Generative Adversarial Networks for Brain Images Synthesis: A Review

TL;DR

This review discusses how generative adversarial networks (GANs) are used to synthesize different brain imaging modalities, improving medical diagnosis by generating missing images efficiently.

Contribution

It provides a comprehensive overview of recent GAN-based methods for cross-modality brain image synthesis, highlighting advancements and applications.

Findings

GANs effectively generate missing brain imaging modalities.

Recent methods improve synthesis quality and accuracy.

GAN-based synthesis reduces need for costly imaging procedures.

Abstract

In medical imaging, image synthesis is the estimation process of one image (sequence, modality) from another image (sequence, modality). Since images with different modalities provide diverse biomarkers and capture various features, multi-modality imaging is crucial in medicine. While multi-screening is expensive, costly, and time-consuming to report by radiologists, image synthesis methods are capable of artificially generating missing modalities. Deep learning models can automatically capture and extract the high dimensional features. Especially, generative adversarial network (GAN) as one of the most popular generative-based deep learning methods, uses convolutional networks as generators, and estimated images are discriminated as true or false based on a discriminator network. This review provides brain image synthesis via GANs. We summarized the recent developments of GANs for cross-modality brain image synthesis including CT to PET, CT to MRI, MRI to PET, and vice versa.