机构：[1]Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA[2]Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA[3]Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Science, Beijing Tongren Hospital, Capital Medical University. Beijing, China首都医科大学附属北京同仁医院首都医科大学附属同仁医院[4]Shanxi Eye Hospital, Taiyuan, Shanxi, China
Accurate identification and segmentation of choroidal neovascularization (CNV) is essential for the diagnosis and management of exudative age-related macular degeneration (AMD). Projection-resolved optical coherence tomographic angiography (PR-OCTA) enables both cross-sectional and en face visualization of CNV. However, CNV identification and segmentation remains difficult even with PR-OCTA due to the presence of residual artifacts. In this paper, a fully automated CNV diagnosis and segmentation algorithm using convolutional neural networks (CNNs) is described. This study used a clinical dataset, including both scans with and without CNV, and scans of eyes with different pathologies. Furthermore, no scans were excluded due to image quality. In testing, all CNV cases were diagnosed from non-CNV controls with 100% sensitivity and 95% specificity. The mean intersection over union of CNV membrane segmentation was as high as 0.88. By enabling fully automated categorization and segmentation, the proposed algorithm should offer benefits for CNV diagnosis, visualization monitoring. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
基金：
National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P30 EY010572, R01 EY024544, R01 EY027833]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81971697]; Research to Prevent BlindnessResearch to Prevent Blindness (RPB)
语种：
外文
被引次数：
WOS：
PubmedID：
中科院(CAS)分区：
出版当年[2019]版：
大类|2 区医学
小类|2 区光学2 区核医学3 区生化研究方法
最新[2025]版：
大类|3 区医学
小类|2 区生化研究方法3 区光学3 区核医学
JCR分区：
出版当年[2018]版：
Q1BIOCHEMICAL RESEARCH METHODSQ1OPTICSQ1RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版：
Q2BIOCHEMICAL RESEARCH METHODSQ2OPTICSQ2RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
第一作者机构：[1]Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA[2]Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
通讯作者：
通讯机构：[1]Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA[2]Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
推荐引用方式(GB/T 7714)：
Wang Jie,Hormel Tristan T.,Gao Liqin,et al.Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning[J].BIOMEDICAL OPTICS EXPRESS.2020,11(2):927-944.doi:10.1364/BOE.379977.
APA：
Wang, Jie,Hormel, Tristan T.,Gao, Liqin,Zang, Pengxiao,Guo, Yukun...&Jia, Yali.(2020).Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning.BIOMEDICAL OPTICS EXPRESS,11,(2)
MLA：
Wang, Jie,et al."Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning".BIOMEDICAL OPTICS EXPRESS 11..2(2020):927-944
