Memory-efficient GAN

PyTorch implementation of Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images. There are a few differences to the original paper. For exmple we use open source brain MRI images here. Also the reception field on every lower-resolution scale is made larger than the reception field of every high-resolution scale, which perfoms better.

Real LPBA	Real T2	Fake T2

Prerequisites

• Linux or MacOS
• Python 3
• PyTorch>0.4
• NVIDIA GPU + CUDA CuDNN
• MATLAB (Optional, but you will need to write a new edge extraction function)

Getting started

Instalation

• Clone this repo
• Install the requirements

Datasets

Here we establish image translation between the LPBA40 dataset and BRATS challenge image data. You need to download both datasets before you start.

• Training: We use the BRATS T2 HGG and LGG data for training and train to translate image edges to gray value images.
• Testing: For testing we extract the edges from all 40 LPBA images. We use the native-space skull-stripped LPBA images and transform them to delineation space.

Folder structure

This code works with following directory structure. You can choose another structure, however, think about replacing the default options of the root directories.

.
├── Data
│   ├── SKETCH2BRATST23D
│   │   ├── A
│   │   │   ├── test
│   │   │   │   ├── data_list.txt
│   │   │   └── train
│   │   │       ├── data_list.txt
│   │   │       └── data_list_valid.txt
│   │   └── B
│   │       ├── test
│   │       │   ├── data_list.txt
│   │       └── train
│   │           ├── data_list.txt
│   │           └── data_list_valid.txt
│   ├── BRATST2_3D #preprocessed BRATS and sketches
│   └── LPBA3D #preprocessed LPBA and sketches
│
├── Results
│   ├── SKETCH2BRATST23D 
│   │   └── LPBATest #test predicted images
│   ├── SKETCH2BRATST23D_HR1 #high resolution scale 1, chekpoints
│   │   └── Val #validation images
│   ├── SKETCH2BRATST23D_HR2
│   │   └── Val
│   ├── SKETCH2BRATST23D_HR3
│   │   └── Val
│   └── SKETCH2BRATST23D_LR #low resolution scale 0, chekpoints
│       └── Val
│       
└── Code
    └── MEGAN
         ├── README.md
         ├── requirements.txt
         ├── ...

Usage

Preprocessing

If you wish to use the same datasets as ours, you can use our preprocessing scripts.

$ python MEGAN/preprpcess_BRATS3D.py --in_img_dir path/to/BRATS/images
$ python MEGAN/preprpcess_LPBA3D.py --in_img_dir path/to/LPBA/images

Training

To train each level:

$ python MEGAN/train_BRATS3D_LR.py 
$ python MEGAN/train_BRATS3D_HR.py --LR_size 32 --HR_size 64 --level_name HR1
$ python MEGAN/train_BRATS3D_HR.py --LR_size 64 --HR_size 128 --level_name HR2
$ python MEGAN/train_BRATS3D_HR.py --LR_size 128 --HR_size 256 --level_name HR3 

Don't forget to set batch sizes using the argument batch_size.

Testing

To get high resolution images when the GANs are trained simply run:

$ python MEGAN/test_BRATS3D.py

Citation

This work has been accepted to the MICCAI 2019. If you use this code, please cite as follows:

@inproceedings{MEGAN,
	title = {Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images},
	booktitle = {International Conference on  Medical Image Computing and Computer Assisted Intervention, MICCAI 2019},
	year = {In Press},
	address = {Shenzhen, China},
	author = {Uzunova, Hristina and Ehrhardt, Jan and Jacob, Fabian and Frydrychowicz, Alex and Handels, Heinz}
}