From: Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey
Publication | Basic technique | Domain | Target | Summary |
---|---|---|---|---|
Iqbal et al. [27] | Conditional GAN | Fundus photography | Retinal vessels | The framework achieved retinal vessels image segmentation from a small training set |
Wang et al. [33] | Conditional GAN (using PatchGAN) | Fundus photography | Optic disc and optic cup | Unsupervised domain adaptation for joint optic disc and cup segmentation using a patch-based discriminator |
Son et al. [28] | Conditional GAN (using U-Net as a generator) | Fundus photography | Retinal vessels | The GAN model segmented retinal vasculature and the optic disc using a dataset, which consisted of fundoscopic images and manual segmentation of the vessels |
Rammy et al. [29] | Conditional GAN | Fundus photography | Retinal vessels | Patch-based GAN with additional loss function to learn thin and thick vessel segmented retinal vessels with the enhanced performance |
Park et al. [30] | Conditional GAN | Fundus photography | Retinal vessels | The proposed GAN model with a multi-kernel pooling and false negative loss could segment retinal blood vessels more robustly |
Heisler et al. [36] | Pix2Pix (conditional GAN) | Peripapillary retinal OCT | Nerve fiber layer, Bruch’s membrane, choroid-sclera boundary | The use of a generative adversarial network and unlabeled data can improve the performance of segmentation for the 3D morphometric analysis of glaucomatous optic nerve head volumes |
Yang et al. [31] | Conditional GAN (topological structure-constrained) | Fundus photography | Retinal vessels | The topological structure-constrained proposed GAN model identified retinal arteries and veins via segmentation from the complex background of retinal images |
Yang et al. [106] | Conditional GAN | Fundus photography | Retinal vessels | To separate blood vessels from fundus image, the model could detect more tiny vessels and locate the edge of blood vessels more accurately |
Zhao et al. [32] | Conditional GAN (with a large receptive field) | Fundus photography | Retinal vessels | The proposed retinal vessel segmentation algorithm using GAN with a large receptive field could capture large-scale high-level semantic vessel features |
Kadambi et al. [34] | Wasserstein GAN | Fundus photography | Optic disc and optic cup | WGAN-based domain adaptation showed a better performance than baseline models for the joint optic disc-and-cup segmentation in fundus images |
Bian et al. [35] | Conditional GAN (using U-Net as a generator) | Fundus photography | Optic disc and optic cup | The proposed method successfully performed optic disc and cup segmentation. The cup-to-disc ratio was automatically calculated with a good performance |
Khan et al. [38] | Conditional GAN | Meibography infrared images | Meibomian gland | The proposed GAN automatically identified the area of the meibomian glands and outperformed the state-of-art methods |
Yildiz et al. [37] | Conditional GAN | In vivo corneal confocal microscopy images | Sub-basal nerves | Automatic segmentation of sub-basal nerves in in vivo confocal microscopy images was performed using the U-Net and GAN-based techniques as a diagnostic tool for corneal diseases |
Zhou et al. [107] | Conditional GAN (using U-Net as a baseline architecture) | Fundus photography | Retinal vessels | The GAN model strengthened retinal vessel segmentation in the low-contrast background using a symmetric equilibrium GAN (U-Net-based), multi-scale features refine blocks and attention mechanism |