trane293 / mm-gan

MM-GAN: Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network

This repository contains source code for MM-GAN: Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network. MM-GAN is a novel GAN-based approach that allows synthesizing missing pulse sequences (modalities) for an MRI scan. For more details, please refer to the paper.

To cite the paper (IEEE TMI):

@ARTICLE{Sharma20,
  author={A. {Sharma} and G. {Hamarneh}},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network}, 
  year={2020},
  volume={39},
  number={4},
  pages={1170-1183},}

To cite this repository:

@misc{Sharma20Code,
  author = {A. {Sharma}},
  title = {MM-GAN: Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/trane293/mm-gan}}
}

How to Run

In order to run the code, we recommend that you use Anaconda distribution. We provivde an environment.yml file that can be used to recreate the exact same Python environment that can be used to run this code.

Once Anaconda is installed, simply do:

conda env create -f environment.yml

Download BRATS2018 dataset from the official website https://www.med.upenn.edu/sbia/brats2018/data.html and extract both the Training and Validation zip files to a folder, say:

/local-scratch/data/BRATS2018/Training/
    /local-scratch/data/BRATS2018/Training/HGG
    /local-scratch/data/BRATS2018/Training/LGG

Validation patients do not have HGG/LGG labels.

/local-scratch/data/BRATS2018/Validation/

Open modules/configfile.py and change the following paths to match your folder structure:

config['data_dir_prefix'] = os.path.join(mount_path_prefix, 'BRATS2018_Full/') # this should be top level path
config['hdf5_filepath_prefix'] = os.path.join(mount_path_prefix, 'BRATS2018/HDF5_Datasets/BRATS2018_Unprocessed.h5') # top level path
config['hdf5_filepath_prefix_2017'] = os.path.join(mount_path_prefix, 'scratch/asa224/Datasets/BRATS2017/HDF5_Datasets/BRATS.h5') # top level path
config['hdf5_combined'] = os.path.join(os.sep.join(config['hdf5_filepath_prefix'].split(os.sep)[0:-1]), 'BRATS_Combined_Unprocessed.h5')
config['hdf5_filepath_cropped'] = os.path.join(mount_path_prefix, 'BRATS2018/HDF5_Datasets/BRATS2018_Cropped_Normalized_Unprocessed.h5') # top level path

Once done, switch to the conda virtualenv, and run:

python modules/create_hdf5_file.py

Once it's finished, run:

python preprocess.py

You should have two HDF5 datasets now, one of which is raw BRATS2018 dataset, and one that is cropped to coordinates.

In order to train MM-GAN on HGG data do:

./mmgan_hgg.sh

Similarly for training on LGG data:

./mmgan_lgg.sh

And it should start the network training process.

Directory Structure

Folder Name	Purpose
modules	contains modules and helper functions, along with network architectures
modules/advanced_gans	contains the main Pix2Pix implementation
notebooks	contains rough notebooks for quick prototyping
prep_BRATS2015	contains code to prepare BRATS2015 data for training/evaluation
prep_ISLES2015	contains code to prepare ISLES2015 data for training/validation