Contour-Aware Consistency for Semi-Supervised Medical Image Segmentation

by Lei Li*, Sheng Lian, Zhiming Luo, Beizhan Wang, Shaozi Li

Introduction

This repository is for our paper: 'Contour-Aware Consistency for Semi-Supervised Medical Image Segmentation'.

Requirements

This repository is based on PyTorch 1.8.0, CUDA 11.1 and Python 3.8.0; All experiments in our paper were conducted on a single NVIDIA GeForce RTX 3090 GPU.

Usage

1. Clone the repo.;

git clone https://github.com/SmileJET/CAC4SSL.git

2. Put the data in './CAC4SSL/data';

3. Train the model;

cd CAC4SSL
# e.g., for 20% labels on LA
python ./code/train_3d.py --dataset_name LA --model cacnet3d_emb_64 --labelnum 16 --gpu 0 --temperature 0.1 --exp cacnet_sample_50

4. Test the model;

cd CAC4SSL
# e.g., for 20% labels on LA
python ./code/test_3d.py --dataset_name LA --model cacnet3d_emb_64 --exp cacnet_sample_50 --labelnum 16 --gpu 0

5. Test the biou;

cd CAC4SSL
# e.g., for acdc
python ./test_acdc_biou.py

Acknowledgements:

Our code is origin from MC-Net, 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.