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Published on: November 30, 2022
Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep
Leyuan Fang1,2, David Cunefare1, Chong Wang2
1Departments of Biomedical Engineering Duke University, Durham, NC 27708, USA.
Abstract:
We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.
