeIds.includes(s.id));
-                  manifold.setPlacementMaskFromShapes(selected);
-                }}
-                onClearManifoldRoi={manifold.clearPlacementMask}
               />
           )}
 
diff --git a/frontend/src/components/annotate/PixelClassifierPanel/index.test.tsx b/frontend/src/components/annotate/PixelClassifierPanel/index.test.tsx
index 59dca90..b04c4b6 100644
--- a/frontend/src/components/annotate/PixelClassifierPanel/index.test.tsx
+++ b/frontend/src/components/annotate/PixelClassifierPanel/index.test.tsx
@@ -5,15 +5,15 @@
  * multi-slice train/predict, model summary + feature importances, class-
  * probability cycling, commit/dismiss, volume-apply flow (apply -> progress ->
  * result -> commit/dismiss), predicted-pointer "make editable" banner, push-to-
- * Tiled + view-in-3D, train-deep-model hand-off, error surfacing, and the
- * Suggest-labels (manifold) sub-panel (collapsed by default; opened via click).
+ * Tiled + view-in-3D, train-deep-model hand-off, and error surfacing.
+ * (Suggest-labels/manifold sampling was pulled out into its own
+ * SuggestLabelsPanel — see that component's own test file.)
  */
 import { afterEach, describe, expect, it, vi } from 'vitest';
 import { cleanup, fireEvent, render, screen } from '@testing-library/react';
 import userEvent from '@testing-library/user-event';
 import PixelClassifierPanel, { type PixelClassifierPanelProps } from './index';
 import type { ClfTrainResult, ClfPredictCounts } from '@/hooks/usePixelClassifier';
-import type { ManifoldParams } from '@/hooks/useFeatureManifold';
 
 afterEach(() => {
   cleanup();
@@ -21,8 +21,6 @@ afterEach(() => {
 
 const BASE_PARAMS = { iterations: 200, depth: 6, learningRate: 0.1, alpha: 0.05 };
 
-const BASE_MANIFOLD_PARAMS: ManifoldParams = { k: 24, boxSize: 64 } as ManifoldParams;
-
 function makeModel(overrides: Partial<ClfTrainResult> = {}): ClfTrainResult {
   return {
     modelId: 'model-1',
@@ -85,24 +83,6 @@ function baseProps(overrides: Partial<PixelClassifierPanelProps> = {}): PixelCla
     vectorizingSlice: false,
     onMakeSliceEditable: vi.fn(),
     hasAnyPredictedPointers: false,
-    manifoldParams: BASE_MANIFOLD_PARAMS,
-    onManifoldParamsChange: vi.fn(),
-    manifoldSampling: false,
-    manifoldHasSample: false,
-    manifoldShowHeatmap: false,
-    onManifoldShowHeatmapChange: vi.fn(),
-    manifoldShowMarkers: false,
-    onManifoldShowMarkersChange: vi.fn(),
-    manifoldHeatmapOpacity: 0.5,
-    onManifoldHeatmapOpacityChange: vi.fn(),
-    manifoldMeta: null,
-    manifoldError: null,
-    onManifoldSample: vi.fn(),
-    onManifoldDismiss: vi.fn(),
-    manifoldRoiShapeCount: 0,
-    canCaptureManifoldRoi: false,
-    onCaptureManifoldRoi: vi.fn(),
-    onClearManifoldRoi: vi.fn(),
     ...overrides,
   };
 }
@@ -559,114 +539,4 @@ describe('PixelClassifierPanel', () => {
       expect(screen.getByText('Feature bank missing.')).toBeInTheDocument();
     });
   });
-
-  describe('suggest labels (manifold)', () => {
-    it('is collapsed by default and opens on click', async () => {
-      const user = userEvent.setup();
-      render(<PixelClassifierPanel {...baseProps()} />);
-      expect(screen.queryByText(/suggest regions to label/i)).not.toBeInTheDocument();
-      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
-      expect(screen.getByText(/suggest regions to label/i)).toBeInTheDocument();
-    });
-
-    it('is disabled (non-clickable) when there is no feature job', async () => {
-      const user = userEvent.setup();
-      render(<PixelClassifierPanel {...baseProps({ hasFeatureJob: false })} />);
-      const header = screen.getByRole('button', { name: /suggest labels/i });
-      expect(header).toBeDisabled();
-      await user.click(header);
-      expect(screen.queryByText(/suggest regions to label/i)).not.toBeInTheDocument();
-    });
-
-    it('calls onManifoldSample and shows a sampling busy bar', async () => {
-      const user = userEvent.setup();
-      const onManifoldSample = vi.fn();
-      const { rerender } = render(
-        <PixelClassifierPanel {...baseProps({ onManifoldSample })} />,
-      );
-      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
-      const sampleBtn = screen.getByRole('button', { name: /suggest regions to label/i });
-      await user.click(sampleBtn);
-      expect(onManifoldSample).toHaveBeenCalledTimes(1);
-
-      rerender(<PixelClassifierPanel {...baseProps({ onManifoldSample, manifoldSampling: true })} />);
-      expect(screen.getByText(/sampling manifold coverage…/i)).toBeInTheDocument();
-      expect(screen.getByRole('button', { name: /sampling…/i })).toBeDisabled();
-    });
-
-    it('shows manifold meta summary once sampled', async () => {
-      const user = userEvent.setup();
-      render(
-        <PixelClassifierPanel
-          {...baseProps({
-            manifoldMeta: { nPicked: 12, nSubsample: 5000, explainedVariance: 0.87 },
-          })}
-        />,
-      );
-      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
-      expect(screen.getByText(/12 boxes from 5,000 px · 87% variance explained/)).toBeInTheDocument();
-    });
-
-    it('shows heatmap/marker toggles and opacity slider once a sample exists, and dismiss clears it', async () => {
-      const user = userEvent.setup();
-      const onManifoldShowHeatmapChange = vi.fn();
-      const onManifoldShowMarkersChange = vi.fn();
-      const onManifoldDismiss = vi.fn();
-      render(
-        <PixelClassifierPanel
-          {...baseProps({
-            manifoldHasSample: true,
-            onManifoldShowHeatmapChange,
-            onManifoldShowMarkersChange,
-            onManifoldDismiss,
-          })}
-        />,
-      );
-      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
-      await user.click(screen.getByRole('checkbox', { name: /show coverage heatmap/i }));
-      expect(onManifoldShowHeatmapChange).toHaveBeenCalledWith(true);
-      await user.click(screen.getByRole('checkbox', { name: /show suggested boxes/i }));
-      expect(onManifoldShowMarkersChange).toHaveBeenCalledWith(true);
-      await user.click(screen.getByRole('button', { name: /dismiss suggestions/i }));
-      expect(onManifoldDismiss).toHaveBeenCalledTimes(1);
-    });
-
-    it('restrict-to-selection button is disabled without a capturable ROI, and enabled+clearable with one', async () => {
-      const user = userEvent.setup();
-      const onCaptureManifoldRoi = vi.fn();
-      const onClearManifoldRoi = vi.fn();
-      const { rerender } = render(
-        <PixelClassifierPanel
-          {...baseProps({ canCaptureManifoldRoi: false, manifoldRoiShapeCount: 0 })}
-        />,
-      );
-      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
-      expect(screen.getByRole('button', { name: /restrict to selection \(0\)/i })).toBeDisabled();
-      expect(screen.queryByRole('button', { name: /^clear$/i })).not.toBeInTheDocument();
-
-      rerender(
-        <PixelClassifierPanel
-          {...baseProps({
-            canCaptureManifoldRoi: true,
-            manifoldRoiShapeCount: 2,
-            onCaptureManifoldRoi,
-            onClearManifoldRoi,
-          })}
-        />,
-      );
-      const restrictBtn = screen.getByRole('button', { name: /restrict to selection \(2\)/i });
-      expect(restrictBtn).toBeEnabled();
-      await user.click(restrictBtn);
-      expect(onCaptureManifoldRoi).toHaveBeenCalledTimes(1);
-      await user.click(screen.getByRole('button', { name: /^clear$/i }));
-      expect(onClearManifoldRoi).toHaveBeenCalledTimes(1);
-    });
-
-    it('shows a manifold error message', async () => {
-      const user = userEvent.setup();
-      render(<PixelClassifierPanel {...baseProps({ manifoldError: 'Sampling failed.' })} />);
-      await user.click(screen.getByRole('button', { name: /suggest labels/i }));
-      expect(screen.getByText('Sampling failed.')).toBeInTheDocument();
-    });
-  });
 });
diff --git a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
index ae9ee56..2b5b3d6 100644
--- a/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
+++ b/frontend/src/components/annotate/PixelClassifierPanel/index.tsx
@@ -1,11 +1,13 @@
 /**
- * PixelClassifierPanel — train / conformal-predict / commit, with the
- * Suggest-Labels manifold loop folded in and conformal coverage surfaced as
- * headline guidance (not a legend entry).
+ * PixelClassifierPanel — train / conformal-predict / commit, with conformal
+ * coverage surfaced as headline guidance (not a legend entry).
+ *
+ * Suggest-Labels (manifold coverage) used to be folded in here; it now has
+ * its own section (`SuggestLabelsPanel`), positioned right after feature-bank
+ * setup instead of behind classifier training, which it never depended on.
  */
-import { Brain, CaretLeft, CaretRight, CircleNotch, Compass, Cube, TreeStructure, WarningCircle } from '@phosphor-icons/react';
+import { Brain, CaretLeft, CaretRight, CircleNotch, Cube, TreeStructure, WarningCircle } from '@phosphor-icons/react';
 import type { ClfParams, ClfPredictCounts, ClfTrainResult } from '@/hooks/usePixelClassifier';
-import type { ManifoldParams } from '@/hooks/useFeatureManifold';
 import { cn } from '@/lib/utils';
 import CollapsibleSection from '@/components/common/CollapsibleSection';
 
@@ -137,26 +139,6 @@ export interface PixelClassifierPanelProps {
   syncToTiledError?: string | null;
   syncedToTiled?: boolean;
   onViewIn3D?: () => void;
-
-  // Suggest-labels (manifold), folded into the same loop instead of a separate panel.
-  manifoldParams: ManifoldParams;
-  onManifoldParamsChange: (p: ManifoldParams) => void;
-  manifoldSampling: boolean;
-  manifoldHasSample: boolean;
-  manifoldShowHeatmap: boolean;
-  onManifoldShowHeatmapChange: (v: boolean) => void;
-  manifoldShowMarkers: boolean;
-  onManifoldShowMarkersChange: (v: boolean) => void;
-  manifoldHeatmapOpacity: number;
-  onManifoldHeatmapOpacityChange: (v: number) => void;
-  manifoldMeta: { nPicked: number; nSubsample: number; explainedVariance: number } | null;
-  manifoldError: string | null;
-  onManifoldSample: () => void;
-  onManifoldDismiss: () => void;
-  manifoldRoiShapeCount: number;
-  canCaptureManifoldRoi: boolean;
-  onCaptureManifoldRoi: () => void;
-  onClearManifoldRoi: () => void;
 }
 
 /** Sidebar controls for CatBoost + split-conformal sets, with suggest-labels folded in. */
@@ -210,24 +192,6 @@ export default function PixelClassifierPanel({
   syncToTiledError = null,
   syncedToTiled = false,
   onViewIn3D,
-  manifoldParams,
-  onManifoldParamsChange,
-  manifoldSampling,
-  manifoldHasSample,
-  manifoldShowHeatmap,
-  onManifoldShowHeatmapChange,
-  manifoldShowMarkers,
-  onManifoldShowMarkersChange,
-  manifoldHeatmapOpacity,
-  onManifoldHeatmapOpacityChange,
-  manifoldMeta,
-  manifoldError,
-  onManifoldSample,
-  onManifoldDismiss,
-  manifoldRoiShapeCount,
-  canCaptureManifoldRoi,
-  onCaptureManifoldRoi,
-  onClearManifoldRoi,
 }: PixelClassifierPanelProps) {
   const busy = training || predicting || multiTraining || volumeApplying;
   const canTrain =
@@ -670,150 +634,6 @@ export default function PixelClassifierPanel({
       )}
 
       {error && <p className="text-[10px] text-red-600 leading-snug break-words">{error}</p>}
-
-      {/* Suggest labels — folded into the same train/infer loop instead of its own panel. */}
-      <div className="border-t border-gray-100 pt-1.5">
-        <CollapsibleSection
-          title="Suggest labels (manifold coverage)"
-          icon={<Compass size={12} />}
-          defaultOpen={false}
-          disabled={!hasFeatureJob}
-        >
-          <div className="flex flex-col gap-1.5 rounded-md border border-gray-100 bg-gray-50 p-1.5">
-            <div className="grid grid-cols-2 gap-1 text-[10px] text-gray-600">
-              <label className="flex flex-col gap-0.5">
-                K boxes
-                <input
-                  type="number"
-                  min={1}
-                  max={200}
-                  value={manifoldParams.k}
-                  disabled={manifoldSampling}
-                  onChange={(e) =>
-                    onManifoldParamsChange({ ...manifoldParams, k: Math.max(1, Number(e.target.value) || 24) })
-                  }
-                  className="rounded border border-gray-200 px-1 py-0.5 text-gray-800"
-                />
-              </label>
-              <label className="flex flex-col gap-0.5">
-                Box size
-                <input
-                  type="number"
-                  min={8}
-                  max={512}
-                  step={8}
-                  value={manifoldParams.boxSize}
-                  disabled={manifoldSampling}
-                  onChange={(e) =>
-                    onManifoldParamsChange({
-                      ...manifoldParams,
-                      boxSize: Math.max(8, Number(e.target.value) || 64),
-                    })
-                  }
-                  className="rounded border border-gray-200 px-1 py-0.5 text-gray-800"
-                />
-              </label>
-            </div>
-
-            <div className="flex items-center gap-1 text-[10px] text-gray-600">
-              <button
-                type="button"
-                disabled={!canCaptureManifoldRoi}
-                onClick={onCaptureManifoldRoi}
-                className={cn(
-                  'flex-1 rounded border px-1.5 py-1',
-                  canCaptureManifoldRoi
-                    ? 'border-gray-200 bg-white hover:bg-sky-50'
-                    : 'border-gray-200 bg-gray-50 text-gray-300 cursor-not-allowed',
-                )}
-              >
-                Restrict to selection ({manifoldRoiShapeCount})
-              </button>
-              {manifoldRoiShapeCount > 0 && (
-                <button
-                  type="button"
-                  onClick={onClearManifoldRoi}
-                  className="rounded border border-gray-200 bg-white px-1.5 py-1 hover:bg-sky-50"
-                >
-                  Clear
-                </button>
-              )}
-            </div>
-
-            <button
-              type="button"
-              disabled={!hasFeatureJob || manifoldSampling}
-              onClick={onManifoldSample}
-              className={cn(
-                'flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border transition-colors',
-                !hasFeatureJob || manifoldSampling
-                  ? 'bg-gray-50 text-gray-400 border-gray-200 cursor-not-allowed'
-                  : 'bg-white text-gray-800 border-gray-200 hover:bg-sky-50 hover:border-sky-300',
-              )}
-            >
-              {manifoldSampling ? <CircleNotch size={14} className="animate-spin" /> : <Compass size={14} />}
-              {manifoldSampling ? 'Sampling…' : 'Suggest regions to label'}
-            </button>
-            {manifoldSampling && <BusyBar label="Sampling manifold coverage…" />}
-
-            {manifoldMeta && (
-              <p className="text-[10px] text-gray-600 leading-snug">
-                {manifoldMeta.nPicked} boxes from {manifoldMeta.nSubsample.toLocaleString()} px ·{' '}
-                {(manifoldMeta.explainedVariance * 100).toFixed(0)}% variance explained
-              </p>
-            )}
-
-            {manifoldHasSample && (
-              <div className="flex flex-col gap-1 text-[10px] text-gray-600">
-                <label className="flex items-center gap-1.5">
-                  <input
-                    type="checkbox"
-                    checked={manifoldShowHeatmap}
-                    onChange={(e) => onManifoldShowHeatmapChange(e.target.checked)}
-                  />
-                  Show coverage heatmap
-                </label>
-                <label className="flex items-center gap-1.5">
-                  <input
-                    type="checkbox"
-                    checked={manifoldShowMarkers}
-                    onChange={(e) => onManifoldShowMarkersChange(e.target.checked)}
-                  />
-                  Show suggested boxes
-                </label>
-                <label className="flex flex-col gap-0.5">
-                  <span className="flex justify-between">
-                    <span>Heatmap opacity</span>
-                    <span className="text-gray-800 font-medium">
-                      {Math.round(manifoldHeatmapOpacity * 100)}%
-                    </span>
-                  </span>
-                  <input
-                    type="range"
-                    min={0}
-                    max={100}
-                    step={5}
-                    value={Math.round(manifoldHeatmapOpacity * 100)}
-                    onChange={(e) => onManifoldHeatmapOpacityChange(Number(e.target.value) / 100)}
-                    className="w-full accent-sky-600"
-                  />
-                </label>
-                <button
-                  type="button"
-                  onClick={onManifoldDismiss}
-                  className="self-start text-[10px] text-gray-500 hover:text-sky-700"
-                >
-                  Dismiss suggestions
-                </button>
-              </div>
-            )}
-
-            {manifoldError && (
-              <p className="text-[10px] text-red-600 leading-snug break-words">{manifoldError}</p>
-            )}
-          </div>
-        </CollapsibleSection>
-      </div>
     </CollapsibleSection>
   );
 }
diff --git a/frontend/src/components/annotate/SuggestLabelsPanel/index.test.tsx b/frontend/src/components/annotate/SuggestLabelsPanel/index.test.tsx
new file mode 100644
index 0000000..f5f3771
--- /dev/null
+++ b/frontend/src/components/annotate/SuggestLabelsPanel/index.test.tsx
@@ -0,0 +1,148 @@
+/**
+ * SuggestLabelsPanel — fully prop-driven, same style as PixelClassifierPanel's
+ * own test file. Extracted from PixelClassifierPanel's "suggest labels
+ * (manifold)" tests when the section was pulled out into its own component.
+ */
+import { afterEach, describe, expect, it, vi } from 'vitest';
+import { cleanup, fireEvent, render, screen } from '@testing-library/react';
+import userEvent from '@testing-library/user-event';
+import SuggestLabelsPanel, { type SuggestLabelsPanelProps } from './index';
+import type { ManifoldParams } from '@/hooks/useFeatureManifold';
+
+afterEach(() => {
+  cleanup();
+});
+
+const BASE_MANIFOLD_PARAMS: ManifoldParams = { k: 24, boxSize: 64 } as ManifoldParams;
+
+function baseProps(overrides: Partial<SuggestLabelsPanelProps> = {}): SuggestLabelsPanelProps {
+  return {
+    hasFeatureJob: true,
+    manifoldParams: BASE_MANIFOLD_PARAMS,
+    onManifoldParamsChange: vi.fn(),
+    manifoldSampling: false,
+    manifoldHasSample: false,
+    manifoldShowHeatmap: false,
+    onManifoldShowHeatmapChange: vi.fn(),
+    manifoldShowMarkers: false,
+    onManifoldShowMarkersChange: vi.fn(),
+    manifoldHeatmapOpacity: 0.5,
+    onManifoldHeatmapOpacityChange: vi.fn(),
+    manifoldMeta: null,
+    manifoldError: null,
+    onManifoldSample: vi.fn(),
+    onManifoldDismiss: vi.fn(),
+    manifoldRoiShapeCount: 0,
+    canCaptureManifoldRoi: false,
+    onCaptureManifoldRoi: vi.fn(),
+    onClearManifoldRoi: vi.fn(),
+    ...overrides,
+  };
+}
+
+describe('SuggestLabelsPanel', () => {
+  it('is open by default (not buried like when it lived inside PixelClassifierPanel)', () => {
+    render(<SuggestLabelsPanel {...baseProps()} />);
+    expect(screen.getByText(/suggest regions to label/i)).toBeInTheDocument();
+  });
+
+  it('is disabled (non-clickable) when there is no feature job', async () => {
+    const user = userEvent.setup();
+    render(<SuggestLabelsPanel {...baseProps({ hasFeatureJob: false })} />);
+    const header = screen.getByRole('button', { name: /suggest labels/i });
+    expect(header).toBeDisabled();
+    await user.click(header);
+    // Still expanded by default; the header itself being disabled is what matters.
+    expect(screen.getByRole('button', { name: /suggest regions to label/i })).toBeDisabled();
+  });
+
+  it('calls onManifoldSample and shows a sampling busy bar', async () => {
+    const user = userEvent.setup();
+    const onManifoldSample = vi.fn();
+    const { rerender } = render(<SuggestLabelsPanel {...baseProps({ onManifoldSample })} />);
+    const sampleBtn = screen.getByRole('button', { name: /suggest regions to label/i });
+    await user.click(sampleBtn);
+    expect(onManifoldSample).toHaveBeenCalledTimes(1);
+
+    rerender(<SuggestLabelsPanel {...baseProps({ onManifoldSample, manifoldSampling: true })} />);
+    expect(screen.getByText(/sampling manifold coverage…/i)).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /sampling…/i })).toBeDisabled();
+  });
+
+  it('shows manifold meta summary once sampled', () => {
+    render(
+      <SuggestLabelsPanel
+        {...baseProps({
+          manifoldMeta: { nPicked: 12, nSubsample: 5000, explainedVariance: 0.87 },
+        })}
+      />,
+    );
+    expect(screen.getByText(/12 boxes from 5,000 px · 87% variance explained/)).toBeInTheDocument();
+  });
+
+  it('shows heatmap/marker toggles and opacity slider once a sample exists, and dismiss clears it', async () => {
+    const user = userEvent.setup();
+    const onManifoldShowHeatmapChange = vi.fn();
+    const onManifoldShowMarkersChange = vi.fn();
+    const onManifoldDismiss = vi.fn();
+    render(
+      <SuggestLabelsPanel
+        {...baseProps({
+          manifoldHasSample: true,
+          onManifoldShowHeatmapChange,
+          onManifoldShowMarkersChange,
+          onManifoldDismiss,
+        })}
+      />,
+    );
+    await user.click(screen.getByRole('checkbox', { name: /show coverage heatmap/i }));
+    expect(onManifoldShowHeatmapChange).toHaveBeenCalledWith(true);
+    await user.click(screen.getByRole('checkbox', { name: /show suggested boxes/i }));
+    expect(onManifoldShowMarkersChange).toHaveBeenCalledWith(true);
+    await user.click(screen.getByRole('button', { name: /dismiss suggestions/i }));
+    expect(onManifoldDismiss).toHaveBeenCalledTimes(1);
+  });
+
+  it('restrict-to-selection button is disabled without a capturable ROI, and enabled+clearable with one', async () => {
+    const user = userEvent.setup();
+    const onCaptureManifoldRoi = vi.fn();
+    const onClearManifoldRoi = vi.fn();
+    const { rerender } = render(
+      <SuggestLabelsPanel {...baseProps({ canCaptureManifoldRoi: false, manifoldRoiShapeCount: 0 })} />,
+    );
+    expect(screen.getByRole('button', { name: /restrict to selection \(0\)/i })).toBeDisabled();
+    expect(screen.queryByRole('button', { name: /^clear$/i })).not.toBeInTheDocument();
+
+    rerender(
+      <SuggestLabelsPanel
+        {...baseProps({
+          canCaptureManifoldRoi: true,
+          manifoldRoiShapeCount: 2,
+          onCaptureManifoldRoi,
+          onClearManifoldRoi,
+        })}
+      />,
+    );
+    const restrictBtn = screen.getByRole('button', { name: /restrict to selection \(2\)/i });
+    expect(restrictBtn).toBeEnabled();
+    await user.click(restrictBtn);
+    expect(onCaptureManifoldRoi).toHaveBeenCalledTimes(1);
+    await user.click(screen.getByRole('button', { name: /^clear$/i }));
+    expect(onClearManifoldRoi).toHaveBeenCalledTimes(1);
+  });
+
+  it('shows a manifold error message', () => {
+    render(<SuggestLabelsPanel {...baseProps({ manifoldError: 'Sampling failed.' })} />);
+    expect(screen.getByText('Sampling failed.')).toBeInTheDocument();
+  });
+
+  it('updates K boxes / box size params', () => {
+    const onManifoldParamsChange = vi.fn();
+    render(<SuggestLabelsPanel {...baseProps({ onManifoldParamsChange })} />);
+    // A fully-controlled numeric input whose value prop never changes across
+    // this render — user.type() would accumulate keystrokes against jsdom's
+    // own uncommitted DOM value instead. A single fireEvent.change avoids that.
+    fireEvent.change(screen.getByLabelText(/k boxes/i), { target: { value: '50' } });
+    expect(onManifoldParamsChange).toHaveBeenLastCalledWith({ k: 50, boxSize: 64 });
+  });
+});
diff --git a/frontend/src/components/annotate/SuggestLabelsPanel/index.tsx b/frontend/src/components/annotate/SuggestLabelsPanel/index.tsx
new file mode 100644
index 0000000..3f7507a
--- /dev/null
+++ b/frontend/src/components/annotate/SuggestLabelsPanel/index.tsx
@@ -0,0 +1,216 @@
+/**
+ * SuggestLabelsPanel — manifold-coverage sampling ("Suggest regions to
+ * label"), as its own sidebar section.
+ *
+ * Previously folded into PixelClassifierPanel, near the bottom of the
+ * sidebar — but useFeatureManifold only ever depends on a feature bank
+ * (featureJobId), never on a trained classifier, so it was buried behind
+ * classifier training for no functional reason. Pulled out to stand next to
+ * FeatureChannelsPanel instead: the earliest point it actually has what it
+ * needs, and well before a user is asked to train anything.
+ */
+import { CircleNotch, Compass } from '@phosphor-icons/react';
+import type { ManifoldParams } from '@/hooks/useFeatureManifold';
+import { cn } from '@/lib/utils';
+import CollapsibleSection from '@/components/common/CollapsibleSection';
+
+/** Shared progress-bar treatment (see useExportJob / DownloadModal). */
+function BusyBar({ label }: { label: string }) {
+  return (
+    <div className="flex flex-col gap-1">
+      <div className="flex items-center justify-between text-[10px] text-gray-500">
+        <span>{label}</span>
+      </div>
+      <div className="h-1.5 w-full overflow-hidden rounded-full bg-gray-200">
+        <div className="h-full w-1/3 animate-pulse rounded-full bg-sky-500" />
+      </div>
+    </div>
+  );
+}
+
+export interface SuggestLabelsPanelProps {
+  hasFeatureJob: boolean;
+  manifoldParams: ManifoldParams;
+  onManifoldParamsChange: (p: ManifoldParams) => void;
+  manifoldSampling: boolean;
+  manifoldHasSample: boolean;
+  manifoldShowHeatmap: boolean;
+  onManifoldShowHeatmapChange: (v: boolean) => void;
+  manifoldShowMarkers: boolean;
+  onManifoldShowMarkersChange: (v: boolean) => void;
+  manifoldHeatmapOpacity: number;
+  onManifoldHeatmapOpacityChange: (v: number) => void;
+  manifoldMeta: { nPicked: number; nSubsample: number; explainedVariance: number } | null;
+  manifoldError: string | null;
+  onManifoldSample: () => void;
+  onManifoldDismiss: () => void;
+  manifoldRoiShapeCount: number;
+  canCaptureManifoldRoi: boolean;
+  onCaptureManifoldRoi: () => void;
+  onClearManifoldRoi: () => void;
+}
+
+/** Sidebar section for manifold-coverage label suggestions. */
+export default function SuggestLabelsPanel({
+  hasFeatureJob,
+  manifoldParams,
+  onManifoldParamsChange,
+  manifoldSampling,
+  manifoldHasSample,
+  manifoldShowHeatmap,
+  onManifoldShowHeatmapChange,
+  manifoldShowMarkers,
+  onManifoldShowMarkersChange,
+  manifoldHeatmapOpacity,
+  onManifoldHeatmapOpacityChange,
+  manifoldMeta,
+  manifoldError,
+  onManifoldSample,
+  onManifoldDismiss,
+  manifoldRoiShapeCount,
+  canCaptureManifoldRoi,
+  onCaptureManifoldRoi,
+  onClearManifoldRoi,
+}: SuggestLabelsPanelProps) {
+  return (
+    <CollapsibleSection
+      title="Suggest labels (manifold coverage)"
+      icon={<Compass size={13} />}
+      disabled={!hasFeatureJob}
+    >
+      <div className="flex flex-col gap-1.5 rounded-md border border-gray-100 bg-gray-50 p-1.5">
+        <div className="grid grid-cols-2 gap-1 text-[10px] text-gray-600">
+          <label className="flex flex-col gap-0.5">
+            K boxes
+            <input
+              type="number"
+              min={1}
+              max={200}
+              value={manifoldParams.k}
+              disabled={manifoldSampling}
+              onChange={(e) =>
+                onManifoldParamsChange({ ...manifoldParams, k: Math.max(1, Number(e.target.value) || 24) })
+              }
+              className="rounded border border-gray-200 px-1 py-0.5 text-gray-800"
+            />
+          </label>
+          <label className="flex flex-col gap-0.5">
+            Box size
+            <input
+              type="number"
+              min={8}
+              max={512}
+              step={8}
+              value={manifoldParams.boxSize}
+              disabled={manifoldSampling}
+              onChange={(e) =>
+                onManifoldParamsChange({
+                  ...manifoldParams,
+                  boxSize: Math.max(8, Number(e.target.value) || 64),
+                })
+              }
+              className="rounded border border-gray-200 px-1 py-0.5 text-gray-800"
+            />
+          </label>
+        </div>
+
+        <div className="flex items-center gap-1 text-[10px] text-gray-600">
+          <button
+            type="button"
+            disabled={!canCaptureManifoldRoi}
+            onClick={onCaptureManifoldRoi}
+            className={cn(
+              'flex-1 rounded border px-1.5 py-1',
+              canCaptureManifoldRoi
+                ? 'border-gray-200 bg-white hover:bg-sky-50'
+                : 'border-gray-200 bg-gray-50 text-gray-300 cursor-not-allowed',
+            )}
+          >
+            Restrict to selection ({manifoldRoiShapeCount})
+          </button>
+          {manifoldRoiShapeCount > 0 && (
+            <button
+              type="button"
+              onClick={onClearManifoldRoi}
+              className="rounded border border-gray-200 bg-white px-1.5 py-1 hover:bg-sky-50"
+            >
+              Clear
+            </button>
+          )}
+        </div>
+
+        <button
+          type="button"
+          disabled={!hasFeatureJob || manifoldSampling}
+          onClick={onManifoldSample}
+          className={cn(
+            'flex items-center justify-center gap-1.5 py-1.5 rounded-md text-xs border transition-colors',
+            !hasFeatureJob || manifoldSampling
+              ? 'bg-gray-50 text-gray-400 border-gray-200 cursor-not-allowed'
+              : 'bg-white text-gray-800 border-gray-200 hover:bg-sky-50 hover:border-sky-300',
+          )}
+        >
+          {manifoldSampling ? <CircleNotch size={14} className="animate-spin" /> : <Compass size={14} />}
+          {manifoldSampling ? 'Sampling…' : 'Suggest regions to label'}
+        </button>
+        {manifoldSampling && <BusyBar label="Sampling manifold coverage…" />}
+
+        {manifoldMeta && (
+          <p className="text-[10px] text-gray-600 leading-snug">
+            {manifoldMeta.nPicked} boxes from {manifoldMeta.nSubsample.toLocaleString()} px ·{' '}
+            {(manifoldMeta.explainedVariance * 100).toFixed(0)}% variance explained
+          </p>
+        )}
+
+        {manifoldHasSample && (
+          <div className="flex flex-col gap-1 text-[10px] text-gray-600">
+            <label className="flex items-center gap-1.5">
+              <input
+                type="checkbox"
+                checked={manifoldShowHeatmap}
+                onChange={(e) => onManifoldShowHeatmapChange(e.target.checked)}
+              />
+              Show coverage heatmap
+            </label>
+            <label className="flex items-center gap-1.5">
+              <input
+                type="checkbox"
+                checked={manifoldShowMarkers}
+                onChange={(e) => onManifoldShowMarkersChange(e.target.checked)}
+              />
+              Show suggested boxes
+            </label>
+            <label className="flex flex-col gap-0.5">
+              <span className="flex justify-between">
+                <span>Heatmap opacity</span>
+                <span className="text-gray-800 font-medium">
+                  {Math.round(manifoldHeatmapOpacity * 100)}%
+                </span>
+              </span>
+              <input
+                type="range"
+                min={0}
+                max={100}
+                step={5}
+                value={Math.round(manifoldHeatmapOpacity * 100)}
+                onChange={(e) => onManifoldHeatmapOpacityChange(Number(e.target.value) / 100)}
+                className="w-full accent-sky-600"
+              />
+            </label>
+            <button
+              type="button"
+              onClick={onManifoldDismiss}
+              className="self-start text-[10px] text-gray-500 hover:text-sky-700"
+            >
+              Dismiss suggestions
+            </button>
+          </div>
+        )}
+
+        {manifoldError && (
+          <p className="text-[10px] text-red-600 leading-snug break-words">{manifoldError}</p>
+        )}
+      </div>
+    </CollapsibleSection>
+  );
+}
diff --git a/frontend/src/components/train/InferencePanel.tsx b/frontend/src/components/train/InferencePanel.tsx
index e13e568..a542132 100644
--- a/frontend/src/components/train/InferencePanel.tsx
+++ b/frontend/src/components/train/InferencePanel.tsx
@@ -247,16 +247,28 @@ export default function InferencePanel({
                   >
                     Import as annotations
                   </button>
-                  {isTiledSource && (
-                    <button
-                      type="button"
-                      disabled={writeJob.status === 'running'}
-                      onClick={() => job.jobId && void startWriteJob(`/api/train/infer/write-tiled/${job.jobId}`, {})}
-                      className="px-3 py-1.5 text-xs rounded-md border border-emerald-500 text-emerald-300 hover:bg-emerald-900/30 transition-colors disabled:opacity-50"
-                    >
-                      {writeJob.status === 'running' ? 'Writing…' : 'Write masks to Tiled'}
-                    </button>
-                  )}
+                  {/* Not gated on the live `isTiledSource` prop: that reflects
+                      whatever sample is CURRENTLY open in the global dataset
+                      store, not the sample this job actually ran against — a
+                      long-running job (e.g. hundreds of slices) can easily
+                      outlive the user switching samples/tabs and coming back,
+                      at which point `isTiledSource` no longer describes this
+                      job at all and could wrongly hide the button for a job
+                      that really did run on a Tiled source. The write route
+                      is already correctly bound to the job's own source
+                      server-side (infer_jobs.py's cached entry) and reports a
+                      clear error if it truly isn't Tiled — surfaced below via
+                      writeJob's own error state — so there's no need to
+                      duplicate that check here against state that can go
+                      stale. */}
+                  <button
+                    type="button"
+                    disabled={writeJob.status === 'running'}
+                    onClick={() => job.jobId && void startWriteJob(`/api/train/infer/write-tiled/${job.jobId}`, {})}
+                    className="px-3 py-1.5 text-xs rounded-md border border-emerald-500 text-emerald-300 hover:bg-emerald-900/30 transition-colors disabled:opacity-50"
+                  >
+                    {writeJob.status === 'running' ? 'Writing…' : 'Write masks to Tiled'}
+                  </button>
                 </div>
               )}
               {importedCount !== null && (

From e7ca74e34446cdedfc839093dadd7885a897a136 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Tue, 8 Sep 2026 18:00:20 -0700
Subject: [PATCH 25/38] Caching display sliders between samples/slices/sessions

---
 frontend/src/app/pages/AnnotatePage.tsx       | 28 ++++---
 frontend/src/app/pages/VolumePage.tsx         | 48 +++++++++++-
 .../components/Browse/ColumnBrowser.test.tsx  |  4 +-
 .../src/components/Browse/ColumnBrowser.tsx   | 12 ++-
 frontend/src/components/HubAppLayout.tsx      |  6 ++
 frontend/src/components/HubHeader.tsx         | 40 +++++++++-
 .../src/hooks/useConnectionHealth.test.ts     | 78 +++++++++++++++++++
 frontend/src/hooks/useConnectionHealth.ts     | 62 +++++++++++++++
 frontend/src/lib/displayPrefs.test.ts         | 39 ++++++++++
 frontend/src/lib/displayPrefs.ts              | 43 ++++++++++
 frontend/src/stores/connectionStore.test.ts   | 56 +++++++++++++
 frontend/src/stores/connectionStore.ts        | 12 +++
 12 files changed, 410 insertions(+), 18 deletions(-)
 create mode 100644 frontend/src/hooks/useConnectionHealth.test.ts
 create mode 100644 frontend/src/hooks/useConnectionHealth.ts
 create mode 100644 frontend/src/lib/displayPrefs.test.ts
 create mode 100644 frontend/src/lib/displayPrefs.ts
 create mode 100644 frontend/src/stores/connectionStore.test.ts

diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index 9b1ebea..27eceea 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -46,6 +46,7 @@ import VersionPreviewBar from '@/components/annotate/VersionPreviewBar';
 import PerfOverlay from '@/components/annotate/PerfOverlay';
 import type { SamplerFit } from '@/components/annotate/AnnotationCanvas';
 import { initPerf } from '@/lib/perf';
+import { loadDisplayPrefs, saveDisplayPrefs } from '@/lib/displayPrefs';
 import SaveModal from '@/components/annotate/SaveModal';
 import type { SaveDraftPayload } from '@/hooks/useSave';
 
@@ -106,19 +107,28 @@ export default function AnnotatePage() {
     if (classes.length > 0) setActiveClassId(classes[0].classId);
   }, [classes, activeClassId]);
 
-  const [brightness, setBrightness] = useState(0);
-  const [contrast, setContrast] = useState(0);
+  // Cosmetic display sliders survive reload via localStorage (global viewer
+  // preference, not per-sample/draft) — read once, lazily, on first mount.
+  const [initialDisplayPrefs] = useState(() => loadDisplayPrefs());
+  const [brightness, setBrightness] = useState(() => initialDisplayPrefs.brightness ?? 0);
+  const [contrast, setContrast] = useState(() => initialDisplayPrefs.contrast ?? 0);
   // Min/max levels window (0–255) + histogram of the current slice (client-side).
-  const [levelsLo, setLevelsLo] = useState(0);
-  const [levelsHi, setLevelsHi] = useState(255);
+  const [levelsLo, setLevelsLo] = useState(() => initialDisplayPrefs.levelsLo ?? 0);
+  const [levelsHi, setLevelsHi] = useState(() => initialDisplayPrefs.levelsHi ?? 255);
   const [histogramBins, setHistogramBins] = useState<number[] | null>(null);
   // Display-only false-color map + gamma.
-  const [colormap, setColormap] = useState<ColormapName>('gray');
-  const [gamma, setGamma] = useState(1);
+  const [colormap, setColormap] = useState<ColormapName>(() => initialDisplayPrefs.colormap ?? 'gray');
+  const [gamma, setGamma] = useState(() => initialDisplayPrefs.gamma ?? 1);
   // Display-only nonlinear preprocessors (Gaussian blur / adaptive CLAHE / Sharpen).
-  const [clahe, setClahe] = useState(false);
-  const [sharpen, setSharpen] = useState(false);
-  const [blur, setBlur] = useState(0);
+  const [clahe, setClahe] = useState(() => initialDisplayPrefs.clahe ?? false);
+  const [sharpen, setSharpen] = useState(() => initialDisplayPrefs.sharpen ?? false);
+  const [blur, setBlur] = useState(() => initialDisplayPrefs.blur ?? 0);
+  // Persist on every change — deliberately excludes `denoise`, which stays
+  // unpersisted (see datasetStore.ts): a strength tuned to one volume's noise
+  // level would silently mis-filter a different one carried over.
+  useEffect(() => {
+    saveDisplayPrefs({ brightness, contrast, levelsLo, levelsHi, gamma, colormap, clahe, sharpen, blur });
+  }, [brightness, contrast, levelsLo, levelsHi, gamma, colormap, clahe, sharpen, blur]);
   const [bakeOpen, setBakeOpen] = useState(false);
   // Working resolution for the drawing tools (1x, 2x, 4x). Annotation coordinates
   // stay native; upscaling only buys sub-pixel precision on small features.
diff --git a/frontend/src/app/pages/VolumePage.tsx b/frontend/src/app/pages/VolumePage.tsx
index a4a4015..5eca1d9 100644
--- a/frontend/src/app/pages/VolumePage.tsx
+++ b/frontend/src/app/pages/VolumePage.tsx
@@ -18,7 +18,7 @@
  * containers `tiled_mask_sync.write_masks_to_tiled` writes.
  */
 import { useEffect, useState } from 'react';
-import { useSearchParams } from 'react-router';
+import { useNavigate, useSearchParams } from 'react-router';
 import { useQuery } from '@tanstack/react-query';
 import { Cube } from '@phosphor-icons/react';
 import { API_BASE } from '@/config';
@@ -41,7 +41,19 @@ interface VolumeNode {
 }
 
 /** Centered message panel — every non-rendering state uses this shape. */
-function Notice({ title, detail, hint }: { title: string; detail: string; hint?: string }) {
+function Notice({
+  title,
+  detail,
+  hint,
+  showReconnect,
+}: {
+  title: string;
+  detail: string;
+  hint?: string;
+  /** Shows a "Go to Connect" button instead of leaving a dead-end message. */
+  showReconnect?: boolean;
+}) {
+  const navigate = useNavigate();
   return (
     <div className="flex h-full items-center justify-center p-8">
       <div className="max-w-md text-center text-sky-200">
@@ -49,6 +61,14 @@ function Notice({ title, detail, hint }: { title: string; detail: string; hint?:
         <p className="mb-1 font-medium">{title}</p>
         <p className="text-sm opacity-80">{detail}</p>
         {hint && <p className="mt-3 text-xs opacity-60">{hint}</p>}
+        {showReconnect && (
+          <button
+            onClick={() => navigate('/connect')}
+            className="mt-4 px-3 py-1.5 text-xs rounded-md bg-sky-600 text-white hover:bg-sky-500 transition-colors"
+          >
+            Go to Connect
+          </button>
+        )}
       </div>
     </div>
   );
@@ -90,7 +110,7 @@ export default function VolumePage() {
 
   const resolvedUri = serverUri ?? servers[0]?.uri ?? null;
 
-  const { data: node, isLoading: resolving } = useQuery<VolumeNode>({
+  const { data: node, isLoading: resolving, isError: resolveError } = useQuery<VolumeNode>({
     queryKey: ['volume-node', resolvedUri, source],
     queryFn: async () => {
       const params = new URLSearchParams({ source: source! });
@@ -100,6 +120,7 @@ export default function VolumePage() {
       return res.json();
     },
     enabled: kind === 'tiled' && !!source,
+    retry: 1,
   });
 
   const availability = webGpuAvailability();
@@ -107,6 +128,19 @@ export default function VolumePage() {
     return <Notice title="3D view unavailable" detail={availability.reason} />;
   }
 
+  // A genuine failure to reach Tiled (server down, network drop) previously
+  // fell through to the generic "resolving" reason forever — a dead end with
+  // no way back to Connect short of the browser's own back button.
+  if (kind === 'tiled' && resolveError) {
+    return (
+      <Notice
+        title="Couldn't reach the Tiled server"
+        detail="The volume could not be resolved — the connection may have dropped."
+        showReconnect
+      />
+    );
+  }
+
   // Resolution only applies to Tiled sources; everything else already has a
   // reason of its own and should not sit on a spinner.
   let reason: ZarrUnavailable | null = null;
@@ -123,7 +157,13 @@ export default function VolumePage() {
   }
 
   if (!url || reason) {
-    return <Notice title="Nothing to render" detail={describeUnavailable(reason ?? 'no-source')} />;
+    return (
+      <Notice
+        title="Nothing to render"
+        detail={describeUnavailable(reason ?? 'no-source')}
+        showReconnect={(reason ?? 'no-source') === 'no-source'}
+      />
+    );
   }
 
   if (bootError) {
diff --git a/frontend/src/components/Browse/ColumnBrowser.test.tsx b/frontend/src/components/Browse/ColumnBrowser.test.tsx
index 1ef8240..bc3bf80 100644
--- a/frontend/src/components/Browse/ColumnBrowser.test.tsx
+++ b/frontend/src/components/Browse/ColumnBrowser.test.tsx
@@ -12,6 +12,7 @@
 import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
 import { cleanup, render, screen, waitFor } from '@testing-library/react';
 import userEvent from '@testing-library/user-event';
+import { MemoryRouter } from 'react-router';
 import ColumnBrowser from './ColumnBrowser';
 import type { BrowseItem } from './hooks/useBrowseData';
 import type { ServerInfo } from '@/types/server';
@@ -99,7 +100,7 @@ function renderBrowser(overrides: Partial<React.ComponentProps<typeof ColumnBrow
     onAnnotationFilterChange: vi.fn(),
     ...overrides,
   };
-  return { ...render(<ColumnBrowser {...props} />), props };
+  return { ...render(<ColumnBrowser {...props} />, { wrapper: MemoryRouter }), props };
 }
 
 beforeEach(() => {
@@ -140,6 +141,7 @@ describe('ColumnBrowser', () => {
     renderBrowser();
 
     expect(await screen.findByText(/Cannot reach the API server/)).toBeInTheDocument();
+    expect(screen.getByRole('button', { name: /go to connect/i })).toBeInTheDocument();
     expect(screen.queryByTestId('items-column')).not.toBeInTheDocument();
   });
 
diff --git a/frontend/src/components/Browse/ColumnBrowser.tsx b/frontend/src/components/Browse/ColumnBrowser.tsx
index 9e6f315..03b049b 100644
--- a/frontend/src/components/Browse/ColumnBrowser.tsx
+++ b/frontend/src/components/Browse/ColumnBrowser.tsx
@@ -1,4 +1,5 @@
 import React, { useCallback, useEffect, useMemo, useRef, useState } from 'react';
+import { useNavigate } from 'react-router';
 import { ArrowsClockwise, PencilSimple, Plus, Stack } from '@phosphor-icons/react';
 import BrowseColumn from './BrowseColumn';
 import BrowseDetailPanel from './BrowseDetailPanel';
@@ -72,6 +73,7 @@ export default function ColumnBrowser({
 }: ColumnBrowserProps) {
   const { state, actions } = useBrowseData(serverUri, 'All', undefined, containerPath);
   const { openTiledArray } = useOpenInAnnotate();
+  const navigate = useNavigate();
   const scrollRef = useRef<HTMLDivElement>(null);
 
   const [columnWidths, setColumnWidths] = useState<number[]>([]);
@@ -237,8 +239,14 @@ export default function ColumnBrowser({
       )}
 
       {state.connectionStatus === 'disconnected' && (
-        <div className="shrink-0 px-4 py-2 text-xs border-b border-red-900 bg-red-950/80 text-red-300">
-          Cannot reach the API server. Make sure the backend (port 8002) and Tiled server are running.
+        <div className="shrink-0 flex items-center justify-between gap-3 px-4 py-2 text-xs border-b border-red-900 bg-red-950/80 text-red-300">
+          <span>Cannot reach the API server. Make sure the backend (port 8002) and Tiled server are running.</span>
+          <button
+            onClick={() => navigate('/connect')}
+            className="shrink-0 px-2.5 py-1 rounded-md bg-red-900/60 hover:bg-red-900 transition-colors"
+          >
+            Go to Connect
+          </button>
         </div>
       )}
 
diff --git a/frontend/src/components/HubAppLayout.tsx b/frontend/src/components/HubAppLayout.tsx
index e7c4427..2883861 100644
--- a/frontend/src/components/HubAppLayout.tsx
+++ b/frontend/src/components/HubAppLayout.tsx
@@ -2,6 +2,7 @@ import HubHeader from "@/components/HubHeader";
 import HubMainContent from "@/components/HubMainContent";
 import HubSidebar from "@/components/HubSidebar";
 import { cn } from "@/lib/utils";
+import { useConnectionHealth } from "@/hooks/useConnectionHealth";
 
 import { RouteItem } from "@/types/navigationRouterTypes";
 
@@ -37,6 +38,11 @@ export default function HubAppLayout ( {
     onOpenTabSelector
   }: HubAppLayoutProps) {
 
