Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization
Renmin University of China（中国人民大学）；Visionary Intelligence Ltd.（远见智能有限公司）；Chinese Academy of Medical Sciences（中国医学科学院）；Peking Union Medical College Hospital（北京协和医院）
This paper tackles automated categorization of Age-related Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus photograph (CFP) or an OCT B-scan image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the CFP and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based CFP/OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a CFP image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,094 CFP images and 1,289 OCT images acquired from 1,093 distinct eyes show that the proposed solution obtains better F1 and Accuracy than multiple baselines for multi-modal AMD categorization. Code and data are available at https://github.com/li-xirong/mmc-amd.
Age related macular degeneration (AMD) is one of the major causes for blindness in the elderly population. In this report, we propose deep learning based methods for retinal analysis using color fundus images for computer aided diagnosis of AMD. We leverage the recent state of the art deep networks for building a single fundus image based AMD classification pipeline. We also propose methods for the other directly relevant and auxiliary tasks such as lesions detection and segmentation, fovea detection and optic disc segmentation. We propose the use of generative adversarial networks (GANs) for …
We report, to our knowledge, the first end-to-end application of Generative Adversarial Networks (GANs) towards the synthesis of Optical Coherence Tomography (OCT) images of the retina. Generative models have gained recent attention for the increasingly realistic images they can synthesize, given a sampling of a data type. In this paper, we apply GANs to a sampling distribution of OCTs of the retina. We observe the synthesis of realistic OCT images depicting recognizable pathology such as macular holes, choroidal neovascular membranes, myopic degeneration, cystoid macular edema, and central se…
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,…
Kaunas University of Technology（考纳斯理工大学）；Lithuanian University of Health Sciences（立陶宛健康科学大学）
This work aims to research an automatic method for detecting Age-related Macular Degeneration (AMD) lesions in RGB eye fundus images. For this, we align invasively obtained eye fundus contrast images (the "golden standard" diagnostic) to the RGB ones and use them to hand-annotate the lesions. This is done using our custom-made tool. Using the data, we train and test five different convolutional neural networks: a custom one to classify healthy and AMD-affected eye fundi, and four well-known networks: ResNet50, ResNet101, MobileNetV3, and UNet to segment (localize) the AMD lesions in the affect…
Background: Patients with neovascular age-related macular degeneration (AMD) can avoid vision loss via certain therapy. However, methods to predict the progression to neovascular age-related macular degeneration (nvAMD) are lacking. Purpose: To develop and validate a deep learning (DL) algorithm to predict 1-year progression of eyes with no, early, or intermediate AMD to nvAMD, using color fundus photographs (CFP). Design: Development and validation of a DL algorithm. Methods: We trained a DL algorithm to predict 1-year progression to nvAMD, and used 10-fold cross-validation to evaluate this a…
Multiscale Color Guided Attention Ensemble Classifier for Age-Related Macular Degeneration using Concurrent Fundus and Optical Coherence Tomography Images
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；School of Medical Science and Technology, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校医学科学与技术学院）；Department of Mathematics, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校数学系）
University of California, Berkeley（加利福尼亚大学伯克利分校）；International Computer Science Institute（国际计算机科学研究所）；University of California, Davis（加利福尼亚大学戴维斯分校）
Many diseases are classified based on human-defined rubrics that are prone to bias. Supervised neural networks can automate the grading of retinal fundus images, but require labor-intensive annotations and are restricted to the specific trained task. Here, we employed an unsupervised network with Non-Parametric Instance Discrimination (NPID) to grade age-related macular degeneration (AMD) severity using fundus photographs from the Age-Related Eye Disease Study (AREDS). Our unsupervised algorithm demonstrated versatility across different AMD classification schemes without retraining, and achiev…
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院（NIH）国家医学图书馆（NLM）国家生物技术信息中心（NCBI））；National Eye Institute (NEI), National Institutes of Health (NIH)（美国国立卫生研究院（NIH）国家眼科研究所（NEI））
Age-related Macular Degeneration (AMD) is a leading cause of blindness. Although the Age-Related Eye Disease Study group previously developed a 9-step AMD severity scale for manual classification of AMD severity from color fundus images, manual grading of images is time-consuming and expensive. Built on our previous work DeepSeeNet, we developed a novel deep learning model for automated classification of images into the 9-step scale. Instead of predicting the 9-step score directly, our approach simulates the reading center grading process. It first detects four AMD characteristics (drusen area…
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…
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）
University of Washington School of Medicine（华盛顿大学医学院）
