mmsegmentation/projects/medical/2d_image/histopathology/consep

Colorectal Nuclear Segmentation and Phenotypes (CoNSeP) Dataset

Description

This project supports Colorectal Nuclear Segmentation and Phenotypes (CoNSeP) Dataset, which can be downloaded from here.

Dataset Overview

The CoNSeP (Colon Segmentation and Phenotyping) dataset consists of 41 H&E stained image tiles, each with a size of 1,000×1,000 pixels and a magnification of 40x. These images were extracted from 16 colorectal adenocarcinoma (CRA) whole slide images (WSI), each of which belonged to a separate patient and was scanned using an Omnyx VL120 scanner at the Pathology Department of the University Hospitals Coventry and Warwickshire NHS Trust, UK. This dataset was first used in paper named, "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images".

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	-

Class Name	Num. Train	Pct. Train	Num. Val	Pct. Val	Num. Test	Pct. Test
background	27	83.61	14	80.4	-	-
other	17	0.17	9	0.52	-	-
inflammatory	25	2.66	14	2.14	-	-
healthy epithelial	3	1.47	2	1.58	-	-
dysplastic/malignant epithelial	10	7.17	8	9.16	-	-
fibroblast	23	3.84	14	4.63	-	-
muscle	8	1.05	3	1.42	-	-
endothelial	7	0.02	4	0.15	-	-

Note:

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

Visualization

Prerequisites

• Python v3.8
• PyTorch v1.10.0
• 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 decompress data to path 'data/'.
• 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
  │   │   │   │   ├── consep
  │   │   │   │   │   ├── configs
  │   │   │   │   │   ├── datasets
  │   │   │   │   │   ├── tools
  │   │   │   │   │   ├── data
  │   │   │   │   │   │   ├── train.txt
  │   │   │   │   │   │   ├── val.txt
  │   │   │   │   │   │   ├── images
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png
  │   │   │   │   │   │   ├── masks
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png

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}

Dataset Citation

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

@article{graham2019hover,
  title={Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images},
  author={Graham, Simon and Vu, Quoc Dang and Raza, Shan E Ahmed and Azam, Ayesha and Tsang, Yee Wah and Kwak, Jin Tae and Rajpoot, Nasir},
  journal={Medical Image Analysis},
  volume={58},
  pages={101563},
  year={2019},
  publisher={Elsevier}
}

Checklist

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

  • Finish the code

  • Basic docstrings & proper citation

  • Test-time correctness

  • 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.