R09.01.01.03 · 任务小类

全切片病理标注

Whole-slide pathology annotation

Navigate gigapixel slides and annotate regions to reference labels.

20 条本项目编写的提案 · 分类 v1.1 · 新增基准运行 0 次。下方验证方式与部分得分规则是设计，不是执行结果。

典型应用: QuPath · ASAP · Aperio ImageScope
小类 GUI 需求标签: G3 · 仅 GUI

任务提案 · 20

展开查看完整原文与建议检查方式；两种界面均保留提案英文原文。

R09.01.01.03-T01 Outline all metastatic foci in a lymph-node whole-slide image in QuPath

原始指令

QuPath project input/ln_project/ contains LN_07.svs (H&E sentinel lymph node, public-dataset style). Find every metastatic focus, outline each with the brush or polygon tool, classify it 'Tumor', and export all annotations to output/LN_07.geojson.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: path · 连续路径 nav · 平移 / 缩放 / 滚动 vis · 视觉判断
工作产物: WP10 · 标注
条件: 未声明
提案难度: 4 / 5

建议验证方式

Lesion-level detection F1>=0.8 (a lesion counts when a Tumor annotation centroid lies inside the gold polygon) and pixel Dice>=0.5 at level 4. QuPath script editor is disabled and scripting events in the project log fail the task.

建议部分得分

15% annotations exported with class; 45% lesion detection F1; 40% Dice

R09.01.01.03-T02 Find the mitotic hotspot and count mitoses in 10 HPFs in QuPath

原始指令

In QuPath open breast carcinoma slide BRCA_112.svs. Find the area with the highest mitotic activity at the tumour periphery, place 10 contiguous high-power-field annotations (0.237 mm^2 each) classified 'HPF', mark each mitotic figure with a Points annotation 'Mitosis', and export output/mitoses.geojson plus total count in output/count.txt.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: nav · 平移 / 缩放 / 滚动 vis · 视觉判断 click · 指向与点击
工作产物: WP10 · 标注
条件: long · 长流程
提案难度: 5 / 5

建议验证方式

HPF set must overlap the gold hotspot (>=50% of fields inside); point-level F1>=0.7 vs pathologist mitoses within the chosen HPFs (8 um match radius); total count within +-3 of gold hotspot count; field areas within 2%. QuPath's script editor is disabled and the VM has no shell for the QuPath CLI, Python or OpenSlide, so annotations can only be made in the viewer; project writes by other processes are caught by file-access auditing.

建议部分得分

20% 10 HPFs correct size; 30% hotspot location; 35% point F1; 15% total count

R09.01.01.03-T03 Compute a Ki-67 index in a hotspot with positive cell detection in QuPath

原始指令

In QuPath set image type Brightfield H-DAB for KI67_09.svs, estimate stain vectors from a representative region, draw a 1 mm^2 annotation 'Hotspot' over the highest proliferation area, run Positive cell detection with a DAB threshold you tune visually, and record the Ki-67 index in output/ki67.json. Save the project.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: path · 连续路径 vis · 视觉判断 click · 指向与点击
工作产物: WP12 · 答案 / 决策
条件: 未声明
提案难度: 3 / 5

建议验证方式

Hotspot annotation area 0.95-1.05 mm^2 overlapping the gold hotspot; Ki-67 index within 3 percentage points of the pathologist reference for that region; detection settings saved in the project workflow. QuPath's script editor is disabled and the VM has no shell for the QuPath CLI or Python, so the analysis must be run in the GUI; project writes by other processes are caught by file-access auditing.

建议部分得分

15% image type and stain vectors; 25% hotspot placement; 60% Ki-67 index

R09.01.01.03-T04 Measure Breslow thickness on a melanoma slide in Aperio ImageScope

原始指令

In Aperio ImageScope open MEL_21.svs. Measure Breslow thickness with the ruler tool from the top of the granular layer to the deepest invasive melanoma cell, save the annotation layer to output/MEL_21.xml and write the value in mm to output/breslow.txt.

