Deep Learning–Based Automated Segmentation and Quantification of Ellipsoid Zone and RPE–Bruch’s Membrane Complex in Healthy Subjects and Geographic Atrophy

Purpose: To validate a deep learning algorithm for automated segmentation and quantitative assessment of the ellipsoid zone (EZ) and RPE–Bruch’s membrane (BM) complex in healthy and geographic atrophy (GA) eyes. Methods: In this retrospective study, SD-OCT volume scans from 30 healthy and 30 eyes with GA were analysed. NMI-Outer Retina Analyzer was used to segment the inner EZ, inner RPE, and outer BM. Average thicknesses of EZ-RPE, EZ-BM, and RPE-BM were calculated from volumes and across nine ETDRS sectors. Manual segmentations were corrected by two masked expert graders and were compared using ICC. Dice coefficients (DC), Pearson correlation, and absolute thickness differences were used to assess agreement between automated and manual segmentation. Heat maps were generated to visualize thicknesses. Results: Thirty healthy eyes and thirty GA eyes were included in the analysis. Mean EZ–RPE, EZ–BM, and RPE–BM thicknesses were 47.55 ± 6.75 µm, 69.49 ± 6.92 µm, and 21.94 ± 3.46 µm, in the healthy eyes and 15.65 ± 11.09 µm, 39.18 ± 23.28 µm, and 23.52 ± 16.21 µm in GA eyes respectively. The model demonstrated high segmentation accuracy, with mean DC of 0.998 in healthy eyes and 0.995–0.998 in GA eyes. In healthy eyes, differences between automated and manual measurements were minimal (1.42 ± 3.39 μm (2.98%) for EZ–RPE, 1.31 ± 3.18 μm (1.88%) for EZ–BM, and 0.67 ± 1.71 μm (3.05%) for RPE–BM) which is within 1.88-3.05% from the gold standard (manual corrections), whereas GA eyes showed greater variability (mean differences of 3.61 ± 8.62 μm (23.06%) for EZ–RPE, 4.28 ± 11.34 μm (10.92%) for EZ–BM, and 4.4 ± 10.45 μm (18.71%) for RPE–BM). Heat maps revealed increased variability at the junctional zone surrounding atrophy. Automated and manual measurements showed strong correlations across all sectors in GA eyes (r = 0.97 for EZ–BM, 0.96 for EZ–RPE, and 0.89 for RPE–BM). Conclusions: The NMI-ORA enables accurate, automated segmentation and quantification of outer retinal layers, with performance comparable to expert graders.