CrossMatch
Code for this paper: CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation
🎉🎉🎉 This paper has been accepted by IEEE Journal of Biomedical and Health Informatics !
CrossMatch Paper:
Requirements
- Create conda environment:
conda create -n CrossMatch python=3.11 - Clone the repo:
git clone https://github.com/AiEson/CrossMatch.git - Activate the environment:
conda activate CrossMatch - Install the requirements:
cd CrossMatch pip install -r requirements.txt
Usage
LA dataset
One click to run:
cd LA/code
bash train.shACDC dataset
One click to run:
cd ACDC
bash scripts/train.sh gpu_num port
# like `bash scripts/train.sh 4 12333` for 4 GPUs and port 12333Results
LA dataset results
- The training set consists of 8 labeled scans and 72 unlabeled scans and the testing set includes 20 scans.
| Method | Reference | Dice(%)↑ | Jaccard(%)↑ | 95HD(voxel)↓ | ASD(voxel)↓ |
|---|---|---|---|---|---|
| UA-MT | (MICCAI'19) | 85.81 | 75.41 | 18.25 | 5.04 |
| SASSNet | (MICCAI'20) | 85.71 | 75.35 | 14.74 | 4.00 |
| DTC | (AAAI'21) | 84.55 | 73.91 | 13.80 | 3.69 |
| MC-Net | (MICCAI'21) | 86.87 | 78.49 | 11.17 | 2.18 |
| URPC | (MedIA'22) | 83.37 | 71.99 | 17.91 | 4.41 |
| SS-Net | (MICCAI'22) | 86.56 | 76.61 | 12.76 | 3.02 |
| MC-Net+ | (MedIA'22) | 87.68 | 78.27 | 10.35 | 1.85 |
| DMD | (MICCAI'23) | 89.70 | 81.42 | 6.88 | 1.78 |
| BCP | (CVPR'23) | 89.55 | 81.22 | 7.10 | 1.69 |
| UniMatch | (CVPR'23) | 89.09 | 80.47 | 12.50 | 3.59 |
| CAML | (MICCAI'23) | 89.62 | 81.28 | 8.76 | 2.02 |
| Ours | 91.33 | 84.11 | 5.29 | 1.53 |
- The training set consists of 16 labeled scans and 64 unlabeled scans and the testing set includes 20 scans.
| Method | Reference | Dice(%)↑ | Jaccard(%)↑ | 95HD(voxel)↓ | ASD(voxel)↓ |
|---|---|---|---|---|---|
| UA-MT | (MICCAI'19) | 88.18 | 79.09 | 9.66 | 2.62 |
| SASSNet | (MICCAI'20) | 88.11 | 79.08 | 12.31 | 3.27 |
| DTC | (AAAI'21) | 87.79 | 78.52 | 10.29 | 2.50 |
| MC-Net | (MICCAI'21) | 90.43 | 82.69 | 6.52 | 1.66 |
| URPC | (MedIA'22) | 87.68 | 78.36 | 14.39 | 3.52 |
| SS-Net | (MICCAI'22) | 88.19 | 79.21 | 8.12 | 2.20 |
| MC-Net+ | (MedIA'22) | 90.60 | 82.93 | 6.27 | 1.58 |
| DMD | (MICCAI'23) | 90.46 | 82.66 | 6.39 | 1.62 |
| BCP | (CVPR'23) | 90.18 | 82.36 | 6.64 | 1.61 |
| UniMatch | (CVPR'23) | 90.77 | 83.18 | 7.21 | 2.05 |
| CAML | (MICCAI'23) | 90.78 | 83.19 | 6.11 | 1.68 |
| Ours | 91.61 | 84.57 | 5.36 | 1.57 |
ACDC dataset results
- The training set consists of 3 labeled scans and 67 unlabeled scans and the testing set includes 20 scans.
| Method | Reference | Dice(%)↑ | Jaccard(%)↑ | 95HD(voxel)↓ | ASD(voxel)↓ |
|---|---|---|---|---|---|
| UA-MT | (MICCAI'19) | 46.04 | 35.97 | 20.08 | 7.75 |
| SASSNet | (MICCAI'20) | 57.77 | 46.14 | 20.05 | 6.06 |
| DTC | (AAAI'21) | 56.90 | 45.67 | 23.36 | 7.39 |
| MC-Net | (MICCAI'21) | 62.85 | 52.29 | 7.62 | 2.33 |
| URPC | (MedIA'22) | 55.87 | 44.64 | 13.60 | 3.74 |
| SS-Net | (MICCAI'22) | 65.82 | 55.38 | 6.67 | 2.28 |
| DMD | (MICCAI'23) | 80.60 | 69.08 | 5.96 | 1.90 |
| UniMatch | (CVPR'23) | 84.38 | 75.54 | 5.06 | 1.04 |
| Ours | 88.27 | 80.17 | 1.53 | 0.46 |
- The training set consists of 7 labeled scans and 63 unlabeled scans and the testing set includes 20 scans.
| Method | Reference | Dice(%)↑ | Jaccard(%)↑ | 95HD(voxel)↓ | ASD(voxel)↓ |
|---|---|---|---|---|---|
| UA-MT | (MICCAI'19) | 81.65 | 70.64 | 6.88 | 2.02 |
| SASSNet | (MICCAI'20) | 84.50 | 74.34 | 5.42 | 1.86 |
| DTC | (AAAI'21) | 84.29 | 73.92 | 12.81 | 4.01 |
| MC-Net | (MICCAI'21) | 86.44 | 77.04 | 5.50 | 1.84 |
| URPC | (MedIA'22) | 83.10 | 72.41 | 4.84 | 1.53 |
| SS-Net | (MICCAI'22) | 86.78 | 77.67 | 6.07 | 1.40 |
| DMD | (MICCAI'23) | 87.52 | 78.62 | 4.81 | 1.60 |
| UniMatch | (CVPR'23) | 88.08 | 80.10 | 2.09 | 0.45 |
| Ours | 89.08 | 81.44 | 1.52 | 0.52 |
Qualitative results
Citation
If you find this project useful, please consider citing:
@ARTICLE{CrossMatch,
author={Zhao, Bin and Wang, Chunshi and Ding, Shuxue},
journal={IEEE Journal of Biomedical and Health Informatics},
title={CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation},
year={2024},
volume={},
number={},
pages={1-13},
keywords={Perturbation methods;Data models;Predictive models;Biomedical imaging;Decoding;Accuracy;Training;Semi-supervised segmentation;Self-knowledge distillation;Image perturbation},
doi={10.1109/JBHI.2024.3463711}}