PanNuke Evaluation Metrics

This repository calculates metrics on the PanNuke dataset, as reported in:

"PanNuke Dataset Extension, Insights and Baselines"

The PanNuke dataset can be downloaded here.
In the repository, the metrics that are calculated are:

• Binary PQ (bPQ): Assumes all nuclei belong to same class and reports the average PQ across tissue types.
  
• Multi-Class PQ (mPQ): Reports the average PQ across the classes and tissue types.
  
• Neoplastic PQ: Reports the PQ for the neoplastic class on all tissues.
  
• Non-Neoplastic PQ: Reports the PQ for the non-neoplastic class on all tissues.
  
• Inflammatory PQ: Reports the PQ for the inflammatory class on all tissues.
  
• Connective PQ: Reports the PQ for the connective class on all tissues.
  
• Dead PQ: Reports the PQ for the dead class on all tissues.
  

For detection based metrics, we used this function.

Set up envrionment

conda create --name pannuke python=3.6
conda activate pannuke
pip install -r requirements.txt

Running the Code

Usage:

"""run.

Usage:
  run.py --true_path=<n> --pred_path=<n> --save_path=<n>
  run.py (-h | --help)
  run.py --version

Options:

  -h --help          Show this string.
  --version          Show version.
  --true_path=<n>    Root path to where the ground-truth is saved.
  --pred_path=<n>    Root path to where the predictions are saved.
  --save_path=<n>    Path where the prediction CSV files will be saved

Before running the code, ground truth and predictions must be saved in the following structure:

• True Masks:
  • <true_path>/masks.npy
• True Tissue Types:
  • <true_path>/types.npy
• Prediction Masks:
  • <pred_path>/masks.npy

Here, prediction masks are saved in the same format as the true masks. i.e a single Nx256x256xC array, where N is the number of test images in that specific fold and C is the number of positive classes. The ordering of the channels from index 0 to 4 is neoplastic, inflammatory, connective tissue, dead and non-neoplastic epithelial.

Citation

If using this code, please cite:

@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}
}

@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}
}