README.md

metadata

license: cc-by-nc-sa-4.0
task_categories:
  - image-segmentation
tags:
  - medical
  - histopathology
  - h-and-e
  - nuclei
  - instance-segmentation
  - pan-cancer
  - tcga
  - gtex
size_categories:
  - 1K<n<10K

PanNuke

Pan-Cancer H&E Nuclei Instance Segmentation and Classification dataset (Gamper et al., ECDP 2019; arXiv:2003.10778). Mirrored from the Warwick TIA Centre release.

Composition

• 7,901 RGB patches of 256x256 at 40x magnification (~0.25 um/pixel)
• 19 tissue types pooled from TCGA / GTEx (Breast, Colon, Lung, Kidney, Prostate, Stomach, Ovarian, Bladder, Esophagus, Pancreatic, Thyroid, Skin, Cervix, Adrenal_gland, Bile-duct, Liver, HeadNeck, Testis, Uterus)
• ~189,744 annotated nuclei, multi-pathologist QC
• 3 folds (PanNuke is designed for 3-fold cross-validation):
  • fold1: 2,656 patches
  • fold2: 2,523 patches
  • fold3: 2,722 patches

Schema

Field	Type	Description
image	PIL RGB 256x256	H&E patch, uint8
inst_map	PIL grayscale uint16 256x256	Global per-pixel instance ID (0 = background)
type_map	PIL grayscale uint8 256x256	Semantic class per pixel (see table below)
tissue	int	Tissue ID 0-18 (alphabetical)
tissue_name	str	Tissue type name
fold	int	Source fold (1, 2, or 3)
sample_id	str	Unique ID, foldN_NNNN

Semantic class encoding (type_map)

Value	Class
0	Background
1	Neoplastic
2	Inflammatory
3	Connective / Soft tissue
4	Dead
5	Epithelial

Instance map (inst_map)

Each connected nucleus is assigned a unique 16-bit integer ID per patch (starting from 1). IDs are derived from the original 6-channel mask arrays released by Warwick: for each foreground class channel, instance IDs are offset by the running count of nuclei in previous channels so the result is globally unique within the patch.

License

CC BY-NC-SA 4.0 (research / non-commercial use). Same license as the original Warwick release.

Citation

@article{gamper2020pannuke,
  title={PanNuke Dataset Extension, Insights and Baselines},
  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Graham, Simon and Jahanifar, Mostafa and Benet, Ksenija and Khurram, Syed Ali and Azam, Ayesha and Hewitt, Katherine and Rajpoot, Nasir},
  journal={arXiv preprint arXiv:2003.10778},
  year={2020}
}

@inproceedings{gamper2019pannuke,
  title={Pannuke: An open pan-cancer histology dataset for nuclei instance segmentation and classification},
  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir},
  booktitle={European Congress on Digital Pathology},
  pages={11--19},
  year={2019},
  organization={Springer}
}