This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images. For an effective use of existing large amount of labeled color fundus photo (CFP) data and the relatively small amount of WF and UWF data, we propose a supervised domain adaptation method named Cross-domain Collaborative Learning (CdCL). Inspired by the success of fixed-ratio based mixup in unsupervised domain adaptation, we re-purpose this strategy for the current task. Due to the intrinsic disparity between the field-of-view of CFP and WF/UWF images, a scale bias naturally exists in a mixup sample that the anatomic structure from a CFP image will be considerably larger than its WF/UWF counterpart. The CdCL method resolves the issue by Scale-bias Correction, which employs Transformers for producing scale-invariant features. As demonstrated by extensive experiments on multiple datasets covering both WF and UWF images, the proposed method compares favorably against a number of competitive baselines.
Observing retinal fundus images by an ophthalmologist is a major diagnosis approach for glaucoma. However, it is still difficult to distinguish the features of the lesion solely through manual observations, especially, in glaucoma early phase. In this paper, we present two deep learning-based automated algorithms for glaucoma detection and optic disc and cup segmentation. We utilize the attention mechanism to learn pixel-wise features for accurate prediction. In particular, we present two convolutional neural networks that can focus on learning various pixel-wise level features. In addition, w…
Airdoc LLC（爱丁堡医疗科技有限公司）；Monash University（莫纳什大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）
For decades, advances in retinal imaging technology have enabled effective diagnosis and management of retinal disease using fundus cameras. Recently, ultra-wide-field (UWF) fundus imaging by Optos camera is gradually put into use because of its broader insights on fundus for some lesions that are not typically seen in traditional fundus images. Research on traditional fundus images is an active topic but studies on UWF fundus images are few. One of the most important reasons is that UWF fundus images are hard to obtain. In this paper, for the first time, we explore domain adaptation from the …
Technische Universität Kaiserslautern（凯泽斯劳滕工业大学）；German Research Center for Artificial Intelligence GmbH (DFKI)（德国人工智能研究中心）；Opthalmology Clinic（眼科诊所）；National University of Science and Technology (NUST)（国立科技大学）；National Center of Artificial Intelligence（国家人工智能中心）；Smart Data and Knowledge Services（智能数据与知识服务部）
Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD). A few small datasets that are available for research community usually suffer from impractical image capturing conditions and stringent inclusion criteria. These shortcomings in already limited choice of existing datasets make it challenging to mature a CAD system so that it can perform in real-world environment. In this paper we present a large publicly available retin…
PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Baidu Inc.（百度公司）；Sun Yat-sen University（中山大学）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（阿根廷国家科学与技术研究理事会）；UNICEN（阿根廷国立中央大学）；Medical University of Vienna（维也纳医科大学）
GrInAdapt: Scaling Retinal Vessel Structural Map Segmentation Through Grounding, Integrating and Adapting Multi-device, Multi-site, and Multi-modal Fundus Domains
Department of Bioengineering, University of Washington（华盛顿大学生物工程系）；Department of Electrical and Computer Engineering, University of Washington（华盛顿大学电气与计算机工程系）；Department of Ophthalmology, University of Washington（华盛顿大学眼科系）
MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images
Hong Kong University of Science and Technology（香港科技大学）；Guangdong Weiren Meditech Co., Ltd.（广东伟仁医疗科技有限公司）；Yunnan United Vision Innovations Technology Co., Ltd.（云南联合视力创新科技有限公司）；Guangdong Hospital of Integrated Traditional Chinese and Western Medicine（广东省中西医结合医院）；Second People’s Hospital of Foshan（佛山市第二人民医院）
McGill University（麦吉尔大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Ottawa（渥太华大学）
Deep learning has seen tremendous interest in medical imaging, particularly in the use of convolutional neural networks (CNNs) for developing automated diagnostic tools. The facility of its non-invasive acquisition makes retinal fundus imaging amenable to such automated approaches. Recent work in analyzing fundus images using CNNs relies on access to massive data for training and validation - hundreds of thousands of images. However, data residency and data privacy restrictions stymie the applicability of this approach in medical settings where patient confidentiality is a mandate. Here, we sh…
Monash University（莫纳什大学）；Airdoc；NVIDIA AI Technology Center（英伟达人工智能技术中心）
