Med-DANet（ECCV 2022）and Med-DANet V2（WACV 2024）

This repo is the official implementation for: Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation and Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation.

[Project page]

Requirements

• python 3.7
• pytorch 1.6.0
• torchvision 0.7.0
• pickle
• nibabel
• fvcore
• kornia
• setproctitle
• tensorboardX
• pickle

Data Acquisition

• The multimodal brain tumor datasets (BraTS 2019 & BraTS 2020) could be acquired from here.

• The liver tumor dataset LiTS 2017 could be acquired from here.

Data Preprocess (BraTS 2019 & BraTS 2020)

After downloading the dataset from here, data preprocessing is needed which is to convert the .nii files as .pkl files and realize date normalization.

python3 ./data/preprocess.py

Training

Run the training script on BraTS dataset. Distributed training is available for training the proposed TransBTS.

sh train.sh

Testing

If you want to test the model which has been trained on the BraTS dataset, run the testing script as following.

python3 test.py

After the testing process stops, you can upload the submission file to here for the final Dice_scores.

Citation

If you use our code or models in your work or find it is helpful, please cite the corresponding paper:

• Med-DANet:

@inproceedings{wang2022med,
  title={Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation},
  author={Wang, Wenxuan and Chen, Chen and Wang, Jing and Zha, Sen and Zhang, Yan and Li, Jiangyun},
  booktitle={European Conference on Computer Vision},
  pages={506--522},
  year={2022},
  organization={Springer}
}

• Med-DANet V2:

@inproceedings{shen2024med,
  title={Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation},
  author={Shen, Haoran and Zhang, Yifu and Wang, Wenxuan and Chen, Chen and Liu, Jing and Song, Shanshan and Li, Jiangyun},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={7871--7881},
  year={2024}
}