Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/Beckschen/TransUNet
This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
https://github.com/Beckschen/TransUNet
Last synced: about 2 months ago
JSON representation
This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
- Host: GitHub
- URL: https://github.com/Beckschen/TransUNet
- Owner: Beckschen
- License: apache-2.0
- Created: 2021-02-08T06:12:54.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-07-26T22:37:49.000Z (5 months ago)
- Last Synced: 2024-10-14T20:04:26.346Z (about 2 months ago)
- Language: Python
- Homepage:
- Size: 37.1 KB
- Stars: 2,370
- Watchers: 12
- Forks: 497
- Open Issues: 125
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome_vision_transformer - code
README
# TransUNet
This repo holds code for [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://arxiv.org/pdf/2102.04306.pdf)
## 📰 News
- [7/26/2024] TransUNet, which supports both 2D and 3D data and incorporates a Transformer encoder and decoder, has been featured in the journal Medical Image Analysis ([link](https://www.sciencedirect.com/science/article/pii/S1361841524002056)).
```bibtex
@article{chen2024transunet,
title={TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers},
author={Chen, Jieneng and Mei, Jieru and Li, Xianhang and Lu, Yongyi and Yu, Qihang and Wei, Qingyue and Luo, Xiangde and Xie, Yutong and Adeli, Ehsan and Wang, Yan and others},
journal={Medical Image Analysis},
pages={103280},
year={2024},
publisher={Elsevier}
}
```
- [10/15/2023] 🔥 3D version of TransUNet is out! Our 3D TransUNet surpasses nn-UNet with 88.11% Dice score on the BTCV dataset and outperforms the top-1 solution in the BraTs 2021 challenge and secure the second place in BraTs 2023 challenge. Please take a look at the [code](https://github.com/Beckschen/3D-TransUNet/tree/main) and [paper](https://arxiv.org/abs/2310.07781).
## Usage
### 1. Download Google pre-trained ViT models
* [Get models in this link](https://console.cloud.google.com/storage/vit_models/): R50-ViT-B_16, ViT-B_16, ViT-L_16...
```bash
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 (All data are available!)
All data are available so no need to send emails for data. Please use the [BTCV preprocessed data](https://drive.google.com/drive/folders/1ACJEoTp-uqfFJ73qS3eUObQh52nGuzCd?usp=sharing) and [ACDC data](https://drive.google.com/drive/folders/1KQcrci7aKsYZi1hQoZ3T3QUtcy7b--n4?usp=drive_link).
### 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. The batch size can be reduced to 12 or 6 to save memory (please also decrease the base_lr linearly), and both can reach similar performance.
```bash
CUDA_VISIBLE_DEVICES=0 python train.py --dataset Synapse --vit_name R50-ViT-B_16
```
- Run the test script on synapse dataset. It supports testing for both 2D images and 3D volumes.
```bash
python test.py --dataset Synapse --vit_name R50-ViT-B_16
```
## Reference
* [Google ViT](https://github.com/google-research/vision_transformer)
* [ViT-pytorch](https://github.com/jeonsworld/ViT-pytorch)
* [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch)
## Citations
```bibtex
@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}
}
```