Open AccessAutomatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search
Author(s) -
Leyuan Fang,
David Cunefare,
Chong Wang,
Robyn H. Guymer,
Shutao Li,
Sina Farsiu
Publication year - 2017
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.8.002732
Subject(s) - optical coherence tomography , computer science , convolutional neural network , artificial intelligence , segmentation , retinal , pattern recognition (psychology) , classifier (uml) , graph , deep learning , image segmentation , computer vision , ophthalmology , medicine , theoretical computer science
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.