Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography
Imperial College London（帝国理工学院）；UCLA（加州大学洛杉矶分校）；Jules Stein Eye Institute（朱尔斯·斯坦眼科研究所）；California State University（加州州立大学）；David Geffen School of Medicine（大卫·格芬医学院）；Technische Universität München（慕尼黑工业大学）
Osama N. Hassan, Serhat Sahin, Vahid Mohammadzadeh, Xiaohe Yang, Navid Amini, Apoorva Mylavarapu, Jack Martinyan, Tae Hong, Golnoush Mahmoudinezhad, Daniel Ruec… 展开作者
Osama N. Hassan, Serhat Sahin, Vahid Mohammadzadeh, Xiaohe Yang, Navid Amini, Apoorva Mylavarapu, Jack Martinyan, Tae Hong, Golnoush Mahmoudinezhad, Daniel Rueckert, Kouros Nouri-Mahdavi, Fabien Scalzo
The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generative deep learning model using the conditional GAN architecture to predict glaucoma progression over time. The patient's OCT scan is predicted from three or two prior measurements. The predicted images demonstrate high similarity with the ground truth images. In addition, our results suggest that OCT scans obtained from only two prior visits may actually be sufficient to predict the next OCT scan of the patient after six months.
X-GAN: A Generative AI-Powered Unsupervised Model for Main Vessel Segmentation of Glaucoma Screening
Southern Methodist University（南方 Methodist 大学）；National Aeronautics and Space Administration（美国国家航空航天局）；University of Texas Southwestern Medical Center（德克萨斯西南医学中心）；Northeastern University（东北大学）
National University of Sciences and Technology（国立科技大学）；Khalifa University（哈利法大学）
Objective: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by measuring cup-to-disc ratios from fundus and optical coherence tomography scans. However, this paper presents a novel strategy that pays attention to the RGC atrophy for screening glaucomatous pathologies and grading their severity. Methods: The proposed framework encompasses a hybrid convolutional network that extracts the retinal nerve fiber layer, ganglion cell with …
Beyond Point Estimates for Glaucoma Visual Field Forecasting with Diffusion Models
ARTORG Center for Biomedical Engineering Research, UniBe, Switzerland（瑞士UniBe生物医学工程研究中心ARTORG）；PeriVision SA, Epalinges, Switzerland（瑞士Epalinges市PeriVision公司）；Department of Ophthalmology, Inselspital, Bern, Switzerland（瑞士伯恩Inselspital眼科部门）
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（新加坡国家眼科中心）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学医学院）；Nanyang Technological University Singapore（新加坡南洋理工大学）；Medical University of Vienna（维也纳医科大学）；Institute of Molecular and Clinical Ophthalmology, Basel（巴塞尔分子与临床眼科研究所）；Abyss Processing Pte Ltd（深渊处理私人有限公司）
Purpose: (1) To develop a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field optical coherence tomography (OCT) scans; (2) To assess whether 3D macula or ONH structures (or the combination of both) provide the best diagnostic power for glaucoma. Methods: A cross-sectional comparative study was performed which included wide-field swept-source OCT scans from 319 glaucoma subjects and 298 non-glaucoma subjects. All scans were compensated to improve deep-tissue visibility. We developed a deep learning algorithm to automatically lab…
Self-supervised denoising of visual field data improves detection of glaucoma progression
Pepperdine University（佩珀代因大学）；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Stein Eye Institute（斯坦眼科研究所）；David Geffen School of Medicine（戴维·格芬医学院）；Columbia University（哥伦比亚大学）；University of California Riverside（加利福尼亚大学河滨分校）
Imperial College London（帝国理工学院）；Medical University of Vienna（维也纳医科大学）；Moorfields Eye Hospital（摩尔菲尔德眼科医院）；Institute of Molecular and Clinical Ophthalmology Basel（巴塞尔分子与临床眼科研究所）；University of Southampton（南安普顿大学）；Technical University of Munich（慕尼黑工业大学）；University of Michigan（密歇根大学）；Universitat Basel（巴塞尔大学）
Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories and are unable to predict future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we present the first method to automatically discover biomarkers that capture temporal dynamics of disease progression. Our method represents patient time series as trajectories in a latent feature space b…
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）
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 …
Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Glaucoma manifest in the retina, can cause irreversible blindness or severely impair the central version. The Optical Coherence Tomography (OCT), a 3D scan of the retina with high qualitative information about the retinal morphology, can be used to diagnose and monitor changes in the retinal anatomy. Many Deep Learning (DL) methods have shared the success of developing an automated tool to monitor pathological changes in the retina. However, the success of these methods depend mainly on large datas…
Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；National University of Singapore（新加坡国立大学）；Aravind Eye Care Systems（阿拉温德眼科护理系统）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）
The optic nerve head (ONH) typically experiences complex neural- and connective-tissue structural changes with the development and progression of glaucoma, and monitoring these changes could be critical for improved diagnosis and prognosis in the glaucoma clinic. The gold-standard technique to assess structural changes of the ONH clinically is optical coherence tomography (OCT). However, OCT is limited to the measurement of a few hand-engineered parameters, such as the thickness of the retinal nerve fiber layer (RNFL), and has not yet been qualified as a stand-alone device for glaucoma diagnos…
