The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i.e., exudate segmentation and imbalanced datasets. The proposed approach replaces the conventional exudate segmentation based feature extraction by combining a pre-trained deep neural network with meta-heuristic feature selection. A feature space over-sampling technique is being used to overcome the effects of skewed datasets and the screening is accomplished by a k-NN based classifier. The role of each data-processing step (e.g., class balancing, feature selection) and the effects of limiting the region-of-interest to fovea on the classification performance are critically analyzed. Finally, the selection and implication of operating point on Receiver Operating Characteristic curve are discussed. The results of this study convincingly demonstrate that by following these fundamental practices of machine learning, a basic k-NN based classifier could effectively accomplish the CSME screening.
Technion Israel Institute of Technology（以色列理工学院）；Rambam Health Care Campus（兰巴姆医疗中心）；University of Adelaide（阿德莱德大学）；Royal Adelaide Hospital（阿德莱德皇家医院）；South Australian Institute of Ophthalmology（南澳大利亚眼科研究所）；Clinical Research Institute at Rambam（兰巴姆临床研究所）
To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME).
Aalto University School of Science（阿尔托大学理学院）；Digifundus Ltd.（Digifundus有限公司）；Central Finland Central Hospital（中芬兰中心医院）
Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening. This labor-intensive task could greatly benefit from automatic detection using deep learning technique. Here we present a deep learning system that identifies referable diabetic retinopathy comparably or better than presented in the previous studies, although we use only a small fraction of images (<1/4) in training but are aided with higher image resolutions. We also …
This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation. In particular, we investigate several different heavily regularized architectures. We find that, contrary to popular belief, neural architectures within this application setting are able to achieve close to human-level performance on unseen test images without requiring large numbers of training examples. Annotating these 3D datasets is difficult, with multiple criteria required. It takes an experienced clinician two days to annotate a si…
School of Advanced Technologies in Medicine（医学先进技术学院）；Isfahan University of Medical Sciences（伊斯法罕医科大学）
Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the major causes of vision loss in developed countries. Alteration of retinal layer structure and appearance of exudate are the most significant signs of these diseases. With the aim of automatic classification of DME, AMD and normal subjects from Optical Coherence Tomography (OCT) images, we proposed a classification algorithm. The two important issues intended in this approach are, not utilizing retinal layer segmentation which by itself is a challenging task and attempting to identify diseases in their early stages,…
Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of permanent blindness in the working-age population. Automatic grading of DR and DME helps ophthalmologists design tailored treatments to patients, thus is of vital importance in the clinical practice. However, prior works either grade DR or DME, and ignore the correlation between DR and its complication, i.e., DME. Moreover, the location information, e.g., macula and soft hard exhaust annotations, are widely used as a prior for grading. Such annotations are costly to obtain, hence it is desirable to develop aut…
Given the capacity of Optical Coherence Tomography (OCT) imaging to display symptoms of a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called ''Livelayer'' which is designe…
Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition
School of Optometry, The Hong Kong Polytechnic University（香港理工大学光学学院）；Department of Ophthalmology, College of Medicine, Rangsit University（皇家理工大学医学院眼科系）；Asia Pacific Tele-Ophthalmology Society（亚太远程眼科学会）；State Key Laboratory (Ophthalmology), Zhongshan Ophthalmic Center, Sun Yat-Sen University（中山眼科中心，中山大学国家重点实验室（眼科））；Duke University（杜克大学）；Texas A&M University（德克萨斯A&M大学）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Xidian University（西电大学）；Svision Imaging Ltd.（Svision影像有限公司）；Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University（SHARP视觉研究中心（RCSV），香港理工大学）；Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park（眼与视觉研究所以（CEVR），香港科学公园17W）；Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong（香港中文大学眼科及视觉科学系）；Faculty of Engineering, The University of Hong Kong（香港大学工程学院）；Faculty of Engineering, The Hong Kong Polytechnic University（香港理工大学工程学院）；Faculty of Health and Life Sciences, University of Liverpool, United Kingdom（利物浦大学健康与生命科学学院，英国）；Shenzhen Technology University, China（深圳技术大学，中国）
本文介绍 APTOS 2021 竞赛，利用 2000 名患者的数万张 OCT 图像预测抗 VEGF 治疗 DME 的反应，最佳模型 AUC 达 80.06%，验证了 AI 在个性化治疗中的价值。
Purpose: to assess the efficacy of THEIA, an artificial intelligence for screening diabetic retinopathy in a multi-center prospective study. To validate the potential application of THEIA as clinical decision making assistant in a national screening program. Methods: 902 patients were recruited from either an urban large eye hospital, or a semi-rural optometrist led screening provider, as they were attending their appointment as part of New Zealand Diabetic Screening programme. These clinics used a variety of retinal cameras and a range of operators. The de-identified images were then graded i…
Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection
Faculty of Biomedical Engineering, Technion-IIT（技术学院生物医学工程系）；Taub Faculty of Computer Science, Technion-IIT（技术学院计算机科学系）；Faculty of Mathematics, Technion-IIT（技术学院数学系）；Department of Ophthalmology, Hillel Yaffe Medical Center（希勒尔·亚法医疗中心眼科部）；The Ruth and Rappaport Faculty of Medicine, Technion-IIT（技术学院医学院）；Ophthalmology Department, São Paulo Federal University（圣保罗联邦大学眼科部）；Medical Engineering and Science, Massachusetts Institute of Technology（麻省理工学院医学工程与科学系）；The Adelson School of Medicine, Ariel University（阿里尔大学阿德勒医学学校）
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…
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；School of Physics and Optoelectronic Engineering, Foshan University（佛山大学物理与光电工程学院）；Department of Biomedical Engineering, Oregon Health & Science University（俄勒冈健康与科学大学生物医学工程系）
Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography (OCT) volume. Methods: 3 x 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc.) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and 6 healthy controls). A CNN with U-Net-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training an…
Fundus photography (FP) remains the primary imaging modality in screening various retinal diseases including age-related macular degeneration, diabetic retinopathy and glaucoma. FP allows the clinician to examine the ocular fundus structures such as the macula, the optic disc (OD) and retinal vessels, whose visibility and clarity in an FP image remain central to ensuring diagnostic accuracy, and hence determine the diagnostic quality (DQ). Images with low DQ, resulting from eye movement, improper illumination and other possible causes, should obviously be recaptured. However, the technician, o…
OCTolyzer: Fully automatic toolkit for segmentation and feature extracting in optical coherence tomography and scanning laser ophthalmoscopy data
University of Edinburgh（爱丁堡大学）；Institute for Regeneration and Repair（再生与修复研究所）；Glasgow Caledonian University（格拉斯哥卡利多尼亚大学）；British Heart Foundation Centre for Cardiovascular Science（英国心脏基金会心血管科学中心）；Clinical Research Facility and Imaging（临床研究设施与影像中心）；Centre for Clinical Brain Sciences（临床脑科学中心）；School of Informatics（信息学院）；Institute for Adaptive and Neural Computation（自适应与神经计算研究所）；School of Mathematics（数学学院）
Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization
University of Ibadan（伊巴丹大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；Hertie Institute for AI in Brain Health（赫蒂脑健康人工智能研究所）；University of Tübingen（蒂宾根大学）；University of Bern（伯尔尼大学）；University College London（伦敦大学学院）；Tübingen AI Center（蒂宾根人工智能中心）
