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[MICCAI2022] This is an official PyTorch implementation for A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation
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# VT-UNet
This repo contains the supported pytorch code and configuration files to reproduce 3D medical image segmentaion results of [VT-UNet](https://arxiv.org/pdf/2111.13300.pdf).
Our previous Code for A Volumetric Transformer for Accurate 3D Tumor Segmentation can be found iside version 1 folder.
# VT-UNet: A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation
Parts of codes are borrowed from [nn-UNet](https://github.com/MIC-DKFZ/nnUNet).
## System requirements
This software was originally designed and run on a system running Ubuntu.
## Dataset Preparation
- Create a folder under VTUNet as DATASET
- Download MSD BraTS dataset (http://medicaldecathlon.com/) and put it under DATASET/vtunet_raw/vtunet_raw_data
- Rename folder as Task03_tumor
- Move dataset.json file to Task03_tumor
## Pre-trained weights
- Download swin-T pretrained weights : https://github.com/SwinTransformer/storage/releases/download/v1.0.8/swin_tiny_patch4_window7_224_22k.pth
- Add it under pretrained_ckpt folder.
## Create Environment variables
vi ~/.bashrc
- export vtunet_raw_data_base="/home/VTUNet/DATASET/vtunet_raw/vtunet_raw_data"
- export vtunet_preprocessed="/home/VTUNet/DATASET/vtunet_preprocessed"
- export RESULTS_FOLDER_VTUNET="/home/VTUNet/DATASET/vtunet_trained_models"
source ~/.bashrc
## Environment setup
Create a virtual environment
- virtualenv -p /usr/bin/python3.8 venv
- source venv/bin/activate
Install torch
- pip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
Install other dependencies
- pip install -r requirements.txt
## Preprocess Data
cd VTUNet
pip install -e .
- vtunet_convert_decathlon_task -i /home/VTUNet/DATASET/vtunet_raw/vtunet_raw_data/Task03_tumor
- vtunet_plan_and_preprocess -t 3
## Train Model
cd vtunet
- CUDA_VISIBLE_DEVICES=0 nohup vtunet_train 3d_fullres vtunetTrainerV2_vtunet_tumor 3 0 &> small.out &
- CUDA_VISIBLE_DEVICES=0 nohup vtunet_train 3d_fullres vtunetTrainerV2_vtunet_tumor_base 3 0 &> base.out &
## Test Model
cd /home/VTUNet/DATASET/vtunet_raw/vtunet_raw_data/vtunet_raw_data/Task003_tumor/
- CUDA_VISIBLE_DEVICES=0 vtunet_predict -i imagesTs -o inferTs/vtunet_tumor -m 3d_fullres -t 3 -f 0 -chk model_best -tr vtunetTrainerV2_vtunet_tumor
- python vtunet/inference_tumor.py vtunet_tumor
## Trained model Weights
- [VT-UNet-S](https://drive.google.com/drive/folders/1t7RTwHNwAqh2fiIqUpFTGTG0FHY8k3pQ?usp=sharing) - (fold 0 only)
- VT-UNet-B (To be updated)
## Acknowledgements
This repository makes liberal use of code from [open_brats2020](https://github.com/lescientifik/open_brats2020), [Swin Transformer](https://github.com/microsoft/Swin-Transformer), [Video Swin Transformer](https://github.com/SwinTransformer/Video-Swin-Transformer), [Swin-Unet](https://github.com/HuCaoFighting/Swin-Unet), [nnUNet](https://github.com/MIC-DKFZ/nnUNet) and [nnFormer](https://github.com/282857341/nnFormer)
## References
* [Medical segmentation decathlon](http://medicaldecathlon.com/)
## Citing VT-UNet
```bash
@inproceedings{peiris2022robust,
title={A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation},
author={Peiris, Himashi and Hayat, Munawar and Chen, Zhaolin and Egan, Gary and Harandi, Mehrtash},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={162--172},
year={2022},
organization={Springer}
}
```
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