README.md

---
dataset_info:
  features:
  - name: image
    dtype:
      image:
        mode: RGB
  - name: instances
    sequence:
      image:
        mode: '1'
  - name: categories
    sequence:
      class_label:
        names:
          '0': Neoplastic
          '1': Inflammatory
          '2': Connective
          '3': Dead
          '4': Epithelial
  - name: tissue
    dtype:
      class_label:
        names:
          '0': Adrenal Gland
          '1': Bile Duct
          '2': Bladder
          '3': Breast
          '4': Cervix
          '5': Colon
          '6': Esophagus
          '7': Head & Neck
          '8': Kidney
          '9': Liver
          '10': Lung
          '11': Ovarian
          '12': Pancreatic
          '13': Prostate
          '14': Skin
          '15': Stomach
          '16': Testis
          '17': Thyroid
          '18': Uterus
  splits:
  - name: fold1
    num_bytes: 283673837.64
    num_examples: 2656
  - name: fold2
    num_bytes: 267595457.439
    num_examples: 2523
  - name: fold3
    num_bytes: 293079722.82
    num_examples: 2722
  download_size: 1665092597
  dataset_size: 844349017.8989999
configs:
- config_name: default
  data_files:
  - split: fold1
    path: data/fold1-*
  - split: fold2
    path: data/fold2-*
  - split: fold3
    path: data/fold3-*
license: cc-by-nc-sa-4.0
task_categories:
- image-segmentation
task_ids:
- instance-segmentation
language:
- en
tags:
- medical
- cell nuclei
- H&E
pretty_name: PanNuke
size_categories:
- 1K<n<10K
paperswithcode_id: pannuke
---

# PanNuke

[![](https://github.com/Mr-TalhaIlyas/Prerpcessing-PanNuke-Nuclei-Instance-Segmentation-Dataset/blob/master/screens/img1.png?raw=true)](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke)

## Dataset Description

- **Homepage:** [PanNuke Dataset for Nuclei Instance Segmentation and Classification](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke)
- **Leaderboard:** [Panoptic Segmentation](https://paperswithcode.com/sota/panoptic-segmentation-on-pannuke)

## Description

PanNuke is a semi-automatically generated dataset for nuclei instance segmentation and classification, providing comprehensive nuclei annotations across 19 tissue types and 5 distinct cell categories. The dataset includes a total of **189,744 labeled nuclei**, each accompanied by an instance segmentation mask, and contains **7,901 images**, each sized **256×256 pixels**. The images were captured at **x40 magnification** with a resolution of **0.25 µm/pixel**. The dataset is highly imbalanced, with the **"Dead" nuclei category** being particularly underrepresented.

Please note that the dataset was created by extracting patches from whole-slide images (WSIs). As a result, some nuclei located at the edges of patches may be cropped, with fewer than 10 visible pixels in certain cases.

## Dataset Structure

The dataset is organized into three folds: `fold1`, `fold2`, and `fold3`, consistent with the original dataset structure. Each fold contains data in a tabular format with the following four columns:

- **`image`**: The RGB tile of the sample.
- **`instances`**: A list of nuclei instances. Each instance represents exactly one nucleus and is in binary format (`1` - nucleus, `0` - background)
- **`categories`**: An integer class label for each nucleus, corresponding to one of the following categories:
  0. Neoplastic
  1. Inflammatory
  2. Connective
  3. Dead
  4. Epithelial
- **`tissue`**: The integer tissue type from which the sample originates, belonging to one of these categories:
  0. Adrenal Gland
  1. Bile Duct
  2. Bladder
  3. Breast
  4. Cervix
  5. Colon
  6. Esophagus
  7. Head & Neck
  8. Kidney
  9. Liver
  10. Lung
  11. Ovarian
  12. Pancreatic
  13. Prostate
  14. Skin
  15. Stomach
  16. Testis
  17. Thyroid
  18. Uterus

## Citation

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

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