机构：[1]State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China.[2]Inception Institute of Artificial Intelligence, Abu Dhabi, UAE[3]Intelligent Healthcare Unit, Baidu, Beijing, China[4]School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China[5]National Scientific and Technical Research Council, CONICET, Argentina[6]Yatiris Group, PLADEMA Institute, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN), Tandil, Argentina[7]School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China[8]Tencent Jarvis Lab, Shenzhen, China[9]School of Software Engineering, South China University of Technology, Guangzhou, China[10]School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China[11]School of Electronic and Information Engineering, Shenzhen University, Shenzhen, China[12]Department of Computer Science and Engineering, The Chinese University of Hong Kong, China[13]School of Electronic and Information Engineering, Soochow University, Suzhou, China[14]Ningbo University, Zhejiang, China[15]Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Zhejiang, China[16]Southern University of Science and Technology, Shenzhen, China[17]Shanghai Jiaotong University, Shanghai, China[18]Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria[19]Department of Ophthalmology, Sun Yat-sen Memorial Hospital, SunYat-sen University, Guangzhou, China中山大学附属第二医院[20]Guangzhou aier eye hospital, Guangzhou, China[21]Department of Ophthalmology, The Second Affiliated Hospital of GuiZhou Medi- cal University, Kaili, China[22]Department of Ophthalmology, The Affiliated Tranditional Chinese Medicine Hospital of Guangzhou Medical University, Guangzhou, China[23]Zhongshan Ophthalmic Center, Sun Yat-sen Univer- sity, Guangzhou, China[24]Department of Ophthalmology, The Trandi- tional Chinese Medicine Hospital Of Guangdong Province, Guangzhou, China[25]Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China[26]Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually lead to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a uniform way, which limits progress in the development of automated techniques for angle closure detection and assessment. To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019. The AGE challenge consisted of two tasks: scleral spur localization and angle closure classification. For this challenge, we released a large dataset of 4800 annotated AS-OCT images from 199 patients, and also proposed an evaluation framework to benchmark and compare different models. During the AGE challenge, over 200 teams registered online, and more than 1100 results were submitted for online evaluation. Finally, eight teams participated in the onsite challenge. In this paper, we summarize these eight onsite challenge methods and analyze their corresponding results for the two tasks. We further discuss limitations and future directions. In the AGE challenge, the top-performing approach had an average Euclidean Distance of 10 pixels (10 mu m) in scleral spur localization, while in the task of angle closure classification, all the algorithms achieved satisfactory performances, with two best obtaining an accuracy rate of 100%. These artificial intelligence techniques have the potential to promote new developments in AS-OCT image analysis and image-based angle closure glaucoma assessment in particular. (C) 2020 Elsevier B.V. All rights reserved.
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|1 区计算机：跨学科应用1 区核医学2 区计算机：人工智能2 区工程：生物医学
最新[2025]版：
大类|1 区医学
小类|1 区计算机：人工智能1 区计算机：跨学科应用1 区工程：生物医学1 区核医学
JCR分区：
出版当年[2018]版：
Q1COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEQ1COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONSQ1RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGINGQ1ENGINEERING, BIOMEDICAL
最新[2023]版：
Q1COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEQ1COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONSQ1ENGINEERING, BIOMEDICALQ1RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
医学