Retinal image analysis software for neurodegenerative disease research

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Our goal is to develop and validate a device-independent software application for analysis of optical coherence tomography (OCT) images of the human retina. Our system will make quantitative measurements of retinal layer thicknesses at the macula in support of the generation of biomarkers for measuring onset and progression of ocular and neurodegenerative diseases, including age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, multiple sclerosis (MS), Alzheimer’s disease, Parkinson’s disease, and amytrophic lateral sclerosis (ALS). Retinal layer thicknesses indicate atrophy through thinning and increased fluid or inflammation through thickening. Accurate, device-independent segmentation of retinal layers together with longitudinal analysis of the layer thicknesses can return a number of quantitative biomarkers to correlate with disease onset and progression, and facilitate direct comparison across OCT devices to results from normal to estimate the degree of abnormalities. Specifically, we are aiming to: Aim 1 – Improve our segmentation of diseased eyes Orion (www.voxeleron.com/orion), our current research platform has been validated and used extensively on normal, non-pathologic eyes. Our structural analyses of retinal layers may be complicated, however, by ocular diseases or opacities prevalent in an aging population. Current software, ours included, can perform poorly in the case of disease, a situation we aim to ameliorate by improving our current segmentation algorithm in these cases and validating its performance on a large, hand-segmented dataset including AMD, DR, and glaucoma cases taken from at least 4 different device manufacturers’ OCT cameras. Aim 2 – Add longitudinal analysis to our segmentation software Clinically, static analysis of data has limited utility. We will add longitudinal analysis to our existing segmentation capabilities to measure the change of thicknesses over time. This new clinical workstation will be rigorously tested by determining agreement with expert-generated ground-truth.