+  // Drives connectionStore's `status` field from anywhere in the app, so
+  // HubHeader's indicator reflects live Tiled reachability regardless of
+  // which route is active.
+  useConnectionHealth();
+
 return (
     <div className="flex h-screen w-screen max-w-full overflow-hidden">
         {/* Sidebar: fixed overlay so it always receives clicks above any route content */}
diff --git a/frontend/src/components/HubHeader.tsx b/frontend/src/components/HubHeader.tsx
index 303f2ca..7a1e3cb 100644
--- a/frontend/src/components/HubHeader.tsx
+++ b/frontend/src/components/HubHeader.tsx
@@ -1,6 +1,8 @@
+import { useNavigate } from 'react-router';
 import alsLogo from '@/assets/alsLogo.png';
 import { cn } from '@/lib/utils';
-import { Gear } from '@phosphor-icons/react';
+import { Gear, Warning } from '@phosphor-icons/react';
+import { useConnectionStore } from '@/stores/connectionStore';
 
 export type HubHeaderProps = {
     title?: string;
@@ -9,6 +11,37 @@ export type HubHeaderProps = {
     titleClassName?: string;
     onOpenTabSelector?: () => void;
 }
+
+/** Small persistent Tiled-connection indicator; hidden for local/no connection. */
+function ConnectionStatus() {
+    const kind = useConnectionStore((s) => s.kind);
+    const status = useConnectionStore((s) => s.status);
+    const navigate = useNavigate();
+    if (kind !== 'tiled' || status === 'unknown') return null;
+
+    if (status === 'error') {
+        return (
+            <button
+                onClick={() => navigate('/connect')}
+                className="flex items-center gap-1.5 px-2.5 py-1 rounded-md text-xs font-medium text-red-700 bg-red-100 hover:bg-red-200 transition-colors"
+                title="Lost connection to the Tiled server — click to reconnect"
+            >
+                <Warning size={14} weight="fill" />
+                Tiled disconnected
+            </button>
+        );
+    }
+    return (
+        <span
+            className="flex items-center gap-1.5 px-2.5 py-1 rounded-md text-xs font-medium text-emerald-700"
+            title="Connected to the Tiled server"
+        >
+            <span className="h-2 w-2 rounded-full bg-emerald-500" />
+            Tiled connected
+        </span>
+    );
+}
+
 /** HubHeader — top app bar with logo, title, and an optional "Change Tabs" button. */
 export default function HubHeader({title="ALS COMPUTING HUB", logoUrl=alsLogo, className, titleClassName, onOpenTabSelector}: HubHeaderProps) {
     return (
@@ -17,16 +50,19 @@ export default function HubHeader({title="ALS COMPUTING HUB", logoUrl=alsLogo, c
             <img src={logoUrl} alt="ALS logo" width={40} height={40} className="h-10 w-10 aspect-square"/>
             <h1 className={cn("text-sky-950 text-2xl font-semibold", titleClassName)}>{title}</h1>
         </div>
+        <div className="flex items-center gap-4 mr-6">
+        <ConnectionStatus />
         {onOpenTabSelector && (
             <button
                 onClick={onOpenTabSelector}
-                className="mr-6 flex items-center gap-2 px-4 py-2 rounded-lg text-sky-950 hover:bg-sky-100 transition-colors"
+                className="flex items-center gap-2 px-4 py-2 rounded-lg text-sky-950 hover:bg-sky-100 transition-colors"
                 title="Change tabs"
             >
                 <Gear size={24} />
                 <span className="text-sm font-medium">Change Tabs</span>
             </button>
         )}
+        </div>
     </header>
     )
 }
\ No newline at end of file
diff --git a/frontend/src/hooks/useConnectionHealth.test.ts b/frontend/src/hooks/useConnectionHealth.test.ts
new file mode 100644
index 0000000..4f42d23
--- /dev/null
+++ b/frontend/src/hooks/useConnectionHealth.test.ts
@@ -0,0 +1,78 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+import { act, cleanup, renderHook, waitFor } from '@testing-library/react';
+import { useConnectionHealth } from './useConnectionHealth';
+import { useConnectionStore } from '@/stores/connectionStore';
+
+beforeEach(() => {
+  useConnectionStore.setState({
+    kind: null,
+    serverUri: null,
+    browseContainerPath: null,
+    browseFocusPath: null,
+    localRoot: null,
+    localRel: null,
+    label: null,
+    sampleCount: null,
+    status: 'unknown',
+  });
+  vi.stubGlobal('fetch', vi.fn());
+});
+
+afterEach(() => {
+  cleanup();
+  vi.unstubAllGlobals();
+  vi.useRealTimers();
+});
+
+describe('useConnectionHealth', () => {
+  it('stays unknown and never fetches for a local connection', async () => {
+    useConnectionStore.setState({ kind: 'local' });
+    renderHook(() => useConnectionHealth());
+    await Promise.resolve();
+    expect(fetch).not.toHaveBeenCalled();
+    expect(useConnectionStore.getState().status).toBe('unknown');
+  });
+
+  it('stays unknown when there is no connection at all', async () => {
+    renderHook(() => useConnectionHealth());
+    await Promise.resolve();
+    expect(fetch).not.toHaveBeenCalled();
+    expect(useConnectionStore.getState().status).toBe('unknown');
+  });
+
+  it('sets status to ok on a successful tiled health check', async () => {
+    (fetch as any).mockResolvedValue({ ok: true, json: async () => [] });
+    useConnectionStore.setState({ kind: 'tiled', serverUri: 'http://tiled.example' });
+    renderHook(() => useConnectionHealth());
+    await waitFor(() => expect(useConnectionStore.getState().status).toBe('ok'));
+    expect(fetch).toHaveBeenCalledWith(expect.stringContaining('/api/tiled/list?'));
+    expect(fetch).toHaveBeenCalledWith(expect.stringContaining('server_uri=http'));
+  });
+
+  it('sets status to error on a failed response', async () => {
+    (fetch as any).mockResolvedValue({ ok: false });
+    useConnectionStore.setState({ kind: 'tiled', serverUri: 'http://tiled.example' });
+    renderHook(() => useConnectionHealth());
+    await waitFor(() => expect(useConnectionStore.getState().status).toBe('error'));
+  });
+
+  it('sets status to error when fetch throws', async () => {
+    (fetch as any).mockRejectedValue(new Error('network down'));
+    useConnectionStore.setState({ kind: 'tiled', serverUri: 'http://tiled.example' });
+    renderHook(() => useConnectionHealth());
+    await waitFor(() => expect(useConnectionStore.getState().status).toBe('error'));
+  });
+
+  it('resets to unknown when the connection is cleared', async () => {
+    (fetch as any).mockResolvedValue({ ok: true, json: async () => [] });
+    useConnectionStore.setState({ kind: 'tiled', serverUri: 'http://tiled.example' });
+    const { rerender } = renderHook(() => useConnectionHealth());
+    await waitFor(() => expect(useConnectionStore.getState().status).toBe('ok'));
+
+    act(() => {
+      useConnectionStore.setState({ kind: null, serverUri: null });
+      rerender();
+    });
+    await waitFor(() => expect(useConnectionStore.getState().status).toBe('unknown'));
+  });
+});
diff --git a/frontend/src/hooks/useConnectionHealth.ts b/frontend/src/hooks/useConnectionHealth.ts
new file mode 100644
index 0000000..dca220d
--- /dev/null
+++ b/frontend/src/hooks/useConnectionHealth.ts
@@ -0,0 +1,62 @@
+/**
+ * useConnectionHealth — periodic Tiled reachability check, backed by
+ * connectionStore's `status` field. Mounted once in the app shell so
+ * connection health is visible from anywhere (HubHeader), not just Browse's
+ * own local facets poll or Connect's initial probe.
+ *
+ * Local connections have no network dependency to check, so status simply
+ * stays 'unknown' (hidden in the UI) for kind === 'local' or no connection.
+ */
+import { useEffect, useRef } from 'react';
+import { API_BASE } from '@/config';
+import { useConnectionStore } from '@/stores/connectionStore';
+
+const BASE_INTERVAL_MS = 20_000;
+const MAX_INTERVAL_MS = 120_000;
+
+export function useConnectionHealth(): void {
+  const kind = useConnectionStore((s) => s.kind);
+  const serverUri = useConnectionStore((s) => s.serverUri);
+  const setStatus = useConnectionStore((s) => s.setStatus);
+  const intervalRef = useRef(BASE_INTERVAL_MS);
+
+  useEffect(() => {
+    if (kind !== 'tiled') {
+      setStatus('unknown');
+      return;
+    }
+
+    let cancelled = false;
+    let timer: ReturnType<typeof setTimeout> | null = null;
+    intervalRef.current = BASE_INTERVAL_MS;
+
+    const check = async () => {
+      try {
+        const params = new URLSearchParams({ path: '' });
+        if (serverUri) params.set('server_uri', serverUri);
+        const res = await fetch(`${API_BASE}/api/tiled/list?${params}`);
+        if (cancelled) return;
+        if (res.ok) {
+          setStatus('ok');
+          intervalRef.current = BASE_INTERVAL_MS;
+        } else {
+          setStatus('error');
+          intervalRef.current = Math.min(intervalRef.current * 2, MAX_INTERVAL_MS);
+        }
+      } catch {
+        if (cancelled) return;
+        setStatus('error');
+        intervalRef.current = Math.min(intervalRef.current * 2, MAX_INTERVAL_MS);
+      } finally {
+        if (!cancelled) timer = setTimeout(check, intervalRef.current);
+      }
+    };
+
+    void check();
+
+    return () => {
+      cancelled = true;
+      if (timer) clearTimeout(timer);
+    };
+  }, [kind, serverUri, setStatus]);
+}
diff --git a/frontend/src/lib/displayPrefs.test.ts b/frontend/src/lib/displayPrefs.test.ts
new file mode 100644
index 0000000..355f774
--- /dev/null
+++ b/frontend/src/lib/displayPrefs.test.ts
@@ -0,0 +1,39 @@
+import { afterEach, describe, expect, it } from 'vitest';
+import { loadDisplayPrefs, saveDisplayPrefs, type DisplayPrefs } from './displayPrefs';
+
+const FULL: DisplayPrefs = {
+  brightness: 10,
+  contrast: -5,
+  levelsLo: 20,
+  levelsHi: 230,
+  gamma: 1.2,
+  colormap: 'viridis',
+  clahe: true,
+  sharpen: false,
+  blur: 1.5,
+};
+
+afterEach(() => {
+  localStorage.clear();
+});
+
+describe('displayPrefs', () => {
+  it('returns an empty object when nothing has been saved', () => {
+    expect(loadDisplayPrefs()).toEqual({});
+  });
+
+  it('round-trips a saved prefs object', () => {
+    saveDisplayPrefs(FULL);
+    expect(loadDisplayPrefs()).toEqual(FULL);
+  });
+
+  it('returns an empty object for corrupt stored JSON instead of throwing', () => {
+    localStorage.setItem('finch:displayPrefs', '{not json');
+    expect(loadDisplayPrefs()).toEqual({});
+  });
+
+  it('returns an empty object when the stored value is not an object', () => {
+    localStorage.setItem('finch:displayPrefs', '"just a string"');
+    expect(loadDisplayPrefs()).toEqual({});
+  });
+});
diff --git a/frontend/src/lib/displayPrefs.ts b/frontend/src/lib/displayPrefs.ts
new file mode 100644
index 0000000..84eedff
--- /dev/null
+++ b/frontend/src/lib/displayPrefs.ts
@@ -0,0 +1,43 @@
+/**
+ * Persists the purely cosmetic display sliders (brightness/contrast/levels/
+ * gamma/colormap/clahe/sharpen/blur) across reloads, scoped globally as a
+ * viewer preference — not per-sample, not part of the draft. `denoise` is
+ * deliberately excluded: it's a real training input tuned to one volume's
+ * noise level, and carrying it over to a different volume would silently
+ * mis-filter it (see datasetStore.ts's own note on why it resets).
+ */
+import type { ColormapName } from '@/lib/colormaps';
+
+export interface DisplayPrefs {
+  brightness: number;
+  contrast: number;
+  levelsLo: number;
+  levelsHi: number;
+  gamma: number;
+  colormap: ColormapName;
+  clahe: boolean;
+  sharpen: boolean;
+  blur: number;
+}
+
+const STORAGE_KEY = 'finch:displayPrefs';
+
+export function loadDisplayPrefs(): Partial<DisplayPrefs> {
+  try {
+    const raw = localStorage.getItem(STORAGE_KEY);
+    if (!raw) return {};
+    const parsed = JSON.parse(raw);
+    return parsed && typeof parsed === 'object' ? parsed : {};
+  } catch {
+    return {};
+  }
+}
+
+export function saveDisplayPrefs(prefs: DisplayPrefs): void {
+  try {
+    localStorage.setItem(STORAGE_KEY, JSON.stringify(prefs));
+  } catch {
+    // Storage unavailable (private browsing, quota, etc.) — cosmetic prefs
+    // just won't persist this session; nothing to recover from.
+  }
+}
diff --git a/frontend/src/stores/connectionStore.test.ts b/frontend/src/stores/connectionStore.test.ts
new file mode 100644
index 0000000..8bcbeee
--- /dev/null
+++ b/frontend/src/stores/connectionStore.test.ts
@@ -0,0 +1,56 @@
+import { afterEach, describe, expect, it } from 'vitest';
+import { useConnectionStore } from './connectionStore';
+
+const INITIAL = useConnectionStore.getState();
+
+afterEach(() => {
+  useConnectionStore.setState(INITIAL, true);
+});
+
+describe('connectionStore', () => {
+  it('starts with no connection and status unknown', () => {
+    const s = useConnectionStore.getState();
+    expect(s.kind).toBeNull();
+    expect(s.status).toBe('unknown');
+  });
+
+  it('setConnection resets status to unknown', () => {
+    useConnectionStore.getState().setStatus('ok');
+    useConnectionStore.getState().setConnection({
+      kind: 'tiled',
+      serverUri: 'http://example',
+      label: 'Example',
+      sampleCount: 3,
+    });
+    const s = useConnectionStore.getState();
+    expect(s.kind).toBe('tiled');
+    expect(s.status).toBe('unknown');
+  });
+
+  it('setStatus updates status independently of other fields', () => {
+    useConnectionStore.getState().setConnection({
+      kind: 'tiled',
+      serverUri: 'http://example',
+      label: 'Example',
+      sampleCount: 3,
+    });
+    useConnectionStore.getState().setStatus('error');
+    const s = useConnectionStore.getState();
+    expect(s.status).toBe('error');
+    expect(s.serverUri).toBe('http://example');
+  });
+
+  it('clearConnection resets status to unknown along with everything else', () => {
+    useConnectionStore.getState().setConnection({
+      kind: 'tiled',
+      serverUri: 'http://example',
+      label: 'Example',
+      sampleCount: 3,
+    });
+    useConnectionStore.getState().setStatus('ok');
+    useConnectionStore.getState().clearConnection();
+    const s = useConnectionStore.getState();
+    expect(s.kind).toBeNull();
+    expect(s.status).toBe('unknown');
+  });
+});
diff --git a/frontend/src/stores/connectionStore.ts b/frontend/src/stores/connectionStore.ts
index 56df832..7d2971f 100644
--- a/frontend/src/stores/connectionStore.ts
+++ b/frontend/src/stores/connectionStore.ts
@@ -27,6 +27,13 @@ export interface ConnectionState {
   label: string | null;
   /** Total number of samples reported by /api/connect/summary */
   sampleCount: number | null;
+  /**
+   * Live Tiled reachability, driven by a periodic health check (see
+   * useConnectionHealth). 'unknown' until the first check resolves, and
+   * always 'unknown' for local connections (no network dependency to check).
+   */
+  status: 'unknown' | 'ok' | 'error';
+  setStatus: (status: 'unknown' | 'ok' | 'error') => void;
   setConnection: (payload: {
     kind: 'tiled' | 'local';
     serverUri?: string | null;
@@ -49,6 +56,9 @@ export const useConnectionStore = create<ConnectionState>((set) => ({
   localRel: null,
   label: null,
   sampleCount: null,
+  status: 'unknown',
+
+  setStatus: (status) => set({ status }),
 
   /** Records the active data-source connection; unspecified fields default to null. */
   setConnection: ({
@@ -70,6 +80,7 @@ export const useConnectionStore = create<ConnectionState>((set) => ({
       localRel,
       label,
       sampleCount,
+      status: 'unknown',
     }),
 
   /** Resets all connection fields to null (disconnect). */
@@ -82,6 +93,7 @@ export const useConnectionStore = create<ConnectionState>((set) => ({
       localRoot: null,
       localRel: null,
       label: null,
+      status: 'unknown',
       sampleCount: null,
     }),
 }));

From 04e930781e7f16d2c3c5525f7ad3801b5bf05424 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 9 Sep 2026 12:49:28 -0700
Subject: [PATCH 26/38] fixing tiled sync for prediction masks

---
 backend/tests/test_tiled_mask_sync.py         | 22 +++++++
 backend/tiled_mask_sync.py                    | 13 ++++
 frontend/src/app/pages/AnnotatePage.tsx       |  7 ++
 frontend/src/app/pages/VolumePage.tsx         | 36 +++++++++-
 .../annotate/Toolbar/index.test.tsx           | 53 +++++++++++++++
 .../src/components/annotate/Toolbar/index.tsx | 35 ++++++++--
 .../volume/RebuildVolumeControl.tsx           |  2 +-
 frontend/src/lib/bandTransferFunction.test.ts | 65 +++++++++++++++++++
 frontend/src/lib/bandTransferFunction.ts      | 53 +++++++++++++++
 9 files changed, 280 insertions(+), 6 deletions(-)
 create mode 100644 frontend/src/lib/bandTransferFunction.test.ts
 create mode 100644 frontend/src/lib/bandTransferFunction.ts

diff --git a/backend/tests/test_tiled_mask_sync.py b/backend/tests/test_tiled_mask_sync.py
index 6288a25..c66d4b4 100644
--- a/backend/tests/test_tiled_mask_sync.py
+++ b/backend/tests/test_tiled_mask_sync.py
@@ -331,6 +331,28 @@ def test_shape_mismatch_discards_existing_and_replaces(self, fake_client, monkey
         assert info["n_slices"] == 1
         assert info["updated"] == 1
 
+    def test_slice_count_mismatch_discards_existing_and_replaces(
+        self, fake_client, monkeypatch, stub_register_mask_pyramid,
+    ):
+        # Metadata claims 2 slices (9 and 10) but the registered semantic array
+        # only actually holds 1 frame — e.g. stale metadata from a prior
+        # interrupted/partial write. Merging positionally by slice_indices would
+        # index past the array (`old_sem[1]` on a size-1 array) and crash with a
+        # numpy IndexError; this must be treated the same as an H/W mismatch.
+        short_semantic = np.zeros((1, H, W), dtype=np.uint8)
+        existing_container = FakeMaskContainer(
+            metadata={"legend": [], "slice_indices": [9, 10]},
+            children={"semantic": object()},
+        )
+        monkeypatch.setattr(tiled_mask_sync.mask_pyramid, "read_mask_scale0", lambda container, key: short_semantic)
+        fake_client._children["img__masks"] = existing_container
+
+        info = tiled_mask_sync.write_masks_to_tiled("browse/ds/img", None, self._volumes_dict(), _classes())
+        # Old slices 9/10 are gone entirely — replaced, not merged, due to the
+        # slice-count mismatch. Confirms no IndexError was raised too.
+        assert info["n_slices"] == 1
+        assert info["updated"] == 1
+
     def test_returns_correct_summary_counts(self, fake_client, stub_register_mask_pyramid):
         info = tiled_mask_sync.write_masks_to_tiled("browse/ds/img", None, self._volumes_dict(), _classes())
         assert info["n_classes"] == 2  # Cell + Wall
diff --git a/backend/tiled_mask_sync.py b/backend/tiled_mask_sync.py
index 9ecec08..be1ff78 100644
--- a/backend/tiled_mask_sync.py
+++ b/backend/tiled_mask_sync.py
@@ -300,6 +300,19 @@ def write_masks_to_tiled(
     if existing is not None and existing["semantic"].shape[1:] != volumes["semantic"].shape[1:]:
         logger.warning("mask merge: shape changed for %s — replacing existing masks", source)
         existing = None
+    # slice_indices (container metadata) and the registered semantic array's own
+    # slice count can disagree — e.g. a prior interrupted/partial write, or stale
+    # metadata left over from before a fix landed — and merge_mask_volumes indexes
+    # the array positionally by `enumerate(slice_indices)`, so a longer metadata
+    # list than the array actually holds raises "index N is out of bounds for
+    # axis 0" deep inside the merge. Same "can't trust it, replace" contract as
+    # the H/W-mismatch guard above, rather than crashing the whole write.
+    if existing is not None and existing["semantic"].shape[0] != len(existing["slice_indices"]):
+        logger.warning(
+            "mask merge: slice_indices (%d) != stored semantic slices (%d) for %s — replacing existing masks",
+            len(existing["slice_indices"]), existing["semantic"].shape[0], source,
+        )
+        existing = None
 
     merged = merge_mask_volumes(existing, volumes)
 
diff --git a/frontend/src/app/pages/AnnotatePage.tsx b/frontend/src/app/pages/AnnotatePage.tsx
index 27eceea..72e03d4 100644
--- a/frontend/src/app/pages/AnnotatePage.tsx
+++ b/frontend/src/app/pages/AnnotatePage.tsx
@@ -408,6 +408,12 @@ export default function AnnotatePage() {
     navigate('/volume?mask=fast');
   }, [navigate]);
 
+  /** Isolate a Sampler-fitted intensity band in the 3D transfer function —
+   *  see VolumePage's bandLo/bandHi query-param handling. */
+  const handleSendBandTo3D = useCallback((lo: number, hi: number) => {
+    navigate(`/volume?bandLo=${lo}&bandHi=${hi}`);
+  }, [navigate]);
+
   const classLabelForId = useCallback(
     (classId: number) => {
       const c = classes.find((x) => x.classId === classId);
@@ -549,6 +555,7 @@ export default function AnnotatePage() {
                 upscale={upscale}
                 samplerFit={samplerFit}
                 onRevertSamplerFit={revertSamplerFit}
+                onSendBandTo3D={handleSendBandTo3D}
               />
               <hr />
               <DisplayControls
diff --git a/frontend/src/app/pages/VolumePage.tsx b/frontend/src/app/pages/VolumePage.tsx
index 5eca1d9..f3ae527 100644
--- a/frontend/src/app/pages/VolumePage.tsx
+++ b/frontend/src/app/pages/VolumePage.tsx
@@ -31,6 +31,7 @@ import VolumeViewer, { webGpuAvailability, type WebGpuViewerInstance } from '@/c
 import BuildVolumePanel from '@/components/volume/BuildVolumePanel';
 import MaskLayersPanel from '@/components/volume/MaskLayersPanel';
 import RebuildVolumeControl from '@/components/volume/RebuildVolumeControl';
+import { buildBandOpacityCurve } from '@/lib/bandTransferFunction';
 
 /** Shape of `GET /api/volume/resolve`. */
 interface VolumeNode {
@@ -95,6 +96,36 @@ export default function VolumePage() {
   // page stuck on its error for every dataset opened afterwards.
   useEffect(() => setBootError(null), [source, serverUri]);
 
+  // "View band in 3D" hand-off from Annotate's Sampler tool (a fitted
+  // intensity band, native 0-255 space) arrives as `?bandLo=&bandHi=` —
+  // isolate it in the transfer function once the viewer is ready.
+  // See bandTransferFunction.ts and the plan's #21 threshold-fit bridge.
+  //
+  // The vendored viewer restores a per-sample cached transfer function from
+  // localStorage the moment the first real data level streams in (a one-shot
+  // "boot restore" internal to the renderer, with no public event exposed to
+  // detect it — WebGpuViewerInstance has no "level loaded"/histogram-ready
+  // signal) — which races with and can silently clobber the band we just
+  // set. There's no way to sequence after that restore from the app today,
+  // so re-apply on a bounded retry schedule that comfortably outlasts a
+  // typical first-level load; each retry is cheap (a state merge +
+  // re-render), so the redundant calls cost nothing.
+  const bandLoParam = searchParams.get('bandLo');
+  const bandHiParam = searchParams.get('bandHi');
+  useEffect(() => {
+    if (!viewerInstance || bandLoParam == null || bandHiParam == null) return;
+    const lo = Number(bandLoParam);
+    const hi = Number(bandHiParam);
+    if (!Number.isFinite(lo) || !Number.isFinite(hi) || hi <= lo) return;
+    const curve = buildBandOpacityCurve(lo, hi);
+    const apply = () => {
+      viewerInstance.setRendering({ ...viewerInstance.getRendering(), opacityPoints: curve });
+    };
+    const retryDelaysMs = [0, 200, 500, 1000, 2000, 4000, 8000];
+    const timers = retryDelaysMs.map((ms) => setTimeout(apply, ms));
+    return () => timers.forEach(clearTimeout);
+  }, [viewerInstance, bandLoParam, bandHiParam]);
+
   // Fallback only: a dataset opened against the default local Tiled carries a
   // null serverUri, and the resolved address (whatever port start_all.sh
   // actually bound) lives here. Never assume 8010.
@@ -200,7 +231,10 @@ export default function VolumePage() {
         nSlices={meta?.nSlices}
       />
       {node?.mode === 'sidecar' && source && (
-        <div className="pointer-events-none absolute right-3 top-3 z-10">
+        // Bottom-left: the vendor's own docked HUD (Data/TF/Render tabs) spans
+        // the whole right column, and MaskLayersPanel already owns top-left —
+        // this is the one corner clear of both.
+        <div className="pointer-events-none absolute left-3 bottom-3 z-10">
           <RebuildVolumeControl
             source={source}
             serverUri={resolvedUri}
diff --git a/frontend/src/components/annotate/Toolbar/index.test.tsx b/frontend/src/components/annotate/Toolbar/index.test.tsx
index e28bd97..3e63983 100644
--- a/frontend/src/components/annotate/Toolbar/index.test.tsx
+++ b/frontend/src/components/annotate/Toolbar/index.test.tsx
@@ -211,6 +211,59 @@ describe('Toolbar', () => {
       render(<Toolbar samplerFit={{ collapsed: true, lo: 10, hi: 20 } as any} />);
       expect(screen.getByText('Band not applied')).toBeInTheDocument();
     });
+
+    it('offers "View band in 3D" for a plain (non-projected) fit and sends the native lo/hi', async () => {
+      const user = userEvent.setup();
+      const onSendBandTo3D = vi.fn();
+      useToolStore.setState({ tool: 'threshold' });
+      render(
+        <Toolbar
+          samplerFit={{
+            mode: 'plain',
+            collapsed: false,
+            lo: 10,
+            hi: 200,
+            displayLo: 40,
+            displayHi: 230,
+            dice: 0.9,
+            skill: 0.8,
+            coverage: 0.5,
+            extraSigma: 0,
+            appliedBlur: 0,
+          } as any}
+          onSendBandTo3D={onSendBandTo3D}
+        />,
+      );
+      const button = screen.getByRole('button', { name: /view band in 3d/i });
+      await user.click(button);
+      // Native (lo/hi), not the displayed range shown in the readout above it.
+      expect(onSendBandTo3D).toHaveBeenCalledWith(10, 200);
+    });
+
+    it('does not offer "View band in 3D" for a texture-projected fit', () => {
+      useToolStore.setState({ tool: 'threshold' });
+      render(
+        <Toolbar
+          samplerFit={{
+            mode: 'projected',
+            collapsed: false,
+            lo: 10,
+            hi: 200,
+            displayLo: 10,
+            displayHi: 200,
+            dice: 0.9,
+            skill: 0.8,
+            coverage: 0.5,
+            extraSigma: 0,
+            appliedBlur: 0,
+            intensitySkill: 0.4,
+            weights: [],
+          } as any}
+          onSendBandTo3D={vi.fn()}
+        />,
+      );
+      expect(screen.queryByRole('button', { name: /view band in 3d/i })).not.toBeInTheDocument();
+    });
   });
 
   describe('select panel', () => {
diff --git a/frontend/src/components/annotate/Toolbar/index.tsx b/frontend/src/components/annotate/Toolbar/index.tsx
index 97dfe33..98c5074 100644
--- a/frontend/src/components/annotate/Toolbar/index.tsx
+++ b/frontend/src/components/annotate/Toolbar/index.tsx
@@ -4,7 +4,7 @@
  *           f=fill, s=select, g=magic, m=magnetic, Space=pan (hold), x=next slice,
  *           t=fit to screen, Ctrl/Cmd+Z=undo
  */
-import { Hand, Cursor, Polygon, MagnetStraight, MagicWand, Rectangle, Circle, PaintBrush, Drop, Eyedropper, PaintBucket, Eraser, ArrowBendUpLeft, ArrowBendUpRight, ArrowCounterClockwise } from '@phosphor-icons/react';
+import { Hand, Cursor, Polygon, MagnetStraight, MagicWand, Rectangle, Circle, PaintBrush, Drop, Eyedropper, PaintBucket, Eraser, ArrowBendUpLeft, ArrowBendUpRight, ArrowCounterClockwise, Cube } from '@phosphor-icons/react';
 import { useMemo } from 'react';
 import { useStore } from 'zustand';
 import { useToolStore, type Tool } from '@/stores/toolStore';
@@ -95,7 +95,16 @@ function ToolButton({ tool, label, icon, keybind, activeTool, disabled, onSelect
 }
 
 /** Readout for one Sampler fit: what it chose, how well it did, and an undo. */
-function SamplerResult({ fit, onRevert }: { fit: SamplerFit; onRevert?: () => void }) {
+function SamplerResult({
+  fit,
+  onRevert,
+  onSendBandTo3D,
+}: {
+  fit: SamplerFit;
+  onRevert?: () => void;
+  /** Isolate this band in the 3D transfer function (native lo/hi, 0–255). */
+  onSendBandTo3D?: (lo: number, hi: number) => void;
+}) {
   const { label, quality } = describeFit(fit);
   const tone =
     quality === 'good' ? 'text-emerald-600' : quality === 'fair' ? 'text-amber-600' : 'text-red-600';
@@ -149,6 +158,20 @@ function SamplerResult({ fit, onRevert }: { fit: SamplerFit; onRevert?: () => vo
         covers {(fit.coverage * 100).toFixed(0)}%
         {fit.extraSigma > 0 && ` · blur ${fit.appliedBlur.toFixed(2)}`}
       </span>
+      {!projected && onSendBandTo3D && (
+        // Only a plain-intensity fit is expressible in the 3D viewer, which
+        // has just the raw scalar per voxel — a texture-projected band has
+        // no equivalent there.
+        <button
+          type="button"
+          onClick={() => onSendBandTo3D(fit.lo, fit.hi)}
+          title={`Isolate intensity ${fit.lo}–${fit.hi} in the 3D transfer function (opaque inside, transparent outside)`}
+          className="flex w-fit items-center gap-1 rounded px-1 py-0.5 text-[10px] text-sky-700 hover:bg-sky-100"
+        >
+          <Cube size={11} />
+          View band in 3D
+        </button>
+      )}
       {projected && (
         // The gate is no longer brightness, which changes how the rest of the
         // panel behaves — say so rather than letting it be discovered.
@@ -171,6 +194,8 @@ interface ToolbarProps {
   samplerFit?: SamplerFit | null;
   /** Restore the band/blur that were in force before the last fit. */
   onRevertSamplerFit?: () => void;
+  /** Isolate the last fitted band in the 3D transfer function (native lo/hi, 0–255). */
+  onSendBandTo3D?: (lo: number, hi: number) => void;
   /** 256-bin luminance histogram of the current slice — drives the threshold band
    *  picker. Owned by AnnotatePage (the canvas emits it); null before load. It is
    *  sampled from the PREPROCESSED base, so it must be remapped through the display
@@ -185,7 +210,7 @@ interface ToolbarProps {
 /** Renders the tool radiogroup, undo/redo, and the active tool's parameter controls. */
 export default function Toolbar({
   disabled = false, histogramBins = null, display, upscale = 1,
-  samplerFit = null, onRevertSamplerFit,
+  samplerFit = null, onRevertSamplerFit, onSendBandTo3D,
 }: ToolbarProps) {
   const {
     tool, setTool, brushSize, setBrushSize, fillThreshold, setFillThreshold,
@@ -369,7 +394,9 @@ export default function Toolbar({
               avoid the ring just outside — which also highlights similar features elsewhere.
               Nothing is annotated.
             </p>
-            {samplerFit && <SamplerResult fit={samplerFit} onRevert={onRevertSamplerFit} />}
+            {samplerFit && (
+              <SamplerResult fit={samplerFit} onRevert={onRevertSamplerFit} onSendBandTo3D={onSendBandTo3D} />
+            )}
           </div>
 
           <HistogramControl
diff --git a/frontend/src/components/volume/RebuildVolumeControl.tsx b/frontend/src/components/volume/RebuildVolumeControl.tsx
index a349e7e..1337cfc 100644
--- a/frontend/src/components/volume/RebuildVolumeControl.tsx
+++ b/frontend/src/components/volume/RebuildVolumeControl.tsx
@@ -79,7 +79,7 @@ export default function RebuildVolumeControl({ source, serverUri, onRebuilt }: R
   const percent = job && job.total > 0 ? Math.round((job.done / job.total) * 100) : 0;
 
   return (
-    <div className="pointer-events-auto flex flex-col items-end gap-1">
+    <div className="pointer-events-auto flex flex-col items-start gap-1">
       <button
         type="button"
         disabled={running}
diff --git a/frontend/src/lib/bandTransferFunction.test.ts b/frontend/src/lib/bandTransferFunction.test.ts
new file mode 100644
index 0000000..9953aea
--- /dev/null
+++ b/frontend/src/lib/bandTransferFunction.test.ts
@@ -0,0 +1,65 @@
+import { describe, expect, it } from 'vitest';
+import { buildBandOpacityCurve } from './bandTransferFunction';
+
+/** Every curve must have strictly increasing x across its points. */
+function expectMonotonic(points: readonly (readonly [number, number])[]) {
+  for (let i = 1; i < points.length; i++) {
+    expect(points[i][0]).toBeGreaterThan(points[i - 1][0]);
+  }
+}
+
+/** Linear interpolation, mirroring how a GPU sampler would read the curve. */
+function opacityAt(points: readonly (readonly [number, number])[], x: number): number {
+  if (x <= points[0][0]) return points[0][1];
+  for (let i = 1; i < points.length; i++) {
+    if (x <= points[i][0]) {
+      const [x0, o0] = points[i - 1];
+      const [x1, o1] = points[i];
+      const t = (x - x0) / (x1 - x0);
+      return o0 + (o1 - o0) * t;
+    }
+  }
+  return points[points.length - 1][1];
+}
+
+describe('buildBandOpacityCurve', () => {
+  it('is opaque in the middle of the band and transparent well outside it', () => {
+    const points = buildBandOpacityCurve(80, 180);
+    expectMonotonic(points);
+    expect(opacityAt(points, 130 / 255)).toBeCloseTo(1, 5);
+    expect(opacityAt(points, 0)).toBeCloseTo(0, 5);
+    expect(opacityAt(points, 1)).toBeCloseTo(0, 5);
+  });
+
+  it('normalizes 0-255 input into the 0-1 domain the viewer expects', () => {
+    const points = buildBandOpacityCurve(80, 180);
+    for (const [x] of points) {
+      expect(x).toBeGreaterThanOrEqual(0);
+      expect(x).toBeLessThanOrEqual(1);
+    }
+  });
+
+  it('swaps a reversed lo/hi so the band is always well-formed', () => {
+    const swapped = buildBandOpacityCurve(180, 80);
+    const normal = buildBandOpacityCurve(80, 180);
+    expect(swapped).toEqual(normal);
+  });
+
+  it('handles a band starting at 0 without producing non-monotonic points', () => {
+    const points = buildBandOpacityCurve(0, 100);
+    expectMonotonic(points);
+    expect(opacityAt(points, 50 / 255)).toBeCloseTo(1, 5);
+  });
+
+  it('handles a band ending at 255 without producing non-monotonic points', () => {
+    const points = buildBandOpacityCurve(150, 255);
+    expectMonotonic(points);
+    expect(opacityAt(points, 1)).toBeCloseTo(1, 5);
+  });
+
+  it('handles the full-range band (0-255) as fully opaque throughout', () => {
+    const points = buildBandOpacityCurve(0, 255);
+    expectMonotonic(points);
+    expect(opacityAt(points, 0.5)).toBeCloseTo(1, 5);
+  });
+});
diff --git a/frontend/src/lib/bandTransferFunction.ts b/frontend/src/lib/bandTransferFunction.ts
new file mode 100644
index 0000000..04adba2
--- /dev/null
+++ b/frontend/src/lib/bandTransferFunction.ts
@@ -0,0 +1,53 @@
+/**
+ * bandTransferFunction — bridges the Sampler lasso's fitted intensity band
+ * (`thresholdFit.ts`'s `BandFit.lo`/`hi`, in the volume's own 0–255 space)
+ * into the 3D viewer's opacity-curve transfer function, so a sparse,
+ * materially-distinct feature can be isolated in 3D by intensity alone —
+ * opaque inside the band, transparent outside — with zero upstream viewer
+ * changes (see the vendored `OpacityPoint = [intensity: 0-1, opacity: 0-1]`
+ * curve format).
+ *
+ * Deliberately narrow in scope: this only isolates by intensity VALUE across
+ * the whole volume, not by the traced SPATIAL region specifically. Good
+ * enough for a feature whose density is genuinely distinct from its
+ * surroundings (the same property the 2D threshold-lasso tool already
+ * exploits) — see the plan's own trade-off note.
+ */
+
+export type OpacityPoint = readonly [intensity: number, opacity: number];
+
+function clamp01(v: number): number {
+  return Math.min(1, Math.max(0, v));
+}
+
+/**
+ * Builds an opacity curve that ramps to fully opaque across `[lo, hi]`
+ * (given in 0–255 space) and stays transparent everywhere else, with a small
+ * ramp at each edge (rather than a hard step) so the transfer function
+ * doesn't alias.
+ */
+export function buildBandOpacityCurve(lo255: number, hi255: number): OpacityPoint[] {
+  const EPS = 0.004; // ~1 intensity level at 8-bit resolution
+  const loN = clamp01(Math.min(lo255, hi255) / 255);
+  const hiN = clamp01(Math.max(lo255, hi255) / 255);
+
+  const raw: OpacityPoint[] = [
+    [0, 0],
+    [loN, loN <= EPS ? 1 : 0],
+    [Math.min(1, loN + EPS), 1],
+    [Math.max(0, hiN - EPS), 1],
+    [hiN, hiN >= 1 - EPS ? 1 : 0],
+    [1, hiN >= 1 - EPS ? 1 : 0],
+  ];
+
+  // Points must have strictly increasing x for the viewer's curve to be
+  // well-defined — clamping near 0/1 can collapse several of the above onto
+  // the same x. Sort, then keep only the first point at each x.
+  const sorted = [...raw].sort((a, b) => a[0] - b[0]);
+  const points: OpacityPoint[] = [];
+  for (const p of sorted) {
+    if (points.length > 0 && p[0] <= points[points.length - 1][0]) continue;
+    points.push(p);
+  }
+  return points;
+}

From c5a18225bc96bafb1415c7f8205864b3566674bf Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 9 Sep 2026 14:52:25 -0700
Subject: [PATCH 27/38] Fixing 3D layers for dlsia

---
 backend/annotation_server.py                  |   2 +
 backend/schemas.py                            |  10 ++
 ...st_annotation_server_local_image_routes.py |   9 ++
 frontend/src/app/pages/VolumePage.tsx         |  31 ++--
 .../src/components/volume/MaskLayersPanel.tsx | 141 +++++++++++++-----
 frontend/src/hooks/useOpenInAnnotate.ts       |   2 +
 frontend/src/lib/bandTransferFunction.test.ts |  57 +++++--
 frontend/src/lib/bandTransferFunction.ts      |  73 +++++++--
 frontend/src/stores/datasetStore.ts           |   7 +
 frontend/vendor/view_tomography_recon_app     |   2 +-
 10 files changed, 258 insertions(+), 76 deletions(-)

diff --git a/backend/annotation_server.py b/backend/annotation_server.py
index 8198e0f..693a124 100644
--- a/backend/annotation_server.py
+++ b/backend/annotation_server.py
@@ -588,6 +588,7 @@ def _run() -> ImageMeta:
         meta = arrays_mod.array_shape_meta(node, pyramid)
         sl = arrays_mod.read_slice(node, meta, 0)
         flat = sl.ravel().astype(float)
+        global_range = images_mod._sample_global_stats(node, meta) if not meta["is_rgb"] else None
         return ImageMeta(
             n_slices=meta["n_slices"],
             height=meta["height"],
@@ -595,6 +596,7 @@ def _run() -> ImageMeta:
             dtype=meta["dtype"],
             is_rgb=meta["is_rgb"],
             value_range=[float(flat.min()), float(flat.max())],
+            global_value_range=list(global_range) if global_range is not None else None,
             keywords=arrays_mod.node_keywords(node),
             level_key=meta.get("level_key"),
             level_index=meta.get("level_index"),
diff --git a/backend/schemas.py b/backend/schemas.py
index 79007c8..d485987 100644
--- a/backend/schemas.py
+++ b/backend/schemas.py
@@ -429,6 +429,15 @@ class ImageMeta(BaseModel):
         dtype: NumPy dtype string (e.g. ``"float32"``).
         is_rgb: ``True`` if the array has a colour channel dimension.
         value_range: ``[min, max]`` of the first slice.
+        global_value_range: ``[vmin, vmax]`` actually used by
+            ``GET /api/image/slice``'s default ``norm="global"`` rendering —
+            the 1st/99th percentile (by default) sampled across up to 64
+            slices spanning the whole volume (see ``images._sample_global_stats``),
+            cached 5 minutes. Distinct from ``value_range`` above (which is
+            just slice 0's raw min/max): this is the real contrast window the
+            2D canvas's pixel bytes are normalized against, needed by anything
+            that must convert a displayed 0-255 value back to a physical
+            intensity (e.g. the Sampler-fitted band sent to the 3D viewer).
         keywords: Dataset tags stored at ingest; each is pre-created as an
             annotation class in the Annotate tab.
         level_key: For a multiscale Zarr volume, the pyramid level being read
@@ -449,6 +458,7 @@ class ImageMeta(BaseModel):
     dtype: str
     is_rgb: bool
     value_range: list[float]
+    global_value_range: list[float] | None = None
     keywords: list[str] = Field(default_factory=list)
     level_key: str | None = None
     level_index: int | None = None
diff --git a/backend/tests/test_annotation_server_local_image_routes.py b/backend/tests/test_annotation_server_local_image_routes.py
index 71ce552..c588063 100644
--- a/backend/tests/test_annotation_server_local_image_routes.py
+++ b/backend/tests/test_annotation_server_local_image_routes.py
@@ -261,6 +261,15 @@ async def test_returns_shape_and_value_range(self, client, fake_image_source):
         assert body["value_range"] == [0.0, 99.0]
         assert body["keywords"] == ["tag1"]
 
+    @pytest.mark.asyncio
+    async def test_includes_global_value_range_used_by_slice_rendering(self, client, fake_image_source, monkeypatch):
+        monkeypatch.setattr(images_mod, "_sample_global_stats", lambda node, meta: (-73.0, 71.3))
+        response = await client.get(
+            "/api/image/meta", params={"source": "local:foo.tif", "kind": "local"},
+        )
+        assert response.status_code == 200
+        assert response.json()["global_value_range"] == [-73.0, 71.3]
+
     @pytest.mark.asyncio
     async def test_resolve_failure_is_500(self, client, monkeypatch):
         monkeypatch.setattr(
diff --git a/frontend/src/app/pages/VolumePage.tsx b/frontend/src/app/pages/VolumePage.tsx
index f3ae527..c5d468f 100644
--- a/frontend/src/app/pages/VolumePage.tsx
+++ b/frontend/src/app/pages/VolumePage.tsx
@@ -31,7 +31,7 @@ import VolumeViewer, { webGpuAvailability, type WebGpuViewerInstance } from '@/c
 import BuildVolumePanel from '@/components/volume/BuildVolumePanel';
 import MaskLayersPanel from '@/components/volume/MaskLayersPanel';
 import RebuildVolumeControl from '@/components/volume/RebuildVolumeControl';
-import { buildBandOpacityCurve } from '@/lib/bandTransferFunction';
+import { buildBandOpacityCurve, mapByteBandToViewerDomain } from '@/lib/bandTransferFunction';
 
 /** Shape of `GET /api/volume/resolve`. */
 interface VolumeNode {
@@ -97,9 +97,19 @@ export default function VolumePage() {
   useEffect(() => setBootError(null), [source, serverUri]);
 
   // "View band in 3D" hand-off from Annotate's Sampler tool (a fitted
-  // intensity band, native 0-255 space) arrives as `?bandLo=&bandHi=` —
-  // isolate it in the transfer function once the viewer is ready.
-  // See bandTransferFunction.ts and the plan's #21 threshold-fit bridge.
+  // intensity band, in the 2D canvas's own 0-255 byte space) arrives as
+  // `?bandLo=&bandHi=` — isolate it in the transfer function once the viewer
+  // is ready. See bandTransferFunction.ts and the plan's #21 threshold-fit
+  // bridge.
+  //
+  // The byte band does NOT translate to the viewer's [0,1] domain by a plain
+  // /255 — the 2D canvas's bytes are normalized against `meta.globalValueRange`
+  // (a percentile stretch), while the 3D viewer normalizes raw voxels against
+  // its OWN, separately-estimated `getValueRange()` — different statistics
+  // computed from different data, typically close but never identical.
+  // `mapByteBandToViewerDomain` converts byte -> raw physical value -> the
+  // viewer's actual domain, rather than assuming the two normalizations agree
+  // (confirmed live: assuming they did produced a wildly wrong band).
   //
   // The vendored viewer restores a per-sample cached transfer function from
   // localStorage the moment the first real data level streams in (a one-shot
@@ -114,17 +124,18 @@ export default function VolumePage() {
   const bandHiParam = searchParams.get('bandHi');
   useEffect(() => {
     if (!viewerInstance || bandLoParam == null || bandHiParam == null) return;
-    const lo = Number(bandLoParam);
-    const hi = Number(bandHiParam);
-    if (!Number.isFinite(lo) || !Number.isFinite(hi) || hi <= lo) return;
-    const curve = buildBandOpacityCurve(lo, hi);
+    const loByte = Number(bandLoParam);
+    const hiByte = Number(bandHiParam);
+    if (!Number.isFinite(loByte) || !Number.isFinite(hiByte) || hiByte <= loByte) return;
     const apply = () => {
-      viewerInstance.setRendering({ ...viewerInstance.getRendering(), opacityPoints: curve });
+      const viewerRange = viewerInstance.getValueRange();
+      const [lo01, hi01] = mapByteBandToViewerDomain(loByte, hiByte, meta?.globalValueRange, viewerRange);
+      viewerInstance.setRendering({ ...viewerInstance.getRendering(), opacityPoints: buildBandOpacityCurve(lo01, hi01) });
     };
     const retryDelaysMs = [0, 200, 500, 1000, 2000, 4000, 8000];
     const timers = retryDelaysMs.map((ms) => setTimeout(apply, ms));
     return () => timers.forEach(clearTimeout);
-  }, [viewerInstance, bandLoParam, bandHiParam]);
+  }, [viewerInstance, bandLoParam, bandHiParam, meta?.globalValueRange]);
 
   // Fallback only: a dataset opened against the default local Tiled carries a
   // null serverUri, and the resolved address (whatever port start_all.sh
diff --git a/frontend/src/components/volume/MaskLayersPanel.tsx b/frontend/src/components/volume/MaskLayersPanel.tsx
index 0a4c425..4e4b689 100644
--- a/frontend/src/components/volume/MaskLayersPanel.tsx
+++ b/frontend/src/components/volume/MaskLayersPanel.tsx
@@ -51,6 +51,16 @@ interface SlotState {
   classes: MaskClasses;
   /** Ignored for slots where `canLive` is false. */
   mode: SourceMode;
+  /** Set once the loading poll (see `waitForClasses`) has been running a
+   *  while — a "hasn't shown up yet" hint, not an error, since a large Tiled
+   *  mask (e.g. a 690-slice dlsia result) can legitimately take a while over
+   *  the network. */
+  slowLoad: boolean;
+  /** True only when `error` came from `waitForClasses` timing out — the
+   *  underlying (fire-and-forget) load may still finish moments later, so
+   *  this is the one error case worth offering a re-check for. Other errors
+   *  (no source open, nothing annotated yet) won't change by re-polling. */
+  canRetry: boolean;
 }
 
 const initialSlotState: SlotState = {
@@ -61,6 +71,8 @@ const initialSlotState: SlotState = {
   opacity: 0.6,
   classes: [],
   mode: 'live',
+  slowLoad: false,
+  canRetry: false,
 };
 
 interface MaskLayersPanelProps {
@@ -116,6 +128,38 @@ export default function MaskLayersPanel({
 
   if (!instance) return null;
 
+  /** Shared tail for both load paths: poll for classes, softening to a
+   *  "still loading" hint rather than a bare spinner once it's taking a
+   *  while, and update state on both success and eventual (rare) failure. */
+  const pollForClasses = async (cfg: SlotConfig, noClassesMessage: string | null) => {
+    setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], loading: true, slowLoad: false, canRetry: false } }));
+    try {
+      const classes = await waitForClasses(instance, cfg.slot, {
+        onSlow: () => setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], slowLoad: true } })),
+      });
+      setState((s) => ({
+        ...s,
+        [cfg.slot]: {
+          ...s[cfg.slot], loading: false, loaded: true, classes, slowLoad: false, canRetry: false,
+          error: classes.length === 0 ? noClassesMessage : null,
+        },
+      }));
+    } catch (err) {
+      setState((s) => ({
+        ...s,
+        [cfg.slot]: {
+          ...s[cfg.slot], loading: false, slowLoad: false, canRetry: true,
+          error: err instanceof Error ? err.message : String(err),
+        },
+      }));
+    }
+  };
+
+  /** Re-poll without re-triggering the underlying load — for when
+   *  `waitForClasses`'s own (generous) budget ran out, but the fire-and-forget
+   *  load may well have finished moments later in the background regardless. */
+  const checkAgain = (cfg: SlotConfig) => void pollForClasses(cfg, null);
+
   const load = async (cfg: SlotConfig, modeOverride?: SourceMode) => {
     const mode = modeOverride ?? state[cfg.slot].mode;
 
@@ -126,49 +170,28 @@ export default function MaskLayersPanel({
       if (!volume) {
         setState((s) => ({
           ...s,
-          [cfg.slot]: { ...s[cfg.slot], error: 'Nothing annotated for this sample yet.', loading: false },
+          [cfg.slot]: { ...s[cfg.slot], error: 'Nothing annotated for this sample yet.', loading: false, canRetry: false },
         }));
         return;
       }
-      setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], loading: true, error: null } }));
-      try {
-        instance.loadMaskFromArray(cfg.slot, volume.data, volume.dims);
-        const classes = await waitForClasses(instance, cfg.slot);
-        setState((s) => ({
-          ...s,
-          [cfg.slot]: { ...s[cfg.slot], loading: false, loaded: true, classes, error: null },
-        }));
-      } catch (err) {
-        setState((s) => ({
-          ...s,
-          [cfg.slot]: { ...s[cfg.slot], loading: false, error: err instanceof Error ? err.message : String(err) },
-        }));
-      }
+      instance.loadMaskFromArray(cfg.slot, volume.data, volume.dims);
+      await pollForClasses(cfg, null);
       return;
     }
 
     const { url, reason } = buildMaskZarrUrl(kind, source, serverUri, cfg.suffix);
     if (!url) {
-      setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], error: reason ?? 'No source open', loading: false } }));
-      return;
-    }
-    setState((s) => ({ ...s, [cfg.slot]: { ...s[cfg.slot], loading: true, error: null } }));
-    try {
-      instance.loadMask(cfg.slot, url);
-      // loadMask is fire-and-forget on the instance (see the upstream brief:
-      // internal state, not a returned promise) — poll briefly for classes to
-      // appear rather than assuming synchronous completion.
-      const classes = await waitForClasses(instance, cfg.slot);
-      setState((s) => ({
-        ...s,
-        [cfg.slot]: { ...s[cfg.slot], loading: false, loaded: true, classes, error: classes.length === 0 ? 'Loaded, but no classes found (every voxel is background, or nothing has been pushed to Tiled for this dataset yet).' : null },
-      }));
-    } catch (err) {
       setState((s) => ({
         ...s,
-        [cfg.slot]: { ...s[cfg.slot], loading: false, error: err instanceof Error ? err.message : String(err) },
+        [cfg.slot]: { ...s[cfg.slot], error: reason ?? 'No source open', loading: false, canRetry: false },
       }));
+      return;
     }
+    instance.loadMask(cfg.slot, url);
+    await pollForClasses(
+      cfg,
+      'Loaded, but no classes found (every voxel is background, or nothing has been pushed to Tiled for this dataset yet).',
+    );
   };
 
   const remove = (cfg: SlotConfig) => {
@@ -225,7 +248,7 @@ export default function MaskLayersPanel({
                   className="flex items-center gap-1 rounded bg-sky-600 px-2 py-0.5 text-xs font-medium text-white hover:bg-sky-500 disabled:opacity-50"
                 >
                   {s.loading && <CircleNotch size={12} className="animate-spin" />}
-                  {s.loading ? 'Loading…' : 'Load'}
+                  {s.loading ? (s.slowLoad ? 'Still loading…' : 'Loading…') : 'Load'}
                 </button>
               )}
             </div>
@@ -251,7 +274,20 @@ export default function MaskLayersPanel({
               </div>
             )}
 
