This repository contains the code for the GAN architectures in Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks

Aim: Generating realistic looking image-label patches of TOF-MRA images

Input:

• Generator - Noise vector of size 100
• Discriminator - Output of the generator (fake image-label pair) or PEGASUS patches (real image-label pair).

Output:

• Generator - Generated image-label pair
• Discriminator - Score if image-label pair looks realistic or generated

Architectures:

• Deep Convolution Generative Adversarial Network (DCGAN)
• Wasserstein-GAN with gradient penalty (WGAN-GP)
• WGAN-GP with spectral normalization (WGAN-GP-SN)

The following files are included in the DCGAN and WGAN_GP folders:

• config.py: here you can change the configuration parameters for train.py and test.py
• utils.py: has some utility functions used in train.py and test.py
• model.py: module with the model definition
• train.py: training of generator and discriminator
• test.py: get test images in compressed .npz form

In the figure below, real and synthesized image patches with corresponding labels are shown. (A) to (C) show image-label pairs generated by DCGAN (A), WGAN-GP (B) and WGAN-GP-SN (C) respectively. (D) show real patches and corresponding labels. The synthesized patches resemble real vessel patches and the labels fit well to the patches, especially those generated by WGAN-GP-SN (C).