Purpose To use a deep learning model to develop a fully automated method (fully semantic network and graph search [FS-GS]) of retinal segmentation for optical coherence tomography (OCT) images from patients with Stargardt disease. Methods Eighty-seven manually segmented (ground truth) OCT volume scan sets (5171 B-scans) from 22 patients with Stargardt disease were used for training, validation and testing of a novel retinal boundary detection approach (FS-GS) that combines a fully semantic deep learning segmentation method, which generates a per-pixel class prediction map with a graph-search method to extract retinal boundary positions. The performance was evaluated using the mean absolute boundary error and the differences in two clinical metrics (retinal thickness and volume) compared with the ground truth. The performance of a separate deep learning method and two publicly available software algorithms were also evaluated against the ground truth. Results FS-GS showed an excellent agreement with the ground truth, with a boundary mean absolute error of 0.23 and 1.12 pixels for the internal limiting membrane and the base of retinal pigment epithelium or Bruch's membrane, respectively. The mean difference in thickness and volume across the central 6 mm zone were 2.10 µm and 0.059 mm 3 . The performance of the proposed method was more accurate and consistent than the publicly available OCTExplorer and AURA tools. Conclusions The FS-GS method delivers good performance in segmentation of OCT images of pathologic retina in Stargardt disease. Translational Relevance Deep learning models can provide a robust method for retinal segmentation and support a high-throughput analysis pipeline for measuring retinal thickness and volume in Stargardt disease.

Background: Stargardt disease (STGD1) is an autosomal recessive retinal dystrophy due to mutations in ABCA4, characterized by subretinal deposition of lipofuscin-like substances and bilateral centrifugal vision loss. Despite the tremendous progress made in the understanding of STGD1, there are no approved treatments to date. This review examines the challenges in the development of an effective STGD1 therapy. Materials and Methods: A literature review was performed through to June 2021 summarizing the spectrum of retinal phenotypes in STGD1, the molecular biology of ABCA4 protein, the in vivo and in vitro models used to investigate the mechanisms of ABCA4 mutations and current clinical trials. Results: STGD1 phenotypic variability remains an challenge for clinical trial design and patient selection. Pre-clinical development of therapeutic options has been limited by the lack of animal models reflecting the diverse phenotypic spectrum of STDG1. Patient-derived cell lines have facilitated the characterization of splice mutations but the clinical presentation is not always predicted by the effect of specific mutations on retinoid metabolism in cellular models. Current therapies primarily aim to delay vision loss whilst strategies to restore vision are less well developed. Conclusions: STGD1 therapy development can be accelerated by a deeper understanding of genotypephenotype correlations.