Low-Cost and Fast Epiretinal Membrane Detection and Quantification based on SD-OCT

Abstract

Epiretinal membrane (ERM) is a pathological condition characterized by the formation of a non-vascularized fibrocellular membrane on the inner retinal surface, leading to retinal traction, macular edema, and visual impairment. Optical coherence tomography (OCT) is the primary imaging modality for diagnosing macular ERM, enabling high-resolution visualization of retinal morphology and thickness. Although the transition from Time-Domain (TD-OCT) to Spectral-Domain OCT (SD-OCT) has significantly improved clinical imaging, the latest advancement - Swept-Source OCT (SS-OCT) - remains limited in clinical adoption due to its high cost, complexity, and processing demands. Consequently, many clinics are unable to leverage the intuitive en face visualization and quantitative analysis of ERM provided by SS-OCT. In this study, we propose a novel and cost-effective pipeline for ERM detection and quantification using only SD-OCT B-scans. Our method introduces a new en face projection technique, termed Epiretinal Projection Image (EPI), which enables intuitive visualization of ERM spatial distribution. By leveraging a YOLOv11x-based deep learning model, we achieve high-precision ERM detection on B-scan images (mAP@50: 0.882, mAP@50:95: 0.556) and accurately project detected regions onto the EPI map for objective area quantification. Furthermore, we propose an association scoring mechanism that correlates EPI projections with retinal thickness maps, revealing a strong spatial relationship (correlation score: 0.771) between ERM presence and retinal thickening - a type of analysis not feasible even with SS-OCT. Our results demonstrate that the proposed system provides not only accurate and explainable ERM localization but also enables robust quantitative assessment using widely available SD-OCT devices. This makes it a highly accessible and scalable alternative for ERM diagnosis and monitoring in routine clinical practice. © 2025 Elsevier B.V., All rights reserved.