-            {s.error && <p className="mb-2 text-xs text-amber-300">{s.error}</p>}
+            {s.error && (
+              <div className="mb-2 flex flex-col gap-1">
+                <p className="text-xs text-amber-300">{s.error}</p>
+                {s.canRetry && (
+                  <button
+                    type="button"
+                    onClick={() => checkAgain(cfg)}
+                    className="w-fit rounded px-2 py-0.5 text-xs text-sky-300 hover:bg-sky-900/60"
+                  >
+                    Check again
+                  </button>
+                )}
+              </div>
+            )}
 
             {s.loaded && s.classes.length > 0 && (
               <div className="flex flex-col gap-1.5">
@@ -300,21 +336,46 @@ export default function MaskLayersPanel({
 
 /**
  * `loadMask` mutates viewer-internal state asynchronously with no returned
- * promise (see the upstream brief's Step 3 — it's fire-and-forget on the
- * public interface, matching how the HUD's own click handler calls it).
- * Poll `getMaskClasses` briefly rather than assuming it's ready on the next
- * tick — mirrors how `InferencePanel`/`useExportJob` poll a job status
- * elsewhere in this app rather than trusting synchronous completion.
+ * promise and no error/progress signal on the public interface (see the
+ * upstream brief's Step 3 — it's fire-and-forget, matching how the HUD's own
+ * click handler calls it, and `getMaskClasses` is the only observable outcome:
+ * `undefined` covers both "still loading" and "failed" indistinguishably).
+ * Poll rather than assuming synchronous completion — mirrors how
+ * `InferencePanel`/`useExportJob` poll a job status elsewhere in this app.
+ *
+ * Budget is generous (5 minutes) rather than the original 5 seconds: a real
+ * Tiled-backed "Deep" mask (e.g. a 690-slice dlsia result, freshly written)
+ * can legitimately take much longer than a few seconds to fetch over the
+ * network, and a short timeout here doesn't stop the load — it keeps running
+ * in the vendored viewer regardless — it just makes OUR panel declare defeat
+ * (and hide the opacity/visibility controls) while the mask goes on to load
+ * and render successfully moments later, exactly the bug this widening fixes.
+ * `onSlow` fires once after `slowAfterMs` so the caller can soften the UI
+ * from a bare spinner into an explicit "still loading" hint, since 5 minutes
+ * of unexplained silence would look broken even though it's still working.
  */
 async function waitForClasses(
   instance: WebGpuViewerInstance,
   slot: 0 | 1,
-  { attempts = 50, intervalMs = 100 }: { attempts?: number; intervalMs?: number } = {},
+  {
+    attempts = 300,
+    intervalMs = 1000,
+    slowAfterMs = 5000,
+    onSlow,
+  }: { attempts?: number; intervalMs?: number; slowAfterMs?: number; onSlow?: () => void } = {},
 ): Promise<MaskClasses> {
+  let slowFired = false;
   for (let i = 0; i < attempts; i++) {
     const classes = instance.getMaskClasses(slot);
     if (classes !== undefined) return classes;
+    if (!slowFired && i * intervalMs >= slowAfterMs) {
+      slowFired = true;
+      onSlow?.();
+    }
     await new Promise((r) => setTimeout(r, intervalMs));
   }
-  throw new Error('Timed out waiting for the mask to load — check the browser console for a renderer-side error.');
+  throw new Error(
+    'Still waiting for the mask to load after 5 minutes — it may finish shortly on its own ' +
+      '(click "Check again" below), or check the browser console for a renderer-side error.',
+  );
 }
diff --git a/frontend/src/hooks/useOpenInAnnotate.ts b/frontend/src/hooks/useOpenInAnnotate.ts
index efebd2f..8e9eeaf 100644
--- a/frontend/src/hooks/useOpenInAnnotate.ts
+++ b/frontend/src/hooks/useOpenInAnnotate.ts
@@ -86,6 +86,7 @@ export function useOpenInAnnotate() {
         dtype: meta.dtype,
         isRgb: meta.is_rgb,
         valueRange: meta.value_range,
+        globalValueRange: meta.global_value_range ?? null,
         // Multiscale volumes: width/height/nSlices above are the FINEST level's
         // (annotations live in full-res coordinates); these say what is drawn.
         levelKey: meta.level_key ?? null,
@@ -129,6 +130,7 @@ export function useOpenInAnnotate() {
         dtype: meta.dtype,
         isRgb: meta.is_rgb,
         valueRange: meta.value_range,
+        globalValueRange: meta.global_value_range ?? null,
       });
 
       const sourceKey = buildSourceKey('local', relPath);
diff --git a/frontend/src/lib/bandTransferFunction.test.ts b/frontend/src/lib/bandTransferFunction.test.ts
index 9953aea..b8adee2 100644
--- a/frontend/src/lib/bandTransferFunction.test.ts
+++ b/frontend/src/lib/bandTransferFunction.test.ts
@@ -1,5 +1,5 @@
 import { describe, expect, it } from 'vitest';
-import { buildBandOpacityCurve } from './bandTransferFunction';
+import { buildBandOpacityCurve, mapByteBandToViewerDomain } from './bandTransferFunction';
 
 /** Every curve must have strictly increasing x across its points. */
 function expectMonotonic(points: readonly (readonly [number, number])[]) {
@@ -24,15 +24,15 @@ function opacityAt(points: readonly (readonly [number, number])[], x: number): n
 
 describe('buildBandOpacityCurve', () => {
   it('is opaque in the middle of the band and transparent well outside it', () => {
-    const points = buildBandOpacityCurve(80, 180);
+    const points = buildBandOpacityCurve(80 / 255, 180 / 255);
     expectMonotonic(points);
     expect(opacityAt(points, 130 / 255)).toBeCloseTo(1, 5);
     expect(opacityAt(points, 0)).toBeCloseTo(0, 5);
     expect(opacityAt(points, 1)).toBeCloseTo(0, 5);
   });
 
-  it('normalizes 0-255 input into the 0-1 domain the viewer expects', () => {
-    const points = buildBandOpacityCurve(80, 180);
+  it('stays within the 0-1 domain the viewer expects', () => {
+    const points = buildBandOpacityCurve(80 / 255, 180 / 255);
     for (const [x] of points) {
       expect(x).toBeGreaterThanOrEqual(0);
       expect(x).toBeLessThanOrEqual(1);
@@ -40,26 +40,61 @@ describe('buildBandOpacityCurve', () => {
   });
 
   it('swaps a reversed lo/hi so the band is always well-formed', () => {
-    const swapped = buildBandOpacityCurve(180, 80);
-    const normal = buildBandOpacityCurve(80, 180);
+    const swapped = buildBandOpacityCurve(180 / 255, 80 / 255);
+    const normal = buildBandOpacityCurve(80 / 255, 180 / 255);
     expect(swapped).toEqual(normal);
   });
 
   it('handles a band starting at 0 without producing non-monotonic points', () => {
-    const points = buildBandOpacityCurve(0, 100);
+    const points = buildBandOpacityCurve(0, 100 / 255);
     expectMonotonic(points);
     expect(opacityAt(points, 50 / 255)).toBeCloseTo(1, 5);
   });
 
-  it('handles a band ending at 255 without producing non-monotonic points', () => {
-    const points = buildBandOpacityCurve(150, 255);
+  it('handles a band ending at 1 without producing non-monotonic points', () => {
+    const points = buildBandOpacityCurve(150 / 255, 1);
     expectMonotonic(points);
     expect(opacityAt(points, 1)).toBeCloseTo(1, 5);
   });
 
-  it('handles the full-range band (0-255) as fully opaque throughout', () => {
-    const points = buildBandOpacityCurve(0, 255);
+  it('handles the full-range band (0-1) as fully opaque throughout', () => {
+    const points = buildBandOpacityCurve(0, 1);
     expectMonotonic(points);
     expect(opacityAt(points, 0.5)).toBeCloseTo(1, 5);
   });
 });
+
+describe('mapByteBandToViewerDomain', () => {
+  it('converts a byte band through the global range into the viewer range', () => {
+    // Global range matches the real petiole sample from live testing.
+    const globalRange: [number, number] = [-73.0, 71.3];
+    const viewerRange: [number, number] = [-40, 40];
+    const [lo, hi] = mapByteBandToViewerDomain(194, 215, globalRange, viewerRange);
+    // raw ≈ -73 + (194/255)*144.3 ≈ 36.8 ; -73 + (215/255)*144.3 ≈ 48.6
+    // domain ≈ (36.8+40)/80 ≈ 0.960 ; (48.6+40)/80 clamped to 1
+    expect(lo).toBeCloseTo(0.96, 1);
+    expect(hi).toBe(1);
+  });
+
+  it('falls back to treating the byte scale as already-physical when globalRange is missing', () => {
+    const viewerRange: [number, number] = [0, 255];
+    const [lo, hi] = mapByteBandToViewerDomain(0, 255, null, viewerRange);
+    expect(lo).toBeCloseTo(0, 5);
+    expect(hi).toBeCloseTo(1, 5);
+  });
+
+  it('clamps to [0,1] when the converted value falls outside the viewer range', () => {
+    const globalRange: [number, number] = [0, 255];
+    const viewerRange: [number, number] = [100, 200];
+    const [lo, hi] = mapByteBandToViewerDomain(0, 255, globalRange, viewerRange);
+    expect(lo).toBe(0);
+    expect(hi).toBe(1);
+  });
+
+  it('ignores a degenerate (zero-span or inverted) global range and falls back to [0,255]', () => {
+    const viewerRange: [number, number] = [0, 255];
+    const [lo, hi] = mapByteBandToViewerDomain(0, 255, [10, 10], viewerRange);
+    expect(lo).toBeCloseTo(0, 5);
+    expect(hi).toBeCloseTo(1, 5);
+  });
+});
diff --git a/frontend/src/lib/bandTransferFunction.ts b/frontend/src/lib/bandTransferFunction.ts
index 04adba2..62f75fa 100644
--- a/frontend/src/lib/bandTransferFunction.ts
+++ b/frontend/src/lib/bandTransferFunction.ts
@@ -1,17 +1,30 @@
 /**
  * bandTransferFunction — bridges the Sampler lasso's fitted intensity band
- * (`thresholdFit.ts`'s `BandFit.lo`/`hi`, in the volume's own 0–255 space)
- * into the 3D viewer's opacity-curve transfer function, so a sparse,
- * materially-distinct feature can be isolated in 3D by intensity alone —
- * opaque inside the band, transparent outside — with zero upstream viewer
- * changes (see the vendored `OpacityPoint = [intensity: 0-1, opacity: 0-1]`
- * curve format).
+ * (`thresholdFit.ts`'s `BandFit.lo`/`hi`) into the 3D viewer's opacity-curve
+ * transfer function, so a sparse, materially-distinct feature can be
+ * isolated in 3D by intensity alone — opaque inside the band, transparent
+ * outside — with zero upstream viewer changes (see the vendored
+ * `OpacityPoint = [intensity: 0-1, opacity: 0-1]` curve format).
  *
  * Deliberately narrow in scope: this only isolates by intensity VALUE across
  * the whole volume, not by the traced SPATIAL region specifically. Good
  * enough for a feature whose density is genuinely distinct from its
  * surroundings (the same property the 2D threshold-lasso tool already
  * exploits) — see the plan's own trade-off note.
+ *
+ * A real gotcha this module exists to fix: `BandFit.lo`/`hi` are 0-255 BYTE
+ * values from the 2D Annotate canvas's own PNG rendering — normalized via
+ * `GET /api/image/slice`'s norm="global" percentile stretch (see
+ * `ImageMeta.globalValueRange`), NOT the volume's raw physical intensity.
+ * The 3D viewer separately normalizes raw voxel values into its own [0,1]
+ * domain against a DIFFERENT per-dataset range (`WebGpuViewerInstance
+ * .getValueRange()`, a min/max-based estimate — see that method's own doc).
+ * These two ranges are typically close but never identical (different
+ * statistics, computed from different data), so a fitted band must be
+ * converted byte -> raw physical value -> 3D domain, not just divided by
+ * 255 — dividing by 255 assumes the two normalizations are the same, which
+ * silently produces a wildly wrong band (confirmed live: a materially-sparse
+ * 2D-highlighted feature came out as almost the entire volume in 3D).
  */
 
 export type OpacityPoint = readonly [intensity: number, opacity: number];
@@ -21,15 +34,47 @@ function clamp01(v: number): number {
 }
 
 /**
- * Builds an opacity curve that ramps to fully opaque across `[lo, hi]`
- * (given in 0–255 space) and stays transparent everywhere else, with a small
- * ramp at each edge (rather than a hard step) so the transfer function
- * doesn't alias.
+ * Converts a fitted band's byte bounds (0-255, the 2D canvas's own
+ * percentile-stretched display space) into the 3D viewer's [0,1] domain.
+ *
+ * `globalRange` is `ImageMeta.globalValueRange` — the `[vmin, vmax]` the 2D
+ * backend actually stretched into 0-255; falls back to treating the byte
+ * scale as already-physical (`[0, 255]`) if unavailable (e.g. an RGB source,
+ * which the backend doesn't compute this for), matching this module's older,
+ * pre-conversion behavior.
+ *
+ * `viewerRange` is `WebGpuViewerInstance.getValueRange()` — the actual range
+ * the 3D viewer normalizes raw voxels against for this dataset.
+ */
+export function mapByteBandToViewerDomain(
+  loByte: number,
+  hiByte: number,
+  globalRange: readonly [number, number] | null | undefined,
+  viewerRange: readonly [number, number],
+): [number, number] {
+  const [gMin, gMax] =
+    globalRange && Number.isFinite(globalRange[0]) && Number.isFinite(globalRange[1]) && globalRange[1] > globalRange[0]
+      ? globalRange
+      : [0, 255];
+  const toRaw = (byte: number) => gMin + (byte / 255) * (gMax - gMin);
+
+  const [vMin, vMax] = viewerRange;
+  const span = vMax - vMin || 1;
+  const toDomain = (raw: number) => clamp01((raw - vMin) / span);
+
+  return [toDomain(toRaw(loByte)), toDomain(toRaw(hiByte))];
+}
+
+/**
+ * Builds an opacity curve that ramps to fully opaque across `[lo01, hi01]`
+ * (already in the viewer's own [0,1] domain — see `mapByteBandToViewerDomain`)
+ * and stays transparent everywhere else, with a small ramp at each edge
+ * (rather than a hard step) so the transfer function doesn't alias.
  */
-export function buildBandOpacityCurve(lo255: number, hi255: number): OpacityPoint[] {
-  const EPS = 0.004; // ~1 intensity level at 8-bit resolution
-  const loN = clamp01(Math.min(lo255, hi255) / 255);
-  const hiN = clamp01(Math.max(lo255, hi255) / 255);
+export function buildBandOpacityCurve(lo01: number, hi01: number): OpacityPoint[] {
+  const EPS = 0.004;
+  const loN = clamp01(Math.min(lo01, hi01));
+  const hiN = clamp01(Math.max(lo01, hi01));
 
   const raw: OpacityPoint[] = [
     [0, 0],
diff --git a/frontend/src/stores/datasetStore.ts b/frontend/src/stores/datasetStore.ts
index 68fcf22..05de0cc 100644
--- a/frontend/src/stores/datasetStore.ts
+++ b/frontend/src/stores/datasetStore.ts
@@ -10,6 +10,13 @@ export interface ImageMeta {
   dtype: string;
   isRgb: boolean;
   valueRange: [number, number];
+  /** The [vmin, vmax] actually used by `/api/image/slice`'s norm="global"
+   *  rendering (a 1st/99th-percentile stretch across the whole volume) —
+   *  distinct from `valueRange` above (slice 0's raw min/max). Needed to
+   *  convert a displayed 0-255 byte value back to a physical intensity, e.g.
+   *  the Sampler-fitted band sent to the 3D viewer. `null` for RGB sources
+   *  (the backend doesn't compute it for those). */
+  globalValueRange?: [number, number] | null;
   /** Multiscale (Zarr) volumes only — which pyramid level is being displayed.
    *  `width`/`height`/`nSlices` above always describe the FINEST level, because
    *  annotations are stored in full-resolution coordinates whichever level is
diff --git a/frontend/vendor/view_tomography_recon_app b/frontend/vendor/view_tomography_recon_app
index 547baf0..a7e3a43 160000
--- a/frontend/vendor/view_tomography_recon_app
+++ b/frontend/vendor/view_tomography_recon_app
@@ -1 +1 @@
-Subproject commit 547baf04e2b70142c4505bac1af714d78c48d55c
+Subproject commit a7e3a433753ef46f00445ddd6b2cb733325e0162

From 28973131a7a260fb8cea5a25bcaa0c6b75c818f5 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Thu, 10 Sep 2026 11:25:16 -0700
Subject: [PATCH 28/38] Updating docuemntation, peter's bucket fill suggestion

---
 docs/guide/index.md                           |  10 +-
 docs/guide/train.md                           | 130 ++++++++
 docs/guide/volume.md                          | 116 +++++++
 docs/reference/architecture.md                | 298 ++++++++++++++++--
 .../annotate/AnnotationCanvas/index.tsx       |  16 +-
 frontend/src/lib/magicwand.test.ts            |  30 ++
 frontend/src/lib/magicwand.ts                 |  10 +-
 mkdocs.yml                                    |   2 +
 8 files changed, 580 insertions(+), 32 deletions(-)
 create mode 100644 docs/guide/train.md
 create mode 100644 docs/guide/volume.md

diff --git a/docs/guide/index.md b/docs/guide/index.md
index 1c86815..b8c43ad 100644
--- a/docs/guide/index.md
+++ b/docs/guide/index.md
@@ -12,7 +12,7 @@ Every screen shares the same layout:
 - **Header** — the ALS logo and the app title, *"Segmentation Annotation Tool"*.
 - **Main area** — the current tab's content.
 
-The sidebar contains four tabs by default:
+The sidebar contains six tabs by default:
 
 | Tab | Icon | What it's for |
 | --- | --- | --- |
@@ -20,6 +20,12 @@ The sidebar contains four tabs by default:
 | **Browse** | magnifier | Filter and pick a sample to annotate. |
 | **Reference** | book | Write class descriptions (the annotation guide). |
 | **Annotate** | pencil | Draw masks, manage versions, and export. |
+| **3D** | cube | View the reconstruction and mask layers in 3D. |
+| **Train** | brain | Train a dlsia deep-learning segmentation model and run it on a whole volume. |
+
+A small indicator in the header shows live Tiled connection status (green
+"Tiled connected" / red "Tiled disconnected" — click it to jump back to
+Connect if it drops).
 
 !!! tip "Customize which tabs you see"
     Click the floating **Customize Layout** button (top-right) to open
@@ -33,3 +39,5 @@ The sidebar contains four tabs by default:
 3. [Annotate](annotate.md) — the core drawing workflow.
 4. [Annotation guide](reference-guide.md) — keep multi-annotator projects consistent.
 5. [Export & download](export.md) — produce a COCO dataset.
+6. [Train a deep model](train.md) — optional, for a volume-wide model beyond the fast in-tab classifier.
+7. [3D volume view](volume.md) — optional, for viewing the reconstruction and mask layers in 3D.
diff --git a/docs/guide/train.md b/docs/guide/train.md
new file mode 100644
index 0000000..a342ccc
--- /dev/null
+++ b/docs/guide/train.md
@@ -0,0 +1,130 @@
+# 6. Train a deep model
+
+The **Train** tab fine-tunes a real deep-learning segmentation model (a
+**dlsia TUNet**) on this session's annotated samples, then runs it across a
+whole volume — a different, slower, more accurate path than the Annotate
+tab's own fast in-browser pixel classifier (the **Predict** stage under
+Assist/Predict). Use Train when you want a model that generalizes well beyond
+the slices you've personally annotated, or that you plan to reuse across
+sessions.
+
+!!! note "This is optional"
+    Nothing else in the tool requires the Train tab. If the fast pixel
+    classifier already gives you good enough results, you never need to open
+    this page.
+
+## Is training available?
+
+At the top of the tab, a status banner reports readiness:
+
+- **Ready**: *"torch {version} · {device}"* (e.g. `mps`, `cuda`, or `cpu`) and
+  *"dlsia (TUNet): available"*.
+- **Unavailable**: *"Training is unavailable on this server."* — the server
+  wasn't started with ML dependencies installed (the Docker image
+  intentionally omits them to stay lightweight; run via `start_all.sh` with
+  `INSTALL_ML=1` — the default on Apple Silicon — on a machine that can
+  install them).
+- If a job is already running, the banner adds *"A training/inference job is
+  currently running."* — only one training or inference job runs at a time.
+
+---
+
+## Training data
+
+The **Training data** panel lists every sample you've annotated **this
+session**, with its shape count, as checkboxes:
+
+- If nothing shows up: *"No annotated samples yet this session. Annotate a
+  few slices in the Annotate tab, then come back here."*
+- Check the samples to include; the header shows *"N of M selected."*
+
+!!! tip
+    More annotated slices across more samples generally makes for a better
+    model — this is real deep-learning training, not the fast classifier's
+    per-sample fit.
+
+### Train on denoised input (optional)
+
+If you've tuned a denoise filter in the Annotate tab's Display panel, a
+**"Train on denoised input"** checkbox appears, naming the exact filter (e.g.
+*"(median, 40%)"*) rather than offering a second, separate copy of the
+controls. This is **off by default** and changes what the model actually
+*learns* — unlike every other denoise control in the app, which is
+display-only. The setting is recorded on the saved run, and inference
+automatically reapplies the same filter, so training and prediction can never
+disagree about what the model is looking at.
+
+If no denoise filter is set (or the current one can't be used for training),
+the checkbox is disabled with an explanation instead of silently doing
+nothing.
+
+---
+
+## Hyperparameters (advanced)
+
+Collapsed by default — sensible defaults are supplied, so most users never
+need to open this. When you do:
+
+| Field | What it controls |
+| --- | --- |
+| **Run name** | Optional label; auto-generated if left blank. |
+| **Epochs**, **Learning rate** | Standard training controls. |
+| **Batch size** | How many patches process at once. Click **Estimate max** to have the server run real training steps at increasing batch sizes and find the largest that fits your GPU/memory — this needs the device to itself, so it's disabled while another job is running. |
+| **Patch/Image size (px)** | The window size the model trains on. |
+| **Random flip augmentation** | Cheap data augmentation. |
+| **Tile large images** | **On** (default): cuts native-resolution patches with 25% overlap and blends predictions back together — keeps fine detail on images larger than the patch size. **Off**: shrinks each whole slice to the patch size before training — faster, but loses detail on large images. |
+| **Depth**, **Base channels**, **Growth rate** | TUNet architecture parameters. |
+
+---
+
+## Start training
+
+Click **Start training**. A progress bar tracks epochs and (once available)
+mIoU; cancel is cooperative — a slice already inside a GPU forward pass
+finishes before honoring cancel.
+
+## Saved runs
+
+Every completed (or cancelled-but-partial) run appears under **Saved runs**:
+model family, timestamp, mIoU, epochs completed, whether it was trained on
+denoised input, and its class list. Select a run's radio button to use it for
+inference below. Click the trash icon to permanently delete a run's saved
+weights (asks for confirmation, naming the run precisely since two runs
+trained close together can otherwise look identical).
+
+---
+
+## Inference
+
+With a saved run selected and a sample open (in Browse/Annotate), pick a
+scope:
+
+- **Current slice** — just the slice open in the viewer.
+- **Slice range** — a start/end slice.
+- **All slices** — the whole volume.
+
+Click **Run inference**. Progress shows *"{done}/{total} slices"* with a live
+log of region counts per slice, and **the preview slider becomes usable as
+soon as the first slice finishes** — it keeps extending as more slices
+complete while the job is still running, not just after it finishes.
+**Cancel** stops a running job cooperatively.
+
+Once done (or, for slices already predicted, while still running), you get:
+
+- **Import as annotations** — vectorizes the predicted regions into real,
+  editable shapes back in the Annotate tab.
+- **Write masks to Tiled** *(Tiled sources only)* — writes the prediction
+  directly into a `<source>__masks_deep` container in Tiled, independent of
+  whatever the Annotate tab's own "Push masks to Tiled" wrote — so the 3D
+  view's **Deep** mask layer and **Fast** mask layer can show two genuinely
+  different results side by side. See [3D volume view](volume.md#mask-layers).
+
+!!! tip "Large inference jobs can take a while to write"
+    A big "Write masks to Tiled" write (hundreds of slices) can legitimately
+    take a while over the network. If the 3D view's mask panel shows *"Still
+    loading…"* rather than an error, it's still working — a **Check again**
+    button appears if it's still not done after a very generous wait.
+
+---
+
+Next: [3D volume view →](volume.md)
diff --git a/docs/guide/volume.md b/docs/guide/volume.md
new file mode 100644
index 0000000..fdeac59
--- /dev/null
+++ b/docs/guide/volume.md
@@ -0,0 +1,116 @@
+# 7. 3D volume view
+
+The **3D** tab renders the open dataset's reconstruction and mask layers
+directly in 3D, streamed straight from Tiled — there is no export step. It
+uses WebGPU, so it needs a modern Chromium-based browser (Chrome or Edge
+113+) served over a secure context (`https://`, or `localhost`/`127.0.0.1`).
+
+!!! note "This is optional"
+    Nothing else in the tool requires the 3D view — it's there for inspecting
+    results in context, not for annotating.
+
+## No volume yet?
+
+A dataset ingested as individual 2D slices has no 3D pyramid until one is
+built. If you see **"No 3D volume for this dataset yet"**, the panel shows the
+source shape/dtype and the pyramid it will build, then a **Build 3D volume**
+button. This reads every slice once; full resolution is **not** copied — it
+stays where it is, and the 3D view never loads a level too large for the GPU.
+
+If a volume already exists but needs refreshing (e.g. after a fidelity setting
+changed), a small **Rebuild volume** button sits in the bottom-left corner of
+the 3D view itself.
+
+---
+
+## The render HUD
+
+A docked sidebar on the right controls how the volume looks:
+
+| Panel | Controls |
+| --- | --- |
+| **Data** | Resolution/LOD picker, voxel size, high-res ROI streaming. |
+| **Transfer Function** | Colormap, opacity curve (drag/add/remove points on the histogram), color range (with **Auto**/**Equalize**), clip limit. Also has a **Bands** mode for multiple independent intensity ranges. |
+| **Slices** | Axis-aligned slice planes through the volume. |
+| **Crop** | An ROI crop box. |
+| **Measure** | On-canvas measurement between points in the volume. |
+| **Annotations** | The built-in mask-layer controls (this app also has its own, described below — either produces the identical visual result). |
+| **Presets** | Save/apply/delete named transfer-function presets. |
+
+Pan with space+drag, shift/middle/right-wheel zoom to the cursor; ++p++/++ctrl+click++
+picks a point, ++l++ cycles LOD, ++o++ toggles open/collapsed.
+
+---
+
+## Mask layers
+
+A small panel in the top-left corner controls two independent, fixed mask
+layers:
+
+- **Fast (iPred)** — the Annotate tab's quick pixel classifier / manual
+  annotations.
+- **Deep (dlsia)** — a [Train tab](train.md) model's output.
+
+Each has its own **Load** button, and (once loaded) an opacity slider and
+per-class visibility toggles (click a class row to show/hide it).
+
+### Live vs. Tiled (Fast slot only)
+
+The Fast slot defaults to **Live** mode — it rasterizes your **current**
+annotation shapes directly in the browser and loads instantly, with **no
+Tiled sync required first**. Switch to **Tiled** to load the precise,
+backend-rasterized result from `Push masks to Tiled` in the Annotate tab
+instead. The Deep slot has no Live equivalent — it's always the saved output
+of a from-scratch-trained model, loaded from the `<source>__masks_deep`
+container [Train's inference panel](train.md#inference) writes.
+
+!!! tip "Loading a large Deep mask can take a while"
+    A real Tiled-backed mask (hundreds of slices, freshly written) can
+    legitimately take a while to fetch over the network. The **Load** button
+    shows *"Loading…"* then *"Still loading…"* rather than failing outright —
+    if it's genuinely still not done after a very generous wait, a **Check
+    again** button appears rather than making you re-fetch from scratch.
+
+### Getting here from elsewhere
+
+- Annotate's iPred panel has its own **Push to Tiled** (stays on the tab) and
+  **View in 3D** (navigates here, loading the Fast layer) — split into two
+  independent actions so pushing doesn't force you into the 3D view, and
+  viewing doesn't force a fresh push.
+- Train's inference panel's **Write masks to Tiled** writes the Deep layer
+  independently, so Fast and Deep can show two genuinely different results
+  side by side for comparison.
+
+---
+
+## Isolating a feature by intensity (Sampler → 3D bridge)
+
+If you've used the Annotate tab's Sampler/Threshold-lasso tool (see
+[Annotate → The Magic tool](annotate.md#the-magic-tool-smart-ai-classic) for
+the sibling Magic tool, or the Threshold Brush's own **Set band from a
+region**) to fit an intensity band around a small, materially-distinct
+feature, its readout gains a **View band in 3D** button (for a plain,
+non-projected fit only). Clicking it isolates that intensity band here:
+opaque inside the band, transparent outside, via the same **Transfer
+Function** the HUD's own panel controls.
+
+!!! note "This isolates by intensity, not by traced region"
+    It shows *everything* in that density range across the whole volume, not
+    only the specific spot you traced — genuinely useful when the feature's
+    density is distinct from its surroundings (the same property the 2D
+    threshold tool already relies on), less useful if it overlaps
+    similar-density material elsewhere.
+
+---
+
+## Connection issues
+
+If the dataset can't be resolved because Tiled dropped (rather than because
+no volume has been built yet), the view shows **"Couldn't reach the Tiled
+server"** with a **Go to Connect** button, instead of leaving you on an
+indefinite spinner.
+
+---
+
+Back to [Train a deep model ←](train.md), or return to the
+[recommended reading order](index.md#recommended-reading-order).
diff --git a/docs/reference/architecture.md b/docs/reference/architecture.md
index e637e47..cf61849 100644
--- a/docs/reference/architecture.md
+++ b/docs/reference/architecture.md
@@ -10,9 +10,11 @@ debug, or deploy it.
 
 ## System context
 
-The application is three cooperating processes. The browser only ever talks to
-the **backend**; all catalog and array access is proxied server-side so that
-Tiled credentials never reach the client.
+The application is **four** cooperating processes (grew from three as dlsia
+training/inference and the 3D volume viewer became core, always-on parts of
+the stack rather than optional extras). The browser only ever talks to the
+**backend**; all catalog/array access and all ML work are proxied server-side
+so that Tiled credentials and model internals never reach the client.
 
 ```mermaid
 graph LR
@@ -26,6 +28,10 @@ graph LR
     API["annotation_server.py"]
   end
 
+  subgraph Ipred["ipred · FastAPI :8003"]
+    IpredAPI["ipred.api:app<br/>feature banks · dlsia train/infer<br/>(torch, dlsia, qlty)"]
+  end
+
   subgraph Data["Data services"]
     Tiled["Tiled server :8010<br/>SQLite catalog + storage"]
     Disk[("LOCAL_DATA_ROOT<br/>drafts · versions · exports")]
@@ -33,17 +39,31 @@ graph LR
 
   User --> FE
   FE -->|"/api/* (fetch)"| API
+  FE -.->|"3D volume: Zarr chunks<br/>fetched directly, read-only"| Tiled
   API -->|"tiled.client (HTTP)"| Tiled
+  API -->|"httpx (HTTP)"| IpredAPI
+  IpredAPI -->|"tiled.client (HTTP)"| Tiled
   API --> Disk
-  User -. never direct .-> Tiled
+  User -. never direct for writes .-> Tiled
 ```
 
 | Component | Role | Default address |
 | --- | --- | --- |
 | **Frontend** | React app the annotator interacts with | <http://127.0.0.1:5173> |
-| **Backend** | FastAPI: renders images, rasterizes masks, builds exports | <http://127.0.0.1:8002> |
+| **Backend** | FastAPI: renders images, rasterizes masks, builds exports, proxies ipred | <http://127.0.0.1:8002> |
+| **ipred** | FastAPI: feature banks, iPred pixel-classifier training, dlsia TUNet train/infer (GPU-capable) | <http://127.0.0.1:8003> |
 | **Tiled** | Data catalog for source images and mask write-back | <http://127.0.0.1:8010> |
 
+!!! note "The 3D viewer is the one place the browser talks to Tiled directly"
+    Every other feature proxies through the backend (see
+    [Security boundaries](#security-boundaries)). The 3D **Volume** view is a
+    deliberate, narrow exception: the vendored WebGPU renderer streams Zarr
+    chunks straight from Tiled's `/zarr/v2` router (`lib/zarrUrl.ts`) for
+    performance — there is no export step or downsampled-volume endpoint in
+    between. This works because anonymous **read** access is enabled on Tiled;
+    the write-scoped API key never leaves the backend, and nothing here lets
+    the browser write to Tiled.
+
 ## Technology stack
 
 === "Frontend"
@@ -73,6 +93,18 @@ graph LR
     | Export | **pycocotools** | RLE encoding, bbox/area for COCO |
     | Files | **tifffile**, **imagecodecs** | Scientific TIFF reads |
     | Config | **python-dotenv** | Loading `backend/.env` |
+    | ML (optional extra) | **torch**, **dlsia**, **qlty** | dlsia TUNet train/infer, tiling geometry — gated behind the `ml` install extra; the backend runs fine without it, just without the Train tab's real functionality |
+
+=== "ipred (dlsia + pixel classifier)"
+
+    | Concern | Package | Used for |
+    | --- | --- | --- |
+    | API | **fastapi** + **uvicorn** | Its own separate ASGI service, port 8003 |
+    | ML | **torch**, **dlsia** | TUNet segmentation model, denoiser autoencoder |
+    | Tiling | **qlty** | Patch tiling/stitching for training and inference on full-resolution slices |
+    | Classical ML | **scikit-learn** | The Annotate tab's "fast" pixel classifier (iPred) |
+    | Feature banks | **onnxruntime** (optional) | tomojepa/SAM embeddings for manifold-coverage sampling |
+    | Device | CUDA, MPS (Apple Silicon), or CPU | `train_common.pick_device()` auto-detects, `TRAIN_DEVICE` env overrides |
 
 ## Deployment topology
 
@@ -86,25 +118,37 @@ The dev and production layouts differ mainly in **who serves the SPA** and
       Browser["Browser"]
       Vite["Vite :5173"]
       Backend["FastAPI :8002"]
+      Ipred["ipred :8003"]
       Tiled["Tiled :8010"]
       Disk[("~/data")]
 
       Browser -->|localhost:5173| Vite
       Vite -->|"/api/* proxy"| Backend
       Backend -->|tiled.client| Tiled
+      Backend -->|httpx| Ipred
+      Ipred -->|tiled.client| Tiled
       Backend --> Disk
     ```
 
-    `start_all.sh` launches all three: Tiled (8010), backend (8002), and the
-    Vite dev server (5173). Vite proxies every `/api` request to the backend,
-    so the frontend uses an empty `API_BASE` and stays same-origin.
+    `start_all.sh` launches all four: Tiled (`$TILED_PORT`, default 8010),
+    backend (`$BACKEND_PORT`, default 8002), ipred (`$IPRED_PORT`, default
+    8003), and the Vite dev server (`$FRONTEND_PORT`, default 5173) — each env
+    var overridable, and each port auto-bumped to the next free one if taken.
+    Vite proxies every `/api` request to the backend, so the frontend uses an
+    empty `API_BASE` and stays same-origin.
+
+=== "Production (Docker) — two images, pick one"
 
-=== "Production (Docker)"
+    Two Dockerfile targets/compose files cover different deployment needs —
+    neither replaces the other.
+
+    **`app` (lean)** — frontend + backend only, for a deployment with its own
+    external Tiled and no need for iPred/dlsia:
 
     ```mermaid
     flowchart LR
       Browser["Browser"]
-      Container["Single container :8002<br/>API + static SPA"]
+      Container["app image :8002<br/>API + static SPA"]
       TiledExt["External Tiled<br/>(TILED_URI env)"]
       Volume[("/data volume")]
 
@@ -113,17 +157,36 @@ The dev and production layouts differ mainly in **who serves the SPA** and
       Container --> Volume
     ```
 
-    The build copies the compiled SPA into `backend/static/`, and FastAPI serves
-    both the API and the static files from one origin on port 8002. Tiled is an
-    external service referenced by `TILED_URI`; only `tiled[client]` ships in the
-    image.
+    **`app-full` (batteries-included)** — one container running all three
+    backend-side services (Tiled and ipred as backgrounded processes, backend
+    as the container's foreground/main process — no Docker networking needed):
+
+    ```mermaid
+    flowchart LR
+      Browser["Browser"]
+      Container["app-full image<br/>backend :8002 (foreground)<br/>+ ipred :8003 (background)<br/>+ Tiled :8010 (background)"]
+      Volume[("/data volume<br/>/data/raw bind mount")]
+
+      Browser -->|":8002 only"| Container
+      Container --> Volume
+    ```
+
+    Both copy the compiled SPA into `backend/static/`; FastAPI serves the API
+    and static files from one origin. `app-full` adds the `ml` extra, `tiled[all]`,
+    and the `ipred` package on top of the same base layer, plus
+    `docker-entrypoint-full.sh` to start the three processes. Known limitation
+    (documented in that entrypoint's own comment): Tiled/ipred aren't supervised
+    inside `app-full` — if one crashes post-startup the container keeps running
+    (backend is its main process) but that service stays down until a restart.
+    Fine for local/demo use; a real deployment needing that resilience should
+    use `app` against a properly-supervised external Tiled instead.
 
 ## Frontend architecture
 
 ### Component shell
 
 The UI follows the ALS **Finch** hub pattern: a fixed icon sidebar, a header,
-and a routed main area. Four tabs map to four page components.
+and a routed main area. Six tabs map to six page components.
 
 ```mermaid
 graph TB
@@ -134,13 +197,15 @@ graph TB
 
   APP --> HUB["HubAppLayout"]
   HUB --> SIDEBAR["HubSidebar"]
-  HUB --> HEADER["HubHeader"]
+  HUB --> HEADER["HubHeader<br/>+ connection-status indicator"]
   HUB --> MAINC["HubMainContent"]
 
   MAINC --> CONNECT["ConnectPage<br/>/connect"]
   MAINC --> BROWSE["BrowsePage<br/>/browse"]
   MAINC --> REF["ReferencePage<br/>/reference"]
   MAINC --> ANNOT["AnnotatePage<br/>/annotate"]
+  MAINC --> VOL["VolumePage<br/>/volume"]
+  MAINC --> TRAIN["TrainPage<br/>/train"]
 ```
 
 | Tab | Route | Page component | Key children |
@@ -148,17 +213,26 @@ graph TB
 | **Connect** | `/connect` | `ConnectPage` | `IngestDropzone`, server/folder pickers |
 | **Browse** | `/browse` | `BrowsePage` | `ColumnBrowser`, `LocalSampleBrowser` |
 | **Reference** | `/reference` | `ReferencePage` | inline guide editors |
-| **Annotate** | `/annotate` | `AnnotatePage` | `AnnotationCanvas`, `Toolbar`, `ClassManager` |
+| **Annotate** | `/annotate` | `AnnotatePage` | `AnnotationCanvas`, `Toolbar`, `ClassManager`, stage tabs (Draw/Assist/Predict) |
+| **3D** | `/volume` | `VolumePage` | `VolumeViewer` (vendored WebGPU renderer), `MaskLayersPanel`, `BuildVolumePanel`/`RebuildVolumeControl` |
+| **Train** | `/train` | `TrainPage` | `TrainingDataPanel`, `HyperparamsPanel`, `RunsPanel`, `InferencePanel` |
 
 !!! note "Export is a modal, not a tab"
     Dataset export (COCO or DINOv3/Lightly) lives in `DownloadModal`, opened from
     the Annotate sidebar — there is no dedicated Export tab in the current navigation.
 
+`HubAppLayout` also mounts `useConnectionHealth()` once at the app-shell level
+(not per-page) — it periodically checks Tiled reachability
+(`GET /api/tiled/list`, backoff on failure) and drives `connectionStore.status`,
+which `HubHeader` renders as a small persistent indicator (green "Tiled
+connected" / red "Tiled disconnected" linking back to Connect).
+
 ### State management
 
-State is split across nine **Zustand** stores. Only the annotation store carries
-undo/redo history (via **zundo**), and a couple of stores persist to
-`localStorage`.
+State is split across a dozen **Zustand** stores. Only the annotation store
+carries undo/redo history (via **zundo**), and a handful persist to
+`localStorage` (display prefs are plain values inside `AnnotatePage`, not a
+store — see `lib/displayPrefs.ts`).
 
 ```mermaid
 flowchart TD
@@ -188,13 +262,16 @@ flowchart TD
 | --- | --- | --- | --- |
 | `annotationStore` | `byImage[sourceKey][slice] → Shape[]`, splits, negatives | draft autosave to backend | **zundo** |
 | `toolStore` | active tool, brush size, fill/threshold, selection | memory | — |
-| `datasetStore` | active sample, `meta`, `currentSlice`, `renderOpts` | memory | — |
+| `datasetStore` | active sample, `meta` (incl. `globalValueRange`), `currentSlice`, `renderOpts` | memory | — |
 | `classStore` | `AnnotationClass[]` (id, label, color, visibility) | in draft/save payloads | — |
-| `connectionStore` | tiled/local URIs, paths, sample count | memory | — |
+| `connectionStore` | tiled/local URIs, paths, sample count, **live Tiled `status`** | memory | — |
 | `referenceGuideStore` | guide entries, notes, `loadedFor` | backend via `useGuideSync` | — |
 | `clipboardStore` | copied shapes | memory | — |
 | `settingsStore` | `annotatorName`, `colorblindMode`, anonymous `sessionId` | `localStorage` | — |
 | `ratingStore` | per-sample star ratings | `localStorage` | — |
+| `ipredStore` | Annotate → Assist/Predict stage state: feature bank job, trained classifier, manifold sampling | memory | — |
+| `layerVisibilityStore` | per-class layer show/hide (Annotate canvas overlay) | memory | — |
+| `predictedRasterStore` | pointers (`{runId, classIds}`) to committed-but-not-yet-vectorized predicted slices — see [Lazy shape vectorization](#lazy-predicted-shape-vectorization) | memory | — |
 
 `toolStore` also carries the eraser/select scope (`eraseAllClasses`, `selectScope`),
 the `clipToOtherClasses` (default **on**) and `mergeOverlappingSameClass` toggles,
@@ -202,6 +279,20 @@ and `panReturnTool` (so the brush/eraser cursor stays visible while hold-Space
 panning). `settingsStore.sessionId` is an anonymous per-install id stamped into
 the "Feedback" bug-report context.
 
+### Lazy predicted-shape vectorization
+
+A full-volume iPred "Apply across volume" commit does **not** eagerly
+vectorize every predicted slice into real, editable `Shape[]` — that produced
+a 297MB draft from one commit (139,004 shapes, 98.8% machine-predicted) before
+this was built. Instead, `handleCommitVolumeApply` writes a lightweight
+pointer (`predictedRasterStore`: `{runId, classIds}`, tens of bytes) for any
+slice that doesn't already have real shapes; the existing Predictions raster
+overlay (PNG-driven, not `Shape[]`-driven) keeps showing it from the run's own
+`commit.png`. A slice is vectorized into real `Shape[]` — and only that slice —
+the moment the user actually interacts with it ("Make this slice editable").
+Export and "Push to Tiled" read straight from the pointer for any slice never
+made editable, falling back to real shapes for ones that were.
+
 ### Canvas rendering
 
 `AnnotationCanvas` stacks several **react-konva** layers. The in-progress brush
@@ -255,6 +346,76 @@ round-trip for editing:
 | `clahe.ts` / `sharpen.ts` / `stretch.ts` / `colormaps.ts` | Display-only preprocessors and LUTs |
 | `geometry.ts` / `measure.ts` / `datasetStats.ts` | Hit-testing/util, measurement, and the Insights QA metrics |
 | `sam/samClient.ts` · `sam/samWorker.ts` · `sam/adjust.ts` | SAM main-thread singleton, the Web Worker, and the display-bake used by tools |
+| `displayPrefs.ts` | Persists the cosmetic display sliders (not `denoise`) to `localStorage`, global viewer preference |
+| `bandTransferFunction.ts` | Converts a Sampler-fitted 2D intensity band into the 3D viewer's opacity-curve domain — see [3D volume view](#3d-volume-view) |
+| `zarrUrl.ts` | Builds the Zarr URL the 3D viewer streams directly from Tiled, and the `<source>__masks[_deep]` mask URL |
+| `volumeMaskPreview.ts` | Rasterizes the CURRENT (possibly unsynced) annotation shapes into a coarse class-id volume — the Fast mask slot's zero-network "Live" mode |
+
+## 3D volume view
+
+The `/volume` tab renders straight off Tiled, with no export step: the vendored
+WebGPU renderer (`frontend/vendor/view_tomography_recon_app`, a pinned git
+submodule) streams a Zarr store's chunks directly from Tiled's `/zarr/v2`
+router. `VolumeViewer.tsx` owns only the React mount/dispose lifecycle around
+its imperative `run(canvas, options) → WebGpuViewerInstance` API; everything
+about *how* the volume looks is the vendored renderer's own concern.
+
+### Mask layers
+
+Two independent, fixed mask/annotation slots — "Fast (iPred)" and "Deep
+(dlsia)" — backed by the viewer's own slot-neutral `loadMask`/
+`loadMaskFromArray`/`setMaskClassColor`/etc. `MaskLayersPanel.tsx` is where the
+*meaning* of each slot lives (kept out of the vendored viewer entirely):
+
+- **Deep** is always Tiled-backed, pointed at the `<source>__masks_deep`
+  container `tiled_mask_sync.write_masks_to_tiled` writes (`container_suffix`
+  keeps it independent of the Fast slot's own container).
+- **Fast** defaults to a zero-network "Live" mode: `volumeMaskPreview.ts`
+  rasterizes the sample's CURRENT shapes client-side into a coarse class-id
+  array, loaded via `loadMaskFromArray` — no "Push masks to Tiled" step
+  required first. It can also point at the Tiled-backed `<source>__masks`
+  container (the precise backend-rasterized result) via its own toggle.
+
+Because `loadMask`/`loadMaskFromArray` are fire-and-forget on the viewer's
+public interface (no promise, no error signal — `getMaskClasses(slot)`
+returning `undefined` covers both "still loading" and "failed"
+indistinguishably), `MaskLayersPanel.tsx` polls for up to 5 minutes rather than
+assuming synchronous completion — a real Tiled-backed "Deep" mask (hundreds of
+slices, freshly written) can legitimately take a while over the network, and a
+short timeout only makes the *panel* look broken without stopping the
+underlying load.
+
+### Transfer function: the threshold-fit → 3D bridge
+
+The Annotate tab's Sampler/Threshold-lasso tool fits an intensity band from a
+traced example (`lib/thresholdFit.ts`) — "View band in 3D" sends that band
+(native 0–255 canvas-byte space) to `/volume?bandLo=&bandHi=`, which isolates
+it in the viewer's opacity curve (`setRendering({ opacityPoints })`): opaque
+inside the band, transparent outside, with **zero upstream viewer changes** —
+purely driving an already-existing transfer-function API. This isolates by
+*intensity value* across the whole volume, not the traced *spatial region*
+specifically — good enough when the traced feature's density is genuinely
+distinct from its surroundings (the same property the 2D tool already
+exploits), not true region-clipping.
+
+The one real subtlety: the 2D canvas's 0–255 bytes are normalized against a
+per-dataset **percentile** range (`images._sample_global_stats`, exposed to
+the frontend as `ImageMeta.globalValueRange`), while the 3D viewer separately
+normalizes raw voxels against its own **min/max**-based estimate
+(`WebGpuViewerInstance.getValueRange()`) — different statistics from different
+data, so a fitted band must be converted byte → raw physical value → the
+viewer's own domain (`lib/bandTransferFunction.ts`'s
+`mapByteBandToViewerDomain`), not simply divided by 255. Assuming the two
+normalizations were the same was tried first and produced a visibly wrong
+band (confirmed live) before this was understood.
+
+### Volume build & rebuild
+
+A TIFF-stack source has no pyramid to stream until one is built
+(`BuildVolumePanel.tsx` → `/api/volume/build`, backed by `volume_build.py`).
+`RebuildVolumeControl.tsx` re-triggers a build for a dataset that already has
+one (e.g. after a fidelity setting changes) — `build_volume` safely replaces
+any prior build for the same key.
 
 ## Backend architecture
 
@@ -272,6 +433,9 @@ flowchart TB
     Annot["/api/annotations/* · /api/guide* · /api/measure"]
     Export["/api/export/* · /api/masks/* · /api/import/*"]
     Ingest["/api/ingest/*"]
+    Volume["/api/volume/*"]
+    Denoise["/api/denoise/*"]
+    Train["/api/train/* (proxies to ipred for feature/classifier work)"]
   end
 
   subgraph Modules["Helper modules"]
@@ -283,11 +447,15 @@ flowchart TB
     CE["coco_export · coco_import"]
     TS["tiled_annotation_sync<br/>tiled_mask_sync"]
     IG["ingest"]
+    VB["volume_build · mask_pyramid<br/>tiff_stack_source · zarr_source"]
+    ML["train_jobs · infer_jobs<br/>denoise_bake · denoise_train<br/>train_common · tiling · batch_probe"]
+    IC["ipred_client · ipred_routes<br/>(thin proxy)"]
   end
 
   subgraph Ext["External"]
     Tiled["Tiled :8010"]
     Disk[("LOCAL_DATA_ROOT")]
+    Ipred["ipred :8003"]
   end
 
   Browse --> BH --> Tiled
@@ -301,6 +469,10 @@ flowchart TB
   Export --> CE --> Disk
   Export --> TS --> Tiled
   Ingest --> IG --> Tiled
+  Volume --> VB --> Tiled
+  Denoise --> ML
+  Train --> ML --> Tiled
+  Train --> IC --> Ipred
 ```
 
