UncerGuidedI2I

PyTorch imeplementation of Uncertainty Guided Progressive GANs for Medical Image Translation

Introduction

This repository provides the code for the MICCAI-2021 paper titled "Uncertainty-guided Progressive GANs for Medical Image Translation". We take inspiration from the progressive learning scheme demonstrated at MedGAN and Progressive GANs, and augment the learning with the estimation of intermediate uncertainty maps (as presented here and here), that are used as attention map to focus the image translation in poorly generated (highly uncertain) regions, progressively improving the images over multiple phases.

The structure of the repository is as follows:

root
 |-ckpt/ (will save all the checkpoints)
 |-data/ (save your data and related script)
 |-src/ (contains all the source code)
    |-ds.py 
    |-networks.py
    |-utils.py
    |-losses.py

Getting started

Requirements

python >= 3.6.10
pytorch >= 1.6.0
jupyter lab
torchio
scikit-image
scikit-learn

Preparing Datasets

The experiments of the paper used T1 MRI scans from the IXI dataset and a proprietary PET/CT dataset.

data/IXI/ has jupyter notebooks and scripts to prepare the data for motion correction (data/IXI/prepare_motion_correction_data.py and data/IXI/viz_motion_correction_data.ipynb) as well as undersampled MRI reconstruction (data/IXI/viz_kspace_undersample_data.ipynb). For custom datasets, use the above notebooks as example to prepare the dataset and place them under data/. The dataset class in src/ds.py loads the paired set of images (corrupted and the non-corrupted version).

Learning models with uncertainty

src/networks.py provides the generator and discriminator architectures.

src/utils.py provides two training APIs train_i2i_UNet3headGAN and train_i2i_Cas_UNet3headGAN. The first API is to be used to train the primary GAN, whereas the second API is to be used to train the subsequent GANs.

An example command to use the first API is:

netG_A = CasUNet_3head(1,1)
netD_A = NLayerDiscriminator(1, n_layers=4)
netG_A, netD_A = train_i2i_UNet3headGAN(
    netG_A, netD_A,
    train_loader, test_loader,
    dtype=torch.cuda.FloatTensor,
    device='cuda',
    num_epochs=50,
    init_lr=1e-5,
    ckpt_path='../ckpt/i2i_0_UNet3headGAN',
)

This will save checkpoints in ../ckpt/ named as i2i_0_UNet3headGAN_eph*.pth

An example command to use the second API (here we assumed the primary GAN and first subsequent GAN are trained already):

# first load the prior Generators 
netG_A1 = CasUNet_3head(1,1)
netG_A1.load_state_dict(torch.load('../ckpt/i2i_0_UNet3headGAN_eph49_G_A.pth'))
netG_A2 = UNet_3head(4,1)
netG_A2.load_state_dict(torch.load('../ckpt/i2i_1_UNet3headGAN_eph49_G_A.pth'))

#initialize the current GAN
netG_A3 = UNet_3head(4,1)
netD_A = NLayerDiscriminator(1, n_layers=4)

#train the cascaded framework
list_netG_A, list_netD_A = train_uncorr2CT_Cas_UNet3headGAN(
    [netG_A1, netG_A2, netG_A3], [netD_A],
    train_loader, test_loader,
    dtype=torch.cuda.FloatTensor,
    device='cuda',
    num_epochs=50,
    init_lr=1e-5,
    ckpt_path='../ckpt/i2i_2_UNet3headGAN',
)

Bibtex

If you find the bits from this project helpful, please cite the following works:

@inproceedings{upadhyay2021uncerguidedi2i,
  title={Uncertainty Guided Progressive GANs for Medical Image Translation},
  author={Upadhyay, Uddeshya and Chen, Yanbei and Hebb, Tobias and Gatidis, Sergios and Akata, Zeynep},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  year={2021},
  organization={Springer}
}

and

@article{upadhyay2021uncertainty,
  title={Uncertainty-aware Generalized Adaptive CycleGAN},
  author={Upadhyay, Uddeshya and Chen, Yanbei and Akata, Zeynep},
  journal={arXiv preprint arXiv:2102.11747},
  year={2021}
}