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ProvoGAN

Official Pytorch Implementation of Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery (ProvoGAN) which is described in the following paper:

Mahmut Yurt and Muzaffer Özbey and Salman Ul Hassan Dar and Berk Tınaz and Kader Karlı Oğuz and Tolga Çukur Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery. arXiv. 2022.

Data

The data directory should be organized as the following structure. '.nii' files are for synthesis tasks, '.mat' files are for reconstruction tasks. REconstruction files should contain a variables named images_fs, images_us and map for the fully-sampled, under-sampled and sampling mask of target data:

data
│
└───train
|   |
│   └─── train subject 1
|   |         modality_1.nii
|   |         modality_2.nii
|   |         ...
|   |         target.nii
|   |         ...
|   |         target_Rx.mat
│   └─── train subject 2
|   |         modality_1.nii
|   |         modality_2.nii
|   |         ...
|   |         target.nii
|   |         ...
|   |         target_Rx.mat
│   ...
|
└───val
|   |
│   └─── val subject 1
|   |         modality_1.nii
|   |         modality_2.nii
|   |         ...
|   |         target.nii
|   |         ...
|   |         target_Rx.mat
│   └─── val subject 2
|   |         modality_1.nii
|   |         modality_2.nii
|   |         ...
|   |         target.nii
|   |         ...
|   |         target_Rx.mat
│   ...
|   
└───test
    |
    └─── test subject 1
    |         modality_1.nii
    |         modality_2.nii
    |         ...
|   |         target.nii
|   |         ...
|   |         target_Rx.mat
    └─── test subject 2
    |         modality_1.nii
    |         modality_2.nii
    |         ...
|   |         target.nii
|   |         ...
|   |         target_Rx.mat
    ...

Demo

provoGAN

sample_run.sh file contains 3 consecutive training and testing commend. To run the code please organize your data folder as the explained structure. Also edit the following arguments according to your choices;

name - name of the experiment
lambda_A - weighting of the pixel-wise loss function
niter, n_iter_decay - number of epochs with normal learning rate and number of epochs for which the learning leate is decayed to 0. Total number of epochs is equal to sum of them
save_epoch_freq -frequency of saving models
order -order selection for 3 stage of ProvoGAN
input1 -constrast name of input modelity 1
input2 -constrast name of input modelity 2
out -constrast name of target modelity

sGAN

A sample run commend example for training and testing:

python train.py --dataroot datasets/IXI --name datasets/IXI --model pix2pix_perceptual --which_model_netG resnet_9blocks  --which_direction AtoB --lambda_A 100 --dataset_mode provo_stage1 --norm batch --pool_size 0 --output_nc 1 --input_nc 2 --gpu_ids 0 --niter 50 --niter_decay 50 --save_epoch_freq 5  --checkpoints_dir /checkpoints/revisions/ --input1 T1 --input2 T2 --out PD


python test_sGAN.py --dataroot datasets/IXI --name datasets/IXI --model pix2pix_perceptual --which_model_netG resnet_9blocks  --dataset_mode provo_stage1 --norm batch --phase test --output_nc 1 --input_nc 2 --gpu_ids 0 --serial_batches  --checkpoints_dir /checkpoints/revisions/ --input1 T1 --input2 T2 --out PD

vGAN

A sample run commend example for training and testing:

python train.py --dataroot datasets/IXI --name datasets/IXI --model pix2pix_perceptual_vGAN --which_model_netG resnet_9blocks_3D --which_model_netD basic_3D  --which_direction AtoB --lambda_A 100 --dataset_mode vGAN --norm batch_3D --pool_size 0 --output_nc 1 --input_nc 2 --gpu_ids 0 --niter 50 --niter_decay 50 --save_epoch_freq 5  --checkpoints_dir /checkpoints/revisions/ --input1 T1 --input2 T2 --out PD


python test_vGAN.py --dataroot datasets/IXI --name datasets/IXI --model pix2pix_perceptual_vGAN --which_model_netG resnet_9blocks_3D  --dataset_mode vGAN --norm batch_3D --phase test --output_nc 1 --input_nc 2 --gpu_ids 0 --serial_batches  --checkpoints_dir /checkpoints/revisions/ --input1 T1 --input2 T2 --out PD

transferGAN

For transferGAN, initially, a cross-section-based 2D network is trained, then its weights are used for weight initialization of volumetric 3D network. A sample run commend example for trainings and testing:

2D pretrainig

python train.py --dataroot datasets/IXI --name transferGAN_pre_train --model pix2pix_perceptual --which_model_netG resnet_9blocks  --which_direction AtoB --lambda_A 100 --dataset_mode provo_stage1 --norm batch --pool_size 0 --output_nc 1 --input_nc 2 --gpu_ids 0 --niter 50 --niter_decay 50 --save_epoch_freq 5  --checkpoints_dir /checkpoints/revisions/ --input1 T1 --input2 T2 --out PD

3D volumetric model with weight tranfer

python train.py --dataroot datasets/IXI --name transferGAN_sample --model pix2pix_perceptual_transferGAN --which_model_netG resnet_9blocks_3D --which_model_netD basic_3D  --which_direction AtoB --lambda_A 50 --dataset_mode vGAN --norm batch_3D --pool_size 0 --output_nc 1 --input_nc 2 --gpu_ids 0 --niter 50 --niter_decay 50 --save_epoch_freq 5  --checkpoints_dir /checkpoints/revisions/ --input1 T1 --input2 T2 --out PD --checkpoints_dir_old /checkpoints/revisions/ --which_model_netG_old resnet_9blocks --name_old transferGAN_pre_train


python test_transferGAN.py --dataroot datasets/IXI --name transferGAN_sample --model pix2pix_perceptual_transferGAN --which_model_netG resnet_9blocks_3D  --dataset_mode vGAN --norm batch_3D --phase test --output_nc 1 --input_nc 2 --gpu_ids 0 --serial_batches  --checkpoints_dir /checkpoints/revisions/ --input1 T1 --input2 T2 --out PD

SC-GAN

A sample run commend example for training and testing:

python train.py --dataroot datasets/IXI --name datasets/IXI --model pix2pix_perceptual_vGAN --which_model_netG unet_att_3D --which_model_netD basic_att_3D --which_direction AtoB --lambda_A 100 --dataset_mode vGAN --norm batch_3D --pool_size 0 --output_nc 1 --input_nc 2 --gpu_ids 0 --niter 50 --niter_decay 50 --save_epoch_freq 5  --checkpoints_dir /checkpoints/revisions/ --input1 T1 --input2 T2 --out PD


python test_SC-GAN.py --dataroot datasets/IXI --name datasets/IXI --model pix2pix_perceptual_vGAN --which_model_netG unet_att_3D  --dataset_mode vGAN --norm batch_3D --phase test --output_nc 1 --input_nc 2 --gpu_ids 0 --serial_batches  --checkpoints_dir /checkpoints/revisions/ --input1 T1 --input2 T2 --out PD

refineGAN

A sample run commend example for training and testing:

python train.py --dataroot datasets/IXI --name refineGAN_sample --model pix2pix_perceptual_refineGAN --which_model_netG resnet_9blocks  --which_direction AtoB --lambda_A 10 --dataset_mode refineGAN --norm batch --pool_size 0 --output_nc 1 --input_nc 2 --gpu_ids 0 --niter 50 --niter_decay 50 --save_epoch_freq 5  --checkpoints_dir /checkpoints/revisions/ --Rx 4 --data_type T1


python test_refineGAN.py --dataroot datasets/IXI --name refineGAN_sample --model pix2pix_perceptual_refineGAN --which_model_netG resnet_9blocks  --dataset_mode refineGAN --norm batch --phase test --output_nc 1 --input_nc 2 --gpu_ids 0 --serial_batches  --checkpoints_dir /checkpoints/revisions/ --Rx 4 --data_type T1

Citation

You are encouraged to modify/distribute this code. However, please acknowledge this code and cite the paper appropriately.

@article{yurt2020progressively,
  title={Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery},
  author={Yurt, Mahmut and {\"O}zbey, Muzaffer and Dar, Salman Ul Hassan and T{\i}naz, Berk and O{\u{g}}uz, Kader Karl{\i} and {\c{C}}ukur, Tolga},
  journal={arXiv preprint arXiv:2011.13913},
  year={2020}
}

For any questions, comments and contributions, please contact Muzaffer Özbey (muzaffer[at]ee.bilkent.edu.tr)

(c) ICON Lab 2022

Acknowledgments

This code uses libraries from pGAN and pix2pix repository.