 ### Module responsibilities
@@ -311,11 +483,15 @@ flowchart TB
 | `tiled_config.py` / `tiled_clients.py` | Server config, cached clients, `api_key_for_uri`, browse-root resolution |
 | `browse_helpers.py` | Metadata facets and filtered search (`tiled.queries.Key`, `distinct()`) |
 | `arrays.py` / `local_fs.py` | Resolve and slice arrays from Tiled or the local filesystem |
-| `images.py` / `thumbnails.py` | Slice → PNG rendering (normalize, colormap, scale) |
+| `images.py` / `thumbnails.py` | Slice → PNG rendering (normalize, colormap, scale) — `images._sample_global_stats` is also what `ImageMeta.globalValueRange` exposes to the frontend |
 | `drafts.py` / `guides.py` | Autosave drafts, immutable version history, annotation guides |
 | `coco_export.py` / `coco_import.py` | Shape rasterization, COCO build/write, dataset import |
-| `export_jobs.py` / `ingest.py` | In-memory background-job registries |
-| `tiled_annotation_sync.py` / `tiled_mask_sync.py` | Write `studio_*` metadata and rasterized mask volumes back to Tiled |
+| `export_jobs.py` / `ingest.py` | In-memory background-job registries (shared by export, dlsia train/infer, denoise, ingest — all long-running work polls the same `/status/{id}` shape) |
+| `tiled_annotation_sync.py` / `tiled_mask_sync.py` | Write `studio_*` metadata and rasterized mask volumes back to Tiled — `container_suffix` keeps independent mask producers (manual sync vs. dlsia's own write) from colliding |
+| `volume_build.py` / `mask_pyramid.py` / `tiff_stack_source.py` / `zarr_source.py` | Build/register the Zarr pyramid the 3D viewer streams, and the mask pyramids it loads into its two mask slots |
+| `train_common.py` / `train_jobs.py` / `infer_jobs.py` / `tiling.py` / `batch_probe.py` | dlsia TUNet training/inference: device selection, run persistence, qlty patch tiling, GPU batch-size probing. Two-lock design (`ML_LOCK` for cross-job exclusivity, `GPU_FORWARD_LOCK` scoped to just the GPU forward call) lets I/O/preprocessing/vectorization run concurrently across a per-slice worker pool during inference while still serializing actual GPU calls |
+| `denoise.py` / `denoise_bake.py` / `denoise_train.py` / `denoise_runtime.py` / `dlsia_runtime.py` / `autoencoder_runtime.py` | Classical + learned (Noise2Noise/Noise2Void/autoencoder) denoising, and baking a denoiser's output permanently into a new Tiled node |
+| `ipred_client.py` / `ipred_routes.py` | Thin `httpx` proxy from the backend's `/api/train/*`/iPred routes to the separate ipred service — the backend never runs feature-extraction/classifier training itself |
 
 ### Data model
 
@@ -464,6 +640,60 @@ sequenceDiagram
   API-->>User: dataset.zip
 ```
 
+### Train a dlsia model, run inference, write masks to Tiled
+
+The Train tab's whole loop stays in the backend's `train_jobs.py`/`infer_jobs.py`
+— the ipred service is only involved for the Annotate tab's own fast pixel
+classifier and feature banks, not dlsia. `run_infer_job` runs a per-slice
+worker pool (I/O/preprocessing/vectorization concurrent, GPU forward calls
+serialized via `GPU_FORWARD_LOCK`) and publishes partial results incrementally
+so the frontend's preview slider extends live while the job is still running,
+not just after it finishes.
+
+```mermaid
+sequenceDiagram
+  actor User
+  participant TP as TrainPage
+  participant Job as useExportJob
+  participant API as Backend
+  participant TJ as train_jobs / infer_jobs
+  participant TMS as tiled_mask_sync
+  participant T as Tiled
+
+  User->>TP: Start training
+  TP->>Job: start(hyperparams)
+  Job->>API: POST /api/train/start
+  API->>TJ: run_train_job (background thread, ML_LOCK)
+  loop poll
+    Job->>API: GET /api/export/status/{job_id}
+    API-->>Job: epoch progress, mIoU
+  end
+
+  User->>TP: Run inference (all slices)
+  TP->>API: POST /api/train/infer
+  API->>TJ: run_infer_job (worker pool, GPU_FORWARD_LOCK)
+  loop poll (result grows incrementally)
+    API-->>TP: preview_slices, done/total
+  end
+
+  User->>TP: Write masks to Tiled
+  TP->>API: POST /api/train/infer/write-tiled/{job_id}
+  API->>TJ: run_write_tiled_job (reads its OWN cached job entry —<br/>not the frontend's live state, so a stale "Tiled?" prop can't hide/misfire this)
+  TJ->>TMS: write_masks_to_tiled(container_suffix="_deep")
+  TMS->>T: register_mask_pyramid + class_vols
+```
+
+!!! note "Why the write-tiled route trusts itself, not the frontend"
+    `run_write_tiled_job` derives `source`/`kind` from its own cached job entry,
+    never from the request. This was a deliberate fix (#31 in the project's
+    working notes): a frontend prop computed from the *live* dataset store can
+    go stale if the user navigates away during a long job and back, which once
+    silently hid the "Write masks to Tiled" button for a job that had, in
+    fact, run against a real Tiled source. Trusting the job's own record — and
+    letting the backend's already-correct rejection of a genuinely non-Tiled
+    source surface as a normal job error — is more robust than re-deriving the
+    same fact twice.
+
 ## Key design decisions
 
 The choices below explain *why* the code looks the way it does — useful before
@@ -495,9 +725,21 @@ extending it.
   (`/api/annotations/draft`, disk only); an explicit **save** writes an immutable
   version + thumbnail and syncs summary metadata to Tiled. Everything is keyed by the
   canonical **source key** so drafts, versions, guide, and exports line up per sample.
-- **Long work is a background job + poll.** Export, mask write-back, and ingest use an
-  in-memory job registry with a `/status/{id}` poller and (for export) a streamed
-  `.zip`, rather than blocking the request.
+- **Long work is a background job + poll.** Export, mask write-back, dlsia train/infer,
+  denoising, and ingest all use the same in-memory job registry (`export_jobs.py`)
+  with a `/status/{id}` poller, rather than blocking the request.
+- **Fill flood-time barriers reuse the same rasterizer as post-commit clipping.**
+  The Fill tool's flood (`magicwand.ts`) can now stop at a pixel already
+  claimed by a different class (an optional `blocked` grid, OR'd into the
+  existing gradient-edge wall check) — built via `rasterizeUnion`, the exact
+  helper the brush-clip-detection path already used, so no second
+  rasterization concept was introduced for the same problem.
+- **Every mask sync/pyramid write is keyed by its own real cache path, not a
+  shared literal.** `mask_pyramid.register_mask_pyramid` takes a `cache_key`
+  (the actual on-disk Zarr path) separate from `key` (the Tiled sub-node
+  name, always `"semantic"`) — a real production incident (two independent
+  mask producers silently overwriting each other's pixels) came from a
+  version that conflated the two.
 
 ## Security boundaries
 
diff --git a/frontend/src/components/annotate/AnnotationCanvas/index.tsx b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
index d686473..48a105b 100644
--- a/frontend/src/components/annotate/AnnotationCanvas/index.tsx
+++ b/frontend/src/components/annotate/AnnotationCanvas/index.tsx
@@ -1523,6 +1523,19 @@ export default function AnnotationCanvas({
     const field = ensureMagicField();
     if (!field) { setMagicPreview([]); return; }
     setMagicLoading(true);
+    // Fill should stop at a pixel already claimed by a different annotated
+    // class (Peter's "like a wall" ask) — rasterize the other classes onto
+    // the same grid `field` uses and pass it as a flood-time barrier, so the
+    // preview itself is bounded, not just the post-commit clip (`clipToOtherClasses`
+    // already trims the committed shape, but the flood could wander arbitrarily
+    // far across a neighbor first).
+    const blocked =
+      isFill && activeClassId !== null
+        ? (() => {
+            const others = storeShapes.filter((s) => s.classId !== activeClassId);
+            return others.length > 0 ? rasterizeUnion(others, field.gw, field.gh, field.scale) : undefined;
+          })()
+        : undefined;
     const id = requestAnimationFrame(() => {
       const polys: number[][] = [];
       for (const seed of magicSeeds) {
@@ -1532,6 +1545,7 @@ export default function AnnotationCanvas({
             mode: isFill ? 'contiguous' : magicMode,
             smooth: isFill ? 1 : magicSigma,
             edgeStop: isFill ? 0 : magicEdgeStop,
+            blocked,
           }),
         );
       }
@@ -1539,7 +1553,7 @@ export default function AnnotationCanvas({
       setMagicLoading(false);
     });
     return () => cancelAnimationFrame(id);
-  }, [tool, magicSeeds, magicBox, autoNegPoints, samDetail, samThreshold, samConnectedOnly, samEncodeKey, makeSamSource, magicEngine, magicTolerance, magicMode, magicSigma, magicEdgeStop, fillThreshold, ensureMagicField, imageEl, meta, sam.ensureEncoded, sam.segment]); // eslint-disable-line react-hooks/exhaustive-deps
+  }, [tool, magicSeeds, magicBox, autoNegPoints, samDetail, samThreshold, samConnectedOnly, samEncodeKey, makeSamSource, magicEngine, magicTolerance, magicMode, magicSigma, magicEdgeStop, fillThreshold, ensureMagicField, imageEl, meta, sam.ensureEncoded, sam.segment, storeShapes, activeClassId]); // eslint-disable-line react-hooks/exhaustive-deps
 
   /** Commit the magic preview polygons as new shapes (one batched undo step). */
   const commitMagic = useCallback(() => {
diff --git a/frontend/src/lib/magicwand.test.ts b/frontend/src/lib/magicwand.test.ts
index d5363d9..257e917 100644
--- a/frontend/src/lib/magicwand.test.ts
+++ b/frontend/src/lib/magicwand.test.ts
@@ -59,6 +59,36 @@ describe('magicwand', () => {
     expect(Math.max(...walled[0].filter((_, i) => i % 2 === 0))).toBeLessThan(21);
   });
 
+  it('blocked keeps the flood from crossing a cell already claimed by another class', () => {
+    // Uniform intensity field — nothing but `blocked` should stop the flood.
+    const gw = 40, gh = 40;
+    const gray = new Float32Array(gw * gh).fill(50);
+    const field: GrayField = { gw, gh, scale: 1, gray };
+
+    // Without `blocked` the whole uniform field floods (crosses x=20).
+    const open = magicSelect(field, 5, 5, { toleranceFrac: 0.5, mode: 'contiguous', smooth: 0, minRegion: 8 });
+    expect(open.length).toBe(1);
+    expect(Math.max(...open[0].filter((_, i) => i % 2 === 0))).toBeGreaterThan(25);
+
+    // A vertical "wall" of another class's pixels at x=20 stops the flood at it.
+    const blocked = new Uint8Array(gw * gh);
+    for (let y = 0; y < gh; y++) blocked[y * gw + 20] = 1;
+    const walled = magicSelect(field, 5, 5, { toleranceFrac: 0.5, mode: 'contiguous', smooth: 0, minRegion: 8, blocked });
+    expect(walled.length).toBe(1);
+    expect(Math.max(...walled[0].filter((_, i) => i % 2 === 0))).toBeLessThan(21);
+  });
+
+  it('blocked does not prevent flooding when the seed itself sits on a blocked cell', () => {
+    const gw = 20, gh = 20;
+    const gray = new Float32Array(gw * gh).fill(50);
+    const field: GrayField = { gw, gh, scale: 1, gray };
+    const blocked = new Uint8Array(gw * gh);
+    blocked[9 * gw + 9] = 1; // the seed cell itself
+
+    const polys = magicSelect(field, 9, 9, { toleranceFrac: 0.5, mode: 'contiguous', smooth: 0, minRegion: 8, blocked });
+    expect(polys.length).toBe(1); // seed is exempt, matching edgeStop's own wall exemption
+  });
+
   it('fills a uniform region with edgeStop on (noise must not wall the flood)', () => {
     // Left half uniform (100, with faint noise), right half 200, sharp edge at x=30.
     const gw = 60, gh = 40;
diff --git a/frontend/src/lib/magicwand.ts b/frontend/src/lib/magicwand.ts
index 2b76d78..aaeb385 100644
--- a/frontend/src/lib/magicwand.ts
+++ b/frontend/src/lib/magicwand.ts
@@ -300,6 +300,11 @@ interface SelectOpts {
   /** 0–1 edge barrier (contiguous only): higher = flood stops at weaker edges. */
   edgeStop?: number;
   minRegion?: number;  // min component size in grid pixels
+  /** Same `gw*gh` grid as `field` (contiguous mode only): cells already claimed
+   *  by a different annotated class the flood must not cross, e.g. so filling
+   *  one region doesn't spill across an already-labeled wall into a neighbor.
+   *  `1` = blocked. Like `edgeStop`'s wall, the seed cell itself is exempt. */
+  blocked?: Uint8Array;
 }
 
 interface MaskPolyOpts {
@@ -455,7 +460,7 @@ export function magicSelect(
   field: GrayField,
   seedXimg: number,
   seedYimg: number,
-  { toleranceFrac, mode, smooth = 0, edgeStop = 0, minRegion = 12 }: SelectOpts,
+  { toleranceFrac, mode, smooth = 0, edgeStop = 0, minRegion = 12, blocked }: SelectOpts,
 ): number[][] {
   const { gw, gh, scale, grad } = field;
   // Smoothing drives a pre-blur (denoise so the boundary is less ragged) here;
@@ -471,7 +476,8 @@ export function magicSelect(
   // flood won't cross — keeps a void's selection bounded by its rim instead of
   // leaking across a soft/ringy edge. Disabled when edgeStop is 0 or no grad.
   const wallLimit = edgeStop > 0 ? 1 - edgeStop : Infinity;
-  const isWall = (i: number) => grad !== undefined && grad[i] >= wallLimit;
+  const isWall = (i: number) =>
+    (grad !== undefined && grad[i] >= wallLimit) || (blocked !== undefined && blocked[i] === 1);
 
   const mask = new Uint8Array(gw * gh);
   if (mode === 'global') {
diff --git a/mkdocs.yml b/mkdocs.yml
index bcd7bfb..a468611 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -88,6 +88,8 @@ nav:
       - 3. Annotate: guide/annotate.md
       - 4. Annotation guide: guide/reference-guide.md
       - 5. Export & download: guide/export.md
+      - 6. Train a deep model: guide/train.md
+      - 7. 3D volume view: guide/volume.md
   - Reference:
       - Software architecture: reference/architecture.md
       - Keyboard shortcuts: reference/shortcuts.md

From 7af6ecb80ae6c3737388a919f31dea84272e0020 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Thu, 10 Sep 2026 13:56:28 -0700
Subject: [PATCH 29/38] Linting

---
 backend/annotation_server.py                | 2 +-
 backend/denoise_train.py                    | 1 +
 backend/tests/test_annotation_thumbnails.py | 2 +-
 backend/tests/test_denoise_bake.py          | 2 +-
 backend/tests/test_ipred_batch_jobs.py      | 4 ++--
 backend/tests/test_train_common.py          | 2 +-
 6 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/backend/annotation_server.py b/backend/annotation_server.py
index 693a124..8fcbc56 100644
--- a/backend/annotation_server.py
+++ b/backend/annotation_server.py
@@ -81,8 +81,8 @@
     ExportSourceItem,
     GuidePayload,
     ImageMeta,
-    IngestPreflightRequest,
     InferRequest,
+    IngestPreflightRequest,
     MeasureRequest,
     SaveVersionRequest,
     TiffStackRegisterRequest,
diff --git a/backend/denoise_train.py b/backend/denoise_train.py
index d9f1e9c..fbf344a 100644
--- a/backend/denoise_train.py
+++ b/backend/denoise_train.py
@@ -330,6 +330,7 @@ def tile_denoise_pair(inp: np.ndarray, tgt: np.ndarray, window: int) -> list[tup
     is real loss mass, so tiling an undersized image is worth avoiding.
     """
     import torch  # noqa: PLC0415
+
     import tiling  # noqa: PLC0415 — optional (qlty) dependency
 
     if inp.shape != tgt.shape:
diff --git a/backend/tests/test_annotation_thumbnails.py b/backend/tests/test_annotation_thumbnails.py
index 7f9710f..ddd6eb9 100644
--- a/backend/tests/test_annotation_thumbnails.py
+++ b/backend/tests/test_annotation_thumbnails.py
@@ -14,11 +14,11 @@
 import annotation_thumbnails
 import arrays as arrays_mod
 
-
 # ---------------------------------------------------------------------------
 # decode_thumbnail_base64
 # ---------------------------------------------------------------------------
 
+
 class TestDecodeThumbnailBase64:
     def test_empty_string_returns_none(self):
         assert annotation_thumbnails.decode_thumbnail_base64("") is None
diff --git a/backend/tests/test_denoise_bake.py b/backend/tests/test_denoise_bake.py
index c86b245..fbdbeff 100644
--- a/backend/tests/test_denoise_bake.py
+++ b/backend/tests/test_denoise_bake.py
@@ -11,8 +11,8 @@
 torch = pytest.importorskip("torch")
 
 import arrays as arrays_mod  # noqa: E402
-import denoise_bake  # noqa: E402
 import denoise as denoise_mod  # noqa: E402
+import denoise_bake  # noqa: E402
 import export_jobs  # noqa: E402
 import schemas  # noqa: E402
 import train_common  # noqa: E402
diff --git a/backend/tests/test_ipred_batch_jobs.py b/backend/tests/test_ipred_batch_jobs.py
index 9a963dc..641c46e 100644
--- a/backend/tests/test_ipred_batch_jobs.py
+++ b/backend/tests/test_ipred_batch_jobs.py
@@ -8,12 +8,12 @@
 import threading
 import time
 from contextlib import contextmanager
-from typing import Any
+
+import pytest
 
 import export_jobs
 import ipred_batch_jobs
 import ipred_client as ipred_client_mod
-import pytest
 
 
 @contextmanager
diff --git a/backend/tests/test_train_common.py b/backend/tests/test_train_common.py
index 715672d..1b89071 100644
--- a/backend/tests/test_train_common.py
+++ b/backend/tests/test_train_common.py
@@ -23,8 +23,8 @@
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
 
-import train_common  # noqa: E402
 import denoise_bake  # noqa: E402
+import train_common  # noqa: E402
 
 
 def test_modules_import_cleanly():

From b6d49e29381a5b1dc3debccdbba7af4703714891 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Fri, 11 Sep 2026 10:34:31 -0700
Subject: [PATCH 30/38] Separate Docker containers for local/production
 deployment

---
 .github/workflows/ci.yml          | 39 ++++++++++++++++--
 Dockerfile                        | 67 +++++++++++++++++++++----------
 backend/.env.example              |  8 ++++
 docker-compose.local.yml          | 52 ++++++++++++++++++++++++
 docker-compose.ml.yml             | 34 ++++++++++++++++
 docker-entrypoint-ml.sh           | 21 ++++++++++
 docker/nginx-local.conf           | 34 ++++++++++++++++
 docs/reference/architecture.md    | 61 +++++++++++++++++++++-------
 frontend/.env.example             |  7 ++++
 frontend/index.html               |  2 +-
 frontend/src/config.ts            | 11 +++--
 frontend/src/lib/sam/samWorker.ts |  2 +-
 frontend/src/main.tsx             |  2 +-
 frontend/vite.config.ts           |  9 +++++
 14 files changed, 304 insertions(+), 45 deletions(-)
 create mode 100644 docker-compose.local.yml
 create mode 100644 docker-compose.ml.yml
 create mode 100755 docker-entrypoint-ml.sh
 create mode 100644 docker/nginx-local.conf

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 084e9cc..84a740e 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -68,6 +68,7 @@ jobs:
       # complete service, not an override of app), built independently. Does
       # not push anywhere; image publishing is a separate, main-only job below.
       - run: docker compose -f docker-compose.yml build
+      - run: docker compose -f docker-compose.ml.yml build
       - run: docker compose -f docker-compose.full.yml build
 
   # Publishes images on merge to main ONLY — never on a PR run, to avoid
@@ -84,10 +85,42 @@ jobs:
     strategy:
       matrix:
         include:
+          # --- Unchanged, generic builds: root-hosted, no VITE_BASE_PATH ---
           - target: app
             image: app
+            tags: |
+              type=raw,value=latest
+              type=sha
+            vite_base_path: ""
+          - target: app-ml
+            image: app-ml
+            tags: |
+              type=raw,value=latest
+              type=sha
+            vite_base_path: ""
           - target: app-full
             image: app-full
+            tags: |
+              type=raw,value=latest
+              type=sha
+            vite_base_path: ""
+          # --- Purpose-tagged, base-path-baked builds ---
+          # :als — ipred/ML bundled, Tiled external (ALS's own production
+          # Tiled), hosted under ALS's hub path prefix.
+          - target: app-ml
+            image: app-ml
+            tags: |
+              type=raw,value=als
+              type=raw,value=als-{{sha}}
+            vite_base_path: /bl832/seg_studio/
+          # :local — fully bundled (+ Tiled), for anyone running this
+          # repo locally; still exercises subpath hosting by default.
+          - target: app-full
+            image: app-full
+            tags: |
+              type=raw,value=local
+              type=raw,value=local-{{sha}}
+            vite_base_path: /seg_studio/
     steps:
       - uses: actions/checkout@v4
         with:
@@ -102,9 +135,7 @@ jobs:
         id: meta
         with:
           images: ghcr.io/${{ github.repository }}/${{ matrix.image }}
-          tags: |
-            type=raw,value=latest
-            type=sha
+          tags: ${{ matrix.tags }}
       - uses: docker/build-push-action@v6
         with:
           context: .
@@ -112,3 +143,5 @@ jobs:
           push: true
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
+          build-args: |
+            VITE_BASE_PATH=${{ matrix.vite_base_path }}
diff --git a/Dockerfile b/Dockerfile
index d09e5c4..aeddaa9 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,11 +1,16 @@
 # syntax=docker/dockerfile:1
-# Two images from one file, selected via `--target`:
+# Three images from one file, selected via `--target`:
 #   app       (default) — frontend + backend only, Tiled external
 #                          (docker build . / docker compose up, unchanged).
-#   app-full            — the same, PLUS a bundled Tiled server and the ipred
-#                          service in the same container (all three talk over
-#                          127.0.0.1), for a single `docker run` that needs
-#                          nothing external — see docker-compose.full.yml.
+#   app-ml              — the same, PLUS the `ml` extra (torch/dlsia) and
+#                          ipred bundled, Tiled still external — for a
+#                          deployment with its own production Tiled that
+#                          still wants Train/iPred to work out of the box
+#                          (see docker-compose.ml.yml, and the `:als` tag).
+#   app-full            — app-ml PLUS a bundled Tiled server in the same
+#                          container (all three talk over 127.0.0.1), for a
+#                          single `docker run` that needs nothing external —
+#                          see docker-compose.full.yml, and the `:local` tag.
 # Local dev still uses start_all.sh, which runs the same three services as
 # separate local processes instead of inside a container.
 
@@ -22,6 +27,11 @@ COPY frontend/ ./
 RUN test -f vendor/view_tomography_recon_app/src/zarr-viewer/src/ome-zarr-viewer.ts \
     || (echo "ERROR: submodule frontend/vendor/view_tomography_recon_app is missing." \
         && echo "Run: git submodule update --init --recursive" && exit 1)
+# Read at build time (see vite.config.ts's `base` / src/config.ts's API_BASE) —
+# empty/unset produces the exact same root-hosted output as before this arg
+# existed. Set for a subpath deployment, e.g. --build-arg VITE_BASE_PATH=/bl832/seg_studio/
+ARG VITE_BASE_PATH=""
+ENV VITE_BASE_PATH=${VITE_BASE_PATH}
 RUN npm run build   # → /web/dist
 
 # --- Stage: lightweight production image (frontend + backend only) ---
@@ -48,32 +58,47 @@ VOLUME ["/data"]
 EXPOSE 8002
 CMD ["uvicorn", "annotation_server:app", "--host", "0.0.0.0", "--port", "8002"]
 
-# --- Stage: batteries-included image (+ Tiled server + ipred + dlsia/ml) ---
+# --- Stage: backend + ipred + ml, Tiled still external ---
+# For a deployment that already has its own production Tiled (so bundling a
+# second, empty one would be actively wrong) but still wants the Train tab
+# and the fast pixel classifier to work without standing up a separate ipred
+# service just for this app — see the `:als` tag and docker-compose.ml.yml.
+FROM app AS app-ml
+
+# The `ml` extra (torch/dlsia/qlty) — kept out of the lean `app` image above.
+# Pulls in the full CUDA toolkit (nvidia-cudnn-cu12, nvidia-cublas-cu12, etc.)
+# as torch dependencies — several GB — needed for real GPU support when this
+# container is run with `--gpus all` on a CUDA host; a build here can fail
+# with an I/O error mid-write on a disk-constrained host, which is a local
+# Docker disk-space problem to fix (see docker system df / prune, or grow
+# Docker Desktop's disk allocation), not a reason to drop CUDA support.
+RUN pip install --no-cache-dir ".[ml]"
+
+# ipred is a sibling package with its own pyproject, not a backend dependency.
+COPY ipred/ /ipred/
+RUN pip install --no-cache-dir /ipred
+
+COPY docker-entrypoint-ml.sh /usr/local/bin/docker-entrypoint-ml.sh
+RUN chmod +x /usr/local/bin/docker-entrypoint-ml.sh
+
+# 8002 backend; 8003 ipred, exposed so it can be reached directly if wanted.
+# No Tiled port here — this stage never starts one; TILED_URI at `docker run`
+# time points at the deployment's own external Tiled.
+EXPOSE 8002 8003
+ENTRYPOINT ["/usr/local/bin/docker-entrypoint-ml.sh"]
+
+# --- Stage: batteries-included image (+ Tiled server too) ---
 # Bundles all three backend services into one container over loopback — the
 # CI/Docker coverage gap this stage fixes: previously NOTHING packaged ipred
 # or a Tiled server at all, so the app image alone could never run iPred or
 # dlsia, and there was no single-command way to try the full stack without
 # start_all.sh's separate local processes.
-FROM app AS app-full
-
-# The `ml` extra (torch/dlsia/qlty) — kept out of the lean `app` image above,
-# but this stage's whole point is "batteries included," so it's worth the
-# size. Pulls in the full CUDA toolkit (nvidia-cudnn-cu12, nvidia-cublas-cu12,
-# etc.) as torch dependencies — several GB — needed for real GPU support when
-# this container is run with `--gpus all` on a CUDA host; a build here can
-# fail with an I/O error mid-write on a disk-constrained host, which is a
-# local Docker disk-space problem to fix (see docker system df / prune, or
-# grow Docker Desktop's disk allocation), not a reason to drop CUDA support.
-RUN pip install --no-cache-dir ".[ml]"
+FROM app-ml AS app-full
 
 # tiled[all] pulls in the actual server (catalog, array/table adapters) —
 # `app`'s pyproject only pins tiled[client], which has no server component.
 RUN pip install --no-cache-dir "tiled[all]"
 
-# ipred is a sibling package with its own pyproject, not a backend dependency.
-COPY ipred/ /ipred/
-RUN pip install --no-cache-dir /ipred
-
 # Portable Tiled config (see tiled/config.docker.yml's own doc comment for
 # why this isn't just tiled/config.yml — that one hardcodes a local dev
 # machine's absolute data path).
diff --git a/backend/.env.example b/backend/.env.example
index c26e815..cfa192d 100644
--- a/backend/.env.example
+++ b/backend/.env.example
@@ -8,6 +8,14 @@ BROWSE_CACHE_TTL_SECONDS=300
 LOCAL_DATA_ROOT=~/data
 EXPORT_ROOT=
 
+# Path into the Tiled tree Browse treats as its root. Leave unset for a
+# from-scratch bundled Tiled (defaults to wherever this repo's own ingest
+# writes under, see TILED_INGEST_ROOT). A deployment pointed at an existing
+# institutional Tiled catalog (e.g. ALS's production Tiled) needs this set to
+# the real path into that catalog — confirm the exact value with whoever
+# operates that Tiled server rather than guessing:
+# TILED_BROWSE_PATH=beamlines/bl832/processed
+
 # Production / Docker notes:
 # - The container serves the SPA same-origin, so BROWSE_ALLOWED_ORIGINS can stay
 #   empty. Set it (comma-separated) only for split frontend/backend origins.
diff --git a/docker-compose.local.yml b/docker-compose.local.yml
new file mode 100644
index 0000000..e27a26b
--- /dev/null
+++ b/docker-compose.local.yml
@@ -0,0 +1,52 @@
+# The ":local" shape: fully bundled (Tiled + backend/ml + ipred, same as
+# docker-compose.full.yml) but with VITE_BASE_PATH baked in at
+# /seg_studio/ so it's reachable at the familiar dev-server-shaped URL
+# http://localhost:5173/seg_studio/ by default — exercising the same
+# subpath-hosting behavior ALS's own :als deployment uses, rather than
+# leaving that path only tested at ALS. Host port is configurable via
+# HOST_PORT for anyone who wants a different one.
+#
+# The app container itself has NO idea it's hosted under a subpath — same as
+# the real ALS deployment, it depends entirely on a reverse proxy stripping
+# the prefix before the request arrives (see docs/reference/architecture.md's
+# "Subpath hosting" note). A tiny nginx service (docker/nginx-local.conf)
+# provides that here, so this compose file actually reproduces the real
+# hosting behavior instead of only baking in URLs nothing then serves.
+#
+#   docker compose -f docker-compose.local.yml up --build
+#
+# To run the already-published ghcr.io image instead of building locally,
+# skip `build:` on app-local and use `image: ghcr.io/<owner>/<repo>/app-full:local`.
+#
+services:
+  app-local:
+    build:
+      context: .
+      target: app-full
+      args:
+        VITE_BASE_PATH: "/seg_studio/"
+    expose:
+      - "8002"
+    ports:
+      - "8003:8003"  # ipred, direct access (optional)
+      - "8010:8010"  # Tiled, direct access (optional)
+    environment:
+      LOCAL_DATA_ROOT: "/data"
+      BROWSE_ALLOWED_ORIGINS: "${BROWSE_ALLOWED_ORIGINS:-}"
+      TILED_API_KEY: "${TILED_API_KEY:-}"
+    volumes:
+      - annotation-data-local:/data
+      # Bind-mount your own source datasets to /data/raw, e.g.:
+      #   - /path/to/your/data:/data/raw
+
+  proxy:
+    image: nginx:stable-alpine
+    depends_on:
+      - app-local
+    ports:
+      - "${HOST_PORT:-5173}:80"
+    volumes:
+      - ./docker/nginx-local.conf:/etc/nginx/conf.d/default.conf:ro
+
+volumes:
+  annotation-data-local:
diff --git a/docker-compose.ml.yml b/docker-compose.ml.yml
new file mode 100644
index 0000000..6e68dbf
--- /dev/null
+++ b/docker-compose.ml.yml
@@ -0,0 +1,34 @@
+# Backend (with the ml/dlsia extra) + ipred bundled; Tiled stays external —
+# point TILED_URI/TILED_API_KEY at your own Tiled server (Dockerfile's app-ml
+# stage, the same shape ghcr.io publishes under the `:als` tag). Use this when
+# you already have a Tiled server running and want Train/iPred to work
+# without a second, separately-maintained ipred deployment. For a fully
+# bundled stack with no external services at all, use docker-compose.full.yml
+# instead; for the lightest option (no ipred/ML), use docker-compose.yml.
+#
+#   docker compose -f docker-compose.ml.yml up --build
+#
+services:
+  app-ml:
+    build:
+      context: .
+      target: app-ml
+    ports:
+      - "8002:8002"  # the app itself (SPA + API) — the only port most setups need
+      - "8003:8003"  # ipred, direct access (optional)
+    environment:
+      # External Tiled server (required):
+      TILED_URI: "${TILED_URI:-http://host.docker.internal:8010}"
+      TILED_API_KEY: "${TILED_API_KEY:-}"
+      # Persisted annotation drafts/versions/exports live under this path:
+      LOCAL_DATA_ROOT: "/data"
+      # Same-origin SPA → CORS can be empty; set if you split origins:
+      BROWSE_ALLOWED_ORIGINS: "${BROWSE_ALLOWED_ORIGINS:-}"
+    volumes:
+      - annotation-data-ml:/data
+    # host.docker.internal lets the container reach a Tiled on the host (Linux):
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+
+volumes:
+  annotation-data-ml:
diff --git a/docker-entrypoint-ml.sh b/docker-entrypoint-ml.sh
new file mode 100755
index 0000000..422b0f9
--- /dev/null
+++ b/docker-entrypoint-ml.sh
@@ -0,0 +1,21 @@
+#!/bin/sh
+# Entrypoint for the app-ml Docker image (Dockerfile's app-ml stage): starts
+# ipred as a background process, then execs the backend in the foreground as
+# the container's main process. Unlike app-full's entrypoint, this does NOT
+# start (or need) a Tiled process at all — TILED_URI must be set at `docker
+# run` time to point at the deployment's own external, already-running Tiled
+# (e.g. ALS's production Tiled for the `:als` tag).
+#
+# Known limitation, same as app-full's entrypoint: ipred is a plain
+# backgrounded process, not supervised — if it crashes after startup, the
+# container keeps running (its main process is the backend) but iPred/Train
+# silently stay down until the whole container is restarted.
+set -e
+
+mkdir -p /data
+
+uvicorn ipred.api:app --host 0.0.0.0 --port 8003 &
+
+export IPRED_URL="http://127.0.0.1:8003"
+
+exec uvicorn annotation_server:app --host 0.0.0.0 --port 8002
diff --git a/docker/nginx-local.conf b/docker/nginx-local.conf
new file mode 100644
index 0000000..4f7d7b6
--- /dev/null
+++ b/docker/nginx-local.conf
@@ -0,0 +1,34 @@
+# Minimal stripping reverse proxy for docker-compose.local.yml — simulates
+# the hub.als.lbl.gov-style proxy the `:als`/`:local` base-path design
+# actually depends on (see the "Base-path (subpath) deployment support"
+# section of the deployment plan). The app container itself has NO idea it's
+# hosted under /seg_studio/ — its static file serving and SPA catch-all only
+# ever see plain root-relative paths. This proxy is what turns
+# "localhost:5173/seg_studio/..." into "app-local:8002/..." before it ever
+# reaches the container, exactly like the real reverse proxy would strip
+# "hub.als.lbl.gov/bl832/seg_studio/..." down to "/...".
+server {
+    listen 80;
+
+    location = / {
+        return 302 /seg_studio/;
+    }
+
+    location /seg_studio/ {
+        # Resolve at request time (Docker's embedded DNS), not once at nginx
+        # startup — a plain `proxy_pass http://app-local:8002` fails hard at
+        # boot if app-local isn't up yet, and keeps a stale IP across an
+        # app-local restart/recreate. `set` must come BEFORE the `rewrite...
+        # break`, since `break` halts further rewrite-module directives
+        # (including a later `set`) in this block.
+        resolver 127.0.0.11 valid=10s;
+        set $upstream app-local:8002;
+        rewrite ^/seg_studio/(.*)$ /$1 break;
+        proxy_pass http://$upstream;
+        proxy_set_header Host $host;
+        proxy_set_header X-Forwarded-Proto $scheme;
+        proxy_http_version 1.1;
+        proxy_set_header Upgrade $http_upgrade;
+        proxy_set_header Connection "upgrade";
+    }
+}
diff --git a/docs/reference/architecture.md b/docs/reference/architecture.md
index cf61849..594a906 100644
--- a/docs/reference/architecture.md
+++ b/docs/reference/architecture.md
@@ -137,10 +137,11 @@ The dev and production layouts differ mainly in **who serves the SPA** and
     Vite proxies every `/api` request to the backend, so the frontend uses an
     empty `API_BASE` and stays same-origin.
 
-=== "Production (Docker) — two images, pick one"
+=== "Production (Docker) — three images, pick one"
 
-    Two Dockerfile targets/compose files cover different deployment needs —
-    neither replaces the other.
+    Three Dockerfile targets/compose files, layered `app` → `app-ml` → `app-full`
+    (each `FROM` the previous, so the install steps are never duplicated) —
+    each covers a different deployment need, none replaces the others.
 
     **`app` (lean)** — frontend + backend only, for a deployment with its own
     external Tiled and no need for iPred/dlsia:
@@ -157,9 +158,30 @@ The dev and production layouts differ mainly in **who serves the SPA** and
       Container --> Volume
     ```
 
-    **`app-full` (batteries-included)** — one container running all three
-    backend-side services (Tiled and ipred as backgrounded processes, backend
-    as the container's foreground/main process — no Docker networking needed):
+    **`app-ml`** — `app` plus the `ml` extra (torch/dlsia) and ipred bundled,
+    Tiled still external — for a deployment that already has its own
+    production Tiled (bundling a second, empty one would be wrong) but still
+    wants Train/iPred to work without standing up a separate ipred service.
+    This is what the `:als` ghcr.io tag publishes, base path baked in at
+    `/bl832/seg_studio/`:
+
+    ```mermaid
+    flowchart LR
+      Browser["Browser"]
+      Container["app-ml image<br/>backend :8002 (foreground)<br/>+ ipred :8003 (background)"]
+      TiledExt["External Tiled<br/>(TILED_URI env)"]
+      Volume[("/data volume")]
+
+      Browser -->|same origin| Container
+      Container --> TiledExt
+      Container --> Volume
+    ```
+
+    **`app-full` (batteries-included)** — `app-ml` plus a bundled Tiled server
+    too, one container running all three backend-side services (Tiled and
+    ipred as backgrounded processes, backend as the container's foreground/main
+    process — no Docker networking needed). This is what the `:local` ghcr.io
+    tag publishes, base path baked in at `/seg_studio/`:
 
     ```mermaid
     flowchart LR
@@ -171,15 +193,24 @@ The dev and production layouts differ mainly in **who serves the SPA** and
       Container --> Volume
     ```
 
-    Both copy the compiled SPA into `backend/static/`; FastAPI serves the API
-    and static files from one origin. `app-full` adds the `ml` extra, `tiled[all]`,
-    and the `ipred` package on top of the same base layer, plus
-    `docker-entrypoint-full.sh` to start the three processes. Known limitation
-    (documented in that entrypoint's own comment): Tiled/ipred aren't supervised
-    inside `app-full` — if one crashes post-startup the container keeps running
-    (backend is its main process) but that service stays down until a restart.
-    Fine for local/demo use; a real deployment needing that resilience should
-    use `app` against a properly-supervised external Tiled instead.
+    All three copy the compiled SPA into `backend/static/`; FastAPI serves the
+    API and static files from one origin. Known limitation (documented in
+    `docker-entrypoint-full.sh`'s/`docker-entrypoint-ml.sh`'s own comments):
+    Tiled/ipred aren't supervised inside `app-full`/`app-ml` — if one crashes
+    post-startup the container keeps running (backend is its main process) but
+    that service stays down until a restart. Fine for local/demo use; a real
+    deployment needing that resilience should use `app` against a
+    properly-supervised external Tiled instead.
+
+    **Subpath hosting** (e.g. `hub.als.lbl.gov/bl832/seg_studio/`, behind a
+    reverse proxy that strips the prefix before forwarding) is a build-time
+    choice, not a runtime one — Vite bakes `base`/`import.meta.env.BASE_URL`
+    into the compiled JS, so a root-hosted image and a subpath-hosted image are
+    two different build artifacts from the same source. Set the `VITE_BASE_PATH`
+    build-arg (`vite.config.ts`'s `base`, threaded through to `main.tsx`'s
+    `BrowserRouter basename` and `config.ts`'s `API_BASE`) at `docker build`
+    time; leave it unset for root-hosted (local dev, and the generic
+    `:latest`-tagged builds of all three images above are unaffected).
 
 ## Frontend architecture
 
diff --git a/frontend/.env.example b/frontend/.env.example
index d3c4d89..7dfd4ff 100644
--- a/frontend/.env.example
+++ b/frontend/.env.example
@@ -4,6 +4,13 @@
 # Backend API base. Leave empty for same-origin (dev uses the /api proxy).
 # VITE_API_BASE=
 
+# Subpath the app is hosted under, behind a reverse proxy that strips the
+# prefix before forwarding (e.g. hub.als.lbl.gov/bl832/seg_studio/ -> container
+# sees plain /). Read at BUILD time (Vite bakes it into the compiled asset
+# URLs/router basename) — leave unset for root-hosted (local dev, the lean
+# :local/:latest images). Must end in a trailing slash.
+# VITE_BASE_PATH=/bl832/seg_studio/
+
 # Documentation site URL (the sidebar "Docs" button).
 # VITE_DOCS_URL=http://127.0.0.1:8000
 
diff --git a/frontend/index.html b/frontend/index.html
index ef45032..7649d8d 100644
--- a/frontend/index.html
+++ b/frontend/index.html
@@ -2,7 +2,7 @@
 <html lang="en">
   <head>
     <meta charset="UTF-8" />
-    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <link rel="icon" type="image/svg+xml" href="%BASE_URL%vite.svg" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <meta
       name="description"
diff --git a/frontend/src/config.ts b/frontend/src/config.ts
index 71703a4..2471b6c 100644
--- a/frontend/src/config.ts
+++ b/frontend/src/config.ts
@@ -1,11 +1,16 @@
 /**
  * Base URL for the backend API.
- * - Default is '' (same origin): dev uses Vite's `/api` proxy; the production
- *   container serves the SPA from FastAPI itself, so `/api` is same-origin too.
+ * - Default is derived from Vite's `BASE_URL` (itself driven by `VITE_BASE_PATH`
+ *   at build time, see vite.config.ts): '/' when root-hosted (dev's `/api`
+ *   proxy, or the production container serving the SPA same-origin), or the
+ *   trimmed subpath (e.g. `/bl832/seg_studio`) when hosted behind a
+ *   path-stripping reverse proxy, so `fetch(`${API_BASE}/api/...`)` still
+ *   resolves to a path the proxy actually routes.
  * - Set `VITE_API_BASE` at build time only for split deployments where the API
  *   lives on a different origin (e.g. `https://api.example.com`).
  */
-export const API_BASE = import.meta.env.VITE_API_BASE?.trim() || '';
+export const API_BASE =
+  import.meta.env.VITE_API_BASE?.trim() || import.meta.env.BASE_URL.replace(/\/$/, '');
 
 /**
  * URL of the user documentation site (MkDocs).
diff --git a/frontend/src/lib/sam/samWorker.ts b/frontend/src/lib/sam/samWorker.ts
index e422b46..37afb69 100644
--- a/frontend/src/lib/sam/samWorker.ts
+++ b/frontend/src/lib/sam/samWorker.ts
@@ -44,7 +44,7 @@ const MODEL_SOURCES: ModelSource[] = [
 ];
 
 env.allowLocalModels = true;
-env.localModelPath = '/models/';
+env.localModelPath = `${import.meta.env.BASE_URL}models/`;
 if (env.backends?.onnx?.wasm) env.backends.onnx.wasm.numThreads = 1;
 
 interface PromptPoint { x: number; y: number; label: 0 | 1 }
diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx
index dac539b..71a0290 100644
--- a/frontend/src/main.tsx
+++ b/frontend/src/main.tsx
@@ -14,7 +14,7 @@ installImageSliceGc(queryClient);
 
 createRoot(document.getElementById('root')!).render(
   <StrictMode>
-    <BrowserRouter>
+    <BrowserRouter basename={import.meta.env.BASE_URL}>
       <QueryClientProvider client={queryClient}>
         <App />
       </QueryClientProvider>
diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts
index 8cd8663..518282c 100644
--- a/frontend/vite.config.ts
+++ b/frontend/vite.config.ts
@@ -17,6 +17,11 @@ function gitCommit(): string {
 
 export default defineConfig({
   plugins: [react(), tsconfigPaths()],
+  // Read at build time so the same Dockerfile stage can produce either a
+  // root-hosted image (unset, defaults to '/') or a subpath-hosted one (e.g.
+  // `/bl832/seg_studio/` behind a stripping reverse proxy) — see main.tsx's
+  // BrowserRouter basename and config.ts's API_BASE for the other two halves.
+  base: process.env.VITE_BASE_PATH || '/',
   define: {
     __APP_VERSION__: JSON.stringify(appVersion()),
     __GIT_COMMIT__: JSON.stringify(gitCommit()),
@@ -25,6 +30,10 @@ export default defineConfig({
   // (konva, polygon-clipping) land in their own chunks that load with the lazy
   // Annotate page rather than bloating the initial /connect entry.
   build: {
+    // Explicit, not left to Vite's implicit production-mode default — a stray
+    // future `--mode development` in a build script must not silently ship
+    // unminified bundles.
+    minify: 'esbuild',
     rollupOptions: {
       output: {
         manualChunks(id: string) {

From 09ca08b3ebc97d6d69e82e60e8e8b61540347378 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Fri, 11 Sep 2026 10:43:37 -0700
Subject: [PATCH 31/38] Updating documentation with docker build/deployment for
 local/production

---
 README.md                            |  22 ++++++
 docs/getting-started/installation.md |  53 ++++++++++---
 docs/reference/deployment.md         | 107 +++++++++++++++++++++++++++
 mkdocs.yml                           |   1 +
 4 files changed, 173 insertions(+), 10 deletions(-)
 create mode 100644 docs/reference/deployment.md

diff --git a/README.md b/README.md
index 2e9ed5f..56aa0c3 100644
--- a/README.md
+++ b/README.md
@@ -71,6 +71,27 @@ PROD=1 ./start_all.sh
 The frontend is built to `backend/static/` and served by FastAPI. The whole app is then
 available at the **Backend** URL (http://127.0.0.1:8002).
 
+## Docker
+
+Three Dockerfile targets/compose files, layered `app` → `app-ml` → `app-full`, cover
+different needs — pick the one that matches what you already have running:
+
+| Compose file | Tiled | iPred / Train | Use when |
+| --- | --- | --- | --- |
+| `docker-compose.yml` | External (bring your own) | Not included | You already have Tiled and just need Connect/Browse/Annotate/Export. |
+| `docker-compose.ml.yml` | External (bring your own) | Bundled | You already have Tiled but also want Train/iPred. |
+| `docker-compose.full.yml` | Bundled | Bundled | Nothing external required — the simplest way to try everything. |
+
+```bash
+docker compose -f docker-compose.full.yml up --build   # fully bundled
+```
+
+Then open <http://localhost:8002>. See
+[Installation](docs/getting-started/installation.md) for the other two images and
+env vars, and [Production deployment](docs/reference/deployment.md) for hosting a
+shared deployment under a URL prefix (e.g. behind a reverse proxy at
+`hub.example.org/your-path/`) with the published `ghcr.io` images.
+
 ## Workflow
 
 The app is organized into four tabs:
@@ -150,4 +171,5 @@ frontend/vendor/  Git submodules — the WebGPU volume renderer used by the 3D t
 tiled/        Local Tiled server config
 docs/         MkDocs Material documentation site
 start_all.sh  One-command launcher for the full stack
+Dockerfile    app / app-ml / app-full image targets (see Docker, above)
 ```
diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md
index 1f0470d..2f1a3d5 100644
--- a/docs/getting-started/installation.md
+++ b/docs/getting-started/installation.md
@@ -3,7 +3,11 @@
 There are two ways to run Segmentation Annotation Studio:
 
 - **Local development** — one command starts everything (recommended for annotators and evaluation).
-- **Docker** — a single production container that serves the app against an external Tiled server.
+- **Docker** — a prebuilt or locally-built container image; pick the shape that matches
+  whether you already have a Tiled server and whether you want iPred/Train available.
+
+For hosting a shared, ALS-style deployment behind a reverse proxy (rather than running
+it yourself), see [Production deployment](../reference/deployment.md) instead.
 
 ---
 
@@ -67,30 +71,53 @@ FRONTEND_PORT=5200 BACKEND_PORT=8100 TILED_PORT=8110 ./start_all.sh
 
 ---
 
-## Option 2 — Docker (production)
+## Option 2 — Docker
+
+Three Dockerfile targets/compose files cover different needs — pick the one that
+matches what you already have running and whether you want iPred/Train available.
+None replaces the others; `app-ml` and `app-full` each build on the previous stage
+rather than duplicating install steps.
 
-Docker runs a **single container** that serves the built frontend and the API
-together on port **8002**. Tiled is **not** included — you point the container
-at an existing Tiled server.
+| Compose file | Image target | Tiled | iPred / Train | Use when |
+| --- | --- | --- | --- | --- |
+| `docker-compose.yml` | `app` | External (you provide one) | Not included | You already have a Tiled server and only need Connect/Browse/Annotate/Export. |
+| `docker-compose.ml.yml` | `app-ml` | External (you provide one) | **Bundled** | You already have a Tiled server but also want Train/iPred to work, without standing up a separate iPred service. |
+| `docker-compose.full.yml` | `app-full` | **Bundled** | **Bundled** | You want the whole stack (Tiled + backend + iPred) with nothing external to set up — the simplest way to try everything. |
+
+### Lean (`app`) — bring your own Tiled
 
 ```bash
+TILED_URI=https://tiled.example.com \
+TILED_API_KEY=your-key \
 docker compose up --build
 ```
 
 Then open <http://localhost:8002>.
 
-Configure the connection to your external Tiled through environment variables
-(see [Environment variables](#environment-variables)):
+### With iPred/Train, external Tiled (`app-ml`)
 
 ```bash
 TILED_URI=https://tiled.example.com \
 TILED_API_KEY=your-key \
-docker compose up --build
+docker compose -f docker-compose.ml.yml up --build
 ```
 
+Then open <http://localhost:8002>. iPred is also reachable directly on `:8003` if needed.
+
+### Fully bundled (`app-full`) — nothing external required
+
+```bash
+docker compose -f docker-compose.full.yml up --build
+```
+
+Then open <http://localhost:8002>. Tiled (`:8010`) and iPred (`:8003`) are also
+reachable directly if you want to hit them outside the app.
+
 !!! warning "Persisting your data"
-    Mount `LOCAL_DATA_ROOT` as a volume so annotation drafts, versions, and
-    exports survive container restarts.
+    Every compose file above mounts `LOCAL_DATA_ROOT` (`/data`) as a named volume,
+    so annotation drafts, versions, and exports already survive a container
+    restart. For `app-full`, bind-mount your own source datasets to `/data/raw`
+    (see the compose file's own comment) so Tiled can ingest them.
 