Retinal vessel segmentation plays a key role in computer-aided screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Recently, deep learning-based retinal vessel segmentation algorithms have achieved remarkable performance. However, due to the domain shift problem, the performance of these algorithms often degrades when they are applied to new data that is different from the training data. Manually labeling new data for each test domain is often a time-consuming and laborious task. In this work, we explore unsupervised domain adaptation in retinal vessel segmen…
RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation
The University of Queensland（昆士兰大学）；University of Technology Sydney（悉尼科技大学）；Australian National University（澳大利亚国立大学）；University of New South Wales（新南威尔士大学）；Victoria University（维多利亚大学）；Deakin University（迪肯大学）
Segmenting blood vessels in fundus imaging plays an important role in medical diagnosis. Many algorithms have been proposed. While deep Neural Networks have been attracting enormous attention from computer vision community recent years and several novel works have been done in terms of its application in retinal blood vessel segmentation, most of them are based on supervised learning which requires amount of labeled data, which is both scarce and expensive to obtain. We leverage the power of Deep Convolutional Neural Networks (DCNN) in feature learning, in this work, to achieve this ultimate g…
Eötvös Loránd University（罗兰大学）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Jiangxi Normal University（江西师范大学）；Chinese Academy of Science（中国科学院）；Qinghai University（青海大学）
Reliable segmentation of retinal vessels can be employed as a way of monitoring and diagnosing certain diseases, such as diabetes and hypertension, as they affect the retinal vascular structure. In this work, we propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation. RSAN employs a modified residual block structure that integrates DropBlock, which can not only be utilized to construct deep networks to extract more complex vascular features, but can also effectively alleviate the overfitting. Moreover, in order to further improve the representation capability of t…
Inception Institute of Artificial Intelligence（人工智能起源研究所）
Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have a large variation in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. However, due to the special optical beam of fundus imaging and structure of the retina, natural image enhancement methods cannot be utilized directly to address this. In this paper, we first analyze the ophthalmoscope imaging system and simulate a reliable degradation of major inf…
University of Nevada, Reno（内华达大学里诺分校）；Deakin University（迪肯大学）；Houston Eye Associates（休斯顿眼科协会）；University of Nevada School of Medicine（内华达大学医学院）
Carrying out clinical diagnosis of retinal vascular degeneration using Fluorescein Angiography (FA) is a time consuming process and can pose significant adverse effects on the patient. Angiography requires insertion of a dye that may cause severe adverse effects and can even be fatal. Currently, there are no non-invasive systems capable of generating Fluorescein Angiography images. However, retinal fundus photography is a non-invasive imaging technique that can be completed in a few seconds. In order to eliminate the need for FA, we propose a conditional generative adversarial network (GAN) to…
In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy the majority of the data, while most classes have only a limited number of samples), which results in a challenging long-tailed learning scenario. Some recently published datasets in ophthalmology AI consist of more than 40 kinds of retinal diseases with complex abnormalities and variable morbidity. Nevertheless, more than 30 conditions are rarely seen in global patient cohorts. From a modeling perspective, most deep learning models trained on these datasets may lack the ability to gen…
Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models
Seoul National University College of Medicine（首尔大学医学院）；Seoul National University Bundang Hospital（首尔大学盆唐医院）；VUNO, Inc.（VUNO公司）；Kookmin University（国民大学）
Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images
Ahsanullah University of Science and Technology（阿萨努拉科技大学）
Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images
The Hong Kong University of Science and Technology（香港科技大学）；Guangdong Provincial Hospital of Integrated Traditional Chinese and Western Medicine（广东省中西医结合医院）；Guangdong Weiren Meditech Co., Ltd（广东唯仁医疗科技有限公司）
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and cup (OC) from fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks demonstrate promising progress in joint OD and OC segmentation. However, affected by the domain shift among different datasets, deep networks are severely hindered in generalizing across different scanners and institutions. In this paper, we present a novel patchbased Output Space Adversarial Learning framework (pOSAL) to jointly and robustly segment the OD and OC from diffe…
