AutoEncoder Driven Multimodal Collaborative Learning for Medical Image Synthesis

The PyTorch implements of AutoEncoder Driven Multimodal Collaborative Learning for Medical Image Synthesis (IJCV 2023).

The overview of our AE-GAN framework.

Our method can synthesis clear and nature images and outperforms other state-of-the-art methods on many datasets.

Experimental results on BraTS2020 dataset.

Experimental results on ISLES2015 dataset.

Experimental results on CBMFM dataset.

Environment

python              3.8.10
pytorch             1.8.1
torchvision         0.9.1
tqdm                4.62.1
numpy               1.20.3
SimpleITK           2.1.0
scikit-learn        0.24.2
opencv-python       4.5.3.56
easydict            1.9
tensorboard         2.5.0
Pillow              8.3.1

Datasets

Download the datasets from the official way and rearrange the files to the following structure. The dataset path can be modified in the AE-GAN/options/*.yaml file.

BraTS2020

MICCAI_BraTS2020_TrainingData
├── flair
│   ├── BraTS20_Training_001_flair.nii.gz
│   ├── BraTS20_Training_002_flair.nii.gz
│   ├── BraTS20_Training_003_flair.nii.gz
│   ├── ...
├── t2
│   ├── BraTS20_Training_001_t2.nii.gz
│   ├── BraTS20_Training_002_t2.nii.gz
│   ├── BraTS20_Training_003_t2.nii.gz
│   ├── ...
├── t1
│   ├── BraTS20_Training_001_t1.nii.gz
│   ├── BraTS20_Training_002_t1.nii.gz
│   ├── BraTS20_Training_003_t1.nii.gz
│   ├── ...
├── t1ce
│   ├── BraTS20_Training_001_t1ce.nii.gz
│   ├── BraTS20_Training_002_t1ce.nii.gz
│   ├── BraTS20_Training_003_t1ce.nii.gz
│   ├── ...

ISLES2015

SISS2015_Training
├── 1
│   ├── VSD.Brain.XX.O.MR_T2.70616
│        ├── VSD.Brain.XX.O.MR_T2.70616.nii
│   ├── VSD.Brain.XX.O.MR_T1.70615
│        ├── VSD.Brain.XX.O.MR_T1.70615.nii
│   ├── VSD.Brain.XX.O.MR_Flair.70614
│        ├── VSD.Brain.XX.O.MR_Flair.70614.nii
│   ├── VSD.Brain.XX.O.MR_DWI.70613
│        ├── VSD.Brain.XX.O.MR_DWI.70613.nii
├── 2
│   ├── VSD.Brain.XX.O.MR_T2.70622
│        ├── VSD.Brain.XX.O.MR_T2.70622.nii
│   ├── VSD.Brain.XX.O.MR_T1.70621
│        ├── VSD.Brain.XX.O.MR_T1.70621.nii
│   ├── VSD.Brain.XX.O.MR_Flair.70620
│        ├── VSD.Brain.XX.O.MR_Flair.70620.nii
│   ├── VSD.Brain.XX.O.MR_DWI.70619
│        ├── VSD.Brain.XX.O.MR_DWI.70619.nii
├── 3
│   ├── ...

Checkpoints

Our pre-trained models are available at: Google Drive | OneDrive | Baidu Drive.

Train

Edit the .yaml file of the corresponding dataset for training configuration and run the following command to train.

python train.py options/brats.yaml

Test

Edit the .yaml file of the corresponding dataset for testing configuration and run the following command to test.

python test.py options/brats.yaml

Citation

If you find this code useful in your research, please consider citing:

@article{cao2023autoencoder,
  title={AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis},
  author={Cao, Bing and Bi, Zhiwei and Hu, Qinghua and Zhang, Han and Wang, Nannan and Gao, Xinbo and Shen, Dinggang},
  journal={International Journal of Computer Vision},
  pages={1--20},
  year={2023},
  publisher={Springer}
}

bcaosudo/AE-GAN