基于transformer的医学CT 和 MRI图像分割内含数据集和环境搭建说明.zip

# TransBTS（MICCAI2021）& TransBTSV2 (To Be Updated) This repo is the official implementation for: 1) [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://arxiv.org/abs/2103.04430). 2) [TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images](https://arxiv.org/abs/2201.12785). The details of the our TransBTS and TransBTSV2 can be found at the models directory ([TransBTS](https://github.com/Wenxuan-1119/TransBTS/tree/main/models/TransBTS) and [TransBTSV2](https://github.com/Wenxuan-1119/TransBTS/tree/main/models/TransBTSV2)) in this repo or in the original paper. ## Requirements - python 3.7 - pytorch 1.6.0 - torchvision 0.7.0 - pickle - nibabel ## Data Acquisition - The multimodal brain tumor datasets (**BraTS 2019** & **BraTS 2020**) could be acquired from [here](https://ipp.cbica.upenn.edu/). - The liver tumor dataset **LiTS 2017** could be acquired from [here](https://competitions.codalab.org/competitions/17094#participate-get-data). - The kidney tumor dataset **KiTS 2019** could be acquired from [here](https://kits19.grand-challenge.org/data/). ## Data Preprocess (BraTS 2019 & BraTS 2020) After downloading the dataset from [here](https://ipp.cbica.upenn.edu/), data preprocessing is needed which is to convert the .nii files as .pkl files and realize data normalization. `python3 preprocess.py` ## Training Run the training script on BraTS dataset. Distributed training is available for training the proposed TransBTS, where --nproc_per_node decides the numer of gpus and --master_port implys the port number. `python3 -m torch.distributed.launch --nproc_per_node=4 --master_port 20003 train.py` ## Testing If you want to test the model which has been trained on the BraTS dataset, run the testing script as following. `python3 test.py` After the testing process stops, you can upload the submission file to [here](https://ipp.cbica.upenn.edu/) for the final Dice_scores. ## Reference 1.[setr-pytorch](https://github.com/gupta-abhay/setr-pytorch) 2.[BraTS2017](https://github.com/MIC-DKFZ/BraTS2017)