kangzi / TransUNet

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

TransUNet

This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Usage

1. Download Google pre-trained ViT models

• Get models in this link: R50-ViT-B_16, ViT-B_16, ViT-L_16...

wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz &&
mkdir ../model/vit_checkpoint/imagenet21k &&
mv {MODEL_NAME}.npz ../model/vit_checkpoint/imagenet21k/{MODEL_NAME}.npz

2. Prepare data

Please go to "./datasets/README.md" for details, or please send an Email to jienengchen01 AT gmail.com to request the preprocessed data.

3. Environment

Please prepare an environment with python=3.7, and then use the command "pip install -r requirements.txt" for the dependencies.

4. Train/Test

• run the train script on synapse dataset

python train.py --dataset Synapse --vit_name R50-ViT-B_16

• run the test script on synapse dataset

python test.py --dataset Synapse --vit_name R50-ViT-B_16

Reference

• Google ViT

Citations

@article{chen2021transunet,
  title={TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation},
  author={Chen, Jieneng and Lu, Yongyi and Yu, Qihang and Luo, Xiangde and Adeli, Ehsan and Wang, Yan and Lu, Le and Yuille, Alan L., and Zhou, Yuyin},
  journal={arXiv preprint arXiv:2102.04306},
  year={2021}
}