Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography

Abstract

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.

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

“…Figure 1 illustrates a representative retinal OCT image of a patient with non-exudative AMD, with layers labeled. Note that following the terminology of our previous work [52], we refer to the area between the apex of the drusen and RPE layer to Bruch's membrane as retinal pigment epithelium and drusen complex (RPEDC) [53]. The difficulty in segmenting OCT images of non-exudative AMD as compared to normal eyes is the abnormal deformation (and ultimately atrophy) of the retinal layers, especially in the RPE layer in the form of drusen (highlighted by pink rectangles in Fig.…”

Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images

“…Segmented layer thicknesses are compared to the corresponding thickness measurements from normative databases to help identify retinal diseases [19][20][21][22]. Aside from measuring retinal layer thicknesses, a few papers have focused on segmenting the fluid regions seen in retinal OCT images such as edema or cystic structures, which are often observed in advanced stages of diseases such as diabetic macular edema (DME) and wet (exudative) age-related macular degeneration [23,24].…”

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

“…The automatic segmentation of the OCTbased retina image into these intra-retinal layers has been solved by the Minimum-cost closed set algorithm [6] and Dynamic Programming algorithm [7] and Directional Graph Search algorithm [8]. A few studies tried to investigate the binary classification of age-related macular degeneration (AMD) [9] or drusen [10] based on the OCT images.…”

“…Farsiu et al assessed the thickness of human retinal layers to discriminate AMD patients from the normal controls [9]. The distributions of grayscale features in the digital fundus images may also be integrated with the entropies and higher order spectra to detect the AMD samples [18].…”