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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Fang, L; Cunefare, D; Wang, C; Guymer, RH; Li, S; Farsiu, S

Published Date

  • May 2017

Published In

Volume / Issue

  • 8 / 5

Start / End Page

  • 2732 - 2744

PubMed ID

  • 28663902

Pubmed Central ID

  • 28663902

Electronic International Standard Serial Number (EISSN)

  • 2156-7085

International Standard Serial Number (ISSN)

  • 2156-7085

Digital Object Identifier (DOI)

  • 10.1364/BOE.8.002732

Language

  • eng