平台: win · Windows
应用: Aperio ImageScope
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G3 · 仅 GUI
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: geom · 精确几何 nav · 平移 / 缩放 / 滚动 vis · 视觉判断
工作产物: WP12 · 答案 / 决策
条件: 未声明
提案难度: 3 / 5

建议验证方式

Value within 0.1 mm of dermatopathologist gold; ruler endpoints within 50 um of the gold start and deepest-point annotations; XML layer contains the ruler. The VM has no shell, text editor, Python or OpenSlide, so the slide and annotation XML are reachable only in ImageScope; file writes by other processes are caught by file-access auditing.

建议部分得分

60% thickness value; 30% endpoints placement; 10% XML saved

R09.01.01.03-T05 Annotate tumour regions on three slides with polygons in ASAP

原始指令

In ASAP open slides CRC_01..03.tif (colorectal resections). Outline invasive tumour regions with the polygon tool in group 'tumor', and stroma-free mucosa in group 'normal', then save each as output/CRC_0N.xml.

平台: win · Windows lin · Linux
应用: ASAP
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G3 · 仅 GUI
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: path · 连续路径 nav · 平移 / 缩放 / 滚动 vis · 视觉判断
工作产物: WP10 · 标注
条件: 未声明
提案难度: 4 / 5

建议验证方式

Per slide tumour Dice>=0.7 and normal Dice>=0.5 at level 3 vs gold; XML schema valid with correct group names; ASAP has no scripting and file edits are detected by missing ASAP XML metadata.

建议部分得分

60% tumour Dice (equal share per slide); 25% normal Dice; 15% valid XML groups

R09.01.01.03-T06 Detect tissue and remove pen-mark and fold artifacts in QuPath

原始指令

In QuPath, run simple tissue detection on biopsy slide BX_44.svs (threshold of your choice), then edit the resulting annotation to remove the blue pen marks, the tissue fold and out-of-focus edge, and classify the final annotation 'Tissue'. Export output/BX_44_tissue.geojson.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: path · 连续路径 vis · 视觉判断 click · 指向与点击
工作产物: WP10 · 标注
条件: 未声明
提案难度: 3 / 5

建议验证方式

IoU>=0.9 vs gold artifact-free tissue mask; pen-mark and fold gold masks overlap the result by <5%; class 'Tissue'. QuPath's script editor is disabled and the VM has no shell for the QuPath CLI, Python or OpenSlide, so editing happens only in the viewer; project writes by other processes are caught by file-access auditing.

建议部分得分

40% tissue IoU; 25% pen marks removed; 25% fold removed; 10% class

R09.01.01.03-T07 Dearray a tissue microarray and fix misplaced cores in QuPath

原始指令

In QuPath open TMA slide TMA_B2.svs (12 x 8 grid). Run the TMA dearrayer with the core diameter from input/tma_spec.txt, move the 5 cores that are misplaced, mark the 3 missing cores as missing, and add core IDs from input/tma_map.csv. Export the TMA map to output/tma_cores.csv.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: drag · 拖放 vis · 视觉判断 click · 指向与点击
工作产物: WP10 · 标注
条件: 未声明
提案难度: 3 / 5

建议验证方式

Exported core centres within 100 um of gold for all 96 positions; missing flags exactly the 3 gold cores; core IDs exact per grid position. QuPath's script editor is disabled and the VM has no shell for the QuPath CLI, Python or OpenSlide, so cores can only be fixed in the viewer; project writes by other processes are caught by file-access auditing.

建议部分得分

15% grid dearrayed; 45% core positions; 20% missing flags; 20% IDs

R09.01.01.03-T08 Annotate tumour, stroma and necrosis layers with holes in ImageScope

原始指令

In Aperio ImageScope open GBM_05.svs and create three annotation layers named Tumor, Stroma and Necrosis. Outline each region with the pen tool, using negative regions to exclude embedded necrosis from tumour areas. Save output/GBM_05.xml.

平台: win · Windows
应用: Aperio ImageScope
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G3 · 仅 GUI
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: path · 连续路径 nav · 平移 / 缩放 / 滚动 vis · 视觉判断
工作产物: WP10 · 标注
条件: 未声明
提案难度: 4 / 5

建议验证方式

Rasterized at level 2 with negative regions subtracted: per-class Dice Tumor>=0.7, Stroma>=0.5, Necrosis>=0.6 vs gold; layer names exact. The VM has no shell, text editor, Python or OpenSlide, so the slide and annotation XML are reachable only in ImageScope; file writes by other processes are caught by file-access auditing.

