Disease complications can alter vascular network morphology and disrupt tissue functioning. Diabetic retinopathy, for example, is a complication of types 1 and 2 diabetes mellitus that can cause blindness. Microvascular diseases are assessed by visual inspection of retinal images, but this can be challenging when diseases exhibit silent symptoms or patients cannot attend in-person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease when trained on statistical and topological summaries of segmented retinal vascular images. We apply our methods to three publicly-available datasets and find that, among the 13 total descriptor vectors we consider, either a statistical Box-counting descriptor vector or a topological Flooding descriptor vector achieves the highest accuracy levels on these datasets. We then created a fourth dataset by merging several datasets: the Box-counting vector outperforms all descriptors on this dataset, including the topological Flooding vector which is sensitive to differences in the annotation styles within the combined dataset. Our work represents a first step to establishing which computational methods are most suitable for identifying microvascular disease as well as some of their current limitations. In the longer term, these methods could be incorporated into automated disease assessment tools.
Zhejiang University（浙江大学）；University of Alberta（阿尔伯塔大学）
Topological and geometrical analysis of retinal blood vessel is a cost-effective way for early detection of many common diseases. Meanwhile, automated vessel segmentation and vascular tree analysis are still lacking in terms of generalization capability. In this work, we construct a novel benchmark RETA with 81 labeled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow is proposed to annotating vessel pixels. During dataset construction, we strived to control inter-annotator variability and intra-annotator variability by performing multi-stage a…
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…
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）；the Second Affiliated Hospital of Zhejiang University（浙江大学医学院附属第二医院）；Ningbo First Hospital（宁波市第一医院）；Edge Hill University（边山大学）；Ningbo University of Technology（宁波工程学院）
Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: featur…
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 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）；VoxelCloud, Inc.（体素云公司）
The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary involvement in such conditions, and the retina is the only anatomical site where the microvasculature can be directly observed. The objective assessment of retinal vessels has long been considered a surrogate biomarker for systemic vascular diseases, and with recent advancements in retinal imaging and computer vision technologies, this topic has become the subject of renewed attention. In this paper, we present a novel…
FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures
Skolkovo Institute of Science and Technology (Skoltech)（斯科尔科沃科学技术研究院（斯科尔泰克））；Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））；University of Sharjah（沙迦大学）；Bian Que AI（扁鹊人工智能）；University College London（伦敦大学学院）；Research Institute for Medical and Health Sciences（医学与健康科学研究院）
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Unsupervised domain adaptation has been proposed recently to tackle the so-called domain shift between training data and test data with different distributions. However, most of them only focus on single-target domain adaptation and cannot be applied to the scenario with multiple target domains. In this paper, we propose RVms, a novel unsupervised multi-target domain adaptation approach to segment retinal vessels (RVs) from multimodal and multicenter retinal images. RVms mainly consists of a style augmentation and transfer (SAT) module and a dual-teacher knowledge distillation (DTKD) module. S…
Deep convolutional neural networks have proved effective in segmenting lesions and anatomies in various medical imaging modalities. However, in the presence of small sample size and domain shift problems, these models often produce masks with non-intuitive segmentation mistakes. In this paper, we propose a segmentation framework called ErrorNet, which learns to correct these segmentation mistakes through the repeated process of injecting systematic segmentation errors to the segmentation result based on a learned shape prior, followed by attempting to predict the injected error. During inferen…
BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Images
University of Electronic Science and Technology of China（电子科技大学）；Southern Methodist University（南方法律大学）；University of Texas Southwestern Medical Center（德克萨斯西南医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Georgetown University（杰斐逊大学）；Sichuan Provincial People’s Hospital（四川省级人民医院）；University of Connecticut（康涅狄格大学）
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；Helmholtz Zentrum Muenchen（亥姆霍兹慕尼黑中心）；University of Innsbruck（因斯布鲁克大学）；University of Oxford（牛津大学）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔德眼科医院国民保健服务基金会信托）；Boehringer Ingelheim Limited（勃林格殷格翰有限公司）；University College London（伦敦大学学院）；Cairo University（开罗大学）；Watany Eye Hospital（瓦塔尼眼科医院）；Heidelberg Engineering GmbH（海德堡工程有限公司）；University of Zurich（苏黎世大学）
Improve Retinal Artery/Vein Classification via Channel Couplin
Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China（北京大学医学部医学技术研究所）；Department of Biomedical Engineering, Peking University, Beijing, China（北京大学生物医学工程系）；National Biomedical Imaging Center, Peking University, Beijing, China（北京大学国家生物医学成像中心）；Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；Emory University, Atlanta, GA, USA（埃默里大学）
Usher Institute, University of Edinburgh（爱丁堡大学厄舍研究所）；Princess Alexandra Eye Pavilion, NHS Lothian（洛锡安国民保健制度亚历山德拉公主眼科馆）；Edinburgh Imaging, University of Edinburgh（爱丁堡大学爱丁堡影像中心）；Health Data Research UK（英国健康数据研究所）；School of Clinical Sciences, University of Edinburgh（爱丁堡大学临床科学学院）；Centre for Clinical Brain Sciences, University of Edinburgh（爱丁堡大学临床脑科学中心）
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality for the visualisation of microvasculature in vivo that has encountered broad adoption in retinal research. OCTA potential in the assessment of pathological conditions and the reproducibility of studies relies on the quality of the image analysis. However, automated segmentation of parafoveal OCTA images is still an open problem. In this study, we generate the first open dataset of retinal parafoveal OCTA images with associated ground truth manual segmentations. Furthermore, we establish a standard for OCTA…
Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging
University College London（伦敦大学学院）；Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Indiana University（印第安纳大学）；European Synchrotron Radiation Facility（欧洲同步辐射装置）；Laboratoire d’Anatomie Des Alpes Françaises（法国阿尔卑斯解剖实验室）；Institute of Pathology, Hannover Medical School（汉诺威医学院病理研究所）；Institute of Functional and Clinical Anatomy, University Medical Center of the Johannes Gutenberg-University Mainz（美因茨大学约翰内斯·古滕贝格大学医学中心功能与临床解剖研究所）；German Center for Lung Research (DZL)（德国肺研究中心）；Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH)（汉诺威终末期及阻塞性肺病生物医学研究中心）
Simon Fraser University（西蒙弗雷泽大学）；University of British Columbia（不列颠哥伦比亚大学）；Medical University of Vienna（维也纳医科大学）；University of Zagreb（萨格勒布大学）
Purpose: Optical Coherence Tomography Angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complication of diabetes. We demonstrate accurate segmentation of the vascular morphology for the superficial capillary plexus and deep vascular complex (SCP and DVC) using a convolutional neural network (CNN) for quantitative analysis. Methods: Retinal OCT-A with a 6x6mm field of view (FOV) were acquired using a Zeiss PlexElite. Multiple-volume acquisition and averaging enhanced the vessel network contrast used for training…
Explainable Multi-Task Retinal Imaging Reveals Microvascular Signals for Systemic Risk Stratification in Type 2 Diabetes: A Pilot Study
Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology（深圳先进技术大学计算机科学与人工智能学院）；Frontier Science Computing Center, Zhuhai Institute of Advanced Technology Chinese Academy of Sciences（中国科学院珠海先进技术研究院前沿科学计算中心）；Chinese University of Hong Kong（香港中文大学）；Zhuhai People's Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University)（珠海人民医院（北京理工大学珠海临床医学院附属医院））；Lampang Inter-Tech College, Lampang Thailand（泰国 Lampang 职业技术学院）
