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Proceedings Paper

Real-time retinal layer segmentation of OCT images: from graph cut to deep learning (Conference Presentation)
Author(s): Svetlana Borkovkina; Worawee Janpongsri; Acner Camino; Marinko Sarunic; Yifan Jian

Paper Abstract

Segmentation of the retinal layers in OCT images is the critical step in analyzing OCT volumetric data for diagnosis and monitoring of retinal disease progression. Real-time retinal layer segmentation has become increasingly desirable with the increasing OCT acquisition speed. In this work we explored methods to accelerate image processing method to segment retinal layers in OCT B-scan images including graph cut and deep learning. We demonstrated ~30-ms and ~3-ms segmentation of 7 retina layers per OCT B-scan with graph cut and deep learning respectively. The accelerated OCT B-scan segmentation was then integrated with our GPU OCT image acquisition software.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11228, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIV, 1122822 (9 March 2020); doi: 10.1117/12.2546490
Show Author Affiliations
Svetlana Borkovkina, Simon Fraser Univ. (Canada)
Worawee Janpongsri, Simon Fraser Univ. (Canada)
Acner Camino, Casey Eye Institute (United States)
Marinko Sarunic, Simon Fraser Univ. (Canada)
Yifan Jian, Casey Eye Institute (United States)


Published in SPIE Proceedings Vol. 11228:
Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIV
Joseph A. Izatt; James G. Fujimoto, Editor(s)

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