建议部分得分

35% tumour; 25% stroma; 25% necrosis; 15% negative regions used correctly

R09.01.01.03-T09 Classify 40 pre-drawn tiles by tissue type in QuPath

原始指令

QuPath project tiles_project contains slide LUNG_08.svs with 40 unclassified square annotations. Inspect each at high magnification and set its class to Tumor, Stroma, Immune or Other (necrosis, blood, mucus). Save the project and export output/tile_classes.csv.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: nav · 平移 / 缩放 / 滚动 vis · 视觉判断 click · 指向与点击
工作产物: WP10 · 标注
条件: 未声明
提案难度: 3 / 5

建议验证方式

Accuracy>=0.85 vs pathologist labels; annotation geometries unchanged; CSV matches the project's stored classes. QuPath's script editor is disabled and the VM has no shell for the QuPath CLI, Python or OpenSlide, so tiles can only be classified in the viewer; project writes by other processes are caught by file-access auditing.

建议部分得分

80% classification accuracy scaled to 0.85; 10% geometry unchanged; 10% CSV consistent

R09.01.01.03-T10 Point-annotate tumour-infiltrating lymphocytes in five ROIs in ASAP

原始指令

In ASAP open BRCA_TIL_03.tif with 5 pre-drawn rectangular ROIs. Place a dot annotation on every stromal lymphocyte inside each ROI (group 'TIL'), then save output/TIL_03.xml and write per-ROI counts to output/til_counts.csv.

平台: win · Windows lin · Linux
应用: ASAP
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G3 · 仅 GUI
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: click · 指向与点击 nav · 平移 / 缩放 / 滚动 vis · 视觉判断
工作产物: WP10 · 标注
条件: long · 长流程
提案难度: 3 / 5

建议验证方式

Per ROI count within 10% of gold; point-level F1>=0.75 (6 um match radius); counts in CSV equal saved points. The VM has no shell or Python, so ASAP's multiresolutionimageinterface bindings and OpenSlide are unusable; annotation XML writes by any process other than ASAP are caught by file-access auditing.

建议部分得分

50% point F1; 40% per-ROI counts; 10% CSV consistent

R09.01.01.03-T11 Train a tumour-stroma pixel classifier interactively in QuPath

原始指令

In QuPath create a pixel classifier for slide HCC_02.svs: draw training scribbles for Tumor, Stroma and Ignore* (background/whitespace) at moderate resolution, iterate with the live preview until boundaries look right, save the classifier 'tumor_stroma', apply it inside the tissue annotation and report tumour area fraction in output/fraction.json.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: path · 连续路径 vis · 视觉判断 click · 指向与点击
工作产物: WP10 · 标注
条件: 未声明
提案难度: 4 / 5

建议验证方式

Saved classifier exists in the project; checker applies it to 3 held-out regions: tumour pixel Dice>=0.75 vs gold; reported tumour fraction within 5 percentage points of gold. QuPath's script editor is disabled and the VM has no shell for the QuPath CLI or Python, so training happens only in the viewer; project writes by other processes are caught by file-access auditing.

建议部分得分

20% classifier saved with 3 classes; 40% held-out Dice; 40% tumour fraction

R09.01.01.03-T12 Navigate to slide coordinates and capture a 20x snapshot in ImageScope

原始指令

In Aperio ImageScope open PROST_17.svs, navigate to x=41,200 um, y=18,750 um (top-left origin) at 20x magnification and save a 1000x1000 px snapshot of that location centred on the point to output/snap.png.

平台: win · Windows
应用: Aperio ImageScope
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G3 · 仅 GUI
GUI 需求 · 普通权限: CL · CLI 足够
交互原语: nav · 平移 / 缩放 / 滚动 click · 指向与点击
工作产物: WP09 · 文件与文件夹
条件: 未声明
提案难度: 2 / 5

建议验证方式

Snapshot size 1000x1000; SSIM>=0.9 vs reference extracted with OpenSlide at the same centre and 20x; centre offset <20 um estimated by template matching. The VM has no shell, Python or OpenSlide, so slide pixels are reachable only in ImageScope; file writes by any process other than ImageScope are caught by file-access auditing.