Objective: The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imaging modality in ophthalmology and represents a dense and rich dataset when combined with labels derived from the EMR. We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD). Me…
Sun Yat-sen University（中山大学）；Baidu Inc.（百度公司）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（国家科学技术研究委员会）；Medical University of Vienna（维也纳医科大学）；VUNO Inc.（VUNO公司）；Inserm（法国国家健康与医学研究院）；Univ Bretagne Occidentale（西布列塔尼大学）；Zasti（Zasti公司）
Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Au…
National Center for Biotechnology Information (NCBI)（美国国家生物技术信息中心）；National Library of Medicine (NLM)（美国国家医学图书馆）；National Institutes of Health (NIH)（美国国立卫生研究院）；National Eye Institute (NEI)（美国国家眼科研究所）
In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score. DeepSeeNet, a deep learning model, was developed to c…
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；National Eye Institute (NEI), National Institutes of Health (NIH)（美国国立卫生研究院国家眼科研究所）
By 2040, age-related macular degeneration (AMD) will affect approximately 288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep learning has shown promise in diagnosing/screening AMD using color fundus photographs, it remains difficult to predict individuals' risks of late AMD accurately. For both tasks, these initial deep learning attempts have remained largely unvalidated in independent cohorts. Here, we demonstrate how d…
Radboud University Medical Center（拉德堡德大学医学中心）；Donders Institute for Brain, Cognition and Behaviour（唐德斯脑、认知与行为研究所）；University Hospital Ramón y Cajal（拉蒙·卡哈尔大学医院）；Ramón y Cajal Health Research Institute (IRYCIS)（拉蒙·卡哈尔健康研究所（IRYCIS））；Clínica Rementería（雷门特利亚诊所）
Purpose: To validate the performance of a commercially-available, CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD) in color fundus (CF) images on a dataset with mixed presence of eye diseases. Methods: Evaluation of joint detection of referable DR and AMD was performed on a DR-AMD dataset with 600 images acquired during routine clinical practice, containing referable and non-referable cases of both diseases. Each image was graded for DR and AMD b…
Monta Vista High School（蒙塔维斯塔高中）；Hanoi-Amsterdam High School（河内-阿姆斯特丹高中）；Churchill Fulshear High School（丘吉尔·福尔瑟高中）；Norfolk Academy（诺福克学院）；John H. Guyer High School（约翰·H·盖尔高中）；Collège Sainte-Anne（圣安妮学院）
Today, more than 12 million people over the age of 40 suffer from ocular diseases. Most commonly, older patients are susceptible to age related macular degeneration, an eye disease that causes blurring of the central vision due to the deterioration of the retina. The former can only be detected through complex and expensive imaging software, markedly a visual field test; this leaves a significant population with untreated eye disease and holds them at risk for complete vision loss. The use of machine learning algorithms has been proposed for treating eye disease. However, the development of th…
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；National Eye Institute, National Institutes of Health（美国国立卫生研究院国家眼科研究所）；University of Wisconsin（威斯康星大学）；Erasmus Medical Center（伊拉斯谟医学中心）；Uniformed Services University of the Health Sciences（美国卫生 uniformed 大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心）
Objective Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and typically require advanced imaging modalities such as fundus autofluorescence (FAF). The objective was to develop and evaluate the performance of a novel 'M3' deep learning framework on RPD detection. Materials and Methods A deep learning framework M3 was developed to detect RPD presence accurately using CFP alone, FAF alone, or both, employing >8000 CFP-FAF image pairs obtained prospectively (Age-Related Eye Diseas…
CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration
Personal Health Data Science Team, Sano Centre for Computational Personalised Medicine（个人健康数据科学团队，Sano计算个性化医学中心）；The Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine Dentistry and Biomedical Science, Queen's University Belfast（Wellcome-Wolfson实验医学研究所，医学牙科与生物医学科学学院，女王大学贝尔法斯特）；Institute of Clinical Sciences Building A, Queen’s University Belfast（临床科学研究所A楼，女王大学贝尔法斯特）；Department of Epidemiology, Erasmus Medical Center（流行病学系，埃拉斯谟医学中心）；Department of Ophthalmology, Erasmus Medical Center（眼科系，埃拉斯谟医学中心）；Department of Ophthalmology, Radboud University Medical Centre（眼科系，拉德堡德大学医学中心）；Institute of Molecular and Clinical Ophthalmology Basel（分子与临床眼科研究所巴塞尔）
National Taiwan University（台湾大学）；King Abdullah University of Science and Technology（沙特阿拉伯国王阿卜杜拉科技大学）；Georgia Institute of Technology（佐治亚理工学院）；Bern University Hospital（伯尔尼大学医院）；Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology（沙特阿拉伯国王阿卜杜拉科技大学生物与环境科学与工程部门、计算机、电气与数学科学与工程部门）；Department of Ophthalmology, Bern University Hospital（伯尔尼大学医院眼科）
Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease which may result in loss of vision. There is limited access to high-quality relevant retinal images and poor understanding of the features defining sub-classes of this disease. Motivated by recent advances in machine learning we specifically explore the potential of generative modeling, using Generative Adversarial Networks (GANs) and style transferring, to facilitate clinical diagnosis and disease understanding by feature extraction. We design an analytic pipeline which first generates synthetic retinal images from clin…
University of Waterloo（滑铁卢大学）；Manipal Institute of Technology（马尼帕尔理工学院）
An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma,diabetic macular edema and diabetic retinopathy are also reported.
