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

Publication ,  Journal Article
Fang, L; Cunefare, D; Wang, C; Guymer, RH; Li, S; Farsiu, S
Published in: Biomedical optics express
May 2017

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.

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Published In

Biomedical optics express

DOI

EISSN

2156-7085

ISSN

2156-7085

Publication Date

May 2017

Volume

8

Issue

5

Start / End Page

2732 / 2744

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4003 Biomedical engineering
  • 3212 Ophthalmology and optometry
  • 0912 Materials Engineering
  • 0205 Optical Physics
 

Citation

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Fang, L., Cunefare, D., Wang, C., Guymer, R. H., Li, S., & Farsiu, S. (2017). Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomedical Optics Express, 8(5), 2732–2744. https://doi.org/10.1364/boe.8.002732
Fang, Leyuan, David Cunefare, Chong Wang, Robyn H. Guymer, Shutao Li, and Sina Farsiu. “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.Biomedical Optics Express 8, no. 5 (May 2017): 2732–44. https://doi.org/10.1364/boe.8.002732.
Fang L, Cunefare D, Wang C, Guymer RH, Li S, Farsiu S. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomedical optics express. 2017 May;8(5):2732–44.
Fang, Leyuan, et al. “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.Biomedical Optics Express, vol. 8, no. 5, May 2017, pp. 2732–44. Epmc, doi:10.1364/boe.8.002732.
Fang L, Cunefare D, Wang C, Guymer RH, Li S, Farsiu S. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomedical optics express. 2017 May;8(5):2732–2744.
Journal cover image

Published In

Biomedical optics express

DOI

EISSN

2156-7085

ISSN

2156-7085

Publication Date

May 2017

Volume

8

Issue

5

Start / End Page

2732 / 2744

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4003 Biomedical engineering
  • 3212 Ophthalmology and optometry
  • 0912 Materials Engineering
  • 0205 Optical Physics