Official Pytorch Implementation of "Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks" (accepted by MICCAI 2019)

This repository provides a PyTorch implementation of 3D brain Generation. It can successfully generates plausible 3-dimensional brain MRI with Generative Adversarial Networks. Trained models are also provided in this page.

Paper

"Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks"

The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention(MICCAI 2019) : (https://arxiv.org/abs/1908.02498)

Cite

@inproceedings{kwon2019generation,
  title={Generation of 3D brain MRI using auto-encoding generative adversarial networks},
  author={Kwon, Gihyun and Han, Chihye and Kim, Dae-shik},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={118--126},
  year={2019},
  organization={Springer}
}

Dependencies

• Python 3.5+
• PyTorch 0.4.0+
• Jupyter Notebook
• Nilearn
• Nibabel

We highly recommend you to use Jupyter Notebook for the better visualization!

Dataset

You can download the Normal MRI data in Alzheimer's Disease Neuroimaging Initiative(ADNI) , Tumor MRI data in BRATS2018 and Stroke MRI data in Anatomical Tracings of Lesions After Stroke (ATLAS).

We converted all the DICOM(.dcm) files of ADNI into Nifti(.nii) file format using SPM12 I/O tools.

ADNI : Download Post-processed(processed with 'recon-all' command of Freesurfer) Structural images labeled as 'Control Normal'.

BRATS : Download dataset from BRATS2018 website.

ATLAS : Download dataset from ATLAS website. Obtain probability maps(masks) from the original .nii images with SPM12 'Segmentation' function. Extract Brain areas with multiplying masks(c1,c2,c3 / GM,WM,CSF) with original images.

Training Details

For each training, run 12,000 iterations (100 epochs in VAE-GAN)

Each run takes ~12 hour with one NVIDIA TITAN X GPU.

Run the Jupyter Notebook code for training (~train.ipynb)

Test Details

You can download our Pre-trained models in our Google Drive

Download the models and save them in the directory './checkpoint' Then you can run the test code ('Test.ipynb')

Quantitative calculation (MS-SSIM / MMD score) & Image sampling is availble in the code.

For the PCA visualization, please follow the PCA tutorial that Nilearn provides.

Model Details

You can get the detailed settings of used models in our model codes

(Model_alphaGAN.py , Model_alphaWGAN.py , Model_VAEGAN.py, Model_WGAN.py)

Details for Dataset

If you have any question about data, feel free to e-mail me!

cyclomon@kaist.ac.kr