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Waterloo（滑铁卢大学）；Bern University Hospital（伯尔尼大学医院）；Tri-Service General Hospital（三军总医院）；National Taiwan University（台湾大学）
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine (SVM) and deep neural networks (DNNs). Furthermore, we present a new clinical retina dataset, called EyeNet2, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet2, our model achieves 90.43\% diagnosis accuracy, and the m…
University of Nevada, Reno（内华达大学雷诺分校）；Deakin University（迪肯大学）；Houston Eye Associates（休斯顿眼科协会）
Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters. FA also requires fluorescein dye that is injected intravenously, which might cause adverse effects ranging from nausea, vomiting to even fatal anaphylaxis. Currently, no other fast and non-invasive technique exists that can generate FA without coupling with Fundus photography. To eradicate the need for an invasive FA extraction procedure, we introduce an Attention-based Generative network that can synthesize Fluorescein Angiography from Fundus imag…
Accurate measurements of abnormalities like Artery-Vein ratio and tortuosity in fundus images is an actively researched task. Most of the research seems to compute such features independently. However, in this work, we have devised a fully automated technique to measure any vascular abnormalities. This paper is a follow-up paper on vessel topology estimation and extraction, we use the extracted topology to perform A-V state-of-the-art Artery-Vein classification, AV ratio calculation, and vessel tortuosity measurement, all fully automated. Existing techniques tend to only work on the partial re…
Beijing University of Posts and Telecommunications（北京邮电大学）
Accurate retinal vessel segmentation is a challenging problem in color fundus image analysis. An automatic retinal vessel segmentation system can effectively facilitate clinical diagnosis and ophthalmological research. Technically, this problem suffers from various degrees of vessel thickness, perception of details, and contextual feature fusion. For addressing these challenges, a deep learning based method has been proposed and several customized modules have been integrated into the well-known encoder-decoder architecture U-net, which is mainly employed in medical image segmentation. Structu…
Universidad Nacional de Colombia（哥伦比亚国立大学）；Fundación Oftalmológica Nacional（国家眼科基金会）；Universidad del Rosario（罗萨里奥大学）
Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. An ophthalmologist performs the diagnosis by screening each patient and analyzing the retinal lesions via ocular imaging. In practice, such analysis is time-consuming and cumbersome to perform. This paper presents a model for automatic DR classification on eye fundus images. The approach identifies the main ocular lesions related to DR and subsequently diagnoses the illness. The proposed method follows the same workflow as the clinicians, provid…
Khalifa University（哈利法大学）；National University of Sciences and Technology（国立科技大学）
Retinal lesions play a vital role in the accurate classification of retinal abnormalities. Many researchers have proposed deep lesion-aware screening systems that analyze and grade the progression of retinopathy. However, to the best of our knowledge, no literature exploits the tendency of these systems to generalize across multiple scanner specifications and multi-modal imagery. Towards this end, this paper presents a detailed evaluation of semantic segmentation, scene parsing and hybrid deep learning systems for extracting the retinal lesions such as intra-retinal fluid, sub-retinal fluid, h…
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…
University of Nevada, Reno（内华达大学里诺分校）；Houston Eye Associates（休斯顿眼科协会）
High fidelity segmentation of both macro and microvascular structure of the retina plays a pivotal role in determining degenerative retinal diseases, yet it is a difficult problem. Due to successive resolution loss in the encoding phase combined with the inability to recover this lost information in the decoding phase, autoencoding based segmentation approaches are limited in their ability to extract retinal microvascular structure. We propose RV-GAN, a new multi-scale generative architecture for accurate retinal vessel segmentation to alleviate this. The proposed architecture uses two generat…
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））
The study of the retinal vasculature is a fundamental stage in the screening and diagnosis of many diseases. A complete retinal vascular analysis requires to segment and classify the blood vessels of the retina into arteries and veins (A/V). Early automatic methods approached these segmentation and classification tasks in two sequential stages. However, currently, these tasks are approached as a joint semantic segmentation task, as the classification results highly depend on the effectiveness of the vessel segmentation. In that regard, we propose a novel approach for the simultaneous segmentat…
