RetOCTNet: Deep Learning-Based Segmentation of OCT Images Following Retinal Ganglion Cell Injury.

Abstract

Purpose: We present RetOCTNet, a deep learning tool to segment the retinal nerve fiber layer (RNFL) and total retinal thickness automatically from optical coherence tomography (OCT) scans in rats following retinal ganglion cell (RGC) injury.

Methods: We created unilateral RGC injury by ocular hypertension (OHT) or optic nerve crush (ONC), and contralateral eyes were not injured. We manually segmented the RNFL and total retina of 3.0-mm radial OCT scans (1000 A-scans per B-scan, 20 frames per B-scan) as ground truth (n = 192 scans). We used these segmentations for training (80%), testing (10%), and validation (10%) to optimize the F1 score. To determine the generalizability of RetOCTNet, we tested it on volumetric scans of a separate cohort at baseline and 4, 8, and 12 weeks post-ONC.

Results: RetOCTNet's segmentations achieved high F1 scores relative to the ground-truth segmentations created by human annotators: 0.88 (RNFL) and 0.98 (retinal thickness) for control eyes, 0.84 and 0.98 for OHT eyes, and 0.78 and 0.96 for ONC eyes, respectively. On volumetric scans 12 weeks post-ONC, RetOCTNet calculated thinning of 29.49% and 10.82% in the RNFL and retina at the optic nerve head (ONH) and thinning of 38.34% and 9.85% in the RNFL and retina superior to the ONH.

Conclusions: RetOCTNet can segment the RNFL and total retinal thickness of both radial and volume OCT scans. RetOCTNet can be applied to longitudinally monitor RNFL in rodent models of RGC injury.

Translational relevance: This tool automates RNFL and retinal thickness measurement for rat OCT scans following RGC injury, reducing analysis time and increasing the consistency between studies.