Journal
BIOMEDICAL OPTICS EXPRESS
Volume 8, Issue 5, Pages 2732-2744Publisher
Optica Publishing Group
DOI: 10.1364/BOE.8.002732
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Funding
- Duke/Duke-NUS pilot collaborative grant
- National Natural Science Foundation of China (NSFC) [61325007, 61501180]
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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. (C) 2017 Optical Society of America
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