Renmin University of China（中国人民大学）；Visionary Intelligence Ltd.（远景智能有限公司）；Peking Union Medical College Hospital（北京协和医院）；Chinese Academy of Medical Sciences（中国医学科学院）
This paper studies automated categorization of age-related macular degeneration (AMD) given a multi-modal input, which consists of a color fundus image and an optical coherence tomography (OCT) image from a specific eye. Previous work uses a traditional method, comprised of feature extraction and classifier training that cannot be optimized jointly. By contrast, we propose a two-stream convolutional neural network (CNN) that is end-to-end. The CNN's fusion layer is tailored to the need of fusing information from the fundus and OCT streams. For generating more multi-modal training instances, we…
Patch Progression Masked Autoencoder with Fusion CNN Network for Classifying Evolution Between Two Pairs of 2D OCT Slices
LaTIM UMR 1101, Inserm（LaTIM UMR 1101，Inserm）；University of Western Brittany（西部布列塔尼大学）；Evolucare Technologies（Evolucare技术公司）；College of Computer Science, Sichuan University（四川大学计算机学院）；United Imaging Healthcare（联合影像医疗）；University of Electronic Science and Technology of China（电子科技大学）；Northwestern University（西北大学）；ShanghaiTech University（上海科技大学）；Wuhan University（武汉大学）；MD Anderson Cancer Center（MD安德森癌症中心）；Guangdong General Hospital（广东人民医院）
Optical coherence tomography (OCT) is a non-invasive imaging modality which is widely used in clinical ophthalmology. OCT images are capable of visualizing deep retinal layers which is crucial for early diagnosis of retinal diseases. In this paper, we describe a comprehensive open-access database containing more than 500 highresolution images categorized into different pathological conditions. The image classes include Normal (NO), Macular Hole (MH), Age-related Macular Degeneration (AMD), Central Serous Retinopathy (CSR), and Diabetic Retinopathy (DR). The images were obtained from a raster s…
We propose a method to predict severity of age related macular degeneration (AMD) from input optical coherence tomography (OCT) images. Although there is no standard clinical severity scale for AMD, we leverage deep learning (DL) based image registration and clustering methods to identify diseased cases and predict their severity. Experiments demonstrate our approach's disease classification performance matches state of the art methods. The predicted disease severity performs well on previously unseen data. Registration output provides better explainability than class activation maps regarding…
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…
Medical University Vienna（维也纳医科大学）；Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University Vienna, Austria（维也纳医科大学生物医学成像与影像引导治疗系计算成像研究实验室）；Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University Vienna, Austria（维也纳医科大学眼科与视光学系眼科图像分析基督教多普勒实验室）；Department of Ophthalmology and Optometry, Medical University Vienna, Austria（维也纳医科大学眼科与视光学系）
The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring. It enables the quantitative assessment of large amounts of data and the corresponding study of disease characteristics. The presence of hyperreflective foci (HRF) is related to disease progression in various retinal diseases. Manual identification of HRF in spectral-domain optical coherence tomography (SD-OCT) scans is error-prone and tedious. We present a fully automated machine learning approach for segmenting HRF in SD-OCT scans. Evaluation on annotated OCT images o…
Modelling Geographic Atrophy Progression using Implicit Neural Representations
University of Basel（巴塞尔大学）；University Hospital Basel（巴塞尔大学医院）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS基金会信托）；Vienna University of Natural Resources and Life Sciences（维也纳自然资源与生命科学大学）
Iran University Of Science and Technology（伊朗科学技术大学）；Lorestan University（洛雷斯坦大学）；Yasouj University（亚苏季大学）
Optical coherence tomography (OCT) as retina imaging technology is currently used by ophthalmologist as a non-invasive and non-contact method for diagnosis of agerelated degeneration (AMD) and diabetic macular edema (DME) diseases. Fluid regions in OCT images reveal the main signs of AMD and DME. In this paper, an efficient and fast clustering in neutrosophic (NS) domain referred as neutrosophic C-means is adapted for fluid segmentation. For this task, a NCM cost function in NS domain is adapted for fluid segmentation and then optimized by gradient descend methods which leads to binary segment…
Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge
