Deep Learning-Based Automated Segmentation and Quantification of the Ellipsoid Zone and the RPE-Bruch's Membrane Complex in Healthy Subjects and in Geographic Atrophy.

Purpose: This study aimed to validate a deep learning algorithm for automated segmentation and quantitative assessment of the ellipsoid zone (EZ) and the retinal pigment epithelium (RPE)-Bruch's membrane (BM) complex in healthy eyes and geographic atrophy (GA) eyes.

Methods

In this retrospective study, spectral-domain optical coherence tomography (SD-OCT) volume scans from 30 healthy eyes and 30 eyes with GA were analyzed. An NMI-Outer Retina Analyzer was used to segment the inner EZ, inner RPE, and outer BM. Average thicknesses of the 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 compared using intraclass correlation coefficients (ICCs). Dice coefficients (DCs), Pearson correlation coefficients, and absolute thickness differences were used to assess agreement between automated and manual segmentation. Heat maps were generated to visualize thickness.

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, respectively, in the healthy eyes and 15.65 ± 11.09 µm, 39.18 ± 23.28 µm, and 23.52 ± 16.21 µm, respectively, in GA eyes. The model demonstrated high segmentation accuracy, with a 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), all within 1.88-3.05% of the gold standard (manual corrections). In contrast, 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 in the junctional zone surrounding the 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 that of expert graders.