title: Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography.
abstract: Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area.
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA ; College of Physics and Electronics, Shandong Normal University, Jinan, China.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA.
authors.forename: Simon S
authors.initials: SS
authors.lastname: Gao
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA.
authors.forename: Steven T
authors.initials: ST
authors.lastname: Bailey
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA.
authors.forename: David
authors.initials: D
authors.lastname: Huang
authors.affiliation: College of Physics and Electronics, Shandong Normal University, Jinan, China.
authors.forename: Dengwang
authors.initials: D
authors.lastname: Li
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA.
authors.forename: Yali
authors.initials: Y
authors.lastname: Jia
keywords: (100.0100) Image processing; (170.3880) Medical and biological imaging; (170.4500) Optical coherence tomography