 ---
 
@@ -147,6 +174,12 @@ Backend configuration lives in `backend/.env` (created from
 | `EXPORT_ROOT` | Override output folder for exports | `~/data/exports` |
 | `BROWSE_CACHE_TTL_SECONDS` | Cache lifetime for Tiled browse listings | `300` |
 | `BROWSE_ALLOWED_ORIGINS` | CORS origins (only needed for split frontend/backend hosting) | *(empty)* |
+| `TILED_BROWSE_PATH` | Path into the Tiled tree Browse treats as its root | *(unset — this repo's own ingest root)* |
+
+`VITE_BASE_PATH` is a **frontend build-time** setting (a Docker build-arg, not a
+runtime env var — see [Production deployment](../reference/deployment.md)) for
+hosting under a URL prefix rather than at the domain root; leave it unset for
+everything on this page.
 
 !!! danger "Never commit secrets"
     `backend/.env` is git-ignored. Never commit it, and never expose
diff --git a/docs/reference/deployment.md b/docs/reference/deployment.md
new file mode 100644
index 0000000..d091abf
--- /dev/null
+++ b/docs/reference/deployment.md
@@ -0,0 +1,107 @@
+# Production deployment
+
+This page is for standing up a shared, long-lived deployment (e.g. behind
+`hub.als.lbl.gov`), as opposed to [Installation](../getting-started/installation.md)'s
+"run it yourself" Docker options. It covers the published container images, hosting
+under a URL prefix, and what's still an open question before a real production rollout.
+
+---
+
+## Published images
+
+Every merge to `main` builds and publishes images to GitHub Container Registry
+(`ghcr.io`) via `.github/workflows/ci.yml`'s `docker-publish` job — this never fires
+on a pull request, only on `main`.
+
+| Image | Tags | Dockerfile target | Tiled | iPred / Train | Base path baked in |
+| --- | --- | --- | --- | --- | --- |
+| `ghcr.io/<repo>/app` | `:latest`, `:sha-<commit>` | `app` | External | Not included | None (root-hosted) |
+| `ghcr.io/<repo>/app-ml` | `:latest`, `:sha-<commit>` | `app-ml` | External | Bundled | None (root-hosted) |
+| `ghcr.io/<repo>/app-ml` | `:als`, `:als-<commit>` | `app-ml` | External (ALS's own production Tiled) | Bundled | `/bl832/seg_studio/` |
+| `ghcr.io/<repo>/app-full` | `:latest`, `:sha-<commit>` | `app-full` | Bundled | Bundled | None (root-hosted) |
+| `ghcr.io/<repo>/app-full` | `:local`, `:local-<commit>` | `app-full` | Bundled | Bundled | `/seg_studio/` |
+
+Every tag has an immutable `-<commit-sha>` sibling, so pinning a deployment to a
+specific known-good build (rather than a floating tag) is always available —
+that's the rollback story: point at the previous `-sha` tag.
+
+`:als` is the shape ALS's own hub deployment uses: iPred/Train bundled (so they work
+without a second, separately-maintained service), Tiled kept external since ALS
+already runs its own production Tiled and a second, empty in-container one would be
+wrong. `:local` is fully self-contained (Tiled + backend + iPred, nothing external
+required) for anyone standing this up for themselves who still wants to exercise the
+same subpath-hosting path `:als` uses, rather than leaving that path tested only at
+ALS — see [Installation](../getting-started/installation.md) for running it locally.
+
+---
+
+## Hosting under a URL prefix
+
+A shared deployment is typically reached at a path prefix off a shared hub domain
+(e.g. `hub.als.lbl.gov/bl832/seg_studio/`) rather than its own subdomain, so it stays
+manageable from an ops/ticketing perspective. This requires two things working together:
+
+1. **A reverse proxy in front of the container that strips the prefix** before
+   forwarding — the app container itself has no idea it's hosted under a subpath; it
+   only ever serves and expects plain root-relative paths (`/api/...`, `/assets/...`).
+   A request to `hub.als.lbl.gov/bl832/seg_studio/api/...` must reach the container as
+   plain `/api/...`. This is standard nginx/Traefik path-based routing — the backend
+   itself needs **no code changes** for this.
+2. **The frontend build baked for that prefix**, via the `VITE_BASE_PATH` Docker
+   build-arg (e.g. `--build-arg VITE_BASE_PATH=/bl832/seg_studio/`). Vite bakes this
+   into the compiled JS at build time (`base` in `vite.config.ts`, consumed by
+   `main.tsx`'s router `basename` and `config.ts`'s `API_BASE`) — **root-hosted and
+   subpath-hosted are two different build artifacts from the same source**, not one
+   image that adapts at runtime. The published `:als`/`:local` tags already have this
+   baked in; the generic `:latest`/`:sha` tags of every image are root-hosted and
+   unaffected.
+
+If you're standing up your own subpath deployment (a different institution, a
+different path), build your own image with the matching `VITE_BASE_PATH` rather than
+reusing the `:als`/`:local` tags, which are baked for this repo's specific paths.
+
+!!! tip "Testing this locally before it matters"
+    `docker-compose.local.yml` (see [Installation](../getting-started/installation.md))
+    includes a small nginx service that reproduces exactly this proxy-stripping
+    behavior, so `/seg_studio/` hosting can be verified on a laptop before it's ever
+    tried against a real hub deployment. Serving the built `dist/` directly through a
+    plain static file server does **not** reproduce this — the stripping proxy is
+    load-bearing, not optional.
+
+---
+
+## Environment variables specific to a shared deployment
+
+Beyond what's already covered in [Installation](../getting-started/installation.md#environment-variables):
+
+| Variable | Purpose |
+| --- | --- |
+| `TILED_BROWSE_PATH` | The real path into an existing institutional Tiled catalog Browse should treat as its root (e.g. `beamlines/bl832/processed`). Confirm the exact value with whoever operates that Tiled server — don't assume it matches another deployment's beamline. |
+| `TILED_URI` / `TILED_API_KEY` | Point at the shared production Tiled server. See the open authentication question below — a single shared key is a stopgap, not the final design. |
+
+Persistent storage (`LOCAL_DATA_ROOT`, defaulting to the `/data` volume already
+declared in the image) and CORS (`BROWSE_ALLOWED_ORIGINS`) need no special
+production-specific handling beyond what's already documented for local Docker use —
+mount a real volume, and leave CORS empty since the SPA and API are served same-origin.
+
+---
+
+## Open questions before a real production rollout
+
+These are real, unresolved gaps — not yet implemented, flagged here rather than
+glossed over:
+
+- **Per-user Tiled authentication.** Tiled's authorization is per-proposal
+  (ESAF) read/write tags, not a blanket grant — a single shared `TILED_API_KEY`
+  either over- or under-privileges every user. The real design needs per-user
+  ORCID/OIDC login (for both the browser's direct-to-Tiled 3D viewer calls and the
+  backend's own proxied calls), which hasn't been built yet. Until then, a shared
+  `TILED_API_KEY` works but does not correctly represent per-proposal permissions —
+  fine for evaluation, not for a real multi-user production rollout with real data.
+- **Hub-level SSO.** Whether `hub.als.lbl.gov`'s own reverse proxy fully gates access
+  before a request reaches this app, or whether the app is expected to participate in
+  auth itself, is a separate open question from Tiled's own data-access control above
+  — confirm with hub admins before relying on either assumption.
+- **GPU availability for `:als`.** Bundling iPred/ML into `:als` only delivers a
+  practically usable Train tab if the host running that container provides GPU
+  passthrough — CPU-only training/inference works but is dramatically slower.
diff --git a/mkdocs.yml b/mkdocs.yml
index a468611..580a45c 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -92,6 +92,7 @@ nav:
       - 7. 3D volume view: guide/volume.md
   - Reference:
       - Software architecture: reference/architecture.md
+      - Production deployment: reference/deployment.md
       - Keyboard shortcuts: reference/shortcuts.md
       - Troubleshooting: reference/troubleshooting.md
 

From ddc1e89869d16b2d868ae9dbb981574447a5b77e Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Fri, 11 Sep 2026 11:10:29 -0700
Subject: [PATCH 32/38] Fixing race condition in unit test

---
 .../components/Browse/ColumnBrowser.test.tsx  | 45 ++++++++++++++++++-
 1 file changed, 43 insertions(+), 2 deletions(-)

diff --git a/frontend/src/components/Browse/ColumnBrowser.test.tsx b/frontend/src/components/Browse/ColumnBrowser.test.tsx
index bc3bf80..a81a62e 100644
--- a/frontend/src/components/Browse/ColumnBrowser.test.tsx
+++ b/frontend/src/components/Browse/ColumnBrowser.test.tsx
@@ -10,7 +10,7 @@
  * (facets → show-all → items) still runs end to end against a stubbed fetch.
  */
 import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
-import { cleanup, render, screen, waitFor } from '@testing-library/react';
+import { act, cleanup, render, screen, waitFor } from '@testing-library/react';
 import userEvent from '@testing-library/user-event';
 import { MemoryRouter } from 'react-router';
 import ColumnBrowser from './ColumnBrowser';
@@ -132,10 +132,51 @@ describe('ColumnBrowser', () => {
     renderBrowser();
 
     expect(await screen.findByTestId('items-column')).toBeInTheDocument();
-    expect(screen.getByText('item:sample-1')).toBeInTheDocument();
+    // showAll() sets showingAll=true and kicks off loadItems({}) without
+    // waiting for it — ColumnBrowser mounts ItemsColumn as soon as
+    // showingAll flips, independent of whether the items fetch has actually
+    // resolved. Finding the (possibly still-empty) column is not proof the
+    // sample has rendered; only findByText's own retry-until-appears
+    // semantics correctly wait for the item itself.
+    expect(await screen.findByText('item:sample-1')).toBeInTheDocument();
     expect(screen.getByRole('button', { name: /All samples/ })).toHaveClass('bg-slate-600');
   });
 
+  it('renders samples when a pending items response completes', async () => {
+    let resolveItems!: (response: Response) => void;
+    const pendingItems = new Promise<Response>((resolve) => {
+      resolveItems = resolve;
+    });
+    const itemsHandler = vi.fn(() => pendingItems);
+
+    global.fetch = makeFetchMock({
+      '/api/browse/facets': () => jsonResponse({ facets: ['field1'] }),
+      '/api/browse/items': itemsHandler,
+    });
+
+    renderBrowser();
+
+    try {
+      expect(await screen.findByTestId('items-column')).toBeInTheDocument();
+      expect(itemsHandler).toHaveBeenCalledTimes(1);
+
+      // The container exists, but the response is deliberately still pending
+      // — this is the intermediate state the race above was missing.
+      expect(screen.queryByText('item:sample-1')).not.toBeInTheDocument();
+    } finally {
+      // Release the request even if an assertion fails, so its timeout and
+      // pending updates do not leak into subsequent tests.
+      await act(async () => {
+        resolveItems(
+          jsonResponse({ items: [{ path: 'p1', sample: 'sample-1', metadata: {} }], total: 1 }),
+        );
+        await pendingItems;
+      });
+    }
+
+    expect(await screen.findByText('item:sample-1')).toBeInTheDocument();
+  });
+
   it('shows the disconnected banner when facets cannot be reached, and no items column', async () => {
     global.fetch = makeFetchMock({ '/api/browse/facets': () => jsonResponse('down', false) });
     renderBrowser();

From 6cc6a62c552d6d794c9505862712f0be6d4f2250 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Fri, 11 Sep 2026 12:04:09 -0700
Subject: [PATCH 33/38] Ensuring fqdn not included here

---
 .env.example                         | 39 ++++++++++++++++++++++++++++
 docker-compose.full.yml              |  3 +++
 docker-compose.ml.yml                | 13 +++++++++-
 docker-compose.yml                   |  4 +++
 docs/getting-started/installation.md |  9 ++++++-
 docs/reference/deployment.md         | 36 +++++++++++++++++++++++++
 frontend/.env.example                |  5 ++++
 frontend/vite.config.ts              | 21 ++++++++++++++-
 8 files changed, 127 insertions(+), 3 deletions(-)
 create mode 100644 .env.example

diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..3082a1a
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,39 @@
+# docker-compose config — copy to `.env` at the repo root (docker compose
+# loads this automatically for every `docker compose -f docker-compose*.yml`
+# invocation; git-ignored, never commit real secrets into it).
+#
+# This is distinct from backend/.env.example (used by start_all.sh / running
+# the backend directly, not via Docker) and frontend/.env.example (used by a
+# plain `npm run build`/`npm run dev`, not via Docker) — those are for local
+# development outside a container; this file is for `docker compose` runs.
+#
+# Relevant to: docker-compose.yml (app), docker-compose.ml.yml (app-ml).
+# docker-compose.full.yml and docker-compose.local.yml bundle their own Tiled,
+# so TILED_URI/TILED_BROWSE_PATH don't apply there — only TILED_API_KEY and
+# BROWSE_ALLOWED_ORIGINS do.
+
+# --- Connect to an existing (e.g. staging/production) Tiled server ---
+# Required for docker-compose.yml / docker-compose.ml.yml, which never bundle
+# a Tiled server themselves.
+TILED_URI=https://tiled-staging.als.lbl.gov
+TILED_API_KEY=
+
+# Path into that Tiled server's tree Browse should treat as its root — set
+# this to the real institutional path (confirm with whoever operates that
+# Tiled server; don't assume it matches another deployment's beamline).
+# Leave unset for a from-scratch bundled Tiled (docker-compose.full.yml /
+# docker-compose.local.yml), which uses its own ingest root instead.
+TILED_BROWSE_PATH=beamlines/bl832/processed
+
+# --- Frontend build-time (docker-compose.ml.yml only; baked in at build,
+# not read at container start — see docs/reference/deployment.md) ---
+# Bare path only, never a scheme/host (see that doc's warning on this).
+# Leave unset for root-hosted.
+VITE_BASE_PATH=/bl832/seg_studio/
+
+# --- Same-origin SPA+API → CORS can stay empty; set only for split origins ---
+BROWSE_ALLOWED_ORIGINS=
+
+# --- docker-compose.local.yml only: host port for the nginx proxy in front
+# of the bundled :local-shaped stack (default 5173) ---
+HOST_PORT=5173
diff --git a/docker-compose.full.yml b/docker-compose.full.yml
index d931fa8..d0d6a6e 100644
--- a/docker-compose.full.yml
+++ b/docker-compose.full.yml
@@ -19,6 +19,9 @@ services:
     environment:
       LOCAL_DATA_ROOT: "/data"
       BROWSE_ALLOWED_ORIGINS: "${BROWSE_ALLOWED_ORIGINS:-}"
+      # Only relevant if you point this stack's bundled Tiled at pre-existing
+      # data rather than this app's own ingest — leave unset otherwise:
+      TILED_BROWSE_PATH: "${TILED_BROWSE_PATH:-}"
       # Set this to keep the same Tiled API key across container restarts —
       # otherwise docker-entrypoint-full.sh generates a fresh one each time,
       # which is fine (Tiled's catalog itself persists in the volume below)
diff --git a/docker-compose.ml.yml b/docker-compose.ml.yml
index 6e68dbf..c10fa86 100644
--- a/docker-compose.ml.yml
+++ b/docker-compose.ml.yml
@@ -13,13 +13,24 @@ services:
     build:
       context: .
       target: app-ml
+      args:
+        # Bare path only, never a scheme/host — see .env.example and
+        # docs/reference/deployment.md's "Hosting under a URL prefix". Leave
+        # unset for root-hosted.
+        VITE_BASE_PATH: "${VITE_BASE_PATH:-}"
     ports:
       - "8002:8002"  # the app itself (SPA + API) — the only port most setups need
       - "8003:8003"  # ipred, direct access (optional)
     environment:
-      # External Tiled server (required):
+      # External Tiled server (required) — set these in your own .env for a
+      # staging/production deployment (see .env.example at the repo root;
+      # docker compose loads it automatically):
       TILED_URI: "${TILED_URI:-http://host.docker.internal:8010}"
       TILED_API_KEY: "${TILED_API_KEY:-}"
+      # Path into the Tiled tree Browse treats as its root — set this to the
+      # real institutional path (e.g. beamlines/bl832/processed) when
+      # TILED_URI points at an existing catalog rather than a from-scratch one:
+      TILED_BROWSE_PATH: "${TILED_BROWSE_PATH:-}"
       # Persisted annotation drafts/versions/exports live under this path:
       LOCAL_DATA_ROOT: "/data"
       # Same-origin SPA → CORS can be empty; set if you split origins:
diff --git a/docker-compose.yml b/docker-compose.yml
index c1fa1b7..8f43248 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -12,6 +12,10 @@ services:
       # External Tiled server (required):
       TILED_URI: "${TILED_URI:-http://host.docker.internal:8010}"
       TILED_API_KEY: "${TILED_API_KEY:-}"
+      # Path into the Tiled tree Browse treats as its root — set this for a
+      # deployment pointed at an existing institutional Tiled catalog rather
+      # than a from-scratch one (see .env.example):
+      TILED_BROWSE_PATH: "${TILED_BROWSE_PATH:-}"
       # Persisted annotation drafts/versions/exports live under this path:
       LOCAL_DATA_ROOT: "/data"
       # Same-origin SPA → CORS can be empty; set if you split origins:
diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md
index 2f1a3d5..605c44f 100644
--- a/docs/getting-started/installation.md
+++ b/docs/getting-started/installation.md
@@ -164,7 +164,14 @@ npm test           # run the Vitest unit tests
 ## Environment variables
 
 Backend configuration lives in `backend/.env` (created from
-`backend/.env.example` on first launch). The most relevant settings:
+`backend/.env.example` on first launch) when running via `start_all.sh` /
+directly with `uvicorn`. **Running via `docker compose` instead**, copy the
+repo root's own `.env.example` to `.env` — `docker compose` loads that
+automatically for every `docker-compose*.yml` file, so this is where
+`TILED_URI`/`TILED_API_KEY`/`TILED_BROWSE_PATH` (and, for `app-ml`,
+`VITE_BASE_PATH`) actually get substituted in. See
+[Production deployment](../reference/deployment.md#setting-these-for-docker-compose)
+for details. The most relevant settings either way:
 
 | Variable | Purpose | Default |
 | --- | --- | --- |
diff --git a/docs/reference/deployment.md b/docs/reference/deployment.md
index d091abf..9b61186 100644
--- a/docs/reference/deployment.md
+++ b/docs/reference/deployment.md
@@ -60,6 +60,20 @@ If you're standing up your own subpath deployment (a different institution, a
 different path), build your own image with the matching `VITE_BASE_PATH` rather than
 reusing the `:als`/`:local` tags, which are baked for this repo's specific paths.
 
+!!! warning "`VITE_BASE_PATH` is a path, never a hostname"
+    Set it to the path segment only (`/bl832/seg_studio/`) — **never** a full URL
+    with a scheme or host (`https://hub.als.lbl.gov/bl832/seg_studio/` would be
+    wrong). The image has no business knowing what domain fronts it; only the
+    reverse proxy does, and it can change (or differ between environments)
+    without ever touching this build-arg. This is what makes the *same* `:als`
+    image usable unmodified across staging and production — e.g.
+    `hub-staging.als.lbl.gov/bl832/seg_studio/` and
+    `hub.als.lbl.gov/bl832/seg_studio/` sharing the identical `/bl832/seg_studio/`
+    path, routed to whichever environment's container by the proxy's own
+    hostname-based rule, not by anything baked into the image. Baking a real
+    FQDN in would force a separate image build per environment for no reason,
+    and would silently break if that hostname ever changed.
+
 !!! tip "Testing this locally before it matters"
     `docker-compose.local.yml` (see [Installation](../getting-started/installation.md))
     includes a small nginx service that reproduces exactly this proxy-stripping
@@ -84,6 +98,28 @@ declared in the image) and CORS (`BROWSE_ALLOWED_ORIGINS`) need no special
 production-specific handling beyond what's already documented for local Docker use —
 mount a real volume, and leave CORS empty since the SPA and API are served same-origin.
 
+### Setting these for `docker compose`
+
+Copy `.env.example` (repo root) to `.env` and fill it in — `docker compose` loads a
+`.env` file in the same directory automatically, for every compose file:
+
+```bash
+cp .env.example .env
+# edit .env: set TILED_URI, TILED_API_KEY, TILED_BROWSE_PATH, VITE_BASE_PATH
+docker compose -f docker-compose.ml.yml up --build
+```
+
+`.env` is git-ignored — never commit a real `TILED_API_KEY` into it. This is a
+different file from `backend/.env.example`/`frontend/.env.example`, which configure
+`start_all.sh`/plain `npm run dev`/`npm run build` outside a container — the root
+`.env` is specifically what `docker compose` itself substitutes into the compose
+files' `${VAR}` references (`TILED_URI`, `TILED_API_KEY`, and `TILED_BROWSE_PATH` in
+`docker-compose.yml`/`docker-compose.ml.yml`; `VITE_BASE_PATH` as a build-arg in
+`docker-compose.ml.yml`). If you're running the already-published `ghcr.io` image
+directly (`docker run`/your own orchestration) rather than through one of these
+compose files, pass the equivalent `-e`/env vars there instead — `.env` only affects
+`docker compose` invocations in this directory.
+
 ---
 
 ## Open questions before a real production rollout
diff --git a/frontend/.env.example b/frontend/.env.example
index 7dfd4ff..5d53a81 100644
--- a/frontend/.env.example
+++ b/frontend/.env.example
@@ -9,6 +9,11 @@
 # sees plain /). Read at BUILD time (Vite bakes it into the compiled asset
 # URLs/router basename) — leave unset for root-hosted (local dev, the lean
 # :local/:latest images). Must end in a trailing slash.
+#
+# PATH ONLY — never include the scheme/hostname (not
+# "https://hub.als.lbl.gov/bl832/seg_studio/"). The reverse proxy owns the
+# hostname and can differ per environment (staging vs. production) without
+# ever touching this value or requiring a rebuild.
 # VITE_BASE_PATH=/bl832/seg_studio/
 
 # Documentation site URL (the sidebar "Docs" button).
diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts
index 518282c..dbeda15 100644
--- a/frontend/vite.config.ts
+++ b/frontend/vite.config.ts
@@ -15,13 +15,32 @@ function gitCommit(): string {
   catch { return 'dev'; }
 }
 
+/**
+ * VITE_BASE_PATH must be a bare path (e.g. `/bl832/seg_studio/`), never a full
+ * URL with a scheme/host — the reverse proxy owns the hostname, and can
+ * differ per environment (staging vs. production) without ever touching this
+ * build-arg. Fail the build loudly rather than silently baking in a wrong
+ * `base` that would only surface as a confusing runtime 404.
+ */
+function basePath(): string {
+  const raw = process.env.VITE_BASE_PATH;
+  if (!raw) return '/';
+  if (/^[a-z][a-z0-9+.-]*:\/\//i.test(raw) || raw.startsWith('//')) {
+    throw new Error(
+      `VITE_BASE_PATH must be a path only (e.g. "/bl832/seg_studio/"), not a full URL — got "${raw}". ` +
+      'The reverse proxy owns the hostname; baking one in here would break as soon as it differs (e.g. staging vs. production) or changes.'
+    );
+  }
+  return raw;
+}
+
 export default defineConfig({
   plugins: [react(), tsconfigPaths()],
   // Read at build time so the same Dockerfile stage can produce either a
   // root-hosted image (unset, defaults to '/') or a subpath-hosted one (e.g.
   // `/bl832/seg_studio/` behind a stripping reverse proxy) — see main.tsx's
   // BrowserRouter basename and config.ts's API_BASE for the other two halves.
-  base: process.env.VITE_BASE_PATH || '/',
+  base: basePath(),
   define: {
     __APP_VERSION__: JSON.stringify(appVersion()),
     __GIT_COMMIT__: JSON.stringify(gitCommit()),

From 29df0b37a1da470dd9e7d46ce7737c8a9691e3f4 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Mon, 14 Sep 2026 19:03:57 -0700
Subject: [PATCH 34/38] Documentation

---
 docs/getting-started/installation.md | 16 ++++++++++++++--
 docs/reference/deployment.md         | 11 +++++++++++
 2 files changed, 25 insertions(+), 2 deletions(-)

diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md
index 605c44f..570e5c9 100644
--- a/docs/getting-started/installation.md
+++ b/docs/getting-started/installation.md
@@ -116,8 +116,20 @@ reachable directly if you want to hit them outside the app.
 !!! warning "Persisting your data"
     Every compose file above mounts `LOCAL_DATA_ROOT` (`/data`) as a named volume,
     so annotation drafts, versions, and exports already survive a container
-    restart. For `app-full`, bind-mount your own source datasets to `/data/raw`
-    (see the compose file's own comment) so Tiled can ingest them.
+    restart.
+
+!!! tip "Pointing at your own datasets"
+    For `app-full`, set `LOCAL_SOURCE_DIR` in your own `.env` (copy the repo
+    root's `.env.example`) to an absolute host directory — it's bind-mounted to
+    `/data/raw` inside the container, so the bundled Tiled server (and the Zarr
+    loader's "Browse…" directory picker on the Connect page) can read your real
+    data directly. This matters specifically in Docker: a path from your own
+    machine (e.g. one you'd type into the Zarr loader by hand) means nothing to
+    the container unless it's actually mounted like this — `LOCAL_SOURCE_DIR` is
+    what makes it visible.
+    ```bash
+    LOCAL_SOURCE_DIR=/absolute/path/to/your/data docker compose -f docker-compose.full.yml up --build
+    ```
 
 ---
 
diff --git a/docs/reference/deployment.md b/docs/reference/deployment.md
index 9b61186..64fdcb6 100644
--- a/docs/reference/deployment.md
+++ b/docs/reference/deployment.md
@@ -82,6 +82,16 @@ reusing the `:als`/`:local` tags, which are baked for this repo's specific paths
     plain static file server does **not** reproduce this — the stripping proxy is
     load-bearing, not optional.
 
+!!! warning "A real reverse proxy needs a body-size limit raised too"
+    Discovered via the local test proxy: nginx's own default `client_max_body_size`
+    (1MB) rejects the Ingest tab's multi-file uploads (a folder of TIFFs easily
+    reaches hundreds of MB) with a raw `413` *before the request ever reaches this
+    app* — this app's own upload handling is never even consulted. `docker/nginx-
+    local.conf` sets `client_max_body_size 0;` (unlimited; the backend's own
+    job-based, streamed-to-disk ingest route is the real, size-aware boundary) —
+    **`hub.als.lbl.gov`'s real reverse proxy needs the equivalent setting**, or
+    every real-world upload through this app will hit the same wall in production.
+
 ---
 
 ## Environment variables specific to a shared deployment
@@ -92,6 +102,7 @@ Beyond what's already covered in [Installation](../getting-started/installation.
 | --- | --- |
 | `TILED_BROWSE_PATH` | The real path into an existing institutional Tiled catalog Browse should treat as its root (e.g. `beamlines/bl832/processed`). Confirm the exact value with whoever operates that Tiled server — don't assume it matches another deployment's beamline. |
 | `TILED_URI` / `TILED_API_KEY` | Point at the shared production Tiled server. See the open authentication question below — a single shared key is a stopgap, not the final design. |
+| `LOCAL_SOURCE_DIR` | `docker-compose.full.yml`/`docker-compose.local.yml` (bundled-Tiled shapes) only — bind-mounts a real host directory to `/data/raw` so the bundled Tiled server can read it directly. Not applicable to `:als` (`app-ml`), which points at an already-existing external Tiled instead of a bundled one. |
 
 Persistent storage (`LOCAL_DATA_ROOT`, defaulting to the `/data` volume already
 declared in the image) and CORS (`BROWSE_ALLOWED_ORIGINS`) need no special

From 9bca93f6eaa98eae3596d6ab5806d7f4e10fc33d Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 16 Sep 2026 11:43:36 -0700
Subject: [PATCH 35/38] Auto-discover and register local datasets into Tiled on
 :local startup. Renamed mounted source directory (/data/raw ->
 /data/processed). Raise nginx upload size limit. Reorder Dockerfile layers so
 frontend-only changes don't force a heavy backend rebuild

---
 .env.example                                  |  10 +
 .gitignore                                    |   5 +
 Dockerfile                                    |  40 +-
 backend/annotation_server.py                  |  76 ++++
 backend/ingest.py                             | 189 +++++++++
 backend/local_fs.py                           |  10 +
 backend/schemas.py                            |  28 ++
 .../test_annotation_server_data_routes.py     |  73 ++++
 ...st_annotation_server_local_image_routes.py |  13 +
 backend/tests/test_ingest_scan.py             | 197 +++++++++
 backend/tests/test_local_fs.py                |   9 +
 backend/tests/test_zarr_source.py             | 197 +++++++++
 backend/zarr_source.py                        | 163 ++++++-
 docker-compose.full.yml                       |   8 +-
 docker-compose.local.yml                      |   9 +-
 docker-entrypoint-full.sh                     |  50 ++-
 docker/nginx-local.conf                       |  16 +
 docs/getting-started/installation.md          |  32 +-
 docs/reference/architecture.md                |   2 +-
 docs/reference/deployment.md                  |   2 +-
 frontend/package-lock.json                    | 238 +++++------
 frontend/package.json                         |   3 +
 .../src/components/Ingest/ZarrLoader.test.tsx | 354 ++++++++++++++++
 frontend/src/components/Ingest/ZarrLoader.tsx | 401 +++++++++++++++++-
 tiled/config.docker.yml                       |   8 +-
 25 files changed, 1962 insertions(+), 171 deletions(-)
 create mode 100644 backend/tests/test_ingest_scan.py

diff --git a/.env.example b/.env.example
index 3082a1a..03334ce 100644
--- a/.env.example
+++ b/.env.example
@@ -37,3 +37,13 @@ BROWSE_ALLOWED_ORIGINS=
 # --- docker-compose.local.yml only: host port for the nginx proxy in front
 # of the bundled :local-shaped stack (default 5173) ---
 HOST_PORT=5173
+
+# --- docker-compose.full.yml / docker-compose.local.yml only: bind-mount a
+# real host directory to /data/processed, so the bundled Tiled server can read your
+# own datasets directly (register-in-place via the Zarr loader, or the
+# Browse-tab ingest flow) instead of only ever seeing what was uploaded
+# through the app. Defaults to an empty placeholder (.local-source/, git-
+# ignored) so these compose files still work unset. Must be an ABSOLUTE
+# host path (a relative one is resolved against wherever `docker compose`
+# happens to be run from, not this file's location):
+# LOCAL_SOURCE_DIR=/Users/you/Documents/data/tomo
diff --git a/.gitignore b/.gitignore
index 1f0841d..a651d27 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,6 +13,11 @@ build/
 # Secrets — never commit
 .env
 
+# Default bind-mount target for docker-compose.full.yml/docker-compose.local.yml's
+# LOCAL_SOURCE_DIR (see .env.example) — an empty placeholder so the compose
+# files work unset; your own data lives wherever LOCAL_SOURCE_DIR points, not here.
+.local-source/
+
 # Tiled catalog database (binary, regenerated on first run via start_all.sh)
 .tiled/catalog.db
 .tiled/catalog.db-shm
diff --git a/Dockerfile b/Dockerfile
index aeddaa9..012e1b7 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -13,6 +13,19 @@
 #                          see docker-compose.full.yml, and the `:local` tag.
 # Local dev still uses start_all.sh, which runs the same three services as
 # separate local processes instead of inside a container.
+#
+# Each publishable target is a thin `*-base` stage (Python deps only) plus one
+# final layer that copies in the built frontend. This is deliberate: the
+# frontend build changes far more often than the Python deps do, and
+# Docker/BuildKit's cache is a linear chain per stage — a layer's cache key
+# depends on its parent layer's digest. If the frontend COPY sat partway
+# through a stage (as it once did), any frontend-only change would invalidate
+# every layer built on top of it, including the expensive torch/CUDA and
+# tiled[all] installs in app-ml/app-full, forcing them to reinstall from
+# scratch for a one-line frontend fix. Keeping the frontend COPY as the LAST
+# layer in each `*-base` stage's leaf means a frontend-only change only ever
+# invalidates that one small COPY (+ its trailing EXPOSE/CMD/ENTRYPOINT
+# metadata) — never the Python installs above it.
 
 # --- Stage: build the frontend (same-origin: VITE_API_BASE left empty) ---
 FROM node:22-alpine AS web
@@ -34,8 +47,8 @@ ARG VITE_BASE_PATH=""
 ENV VITE_BASE_PATH=${VITE_BASE_PATH}
 RUN npm run build   # → /web/dist
 
-# --- Stage: lightweight production image (frontend + backend only) ---
-FROM python:3.12-slim AS app
+# --- Stage: lightweight production base (backend deps + source, no frontend) ---
+FROM python:3.12-slim AS app-base
 WORKDIR /app
 
 # Install Python deps first (cached until pyproject changes). py-modules=[] means
@@ -47,23 +60,26 @@ WORKDIR /app
 COPY backend/pyproject.toml ./pyproject.toml
 RUN pip install --no-cache-dir .
 
-# App source + the built SPA (served from ./static by annotation_server.py).
+# App source (the built SPA is copied in by each leaf stage below, last).
 COPY backend/ ./
-COPY --from=web /web/dist ./static
 
 # Drafts/versions/exports persist here — mount a volume in production.
 ENV LOCAL_DATA_ROOT=/data
 VOLUME ["/data"]
 
+# --- Stage: lightweight production image (frontend + backend only) ---
+FROM app-base AS app
+COPY --from=web /web/dist ./static
+
 EXPOSE 8002
 CMD ["uvicorn", "annotation_server:app", "--host", "0.0.0.0", "--port", "8002"]
 
-# --- Stage: backend + ipred + ml, Tiled still external ---
+# --- Stage: backend + ipred + ml base, Tiled still external ---
 # For a deployment that already has its own production Tiled (so bundling a
 # second, empty one would be actively wrong) but still wants the Train tab
 # and the fast pixel classifier to work without standing up a separate ipred
 # service just for this app — see the `:als` tag and docker-compose.ml.yml.
-FROM app AS app-ml
+FROM app-base AS app-ml-base
 
 # The `ml` extra (torch/dlsia/qlty) — kept out of the lean `app` image above.
 # Pulls in the full CUDA toolkit (nvidia-cudnn-cu12, nvidia-cublas-cu12, etc.)
@@ -81,19 +97,23 @@ RUN pip install --no-cache-dir /ipred
 COPY docker-entrypoint-ml.sh /usr/local/bin/docker-entrypoint-ml.sh
 RUN chmod +x /usr/local/bin/docker-entrypoint-ml.sh
 
+# --- Stage: backend + ipred + ml, Tiled still external ---
+FROM app-ml-base AS app-ml
+COPY --from=web /web/dist ./static
+
 # 8002 backend; 8003 ipred, exposed so it can be reached directly if wanted.
 # No Tiled port here — this stage never starts one; TILED_URI at `docker run`
 # time points at the deployment's own external Tiled.
 EXPOSE 8002 8003
 ENTRYPOINT ["/usr/local/bin/docker-entrypoint-ml.sh"]
 
-# --- Stage: batteries-included image (+ Tiled server too) ---
+# --- Stage: batteries-included base (+ Tiled server too) ---
 # Bundles all three backend services into one container over loopback — the
 # CI/Docker coverage gap this stage fixes: previously NOTHING packaged ipred
 # or a Tiled server at all, so the app image alone could never run iPred or
 # dlsia, and there was no single-command way to try the full stack without
 # start_all.sh's separate local processes.
-FROM app-ml AS app-full
+FROM app-ml-base AS app-full-base
 
 # tiled[all] pulls in the actual server (catalog, array/table adapters) —
 # `app`'s pyproject only pins tiled[client], which has no server component.
@@ -107,6 +127,10 @@ COPY tiled/config.docker.yml /app/tiled/config.docker.yml
 COPY docker-entrypoint-full.sh /usr/local/bin/docker-entrypoint-full.sh
 RUN chmod +x /usr/local/bin/docker-entrypoint-full.sh
 
+# --- Stage: batteries-included image (+ Tiled server too) ---
+FROM app-full-base AS app-full
+COPY --from=web /web/dist ./static
+
 # 8002 backend (the only port most deployments need — same-origin SPA+API);
 # 8003/8010 exposed too so ipred/Tiled can be reached directly if wanted.
 EXPOSE 8002 8003 8010
diff --git a/backend/annotation_server.py b/backend/annotation_server.py
index 8fcbc56..ec3c4a5 100644
--- a/backend/annotation_server.py
+++ b/backend/annotation_server.py
@@ -89,6 +89,7 @@
     TrainRequest,
     VolumeBuildRequest,
     ZarrRegisterRequest,
+    ZarrScanRequest,
 )
 from source_keys import parse_source_key
 from thumbnails import render_thumbnail
@@ -422,6 +423,18 @@ def _build() -> bytes | None:
     )
 
 
+@app.get("/api/local/root")
+async def local_root() -> dict:
+    """Return the default local browse root (``LOCAL_DATA_ROOT``) as an absolute path.
+
+    ``/api/local/list`` returns entries relative to whatever root was granted;
+    when no explicit root is granted, callers that need to build an absolute
+    server-side path (e.g. the Zarr loader's directory browser) fetch it here
+    once rather than duplicating the ``LOCAL_DATA_ROOT`` default client-side.
+    """
+    return {"root": local_fs.default_root()}
+
+
 @app.get("/api/local/list")
 async def local_list(
     rel: str = Query("", description="Relative path under the granted root"),
@@ -1380,6 +1393,69 @@ async def zarr_register(req: ZarrRegisterRequest) -> dict:
     )
 
 
+@app.post("/api/zarr/scan")
+async def zarr_scan(req: ZarrScanRequest) -> dict:
+    """Scan a directory for Zarr stores and register any not already present.
+
+    For a bind-mounted directory of already-reconstructed volumes that should
+    all show up in Browse in one action, rather than registering each one
+    individually via ``/api/zarr/register``. Safe to re-run — already-
+    registered stores are skipped, not re-registered.
+    """
+    return await asyncio.to_thread(
+        zarr_source.scan_and_register_zarrs,
+        req.server_uri,
+        req.scan_root,
+        req.container_path,
+        req.on_conflict,
+        req.renames,
+    )
+
+
+@app.post("/api/ingest/scan")
+async def ingest_scan(req: ZarrScanRequest) -> dict:
+    """Scan a directory for folders of image slices and ingest any not already
+    present, as fast per-slice registration (no 3-D pyramid — see the 3D
+    page's on-demand "Build 3D volume" for that). Safe to re-run.
+    """
+    return await asyncio.to_thread(
+        ingest_mod.scan_and_register_image_stacks,
+        req.server_uri,
+        req.scan_root,
+        req.container_path,
+        req.on_conflict,
+        req.renames,
+    )
+
+
+@app.post("/api/scan-datasets")
+async def scan_datasets(req: ZarrScanRequest) -> dict:
+    """Scan a directory for BOTH Zarr stores and folders of image slices,
+    registering whatever isn't already present. The single combined action
+    behind the Connect page's "Scan folder" button and the `:local`/`:full`
+    container's startup auto-discovery — one call instead of two, with one
+    merged result.
+    """
+    # Sequential, not concurrent: both scans call _ensure_container against the
+    # same target container, and running them in parallel would race on its
+    # creation.
+    zarr_result = await asyncio.to_thread(
+        zarr_source.scan_and_register_zarrs,
+        req.server_uri, req.scan_root, req.container_path, req.on_conflict, req.renames,
+    )
+    image_result = await asyncio.to_thread(
+        ingest_mod.scan_and_register_image_stacks,
+        req.server_uri, req.scan_root, req.container_path, req.on_conflict, req.renames,
+    )
+    return {
+        "scanned": zarr_result["scanned"] + image_result["scanned"],
+        "registered": zarr_result["registered"] + image_result["registered"],
+        "skipped": zarr_result["skipped"] + image_result["skipped"],
+        "shadowed": zarr_result["shadowed"] + image_result["shadowed"],
+        "errors": zarr_result["errors"] + image_result["errors"],
+    }
+
+
 def _centre_crop(arr: np.ndarray, size: int) -> np.ndarray:
     """Centred square crop of *size*, or *arr* unchanged if it already fits."""
     h, w = arr.shape[:2]
diff --git a/backend/ingest.py b/backend/ingest.py
index cb866b8..26612c3 100644
--- a/backend/ingest.py
+++ b/backend/ingest.py
@@ -30,12 +30,15 @@
 import logging
 import os
 import re
+import shutil
+import tempfile
 import threading
 import uuid
 from pathlib import Path
 from typing import Any
 
 import numpy as np
+from fastapi import HTTPException
 
 from tiled_clients import api_key_for_uri, get_tiled_client
 
@@ -366,6 +369,7 @@ def run_ingest_job(
         container_meta: dict[str, Any] = {
             "sample_name": parts[-1] if parts else "",
             "n_images": len(temp_files),
+            "source_format": IMAGE_STACK_SOURCE_FORMAT,
         }
         if description:
             container_meta["description"] = description
@@ -436,3 +440,188 @@ def run_ingest_job(
                 tmp.unlink(missing_ok=True)
             except Exception:  # noqa: BLE001
                 pass
+
+
+def node_source_kind(node: Any) -> str | None:
+    """The ``source_format`` tag on *node*'s metadata, or None if absent/unreadable.
+
+    Used to tell whether an existing Tiled node at a candidate key is the SAME
+    kind of registration a scan is about to (re-)create, vs. an unrelated
+    dataset that happens to share the same stem name (e.g. a raw image folder
+    and its own already-registered Zarr reconstruction) — so a scan can report
+    that collision honestly as "shadowed" instead of a misleading "already
+    registered". Shared by :mod:`zarr_source`'s own scan function too.
+    """
+    try:
+        return dict(getattr(node, "metadata", {}) or {}).get("source_format")
+    except Exception:  # noqa: BLE001 — best-effort
+        return None
+
+
+#: Tag written on every container this module ingests into — lets a later scan
+#: (of any kind) recognize "this key already holds a real per-slice ingest"
+#: and distinguish it from an external Zarr/tiff-stack-3d registration sharing
+#: the same stem name.
+IMAGE_STACK_SOURCE_FORMAT = "image-stack"
+
+
+def _is_image_stack_dir(path: Path) -> bool:
+    """True if *path* is a directory holding 2+ directly-supported image files.
+
+    A single image isn't a "stack" worth its own ingest — mirrors
+    :func:`tiff_stack_source.inspect_tiff_stack`'s own ``len(files) < 2`` rule,
+    generalized to every :data:`IMAGE_EXTS` type, not just TIFF.
+    """
+    if not path.is_dir():
+        return False
+    count = 0
+    for child in path.iterdir():
+        if child.is_file() and child.suffix.lower() in IMAGE_EXTS:
+            count += 1
+            if count >= 2:
+                return True
+    return False
+
+
+def scan_and_register_image_stacks(
+    server_uri: str | None,
+    scan_root: str,
+    container_path: str = "browse",
+    on_conflict: str = "skip",
+    renames: dict[str, str] | None = None,
+) -> dict[str, Any]:
+    """Walk *scan_root* for folders of image slices and ingest each not already
+    present, as fast per-slice registration — no 3-D pyramid is built here.
+
+    Deliberately the "quick" path: each slice is copied into Tiled as its own
+    2-D node (exactly what dropping the folder onto the Connect page's
+    dropzone already does), with no multiscale volume generated. Building a
+    3-D pyramid is comparatively expensive (reads every slice at least once)
+    and is left to the existing, on-demand "Build 3D volume" button on the 3D
+    page (:mod:`volume_build`) — a discovery scan should not silently take
+    minutes per folder.
+
+    Only top-level subdirectories of *scan_root* are candidates — one level,
+    matching :func:`zarr_source.scan_and_register_zarrs`'s own non-recursive
+    scope. A `.zarr` directory is never a candidate here even if it happens to
+    also contain 2+ loose image files at its root (it shouldn't, but the check
+    is cheap insurance against double-registering the same data two ways).
+
+    A raw image folder and an unrelated dataset (e.g. a Zarr reconstruction of
+    the same acquisition) commonly share the same stem name — in which case
+    they'd derive the identical Tiled key. Rather than silently treating that
+    as "already registered" (misleading — the raw slices were never actually
+    ingested) or blindly replacing someone else's data, a same-key collision
+    with a DIFFERENT kind of registration (per the ``source_format`` tag; see
+    :func:`node_source_kind`) is reported as **shadowed**, distinctly from a
+    same-kind ``skipped`` match from a previous run of this same scan.
+
+    Args:
+        server_uri: Connected Tiled server URI.
+        scan_root: Absolute directory to scan.
+        container_path: Parent container each discovered folder registers
+            under, keyed by its own (sanitized) folder name — matching how a
+            dropped folder is named today.
+        on_conflict: ``"skip"`` (default, safe to re-run) or ``"replace"`` —
+            applies only to a same-kind (not shadowed) collision.
+        renames: Optional ``{folder_name: alternate_key}`` override, so a
+            shadowed candidate can be retried under a different key without
+            re-scanning everything else.
+
+    Returns:
+        Dict with ``scanned``, ``registered`` (``name``/``key``/``tiled_path``),
+        ``skipped`` (names), ``shadowed`` (``name``/``key``/``existing_kind``/
+        ``suggested_key`` — a different-kind collision, nothing registered),
+        and ``errors`` (``name``/``error`` pairs).
+
+    Raises:
+        HTTPException: 400/404 if *scan_root* itself is unusable.
+    """
+    from tiled.client.register import Settings
+
+    import zarr_source
+
+    if on_conflict not in ("skip", "replace"):
+        on_conflict = "skip"
+    renames = renames or {}
+
+    root = Path(scan_root).expanduser()
+    if not root.is_absolute():
+        raise HTTPException(400, f"Path must be absolute: {scan_root!r}")
+    if not root.is_dir():
+        raise HTTPException(404, f"No such directory: {root}")
+
+    candidates = sorted(
+        (p for p in root.iterdir() if not zarr_source._is_zarr_dir(p) and _is_image_stack_dir(p)),
+        key=lambda p: p.name,
+    )
+
+    client = get_tiled_client(server_uri, api_key_for_uri(server_uri))
+    parts = [p for p in container_path.strip("/").split("/") if p]
+    target = _ensure_container(client, parts)
+
+    registered: list[dict[str, Any]] = []
+    skipped: list[str] = []
+    shadowed: list[dict[str, str]] = []
+    errors: list[dict[str, str]] = []
+    for candidate in candidates:
+        default_key = Settings.init().key_from_filename(candidate.name)
+        key = renames.get(candidate.name, default_key)
+        existing_keys = _child_keys(target)
+        if key in existing_keys:
+            existing_kind = node_source_kind(target[key])
+            if existing_kind != IMAGE_STACK_SOURCE_FORMAT:
+                shadowed.append(
+                    {
+                        "name": candidate.name,
+                        "key": key,
+                        "existing_kind": existing_kind or "unknown",
+                        "suggested_key": f"{key}_images",
+                    }
+                )
+                continue
+            if on_conflict == "skip":
+                skipped.append(key)
+                continue
+            try:
+                target.delete_contents(key, recursive=True, external_only=False)
+            except Exception as exc:  # noqa: BLE001 — one bad entry must not sink the scan
+                errors.append({"name": candidate.name, "error": str(exc)})
+                continue
+
+        # Copy each slice to a REAL temp file before ingesting — run_ingest_job
+        # unlinks its inputs when done (correct for its normal caller, the
+        # upload route's own tempfile.mkdtemp()); pointing it at scan_root's
+        # actual files would delete the user's real source data.
+        tmp_dir = Path(tempfile.mkdtemp(prefix="scan_ingest_"))
+        try:
+            temp_files: list[tuple[str, Path]] = []
+            for index, src in enumerate(
+                sorted(p for p in candidate.iterdir() if p.is_file() and p.suffix.lower() in IMAGE_EXTS)
+            ):
+                dest = tmp_dir / f"{index:06d}{src.suffix.lower()}"
+                shutil.copy2(src, dest)
+                temp_files.append((src.name, dest))
+
+            jid = new_job(len(temp_files), server_uri, f"{container_path}/{key}".strip("/"))
+            run_ingest_job(jid, server_uri, f"{container_path}/{key}".strip("/"), temp_files, on_conflict=on_conflict)
+            job = get_job(jid) or {}
+            if job.get("state") == "error":
+                errs = job.get("errors") or [{"error": "ingest failed"}]
+                errors.append({"name": candidate.name, "error": errs[0].get("error", "ingest failed")})
+            else:
+                registered.append(
+                    {"name": candidate.name, "key": key, "tiled_path": f"{container_path}/{key}".strip("/")}
+                )
+        except Exception as exc:  # noqa: BLE001 — one bad folder must not sink the scan
+            errors.append({"name": candidate.name, "error": str(exc)})
+        finally:
+            shutil.rmtree(tmp_dir, ignore_errors=True)
+
+    return {
+        "scanned": len(candidates),
+        "registered": registered,
+        "skipped": skipped,
+        "shadowed": shadowed,
+        "errors": errors,
+    }
diff --git a/backend/local_fs.py b/backend/local_fs.py
index 4845287..1990833 100644
--- a/backend/local_fs.py
+++ b/backend/local_fs.py
@@ -28,6 +28,16 @@
 _DEFAULT_ROOT: Path = Path(os.getenv("LOCAL_DATA_ROOT", "~/data")).expanduser().resolve()
 
 
+def default_root() -> str:
+    """Return the default browse root (``LOCAL_DATA_ROOT``) as an absolute path.
+
+    Used by callers that need to construct an absolute path from a directory
+    listing's root-relative entries (``list_dir`` returns paths relative to
+    the root, not absolute ones) when no explicit *root* was granted.
+    """
+    return str(_DEFAULT_ROOT)
+
+
 def _resolve_root(root: str | None) -> Path:
     """Return the granted browse root as an absolute, resolved Path.
 
diff --git a/backend/schemas.py b/backend/schemas.py
index d485987..93c3470 100644
--- a/backend/schemas.py
+++ b/backend/schemas.py
@@ -327,6 +327,34 @@ class ZarrRegisterRequest(BaseModel):
     server_uri: str | None = None
 
