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Proceedings Paper

Automated choroidal neovascularization diagnosis and quantification using convolutional neural networks in OCT angiography (Conference Presentation)
Author(s): Jie Wang; Tristan Hormel; Liqin Gao; Pengxiao Zang; Yukun Guo; Steven T. Bailey; Yali Jia

Paper Abstract

Detecting and quantifying choroidal neovascularization (CNV) is essential for the diagnosis of neovascular age-related macular degeneration (AMD). Projection-resolved OCT angiography (PR-OCTA) has enabled both en face and volumetric visualization of CNV. However, previously described CNV detection methods only quantify CNV that was already diagnosed, and were unable to identify CNV form unknown inputs . Previous methods were also limited by artifacts. A fully automated CNV diagnosis and quantification algorithm using convolutional neural networks (CNNs) was developed. It was able to diagnose CNV and output CNV membrane and vessel area from retinal structural and angiographic images.

Paper Details

Date Published: 9 March 2020
Proc. SPIE 11218, Ophthalmic Technologies XXX, 1121809 (9 March 2020); doi: 10.1117/12.2544490
Show Author Affiliations
Jie Wang, Oregon Health & Science Univ. (United States)
Tristan Hormel, Oregon Health & Science Univ. (United States)
Liqin Gao, Oregon Health & Science Univ. (United States)
Pengxiao Zang, Oregon Health & Science Univ. (United States)
Yukun Guo, Oregon Health & Science Univ. (United States)
Steven T. Bailey, Oregon Health & Science Univ. (United States)
Yali Jia, Oregon Health & Science Univ. (United States)

Published in SPIE Proceedings Vol. 11218:
Ophthalmic Technologies XXX
Fabrice Manns; Arthur Ho; Per G. Söderberg, Editor(s)

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