📝 Table of Contents

• About
• Getting Started
• Usage
• Authors
• Acknowledgments

🧐 About

This is the anonymous code of GVS, which mainly includes training details, pretrained model and the synthetic images of one volume.

🏁 Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

First check your enviroment.

pytorch >= 1.3.1
python >= 3.6
opencv-python >= 4.3

File Tree

│  test.py           # evaluate a model by some index, and extract images
│  README.md                 # this file
│  main.py            # our main code, tarin our model on BraTS
│
|--chechpoints        
   | pretrain.pth     # pretrined model
|
|--data 
   |--test_npy        # test examples
   |  test_brats.txt  # text examples list
   |  train_brats.txt # train examples list    
|
|--Results
   |--eval            # the pseudo-healthy images of test examples          
|
├─unet
   │  unet_model.py          # store basic model
   │  unet_parts.py          # basic part of model
   |  network.py             # baseci part of model
│  
└─utils
    │  dataset.py            # dataloader
    │  init_logging.py       # initial a logger to write a log
    │  ms_ssim.py            # calculate ms-ssim between two images
    │  nii2npy_brats.py      # split .nii in to .npy to train 
    │  nii2npy_lits.py       # split .nii in to .npy to train 
    │  split_cases_brats.py  # split cases into train/val/test set
    │  split_cases_lits.py   # split cases into train/val/test set

🎈 Usage

• Then you can train model by running main.py
• You can evaluate model by running test.py