Generative Adversarial Networks for Brain Images Synthesis: A Review
Firoozeh Shomal Zadeh Sevda Molani Maysam Orouskhani Marziyeh Rezaei Mehrzad Shafiei Hossein Abbasi
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.