Bahcesehir University（巴赫切谢希尔大学）；American University of Malta（马耳他美国大学）
Clinical screening with low-quality fundus images is challenging and significantly leads to misdiagnosis. This paper addresses the issue of improving the retinal image quality and vessel segmentation through retinal image restoration. More specifically, a cycle-consistent generative adversarial network (CycleGAN) with a convolution block attention module (CBAM) is used for retinal image restoration. A modified UNet is used for retinal vessel segmentation for the restored retinal images (CBAM-UNet). The proposed model consists of two generators and two discriminators. Generators translate image…
Eindhoven University of Technology（埃因霍温理工大学）；Maastricht University Medical Center+（马斯特里赫特大学医学中心+）
Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to blindness and cardiovascular disease. Information about early stage T2D might be present in retinal fundus images, but to what extent these images can be used for a screening setting is still unknown. In this study, deep neural networks were employed to differentiate between fundus images from individuals with and without T2D. We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC). A multi-target learning approach to simultaneously output r…
University of Nevada, Reno（内华达大学雷诺分校）；Houston Eye Associates（休斯顿眼科协会）
In Fluorescein Angiography (FA), an exogenous dye is injected in the bloodstream to image the vascular structure of the retina. The injected dye can cause adverse reactions such as nausea, vomiting, anaphylactic shock, and even death. In contrast, color fundus imaging is a non-invasive technique used for photographing the retina but does not have sufficient fidelity for capturing its vascular structure. The only non-invasive method for capturing retinal vasculature is optical coherence tomography-angiography (OCTA). However, OCTA equipment is quite expensive, and stable imaging is limited to s…
Diabetic Retinopathy (DR) is a retinal disorder that affects the people having diabetes mellitus for a long time (20 years). DR is one of the main reasons for the preventable blindness all over the world. If not detected early the patient may progress to severe stages of irreversible blindness. Lack of Ophthalmologists poses a serious problem for the growing diabetes patients. It is advised to develop an automated DR screening system to assist the Ophthalmologist in decision making. Hard exudates develop when DR is present. It is important to detect hard exudates in order to detect DR in an ea…
University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）
Fundus photography has routinely been used to document the presence and severity of retinal degenerative diseases such as age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR) in clinical practice, for which the fovea and optic disc (OD) are important retinal landmarks. However, the occurrence of lesions, drusen, and other retinal abnormalities during retinal degeneration severely complicates automatic landmark detection and segmentation. Here we propose HBA-U-Net: a U-Net backbone enriched with hierarchical bottleneck attention. The network consists of a novel bottle…
This paper presents a computationally efficient method for the detection of optic nerve head in both color fundus and fluorescein angiography images. It involves a combination of Radon transformation and multi-overlapping windows within an optimization framework in order to achieve a robust detection in the presence of various structural, color, and intensity variations in such images. Three databases have been examined and it is shown that the introduced method provides high detection rates while achieving faster proceeding rates than the existing algorithms that possess comparable detection …
Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；University of California, Berkeley（加州大学伯克利分校）；Affiliated Eye Hospital of Nanchang University（南昌大学附属眼科医院）
Retinopathy of prematurity (ROP) is an abnormal blood vessel development in the retina of a prematurely-born infant or an infant with low birth weight. ROP is one of the leading causes for infant blindness globally. Early detection of ROP is critical to slow down and avert the progression to vision impairment caused by ROP. Yet there is limited awareness of ROP even among medical professionals. Consequently, dataset for ROP is limited if ever available, and is in general extremely imbalanced in terms of the ratio between negative images and positive ones. In this study, we formulate the proble…
In recent years, several automatic segmentation methods have been proposed for blood vessels in retinal fundus images, ranging from using cheap and fast trainable filters to complicated neural networks and even deep learning. One example of a filted-based segmentation method is B-COSFIRE. In this approach the image filter is trained with example prototype patterns, to which the filter becomes selective by finding points in a Difference of Gaussian response on circles around the center with large intensity variation. In this paper we discuss and evaluate several of these vessel segmentation met…