建议部分得分

20% size; 40% location offset; 40% SSIM

R09.01.01.03-T13 Measure the closest tumour-to-margin distance in QuPath

原始指令

In QuPath open excision slide BCC_30.svs, which has a pre-drawn polyline annotation 'Inked margin'. Outline the basal cell carcinoma as annotation 'Tumor', then measure the shortest distance from tumour to the inked margin with a line annotation and report it in mm in output/margin.json.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: path · 连续路径 geom · 精确几何 vis · 视觉判断
工作产物: WP12 · 答案 / 决策
条件: 未声明
提案难度: 3 / 5

建议验证方式

Reported distance within 0.2 mm of gold; line endpoints on the tumour boundary and margin polyline (within 30 um); tumour annotation Dice>=0.6. QuPath's script editor is disabled and the VM has no shell for the QuPath CLI, Python or OpenSlide, so measuring happens only in the viewer; project writes by other processes are caught by file-access auditing.

建议部分得分

25% tumour annotation; 25% line endpoints; 50% distance

R09.01.01.03-T14 Phenotype CD8 and PD-L1 cells on a multiplex IF slide in QuPath

原始指令

In QuPath open 4-channel immunofluorescence slide MIF_12.ome.tiff (DAPI, CD8, PD-L1, panCK). Set channel display ranges, run cell detection on DAPI in the 'Tumor region' annotation, create single-measurement classifiers for CD8 and PD-L1 and a composite classifier, and export per-phenotype counts to output/phenotypes.csv.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: CL · CLI 足够
交互原语: vis · 视觉判断 click · 指向与点击 type · 文字与表单
工作产物: WP10 · 标注
条件: tutorial · 阅读文档 / 教程
提案难度: 5 / 5

建议验证方式

Counts for CD8+, PD-L1+, double-positive and negative within 10% of gold; classifiers saved in the project; detected cell count within 10% of gold nuclei count. QuPath's script editor is disabled and the VM has no shell for the QuPath CLI or Python, so the analysis must be run in the GUI; project writes by other processes are caught by file-access auditing.

建议部分得分

20% cell detection; 20% classifiers saved; 60% phenotype counts (equal share each)

R09.01.01.03-T15 Accept or reject AI-proposed regions on a slide in ImageScope

原始指令

Aperio ImageScope opens LN_15.svs with an annotation layer 'AI candidates' of 25 proposed metastasis regions. Review each at high magnification; move true metastases to a new layer 'Confirmed' and delete false positives (sinus histiocytes, germinal centres). Save output/LN_15_review.xml.

平台: win · Windows
应用: Aperio ImageScope
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G3 · 仅 GUI
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: nav · 平移 / 缩放 / 滚动 vis · 视觉判断 click · 指向与点击
工作产物: WP10 · 标注
条件: 未声明
提案难度: 3 / 5

建议验证方式

Set of confirmed candidate IDs vs gold: precision>=0.85 and recall>=0.85; remaining candidates deleted; geometries of confirmed regions unchanged. The VM has no shell, text editor, Python or OpenSlide, so the slide and annotation XML are reachable only in ImageScope; file writes by other processes are caught by file-access auditing.

建议部分得分

45% precision; 45% recall; 10% geometries intact

R09.01.01.03-T16 Control pair of T12: extract the slide region with OpenSlide

原始指令

Control pair of T12. Using Python and OpenSlide, extract a 1000x1000 px region of PROST_17.svs at 20x centred on x=41,200 um, y=18,750 um and save output/snap.png.

平台: lin · Linux mac · macOS win · Windows
应用: Python · OpenSlide
交互表面: term · 终端
GUI 需求 · 任务设定: CL · CLI 足够
GUI 需求 · 普通权限: CL · CLI 足够
交互原语: code · 代码与命令
工作产物: WP09 · 文件与文件夹
条件: 未声明
提案难度: 2 / 5

建议验证方式

Same checks as T12: size, centre offset <20 um, SSIM>=0.9 vs reference; level/downsample selection correct for 20x.

