A column-based deep learning method for the detection and quantification of atrophy associated with AMD in OCT scans.

The objective quantification of retinal atrophy associated with age-related macular degeneration (AMD) is required for clinical diagnosis, follow-up, treatment efficacy evaluation, and clinical research. Spectral Domain Optical Coherence Tomography (OCT) has become an essential imaging technology to evaluate the macula. This paper describes a novel automatic method for the identification and quantification of atrophy associated with AMD in OCT scans and its visualization in the corresponding infrared imaging (IR) image. The method is based on the classification of light scattering patterns in vertical pixel-wide columns (A-scans) in OCT slices (B-scans) in which atrophy appears with a custom column-based convolutional neural network (CNN). The network classifies individual columns with 3D column patches formed by adjacent neighboring columns from the volumetric OCT scan. Subsequent atrophy columns form atrophy segments which are then projected onto the IR image and are used to identify and segment atrophy lesions in the IR image and to measure their areas and distances from the fovea. Experimental results on 106 clinical OCT scans (5,207 slices) in which cRORA atrophy (the end point of advanced dry AMD) was identified in 2,952 atrophy segments and 1,046 atrophy lesions yield a mean F

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Declaration of Competing Interest None of the authors has any conflict of interest. The authors have no personal financial or institutional interest in any of the materials, software or devices described in this article.