Table 5.

Summary of literature review for domain transfer task using GAN in ophthalmology imaging domains

Publication	Basic technique	Domain	Summary
Costa et al. [58]	Conditional GAN	Vessel image → Fundus photography	The study proposed a vessel network to retinal image translation framework producing simplified vessel tree and realistic retinal images by estimating latent space. Autoencoder was used to synthesize new retinal vessel images apart from training of GAN
Zhao et al. [59]	Conditional GAN	Vessel image → Fundus photography	Retinal image synthesis can be effectively learned in a data-driven fashion from a relatively small sample size using a conditional GAN architecture
Yu et al. [60]	Pix2pix (with ResU-net generator) (conditional GAN)	Vessel image → Fundus photography	To enlarge training datasets for facilitating medical image analysis, the multiple-channels-multiple-landmarks (MCML) was developed to synthesize color fundus images from a combination of vessel and optic disc masked images
Wu et al. [61]	Conditional GAN	Volumetric retinal OCT → Fundus autofluorescence	The en-face OCT images were synthesized from volumetric retinal OCT by restricted summed voxel projection. The fundus autofluorescence images were generated from en-face OCT images using GAN to identify the geographic atrophy region
Tavakkoli et al. [62]	Conditional GAN	Fundus photography → Fluorescein angiography	The proposed GAN produced anatomically accurate fluorescein angiography images that were indistinguishable from real angiograms
Yoo et al. [63]	CycleGAN	Ultra-widefield fundus photography → Fundus photography	Ultra-widefield images were successfully translated into traditional fundus photography-style images by CycleGAN, and the main structural information of the retina and optic nerve was retained
Ju et al. [64]	CycleGAN	Fundus photography → Ultra-widefield fundus photography	The CycleGAN model transferred the color fundus photographs to ultra-widefield images to introduce additional data for existing limited ultra-widefield images. The proposed method was adopted for diabetic retinopathy grading and lesion detection
Lazaridis et al. [91, 108]	Wasserstein GAN + perceptual loss (conditional GAN)	Time-domain OCT → spectral-domain OCT	Time-domain OCT was converted to synthetic spectral-domain OCT using GAN. The model improved the statistical power of the measurements when compared with those derived from the original OCT

GAN = generative adversarial network; OCT = optical coherence tomography