CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

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Deep Learning CoTr
Overview

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 nnU-Net.
  • 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},
  journal={arXiv preprint arXiv:2103.03024},
  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 ([email protected])

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Comments
  • reference points解释

    reference points解释

    作者，你好，可以解释下这段代码的意思吗？ def get_reference_points(spatial_shapes, valid_ratios, device): reference_points_list = [] for lvl, (D_, H_, W_) in enumerate(spatial_shapes):

            ref_d, ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, D_ - 0.5, D_, dtype=torch.float32, device=device),
                                             torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
                                             torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))

        ref_d = ref_d.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * D_)
        ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 2] * H_)
        ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * W_)

        ref = torch.stack((ref_d, ref_x, ref_y), -1)   # D W H
        reference_points_list.append(ref)
    reference_points = torch.cat(reference_points_list, 1)
    reference_points = reference_points[:, :, None] * valid_ratios[:, None]
    return reference_points
    opened by 60wanjinbing 0
Owner
null
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