Table 5 Summary of literature review for domain transfer task using GAN in ophthalmology imaging domains

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