Official Code for our paper "Mutual Consistency Learning for Semi-supervised Medical Image Segmentation"

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Admin Panels MC-Net
Overview

Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

by Yicheng Wu*, Zongyuan Ge, Donghao Zhang, Minfeng Xu, Lei Zhang, Yong Xia, and Jianfei Cai.

Semi-supervised Left Atrium Segmentation with Mutual Consistency Training

by Yicheng Wu, Minfeng Xu, Zongyuan Ge, Jianfei Cai*, and Lei Zhang.

News

<01.07.2022> Our paper entitled "Mutual Consistency Learning for Semi-supervised Medical Image Segmentation" has been accepted by Medical Image Analysis;
<18.04.2022> We provided our pre-trained models on the LA, Pancreas-CT and ACDC datasets, see './MC-Net/pretrained_pth/';
<16.04.2022> We released the codes;

Introduction

This repository is for our paper: 'Mutual Consistency Learning for Semi-supervised Medical Image Segmentation'. Note that, the MC-Net+ model is named as mcnet3d_v2 in our repository and we also provide the mcnet2d_v1 and mcnet3d_v1 versions, which are similar to the MC-Net model in MICCAI 2021: 'Semi-supervised Left Atrium Segmentation with Mutual Consistency Training'.

Requirements

This repository is based on PyTorch 1.8.0, CUDA 11.2 and Python 3.8.10; All experiments in our paper were conducted on a single NVIDIA Tesla V100 GPU.

Usage

  1. Clone the repo.;
git clone https://github.com/ycwu1997/MC-Net.git
  1. Put the data in './MC-Net/data';

  2. Train the model;

cd MC-Net
# e.g., for 20% labels on LA
python ./code/train_mcnet_3d.py --dataset_name LA --model mcnet3d_v2 --labelnum 16 --gpu 0 --temperature 0.1
  1. Test the model;
cd MC-Net
# e.g., for 20% labels on LA
python ./code/test_3d.py --dataset_name LA --model mcnet3d_v2 --exp MCNet --labelnum 16 --gpu 0

Citation

If our MC-Net+ model is useful for your research, please consider citing:

  @inproceedings{wu2021semi,
    title={Semi-supervised left atrium segmentation with mutual consistency training},
    author={Wu, Yicheng and Xu, Minfeng and Ge, Zongyuan and Cai, Jianfei and Zhang, Lei},
    booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
    pages={297--306},
    year={2021},
    organization={Springer}
    }
  @article{wu2022mutual,
    title={Mutual Consistency Learning for Semi-supervised Medical Image Segmentation},
    author={Wu, Yicheng and Ge, Zongyuan and Zhang, Donghao and Xu, Minfeng and Zhang, Lei and Xia, Yong and Cai, Jianfei},
    journal={Medical Image Analysis},
    volume={81},
    pages={102530},
    year={2022},
    publisher={Elsevier}
    }

Acknowledgements:

Our code is origin from UAMT, SASSNet, DTC, URPC and SSL4MIS. Thanks for these authors for their valuable works and hope our model can promote the relevant research as well.

Questions

If any questions, feel free to contact me at '[email protected]'

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Comments
  • Did you crop raw samples on the z axis?

    Did you crop raw samples on the z axis?

    Thank you for your work and sharing the code. But I have a question about data preprocessing.

    The papersays that "we cropped the 3D samples according to the ground truth, with enlarged margins i.e. [10 ∼ 20, 10 ∼ 20, 5 ∼ 10] or [25, 25, 25] voxels on LA or PancreasCT, respectively", but your preprocessing code just cropped the samples on the x axis and y axis.

    Why they are different? Which method is used to produce the performance reported in the paper, like 91.07 on LA and 79.37 on PancreasCT?

    opened by zhangwang997 1
  • train_mcnet_2d.py

    train_mcnet_2d.py

    There are two num_classes in the CT slice of my own dataset, but these two categories will not appear in the same slice, that is, each slice only shows one category. I found that MEAN_dice and HD95 were displayed as 0 in most of the time during the training, I wonder if they are related to my dataset itself. Experiments with the ACDC dataset are fine

    opened by GuozhengSui 3
Owner
Eli Wu
Eli Wu
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