University of Western Brittany, Brest, France；University of Tlemcen, Algeria；Ophthalmology Department, CHRU Brest, Brest, France；Imperial College London, United Kingdom；Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan；Evolucare Technologies, France；College of Computer Science, Sichuan University, China；Medical University of Vienna, Austria；TNO, The Hague, The Netherlands；Image Sciences Institute, UMC Utrecht, Utrecht, The Netherlands；Johannes Kepler University Linz, Austria；University of Zagreb, Faculty of Electrical Engineering；Department of Radiology, University of Calgary, Calgary, AB, Canada；Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada；Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada；Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada；Department of Pediatrics, University of Calgary, Calgary, AB, Canada；Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada；Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada；University of Calgary, Calgary, AB, Canada；German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany；Medical Faculty Heidelberg, Heidelberg University, Germany；Biomedical Image Technologies (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid, Spain；Ophthalmology Service of the Provincial Ophthalmic Institute, Hospital Universitario Gregorio Marañón, Madrid, Spain；University of Edinburgh, Scotland；Lung Institute, Faculty of Medicine, Imperial College London, United Kingdom；Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom
Deep Learning-assisted AMD Staging based on OCT and OCT Angiography
Casey Eye Institute, Oregon Health & Science University（奥勒冈健康与科学大学凯斯眼科研究所）；Department of Biomedical Engineering, Oregon Health & Science University（奥勒冈健康与科学大学生物医学工程系）；Department of Ophthalmology, Mackay Memorial Hospital（Mackay纪念医院眼科部）
OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；Ural Federal University Named after the First President of Russia B. N. Yeltsin（俄罗斯首任总统B.N.叶利钦命名的乌拉尔联邦大学）；Ophthalmosurgery Clinic "Professorskaya Plus"（“Professorskaya Plus”眼科诊所）；Ural State Medical University（乌拉尔国立医科大学）
Multiple-surface segmentation in Optical Coherence Tomography (OCT) images is a challenge problem, further complicated by the frequent presence of weak image boundaries. Recently, many deep learning (DL) based methods have been developed for this task and yield remarkable performance. Unfortunately, due to the scarcity of training data in medical imaging, it is challenging for DL networks to learn the global structure of the target surfaces, including surface smoothness. To bridge this gap, this study proposes to seamlessly unify a U-Net for feature learning with a constrained differentiable d…
The presence of drusen is the main hallmark of early/intermediate age-related macular degeneration (AMD). Therefore, automated drusen segmentation is an important step in image-guided management of AMD. There are two common approaches to drusen segmentation. In the first, the drusen are segmented directly as a binary classification task. In the second approach, the surrounding retinal layers (outer boundary retinal pigment epithelium (OBRPE) and Bruch's membrane (BM)) are segmented and the remaining space between these two layers is extracted as drusen. In this work, we extend the standard U-N…
University of Isfahan（伊斯法罕大学）；Hunan University（湖南大学）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Sichuan University（四川大学）
In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Due to the coherent nature of the image formation process, optical coherence tomography (OCT) images are inevitably affected by noise. This paper proposes a new method named the multi-input fully-convolutional networks (MIFCN) for denoising of OCT images. In contrast to recently proposed natural image denoising CNNs, the proposed architecture allows the exploitation of high degrees of correlation and complementary information amon…
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 …
Monash University（莫纳什大学）；Airdoc；Kyushu Institute of Technology（九州工业大学）；Inception Institute of Artificial Intelligence（Inception人工智能研究院）
The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as age-related macular degeneration (AMD) and diabetic retinopathy (DR). Some studies show that AMD and DR share some common features like hemorrhagic points and exudation but most classification algorithms only train those disease models independently. Inspired by knowledge distillation where additional monitoring signals from various sour…
Centro de Investigación CITIC（CITIC研究中心）；Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所（INIBIC））；Hospital Clínico San Carlos（圣卡洛斯临床医院）；Instituto de Investigación Sanitaria (IdISSC)（健康研究所（IdISSC））；Centro Internacional de Oftalmología Avanzada（国际高级眼科中心）