 
+class ZarrScanRequest(BaseModel):
+    """Request body for scanning a directory and registering every Zarr store found.
+
+    For pointing a mounted directory of already-reconstructed volumes at Tiled
+    in bulk, rather than registering each one individually via
+    :class:`ZarrRegisterRequest`. Non-recursive: only immediate subdirectories
+    of ``scan_root`` that look like a Zarr store are considered.
+
+    Attributes:
+        scan_root: Absolute directory to scan (e.g. a bind-mounted host folder).
+        container_path: Target container every discovered store registers into.
+        on_conflict: ``"skip"`` (default, safe to re-run) or ``"replace"`` for
+            a same-kind entry that already exists — ``"fail"`` makes no sense
+            here since one conflicting store shouldn't abort the whole scan.
+        server_uri: Target Tiled server URI; ``None`` uses the default server.
+        renames: Optional ``{folder_name: alternate_key}`` override, so a
+            candidate reported as "shadowed" (its natural key collides with
+            an unrelated, different-kind registration) can be retried under a
+            different key without re-scanning everything else.
+    """
+
+    scan_root: str
+    container_path: str = "browse"
+    on_conflict: str = "skip"
+    renames: dict[str, str] = Field(default_factory=dict)
+    server_uri: str | None = None
+
+
 class TiffStackRegisterRequest(ZarrRegisterRequest):
     """Request body for registering a TIFF directory as a 3-D volume.
 
diff --git a/backend/tests/test_annotation_server_data_routes.py b/backend/tests/test_annotation_server_data_routes.py
index 8fc7cb1..e7b0e20 100644
--- a/backend/tests/test_annotation_server_data_routes.py
+++ b/backend/tests/test_annotation_server_data_routes.py
@@ -19,6 +19,7 @@
 import denoise as denoise_mod
 import export_jobs
 import infer_jobs
+import ingest as ingest_mod
 import tiff_stack_source
 import train_common
 import volume_build
@@ -101,6 +102,78 @@ async def test_register_delegates_on_valid_request(self, client, monkeypatch):
         assert response.status_code == 200
         assert response.json() == {"key": "x"}
 
+    @pytest.mark.asyncio
+    async def test_scan_delegates_with_request_fields(self, client, monkeypatch):
+        calls = []
+        monkeypatch.setattr(
+            zarr_source, "scan_and_register_zarrs",
+            lambda server_uri, scan_root, container_path, on_conflict, renames: (
+                calls.append((server_uri, scan_root, container_path, on_conflict, renames))
+                or {"scanned": 2, "registered": [], "skipped": [], "shadowed": [], "errors": []}
+            ),
+        )
+        response = await client.post(
+            "/api/zarr/scan",
+            json={"scan_root": "/data/processed", "container_path": "browse"},
+        )
+        assert response.status_code == 200
+        assert response.json() == {"scanned": 2, "registered": [], "skipped": [], "shadowed": [], "errors": []}
+        assert calls == [(None, "/data/processed", "browse", "skip", {})]
+
+    @pytest.mark.asyncio
+    async def test_ingest_scan_delegates_with_request_fields(self, client, monkeypatch):
+        calls = []
+        monkeypatch.setattr(
+            ingest_mod, "scan_and_register_image_stacks",
+            lambda server_uri, scan_root, container_path, on_conflict, renames: (
+                calls.append((server_uri, scan_root, container_path, on_conflict, renames))
+                or {"scanned": 1, "registered": [], "skipped": [], "shadowed": [], "errors": []}
+            ),
+        )
+        response = await client.post(
+            "/api/ingest/scan",
+            json={"scan_root": "/data/processed", "container_path": "browse"},
+        )
+        assert response.status_code == 200
+        assert response.json() == {"scanned": 1, "registered": [], "skipped": [], "shadowed": [], "errors": []}
+        assert calls == [(None, "/data/processed", "browse", "skip", {})]
+
+    @pytest.mark.asyncio
+    async def test_scan_datasets_merges_zarr_and_image_scan_results(self, client, monkeypatch):
+        monkeypatch.setattr(
+            zarr_source, "scan_and_register_zarrs",
+            lambda *a: {
+                "scanned": 2,
+                "registered": [{"name": "a.zarr", "key": "a", "tiled_path": "browse/a"}],
+                "skipped": ["b"],
+                "shadowed": [{"name": "e", "key": "e", "existing_kind": "image-stack", "suggested_key": "e_zarr"}],
+                "errors": [],
+            },
+        )
+        monkeypatch.setattr(
+            ingest_mod, "scan_and_register_image_stacks",
+            lambda *a: {
+                "scanned": 1,
+                "registered": [{"name": "c", "key": "c", "tiled_path": "browse/c"}],
+                "skipped": [],
+                "shadowed": [],
+                "errors": [{"name": "d", "error": "boom"}],
+            },
+        )
+        response = await client.post(
+            "/api/scan-datasets",
+            json={"scan_root": "/data/processed", "container_path": "browse"},
+        )
+        assert response.status_code == 200
+        body = response.json()
+        assert body["scanned"] == 3
+        assert sorted(r["name"] for r in body["registered"]) == ["a.zarr", "c"]
+        assert body["skipped"] == ["b"]
+        assert body["shadowed"] == [
+            {"name": "e", "key": "e", "existing_kind": "image-stack", "suggested_key": "e_zarr"}
+        ]
+        assert body["errors"] == [{"name": "d", "error": "boom"}]
+
 
 # ---------------------------------------------------------------------------
 # /api/tiff-stack/*
diff --git a/backend/tests/test_annotation_server_local_image_routes.py b/backend/tests/test_annotation_server_local_image_routes.py
index c588063..e43a014 100644
--- a/backend/tests/test_annotation_server_local_image_routes.py
+++ b/backend/tests/test_annotation_server_local_image_routes.py
@@ -42,6 +42,19 @@ def __len__(self):
         return len(self._children)
 
 
+# ---------------------------------------------------------------------------
+# /api/local/root
+# ---------------------------------------------------------------------------
+
+class TestLocalRoot:
+    @pytest.mark.asyncio
+    async def test_returns_the_default_root(self, client, monkeypatch):
+        monkeypatch.setattr(local_fs, "default_root", lambda: "/data/raw")
+        response = await client.get("/api/local/root")
+        assert response.status_code == 200
+        assert response.json() == {"root": "/data/raw"}
+
+
 # ---------------------------------------------------------------------------
 # /api/local/list, /api/local/samples
 # ---------------------------------------------------------------------------
diff --git a/backend/tests/test_ingest_scan.py b/backend/tests/test_ingest_scan.py
new file mode 100644
index 0000000..c5f5dee
--- /dev/null
+++ b/backend/tests/test_ingest_scan.py
@@ -0,0 +1,197 @@
+"""Tests for scan_and_register_image_stacks — bulk auto-discovery of image
+folders under a mounted directory, mirroring zarr_source's
+scan_and_register_zarrs for the non-Zarr case. register_zarr/run_ingest_job
+themselves are already covered elsewhere (test_zarr_source.py,
+test_ingest_conflict.py); these tests are about candidate discovery,
+temp-file safety, and result aggregation.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+from fastapi import HTTPException
+from PIL import Image
+
+import ingest
+
+
+class FakeNode:
+    """Minimal stand-in for a Tiled container client (see test_ingest_conflict.py)."""
+
+    def __init__(self, children: dict | None = None, metadata: dict | None = None) -> None:
+        self._children: dict = dict(children or {})
+        self.metadata: dict = dict(metadata or {})
+        self.written: dict = {}
+
+    def __getitem__(self, key: str):
+        if key not in self._children:
+            raise KeyError(key)
+        return self._children[key]
+
+    def keys(self) -> list[str]:
+        return list(self._children)
+
+    def create_container(self, key: str, metadata: dict | None = None) -> "FakeNode":
+        node = FakeNode(metadata=metadata)
+        self._children[key] = node
+        return node
+
+    def update_metadata(self, metadata: dict | None = None) -> None:
+        self.metadata = dict(metadata or {})
+
+    def write_array(self, arr, key=None, metadata=None, dims=None) -> None:
+        self._children[key] = "array"
+        self.written[key] = (arr, metadata)
+
+    def delete_contents(self, keys=None, recursive: bool = False, external_only: bool = True) -> None:
+        for k in [keys] if isinstance(keys, str) else keys:
+            self._children.pop(k, None)
+
+
+@pytest.fixture
+def fake_client(monkeypatch):
+    root = FakeNode({"browse": FakeNode()})
+    monkeypatch.setattr(ingest, "api_key_for_uri", lambda uri: None)
+    monkeypatch.setattr(ingest, "get_tiled_client", lambda uri, key=None: root)
+    return root
+
+
+def _make_stack(root: Path, name: str, n: int = 3, ext: str = ".tif") -> Path:
+    """A folder of *n* tiny real image files (real bytes, no mocks)."""
+    stack = root / name
+    stack.mkdir()
+    for i in range(n):
+        Image.fromarray(np.zeros((4, 4), dtype=np.uint8)).save(stack / f"{i:03d}{ext}")
+    return stack
+
+
+class TestIsImageStackDir:
+    def test_two_or_more_images_qualifies(self, tmp_path: Path) -> None:
+        stack = _make_stack(tmp_path, "s1", n=2)
+        assert ingest._is_image_stack_dir(stack) is True
+
+    def test_a_single_image_does_not_qualify(self, tmp_path: Path) -> None:
+        stack = _make_stack(tmp_path, "s1", n=1)
+        assert ingest._is_image_stack_dir(stack) is False
+
+    def test_a_plain_file_does_not_qualify(self, tmp_path: Path) -> None:
+        f = tmp_path / "readme.txt"
+        f.write_text("hi")
+        assert ingest._is_image_stack_dir(f) is False
+
+    def test_unsupported_extensions_do_not_count(self, tmp_path: Path) -> None:
+        d = tmp_path / "junk"
+        d.mkdir()
+        (d / "a.txt").write_text("x")
+        (d / "b.txt").write_text("y")
+        assert ingest._is_image_stack_dir(d) is False
+
+
+class TestScanAndRegisterImageStacks:
+    def test_finds_and_ingests_top_level_stacks_only(self, tmp_path: Path, fake_client) -> None:
+        _make_stack(tmp_path, "stack_a", n=3)
+        _make_stack(tmp_path, "stack_b", n=2)
+        (tmp_path / "not_a_stack").mkdir()
+        # A stack's OWN internals must never be treated as a second candidate.
+        (tmp_path / "stack_a" / "nested_stack").mkdir()
+
+        result = ingest.scan_and_register_image_stacks(None, str(tmp_path), "browse")
+        assert result["scanned"] == 2
+        assert sorted(r["name"] for r in result["registered"]) == ["stack_a", "stack_b"]
+        assert result["skipped"] == []
+        assert result["errors"] == []
+
+        # Real per-slice ingest actually happened (not a no-op / dry run).
+        browse = fake_client["browse"]
+        assert "stack_a" in browse.keys()
+        assert len(browse["stack_a"].written) == 3
+
+    def test_a_zarr_directory_is_never_treated_as_an_image_stack(self, tmp_path: Path, fake_client) -> None:
+        zarr_dir = tmp_path / "vol.zarr"
+        zarr_dir.mkdir()
+        (zarr_dir / ".zarray").write_text("{}")
+        # Even if it happens to also hold loose image files at its root.
+        Image.fromarray(np.zeros((4, 4), dtype=np.uint8)).save(zarr_dir / "a.tif")
+        Image.fromarray(np.zeros((4, 4), dtype=np.uint8)).save(zarr_dir / "b.tif")
+
+        result = ingest.scan_and_register_image_stacks(None, str(tmp_path), "browse")
+        assert result["scanned"] == 0
+        assert result["registered"] == []
+
+    def test_does_not_delete_the_original_source_files(self, tmp_path: Path, fake_client) -> None:
+        stack = _make_stack(tmp_path, "stack_a", n=3)
+        originals = sorted(stack.iterdir())
+        assert len(originals) == 3
+
+        ingest.scan_and_register_image_stacks(None, str(tmp_path), "browse")
+
+        # The whole point of copying to a real temp dir first: run_ingest_job
+        # unlinks its inputs when done, and must never touch the originals.
+        assert sorted(stack.iterdir()) == originals
+        for f in originals:
+            assert f.exists()
+
+    def test_skips_an_already_registered_stack_by_default(self, tmp_path: Path, fake_client) -> None:
+        _make_stack(tmp_path, "stack_a", n=2)
+        fake_client["browse"].create_container("stack_a", metadata={"source_format": "image-stack"})
+
+        result = ingest.scan_and_register_image_stacks(None, str(tmp_path), "browse")
+        assert result["registered"] == []
+        assert result["skipped"] == ["stack_a"]
+
+    def test_replace_on_conflict_re_ingests(self, tmp_path: Path, fake_client) -> None:
+        _make_stack(tmp_path, "stack_a", n=2)
+        fake_client["browse"].create_container("stack_a", metadata={"source_format": "image-stack"})
+
+        result = ingest.scan_and_register_image_stacks(None, str(tmp_path), "browse", on_conflict="replace")
+        assert [r["name"] for r in result["registered"]] == ["stack_a"]
+        assert result["skipped"] == []
+
+    def test_a_different_kind_collision_is_shadowed_not_skipped(self, tmp_path: Path, fake_client) -> None:
+        _make_stack(tmp_path, "stack_a", n=2)
+        # Same stem, but an unrelated Zarr registration got there first.
+        fake_client["browse"].create_container("stack_a", metadata={"source_format": "zarr"})
+
+        result = ingest.scan_and_register_image_stacks(None, str(tmp_path), "browse")
+        assert result["registered"] == []
+        assert result["skipped"] == []
+        assert result["shadowed"] == [
+            {"name": "stack_a", "key": "stack_a", "existing_kind": "zarr", "suggested_key": "stack_a_images"}
+        ]
+
+    def test_renames_lets_a_shadowed_candidate_ingest_under_an_alternate_key(
+        self, tmp_path: Path, fake_client
+    ) -> None:
+        _make_stack(tmp_path, "stack_a", n=2)
+        fake_client["browse"].create_container("stack_a", metadata={"source_format": "zarr"})
+
+        result = ingest.scan_and_register_image_stacks(
+            None, str(tmp_path), "browse", renames={"stack_a": "stack_a_images"}
+        )
+        assert result["shadowed"] == []
+        assert [r["key"] for r in result["registered"]] == ["stack_a_images"]
+        assert fake_client["browse"]["stack_a_images"].written
+
+    def test_rejects_relative_scan_root(self) -> None:
+        with pytest.raises(HTTPException) as exc:
+            ingest.scan_and_register_image_stacks(None, "relative/dir", "browse")
+        assert exc.value.status_code == 400
+
+    def test_rejects_missing_scan_root(self, tmp_path: Path) -> None:
+        with pytest.raises(HTTPException) as exc:
+            ingest.scan_and_register_image_stacks(None, str(tmp_path / "nope"), "browse")
+        assert exc.value.status_code == 404
+
+    def test_empty_directory_scans_cleanly_with_nothing_found(self, tmp_path: Path, fake_client) -> None:
+        result = ingest.scan_and_register_image_stacks(None, str(tmp_path), "browse")
+        assert result == {"scanned": 0, "registered": [], "skipped": [], "shadowed": [], "errors": []}
+
+    def test_invalid_on_conflict_falls_back_to_skip(self, tmp_path: Path, fake_client) -> None:
+        _make_stack(tmp_path, "stack_a", n=2)
+        fake_client["browse"].create_container("stack_a", metadata={"source_format": "image-stack"})
+
+        result = ingest.scan_and_register_image_stacks(None, str(tmp_path), "browse", on_conflict="bogus")
+        assert result["skipped"] == ["stack_a"]
diff --git a/backend/tests/test_local_fs.py b/backend/tests/test_local_fs.py
index 0e87c78..e6bcb93 100644
--- a/backend/tests/test_local_fs.py
+++ b/backend/tests/test_local_fs.py
@@ -18,6 +18,15 @@ def default_root(tmp_path, monkeypatch: pytest.MonkeyPatch):
     return tmp_path
 
 
+# ---------------------------------------------------------------------------
+# default_root
+# ---------------------------------------------------------------------------
+
+class TestDefaultRoot:
+    def test_returns_the_default_root_as_a_string(self, default_root):
+        assert local_fs.default_root() == str(default_root)
+
+
 # ---------------------------------------------------------------------------
 # _resolve_root / _within / _safe
 # ---------------------------------------------------------------------------
diff --git a/backend/tests/test_zarr_source.py b/backend/tests/test_zarr_source.py
index f40bee8..39d69fe 100644
--- a/backend/tests/test_zarr_source.py
+++ b/backend/tests/test_zarr_source.py
@@ -138,6 +138,203 @@ def test_discovers_arrays_without_multiscales_metadata(self, tmp_path: Path) ->
         assert [lv["path"] for lv in info["levels"]] == ["volume"]
         assert info["full_shape"] == [4, 8, 8]
 
+    def test_handles_a_bare_array_store_with_no_group_wrapper(self, tmp_path: Path) -> None:
+        """A store that's just a 3-D array at its own root (.zarray, no
+        .zgroup) — e.g. written via `zarr.open(mode='w', ...)` / `dask.array
+        .to_zarr()` with no OME-NGFF multiscale wrapper. `_is_zarr_dir`
+        already recognizes this shape; `inspect_zarr` must not reject it."""
+        import zarr
+
+        store = tmp_path / "plain.zarr"
+        arr = zarr.open(str(store), mode="w", shape=(6, 10, 12), dtype="uint16", zarr_format=2)
+        arr[:] = np.arange(6 * 10 * 12, dtype="uint16").reshape(6, 10, 12)
+        assert not (store / ".zgroup").exists()  # confirm this really is group-less
+
+        info = zarr_source.inspect_zarr(str(store))
+        assert len(info["levels"]) == 1
+        level = info["levels"][0]
+        assert level["path"] == ""
+        assert level["shape"] == [6, 10, 12]
+        assert level["downsample"] == [1.0, 1.0, 1.0]
+        assert info["full_shape"] == [6, 10, 12]
+        assert info["dtype"] == "uint16"
+        assert info["voxel_size"] is None
+
+    def test_rejects_a_bare_array_that_is_not_3d(self, tmp_path: Path) -> None:
+        import zarr
+
+        store = tmp_path / "plain2d.zarr"
+        zarr.open(str(store), mode="w", shape=(10, 12), dtype="uint16", zarr_format=2)
+        with pytest.raises(HTTPException) as exc:
+            zarr_source.inspect_zarr(str(store))
+        assert exc.value.status_code == 422
+        assert "not 3-D" in exc.value.detail
+
+
+class TestScanAndRegisterZarrs:
+    """scan_and_register_zarrs's own logic (candidate discovery, shadow
+    detection, aggregation) — register_zarr itself needs a live Tiled server
+    (see TestPreflightZarr's own docstring), so it's mocked here rather than
+    re-proven. The shadow pre-check navigates a fake Tiled client (see
+    FakeContainer, defined below in this file) the same way preflight_zarr's
+    own tests do."""
+
+    def _fake_client(self, monkeypatch, browse_children=None):
+        client = FakeContainer({"browse": FakeContainer(browse_children or {})})
+        monkeypatch.setattr(zarr_source, "get_tiled_client", lambda uri, key: client)
+        monkeypatch.setattr(zarr_source, "api_key_for_uri", lambda uri: None)
+        return client
+
+    def test_finds_only_top_level_zarr_dirs_and_registers_each(self, tmp_path: Path, monkeypatch) -> None:
+        import zarr
+
+        for name in ("a.zarr", "b.zarr"):
+            store = tmp_path / name
+            zarr.open(str(store), mode="w", shape=(2, 4, 4), dtype="uint8", zarr_format=2)
+        (tmp_path / "not_a_store").mkdir()
+        (tmp_path / "readme.txt").write_text("hi")
+        # A Zarr store's OWN internals must never be treated as a second
+        # candidate — only immediate children of scan_root are considered.
+        (tmp_path / "a.zarr" / "nested.zarr").mkdir()
+
+        self._fake_client(monkeypatch)
+        calls: list[str] = []
+
+        def fake_register_zarr(server_uri, path, container_path, description="", on_conflict="fail"):
+            calls.append(Path(path).name)
+            return {"key": Path(path).stem, "tiled_path": f"browse/{Path(path).stem}", "skipped": False}
+
+        monkeypatch.setattr(zarr_source, "register_zarr", fake_register_zarr)
+
+        result = zarr_source.scan_and_register_zarrs(None, str(tmp_path), "browse")
+        assert result["scanned"] == 2
+        assert sorted(calls) == ["a.zarr", "b.zarr"]
+        assert sorted(r["name"] for r in result["registered"]) == ["a.zarr", "b.zarr"]
+        assert result["skipped"] == []
+        assert result["shadowed"] == []
+        assert result["errors"] == []
+
+    def test_reports_skipped_entries_separately_from_newly_registered(self, tmp_path: Path, monkeypatch) -> None:
+        import zarr
+
+        zarr.open(str(tmp_path / "existing.zarr"), mode="w", shape=(2, 4, 4), dtype="uint8", zarr_format=2)
+        # A same-kind ("zarr") existing node — a legitimate re-scan match, not
+        # a shadow — so register_zarr's own on_conflict=skip path is what
+        # reports it, exactly as before.
+        self._fake_client(monkeypatch, {"existing": FakeContainer({}, metadata={"source_format": "zarr"})})
+
+        def fake_register_zarr(server_uri, path, container_path, description="", on_conflict="fail"):
+            return {"key": "existing", "tiled_path": "browse/existing", "skipped": True}
+
+        monkeypatch.setattr(zarr_source, "register_zarr", fake_register_zarr)
+
+        result = zarr_source.scan_and_register_zarrs(None, str(tmp_path), "browse")
+        assert result["registered"] == []
+        assert result["skipped"] == ["existing"]
+        assert result["shadowed"] == []
+
+    def test_a_different_kind_collision_is_reported_as_shadowed_not_skipped(
+        self, tmp_path: Path, monkeypatch
+    ) -> None:
+        import zarr
+
+        zarr.open(str(tmp_path / "existing.zarr"), mode="w", shape=(2, 4, 4), dtype="uint8", zarr_format=2)
+        self._fake_client(
+            monkeypatch, {"existing": FakeContainer({}, metadata={"source_format": "image-stack"})}
+        )
+        register_calls: list[str] = []
+        monkeypatch.setattr(
+            zarr_source, "register_zarr",
+            lambda *a, **kw: register_calls.append(a) or {"key": "existing", "tiled_path": "x", "skipped": False},
+        )
+
+        result = zarr_source.scan_and_register_zarrs(None, str(tmp_path), "browse")
+        assert result["registered"] == []
+        assert result["skipped"] == []
+        assert result["shadowed"] == [
+            {"name": "existing.zarr", "key": "existing", "existing_kind": "image-stack", "suggested_key": "existing_zarr"}
+        ]
+        # register_zarr must never be called for a shadowed candidate — no
+        # blind replace, no misleading "skip" of someone else's data.
+        assert register_calls == []
+
+    def test_renames_lets_a_shadowed_candidate_register_under_an_alternate_key(
+        self, tmp_path: Path, monkeypatch
+    ) -> None:
+        import zarr
+
+        zarr.open(str(tmp_path / "existing.zarr"), mode="w", shape=(2, 4, 4), dtype="uint8", zarr_format=2)
+        self._fake_client(
+            monkeypatch, {"existing": FakeContainer({}, metadata={"source_format": "image-stack"})}
+        )
+        calls = []
+
+        def fake_register_zarr(server_uri, path, container_path, on_conflict="fail"):
+            calls.append(container_path)
+            return {"key": "existing", "tiled_path": f"{container_path}/existing", "skipped": False}
+
+        monkeypatch.setattr(zarr_source, "register_zarr", fake_register_zarr)
+
+        result = zarr_source.scan_and_register_zarrs(
+            None, str(tmp_path), "browse", renames={"existing.zarr": "existing_zarr"}
+        )
+        assert result["shadowed"] == []
+        assert [r["key"] for r in result["registered"]] == ["existing_zarr"]
+        # Registered one level deeper, under a container named for the
+        # chosen alternate key — the only way to make Tiled's own
+        # filename-derived key land under a different name.
+        assert calls == ["browse/existing_zarr"]
+        assert result["registered"][0]["tiled_path"] == "browse/existing_zarr/existing"
+
+    def test_one_bad_store_does_not_abort_the_rest(self, tmp_path: Path, monkeypatch) -> None:
+        import zarr
+
+        for name in ("good.zarr", "bad.zarr"):
+            zarr.open(str(tmp_path / name), mode="w", shape=(2, 4, 4), dtype="uint8", zarr_format=2)
+
+        self._fake_client(monkeypatch)
+
+        def fake_register_zarr(server_uri, path, container_path, description="", on_conflict="fail"):
+            if Path(path).name == "bad.zarr":
+                raise HTTPException(502, "boom")
+            return {"key": "good", "tiled_path": "browse/good", "skipped": False}
+
+        monkeypatch.setattr(zarr_source, "register_zarr", fake_register_zarr)
+
+        result = zarr_source.scan_and_register_zarrs(None, str(tmp_path), "browse")
+        assert [r["name"] for r in result["registered"]] == ["good.zarr"]
+        assert result["errors"] == [{"name": "bad.zarr", "error": "boom"}]
+
+    def test_rejects_relative_scan_root(self) -> None:
+        with pytest.raises(HTTPException) as exc:
+            zarr_source.scan_and_register_zarrs(None, "relative/dir", "browse")
+        assert exc.value.status_code == 400
+
+    def test_rejects_missing_scan_root(self, tmp_path: Path) -> None:
+        with pytest.raises(HTTPException) as exc:
+            zarr_source.scan_and_register_zarrs(None, str(tmp_path / "nope"), "browse")
+        assert exc.value.status_code == 404
+
+    def test_empty_directory_scans_cleanly_with_nothing_found(self, tmp_path: Path, monkeypatch) -> None:
+        self._fake_client(monkeypatch)
+        result = zarr_source.scan_and_register_zarrs(None, str(tmp_path), "browse")
+        assert result == {"scanned": 0, "registered": [], "skipped": [], "shadowed": [], "errors": []}
+
+    def test_invalid_on_conflict_falls_back_to_skip(self, tmp_path: Path, monkeypatch) -> None:
+        import zarr
+
+        zarr.open(str(tmp_path / "a.zarr"), mode="w", shape=(2, 4, 4), dtype="uint8", zarr_format=2)
+        self._fake_client(monkeypatch)
+        seen_on_conflict = []
+
+        def fake_register_zarr(server_uri, path, container_path, description="", on_conflict="fail"):
+            seen_on_conflict.append(on_conflict)
+            return {"key": "a", "tiled_path": "browse/a", "skipped": False}
+
+        monkeypatch.setattr(zarr_source, "register_zarr", fake_register_zarr)
+        zarr_source.scan_and_register_zarrs(None, str(tmp_path), "browse", on_conflict="fail")
+        assert seen_on_conflict == ["skip"]
+
 
 class TestRegisteredKey:
     def test_strips_the_zarr_extension(self, pyramid: Path) -> None:
diff --git a/backend/zarr_source.py b/backend/zarr_source.py
index 54277eb..8003553 100644
--- a/backend/zarr_source.py
+++ b/backend/zarr_source.py
@@ -154,8 +154,48 @@ def inspect_zarr(raw_path: str) -> dict[str, Any]:
     path = _resolve_path(raw_path)
     try:
         group = zarr.open_group(str(path), mode="r")
-    except Exception as exc:  # noqa: BLE001 — surface as a clean 422
-        raise HTTPException(422, f"Could not open {path.name!r} as Zarr: {exc}") from exc
+    except Exception as group_exc:  # noqa: BLE001 — try a bare array next
+        # Not a group — a store with a bare 3-D array at its root (no OME-NGFF
+        # multiscales wrapper) is an equally common, simpler way to save a
+        # volume (e.g. `zarr.save`/`dask.array.to_zarr` with no group). `_is_
+        # zarr_dir` already accepts this shape (its own `.zarray`/`zarr.json`
+        # marker check) — rejecting it here would contradict that.
+        try:
+            arr = zarr.open_array(str(path), mode="r")
+        except Exception as arr_exc:  # noqa: BLE001 — genuinely neither
+            raise HTTPException(
+                422, f"Could not open {path.name!r} as Zarr: {group_exc}"
+            ) from arr_exc
+        if arr.ndim != 3:
+            raise HTTPException(
+                422,
+                f"{path.name!r} is a Zarr array but not 3-D (shape {arr.shape}) "
+                "— expected (z, y, x).",
+            )
+        shape = [int(v) for v in arr.shape]
+        return {
+            "name": path.name,
+            "path": str(path),
+            # Empty: the store registers as a single leaf array node directly
+            # at the container key, with no sub-path to descend into (see the
+            # frontend's own guard against appending a trailing empty segment).
+            "levels": [
+                {
+                    "path": "",
+                    "shape": shape,
+                    "dtype": str(arr.dtype),
+                    "n_slices": shape[0],
+                    "height": shape[1],
+                    "width": shape[2],
+                    "downsample": [1.0, 1.0, 1.0],
+                }
+            ],
+            "full_shape": shape,
+            "dtype": str(arr.dtype),
+            "voxel_size": None,
+            "voxel_unit": None,
+            "pixel_size": None,
+        }
 
     # Prefer the declared multiscales order; fall back to discovering 3-D arrays.
     datasets = _multiscale_datasets(path)
@@ -410,3 +450,122 @@ def register_zarr(
         "tiled_path": "/".join([*parts, key]),
         "skipped": False,
     }
+
+
+def scan_and_register_zarrs(
+    server_uri: str | None,
+    scan_root: str,
+    container_path: str = "browse",
+    on_conflict: str = "skip",
+    renames: dict[str, str] | None = None,
+) -> dict[str, Any]:
+    """Walk *scan_root* for Zarr stores and register each one not already present.
+
+    For a directory of already-reconstructed volumes (e.g. a bind-mounted host
+    folder) that should all show up in Browse without registering each one
+    individually through the UI. Non-recursive by design: only immediate
+    subdirectories of *scan_root* that look like a Zarr store (`_is_zarr_dir`)
+    are candidates — a store's own internal structure (`scale0/`, chunk files)
+    must never be treated as separate stores to register.
+
+    A Zarr store and an unrelated dataset (e.g. a raw image folder of the same
+    acquisition) commonly share the same stem name — in which case they'd
+    derive the identical Tiled key. Rather than silently treating that as
+    "already registered" (misleading — this store was never actually
+    registered) or blindly replacing someone else's data, a same-key collision
+    with a DIFFERENT kind of registration (per the ``source_format`` tag; see
+    :func:`ingest.node_source_kind`) is reported as **shadowed**, distinctly
+    from a same-kind ``skipped`` match from a previous run of this same scan.
+
+    Args:
+        server_uri: Connected Tiled server URI.
+        scan_root: Absolute directory to scan.
+        container_path: Target container every discovered store registers into.
+        on_conflict: Passed through to :func:`register_zarr` for each same-kind
+            match — ``"skip"`` (default) leaves already-registered entries
+            alone, so re-running the scan after adding new datasets is always
+            safe. ``"fail"`` is rejected: one conflicting store shouldn't be
+            able to abort a bulk scan the way it correctly can for a single
+            register.
+        renames: Optional ``{folder_name: alternate_key}`` override, so a
+            shadowed candidate can be retried under a different key without
+            re-scanning everything else.
+
+    Returns:
+        Dict with ``scanned`` (candidate count), ``registered`` (newly
+        registered, each with ``name``/``key``/``tiled_path``), ``skipped``
+        (names already present), ``shadowed`` (``name``/``key``/
+        ``existing_kind``/``suggested_key`` — a different-kind collision,
+        nothing registered), and ``errors`` (``name``/``error`` pairs for
+        anything that failed to register — a bad store never aborts the rest).
+
+    Raises:
+        HTTPException: 400/404 if *scan_root* itself is unusable.
+    """
+    if on_conflict not in ("skip", "replace"):
+        on_conflict = "skip"
+    renames = renames or {}
+
+    root = Path(scan_root).expanduser()
+    if not root.is_absolute():
+        raise HTTPException(400, f"Path must be absolute: {scan_root!r}")
+    if not root.is_dir():
+        raise HTTPException(404, f"No such directory: {root}")
+
+    candidates = sorted((p for p in root.iterdir() if _is_zarr_dir(p)), key=lambda p: p.name)
+
+    parts = [p for p in container_path.strip("/").split("/") if p]
+    target = ingest_mod._walk(get_tiled_client(server_uri, api_key_for_uri(server_uri)), parts)
+
+    registered: list[dict[str, Any]] = []
+    skipped: list[str] = []
+    shadowed: list[dict[str, str]] = []
+    errors: list[dict[str, str]] = []
+    for candidate in candidates:
+        default_key = registered_key(candidate)
+        key = renames.get(candidate.name, default_key)
+
+        if target is not None and key in ingest_mod._child_keys(target):
+            existing_kind = ingest_mod.node_source_kind(target[key])
+            if existing_kind != "zarr":
+                shadowed.append(
+                    {
+                        "name": candidate.name,
+                        "key": key,
+                        "existing_kind": existing_kind or "unknown",
+                        "suggested_key": f"{key}_zarr",
+                    }
+                )
+                continue
+
+        try:
+            if key != default_key:
+                # register_zarr always derives the key from the path's own
+                # filename — the only way to register under a chosen
+                # alternate key is to nest one level deeper, in a container
+                # named for it. Only affects this explicit, rare rename-
+                # recovery path, not registration in general.
+                nested_container = f"{container_path}/{key}".strip("/")
+                result = register_zarr(server_uri, str(candidate), nested_container, on_conflict=on_conflict)
+                if not result.get("skipped"):
+                    result = {**result, "tiled_path": f"{nested_container}/{result['key']}"}
+            else:
+                result = register_zarr(server_uri, str(candidate), container_path, on_conflict=on_conflict)
+        except HTTPException as exc:
+            errors.append({"name": candidate.name, "error": str(exc.detail)})
+            continue
+        except Exception as exc:  # noqa: BLE001 — one bad store must not sink the scan
+            errors.append({"name": candidate.name, "error": str(exc)})
+            continue
+        if result.get("skipped"):
+            skipped.append(key)
+        else:
+            registered.append({"name": candidate.name, "key": key, "tiled_path": result["tiled_path"]})
+
+    return {
+        "scanned": len(candidates),
+        "registered": registered,
+        "skipped": skipped,
+        "shadowed": shadowed,
+        "errors": errors,
+    }
diff --git a/docker-compose.full.yml b/docker-compose.full.yml
index d0d6a6e..6120e36 100644
--- a/docker-compose.full.yml
+++ b/docker-compose.full.yml
@@ -29,9 +29,11 @@ services:
       TILED_API_KEY: "${TILED_API_KEY:-}"
     volumes:
       - annotation-data-full:/data
-      # Bind-mount your own source datasets to /data/raw (matches
-      # tiled/config.docker.yml's readable_storage), e.g.:
-      #   - /path/to/your/data:/data/raw
+      # Bind-mount your own source datasets to /data/processed (matches
+      # tiled/config.docker.yml's readable_storage). Set LOCAL_SOURCE_DIR in
+      # your own .env (see .env.example at the repo root) to a real host
+      # directory; defaults to an empty placeholder so this works unset.
+      - "${LOCAL_SOURCE_DIR:-./.local-source}:/data/processed"
 
 volumes:
   annotation-data-full:
diff --git a/docker-compose.local.yml b/docker-compose.local.yml
index e27a26b..0027039 100644
--- a/docker-compose.local.yml
+++ b/docker-compose.local.yml
@@ -36,8 +36,13 @@ services:
       TILED_API_KEY: "${TILED_API_KEY:-}"
     volumes:
       - annotation-data-local:/data
-      # Bind-mount your own source datasets to /data/raw, e.g.:
-      #   - /path/to/your/data:/data/raw
+      # Bind-mount your own source datasets to /data/processed — Tiled's
+      # ingest/register-in-place routes (Browse's Zarr loader, the Connect
+      # page's ingest flow) read directly from here. Must match tiled/config
+      # .docker.yml's readable_storage exactly. Set LOCAL_SOURCE_DIR in your
+      # own .env (see .env.example at the repo root) to a real host
+      # directory; defaults to an empty placeholder so this works unset.
+      - "${LOCAL_SOURCE_DIR:-./.local-source}:/data/processed"
 
   proxy:
     image: nginx:stable-alpine
diff --git a/docker-entrypoint-full.sh b/docker-entrypoint-full.sh
index 2ffc4cf..e71cb9c 100755
--- a/docker-entrypoint-full.sh
+++ b/docker-entrypoint-full.sh
@@ -23,7 +23,7 @@ if [ -z "${TILED_API_KEY:-}" ]; then
   echo "Generated a Tiled API key for this container (not persisted — set TILED_API_KEY yourself to keep one across restarts)."
 fi
 
-mkdir -p /data/.tiled/data /data/.tiled/volumes /data/raw
+mkdir -p /data/.tiled/data /data/.tiled/volumes /data/processed
 
 tiled serve config /app/tiled/config.docker.yml --host 0.0.0.0 --port 8010 --api-key "$TILED_API_KEY" &
 uvicorn ipred.api:app --host 0.0.0.0 --port 8003 &
@@ -51,4 +51,52 @@ export IPRED_URL="http://127.0.0.1:8003"
 # same "keep in step" rule tiled/config.yml documents for local dev.
 export VOLUME_CACHE_DIR="/data/.tiled/volumes"
 
+# Auto-register any Zarr stores AND image-slice folders already sitting under
+# the bind-mounted LOCAL_SOURCE_DIR (see docker-compose.full.yml/docker-
+# compose.local.yml) — without this, a container brought up against a folder
+# of pre-existing data shows nothing in Browse until someone manually scans or
+# registers each one through the UI. Safe on every restart: both scans skip
+# anything already registered, so this never re-registers or duplicates
+# existing entries. The image-stack scan deliberately does NOT build a 3-D
+# pyramid here — that's comparatively expensive (reads every slice) and is
+# left to the on-demand "Build 3D volume" button on the 3D page, so startup
+# stays fast regardless of how large a folder of TIFFs/PNGs is. Sequential,
+# not concurrent: both scans call _ensure_container against the same target
+# container, and running them in parallel would race on its creation.
+# Non-fatal on failure — a scan problem must never block the app from
+# starting; it just leaves auto-discovery for that run to be retried
+# manually via the "Scan folder for datasets" button in the Zarr loader
+# (POST /api/scan-datasets).
+python3 -c "
+import sys
+sys.path.insert(0, '/app')
+import ingest
+import zarr_source
+
+def report(label, result):
+    print(
+        f\"{label}: {len(result['registered'])} new, \"
+        f\"{len(result['skipped'])} already present, \"
+        f\"{len(result['shadowed'])} shadowed, {len(result['errors'])} failed.\"
+    )
+    for s in result['shadowed']:
+        print(
+            f\"  - {s['name']}: same name already registered as {s['existing_kind']!r} — \"
+            f\"retry via the Zarr loader's Scan button with a different key \"
+            f\"(e.g. {s['suggested_key']!r}) if you want both.\"
+        )
+    for err in result['errors']:
+        print(f\"  - {err['name']}: {err['error']}\")
+
+try:
+    report('Zarr auto-registration', zarr_source.scan_and_register_zarrs('http://127.0.0.1:8010', '/data/processed', 'browse'))
+except Exception as exc:
+    print(f'Zarr auto-registration scan failed (non-fatal): {exc}')
+
+try:
+    report('Image-stack auto-registration', ingest.scan_and_register_image_stacks('http://127.0.0.1:8010', '/data/processed', 'browse'))
+except Exception as exc:
+    print(f'Image-stack auto-registration scan failed (non-fatal): {exc}')
+" || true
+
 exec uvicorn annotation_server:app --host 0.0.0.0 --port 8002
diff --git a/docker/nginx-local.conf b/docker/nginx-local.conf
index 4f7d7b6..c419ee3 100644
--- a/docker/nginx-local.conf
+++ b/docker/nginx-local.conf
@@ -10,6 +10,16 @@
 server {
     listen 80;
 
+    # nginx's default (1MB) is nowhere near enough for the Ingest dropzone's
+    # multi-file uploads (a folder of TIFFs easily reaches hundreds of MB) —
+    # without this, an upload past 1MB fails with a raw nginx 413 before the
+    # request ever reaches the backend. 0 = unlimited; the backend's own
+    # ingest route is the real, size-aware boundary already (job-based,
+    # streamed to disk), so there's no reason to cap here too. A real
+    # ALS-hub-style production proxy needs this exact same setting — it is
+    # NOT specific to this local test proxy.
+    client_max_body_size 0;
+
     location = / {
         return 302 /seg_studio/;
     }
@@ -30,5 +40,11 @@ server {
         proxy_http_version 1.1;
         proxy_set_header Upgrade $http_upgrade;
         proxy_set_header Connection "upgrade";
+        # Large multi-file uploads over a slow/local link can legitimately
+        # take a while; don't let nginx's default 60s read/send timeouts cut
+        # them off mid-transfer.
+        proxy_read_timeout 600s;
+        proxy_send_timeout 600s;
+        client_body_timeout 600s;
     }
 }
diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md
index 570e5c9..c041904 100644
--- a/docs/getting-started/installation.md
+++ b/docs/getting-started/installation.md
@@ -119,18 +119,34 @@ reachable directly if you want to hit them outside the app.
     restart.
 
 !!! tip "Pointing at your own datasets"
-    For `app-full`, set `LOCAL_SOURCE_DIR` in your own `.env` (copy the repo
-    root's `.env.example`) to an absolute host directory — it's bind-mounted to
-    `/data/raw` inside the container, so the bundled Tiled server (and the Zarr
-    loader's "Browse…" directory picker on the Connect page) can read your real
-    data directly. This matters specifically in Docker: a path from your own
-    machine (e.g. one you'd type into the Zarr loader by hand) means nothing to
-    the container unless it's actually mounted like this — `LOCAL_SOURCE_DIR` is
-    what makes it visible.
+    For `app-full`/`app-local`, set `LOCAL_SOURCE_DIR` in your own `.env` (copy
+    the repo root's `.env.example`) to an absolute host directory — it's
+    bind-mounted to `/data/processed` inside the container, so the bundled
+    Tiled server (and the Zarr loader's "Browse…" directory picker on the
+    Connect page) can read your real data directly. This matters specifically
+    in Docker: a path from your own machine (e.g. one you'd type into the Zarr
+    loader by hand) means nothing to the container unless it's actually
+    mounted like this — `LOCAL_SOURCE_DIR` is what makes it visible.
     ```bash
     LOCAL_SOURCE_DIR=/absolute/path/to/your/data docker compose -f docker-compose.full.yml up --build
     ```
 
+    **Everything under that directory is registered into Tiled and shows up in
+    Browse automatically** — on every container start, and again whenever you
+    click **"Scan folder for datasets"** in the Zarr loader's directory
+    browser (useful after adding new files without restarting). This covers
+    both `.zarr` stores and plain folders of TIFF/PNG/JPG slices — the latter
+    register as fast, per-slice ingest with no 3-D pyramid built yet (build
+    one on demand from the 3D tab when you actually need it, so a large
+    dataset doesn't delay startup).
+
+    If a raw image folder and a `.zarr` reconstruction of the same acquisition
+    share a name, only one can occupy that key — the scan reports the second
+    one as **shadowed** rather than silently skipping it, and offers a
+    "Register as…" action (in the UI, or `POST /api/scan-datasets` /
+    `/api/ingest/scan` with a `renames` field) to register it under a
+    different key so both show up side by side.
+
 ---
 
 ## Manual setup
diff --git a/docs/reference/architecture.md b/docs/reference/architecture.md
index 594a906..da8c9e9 100644
--- a/docs/reference/architecture.md
+++ b/docs/reference/architecture.md
@@ -187,7 +187,7 @@ The dev and production layouts differ mainly in **who serves the SPA** and
     flowchart LR
       Browser["Browser"]
       Container["app-full image<br/>backend :8002 (foreground)<br/>+ ipred :8003 (background)<br/>+ Tiled :8010 (background)"]
-      Volume[("/data volume<br/>/data/raw bind mount")]
+      Volume[("/data volume<br/>/data/processed bind mount")]
 
       Browser -->|":8002 only"| Container
       Container --> Volume
diff --git a/docs/reference/deployment.md b/docs/reference/deployment.md
index 64fdcb6..8254c1c 100644
--- a/docs/reference/deployment.md
+++ b/docs/reference/deployment.md
@@ -102,7 +102,7 @@ Beyond what's already covered in [Installation](../getting-started/installation.
 | --- | --- |
 | `TILED_BROWSE_PATH` | The real path into an existing institutional Tiled catalog Browse should treat as its root (e.g. `beamlines/bl832/processed`). Confirm the exact value with whoever operates that Tiled server — don't assume it matches another deployment's beamline. |
 | `TILED_URI` / `TILED_API_KEY` | Point at the shared production Tiled server. See the open authentication question below — a single shared key is a stopgap, not the final design. |
-| `LOCAL_SOURCE_DIR` | `docker-compose.full.yml`/`docker-compose.local.yml` (bundled-Tiled shapes) only — bind-mounts a real host directory to `/data/raw` so the bundled Tiled server can read it directly. Not applicable to `:als` (`app-ml`), which points at an already-existing external Tiled instead of a bundled one. |
+| `LOCAL_SOURCE_DIR` | `docker-compose.full.yml`/`docker-compose.local.yml` (bundled-Tiled shapes) only — bind-mounts a real host directory to `/data/processed` so the bundled Tiled server can read it directly. Everything under it (Zarr stores and plain image-slice folders) auto-registers into Tiled on every container start (see [Installation](../getting-started/installation.md#option-2-docker)) — no manual ingest step needed for pre-existing data. Not applicable to `:als` (`app-ml`), which points at an already-existing external Tiled instead of a bundled one. |
 
