Table 2. A summary of the research on segmentation in the article.

Reference	Method	Optimisation strategy	Images (n)	Disease	Segmentation type	Performance
Mishra et al50	U-Net	Shortest path algorithm	1343	AMD	DursenRPD11 layers of retina	Drusen: average difference between automatic and manual segmentation 0.75±1.99 pixelsRPD: average difference between automatic and manual segmentation 0.41±1.97 pixels
Lu et al51	Deep learning	Binary map	29	Non-exudative AMD	Calcified drusen	DSC 68.27±11.09%
Ji et al52	Deep learning	Stochastic gradient descent	105	Non-exudative AMD	GA	Dataset 1: mean OR 86.94%, AAD 11.49%, CC 0.9857Dataset 2: mean OR 81.66%, AAD 8.30%, CC 0.9952
Elsawy et al53	Deep-GA-Net	3D loss-based attention layer	1284	AMD	GA	Accuracy 93%
Fernández54	GVF Snake algorithm	Multiscale edge detection scheme	7	Wet AMD	SRFIRFPED	Similar to clinical experts
Rashno et al55	GCKGC	Transform OCT scans to neutrosophic domain and cost functions	796	AMD	SRFIRFPED	GC: dice coefficient 76.10%, sensitivity 80.54%, precision 90.34%KGC: dice coefficient 70.97%, sensitivity 86.40%, precision 77.17%
Moraes et al56	Deep learning	NA	2966	Wet AMD	Neurosensory RetinaRPEIRFSRFSHRMHyper-reflective fociDrusenFibrovascular PEDSerous PED	SRF: accuracy 90.3%IRF: accuracy 72.7%
Xie et al57	U-NetDDP	Smoothness constraints and loss functions	384	AMDNormal	Inner limiting membraneInner retinal pigment epithelium-drusen complexThe outer aspect of the Bruch membrane	Mean absolute surface distance±standard deviation (µm): 1.88±1.96
Pawloff et al58	Deep learning	End to end	41 147	Wet AMD	IRFSRF	HAWK: AUC of 85% for IRF and 87% for SRF in the central millimetreHARRIER: AUC of 93% for IRF and 87% for SRF in the central millimetre
Prabha et al59	AR U-Net++	Attention blocks and residual blocks	2272	Wet AMD	ILMIPLRPEBMIRFSRFPED	Accuracy 99.67%Mean IoU 84%Dice coefficient 94%
Feng et al60	U-Net	ResNeSt block and pyramid pooling module	116	Wet AMD	CNV	AUC 94.76%Specificity 99.5%Sensitivity 72.71%

AADabsolute area differenceAMDage-related macular degenerationARattention residualAUCarea under curveBMBruch’s membraneCCcorrelation coefficientCNVchoroidal neovascularization3Dthree-dimensionalDDPdistribute data parallelDSCdice similarity coefficientGAgeographic atrophyGCgraph cut GVFgradient vector flowILMinternal limiting membraneIoUintersection over unionIPLinner plexiform layerIRFintraretinal fluidKGCkernel graph cutOCToptical coherence tomographyORoverlap ratioPEDpigment epithelial detachmentRPDreticular pseudodrusenRPEretinal pigment epitheliumSHRMSubretinal hyperreflective materialSRFsubretinal fluid