Your browser doesn't support javascript.
loading
AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography.
Fu, Huazhu; Li, Fei; Sun, Xu; Cao, Xingxing; Liao, Jingan; Orlando, José Ignacio; Tao, Xing; Li, Yuexiang; Zhang, Shihao; Tan, Mingkui; Yuan, Chenglang; Bian, Cheng; Xie, Ruitao; Li, Jiongcheng; Li, Xiaomeng; Wang, Jing; Geng, Le; Li, Panming; Hao, Huaying; Liu, Jiang; Kong, Yan; Ren, Yongyong; Bogunovic, Hrvoje; Zhang, Xiulan; Xu, Yanwu.
Afiliação
  • Fu H; Inception Institute of Artificial Intelligence, Abu Dhabi, UAE.
  • Li F; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China.
  • Sun X; Intelligent Healthcare Unit, Baidu, Beijing, China.
  • Cao X; Intelligent Healthcare Unit, Baidu, Beijing, China.
  • Liao J; School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China.
  • Orlando JI; National Scientific and Technical Research Council, CONICET, Argentina; Yatiris Group, PLADEMA Institute, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN), Tandil, Argentina.
  • Tao X; School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.
  • Li Y; Tencent Jarvis Lab, Shenzhen, China.
  • Zhang S; School of Software Engineering, South China University of Technology, Guangzhou, China.
  • Tan M; School of Software Engineering, South China University of Technology, Guangzhou, China.
  • Yuan C; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
  • Bian C; Tencent Jarvis Lab, Shenzhen, China.
  • Xie R; School of Electronic and Information Engineering, Shenzhen University, Shenzhen, China.
  • Li J; School of Electronic and Information Engineering, Shenzhen University, Shenzhen, China.
  • Li X; Department of Computer Science and Engineering, The Chinese University of Hong Kong, China.
  • Wang J; Department of Computer Science and Engineering, The Chinese University of Hong Kong, China.
  • Geng L; School of Electronic and Information Engineering, Soochow University, Suzhou, China.
  • Li P; School of Electronic and Information Engineering, Soochow University, Suzhou, China.
  • Hao H; Ningbo University, Zhejiang, China; Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Zhejiang, China.
  • Liu J; Southern University of Science and Technology, Shenzhen, China; Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Zhejiang, China.
  • Kong Y; Shanghai Jiaotong University, Shanghai, China.
  • Ren Y; Shanghai Jiaotong University, Shanghai, China.
  • Bogunovic H; Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria.
  • Zhang X; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China.. Electronic address: zhangxl2@mail.sysu.edu.cn.
  • Xu Y; Intelligent Healthcare Unit, Baidu, Beijing, China. Electronic address: ywxu@ieee.org.
Med Image Anal ; 66: 101798, 2020 12.
Article em En | MEDLINE | ID: mdl-32896781
ABSTRACT
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 µm) 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.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Glaucoma de Ângulo Fechado / Glaucoma de Ângulo Aberto Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Glaucoma de Ângulo Fechado / Glaucoma de Ângulo Aberto Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article