Pan-Cancer-Nuclei-Seg — Manual Subset

Gold-standard manual nucleus segmentation patches from the TCIA Pan-Cancer-Nuclei-Seg resource (Hou et al., Scientific Data 2020): 1,356 manually segmented 256×256 H&E patches with per-nucleus instance masks, spanning 14 TCGA cancer types.

Scope — please read. This repository contains only the manual subset, not the full Pan-Cancer-Nuclei-Seg resource. The headline "~5 billion nuclei" component (5,060 whole-slide images, ~666 GB of CSV polygon vertices) is algorithm-generated (a U-Net pipeline, not manual ground truth), is distributed without source images (the H&E WSIs live in TCGA/GDC), and is not hosted here. The 1,356 manual patches are the only part that is both gold-standard and image-paired.

• Modality: Histopathology — H&E brightfield, 256×256 patches at 40× (~0.25 µm/px)
• Target: nucleus segmentation (single foreground class)
• Ground truth: manually corrected Mask R-CNN masks agreed by annotators A, B and C collectively (per the dataset readme)
• License: CC BY 3.0
• Source: TCIA Pan-Cancer-Nuclei-Seg · Stony Brook BMI Box cnn-nuclear-segmentations-2019

Cancer types (14)

BLCA, BRCA, CESC, COAD, GBM, LUAD, LUSC, PAAD, PRAD, READ, SKCM, STAD, UCEC, UVM (~91–100 patches each, balanced).

Columns

Column	Type	Notes
patch_id	int32	Patch-ID from the dataset readme (non-contiguous, 1..1365 with gaps)
image	Image (RGB)	256×256 H&E patch ({id}_crop.png), original PNG bytes
instance_mask	Image (16-bit, mode I;16)	Consensus manual mask ({id}_labeled_mask_corrected.png); 0 = background, 1..N = per-nucleus instance IDs. Original PNG bytes (lossless)
cancer_type	string	One of the 14 TCGA codes (lowercase)
wsi_id	string	TCGA slide barcode the patch was cropped from (e.g. TCGA-2F-A9KR-01Z-00-DX1) — use as the cross-dataset dedup key
x, y	int32	Top-left coordinate of the crop in the source WSI
size_original	int32	Crop side length in source-WSI pixels before resize
size_in_40x	int32	Crop side length at 40× (400)
num_instances	int32	Number of nuclei in the patch (= instance_mask.max())
has_multirater	bool	True for the 27 patches that also have per-annotator masks at the source

For binary nucleus-vs-background segmentation, treat instance_mask > 0 as foreground. Read the mask via numpy (np.array(mask)) — passing a 16-bit I;16 PNG through PIL.Image.convert("L") divides values by 256 and erases most instance IDs.

Empty masks: 32 of the 1,356 patches contain no annotated nuclei (num_instances == 0, background/stroma-only tissue) — these are faithful to the source, not a packaging error. Filter on num_instances > 0 if your task requires at least one nucleus (instance counts range 0–141, mean ≈ 29).

Provenance, naming and cross-dataset overlap

• Provenance: official, author-hosted (Saltz/Kurc, Stony Brook BMI). 1,356 patches matches the paper.
• Faithful naming: this is the manual subset only (see scope note above).
• Ground-truth tier: the bulk Pan-Cancer-Nuclei-Seg masks are algorithm-generated; only these 1,356 patches are manual. 27 patches carry additional per-annotator masks at the source.
• Overlap (leakage hazard): every patch is TCGA-derived, so it shares source slides with other TCGA histopathology sets — notably PanNuke, MoNuSeg, MoNuSAC, NuCLS and TIGER (on BRCA/PRAD/BLCA/LUAD). Deduplicate against those by matching the wsi_id TCGA barcode before any joint benchmark.

Citation

@article{hou2020pancancernucleiseg,
  title   = {Dataset of segmented nuclei in hematoxylin and eosin stained
             histopathology images of ten cancer types},
  author  = {Hou, Le and Gupta, Rajarsi and Van Arnam, John S. and Zhang, Yuwei
             and Sivalenka, Kaustubh and Samaras, Dimitris and Kurc, Tahsin M.
             and Saltz, Joel H.},
  journal = {Scientific Data},
  volume  = {7},
  number  = {1},
  pages   = {185},
  year    = {2020},
  doi     = {10.1038/s41597-020-0528-1}
}

TCIA data citation: Hou, L. et al. (2019). Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of Ten Cancer Types (Pan-Cancer-Nuclei-Seg). The Cancer Imaging Archive. doi:10.7937/TCIA.2019.4A4DKP9U