CoTr: A Python repository from eliseyang

CoTr: Efficient 3D Medical Image Segmentation by bridging CNN and Transformer

This is the official pytorch implementation of the CoTr:

Paper: CoTr: Efficient 3D Medical Image Segmentation by bridging CNN and Transformer.

Requirements

CUDA 11.0
Python 3.7
Pytorch 1.7
Torchvision 0.8.2

Usage

0. Installation

Install Pytorch1.7, nnUNet and CoTr as below

pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

cd nnUNet
pip install -e .

cd CoTr_package
pip install -e .

1. Data Preparation

Download BCV dataset
Preprocess the BCV dataset according to the uploaded nnUNet package.
Training and Testing ID are in data/splits_final.pkl.

2. Training

cd CoTr_package/CoTr/run

Run nohup python run_training.py -gpu='0' -outpath='CoTr' 2>&1 & for training.

3. Testing

Run nohup python run_training.py -gpu='0' -outpath='CoTr' -val --val_folder='validation_output' 2>&1 & for validation.

4. Citation

If this code is helpful for your study, please cite:

@article{xie2021cotr,
  title={CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation},
  author={Xie, Yutong and Zhang, Jianpeng and Shen, Chunhua and Xia, Yong},
  booktitle={MICCAI},
  year={2021}
}

5. Acknowledgements

Part of codes are reused from the nnU-Net. Thanks to Fabian Isensee for the codes of nnU-Net.

Contact

Yutong Xie (xuyongxie@mail.nwpu.edu.cn)

eliseyang/CoTr