Chapter 7 - Generative adversarial networks for medical image synthesis

Abstract

This chapter reviews recent developments of generative adversarial network (GAN)-based methods for medical and biomedical image synthesis tasks. These methods are classified into GAN, conditional GAN (cGAN), and cycle-consistent GAN (Cycle-GAN) according to the network architecture designs. For each category, a literature survey is given, which covers discussions of the network architecture designs, loss functions used to supervise the network, and challenges of training the network. We then introduce some practical aspects of these GAN-based methods, such as network setting for different tasks' aim, image pre-processing to enhance the input image quality, and data augmentation to enlarge the training data variation. We also briefly introduce some specific applications of cGAN and Cycle-GAN. Finally, a conclusion with highlighted important contributions and discussion of some identified specific challenges for GAN-based methods are given.