/TransHRNet

Primary LanguagePython

3D Medical Image Segmentation using Parallel Transformers

This is the official pytorch implementation of the TransHRNet

Requirements

CUDA 11.0

Python 3.7

Pytorch 1.7

Torchvision 0.8.2

Usage

1. Data Preparation

  • Download BCV dataset (https://www.synapse.org/#!Synapse:syn3193805/wiki/217789)

  • Preprocess the BCV dataset use the nnUNet package.

    cd nnUNet/nnunet/dataset_conversion 
Run python Task017_BeyondCranialVaultAbdominalOrganSegmentation.py

  • Training and Testing ID are in data/splits_final.pkl

2. Training

cd TransHRNet_package/TransHRNet/run

  • Run python run_training.py -gpu="0" -outpath="TransHRNet_result" for training.

3. Testing

cd TransHRNet_package/TransHRNet/run

  • Run python run_training.py -gpu='0' -outpath="TransHRNet_result" -val --val_folder='validation_result' for validation.

4. Performance on Multi-Atlas Labeling Beyond the Cranial Vault (BCV) dataset

Acknowledge

Part of codes are reused from the CoTr (https://github.com/YtongXie/CoTr). Thanks to Fabian Isensee for the codes of CoTr.