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）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；National University of Singapore（新加坡国立大学）；Duke-NUS Graduate Medical School（杜克-新加坡国立大学医学院）；Vilnius University（维尔纽斯大学）；Vilnius University Hospital Santaros Klinikos（维尔纽斯大学圣罗克利什科斯医院）；Massachusetts Institute of Technology（麻省理工学院）；Institute for Molecular and Clinical Ophthalmology（分子与临床眼科研究所）
Purpose: The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma. Optical coherence tomography (OCT) is the current gold standard to visualize and quantify these changes, however the resulting 3D deep-tissue information has not yet been fully exploited for the diagnosis and prognosis of glaucoma. To this end, we aimed: (1) To compare the performance of two relatively recent geometric deep learning techniques in diagnosing glaucoma from a single OCT scan of the ONH; and (2) To identify the 3D structural features of the ON…
A Hybrid Deep Learning Classification of Perimetric Glaucoma Using Peripapillary Nerve Fiber Layer Reflectance and Other OCT Parameters from Three Anatomy Regions
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；OHSU-PSU School of Public Health（俄勒冈健康与科学大学-波特兰州立大学公共卫生学院）；Bascom Palmer Eye Institute, University of Miami（迈阿密大学巴斯康帕尔默眼科研究所）；Doheny Eye Center and David Geffen School of Medicine at UCLA（加州大学洛杉矶分校多希尼眼科中心与大卫·格芬医学院）；Southern California Eye Institute（南加州眼科研究所）；Wills Eye Hospital（威尔斯眼科医院）
Introducing the Biomechanics-Function Relationship in Glaucoma: Improved Visual Field Loss Predictions from intraocular pressure-induced Neural Tissue Strains
Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡全国眼科中心新加坡眼科研究所）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Department of Statistics & Data Science, National University of Singapore（新加坡国立大学统计与数据科学系）；Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院科研联盟）；Department of Mechanical Engineering, Massachusetts Institute of Technology（麻省理工学院机械工程系）；Department of Ophthalmology, Emory University School of Medicine（埃默里大学医学院眼科系）；Department of Biomedical Engineering, Georgia Institute of Technology/Emory University（佐治亚理工学院/埃默里大学生物医学工程系）；Emory Empathetic AI for Health Institute, Emory University（埃默里大学健康共情人工智能研究所）
Automated drusen segmentation in retinal optical coherence tomography (OCT) scans is relevant for understanding age-related macular degeneration (AMD) risk and progression. This task is usually performed by segmenting the top/bottom anatomical interfaces that define drusen, the outer boundary of the retinal pigment epithelium (OBRPE) and the Bruch's membrane (BM), respectively. In this paper we propose a novel multi-decoder architecture that tackles drusen segmentation as a multitask problem. Instead of training a multiclass model for OBRPE/BM segmentation, we use one decoder per target class …
Massachusetts Institute of Technology（麻省理工学院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Tufts Medical Center（塔夫茨医疗中心）
Recent advances in optical coherence tomography such as the development of high speed ultrahigh resolution scanners and corresponding signal processing techniques may reveal new potential biomarkers in retinal diseases. Newly visible features are, for example, small hyperreflective specks in age-related macular degeneration. Identifying these new markers is crucial to investigate potential association with disease progression and treatment outcomes. Therefore, it is necessary to reliably detect these features in 3D volumetric scans. Because manual labeling of entire volumes is infeasible a nee…
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT
Medical University of Vienna（维也纳医科大学）；Imperial College London（伦敦帝国学院）；Technical University Munich（慕尼黑工业大学）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS基金会信托）；University of Basel（巴塞尔大学）；University of Southampton（南安普顿大学）
Simon Fraser University（西蒙弗雷泽大学）；Wake Forest University School of Medicine（维克森林大学医学院）；Wake Forest School of Medicine（维克森林大学医学院）；University of Nottingham（诺丁汉大学）；Tencent Jarvis Lab（腾讯Jarvis实验室）；Memorial University of Newfoundland（纽芬兰纪念大学）；University of British Columbia（不列颠哥伦比亚大学）；The University of British Columbia（不列颠哥伦比亚大学）；University College London（伦敦大学学院）
Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions. It has been widely used as a auxiliary imaging reference to detect eye-related pathology and predict longitudinal progression of the disease characteristics. Retina layer segmentation is one of the crucial feature extraction techniques, where the variations of retinal layer thicknesses and the retinal layer deformation due to the presence of the fluid are highly correlated with multiple epidemic eye diseases like Diabetic Retinopathy(DR) and Age-related Macular…
Ophthalmic Engineering and Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore（新加坡国立大学工程学院生物医学工程系眼科工程与创新实验室）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡国立眼科中心新加坡眼科研究所）；Rajan Eye Care Hospital（拉詹眼科护理医院）
Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur along with the progression of glaucoma. Although several deep learning (DL) techniques have been recently proposed for the automated extraction (segmentation) and quantification of these morphological changes, the device specific nature and the difficulty in preparing manual segmentations (training data) limit their clinical adoption. With several new manufacturers and next-generation OCT devices entering the market, t…
