mmsegmentation/projects/medical/2d_image/histopathology/conic2022_seg

CoNIC: Colon Nuclei Identification and Counting Challenge

Description

This project supports CoNIC: Colon Nuclei Identification and Counting Challenge, which can be downloaded from here.

Dataset Overview

Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath). To help drive forward research and innovation for automatic nuclei recognition in CPath, we organise the Colon Nuclei Identification and Counting (CoNIC) Challenge. The challenge requires researchers to develop algorithms that perform segmentation, classification and counting of 6 different types of nuclei within the current largest known publicly available nuclei-level dataset in CPath, containing around half a million labelled nuclei.

Task Information

The CONIC challenge has 2 tasks:

• Task 1: Nuclear segmentation and classification.

The first task requires participants to segment nuclei within the tissue, while also classifying each nucleus into one of the following categories: epithelial, lymphocyte, plasma, eosinophil, neutrophil or connective tissue.

• Task 2: Prediction of cellular composition.

For the second task, we ask participants to predict how many nuclei of each class are present in each input image.

The output of Task 1 can be directly used to perform Task 2, but these can be treated as independent tasks. Therefore, if it is preferred, prediction of cellular composition can be treated as a stand alone regression task.

NOTE：We only consider Task 1 in the following sections.

Original Statistic Information

Dataset name	Anatomical region	Task type	Modality	Num. Classes	Train/Val/Test Images	Train/Val/Test Labeled	Release Date	License
CoNIC202	abdomen	segmentation	histopathology	7	4981/-/-	yes/-/-	2022	Attribution-NonCommercial-ShareAlike 4.0 International

Class Name	Num. Train	Pct. Train	Num. Val	Pct. Val	Num. Test	Pct. Test
background	4981	83.97	-	-	-	-
neutrophil	1218	0.13	-	-	-	-
epithelial	4256	10.31	-	-	-	-
lymphocyte	4473	1.85	-	-	-	-
plasma	3316	0.55	-	-	-	-
eosinophil	1456	0.1	-	-	-	-
connective	4613	3.08	-	-	-	-

Note:

• Pct means percentage of pixels in this category in all pixels.

Visualization

Prerequisites

• Python v3.8
• PyTorch v1.10.0
• pillow(PIL) v9.3.0
• scikit-learn(sklearn) v1.2.0
• MIM v0.3.4
• MMCV v2.0.0rc4
• MMEngine v0.2.0 or higher
• MMSegmentation v1.0.0rc5

All the commands below rely on the correct configuration of PYTHONPATH, which should point to the project's directory so that Python can locate the module files. In conic2022_seg/ root directory, run the following line to add the current directory to PYTHONPATH:

export PYTHONPATH=`pwd`:$PYTHONPATH

Dataset preparing

• download dataset from here and move data to path 'data/CoNIC_Challenge'. The directory should be like:

  data/CoNIC_Challenge
        ├── README.txt
        ├── by-nc-sa.md
        ├── counts.csv
        ├── images.npy
        ├── labels.npy
        └── patch_info.csv
  

• run script "python tools/prepare_dataset.py" to format data and change folder structure as below.
• run script "python ../../tools/split_seg_dataset.py" to split dataset and generate train.txt, val.txt and test.txt. If the label of official validation set and test set can't be obtained, we generate train.txt and val.txt from the training set randomly.

  mmsegmentation
  ├── mmseg
  ├── projects
  │   ├── medical
  │   │   ├── 2d_image
  │   │   │   ├── histopathology
  │   │   │   │   ├── conic2022_seg
  │   │   │   │   │   ├── configs
  │   │   │   │   │   ├── datasets
  │   │   │   │   │   ├── tools
  │   │   │   │   │   ├── data
  │   │   │   │   │   │   ├── train.txt
  │   │   │   │   │   │   ├── val.txt
  │   │   │   │   │   │   ├── images
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png
  │   │   │   │   │   │   ├── masks
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png

Divided Dataset Information

Note: The table information below is divided by ourselves.

Class Name	Num. Train	Pct. Train	Num. Val	Pct. Val	Num. Test	Pct. Test
background	3984	84.06	997	83.65	-	-
neutrophil	956	0.12	262	0.13	-	-
epithelial	3400	10.26	856	10.52	-	-
lymphocyte	3567	1.83	906	1.96	-	-
plasma	2645	0.55	671	0.56	-	-
eosinophil	1154	0.1	302	0.1	-	-
connective	3680	3.08	933	3.08	-	-

Training commands

Train models on a single server with one GPU.

mim train mmseg ./configs/${CONFIG_FILE}

Testing commands

Test models on a single server with one GPU.

mim test mmseg ./configs/${CONFIG_FILE}  --checkpoint ${CHECKPOINT_PATH}

Organizers

• Simon Graham (TIA, PathLAKE)
• Mostafa Jahanifar (TIA, PathLAKE)
• Dang Vu (TIA)
• Giorgos Hadjigeorghiou (TIA, PathLAKE)
• Thomas Leech (TIA, PathLAKE)
• David Snead (UHCW, PathLAKE)
• Shan Raza (TIA, PathLAKE)
• Fayyaz Minhas (TIA, PathLAKE)
• Nasir Rajpoot (TIA, PathLAKE)

TIA: Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, United Kingdom

UHCW: Department of Pathology, University Hospitals Coventry and Warwickshire, United Kingdom

PathLAKE: Pathology Image Data Lake for Analytics Knowledge & Education, , University Hospitals Coventry and Warwickshire, United Kingdom

Dataset Citation

If this work is helpful for your research, please consider citing the below paper.

@inproceedings{graham2021lizard,
  title={Lizard: A large-scale dataset for colonic nuclear instance segmentation and classification},
  author={Graham, Simon and Jahanifar, Mostafa and Azam, Ayesha and Nimir, Mohammed and Tsang, Yee-Wah and Dodd, Katherine and Hero, Emily and Sahota, Harvir and Tank, Atisha and Benes, Ksenija and others},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={684--693},
  year={2021}
}
@article{graham2021conic,
  title={Conic: Colon nuclei identification and counting challenge 2022},
  author={Graham, Simon and Jahanifar, Mostafa and Vu, Quoc Dang and Hadjigeorghiou, Giorgos and Leech, Thomas and Snead, David and Raza, Shan E Ahmed and Minhas, Fayyaz and Rajpoot, Nasir},
  journal={arXiv preprint arXiv:2111.14485},
  year={2021}
}

Checklist

• Milestone 1: PR-ready, and acceptable to be one of the projects/.

  • Finish the code

  • Basic docstrings & proper citation

  • A full README

• Milestone 2: Indicates a successful model implementation.

  • Training-time correctness

• Milestone 3: Good to be a part of our core package!

  • Type hints and docstrings

  • Unit tests

  • Code polishing

  • Metafile.yml

• Move your modules into the core package following the codebase's file hierarchy structure.

• Refactor your modules into the core package following the codebase's file hierarchy structure.