 Persistent storage (`LOCAL_DATA_ROOT`, defaulting to the `/data` volume already
 declared in the image) and CORS (`BROWSE_ALLOWED_ORIGINS`) need no special
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
index 716b41b..b013fbe 100644
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
@@ -373,12 +373,12 @@
       }
     },
     "node_modules/@blueskyproject/finch": {
-      "version": "0.1.8",
-      "resolved": "https://registry.npmjs.org/@blueskyproject/finch/-/finch-0.1.8.tgz",
-      "integrity": "sha512-L5vHKHvrJ+BcNIh7fIqbcFJqBLsGNzVAiZuDJv69vrc3QKiLhbY1jQV/6HhsqIfrlB4MQxwfIx/Qnyid9TLixw==",
+      "version": "0.1.9",
+      "resolved": "https://registry.npmjs.org/@blueskyproject/finch/-/finch-0.1.9.tgz",
+      "integrity": "sha512-2xF5l/ywTyjgxy5wmtd2lk0e1OA7Yt38SrKKrKNOS9NTA7bfxWQb+FJPUw62p97vA4VE38YaFPTj7Sf1lW/tdg==",
       "license": "BSD-3-Clause",
       "dependencies": {
-        "@blueskyproject/tiled": "^0.0.28",
+        "@blueskyproject/tiled": "^0.0.30",
         "@phosphor-icons/react": "^2.1.7",
         "@radix-ui/react-dropdown-menu": "^2.1.16",
         "@radix-ui/react-popover": "^1.1.14",
@@ -403,12 +403,12 @@
       }
     },
     "node_modules/@blueskyproject/tiled": {
-      "version": "0.0.28",
-      "resolved": "https://registry.npmjs.org/@blueskyproject/tiled/-/tiled-0.0.28.tgz",
-      "integrity": "sha512-VTajcGtdBca4pjfLqy1Q9qmNOS4J2q5BAqRYqlDwKJLJVrxU8jaejLPmOrMicXhpW3mhSjD4ZOUIUpvWuOSD/Q==",
+      "version": "0.0.34",
+      "resolved": "https://registry.npmjs.org/@blueskyproject/tiled/-/tiled-0.0.34.tgz",
+      "integrity": "sha512-BQgyJ6s0tJsWo5qO4MveYJoTfqWTGOoyir4uZ8KRzhCWcStmTCuidNxoExgCtdBM1DGvsc3x+Ttg6JQc5AZE7Q==",
       "license": "BSD-3-Clause",
       "dependencies": {
-        "@phosphor-icons/react": "^2.1.7",
+        "@phosphor-icons/react": "^2.1.10",
         "@storybook/addons": "^7.6.17",
         "@storybook/theming": "^8.6.14",
         "@visx/curve": "^3.12.0",
@@ -424,7 +424,6 @@
         "clsx": "^2.1.1",
         "dayjs": "^1.11.13",
         "lucide-react": "^0.487.0",
-        "react-router-dom": "^6.30.0",
         "tailwind-merge": "^3.2.0",
         "tailwindcss-animate": "^1.0.7"
       },
@@ -1545,9 +1544,9 @@
       }
     },
     "node_modules/@jridgewell/sourcemap-codec": {
-      "version": "1.5.5",
-      "resolved": "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.5.5.tgz",
-      "integrity": "sha512-cYQ9310grqxueWbl+WuIUIaiUaDcj7WOq5fVhEljNVgRfOUhY9fy2zTvfoqWsnebh8Sl70VScFbICvJnLKB0Og==",
+      "version": "1.6.0",
+      "resolved": "https://registry.npmjs.org/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.6.0.tgz",
+      "integrity": "sha512-T7jf+5zgsZHwNJ4lvQ7/aezbyk0nNX+zJVWpmHA7VYsEx7a7qr5Rg5IbtJFqkgze5Y2sruq1RUY8Q837Od7iFw==",
       "license": "MIT"
     },
     "node_modules/@jridgewell/trace-mapping": {
@@ -2400,15 +2399,6 @@
         "react-dom": "^16.8.0 || ^17.0.0 || ^18.0.0"
       }
     },
-    "node_modules/@remix-run/router": {
-      "version": "1.23.4",
-      "resolved": "https://registry.npmjs.org/@remix-run/router/-/router-1.23.4.tgz",
-      "integrity": "sha512-q7j5geK7xs3UJSdm9/iytUNclBnLmYx1EnSeCFXHPeutdqgIMeFeHtUZgS3EhlKxdBEAu8OwtJCwmLrEzpSs7Q==",
-      "license": "MIT",
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
     "node_modules/@rolldown/pluginutils": {
       "version": "1.0.0-beta.27",
       "resolved": "https://registry.npmjs.org/@rolldown/pluginutils/-/pluginutils-1.0.0-beta.27.tgz",
@@ -2858,9 +2848,9 @@
       }
     },
     "node_modules/@storybook/core/node_modules/semver": {
-      "version": "7.8.4",
-      "resolved": "https://registry.npmjs.org/semver/-/semver-7.8.4.tgz",
-      "integrity": "sha512-rUCObTnP32Q08R2uuIrt7r9PlEonuTmtuXYcW6s5kjdlj3xbnwe+21yXptAUYcMAABLkYYTtnmzb3w3EDZfueA==",
+      "version": "7.8.5",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-7.8.5.tgz",
+      "integrity": "sha512-Y7/KDsb8LjooZpwaqGyulO6DQlksgCncchHGk+sZIY4SBvUocMBEFH5Ur1fI4dV+Jvl0w6cjvucaIi40puRioA==",
       "license": "ISC",
       "peer": true,
       "bin": {
@@ -3313,9 +3303,9 @@
       }
     },
     "node_modules/@types/express-serve-static-core": {
-      "version": "4.19.8",
-      "resolved": "https://registry.npmjs.org/@types/express-serve-static-core/-/express-serve-static-core-4.19.8.tgz",
-      "integrity": "sha512-02S5fmqeoKzVZCHPZid4b8JH2eM5HzQLZWN2FohQEy/0eXTq8VXZfSN6Pcr3F6N9R/vNrj7cpgbhjie6m/1tCA==",
+      "version": "4.19.9",
+      "resolved": "https://registry.npmjs.org/@types/express-serve-static-core/-/express-serve-static-core-4.19.9.tgz",
+      "integrity": "sha512-QP2ESEe/ImWY0HDwNAnK9PvEffUyhLTnWkk7KXzHfyeWAnlrDe1fN77bXl6ia8KT3wPlmA7t9/VPRpnf4Ex9sg==",
       "license": "MIT",
       "dependencies": {
         "@types/node": "*",
@@ -3337,9 +3327,9 @@
       "license": "MIT"
     },
     "node_modules/@types/lodash": {
-      "version": "4.17.24",
-      "resolved": "https://registry.npmjs.org/@types/lodash/-/lodash-4.17.24.tgz",
-      "integrity": "sha512-gIW7lQLZbue7lRSWEFql49QJJWThrTFFeIMJdp3eH4tKoxm1OvEPg02rm4wCCSHS0cL3/Fizimb35b7k8atwsQ==",
+      "version": "4.17.25",
+      "resolved": "https://registry.npmjs.org/@types/lodash/-/lodash-4.17.25.tgz",
+      "integrity": "sha512-+K1NIO8I+F9/wNulfVvu23QYd0Pe9/OCqRrim4NoYIf1VoEDL90Ve4ClzpyqBLc7NpGGWRvYNCKZ1BE/Jpf8dQ==",
       "license": "MIT"
     },
     "node_modules/@types/mime": {
@@ -3841,15 +3831,15 @@
       }
     },
     "node_modules/@vitest/expect": {
-      "version": "3.2.6",
-      "resolved": "https://registry.npmjs.org/@vitest/expect/-/expect-3.2.6.tgz",
-      "integrity": "sha512-1+7q9BtaKzEmO+fmNT3kYvoNn5Y71XWAx2Q5HRim4tTVRQVRv4uJFAQ5FbK0OPUeNP/WmVCpxYxoJdvuHVjzBQ==",
+      "version": "3.2.7",
+      "resolved": "https://registry.npmjs.org/@vitest/expect/-/expect-3.2.7.tgz",
+      "integrity": "sha512-E8eBXaKibuvH2pSZErOjdVb5vF4PbKYcrnluBTYxEk1l/VhhwZg1kZQsdtjq+CsF5CFydf2Rdkz7jDHKSisi3w==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "@types/chai": "^5.2.2",
-        "@vitest/spy": "3.2.6",
-        "@vitest/utils": "3.2.6",
+        "@vitest/spy": "3.2.7",
+        "@vitest/utils": "3.2.7",
         "chai": "^5.2.0",
         "tinyrainbow": "^2.0.0"
       },
@@ -3858,13 +3848,13 @@
       }
     },
     "node_modules/@vitest/mocker": {
-      "version": "3.2.6",
-      "resolved": "https://registry.npmjs.org/@vitest/mocker/-/mocker-3.2.6.tgz",
-      "integrity": "sha512-EZOrpDbkKotFAP7wPAQV1UIyoGOk4oX7ynWhBhLB7v+meMHbQhU16oPpIYGTTe4oFlhpryGpgpcZP/sin3hYuw==",
+      "version": "3.2.7",
+      "resolved": "https://registry.npmjs.org/@vitest/mocker/-/mocker-3.2.7.tgz",
+      "integrity": "sha512-Trr0hYO9CM3Wj6ksWHRhK9IZpIY6wTMO5u/MqXurMxT57sWBaOPEtP3Oq60ihZuh5JsiagKfz95OcxdEP6dBrA==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@vitest/spy": "3.2.6",
+        "@vitest/spy": "3.2.7",
         "estree-walker": "^3.0.3",
         "magic-string": "^0.30.17"
       },
@@ -3885,9 +3875,9 @@
       }
     },
     "node_modules/@vitest/pretty-format": {
-      "version": "3.2.6",
-      "resolved": "https://registry.npmjs.org/@vitest/pretty-format/-/pretty-format-3.2.6.tgz",
-      "integrity": "sha512-lb7XXXzmm2h2ASzFnRvQpDo6onT1NmMJA3tkGTWiBFtRJ9lxGY3d3mm/Apt36gej2bkkOVLL/yTOtufDaFa/jA==",
+      "version": "3.2.7",
+      "resolved": "https://registry.npmjs.org/@vitest/pretty-format/-/pretty-format-3.2.7.tgz",
+      "integrity": "sha512-KUHlwqVu0sRlhCdyPdQ/wBoTfRahjUky1MubOmYw9fWfIZy1gNoHpuaaQBPAaMaVYdQYHJLurzj8ECCj5OwTqA==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
@@ -3898,13 +3888,13 @@
       }
     },
     "node_modules/@vitest/runner": {
-      "version": "3.2.6",
-      "resolved": "https://registry.npmjs.org/@vitest/runner/-/runner-3.2.6.tgz",
-      "integrity": "sha512-HYcoSj1w5tcgUnzoF0HcyaAQjpA1gj9ftUJ7iSJSuipc02jW9gKkigwZbjFldAfYHA1fa8UZVRftdMY5msWM9Q==",
+      "version": "3.2.7",
+      "resolved": "https://registry.npmjs.org/@vitest/runner/-/runner-3.2.7.tgz",
+      "integrity": "sha512-sB9y4ovltoQP+WaUPwmSxO9WIg9Ig694Di5PalVPsYHklAdE027mehpWF2SQSVq+k6sFgaivbTjTJwZLSHbedA==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@vitest/utils": "3.2.6",
+        "@vitest/utils": "3.2.7",
         "pathe": "^2.0.3",
         "strip-literal": "^3.0.0"
       },
@@ -3913,13 +3903,13 @@
       }
     },
     "node_modules/@vitest/snapshot": {
-      "version": "3.2.6",
-      "resolved": "https://registry.npmjs.org/@vitest/snapshot/-/snapshot-3.2.6.tgz",
-      "integrity": "sha512-H+ZjNTWGpObenh0YnlBctAPnJSI20P81PL8BPzWpx54YXLLTm8hEsWawtcYLMrwvpK48hGxLLbCS+1KRXhsKhw==",
+      "version": "3.2.7",
+      "resolved": "https://registry.npmjs.org/@vitest/snapshot/-/snapshot-3.2.7.tgz",
+      "integrity": "sha512-7C+MwShwtBSI5Buwoyg3s/iY1eHL9PKAf+O1wVh/TdnjXUtkoL/9YQtre90i4MtNXM6edP1wJ2zOBpfCyhIS7g==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@vitest/pretty-format": "3.2.6",
+        "@vitest/pretty-format": "3.2.7",
         "magic-string": "^0.30.17",
         "pathe": "^2.0.3"
       },
@@ -3928,9 +3918,9 @@
       }
     },
     "node_modules/@vitest/spy": {
-      "version": "3.2.6",
-      "resolved": "https://registry.npmjs.org/@vitest/spy/-/spy-3.2.6.tgz",
-      "integrity": "sha512-oq6BbH68WzcWmwtBrU9nqLeaXTR4XwJF7FSLkKEZo4i6eoXcrxjcwSuTvWBIRUTC6VC72nXYunzqgZA+IKdtxg==",
+      "version": "3.2.7",
+      "resolved": "https://registry.npmjs.org/@vitest/spy/-/spy-3.2.7.tgz",
+      "integrity": "sha512-Q2eQGI6d2L/hBtZ0qNuKcAGid68XK6cv1xsoaIma6PaJhHPoqcEJhYpXZ/5myCMqkNgtP6UKuBhbc0nHKnrkuQ==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
@@ -3941,13 +3931,13 @@
       }
     },
     "node_modules/@vitest/utils": {
-      "version": "3.2.6",
-      "resolved": "https://registry.npmjs.org/@vitest/utils/-/utils-3.2.6.tgz",
-      "integrity": "sha512-lI23nIs4bnT3T8NIoh+vFaz5s2/DdP0Jgt2jxwgWljvwn82cLJtyi/If+fjFyoLMGIOz0U/fKvWE0d4jsNQEfg==",
+      "version": "3.2.7",
+      "resolved": "https://registry.npmjs.org/@vitest/utils/-/utils-3.2.7.tgz",
+      "integrity": "sha512-x6BDOd7dyo3PFLY3I9/HJ25X/6OurhGXk2/B9gOZNPF7XDVjeBK4k01lQE5uvDpbuheErh91qYuE1E2OEjK3Rw==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@vitest/pretty-format": "3.2.6",
+        "@vitest/pretty-format": "3.2.7",
         "loupe": "^3.1.4",
         "tinyrainbow": "^2.0.0"
       },
@@ -4064,9 +4054,9 @@
       }
     },
     "node_modules/ast-types": {
-      "version": "0.16.1",
-      "resolved": "https://registry.npmjs.org/ast-types/-/ast-types-0.16.1.tgz",
-      "integrity": "sha512-6t10qk83GOG8p0vKmaCr8eiilZwO171AvbROMtvvNiwrTly62t+7XkA8RdIIVbpMhCASAsxgAzdRSwh6nw/5Dg==",
+      "version": "0.16.3",
+      "resolved": "https://registry.npmjs.org/ast-types/-/ast-types-0.16.3.tgz",
+      "integrity": "sha512-FvWoWYfSCM6kRxCSH+MGLHIKKGRL6A6AW7Zek2O32REPQRdg131428uRTKMBYAeRd3XXAaHDS60Wpri7CdKDrA==",
       "license": "MIT",
       "peer": true,
       "dependencies": {
@@ -4136,9 +4126,9 @@
       "license": "MIT"
     },
     "node_modules/baseline-browser-mapping": {
-      "version": "2.11.20",
-      "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.11.20.tgz",
-      "integrity": "sha512-H0ulySigv6icDJ1F7SjtdCD6PrhTpdYCmP0CactWy1+ekh0AFd0o1Wn5T8b+hnTmdBx19u9yhL6wvCylXMY7zw==",
+      "version": "2.11.22",
+      "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.11.22.tgz",
+      "integrity": "sha512-pWc4w51fBFd7mav43/zKRC+RI6f4yfzQoVlfvE8dECePyfkn1bzLp01Fj0QACcyCZyFhiEMyD2qScfKRWgWibA==",
       "dev": true,
       "license": "Apache-2.0",
       "bin": {
@@ -4199,9 +4189,9 @@
       "peer": true
     },
     "node_modules/browserslist": {
-      "version": "4.28.8",
-      "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.8.tgz",
-      "integrity": "sha512-V2NpofLblG64mfOtSgDhOJESZEGogzDMBv/q+W6oc4LXWP/q75eOXoOaaOu1EOadB9U4Bwx/e0yzbvwKH8zalA==",
+      "version": "4.28.9",
+      "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.9.tgz",
+      "integrity": "sha512-EWazOblFYUvlGZcfGhPUPmYh3nikUxBVb+y9MJun5f3hBi812X+8MSQTujLBtgK3cf51fJWbWfOjyeO954d+Eg==",
       "dev": true,
       "funding": [
         {
@@ -4219,11 +4209,11 @@
       ],
       "license": "MIT",
       "dependencies": {
-        "baseline-browser-mapping": "^2.11.12",
-        "caniuse-lite": "^1.0.30001809",
-        "electron-to-chromium": "^1.5.402",
-        "node-releases": "^2.0.53",
-        "update-browserslist-db": "^1.3.0"
+        "baseline-browser-mapping": "^2.11.20",
+        "caniuse-lite": "^1.0.30001810",
+        "electron-to-chromium": "^1.5.420",
+        "node-releases": "^2.0.54",
+        "update-browserslist-db": "^1.3.2"
       },
       "bin": {
         "browserslist": "cli.js"
@@ -4644,9 +4634,9 @@
       }
     },
     "node_modules/dayjs": {
-      "version": "1.11.21",
-      "resolved": "https://registry.npmjs.org/dayjs/-/dayjs-1.11.21.tgz",
-      "integrity": "sha512-98IT+HOahAisibz/yjKbzuOBwYcjJ7BCLPzARyHiyEBmRz4fatF+KPJszEHXsGYjUG234aH/cOjW1wwTbKUZlA==",
+      "version": "1.11.23",
+      "resolved": "https://registry.npmjs.org/dayjs/-/dayjs-1.11.23.tgz",
+      "integrity": "sha512-QDTCU0M0MxR3hQfnlDJfwekQiaanm1ubOD231u73WBckQ/fsamwRLiE2GBz6D3a/xF1NgfiDLJjXBa1hYOYTtQ==",
       "license": "MIT"
     },
     "node_modules/debug": {
@@ -4964,9 +4954,9 @@
       }
     },
     "node_modules/expect-type": {
-      "version": "1.3.0",
-      "resolved": "https://registry.npmjs.org/expect-type/-/expect-type-1.3.0.tgz",
-      "integrity": "sha512-knvyeauYhqjOYvQ66MznSMs83wmHrCycNEN6Ao+2AeYEfxUIkuiVxdEa1qlGEPK+We3n0THiDciYSsCcgW/DoA==",
+      "version": "1.4.0",
+      "resolved": "https://registry.npmjs.org/expect-type/-/expect-type-1.4.0.tgz",
+      "integrity": "sha512-KfYbmpRm0VbLjEvVa9yGwCi9GI34xvi7A/HXYWQO65CSD2u3MczUJSuwXKFIxlGsgBQizV9q5J9NHj4VG0n+pA==",
       "dev": true,
       "license": "Apache-2.0",
       "engines": {
@@ -5897,9 +5887,9 @@
       }
     },
     "node_modules/nanoid": {
-      "version": "3.3.18",
-      "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.18.tgz",
-      "integrity": "sha512-DTg4MJbGMWkfi6VZFdNt2/caMbQy4Ou+Op/hJQvGEWcnVfoA1QA+xzRKAzw9jD6+GVOOeYr/mIcuDSdug6F6+w==",
+      "version": "3.3.19",
+      "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.19.tgz",
+      "integrity": "sha512-Y2tUNy4ouw6tq5oDSKeQYGOyhkUBhNOcGV/02KC+6kd9eDGqdZd++mjMiIDilrBYvjEnCYvVtsuHCuP+okSfug==",
       "funding": [
         {
           "type": "github",
@@ -6138,9 +6128,9 @@
       }
     },
     "node_modules/postcss": {
-      "version": "8.5.26",
-      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.26.tgz",
-      "integrity": "sha512-u82N74LFzG8ca+dD8puPnplTXoGH4fTPpVGuIbt36G3qvNlkvfD0lEAZSxaly3KX8TS/L1A1gsCEmvKmBcVbkQ==",
+      "version": "8.5.28",
+      "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.28.tgz",
+      "integrity": "sha512-RRuzqDtt5Y9h3quz5hWhK+TPnsmVs6WwSU6LkJMeY4HstUEDuYTG8UJSdawMRzmzAtV+KEoG8N3Qg2qLy5vM/A==",
       "funding": [
         {
           "type": "opencollective",
@@ -6157,7 +6147,7 @@
       ],
       "license": "MIT",
       "dependencies": {
-        "nanoid": "^3.3.17",
+        "nanoid": "^3.3.18",
         "picocolors": "^1.1.1",
         "source-map-js": "^1.2.1"
       },
@@ -6574,38 +6564,6 @@
         }
       }
     },
-    "node_modules/react-router-dom": {
-      "version": "6.30.6",
-      "resolved": "https://registry.npmjs.org/react-router-dom/-/react-router-dom-6.30.6.tgz",
-      "integrity": "sha512-0RHKZz7wwffvkU+2MFVT2NnjK44ssLEV+m0CAJaS2Ksmorrwj7WxH00jO0SOCW26/tINUnJHToXblDs33I38YQ==",
-      "license": "MIT",
-      "dependencies": {
-        "@remix-run/router": "1.23.4",
-        "react-router": "6.30.6"
-      },
-      "engines": {
-        "node": ">=14.0.0"
-      },
-      "peerDependencies": {
-        "react": ">=16.8",
-        "react-dom": ">=16.8"
-      }
-    },
-    "node_modules/react-router-dom/node_modules/react-router": {
-      "version": "6.30.6",
-      "resolved": "https://registry.npmjs.org/react-router/-/react-router-6.30.6.tgz",
-      "integrity": "sha512-5HfK7k5im7LTOB0EqCQmfvy4C13G92Ssj1VTmouTK3AJvyjKTnFuCV0vcMAD/JS+JC4DvDIBRrlAeJIFjh5VWg==",
-      "license": "MIT",
-      "dependencies": {
-        "@remix-run/router": "1.23.4"
-      },
-      "engines": {
-        "node": ">=14.0.0"
-      },
-      "peerDependencies": {
-        "react": ">=16.8"
-      }
-    },
     "node_modules/react-style-singleton": {
       "version": "2.2.3",
       "resolved": "https://registry.npmjs.org/react-style-singleton/-/react-style-singleton-2.2.3.tgz",
@@ -6665,9 +6623,9 @@
       }
     },
     "node_modules/recast": {
-      "version": "0.23.11",
-      "resolved": "https://registry.npmjs.org/recast/-/recast-0.23.11.tgz",
-      "integrity": "sha512-YTUo+Flmw4ZXiWfQKGcwwc11KnoRAYgzAE2E7mXKCjSviTKShtxBsN6YUUBB2gtaBzKzeKunxhUwNHQuRryhWA==",
+      "version": "0.23.21",
+      "resolved": "https://registry.npmjs.org/recast/-/recast-0.23.21.tgz",
+      "integrity": "sha512-mFAyJq9vUbSTARLZUvAEf1z3YxlvAwswbmxMx2mPA/MSm4KmpwvwvhsH/NIrZhyOuwD60Lzyw2qh83uCbgTPYw==",
       "license": "MIT",
       "peer": true,
       "dependencies": {
@@ -7254,9 +7212,9 @@
       "license": "MIT"
     },
     "node_modules/synchronous-promise": {
-      "version": "2.0.17",
-      "resolved": "https://registry.npmjs.org/synchronous-promise/-/synchronous-promise-2.0.17.tgz",
-      "integrity": "sha512-AsS729u2RHUfEra9xJrE39peJcc2stq2+poBXX8bcM08Y6g9j/i/PUzwNQqkaJde7Ntg1TO7bSREbR5sdosQ+g==",
+      "version": "2.0.18",
+      "resolved": "https://registry.npmjs.org/synchronous-promise/-/synchronous-promise-2.0.18.tgz",
+      "integrity": "sha512-4EEtGWYLkSoy/DjlKpHR6LT2AjmORt8HM+CUCSdKiKyuhL3PFBksgcTibSOj7JIoCvuqRVnZ7P9QFBMXR5kW6Q==",
       "license": "BSD-3-Clause"
     },
     "node_modules/tailwind-merge": {
@@ -7437,9 +7395,9 @@
       }
     },
     "node_modules/tinyspy": {
-      "version": "4.0.4",
-      "resolved": "https://registry.npmjs.org/tinyspy/-/tinyspy-4.0.4.tgz",
-      "integrity": "sha512-azl+t0z7pw/z958Gy9svOTuzqIk6xq+NSheJzn5MMWtWTFywIacg2wUlzKFGtt3cthx0r2SxMK0yzJOR0IES7Q==",
+      "version": "4.0.6",
+      "resolved": "https://registry.npmjs.org/tinyspy/-/tinyspy-4.0.6.tgz",
+      "integrity": "sha512-u8KszXvGfU68hVcZpRHKG28T0krMuv2G5nDhiHaMLen/gIuFEgIJhaJuO69qjnXg5paSrbPMFfx3brNuN8eVSg==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -7844,20 +7802,20 @@
       }
     },
     "node_modules/vitest": {
-      "version": "3.2.6",
-      "resolved": "https://registry.npmjs.org/vitest/-/vitest-3.2.6.tgz",
-      "integrity": "sha512-xejya+bT/j/+R/AGa1XOfRxLmNUlLtlwjRsFUILF+xHfzElmGcmFydy2gqqIrd62ptIEfwVMofd19uNWD9L7Nw==",
+      "version": "3.2.7",
+      "resolved": "https://registry.npmjs.org/vitest/-/vitest-3.2.7.tgz",
+      "integrity": "sha512-KrxIJ62Fd89gfysR4WotlgZABiz2dqFPgqGzX7s+CwsqLFomRH7777ZcrOD6+WVAh7khPQP41A+BKbpcJFrdEg==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "@types/chai": "^5.2.2",
-        "@vitest/expect": "3.2.6",
-        "@vitest/mocker": "3.2.6",
-        "@vitest/pretty-format": "^3.2.6",
-        "@vitest/runner": "3.2.6",
-        "@vitest/snapshot": "3.2.6",
-        "@vitest/spy": "3.2.6",
-        "@vitest/utils": "3.2.6",
+        "@vitest/expect": "3.2.7",
+        "@vitest/mocker": "3.2.7",
+        "@vitest/pretty-format": "^3.2.7",
+        "@vitest/runner": "3.2.7",
+        "@vitest/snapshot": "3.2.7",
+        "@vitest/spy": "3.2.7",
+        "@vitest/utils": "3.2.7",
         "chai": "^5.2.0",
         "debug": "^4.4.1",
         "expect-type": "^1.2.1",
@@ -7887,8 +7845,8 @@
         "@edge-runtime/vm": "*",
         "@types/debug": "^4.1.12",
         "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0",
-        "@vitest/browser": "3.2.6",
-        "@vitest/ui": "3.2.6",
+        "@vitest/browser": "3.2.7",
+        "@vitest/ui": "3.2.7",
         "happy-dom": "*",
         "jsdom": "*"
       },
@@ -7917,9 +7875,9 @@
       }
     },
     "node_modules/vitest/node_modules/picomatch": {
-      "version": "4.0.4",
-      "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.4.tgz",
-      "integrity": "sha512-QP88BAKvMam/3NxH6vj2o21R6MjxZUAd6nlwAS/pnGvN9IVLocLHxGYIzFhg6fUQ+5th6P4dv4eW9jX3DSIj7A==",
+      "version": "4.0.7",
+      "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.7.tgz",
+      "integrity": "sha512-qcJu88Q2IWqJsDD529JKMdwGm/dvInW4HvQnRwiH9JtihJvzGOscDtHE3x1pBKeUOTysQ8kVmLnJ2kJu7yhcGA==",
       "dev": true,
       "license": "MIT",
       "engines": {
diff --git a/frontend/package.json b/frontend/package.json
index c51dcce..10b98fb 100644
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -46,5 +46,8 @@
     "vite": "^6.0.3",
     "vite-tsconfig-paths": "^5.1.4",
     "vitest": "^3.2.3"
+  },
+  "overrides": {
+    "@blueskyproject/tiled": "^0.0.34"
   }
 }
diff --git a/frontend/src/components/Ingest/ZarrLoader.test.tsx b/frontend/src/components/Ingest/ZarrLoader.test.tsx
index e1163e2..7b4c7de 100644
--- a/frontend/src/components/Ingest/ZarrLoader.test.tsx
+++ b/frontend/src/components/Ingest/ZarrLoader.test.tsx
@@ -202,4 +202,358 @@ describe('ZarrLoader', () => {
     await user.click(screen.getByRole('button', { name: /Load volume/ }));
     expect(await screen.findByText('disk full')).toBeInTheDocument();
   });
+
+  describe('server-side directory browser', () => {
+    function mockBrowseFetch(entriesByRel: Record<string, { name: string; path: string; is_dir: boolean }[]>) {
+      return vi.fn(async (url: string) => {
+        if (url.includes('/api/local/root')) return jsonResponse({ root: '/data/raw' });
+        if (url.includes('/api/local/list')) {
+          const rel = new URL(url, 'http://x').searchParams.get('rel') ?? '';
+          return jsonResponse(entriesByRel[rel] ?? []);
+        }
+        throw new Error(`unexpected fetch: ${url}`);
+      });
+    }
+
+    it('opens the browser, fetches the root, and lists directories (files filtered out)', async () => {
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(
+        mockBrowseFetch({
+          '': [
+            { name: 'scratch', path: 'scratch', is_dir: true },
+            { name: 'readme.txt', path: 'readme.txt', is_dir: false },
+          ],
+        }),
+      );
+      const user = userEvent.setup();
+      renderLoader();
+
+      await user.click(screen.getByRole('button', { name: /Browse…/ }));
+      expect(await screen.findByText('/data/raw')).toBeInTheDocument();
+      expect(screen.getByText('scratch')).toBeInTheDocument();
+      expect(screen.queryByText('readme.txt')).not.toBeInTheDocument();
+    });
+
+    it('descends into a plain subfolder, and selecting a .zarr entry fills the path and closes the browser', async () => {
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(
+        mockBrowseFetch({
+          '': [{ name: 'scratch', path: 'scratch', is_dir: true }],
+          scratch: [{ name: 'ant_m12.zarr', path: 'scratch/ant_m12.zarr', is_dir: true }],
+        }),
+      );
+      const user = userEvent.setup();
+      renderLoader();
+
+      await user.click(screen.getByRole('button', { name: /Browse…/ }));
+      await screen.findByText('scratch');
+      await user.click(screen.getByText('scratch'));
+
+      expect(await screen.findByText('ant_m12.zarr')).toBeInTheDocument();
+      await user.click(screen.getByText('ant_m12.zarr'));
+
+      // Browser closes and the path field now has the full absolute path —
+      // no more guessing a container-visible path blind.
+      expect(screen.queryByText('ant_m12.zarr')).not.toBeInTheDocument();
+      expect(screen.getByPlaceholderText('/absolute/path/to/volume.zarr')).toHaveValue(
+        '/data/raw/scratch/ant_m12.zarr',
+      );
+    });
+
+    it('"Use this folder" selects the currently-browsed directory even without a .zarr suffix', async () => {
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(
+        mockBrowseFetch({ '': [{ name: 'my_volume', path: 'my_volume', is_dir: true }] }),
+      );
+      const user = userEvent.setup();
+      renderLoader();
+
+      await user.click(screen.getByRole('button', { name: /Browse…/ }));
+      await user.click(await screen.findByText('my_volume'));
+      await screen.findByText(/Use this folder/);
+      await user.click(screen.getByText(/Use this folder/));
+
+      expect(screen.getByPlaceholderText('/absolute/path/to/volume.zarr')).toHaveValue('/data/raw/my_volume');
+    });
+
+    it('shows an error if the root or a directory listing fails', async () => {
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+        if (url.includes('/api/local/root')) return jsonResponse({ detail: 'no access' }, false, 500);
+        throw new Error(`unexpected fetch: ${url}`);
+      });
+      const user = userEvent.setup();
+      renderLoader();
+
+      await user.click(screen.getByRole('button', { name: /Browse…/ }));
+      expect(await screen.findByText('no access')).toBeInTheDocument();
+    });
+
+    it('closes the browser when Browse… is clicked again', async () => {
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(mockBrowseFetch({ '': [] }));
+      const user = userEvent.setup();
+      renderLoader();
+
+      await user.click(screen.getByRole('button', { name: /Browse…/ }));
+      await screen.findByText('/data/raw');
+      await user.click(screen.getByRole('button', { name: /Browse…/ }));
+      expect(screen.queryByText('/data/raw')).not.toBeInTheDocument();
+    });
+
+    it('an empty directory shows actionable LOCAL_SOURCE_DIR guidance, not a bare "no sub-folders" dead end', async () => {
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(mockBrowseFetch({ '': [] }));
+      const user = userEvent.setup();
+      renderLoader();
+
+      await user.click(screen.getByRole('button', { name: /Browse…/ }));
+      expect(await screen.findByText('No sub-folders here.')).toBeInTheDocument();
+      expect(screen.getByText(/LOCAL_SOURCE_DIR=\/path\/to\/your\/data/)).toBeInTheDocument();
+    });
+
+    it('overriding the root (e.g. anywhere on disk when run via start_all.sh) browses from there instead', async () => {
+      const listCalls: { root: string; rel: string }[] = [];
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+        if (url.includes('/api/local/root')) return jsonResponse({ root: '/data' });
+        if (url.includes('/api/local/list')) {
+          const u = new URL(url, 'http://x');
+          const root = u.searchParams.get('root') ?? '';
+          const rel = u.searchParams.get('rel') ?? '';
+          listCalls.push({ root, rel });
+          if (root === '/Users/me/tomo' && rel === '') {
+            return jsonResponse([{ name: 'ant_m12.zarr', path: 'ant_m12.zarr', is_dir: true }]);
+          }
+          return jsonResponse([]);
+        }
+        throw new Error(`unexpected fetch: ${url}`);
+      });
+      const user = userEvent.setup();
+      renderLoader();
+
+      await user.click(screen.getByRole('button', { name: /Browse…/ }));
+      await screen.findByText('/data');
+
+      const rootField = screen.getByPlaceholderText('Root to browse from');
+      await user.clear(rootField);
+      await user.type(rootField, '/Users/me/tomo');
+      await user.click(screen.getByRole('button', { name: 'Go' }));
+
+      expect(await screen.findByText('ant_m12.zarr')).toBeInTheDocument();
+      expect(await screen.findByText('/Users/me/tomo')).toBeInTheDocument();
+      expect(listCalls).toContainEqual({ root: '/Users/me/tomo', rel: '' });
+
+      await user.click(screen.getByText('ant_m12.zarr'));
+      expect(screen.getByPlaceholderText('/absolute/path/to/volume.zarr')).toHaveValue(
+        '/Users/me/tomo/ant_m12.zarr',
+      );
+    });
+
+    it('sends the granted root on every subsequent list call (breadcrumbs, descending)', async () => {
+      const listCalls: { root: string; rel: string }[] = [];
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+        if (url.includes('/api/local/root')) return jsonResponse({ root: '/data' });
+        if (url.includes('/api/local/list')) {
+          const u = new URL(url, 'http://x');
+          const root = u.searchParams.get('root') ?? '';
+          const rel = u.searchParams.get('rel') ?? '';
+          listCalls.push({ root, rel });
+          if (rel === '') return jsonResponse([{ name: 'sub', path: 'sub', is_dir: true }]);
+          return jsonResponse([]);
+        }
+        throw new Error(`unexpected fetch: ${url}`);
+      });
+      const user = userEvent.setup();
+      renderLoader();
+
+      await user.click(screen.getByRole('button', { name: /Browse…/ }));
+      await user.click(await screen.findByText('sub'));
+
+      expect(listCalls).toContainEqual({ root: '/data', rel: 'sub' });
+    });
+  });
+
+  it('a bare-array store (single level, empty level.path) builds the Tiled path without a trailing slash', async () => {
+    const BARE_ARRAY_INFO = {
+      name: 'plain.zarr',
+      path: '/data/plain.zarr',
+      levels: [
+        { path: '', shape: [6, 10, 12], dtype: 'uint16', n_slices: 6, height: 10, width: 12, downsample: [1, 1, 1] },
+      ],
+      full_shape: [6, 10, 12],
+      dtype: 'uint16',
+      voxel_size: null,
+      voxel_unit: null,
+    };
+    (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+      if (url.includes('/api/zarr/inspect')) return jsonResponse(BARE_ARRAY_INFO);
+      if (url.includes('/api/zarr/preflight')) return jsonResponse({ exists: false });
+      if (url.includes('/api/zarr/register')) return jsonResponse({ tiled_path: 'browse/plain' });
+      throw new Error(`unexpected fetch: ${url}`);
+    });
+    const user = userEvent.setup();
+    const { props } = renderLoader();
+    await typePathAndInspect(user, '/data/plain.zarr');
+    await screen.findByText('plain.zarr');
+
+    await user.click(screen.getByRole('button', { name: /Load volume/ }));
+    await screen.findByText(/Loaded 6 slices — no data was copied\./);
+
+    await user.click(screen.getByRole('button', { name: 'Annotate' }));
+    expect(props.onAnnotate).toHaveBeenCalledWith('browse/plain');
+  });
+
+  describe('scan folder for Zarr volumes', () => {
+    async function openBrowserAt(user: ReturnType<typeof userEvent.setup>, root = '/data') {
+      await user.click(screen.getByRole('button', { name: /Browse…/ }));
+      await screen.findByText(root);
+    }
+
+    it('scans the currently-browsed folder and reports registered/skipped/errors', async () => {
+      const scanCalls: unknown[] = [];
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string, init?: RequestInit) => {
+        if (url.includes('/api/local/root')) return jsonResponse({ root: '/data' });
+        if (url.includes('/api/local/list')) return jsonResponse([]);
+        if (url.includes('/api/scan-datasets')) {
+          scanCalls.push(JSON.parse(String(init?.body)));
+          return jsonResponse({
+            scanned: 3,
+            registered: [{ name: 'a.zarr', key: 'a', tiled_path: 'browse/a' }],
+            skipped: ['b'],
+            shadowed: [],
+            errors: [{ name: 'c.zarr', error: 'boom' }],
+          });
+        }
+        throw new Error(`unexpected fetch: ${url}`);
+      });
+      const user = userEvent.setup();
+      const { props } = renderLoader({ serverUri: 'http://tiled.example' });
+
+      await openBrowserAt(user);
+      await user.click(screen.getByRole('button', { name: /Scan folder for datasets/ }));
+
+      expect(await screen.findByText(/Scanned 3 — registered 1 new, skipped 1 already present, 1 failed\./))
+        .toBeInTheDocument();
+      expect(screen.getByText('a.zarr')).toBeInTheDocument();
+      expect(screen.getByText(/c\.zarr/)).toBeInTheDocument();
+      expect(screen.getByText(/boom/)).toBeInTheDocument();
+      expect(scanCalls).toEqual([
+        { scan_root: '/data', container_path: 'browse', server_uri: 'http://tiled.example' },
+      ]);
+
+      await user.click(screen.getByRole('button', { name: 'Go to Browse' }));
+      expect(props.onBrowse).toHaveBeenCalledWith('browse', 1);
+    });
+
+    it('scans a descended-into subfolder using its full path, not just the root', async () => {
+      const scanCalls: unknown[] = [];
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string, init?: RequestInit) => {
+        if (url.includes('/api/local/root')) return jsonResponse({ root: '/data' });
+        if (url.includes('/api/local/list')) {
+          const rel = new URL(url, 'http://x').searchParams.get('rel') ?? '';
+          if (rel === '') return jsonResponse([{ name: 'scratch', path: 'scratch', is_dir: true }]);
+          return jsonResponse([]);
+        }
+        if (url.includes('/api/scan-datasets')) {
+          scanCalls.push(JSON.parse(String(init?.body)));
+          return jsonResponse({ scanned: 0, registered: [], skipped: [], shadowed: [], errors: [] });
+        }
+        throw new Error(`unexpected fetch: ${url}`);
+      });
+      const user = userEvent.setup();
+      renderLoader();
+
+      await openBrowserAt(user);
+      await user.click(await screen.findByText('scratch'));
+      await user.click(await screen.findByRole('button', { name: /Scan folder for datasets \(scratch\)/ }));
+
+      await screen.findByText(/Scanned 0/);
+      expect(scanCalls).toEqual([
+        { scan_root: '/data/scratch', container_path: 'browse', server_uri: 'http://tiled.example' },
+      ]);
+    });
+
+    it('shows an error if the scan request fails', async () => {
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+        if (url.includes('/api/local/root')) return jsonResponse({ root: '/data' });
+        if (url.includes('/api/local/list')) return jsonResponse([]);
+        if (url.includes('/api/scan-datasets')) return jsonResponse({ detail: 'no such directory' }, false, 404);
+        throw new Error(`unexpected fetch: ${url}`);
+      });
+      const user = userEvent.setup();
+      renderLoader();
+
+      await openBrowserAt(user);
+      await user.click(screen.getByRole('button', { name: /Scan folder for datasets/ }));
+      expect(await screen.findByText('no such directory')).toBeInTheDocument();
+    });
+
+    it('does not show "Go to Browse" when nothing new was registered', async () => {
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string) => {
+        if (url.includes('/api/local/root')) return jsonResponse({ root: '/data' });
+        if (url.includes('/api/local/list')) return jsonResponse([]);
+        if (url.includes('/api/scan-datasets')) {
+          return jsonResponse({ scanned: 1, registered: [], skipped: ['already-there'], shadowed: [], errors: [] });
+        }
+        throw new Error(`unexpected fetch: ${url}`);
+      });
+      const user = userEvent.setup();
+      renderLoader();
+
+      await openBrowserAt(user);
+      await user.click(screen.getByRole('button', { name: /Scan folder for datasets/ }));
+      await screen.findByText(/Scanned 1/);
+      expect(screen.queryByRole('button', { name: 'Go to Browse' })).not.toBeInTheDocument();
+    });
+
+    it('shows a shadowed collision distinctly, and retrying it merges into the existing summary', async () => {
+      const scanCalls: unknown[] = [];
+      (global.fetch as ReturnType<typeof vi.fn>).mockImplementation(async (url: string, init?: RequestInit) => {
+        if (url.includes('/api/local/root')) return jsonResponse({ root: '/data' });
+        if (url.includes('/api/local/list')) return jsonResponse([]);
+        if (url.includes('/api/scan-datasets')) {
+          const body = JSON.parse(String(init?.body));
+          scanCalls.push(body);
+          if (body.renames) {
+            return jsonResponse({
+              scanned: 1,
+              registered: [{ name: 'stack_a', key: 'stack_a_images', tiled_path: 'browse/stack_a_images' }],
+              skipped: [],
+              shadowed: [],
+              errors: [],
+            });
+          }
+          return jsonResponse({
+            scanned: 1,
+            registered: [],
+            skipped: [],
+            shadowed: [
+              { name: 'stack_a', key: 'stack_a', existing_kind: 'zarr', suggested_key: 'stack_a_images' },
+            ],
+            errors: [],
+          });
+        }
+        throw new Error(`unexpected fetch: ${url}`);
+      });
+      const user = userEvent.setup();
+      renderLoader();
+
+      await openBrowserAt(user);
+      await user.click(screen.getByRole('button', { name: /Scan folder for datasets/ }));
+
+      expect(await screen.findByText(/Scanned 1 — registered 0 new, skipped 0 already present, 1 shadowed\./))
+        .toBeInTheDocument();
+      expect(screen.getByText('stack_a')).toBeInTheDocument();
+      expect(screen.getByText(/already registered as a/)).toBeInTheDocument();
+      expect(screen.getByText('zarr')).toBeInTheDocument();
+      const input = screen.getByDisplayValue('stack_a_images');
+
+      await user.click(screen.getByRole('button', { name: 'Register as this' }));
+
+      // The shadowed entry is gone once resolved, replaced by a registered
+      // one (rendered by name, so still "stack_a" — merged in, not dangling).
+      await waitFor(() => {
+        expect(screen.queryByRole('button', { name: 'Register as this' })).not.toBeInTheDocument();
+      });
+      expect(screen.getByText(/registered 1 new/)).toBeInTheDocument();
+      expect(scanCalls).toContainEqual(
+        expect.objectContaining({ renames: { stack_a: 'stack_a_images' } }),
+      );
+      expect(input).toBeDefined();
+    });
+  });
 });
diff --git a/frontend/src/components/Ingest/ZarrLoader.tsx b/frontend/src/components/Ingest/ZarrLoader.tsx
index a18b1e9..1d9030d 100644
--- a/frontend/src/components/Ingest/ZarrLoader.tsx
+++ b/frontend/src/components/Ingest/ZarrLoader.tsx
@@ -11,7 +11,7 @@
  * anything is written to the catalog.
  */
 import { useCallback, useState } from 'react';
-import { Stack, Warning, CheckCircle, MagnifyingGlass } from '@phosphor-icons/react';
+import { Stack, Warning, CheckCircle, MagnifyingGlass, Folder, FolderOpen } from '@phosphor-icons/react';
 import { API_BASE } from '@/config';
 
 /** One resolution level of a multiscale volume. */
@@ -56,6 +56,35 @@ function describeLevel(level: ZarrLevel): string {
   return `${z} × ${y} × ${x}`;
 }
 
+interface DirEntry {
+  name: string;
+  path: string;
+  is_dir: boolean;
+}
+
+/** Response of POST /api/scan-datasets — bulk-registers every Zarr store AND
+ * folder of image slices found directly under a directory, for pointing a
+ * mounted folder of already-reconstructed volumes at Tiled in one action
+ * instead of one at a time (or one per data kind). */
+interface ScanResult {
+  scanned: number;
+  registered: { name: string; key: string; tiled_path: string }[];
+  skipped: string[];
+  // A candidate whose natural key collides with an UNRELATED, different-kind
+  // registration sharing the same stem name (e.g. a raw image folder and its
+  // own already-registered Zarr reconstruction) — nothing registered, so it
+  // doesn't silently masquerade as "already present". Retry with a different
+  // key (see `renames`) to keep both.
+  shadowed: { name: string; key: string; existing_kind: string; suggested_key: string }[];
+  errors: { name: string; error: string }[];
+}
+
+/** Join an absolute root with a root-relative path (as returned by /api/local/list). */
+function joinPath(root: string, rel: string): string {
+  if (!rel) return root;
+  return `${root.replace(/\/$/, '')}/${rel}`;
+}
+
 export default function ZarrLoader({ serverUri, onBrowse, onAnnotate }: ZarrLoaderProps) {
   const [path, setPath] = useState('');
   const [containerPath, setContainerPath] = useState('browse');
@@ -67,6 +96,144 @@ export default function ZarrLoader({ serverUri, onBrowse, onAnnotate }: ZarrLoad
   const [existing, setExisting] = useState<ExistingInfo | null>(null);
   const [loaded, setLoaded] = useState<{ tiledPath: string; nSlices: number } | null>(null);
 
+  // Server-side directory browser — the path field takes an absolute path on
+  // the server's own filesystem (nothing is uploaded), which is easy to get
+  // wrong by typing a path from your own machine when that's NOT the same
+  // filesystem the server sees (e.g. inside Docker, only what's actually
+  // bind-mounted is visible). Browsing what the server can really see avoids
+  // that guesswork, mirroring the same root+relative-listing pattern the
+  // Connect page's "Local Folder" mode already uses — including letting the
+  // root itself be overridden: running via start_all.sh (no container), the
+  // backend is a native process with the same filesystem access as the rest
+  // of your machine, so granting a different root here searches anywhere
+  // you'd expect, exactly like "Local Folder" already does. Running via
+  // Docker, the SAME override mechanism naturally can't escape the
+  // container's own sandbox — an unmounted host path just comes back empty,
+  // which is the correct, honest reflection of what's actually mounted
+  // (see LOCAL_SOURCE_DIR in the deployment docs for making a real directory
+  // visible there instead of fighting this).
+  const [browsing, setBrowsing] = useState(false);
+  const [defaultRoot, setDefaultRoot] = useState<string | null>(null);
+  const [rootInput, setRootInput] = useState('');
+  const [browseRoot, setBrowseRoot] = useState<string | null>(null);
+  const [browseRel, setBrowseRel] = useState('');
+  const [browseEntries, setBrowseEntries] = useState<DirEntry[]>([]);
+  const [browseError, setBrowseError] = useState<string | null>(null);
+  const [browseLoading, setBrowseLoading] = useState(false);
+  const [scanning, setScanning] = useState(false);
+  const [scanResult, setScanResult] = useState<ScanResult | null>(null);
+  const [scanError, setScanError] = useState<string | null>(null);
+  const [renameInputs, setRenameInputs] = useState<Record<string, string>>({});
+
+  const listDir = useCallback(async (root: string, rel: string) => {
+    setBrowseLoading(true);
+    setBrowseError(null);
+    try {
+      const res = await fetch(
+        `${API_BASE}/api/local/list?root=${encodeURIComponent(root)}&rel=${encodeURIComponent(rel)}`,
+      );
+      if (!res.ok) throw new Error(await readError(res));
+      const entries: DirEntry[] = await res.json();
+      setBrowseEntries(entries.filter((e) => e.is_dir));
+      setBrowseRoot(root);
+      setBrowseRel(rel);
+    } catch (err) {
+      setBrowseError(err instanceof Error ? err.message : String(err));
+    } finally {
+      setBrowseLoading(false);
+    }
+  }, []);
+
+  const openBrowser = useCallback(async () => {
+    setBrowsing(true);
+    let root = defaultRoot;
+    if (root === null) {
+      try {
+        const res = await fetch(`${API_BASE}/api/local/root`);
+        if (!res.ok) throw new Error(await readError(res));
+        ({ root } = await res.json());
+        setDefaultRoot(root);
+        setRootInput(root ?? '');
+      } catch (err) {
+        setBrowseError(err instanceof Error ? err.message : String(err));
+        return;
+      }
+    }
+    await listDir(browseRoot ?? root ?? '', browseRoot !== null ? browseRel : '');
+  }, [defaultRoot, browseRoot, browseRel, listDir]);
+
+  const grantRoot = () => {
+    const root = rootInput.trim();
+    if (root) void listDir(root, '');
+  };
+
+  const chooseDir = (entry: DirEntry) => {
+    if (browseRoot === null) return;
+    setPath(joinPath(browseRoot, entry.path));
+    setBrowsing(false);
+  };
+
+  const runScan = useCallback(
+    async (renames?: Record<string, string>) => {
+      if (browseRoot === null) return null;
+      const res = await fetch(`${API_BASE}/api/scan-datasets`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({
+          scan_root: joinPath(browseRoot, browseRel),
+          container_path: containerPath,
+          server_uri: serverUri,
+          ...(renames ? { renames } : {}),
+        }),
+      });
+      if (!res.ok) throw new Error(await readError(res));
+      return (await res.json()) as ScanResult;
+    },
+    [browseRoot, browseRel, containerPath, serverUri],
+  );
+
+  const scanFolder = useCallback(async () => {
+    setScanning(true);
+    setScanError(null);
+    setScanResult(null);
+    try {
+      setScanResult(await runScan());
+    } catch (err) {
+      setScanError(err instanceof Error ? err.message : String(err));
+    } finally {
+      setScanning(false);
+    }
+  }, [runScan]);
+
+  /** Retry one shadowed candidate under an alternate key, merging the result
+   * into the existing summary rather than replacing it (the rest of the
+   * scan's findings are still valid and shouldn't disappear from view). */
+  const retryShadowed = useCallback(
+    async (name: string, altKey: string) => {
+      setScanning(true);
+      setScanError(null);
+      try {
+        const retryResult = await runScan({ [name]: altKey });
+        if (!retryResult) return;
+        setScanResult((prev) => {
+          const base = prev ?? { scanned: 0, registered: [], skipped: [], shadowed: [], errors: [] };
+          return {
+            scanned: base.scanned,
+            registered: [...base.registered, ...retryResult.registered],
+            skipped: base.skipped,
+            shadowed: base.shadowed.filter((s) => s.name !== name),
+            errors: [...base.errors, ...retryResult.errors],
+          };
+        });
+      } catch (err) {
+        setScanError(err instanceof Error ? err.message : String(err));
+      } finally {
+        setScanning(false);
+      }
+    },
+    [runScan],
+  );
+
   /** Pull the FastAPI `detail` message out of an error response. */
   const readError = async (res: Response): Promise<string> => {
     try {
@@ -128,8 +295,12 @@ export default function ZarrLoader({ serverUri, onBrowse, onAnnotate }: ZarrLoad
         if (!res.ok) throw new Error(await readError(res));
         const data = await res.json();
         const level = info.levels[levelIdx];
+        // A bare-array store (no OME-NGFF multiscale group) registers as a
+        // single leaf array node directly at the container key — its level
+        // has no sub-path to descend into (level.path === ''), so appending
+        // '/' + '' would produce a wrong, trailing-slash Tiled path.
         setLoaded({
-          tiledPath: `${data.tiled_path}/${level.path}`,
+          tiledPath: level.path ? `${data.tiled_path}/${level.path}` : data.tiled_path,
           nSlices: info.levels[0].n_slices,
         });
         setExisting(null);
@@ -171,8 +342,234 @@ export default function ZarrLoader({ serverUri, onBrowse, onAnnotate }: ZarrLoad
           <MagnifyingGlass size={15} />
           {busy === 'inspect' ? 'Inspecting…' : 'Inspect'}
         </button>
+        <button
+          type="button"
+          onClick={() => (browsing ? setBrowsing(false) : openBrowser())}
+          className="flex items-center gap-1.5 rounded-md border border-white/15 px-3 py-1.5 text-sm font-medium text-sky-100 hover:bg-white/10"
+        >
+          <FolderOpen size={15} />
+          Browse…
+        </button>
       </div>
 
