Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.

We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME.

Objective To define quantitative indicators for the presence of intermediate age-related macular degeneration (AMD) via spectral-domain optical coherence tomography (SD-OCT) imaging of older adults. Design Evaluation of diagnostic test and technology. Participants and Controls One eye from 115 elderly subjects without AMD and 269 subjects with intermediate AMD from the Age-Related Eye Disease Study 2 (AREDS2) Ancillary SD-OCT Study. Methods We semiautomatically delineated the retinal pigment epithelium (RPE) and RPE drusen complex (RPEDC, the axial distance from the apex of the drusen and RPE layer to Bruch's membrane) and total retina (TR, the axial distance between the inner limiting and Bruch's membranes) boundaries. We registered and averaged the thickness maps from control subjects to generate a map of “normal” non-AMD thickness. We considered RPEDC thicknesses larger or smaller than 3 standard deviations from the mean as abnormal, indicating drusen or geographic atrophy (GA), respectively. We measured TR volumes, RPEDC volumes, and abnormal RPEDC thickening and thinning volumes for each subject. By using different combinations of these 4 disease indicators, we designed 5 automated classifiers for the presence of AMD on the basis of the generalized linear model regression framework. We trained and evaluated the performance of these classifiers using the leave-one-out method. Main Outcome Measures The range and topographic distribution of the RPEDC and TR thicknesses in a 5-mm diameter cylinder centered at the fovea. Results The most efficient method for separating AMD and control eyes required all 4 disease indicators. The area under the curve (AUC) of the receiver operating characteristic (ROC) for this classifier was >0.99. Overall neurosensory retinal thickening in eyes with AMD versus control eyes in our study contrasts with previous smaller studies. Conclusions We identified and validated efficient biometrics to distinguish AMD from normal eyes by analyzing the topographic distribution of normal and abnormal RPEDC thicknesses across a large atlas of eyes. We created an online atlas to share the 38 400 SD-OCT images in this study, their corresponding segmentations, and quantitative measurements. Financial Disclosure(s) Proprietary or commercial disclosure may be found after the references.

The automatic algorithm accurately and reproducibly segmented three retinal boundaries in images containing drusen and GA. This automatic approach can reduce time and labor costs and yield objective measurements that potentially reveal quantitative RPE changes in longitudinal clinical AMD studies. (ClinicalTrials.gov number, NCT00734487.).

Purpose To determine the dynamic morphological development of the human fovea in-vivo utilizing portable spectral domain optical coherence tomography (SDOCT). Design Prospective, observational case series. Paticipants 31 prematurely born neonates, nine children and nine adults. Methods Sixty-two neonates were enrolled in this study. SDOCT imaging was performed after examination for retinopathy of prematurity (ROP) at the bedside in non-sedated infants ages 31-41 weeks post-menstrual-age PMA (PMA=gestational age in weeks + chronological age) and at outpatient follow-up ophthalmic examinations. Thirty-one neonates met eligibility criteria. Nine children and nine adults without ocular pathology served as control groups. Semi-automatic retinal layer segmentation was performed. Central foveal thickness (CFT), foveal to parafoveal (FP) ratio (CFT divided by thickness 1000 μm from the foveal center), and 3D thickness maps were analyzed. Main Outcomes Measures In-vivo determination of foveal morphology, layer segmentation, analysis of sub-cellular changes, spatio-temporal layer shifting. Results In contrast to the adult fovea, we observed several signs of immaturity in the neonates: a shallow foveal pit, persistence of inner retinal layers (IRL), and a thin photoreceptor layer (PRL) that was thinnest at the foveal center. Three-dimensional mapping showed displacement of retinal layers out of the foveal center as the fovea matured and the progressive formation of the inner/outer segment band in the opposite direction. The FP-IRL ratios decreased as IRL migrated prior to term and minimally after that, while FP-PRL ratios increased as PRL subcellular elements formed closer to term and into childhood. A surprising finding was the presence of cystoid macular edema in 58% of premature neonates which appeared to affect inner foveal maturation. Conclusions This study provides the first view into development of living cellular layers of the human retina and of subcellular specialization at the fovea in premature infant eyes using portable spectral domain optical coherence tomography. Our work establishes a framework of the timeline of human foveal development, allowing us to identify unexpected retinal abnormalities that may provide new keys to disease activity, and provide a method for mapping of foveal structures from infancy to adulthood that may be integral in future studies of vision and visual cortex development.