July 23, 2025Open Access

Deep Learning Techniques for Retinal Layer Segmentation to Aid Ocular Disease Diagnosis: A Review

OQOliver Jonathan Quintana-QuintanaAutonomous University of Queretaro MAMarco Antonio Aceves-FernándezAutonomous University of Queretaro JPJesús Carlos Pedraza‐OrtegaAutonomous University of Queretaro

Key Points

Deep learning techniques are automating retinal layer segmentation in OCT scans for quicker diagnoses.
U-Net models frequently achieve high Dice scores over 0.90 for layers like the retinal pigment epithelium.
Evaluation of multiple architectures highlights varying strengths and weaknesses in segmentation performance.
Identified challenges include dataset inconsistencies and the need for better integration into clinical practice.

Abstract

Age-related ocular conditions like macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are leading causes of irreversible vision loss globally. Optical coherence tomography (OCT) provides essential non-invasive visualization of retinal structures for early diagnosis, but manual analysis of these images is labor-intensive and prone to variability. Deep learning (DL) techniques have emerged as powerful tools for automating the segmentation of the retinal layer in OCT scans, potentially improving diagnostic efficiency and consistency. This review systematically evaluates the state of the art in DL-based retinal layer segmentation using the PRISMA methodology. We analyze various architectures (including CNNs, U-Net variants, GANs, and transformers), examine the characteristics and availability of datasets, discuss common preprocessing and data augmentation strategies, identify frequently targeted retinal layers, and compare performance evaluation metrics across studies. Our synthesis highlights significant progress, particularly with U-Net-based models, which often achieve Dice scores exceeding 0.90 for well-defined layers, such as the retinal pigment epithelium (RPE). However, it also identifies ongoing challenges, including dataset heterogeneity, inconsistent evaluation protocols, difficulties in segmenting specific layers (e.g., OPL, RNFL), and the need for improved clinical integration. This review provides a comprehensive overview of current strengths, limitations, and future directions to guide research towards more robust and clinically applicable automated segmentation tools for enhanced ocular disease diagnosis.

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