CrossMatch

Code for this paper: CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation

CrossMatch Paper:

Requirements

1. Create conda environment:

   conda create -n CrossMatch python=3.11

2. Clone the repo:

   git clone https://github.com/AiEson/CrossMatch.git

3. Activate the environment:

   conda activate CrossMatch

4. Install the requirements:

   cd CrossMatch
   pip install -r requirements.txt

Usage

LA dataset

One click to run:

cd LA/code
bash train.sh

ACDC 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 12333

Results

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:

@misc{zhao2024crossmatch,
      title={CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation}, 
      author={Bin Zhao and Chunshi Wang and Shuxue Ding},
      year={2024},
      eprint={2405.00354},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgement

• This code is adapted from UA-MT, DTC and UniMatch .
• We thank Lequan Yu, Xiangde Luo and Lihe Yang for their elegant and efficient code base.