Age-related Macular Degeneration (AMD) is the predominant cause of blindness in developed countries, specially in elderly people. Moreover, its prevalence is increasing due to the global population ageing. In this scenario, early detection is crucial to avert later vision impairment. Nonetheless, implementing large-scale screening programmes is usually not viable, since the population at-risk is large and the analysis must be performed by expert clinicians. Also, the diagnosis of AMD is considered to be particularly difficult, as it is characterized by many different lesions that, in many case…
FaraAI Company（法拉人工智能公司）；University of Tehran（德黑兰大学）
Optical coherence tomography (OCT) imaging is a well-known technology for visualizing retinal layers and helps ophthalmologists to detect possible diseases. Accurate and early diagnosis of common retinal diseases can prevent the patients from suffering critical damages to their vision. Computer-aided diagnosis (CAD) systems can significantly assist ophthalmologists in improving their examinations. This paper presents a new enhanced deep ensemble convolutional neural network for detecting retinal diseases from OCT images. Our model generates rich and multi-resolution features by employing the l…
Recent contrastive learning methods achieved state-of-the-art in low label regimes. However, the training requires large batch sizes and heavy augmentations to create multiple views of an image. With non-contrastive methods, the negatives are implicitly incorporated in the loss, allowing different images and modalities as pairs. Although the meta-information (i.e., age, sex) in medical imaging is abundant, the annotations are noisy and prone to class imbalance. In this work, we exploited already existing temporal information (different visits from a patient) in a longitudinal optical coherence…
Faculty of Informatics, Kaunas University of Technology（信息学院，卡斯图技术大学）
Age-related macular degeneration (AMD) is one of the leading causes of irreversible vision impairment in people over the age of 60. This research focuses on semantic segmentation for AMD lesion detection in RGB fundus images, a non-invasive and cost-effective imaging technique. The results of the ADAM challenge - the most comprehensive AMD detection from RGB fundus images research competition and open dataset to date - serve as a benchmark for our evaluation. Taking the U-Net connectivity as a base of our framework, we evaluate and compare several approaches to improve the segmentation model's…
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
The Hong Kong Polytechnic University（香港理工大学）；Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院）；Clemson University（克莱姆森大学）；Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital（澳大利亚眼科研究中心，皇家维多利亚眼耳医院）；Zhejiang University（浙江大学）；Bright Eye Hospital（亮睛眼科医院）；Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital（广东省医学科学院，广东省人民医院）；Capital Medical University（首都医科大学）；Beijing Tongren Hospital（北京同仁医院）；Centre for Eye and Vision Research (CEVR)（眼科与视觉研究中心）
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCT
Medical University of Vienna（维也纳医科大学）；University of Basel（巴塞尔大学）；University of Michigan（密歇根大学）；NIHR Moorfields Biomedical Research Centre（NIHR穆尔菲尔德生物医学研究中心）；Technical University Munich（慕尼黑工业大学）；University of Southampton（南安普顿大学）
Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the leading causes of blindness in the developed world. Automated BM segmentation methods exist, but they usually do not account for the anatomical coherence of the results, neither provide feedback on the confidence of the prediction. These factors limit the applicability of these systems in real-world scenarios. With this in mind, we propose an end-to-end deep learning method for automated BM segmentation in AMD patients.…
KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
Artificial Intelligence Department University of Isfahan（人工智能系伊斯法罕大学）；Labbafinejad Hospital Shahid Beheshti University of Medical Science（拉巴法尼贾医院谢赫·贝赫什提医学科学大学）；Department of Mechanical Engineering Isfahan University of Technology（机械工程系伊斯法罕技术大学）
Age-related macular degeneration (AMD) is one of the leading causes of permanent vision loss in people aged over 60 years. Accurate segmentation of biomarkers such as drusen that points to the early stages of AMD is crucial in preventing further vision impairment. However, segmenting drusen is extremely challenging due to their varied sizes and appearances, low contrast and noise resemblance. Most existing literature, therefore, have focused on size estimation of drusen using classification, leaving the challenge of accurate segmentation less tackled. Additionally, obtaining the pixel-wise ann…