Carnegie Mellon University（卡内基梅隆大学）；INESC TEC；Faculty of Engineering, University of Porto（波尔图大学工程学院）
We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images. Our novel re-interpretation of the image distortion model underlying dehazing theory shows how three existing priors commonly used by the dehazing community and a novel fourth prior are related. We utilize the theory to develop a family of enhancement methods for retinal images, including novel methods for whole image brightening and darkening. We show a novel derivation of the Unsharp Masking algorithm. We evaluate the enhancement methods as a pre-processing step…
A Disease-Specific Foundation Model Using Over 100K Fundus Images: Release and Validation for Abnormality and Multi-Disease Classification on Downstream Tasks
Seoul National University（首尔大学）；Seoul National University Hospital（首尔大学附属医院）
Luminosity-Adaptive Contrast Enhancement Using CLAHE for Retinal Fundus Images with Quantitative Validation and Comparative Analysis
Independent Researcher, Department of Information and Computer Engineering, Scottsdale, AZ, USA（独立研究者，信息与计算机工程系，斯科茨代尔，亚利桑那州，美国）；Seidenberg School of Computer Science and Information Systems, Pace University, New York City, NY, USA（塞登堡计算机科学与信息系统学院，帕克大学，纽约市，纽约州，美国）
With the advancement of powerful image processing and machine learning techniques, CAD has become ever more prevalent in all fields of medicine including ophthalmology. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous. The first stage is based on RCNN and is responsible for localizing and extracting optic disc from a retinal fundus image while the second stage uses Deep CNN to classify the extracted disc into health…
Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view compared to traditional fundus photography. Previous studies showed that deep learning (DL) models are effective for detecting retinal disease in UWF images, but primarily considered individual diseases under less-than-realistic conditions (excluding images with other diseases, artefacts, comorbidities, or borderline cases; and balancing healthy and diseased images) and did not systematically investigate which regions of the UWF images are relevant for disease detection. We first improve on the s…
Indian Institute of Science（印度科学学院）；University of Southern California（南加州大学）
Optic disc (OD) and optic cup (OC) are regions of prominent clinical interest in a retinal fundus image. They are the primary indicators of a glaucomatous condition. With the advent and success of deep learning for healthcare research, several approaches have been proposed for the segmentation of important features in retinal fundus images. We propose a novel approach for the simultaneous segmentation of the OD and OC using a residual encoder-decoder network (REDNet) based regional convolutional neural network (RCNN). The RED-RCNN is motivated by the Mask RCNN (MRCNN). Performance comparisons …
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 …
A Deep Learning-Based Unified Framework for Red Lesions Detection on Retinal Fundus Images
Computer and Information Science College, King Saud University（沙特王后大学计算机与信息科学学院）；Department of Ophthalmology, College of Medicine, Princess Nourah bint Abdulrahman University（普里丝特努尔·阿卜杜勒拉赫曼大学医学院眼科系）
One of the leading causes of blindness is glaucoma, which is challenging to detect since it remains asymptomatic until the symptoms are severe. Thus, diagnosis is usually possible until the markers are easy to identify, i.e., the damage has already occurred. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. However, due to the nature of the disease, researchers still debate which markers qualify as a consistent glaucoma metric. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and an…
Netaji Subhas Institute of Technology（内塔吉·苏巴斯技术学院）；Pandit Deendayal Energy University（潘迪特·迪恩达亚尔能源大学）；National Institute of Technology Delhi（德里国立理工学院）；AMC MET Medical College（AMC MET医学院）
Patients with long-standing diabetes often fall prey to Diabetic Retinopathy (DR) resulting in changes in the retina of the human eye, which may lead to loss of vision in extreme cases. The aim of this study is two-fold: (a) create deep learning models that were trained to grade degraded retinal fundus images and (b) to create a browser-based application that will aid in diagnostic procedures by highlighting the key features of the fundus image. In this research work, we have emulated the images plagued by distortions by degrading the images based on multiple different combinations of Light Tr…
Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and Siamese Networks