+      {browsing && (
+        <div className="space-y-2 rounded-md border border-white/10 bg-black/20 p-2">
+          <div className="flex gap-2">
+            <input
+              type="text"
+              value={rootInput}
+              onChange={(e) => setRootInput(e.target.value)}
+              onKeyDown={(e) => { if (e.key === 'Enter') grantRoot(); }}
+              placeholder="Root to browse from"
+              className="flex-1 min-w-0 rounded-md border border-white/15 bg-white/5 px-2 py-1 font-mono text-xs text-white placeholder:text-white/30"
+            />
+            <button
+              type="button"
+              onClick={grantRoot}
+              disabled={!rootInput.trim()}
+              className="rounded-md border border-white/15 px-2 py-1 text-xs font-medium text-sky-100 hover:bg-white/10 disabled:opacity-40"
+            >
+              Go
+            </button>
+          </div>
+          <p className="text-[11px] leading-snug text-sky-300/80">
+            Only what this server can actually see is listed — running locally
+            (<code className="text-sky-200">start_all.sh</code>), that's anywhere on
+            your machine; in Docker, only what's bind-mounted (e.g. via{' '}
+            <code className="text-sky-200">LOCAL_SOURCE_DIR</code>).
+          </p>
+
+          {browseRoot !== null && (
+            <div className="flex flex-wrap items-center gap-1 text-xs text-sky-300/90">
+              <button
+                type="button"
+                className="rounded px-1.5 py-0.5 font-mono hover:bg-white/10 text-sky-200"
+                onClick={() => listDir(browseRoot, '')}
+              >
+                {browseRoot}
+              </button>
+              {browseRel.split('/').filter(Boolean).map((seg, i, arr) => {
+                const target = arr.slice(0, i + 1).join('/');
+                return (
+                  <span key={target} className="flex items-center gap-1">
+                    <span>/</span>
+                    <button
+                      type="button"
+                      className="rounded px-1.5 py-0.5 hover:bg-white/10 text-sky-200"
+                      onClick={() => listDir(browseRoot, target)}
+                    >
+                      {seg}
+                    </button>
+                  </span>
+                );
+              })}
+            </div>
+          )}
+
+          {browseError && (
+            <div className="flex items-start gap-2 rounded-md border border-amber-400/30 bg-amber-400/10 px-2.5 py-2 text-xs text-amber-200">
+              <Warning size={15} className="mt-0.5 flex-shrink-0" />
+              <span>{browseError}</span>
+            </div>
+          )}
+
+          {!browseError && (
+            <div className="max-h-48 overflow-y-auto rounded-md border border-white/10 bg-white/5 text-sm">
+              {browseLoading && (
+                <div className="px-3 py-3 text-sky-300/80 text-xs">Loading…</div>
+              )}
+              {!browseLoading && browseEntries.length === 0 && (
+                <div className="space-y-1.5 px-3 py-3 text-xs text-amber-200">
+                  <p>No sub-folders here.</p>
+                  <p className="leading-snug text-amber-200/90">
+                    If you expected your own data to show up: unlike the image dropzone
+                    above (which uploads through the browser), a Zarr volume has to
+                    already be visible to this <em>server</em> — nothing gets copied. If
+                    you're running this in Docker, that means bind-mounting it in first,
+                    then restarting:
+                  </p>
+                  <pre className="overflow-x-auto rounded bg-black/30 px-2 py-1 font-mono text-[11px] text-amber-100">
+                    LOCAL_SOURCE_DIR=/path/to/your/data docker compose -f docker-compose.full.yml up -d
+                  </pre>
+                  <p className="leading-snug text-amber-200/90">
+                    (or set <code className="text-amber-100">LOCAL_SOURCE_DIR</code> in a{' '}
+                    <code className="text-amber-100">.env</code> file at the repo root —
+                    see the Production deployment docs). Not using Docker? Grant a
+                    different root above instead — the server can see anywhere on that
+                    machine.
+                  </p>
+                </div>
+              )}
+              {!browseLoading && browseEntries.map((entry) => {
+                const isZarr = entry.name.toLowerCase().endsWith('.zarr');
+                return (
+                  <div
+                    key={entry.path}
+                    className="flex items-center gap-2 border-b border-white/10 px-3 py-2 last:border-b-0 hover:bg-white/10"
+                  >
+                    <button
+                      type="button"
+                      className="flex flex-1 items-center gap-2 text-left text-sky-100 min-w-0"
+                      onClick={() => (isZarr || browseRoot === null ? chooseDir(entry) : listDir(browseRoot, entry.path))}
+                    >
+                      {isZarr ? (
+                        <Stack size={14} className="text-emerald-400 shrink-0" />
+                      ) : (
+                        <Folder size={14} className="text-sky-400 shrink-0" />
+                      )}
+                      <span className="font-mono truncate">{entry.name}</span>
+                    </button>
+                    {isZarr && (
+                      <span className="shrink-0 rounded bg-emerald-600/30 px-1.5 py-0.5 text-[10px] font-medium text-emerald-200">
+                        .zarr
+                      </span>
+                    )}
+                  </div>
+                );
+              })}
+            </div>
+          )}
+
+          {browseRoot !== null && !browseError && (
+            <div className="flex flex-wrap items-center gap-3">
+              <button
+                type="button"
+                onClick={() => { setPath(joinPath(browseRoot, browseRel)); setBrowsing(false); }}
+                className="text-xs font-medium text-sky-200 hover:underline"
+              >
+                Use this folder ({browseRel || 'root'})
+              </button>
+              <button
+                type="button"
+                onClick={scanFolder}
+                disabled={scanning}
+                className="flex items-center gap-1.5 rounded-md border border-white/15 px-2 py-1 text-xs font-medium text-sky-100 hover:bg-white/10 disabled:opacity-40"
+              >
+                <FolderOpen size={13} />
+                {scanning
+                  ? 'Scanning…'
+                  : `Scan folder for datasets (${browseRel || 'root'})`}
+              </button>
+            </div>
+          )}
+
+          {scanError && (
+            <div className="flex items-start gap-2 rounded-md border border-amber-400/30 bg-amber-400/10 px-2.5 py-2 text-xs text-amber-200">
+              <Warning size={15} className="mt-0.5 flex-shrink-0" />
+              <span>{scanError}</span>
+            </div>
+          )}
+
+          {scanResult && (
+            <div className="space-y-1.5 rounded-md border border-white/10 bg-black/20 p-2.5 text-xs">
+              <div className="flex items-start gap-2 text-emerald-300">
+                <CheckCircle size={15} className="mt-0.5 flex-shrink-0" />
+                <span>
+                  Scanned {scanResult.scanned} — registered {scanResult.registered.length} new,
+                  skipped {scanResult.skipped.length} already present
+                  {scanResult.shadowed.length > 0 ? `, ${scanResult.shadowed.length} shadowed` : ''}
+                  {scanResult.errors.length > 0 ? `, ${scanResult.errors.length} failed` : ''}.
+                </span>
+              </div>
+              {scanResult.registered.length > 0 && (
+                <ul className="ml-5 list-disc text-sky-200/90">
+                  {scanResult.registered.map((r) => (
+                    <li key={r.key} className="font-mono">{r.name}</li>
+                  ))}
+                </ul>
+              )}
+              {scanResult.shadowed.length > 0 && (
+                <div className="space-y-1.5">
+                  {scanResult.shadowed.map((s) => (
+                    <div key={s.name} className="rounded-md border border-amber-400/30 bg-amber-400/10 p-2">
+                      <p className="text-amber-200">
+                        <span className="font-mono">{s.name}</span> — same name already registered as a{' '}
+                        <span className="font-mono">{s.existing_kind}</span> dataset. Register this one too, under
+                        a different name:
+                      </p>
+                      <div className="mt-1.5 flex gap-2">
+                        <input
+                          type="text"
+                          defaultValue={s.suggested_key}
+                          onChange={(e) => setRenameInputs((prev) => ({ ...prev, [s.name]: e.target.value }))}
+                          className="flex-1 min-w-0 rounded-md border border-white/15 bg-white/5 px-2 py-1 font-mono text-xs text-white"
+                        />
+                        <button
+                          type="button"
+                          disabled={scanning}
+                          onClick={() => retryShadowed(s.name, renameInputs[s.name] ?? s.suggested_key)}
+                          className="rounded-md border border-amber-300/40 px-2 py-1 text-xs font-medium text-amber-100 hover:bg-amber-400/10 disabled:opacity-40"
+                        >
+                          Register as this
+                        </button>
+                      </div>
+                    </div>
+                  ))}
+                </div>
+              )}
+              {scanResult.errors.length > 0 && (
+                <ul className="ml-5 list-disc text-amber-200">
+                  {scanResult.errors.map((e) => (
+                    <li key={e.name}>
+                      <span className="font-mono">{e.name}</span>: {e.error}
+                    </li>
+                  ))}
+                </ul>
+              )}
+              {scanResult.registered.length > 0 && (
+                <button
+                  type="button"
+                  onClick={() => { onBrowse?.(containerPath, scanResult.registered.length); setBrowsing(false); }}
+                  className="rounded-md bg-sky-600 px-3 py-1.5 text-xs font-medium text-white hover:bg-sky-500"
+                >
+                  Go to Browse
+                </button>
+              )}
+            </div>
+          )}
+        </div>
+      )}
+
       {error && (
         <div className="flex items-start gap-2 rounded-md border border-amber-400/30 bg-amber-400/10 px-2.5 py-2 text-xs text-amber-200">
           <Warning size={15} className="mt-0.5 flex-shrink-0" />
diff --git a/tiled/config.docker.yml b/tiled/config.docker.yml
index 6167750..be21762 100644
--- a/tiled/config.docker.yml
+++ b/tiled/config.docker.yml
@@ -22,9 +22,11 @@ trees:
       uri: "sqlite+aiosqlite:////data/.tiled/catalog.db"
       writable_storage: "/data/.tiled/data"
       readable_storage:
-        # Bind-mount your own source datasets here (see
-        # docker-compose.full.yml's volumes). Uploads/ingest also land here.
-        - "/data/raw"
+        # Bind-mount your own source datasets here (see docker-compose.full.yml
+        # / docker-compose.local.yml's volumes). Uploads/ingest also land here.
+        # Must match the bind-mount target in both compose files exactly —
+        # Tiled refuses to register an external asset outside this list.
+        - "/data/processed"
         # Generated 3-D volume pyramids (backend/tiff_stack_source.py) and
         # mask pyramids (backend/mask_pyramid.py) — must stay in step with
         # VOLUME_CACHE_DIR, set by docker-entrypoint-full.sh.

From e8f22a3eca7a74f8967aeccad69884973b16ec33 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 16 Sep 2026 13:53:49 -0700
Subject: [PATCH 36/38] aligning the container names/build/git actions to match
 the view_tomography_reconstruction_app repo

---
 .github/workflows/ci.yml                  | 84 ++------------------
 .github/workflows/publish-image.yml       | 94 ++++++++++++++++++++++
 docker-compose.als-prod.yml               | 45 +++++++++++
 docker-compose.als-staging.yml            | 33 ++++++++
 docker-compose.full.yml                   |  1 +
 docker-compose.local.yml                  | 12 ++-
 docker-compose.ml.yml                     | 15 ++--
 docker-compose.yml                        |  1 +
 docs/getting-started/installation.md      |  3 +-
 docs/reference/architecture.md            |  4 +-
 docs/reference/deployment.md              | 95 +++++++++++++++--------
 frontend/vendor/view_tomography_recon_app |  2 +-
 12 files changed, 265 insertions(+), 124 deletions(-)
 create mode 100644 .github/workflows/publish-image.yml
 create mode 100644 docker-compose.als-prod.yml
 create mode 100644 docker-compose.als-staging.yml

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 84a740e..0745c9c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -63,85 +63,11 @@ jobs:
       - uses: docker/setup-buildx-action@v3
       # Build only — catches a Dockerfile/compose file silently falling
       # behind the app it packages (the exact drift found in this session's
-      # review: no ipred service, no ml extra, no Tiled service). Two
-      # standalone compose files (not overlays — app-full is a separate,
-      # complete service, not an override of app), built independently. Does
-      # not push anywhere; image publishing is a separate, main-only job below.
+      # review: no ipred service, no ml extra, no Tiled service). Three
+      # standalone compose files (not overlays — each is a separate, complete
+      # service, not an override of another), built independently. Does not
+      # push anywhere; image publishing lives in its own workflow
+      # (publish-image.yml), gated on merge to main, not every PR.
       - run: docker compose -f docker-compose.yml build
       - run: docker compose -f docker-compose.ml.yml build
       - run: docker compose -f docker-compose.full.yml build
-
-  # Publishes images on merge to main ONLY — never on a PR run, to avoid
-  # wasted builds and registry clutter. A separate job (not a branch filter
-  # on docker-build above) so the build-and-test signal on PRs stays exactly
-  # what it is today, just with a real build verification added.
-  docker-publish:
-    if: github.event_name == 'push' && github.ref == 'refs/heads/main'
-    needs: [backend, ipred, frontend, docker-build]
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-    strategy:
-      matrix:
-        include:
-          # --- Unchanged, generic builds: root-hosted, no VITE_BASE_PATH ---
-          - target: app
-            image: app
-            tags: |
-              type=raw,value=latest
-              type=sha
-            vite_base_path: ""
-          - target: app-ml
-            image: app-ml
-            tags: |
-              type=raw,value=latest
-              type=sha
-            vite_base_path: ""
-          - target: app-full
-            image: app-full
-            tags: |
-              type=raw,value=latest
-              type=sha
-            vite_base_path: ""
-          # --- Purpose-tagged, base-path-baked builds ---
-          # :als — ipred/ML bundled, Tiled external (ALS's own production
-          # Tiled), hosted under ALS's hub path prefix.
-          - target: app-ml
-            image: app-ml
-            tags: |
-              type=raw,value=als
-              type=raw,value=als-{{sha}}
-            vite_base_path: /bl832/seg_studio/
-          # :local — fully bundled (+ Tiled), for anyone running this
-          # repo locally; still exercises subpath hosting by default.
-          - target: app-full
-            image: app-full
-            tags: |
-              type=raw,value=local
-              type=raw,value=local-{{sha}}
-            vite_base_path: /seg_studio/
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - uses: docker/setup-buildx-action@v3
-      - uses: docker/login-action@v3
-        with:
-          registry: ghcr.io
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-      - uses: docker/metadata-action@v5
-        id: meta
-        with:
-          images: ghcr.io/${{ github.repository }}/${{ matrix.image }}
-          tags: ${{ matrix.tags }}
-      - uses: docker/build-push-action@v6
-        with:
-          context: .
-          target: ${{ matrix.target }}
-          push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          build-args: |
-            VITE_BASE_PATH=${{ matrix.vite_base_path }}
diff --git a/.github/workflows/publish-image.yml b/.github/workflows/publish-image.yml
new file mode 100644
index 0000000..137990e
--- /dev/null
+++ b/.github/workflows/publish-image.yml
@@ -0,0 +1,94 @@
+name: Create and publish image
+
+# Mirrors als-computing/view_tomography_recon_app's own publish-image.yml —
+# same tag names (:local / :als-prod / :als-staging), same one-image-many-
+# tags shape. Runs on merge to main only (never on a PR — that stays in
+# ci.yml's build-only docker-build job, so PRs get a real "does it build"
+# check with no registry pushes or wasted publish minutes).
+on:
+  push:
+    branches: ['main']
+    tags: ['v*']
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          # The 3D viewer is a git submodule (frontend/vendor/) — without it
+          # the frontend build stage fails on an unresolved import.
+          submodules: recursive
+          fetch-depth: 0
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      # :local — fully bundled (Tiled + backend/ml + ipred), for anyone
+      # running this repo locally with nothing external set up. Base path
+      # baked in at /seg_studio/ so it exercises subpath hosting by default,
+      # matching :als-prod/:als-staging below rather than leaving that path
+      # tested only at ALS. See docker-compose.local.yml.
+      - name: Build and push local image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          target: app-full
+          push: true
+          build-args: |
+            VITE_BASE_PATH=/seg_studio/
+          tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:local
+          labels: ${{ steps.meta.outputs.labels }}
+
+      # :als-prod — ipred/ML bundled, Tiled external (ALS's own production
+      # Tiled — see docker-compose.als-prod.yml for the real TILED_URI
+      # default), hosted under ALS's hub path prefix.
+      - name: Build and push ALS production image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          target: app-ml
+          push: true
+          build-args: |
+            VITE_BASE_PATH=/bl832/seg_studio/
+          tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:als-prod
+          labels: ${{ steps.meta.outputs.labels }}
+
+      # :als-staging — identical build to :als-prod today (this app reads
+      # TILED_URI at container-run time, not build time, so the prod/staging
+      # distinction lives in docker-compose.als-staging.yml's default rather
+      # than here) — kept as its own explicit step, not just a second tag on
+      # the same build, so staging and prod can diverge later without
+      # restructuring this workflow.
+      - name: Build and push ALS staging image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          target: app-ml
+          push: true
+          build-args: |
+            VITE_BASE_PATH=/bl832/seg_studio/
+          tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:als-staging
+          labels: ${{ steps.meta.outputs.labels }}
diff --git a/docker-compose.als-prod.yml b/docker-compose.als-prod.yml
new file mode 100644
index 0000000..03085a2
--- /dev/null
+++ b/docker-compose.als-prod.yml
@@ -0,0 +1,45 @@
+# The ":als-prod" shape: backend (with ml/dlsia) + ipred bundled, pointed at
+# ALS's real PRODUCTION Tiled server by default — Dockerfile's app-ml stage,
+# base path baked in at /bl832/seg_studio/ (hub.als.lbl.gov's own path
+# prefix). This is what .github/workflows/publish-image.yml publishes under
+# the `:als-prod` tag; this compose file is for running that published image
+# (or building the same thing locally to test before it ships).
+#
+# Tiled itself stays external — ALS already runs its own production Tiled;
+# bundling a second, empty one here would be actively wrong. Only
+# TILED_API_KEY needs setting per deployment (a per-user/service credential —
+# see docs/reference/deployment.md's open question on per-proposal Tiled auth
+# before this goes live with real data).
+#
+#   docker compose -f docker-compose.als-prod.yml up --build
+#
+# To run the already-published ghcr.io image instead of building locally,
+# skip `build:` and use `image: ghcr.io/<owner>/<repo>:als-prod`.
+#
+name: segmentation_annotation_studio
+services:
+  app-als-prod:
+    build:
+      context: .
+      target: app-ml
+      args:
+        VITE_BASE_PATH: "/bl832/seg_studio/"
+    ports:
+      - "8002:8002"  # the app itself (SPA + API) — the only port most setups need
+      - "8003:8003"  # ipred, direct access (optional)
+    environment:
+      # ALS's real production Tiled. Override via TILED_URI in your own .env
+      # only if this ever needs to point somewhere else temporarily.
+      TILED_URI: "${TILED_URI:-https://tiled.als.lbl.gov}"
+      TILED_API_KEY: "${TILED_API_KEY:-}"
+      # Real institutional path into ALS's production catalog — confirm with
+      # whoever operates that Tiled server before assuming this is exactly
+      # right for a different beamline.
+      TILED_BROWSE_PATH: "${TILED_BROWSE_PATH:-beamlines/bl832/processed}"
+      LOCAL_DATA_ROOT: "/data"
+      BROWSE_ALLOWED_ORIGINS: "${BROWSE_ALLOWED_ORIGINS:-}"
+    volumes:
+      - annotation-data-als-prod:/data
+
+volumes:
+  annotation-data-als-prod:
diff --git a/docker-compose.als-staging.yml b/docker-compose.als-staging.yml
new file mode 100644
index 0000000..b2c303c
--- /dev/null
+++ b/docker-compose.als-staging.yml
@@ -0,0 +1,33 @@
+# The ":als-staging" shape — identical to docker-compose.als-prod.yml except
+# it defaults to ALS's STAGING Tiled server instead of production. See that
+# file for the full explanation; this one exists so pointing at staging vs.
+# production is a matter of which compose file you run, not remembering to
+# override TILED_URI correctly every time.
+#
+#   docker compose -f docker-compose.als-staging.yml up --build
+#
+# To run the already-published ghcr.io image instead of building locally,
+# skip `build:` and use `image: ghcr.io/<owner>/<repo>:als-staging`.
+#
+name: segmentation_annotation_studio
+services:
+  app-als-staging:
+    build:
+      context: .
+      target: app-ml
+      args:
+        VITE_BASE_PATH: "/bl832/seg_studio/"
+    ports:
+      - "8002:8002"
+      - "8003:8003"
+    environment:
+      TILED_URI: "${TILED_URI:-https://tiled-staging.als.lbl.gov}"
+      TILED_API_KEY: "${TILED_API_KEY:-}"
+      TILED_BROWSE_PATH: "${TILED_BROWSE_PATH:-beamlines/bl832/processed}"
+      LOCAL_DATA_ROOT: "/data"
+      BROWSE_ALLOWED_ORIGINS: "${BROWSE_ALLOWED_ORIGINS:-}"
+    volumes:
+      - annotation-data-als-staging:/data
+
+volumes:
+  annotation-data-als-staging:
diff --git a/docker-compose.full.yml b/docker-compose.full.yml
index 6120e36..abae5ce 100644
--- a/docker-compose.full.yml
+++ b/docker-compose.full.yml
@@ -7,6 +7,7 @@
 #
 #   docker compose -f docker-compose.full.yml up --build
 #
+name: segmentation_annotation_studio
 services:
   app-full:
     build:
diff --git a/docker-compose.local.yml b/docker-compose.local.yml
index 0027039..8c221f8 100644
--- a/docker-compose.local.yml
+++ b/docker-compose.local.yml
@@ -2,9 +2,12 @@
 # docker-compose.full.yml) but with VITE_BASE_PATH baked in at
 # /seg_studio/ so it's reachable at the familiar dev-server-shaped URL
 # http://localhost:5173/seg_studio/ by default — exercising the same
-# subpath-hosting behavior ALS's own :als deployment uses, rather than
-# leaving that path only tested at ALS. Host port is configurable via
-# HOST_PORT for anyone who wants a different one.
+# subpath-hosting behavior ALS's own :als-prod/:als-staging deployments use,
+# rather than leaving that path only tested at ALS. Host port is configurable
+# via HOST_PORT for anyone who wants a different one. Published by
+# .github/workflows/publish-image.yml (mirroring als-computing/
+# view_tomography_recon_app's own publish-image.yml — same tag names, same
+# one-image-many-tags shape).
 #
 # The app container itself has NO idea it's hosted under a subpath — same as
 # the real ALS deployment, it depends entirely on a reverse proxy stripping
@@ -16,8 +19,9 @@
 #   docker compose -f docker-compose.local.yml up --build
 #
 # To run the already-published ghcr.io image instead of building locally,
-# skip `build:` on app-local and use `image: ghcr.io/<owner>/<repo>/app-full:local`.
+# skip `build:` on app-local and use `image: ghcr.io/<owner>/<repo>:local`.
 #
+name: segmentation_annotation_studio
 services:
   app-local:
     build:
diff --git a/docker-compose.ml.yml b/docker-compose.ml.yml
index c10fa86..a54afb1 100644
--- a/docker-compose.ml.yml
+++ b/docker-compose.ml.yml
@@ -1,13 +1,18 @@
 # Backend (with the ml/dlsia extra) + ipred bundled; Tiled stays external —
 # point TILED_URI/TILED_API_KEY at your own Tiled server (Dockerfile's app-ml
-# stage, the same shape ghcr.io publishes under the `:als` tag). Use this when
-# you already have a Tiled server running and want Train/iPred to work
-# without a second, separately-maintained ipred deployment. For a fully
-# bundled stack with no external services at all, use docker-compose.full.yml
-# instead; for the lightest option (no ipred/ML), use docker-compose.yml.
+# stage — the same shape ghcr.io publishes under the ALS-specific `:als-prod`/
+# `:als-staging` tags, see docker-compose.als-prod.yml/docker-compose.als-
+# staging.yml for those with real defaults baked in). This file is the
+# generic, no-institution-specific-defaults version — use it when you already
+# have a Tiled server running (ALS's or anyone else's) and want Train/iPred
+# to work without a second, separately-maintained ipred deployment. For a
+# fully bundled stack with no external services at all, use docker-
+# compose.full.yml instead; for the lightest option (no ipred/ML), use
+# docker-compose.yml.
 #
 #   docker compose -f docker-compose.ml.yml up --build
 #
+name: segmentation_annotation_studio
 services:
   app-ml:
     build:
diff --git a/docker-compose.yml b/docker-compose.yml
index 8f43248..16f6ae3 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -3,6 +3,7 @@
 #
 #   docker compose up --build
 #
+name: segmentation_annotation_studio
 services:
   app:
     build: .
diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md
index c041904..5223c78 100644
--- a/docs/getting-started/installation.md
+++ b/docs/getting-started/installation.md
@@ -119,7 +119,8 @@ reachable directly if you want to hit them outside the app.
     restart.
 
 !!! tip "Pointing at your own datasets"
-    For `app-full`/`app-local`, set `LOCAL_SOURCE_DIR` in your own `.env` (copy
+    For `app-full` (or the published `:local` image, which builds from the same
+    target — see [Deployment](../reference/deployment.md)), set `LOCAL_SOURCE_DIR` in your own `.env` (copy
     the repo root's `.env.example`) to an absolute host directory — it's
     bind-mounted to `/data/processed` inside the container, so the bundled
     Tiled server (and the Zarr loader's "Browse…" directory picker on the
diff --git a/docs/reference/architecture.md b/docs/reference/architecture.md
index da8c9e9..25e6f20 100644
--- a/docs/reference/architecture.md
+++ b/docs/reference/architecture.md
@@ -162,8 +162,8 @@ The dev and production layouts differ mainly in **who serves the SPA** and
     Tiled still external — for a deployment that already has its own
     production Tiled (bundling a second, empty one would be wrong) but still
     wants Train/iPred to work without standing up a separate ipred service.
-    This is what the `:als` ghcr.io tag publishes, base path baked in at
-    `/bl832/seg_studio/`:
+    This is what the `:als-prod`/`:als-staging` ghcr.io tags publish, base path
+    baked in at `/bl832/seg_studio/`:
 
     ```mermaid
     flowchart LR
diff --git a/docs/reference/deployment.md b/docs/reference/deployment.md
index 8254c1c..0d47c43 100644
--- a/docs/reference/deployment.md
+++ b/docs/reference/deployment.md
@@ -10,28 +10,51 @@ under a URL prefix, and what's still an open question before a real production r
 ## Published images
 
 Every merge to `main` builds and publishes images to GitHub Container Registry
-(`ghcr.io`) via `.github/workflows/ci.yml`'s `docker-publish` job — this never fires
-on a pull request, only on `main`.
-
-| Image | Tags | Dockerfile target | Tiled | iPred / Train | Base path baked in |
+(`ghcr.io`) via its own dedicated workflow, `.github/workflows/publish-image.yml` —
+this never fires on a pull request, only on `main` (PRs get a build-only check
+instead, in `ci.yml`'s `docker-build` job — same Dockerfile targets, no registry
+push). The structure mirrors
+[als-computing/view_tomography_recon_app](https://github.com/als-computing/view_tomography_recon_app)'s
+own `publish-image.yml` — same tag names, same one-image-many-tags shape, so anyone
+already familiar with that repo's deployment finds this one working the same way.
+
+**One image name, three tags** — the Dockerfile target differs per tag (invisible
+from the registry, but real underneath):
+
+| Tag | Dockerfile target | Tiled | iPred / Train | Base path baked in | Compose file |
 | --- | --- | --- | --- | --- | --- |
-| `ghcr.io/<repo>/app` | `:latest`, `:sha-<commit>` | `app` | External | Not included | None (root-hosted) |
-| `ghcr.io/<repo>/app-ml` | `:latest`, `:sha-<commit>` | `app-ml` | External | Bundled | None (root-hosted) |
-| `ghcr.io/<repo>/app-ml` | `:als`, `:als-<commit>` | `app-ml` | External (ALS's own production Tiled) | Bundled | `/bl832/seg_studio/` |
-| `ghcr.io/<repo>/app-full` | `:latest`, `:sha-<commit>` | `app-full` | Bundled | Bundled | None (root-hosted) |
-| `ghcr.io/<repo>/app-full` | `:local`, `:local-<commit>` | `app-full` | Bundled | Bundled | `/seg_studio/` |
-
-Every tag has an immutable `-<commit-sha>` sibling, so pinning a deployment to a
-specific known-good build (rather than a floating tag) is always available —
-that's the rollback story: point at the previous `-sha` tag.
-
-`:als` is the shape ALS's own hub deployment uses: iPred/Train bundled (so they work
-without a second, separately-maintained service), Tiled kept external since ALS
-already runs its own production Tiled and a second, empty in-container one would be
-wrong. `:local` is fully self-contained (Tiled + backend + iPred, nothing external
+| `:local` | `app-full` | Bundled | Bundled | `/seg_studio/` | `docker-compose.local.yml` |
+| `:als-prod` | `app-ml` | External (ALS's production Tiled) | Bundled | `/bl832/seg_studio/` | `docker-compose.als-prod.yml` |
+| `:als-staging` | `app-ml` | External (ALS's staging Tiled) | Bundled | `/bl832/seg_studio/` | `docker-compose.als-staging.yml` |
+
+Pull with `ghcr.io/<owner>/<repo>:local` / `:als-prod` / `:als-staging`.
+
+`:als-prod` and `:als-staging` build identically today — this app reads `TILED_URI`
+at container-*run* time (an ordinary env var), not at build time, so the
+prod-vs-staging Tiled distinction lives entirely in which compose file's default you
+use, not in the image itself. They're still published as two separate, explicitly
+named steps in `publish-image.yml` (not one build tagged twice) so they can diverge
+later — e.g. if a build-time-only setting ever needs to differ between the two —
+without restructuring the workflow.
+
+`:local` is fully self-contained (Tiled + backend + iPred, nothing external
 required) for anyone standing this up for themselves who still wants to exercise the
-same subpath-hosting path `:als` uses, rather than leaving that path tested only at
-ALS — see [Installation](../getting-started/installation.md) for running it locally.
+same subpath-hosting path `:als-prod`/`:als-staging` use, rather than leaving that
+path tested only at ALS — see [Installation](../getting-started/installation.md) for
+running it locally. `:als-prod`/`:als-staging` bundle iPred/Train (so they work
+without a second, separately-maintained service) but keep Tiled external, since ALS
+already runs its own Tiled and a second, empty in-container one would be wrong.
+
+!!! note "No `:latest`/`-sha` tags — a deliberate simplification"
+    Earlier in this repo's history, every image also published generic `:latest` +
+    immutable `:sha-<commit>` tags alongside the purpose-named ones. That's been
+    dropped in favor of exactly the three tags above, matching the reference repo's
+    own simpler scheme — one clearly-named tag per real deployment target, nothing
+    else. The trade-off: there's no immutable per-commit tag to roll back to
+    anymore; rolling back today means re-running `publish-image.yml` against an
+    older commit (`git push -f` a maintenance branch to `main`, or a manual
+    workflow dispatch pinned to a specific ref) rather than just pointing at a
+    `-sha` tag that's already sitting in the registry.
 
 ---
 
@@ -52,20 +75,21 @@ manageable from an ops/ticketing perspective. This requires two things working t
    into the compiled JS at build time (`base` in `vite.config.ts`, consumed by
    `main.tsx`'s router `basename` and `config.ts`'s `API_BASE`) — **root-hosted and
    subpath-hosted are two different build artifacts from the same source**, not one
-   image that adapts at runtime. The published `:als`/`:local` tags already have this
-   baked in; the generic `:latest`/`:sha` tags of every image are root-hosted and
-   unaffected.
+   image that adapts at runtime. The published `:als-prod`/`:als-staging`/`:local`
+   tags already have this baked in — building your own image (e.g. `docker-
+   compose.yml`/`docker-compose.ml.yml`/`docker-compose.full.yml`'s generic,
+   unpublished targets) with `VITE_BASE_PATH` left unset stays root-hosted.
 
 If you're standing up your own subpath deployment (a different institution, a
 different path), build your own image with the matching `VITE_BASE_PATH` rather than
-reusing the `:als`/`:local` tags, which are baked for this repo's specific paths.
+reusing the `:als-prod`/`:als-staging`/`:local` tags, which are baked for this repo's specific paths.
 
 !!! warning "`VITE_BASE_PATH` is a path, never a hostname"
     Set it to the path segment only (`/bl832/seg_studio/`) — **never** a full URL
     with a scheme or host (`https://hub.als.lbl.gov/bl832/seg_studio/` would be
     wrong). The image has no business knowing what domain fronts it; only the
     reverse proxy does, and it can change (or differ between environments)
-    without ever touching this build-arg. This is what makes the *same* `:als`
+    without ever touching this build-arg. This is what makes the *same* `:als-prod`/`:als-staging`
     image usable unmodified across staging and production — e.g.
     `hub-staging.als.lbl.gov/bl832/seg_studio/` and
     `hub.als.lbl.gov/bl832/seg_studio/` sharing the identical `/bl832/seg_studio/`
@@ -102,7 +126,7 @@ Beyond what's already covered in [Installation](../getting-started/installation.
 | --- | --- |
 | `TILED_BROWSE_PATH` | The real path into an existing institutional Tiled catalog Browse should treat as its root (e.g. `beamlines/bl832/processed`). Confirm the exact value with whoever operates that Tiled server — don't assume it matches another deployment's beamline. |
 | `TILED_URI` / `TILED_API_KEY` | Point at the shared production Tiled server. See the open authentication question below — a single shared key is a stopgap, not the final design. |
-| `LOCAL_SOURCE_DIR` | `docker-compose.full.yml`/`docker-compose.local.yml` (bundled-Tiled shapes) only — bind-mounts a real host directory to `/data/processed` so the bundled Tiled server can read it directly. Everything under it (Zarr stores and plain image-slice folders) auto-registers into Tiled on every container start (see [Installation](../getting-started/installation.md#option-2-docker)) — no manual ingest step needed for pre-existing data. Not applicable to `:als` (`app-ml`), which points at an already-existing external Tiled instead of a bundled one. |
+| `LOCAL_SOURCE_DIR` | `docker-compose.full.yml`/`docker-compose.local.yml` (bundled-Tiled shapes) only — bind-mounts a real host directory to `/data/processed` so the bundled Tiled server can read it directly. Everything under it (Zarr stores and plain image-slice folders) auto-registers into Tiled on every container start (see [Installation](../getting-started/installation.md#option-2-docker)) — no manual ingest step needed for pre-existing data. Not applicable to `:als-prod`/`:als-staging` (`app-ml`), which point at an already-existing external Tiled instead of a bundled one. |
 
 Persistent storage (`LOCAL_DATA_ROOT`, defaulting to the `/data` volume already
 declared in the image) and CORS (`BROWSE_ALLOWED_ORIGINS`) need no special
@@ -116,17 +140,24 @@ Copy `.env.example` (repo root) to `.env` and fill it in — `docker compose` lo
 
 ```bash
 cp .env.example .env
-# edit .env: set TILED_URI, TILED_API_KEY, TILED_BROWSE_PATH, VITE_BASE_PATH
-docker compose -f docker-compose.ml.yml up --build
+# edit .env: set TILED_API_KEY at minimum — TILED_URI/TILED_BROWSE_PATH/
+# VITE_BASE_PATH already default correctly per environment (see below)
+docker compose -f docker-compose.als-prod.yml up --build
 ```
 
+For ALS specifically, use `docker-compose.als-prod.yml`/`docker-compose.als-
+staging.yml` — these already default `TILED_URI` to the real ALS production/staging
+Tiled hostnames and `VITE_BASE_PATH` to `/bl832/seg_studio/`, so only
+`TILED_API_KEY` needs setting per deployment. `docker-compose.ml.yml` is the
+generic, no-institution-specific-defaults version of the same shape (any other
+Tiled server, any base path) — set `TILED_URI`/`TILED_BROWSE_PATH`/`VITE_BASE_PATH`
+yourself there.
+
 `.env` is git-ignored — never commit a real `TILED_API_KEY` into it. This is a
 different file from `backend/.env.example`/`frontend/.env.example`, which configure
 `start_all.sh`/plain `npm run dev`/`npm run build` outside a container — the root
 `.env` is specifically what `docker compose` itself substitutes into the compose
-files' `${VAR}` references (`TILED_URI`, `TILED_API_KEY`, and `TILED_BROWSE_PATH` in
-`docker-compose.yml`/`docker-compose.ml.yml`; `VITE_BASE_PATH` as a build-arg in
-`docker-compose.ml.yml`). If you're running the already-published `ghcr.io` image
+files' `${VAR}` references. If you're running the already-published `ghcr.io` image
 directly (`docker run`/your own orchestration) rather than through one of these
 compose files, pass the equivalent `-e`/env vars there instead — `.env` only affects
 `docker compose` invocations in this directory.
@@ -149,6 +180,6 @@ glossed over:
   before a request reaches this app, or whether the app is expected to participate in
   auth itself, is a separate open question from Tiled's own data-access control above
   — confirm with hub admins before relying on either assumption.
-- **GPU availability for `:als`.** Bundling iPred/ML into `:als` only delivers a
+- **GPU availability for `:als-prod`/`:als-staging`.** Bundling iPred/ML there only delivers a
   practically usable Train tab if the host running that container provides GPU
   passthrough — CPU-only training/inference works but is dramatically slower.
diff --git a/frontend/vendor/view_tomography_recon_app b/frontend/vendor/view_tomography_recon_app
index a7e3a43..46c1e2a 160000
--- a/frontend/vendor/view_tomography_recon_app
+++ b/frontend/vendor/view_tomography_recon_app
@@ -1 +1 @@
-Subproject commit a7e3a433753ef46f00445ddd6b2cb733325e0162
+Subproject commit 46c1e2acae30e3abb62402793f2d236330ba5383

From 363b6444335f0e3b27de2fc6a045099277161c2c Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 16 Sep 2026 15:03:35 -0700
Subject: [PATCH 37/38] Addressing CodeQL alerts

---
 .github/workflows/ci.yml             |  5 ++++
 backend/tests/test_train_common.py   |  6 ++++-
 backend/train_common.py              |  6 ++++-
 ipred/src/ipred/api.py               |  6 ++++-
 ipred/src/ipred/preprocess.py        | 17 +++++++++++---
 ipred/tests/test_channel_png_path.py | 35 ++++++++++++++++++++++++++++
 6 files changed, 69 insertions(+), 6 deletions(-)
 create mode 100644 ipred/tests/test_channel_png_path.py

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 0745c9c..ebe7b36 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -5,6 +5,11 @@ on:
     branches: [main]
   pull_request:
 
+# None of these jobs push commits, comment on PRs, or touch packages — read-only
+# is the least-privilege default (CodeQL actions/missing-workflow-permissions).
+permissions:
+  contents: read
+
 jobs:
   backend:
     runs-on: ubuntu-latest
diff --git a/backend/tests/test_train_common.py b/backend/tests/test_train_common.py
index 1b89071..18d62af 100644
--- a/backend/tests/test_train_common.py
+++ b/backend/tests/test_train_common.py
@@ -399,4 +399,8 @@ def boom():
         monkeypatch.setattr(train_common, "torch_available", boom)
         result = train_common.capability()
         assert "error" in result
-        assert "tiling import exploded" in result["error"]
+        # The raw exception message must never reach an HTTP response (CodeQL
+        # py/stack-trace-exposure) — only a generic marker crosses the API
+        # boundary; the real detail goes to the server log only.
+        assert "tiling import exploded" not in result["error"]
+        assert result["torch_available"] is False
diff --git a/backend/train_common.py b/backend/train_common.py
index 7cde28e..264b628 100644
--- a/backend/train_common.py
+++ b/backend/train_common.py
@@ -861,6 +861,10 @@ def capability() -> dict[str, Any]:
         # PyWavelets, which skimage imports lazily and which is not installed.
         result["denoise"] = {"available": True, "methods": denoise.describe_methods()}
     except Exception as exc:  # noqa: BLE001 — a capability probe must never 500
+        # Full exception detail goes to the server log only — the raw message
+        # (module paths, internal state) must never reach an HTTP response
+        # (CodeQL py/stack-trace-exposure). The client only needs to know the
+        # probe failed; every field above already defaults to unavailable.
         logger.warning("Train capability probe failed: %s", exc)
-        result["error"] = str(exc)
+        result["error"] = "capability probe failed"
     return result
diff --git a/ipred/src/ipred/api.py b/ipred/src/ipred/api.py
index 1981bb8..0c967fa 100644
--- a/ipred/src/ipred/api.py
+++ b/ipred/src/ipred/api.py
@@ -343,7 +343,11 @@ def api_feature_channel(feature_id: str, index: int) -> Response:
     row = get_catalog().get_feature_bank(feature_id)
     if row is None:
         raise HTTPException(404, "feature bank not found")
-    path = preprocess.channel_png_path(row["blob_dir"], index)
+    try:
+        path = preprocess.channel_png_path(row["blob_dir"], index)
+    except ValueError as exc:
+        logger.warning("rejected out-of-root channel path: %s", exc)
+        raise HTTPException(404, "channel not found") from exc
     if not path.is_file():
         raise HTTPException(404, "channel not found")
     return FileResponse(path, media_type="image/png")
diff --git a/ipred/src/ipred/preprocess.py b/ipred/src/ipred/preprocess.py
index c1f7fbd..b717fad 100644
--- a/ipred/src/ipred/preprocess.py
+++ b/ipred/src/ipred/preprocess.py
@@ -14,7 +14,7 @@
 from ipred import array_source, compositions, features
 from ipred.catalog import Catalog
 from ipred.compose_run import run_composition
-from ipred.paths import project_blob_dir
+from ipred.paths import engine_root, project_blob_dir
 
 logger = logging.getLogger(__name__)
 
@@ -152,8 +152,19 @@ def load_feature_bank_arrays(blob_dir: str | Path) -> dict[str, Any]:
 
 
 def channel_png_path(blob_dir: str | Path, index: int) -> Path:
-    """Path to a cached channel PNG."""
-    return Path(blob_dir) / "channels" / f"{index:04d}.png"
+    """Path to a cached channel PNG.
+
+    ``blob_dir`` is read back from the catalog DB keyed by a user-supplied
+    ``feature_id`` — every value ever written there is a server-generated
+    ``uuid4().hex`` under ``engine_root()`` (see ``run_preprocess`` above), so
+    this can never legitimately resolve outside it. Enforce that explicitly
+    rather than trusting the DB round-trip (CodeQL py/path-injection).
+    """
+    root = engine_root().resolve()
+    path = (Path(blob_dir) / "channels" / f"{index:04d}.png").resolve()
+    if path != root and root not in path.parents:
+        raise ValueError(f"channel path {path} escapes engine root")
+    return path
 
 
 def _bank_response(
diff --git a/ipred/tests/test_channel_png_path.py b/ipred/tests/test_channel_png_path.py
new file mode 100644
index 0000000..0da306a
--- /dev/null
+++ b/ipred/tests/test_channel_png_path.py
@@ -0,0 +1,35 @@
+"""channel_png_path's engine-root containment guard (CodeQL py/path-injection)."""
+
+from __future__ import annotations
+
+import pytest
+
+from ipred import preprocess
+
+
+@pytest.fixture()
+def local_data_root(tmp_path, monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setenv("LOCAL_DATA_ROOT", str(tmp_path))
+    return tmp_path
+
+
+def test_normal_blob_dir_resolves_under_engine_root(local_data_root) -> None:
+    from ipred.paths import engine_root
+
+    blob_dir = engine_root() / "projects" / "abc123" / "features" / "def456"
+    path = preprocess.channel_png_path(str(blob_dir), 0)
+    assert path == (blob_dir / "channels" / "0000.png").resolve()
+
+
+def test_traversal_in_blob_dir_is_rejected(local_data_root) -> None:
+    from ipred.paths import engine_root
+
+    escaping = engine_root() / "projects" / ".." / ".." / "etc"
+    with pytest.raises(ValueError, match="escapes engine root"):
+        preprocess.channel_png_path(str(escaping), 0)
+
+
+def test_absolute_blob_dir_outside_root_is_rejected(local_data_root, tmp_path) -> None:
+    outside = tmp_path.parent / "somewhere-else"
+    with pytest.raises(ValueError, match="escapes engine root"):
+        preprocess.channel_png_path(str(outside), 0)

From 317f474f684fe17f3cd3ad66ed1b211e24ccb028 Mon Sep 17 00:00:00 2001
From: David Abramov <dabramov@lbl.gov>
Date: Wed, 16 Sep 2026 15:20:17 -0700
Subject: [PATCH 38/38] Addressing more CodeQL alerts

---
 ipred/src/ipred/api.py               |  4 ++--
 ipred/src/ipred/preprocess.py        | 33 ++++++++++++++++----------
 ipred/tests/test_channel_png_path.py | 35 ++++++++++++++++------------
 3 files changed, 43 insertions(+), 29 deletions(-)

diff --git a/ipred/src/ipred/api.py b/ipred/src/ipred/api.py
index 0c967fa..785cf0c 100644
--- a/ipred/src/ipred/api.py
+++ b/ipred/src/ipred/api.py
@@ -344,9 +344,9 @@ def api_feature_channel(feature_id: str, index: int) -> Response:
     if row is None:
         raise HTTPException(404, "feature bank not found")
     try:
-        path = preprocess.channel_png_path(row["blob_dir"], index)
+        path = preprocess.channel_png_path(row["project_id"], feature_id, index)
     except ValueError as exc:
-        logger.warning("rejected out-of-root channel path: %s", exc)
+        logger.warning("rejected invalid channel path request: %s", exc)
         raise HTTPException(404, "channel not found") from exc
     if not path.is_file():
         raise HTTPException(404, "channel not found")
diff --git a/ipred/src/ipred/preprocess.py b/ipred/src/ipred/preprocess.py
index b717fad..ff937c9 100644
--- a/ipred/src/ipred/preprocess.py
+++ b/ipred/src/ipred/preprocess.py
@@ -4,6 +4,7 @@
 
 import json
 import logging
+import re
 import uuid
 from datetime import datetime, timezone
 from pathlib import Path
@@ -14,7 +15,7 @@
 from ipred import array_source, compositions, features
 from ipred.catalog import Catalog
 from ipred.compose_run import run_composition
-from ipred.paths import engine_root, project_blob_dir
+from ipred.paths import project_blob_dir
 
 logger = logging.getLogger(__name__)
 
@@ -151,20 +152,28 @@ def load_feature_bank_arrays(blob_dir: str | Path) -> dict[str, Any]:
     }
 
 
-def channel_png_path(blob_dir: str | Path, index: int) -> Path:
+_FEATURE_ID_RE = re.compile(r"^[0-9a-f]{32}$")  # exactly uuid.uuid4().hex's shape
+
+
+def channel_png_path(project_id: str, feature_id: str, index: int) -> Path:
     """Path to a cached channel PNG.
 
-    ``blob_dir`` is read back from the catalog DB keyed by a user-supplied
-    ``feature_id`` — every value ever written there is a server-generated
-    ``uuid4().hex`` under ``engine_root()`` (see ``run_preprocess`` above), so
-    this can never legitimately resolve outside it. Enforce that explicitly
-    rather than trusting the DB round-trip (CodeQL py/path-injection).
+    Deliberately does NOT take the catalog's stored ``blob_dir`` string and
+    build a path from it — however safe that value always happens to be in
+    practice (a server-generated ``uuid.uuid4().hex`` under ``engine_root()``,
+    see ``run_preprocess`` above), a static analyzer has no way to know that,
+    and treats any DB value reached via a user-supplied ``feature_id`` lookup
+    as still tainted (CodeQL py/path-injection). Instead, validate the
+    *request's own* ``feature_id`` against the exact fixed shape it is always
+    generated in, before it ever touches a path, and rebuild the directory
+    fresh from known-safe components (``project_blob_dir`` already
+    constrains ``project_id`` the same way via its own hex-digest shape). A
+    request whose ``feature_id`` doesn't match a real feature bank's id can
+    never reach the filesystem layer at all.
     """
-    root = engine_root().resolve()
-    path = (Path(blob_dir) / "channels" / f"{index:04d}.png").resolve()
-    if path != root and root not in path.parents:
-        raise ValueError(f"channel path {path} escapes engine root")
-    return path
+    if not _FEATURE_ID_RE.match(feature_id):
+        raise ValueError(f"invalid feature_id: {feature_id!r}")
+    return project_blob_dir(project_id) / "features" / feature_id / "channels" / f"{index:04d}.png"
 
 
 def _bank_response(
diff --git a/ipred/tests/test_channel_png_path.py b/ipred/tests/test_channel_png_path.py
index 0da306a..3a2b36b 100644
--- a/ipred/tests/test_channel_png_path.py
+++ b/ipred/tests/test_channel_png_path.py
@@ -1,7 +1,10 @@
-"""channel_png_path's engine-root containment guard (CodeQL py/path-injection)."""
+"""channel_png_path validates feature_id against its known-safe shape before
+ever constructing a filesystem path from it (CodeQL py/path-injection)."""
 
 from __future__ import annotations
 
+import uuid
+
 import pytest
 
 from ipred import preprocess
@@ -13,23 +16,25 @@ def local_data_root(tmp_path, monkeypatch: pytest.MonkeyPatch):
     return tmp_path
 
 
-def test_normal_blob_dir_resolves_under_engine_root(local_data_root) -> None:
-    from ipred.paths import engine_root
+def test_valid_feature_id_resolves_under_project_blob_dir(local_data_root) -> None:
+    from ipred.paths import project_blob_dir
+
+    feature_id = uuid.uuid4().hex
+    path = preprocess.channel_png_path("abc123", feature_id, 0)
+    assert path == project_blob_dir("abc123") / "features" / feature_id / "channels" / "0000.png"
 
-    blob_dir = engine_root() / "projects" / "abc123" / "features" / "def456"
-    path = preprocess.channel_png_path(str(blob_dir), 0)
-    assert path == (blob_dir / "channels" / "0000.png").resolve()
 
+def test_traversal_payload_as_feature_id_is_rejected(local_data_root) -> None:
+    with pytest.raises(ValueError, match="invalid feature_id"):
+        preprocess.channel_png_path("abc123", "../../../../etc/passwd", 0)
 
-def test_traversal_in_blob_dir_is_rejected(local_data_root) -> None:
-    from ipred.paths import engine_root
 
-    escaping = engine_root() / "projects" / ".." / ".." / "etc"
-    with pytest.raises(ValueError, match="escapes engine root"):
-        preprocess.channel_png_path(str(escaping), 0)
+def test_wrong_length_feature_id_is_rejected(local_data_root) -> None:
+    with pytest.raises(ValueError, match="invalid feature_id"):
+        preprocess.channel_png_path("abc123", "not-a-real-uuid", 0)
 
 
-def test_absolute_blob_dir_outside_root_is_rejected(local_data_root, tmp_path) -> None:
-    outside = tmp_path.parent / "somewhere-else"
-    with pytest.raises(ValueError, match="escapes engine root"):
-        preprocess.channel_png_path(str(outside), 0)
+def test_uppercase_hex_feature_id_is_rejected(local_data_root) -> None:
+    # uuid.uuid4().hex is always lowercase — anything else can't be a real one.
+    with pytest.raises(ValueError, match="invalid feature_id"):
+        preprocess.channel_png_path("abc123", uuid.uuid4().hex.upper(), 0)