Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network
Inception Institute of Artificial Intelligence（人工智能初创研究院）；Institute for Infocomm Research, Agency for Science, Technology and Research（科学、技术和研究局信息通信研究院）；Baidu Inc.（百度公司）；Microsoft Research（微软研究院）；Singapore Eye Research Institute（新加坡眼科研究院）；Nanyang Technological University（南洋理工大学）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；Southern University of Science and Technology（南方科技大学）
Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Mani Baskaran, Meenakshi Mahesh, Tin Aung, Jiang Liu
备注：
9 pages, accepted by IEEE Transactions on Cybernetics
Irreversible visual impairment is often caused by primary angle-closure glaucoma, which could be detected via Anterior Segment Optical Coherence Tomography (AS-OCT). In this paper, an automated system based on deep learning is presented for angle-closure detection in AS-OCT images. Our system learns a discriminative representation from training data that captures subtle visual cues not modeled by handcrafted features. A Multi-Level Deep Network (MLDN) is proposed to formulate this learning, which utilizes three particular AS-OCT regions based on clinical priors: the global anterior segment structure, local iris region, and anterior chamber angle (ACA) patch. In our method, a sliding window based detector is designed to localize the ACA region, which addresses ACA detection as a regression task. Then, three parallel sub-networks are applied to extract AS-OCT representations for the global image and at clinically-relevant local regions. Finally, the extracted deep features of these sub-networks are concatenated into one fully connected layer to predict the angle-closure detection result. In the experiments, our system is shown to surpass previous detection methods and other deep learning systems on two clinical AS-OCT datasets.
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；Baidu Inc（百度公司）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心）；Southern University of Science and Technology（南方科技大学）；Inception Institute of Artificial Intelligence（启科人工智能研究院）
Anterior chamber angle (ACA) classification is a key step in the diagnosis of angle-closure glaucoma in Anterior Segment Optical Coherence Tomography (AS-OCT). Existing automated analysis methods focus on a binary classification system (i.e., open angle or angle-closure) in a 2D AS-OCT slice. However, clinical diagnosis requires a more discriminating ACA three-class system (i.e., open, narrow, or synechiae angles) for the benefit of clinicians who seek better to understand the progression of the spectrum of angle-closure glaucoma types. To address this, we propose a novel sequence multi-scale …
Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT
Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Guangzhou Shiyuan Electronic Technology Company Limited（广州视源电子科技股份有限公司）；Microsoft Research（微软研究院）；Singapore Eye Research Institute（新加坡眼科研究院）；Cixi Institute of Biomedical Engineering, CAS（中国科学院慈溪生物医学工程研究所）
Inception Institute of Artificial Intelligence（起源人工智能研究院）；State Key Laboratory, Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心国家重点实验室）；Baidu（百度）；South China University of Technology（华南理工大学）；National Scientific and Technical Research Council, CONICET（阿根廷国家科学与技术研究理事会）；Yatiris Group, PLADEMA Institute, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN)（布宜诺斯艾利斯省中央国立大学）
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 p…
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Baidu Inc（百度公司）；Southern University of Science and Technology（南方科技大学）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山大学中山眼科中心）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Precise characterization and analysis of iris shape from Anterior Segment OCT (AS-OCT) are of great importance in facilitating diagnosis of angle-closure-related diseases. Existing methods focus solely on analyzing structural properties identified from the 2D slice, while accurate characterization of morphological changes of iris shape in 3D AS-OCT may be able to reveal in addition the risk of disease progression. In this paper, we propose a novel framework for reconstruction and quantification of 3D iris surface from AS-OCT imagery. We consider it to be the first work to detect angle-closure …
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images
National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Cardiff University（卡迪夫大学）；Newcastle Research and Innovation Institute（纽卡斯尔研究创新研究所）；National University Hospital（国立大学医院）
Tencent Jarvis Lab, Tencent（腾讯杰维斯实验室，腾讯）；National University of Defense Technology（国防科技大学）
Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people. Early detection of PACG is essential, so as to provide timely treatment and minimize the vision loss. In the clinical practice, PACG is diagnosed by analyzing the angle between the cornea and iris with anterior segment optical coherence tomography (AS-OCT). The rapid development of deep learning technologies provides the feasibility of building a computer-aided system for the fast and accurate segmentation of cornea and iris tissues. However, the application of deep learning methods in the …
Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net
IIT Bhubaneswar（印度理工学院布巴内斯瓦尔分校）；Aravind Eye Care Systems（阿拉文德眼科医疗系统）；Singapore Eye Research Institute（新加坡眼科研究所）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）
本研究提出基于 Residual U-Net 架构的增强去噪模型，有效降低 OCT 图像中的散斑噪声并保留组织细节，在 ASOCT 和 PSOCT 两种模态上均显著提升了图像质量指标，助力更精确的临床诊断。
ARCOS: Zero-shot Boundary Localization for Corneal Layer Segmentation Across Optical Coherence Tomography Devices
Laboratoire d’Optique et Biosciences, CNRS, INSERM, École polytechnique, Institut Polytechnique de Paris（光学与生物光学实验室，法国国家科学研究中心，法国健康与医学研究院，巴黎综合理工学院，巴黎理工学院）；GRC 32, Transplantation et Thérapies Innovantes de la Cornée, Sorbonne Université, Centre Hospitalier National d’Ophtalmologie des Quinze-Vingts（GRC 32，角膜创新移植与治疗研究组，索邦大学，十五二十国家眼科中心）
American University of Beirut（贝鲁特美国大学）；Reinstein Vision；London Vision Clinic（伦敦视力诊所）；Columbia University Medical Center（哥伦比亚大学医学中心）；Sorbonne Université（索邦大学）；Biomedical Science Research Institute, Ulster University（阿尔斯特大学生物医学科学研究所）；Bascom Palmer Eye Institute, University of Miami Miller School of Medicine（迈阿密大学米勒医学院巴斯康帕尔默眼科研究所）；Stanford University（斯坦福大学）；Saint Joseph University（圣约瑟夫大学）；American University of Beirut Medical Center（贝鲁特美国大学医学中心）
South China University of Technology（华南理工大学）；National University of Singapore（新加坡国立大学）；Sun Yat-sen University（中山大学）；Baidu Inc.（百度公司）；Pazhou Laboratory（琶洲实验室）；Key Laboratory of Big Data and Intelligent Robot, Ministry of Education（教育部大数据与智能机器人重点实验室）
Deep learning (DL) has made significant progress in angle closure classification with anterior segment optical coherence tomography (AS-OCT) images. These AS-OCT images are often acquired by different imaging devices/conditions, which results in a vast change of underlying data distributions (called "data domains"). Moreover, due to practical labeling difficulties, some domains (e.g., devices) may not have any data labels. As a result, deep models trained on one specific domain (e.g., a specific device) are difficult to adapt to and thus may perform poorly on other domains (e.g., other devices…
Joint Explicit and Implicit Cross-Modal Interaction Network for Anterior Chamber Inflammation Diagnosis
College of Computer Science & Technology, Zhejiang University（浙江大学计算机科学与技术学院）；State Key Laboratory of Ophthalmology, Sun Yat-sen University（中山大学眼科国家重点实验室）
Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle Closure Glaucoma Progression
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学医学院）；Emory University School of Medicine（埃默里大学医学院）；Emory University（埃默里大学）；National University of Singapore（新加坡国立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory Empathetic AI for Health Institute（埃默里健康共情人工智能研究所）