建议部分得分

20% size; 40% location; 40% SSIM

R09.01.01.03-T17 Annotate Gleason patterns on prostate biopsy cores and assign grade group

原始指令

In QuPath open PBX_06.svs (6 biopsy cores). Annotate Gleason pattern 3, 4 and 5 areas with the matching classes, compute the percentage of pattern 4 per core, and write output/gleason.json with per-core primary/secondary patterns, % pattern 4 and the overall ISUP grade group.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: path · 连续路径 nav · 平移 / 缩放 / 滚动 vis · 视觉判断
工作产物: WP10 · 标注
条件: long · 长流程
提案难度: 5 / 5

建议验证方式

Per-class Dice vs consensus gold (pattern 3>=0.5, 4>=0.5); per-core patterns exact; % pattern 4 within 10 points; grade group exact. Annotations must exist in the project (not only the JSON). QuPath's script editor is disabled and the VM has no shell for the QuPath CLI, Python or OpenSlide, so annotating happens only in the viewer; project writes by other processes are caught by file-access auditing.

建议部分得分

35% pattern annotations; 25% per-core patterns; 15% % pattern 4; 25% grade group

R09.01.01.03-T18 Correct a junior annotator's tumour outlines in ASAP

原始指令

In ASAP open OV_14.tif with input/OV_14_junior.xml loaded. Fix the 6 boundary errors (tumour extending into stroma, missed nests) and relabel the 2 regions wrongly assigned to group 'tumor' that are endometriosis. Save output/OV_14_corrected.xml.

平台: win · Windows lin · Linux
应用: ASAP
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G3 · 仅 GUI
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: path · 连续路径 drag · 拖放 vis · 视觉判断
工作产物: WP10 · 标注
条件: 未声明
提案难度: 3 / 5

建议验证方式

Tumour Dice vs gold improves by >=0.1 over the junior file and reaches >=0.75; the 2 regions now in group 'other'; untouched correct regions remain within 1% area. The VM has no shell or Python, so ASAP's multiresolutionimageinterface bindings and OpenSlide are unusable; annotation XML writes by any process other than ASAP are caught by file-access auditing.

建议部分得分

50% boundary fixes (Dice gain); 30% relabeled regions; 20% correct regions intact

R09.01.01.03-T19 Set up a QuPath project with image types and stain vectors

原始指令

Create QuPath project output/her2_project/ with the 8 slides in input/her2_slides/, set image type Brightfield (H-DAB) for all, estimate stain vectors from a representative region on slide 1 and apply them to all slides, and add metadata 'block' and 'stain_batch' from input/slides.csv.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: click · 指向与点击 type · 文字与表单 drag · 拖放
工作产物: WP08 · 应用 / 系统状态
条件: 未声明
提案难度: 2 / 5

建议验证方式

Project file lists 8 images; each image data has type BRIGHTFIELD_H_DAB and identical stain vectors (within 0.02 of reference estimate from the gold region); metadata values exact. QuPath's script editor is disabled and the VM has no shell for the QuPath CLI or Python, so the project must be set up in the GUI; project writes by other processes are caught by file-access auditing.

建议部分得分

25% 8 images added; 20% image types; 35% stain vectors; 20% metadata

R09.01.01.03-T20 Count tumour buds at the invasive front hotspot in QuPath

原始指令

In QuPath open colorectal cancer slide CRC_BUD_04.svs. Scan the invasive front, choose the hotspot, place a 0.785 mm^2 circular annotation 'Hotspot', mark each tumour bud (single cells or clusters of up to 4 cells) with Points 'Bud', and write the count and ITBCC budding grade (Bd1-Bd3) to output/budding.json.

平台: mac · macOS win · Windows
应用: QuPath
交互表面: desk · 桌面 GUI
GUI 需求 · 任务设定: G2 · GUI 必要
GUI 需求 · 普通权限: G2 · GUI 必要
交互原语: nav · 平移 / 缩放 / 滚动 vis · 视觉判断 click · 指向与点击
工作产物: WP10 · 标注
条件: 未声明
提案难度: 4 / 5

建议验证方式

Hotspot circle area within 2% and overlapping the gold hotspot; bud count within +-2 of gold in the chosen field; point F1>=0.7; grade exact. QuPath's script editor is disabled and the VM has no shell for the QuPath CLI, Python or OpenSlide, so counting happens only in the viewer; project writes by other processes are caught by file-access auditing.

建议部分得分

20% hotspot; 40% bud point F1; 20% count; 20% grade

已有研究映射

以下沿用研究目录中的基准与小类映射，不是逐题标注、执行记录或产品背书。

OSWorld-Science

全切片病理标注

任务提案 · 20

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建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

原始指令

建议验证方式

建议部分得分

已有研究映射

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