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 po

SUBMITTER: Sanchez-Rodriguez G 

PROVIDER: S-EPMC11801391 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature