Awesome
CoTr: Efficient 3D Medical Image Segmentation by bridging CNN and Transformer
This is the official pytorch implementation of the CoTr:<br />
Paper: CoTr: Efficient 3D Medical Image Segmentation by bridging CNN and Transformer.
Requirements
CUDA 11.0<br /> Python 3.7<br /> Pytorch 1.7<br /> Torchvision 0.8.2<br />
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 (yutong.xie678@gmail.com)