/RDGAN

RDGAN (Adversarial Training and Dilated Convolutions for Compressed Sensing MR , published in 2019) implemented (Unet on single GPU) in Tensorflow 1.8+. This is an official implementation

Primary LanguagePython

RDGAN

🔥 RDGAN (Adversarial Training and Dilated Convolutions for Compressed Sensing MR , published in 2019) implemented (Unet on single GPU) in Tensorflow 1.8+. This is an official implementation. 🔥

This is the official implementation code for Adversarial Training and Dilated Convolutions for Compressed Sensing MRI

If you use this code for your research, please cite our paper.

@article{xu2019adversarial,
title={Adversarial training and dilated convolutions for compressed sensing MRI},
author={Xu, Chao and Tao, Jinxu and Ye, Zhongfu and Xu, Jinzhang and Kainat, Wajiha},
year={2019}}

If you have any questions about this code, please feel free to contact Chao Xu (chaoxu006@gmail.com).

Prerequisites

The original code is in python 3.6 under the following dependencies:

  1. tensorflow (v1.8.0)
  2. tensorlayer (v1.9.0)
  3. easydict (v1.6)
  4. nibabel (v2.2.1)
  5. scikit-image (v0.19.2)
  6. numpy (v1.15.4)

Code tested in Ubuntu 16.04 with Nvidia GPU + CUDA CuDNN (whose version is compatible to tensorflow v1.8.0).

How to use

  1. Prepare data

    1. Data used in this work are publicly available from the MICCAI 2013 grand challenge (link). We refer users to register with the grand challenge organisers to be able to download the data.
    2. Download training and test data respectively into data/MICCAI13_SegChallenge/Training_100 and data/MICCAI13_SegChallenge/Testing_100 (We randomly included 100 T1-weighted MRI datasets for training and 50 datasets for testing)
    3. run 'python data_loader.py'
    4. after running the code, training/validation/testing data should be saved to 'data/MICCAI13_SegChallenge/' in pickle format.

  2. Download pretrained VGG19 model

    1. Download 'vgg19.npy' from this link
    2. Save 'vgg19.npy' into 'trained_model/VGG19'

  3. Train model

    1. run CUDA_VISIBLE_DEVICES=0 python train.py --model 'unet_refine' --mask 'gaussian1d' -- maskperc 30 --lossfunciton 'gan_loss' --penalty 'no_penalty' --train 'train'

    where you should specify model, mask, maskperc, lossfunction, penalty, train, respectively:

    • model: choose from 'unet' or 'unet_refine'
    • mask: choose from 'radialcartesi', 'gaussian1d', 'gaussian2d'
    • maskperc: choose from '10', '20', '30', '40', '50' (percentage of mask)
    • lossfunciton: choose from 'gan_loss', 'wgan_loss, 'lsgan_loss'
    • penalty: choose from 'no_penalty', 'wgangp_penalty', 'dragan_penalty'
    • train: choose from 'train','evaluate'

  4. Test trained model

    1. the trained model parameters in 'checkpoints/', image which you want to reconstruct in
      'evaluate/samples/'
    2. run CUDA_VISIBLE_DEVICES=0 python train.py --model 'unet_refine' --mask 'gaussian1d' --maskperc 30 --train 'evaluate' where you should specify model, mask, maskperc, train respectively (as above).

Results

:: Results from this reporepository. :: image

image

Please refer to the paper for the other detailed results.

What we are doing and going to do

  • WGAN
  • WGAN-GP
  • LSGAN
  • VGG19
  • SNGAN
  • MultiGPU

References

🍔

Thanks for these source codes porviding me with knowledges to complete this repository.