---
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](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke).

## 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

```bibtex
@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}
}
```