Segmenting of clinically important retinal blood vessels into arteries and veins is a prerequisite for retinal vessel analysis. Such analysis can provide potential insights and bio-markers for identifying and diagnosing various retinal eye diseases. Alteration in the regularity and width of the retinal blood vessels can act as an indicator of the health of the vasculature system all over the body. It can help identify patients at high risk of developing vasculature diseases like stroke and myocardial infarction. Over the years, various Deep Learning architectures have been proposed to perform retinal vessel segmentation. Recently, attention mechanisms have been increasingly used in image segmentation tasks. The work proposes a new Deep Learning approach for artery-vein segmentation. The new approach is based on the Attention mechanism that is incorporated into the WNet Deep Learning model, and we call the model as Attention-WNet. The proposed approach has been tested on publicly available datasets such as HRF and DRIVE datasets. The proposed approach has outperformed other state-of-art models available in the literature.
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…
Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Brigham and Women’s Hospital（布列根和妇女医院）；Harvard Medical School（哈佛医学院）；Northeastern University（东北大学）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Washing University in St. Louis（圣路易斯华盛顿大学）；First Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第一医院）
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…
Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Bi- nary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art;…
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）
Retinal imaging serves as a valuable tool for diagnosis of various diseases. However, reading retinal images is a difficult and time-consuming task even for experienced specialists. The fundamental step towards automated retinal image analysis is vessel segmentation and artery/vein classification, which provide various information on potential disorders. To improve the performance of the existing automated methods for retinal image analysis, we propose a two-step vessel classification. We adopt a UNet-based model, SeqNet, to accurately segment vessels from the background and make prediction on…
Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein
National Clinical Research Center for Cancer/Cancer Hospital Shenzhen Hospital（国家癌症临床研究中心/肿瘤医院深圳医院）；Key Laboratory of System Software (Chinese Academy of Sciences)（系统软件重点实验室（中国科学院））；State Key Laboratory of Computer Science（计算机科学国家重点实验室）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Guangzhou Jiayi Software Technology Co., Ltd.（广州嘉益软件科技有限公司）；R&D Center, Guangxi Huayi Artificial Intelligence Medical Technology Co., Ltd（广西华奕人工智能医疗科技有限公司研发中心）；Department of Computer Science, University of Exeter（埃克塞特大学计算机科学系）；Department of Cardiovascular & Metabolic Medicine, University of Liverpool（利物浦大学心血管与代谢医学系）
Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods A …
We present a fully automatic, graph-based technique for extracting the retinal vascular topology -- that is, how different vessels are connected to each other -- given a single color fundus image. Determining this connectivity is very challenging because vessels cross each other in a 2D image, obscuring their true paths. We quantitatively validated the usefulness of our extraction method by using it to achieve comparable state-of-the-art results in retinal artery-vein classification. Our proposed approach works as follows: We first segment the retinal vessels using our previously developed sta…
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医学机器人研究所）；School of Computer Science and Technology, Guizhou University（贵州大学计算机科学与技术学院）；Shanghai Chest Hospital, Shanghai Jiao Tong University（上海交通大学附属胸科医院）；Université de Lyon, INSA Lyon, CREATIS, CNRS, INSERM（里昂大学、里昂国立应用科学学院、CREATIS实验室、法国国家科学研究中心、法国国家健康与医学研究院）
Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly i…
Fuzhou University（福州大学）；Fujian Medical University Union Hospital（福建医科大学附属协和医院）
With the development of medical computer-aided diagnostic systems, pulmonary artery-vein(A/V) separation plays a crucial role in assisting doctors in preoperative planning for lung cancer surgery. However, distinguishing arterial from venous irrigation in chest CT images remains a challenge due to the similarity and complex structure of the arteries and veins. We propose a novel method for automatic separation of pulmonary arteries and veins from chest CT images. The method consists of three parts. First, global connection information and local feature information are used to construct a compl…
Force Sensing Guided Artery-Vein Segmentation via Sequential Ultrasound Images
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Centre for Artificial Intelligence and Robotics, HK Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；City University of Hong Kong（香港城市大学）
Youtu Lab, Tencent（腾讯优图实验室）；School of Information Science and Engineering, Xiamen University（厦门大学信息科学与工程学院）
Retinal artery/vein (A/V) classification plays a critical role in the clinical biomarker study of how various systemic and cardiovascular diseases affect the retinal vessels. Conventional methods of automated A/V classification are generally complicated and heavily depend on the accurate vessel segmentation. In this paper, we propose a multi-task deep neural network with spatial activation mechanism that is able to segment full retinal vessel, artery and vein simultaneously, without the pre-requirement of vessel segmentation. The input module of the network integrates the domain knowledge of w…
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；The First Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第一医院）；Northeast Forestry University（东北林业大学）；The Fourth Hospital of Harbin Medical University（哈尔滨医科大学附属第四医院）；China-Japan Friendship Hospital（中日友好医院）；Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School（南京大学医学院附属南京鼓楼医院）；Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院）
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）
Retinal artery/vein (A/V) classification lays the foundation for the quantitative analysis of retinal vessels, which is associated with potential risks of various cardiovascular and cerebral diseases. The topological connection relationship, which has been proved effective in improving the A/V classification performance for the conventional graph based method, has not been exploited by the deep learning based method. In this paper, we propose a Topology Ranking Generative Adversarial Network (TR-GAN) to improve the topology connectivity of the segmented arteries and veins, and further to boost…
School of Informatics, Xiamen University（厦门大学信息学院）
Retinal artery/vein (A/V) classification is a critical technique for diagnosing diabetes and cardiovascular diseases. Although deep learning based methods achieve impressive results in A/V classification, their performances usually degrade severely when being directly applied to another database, due to the domain shift, e.g., caused by the variations in imaging protocols. In this paper, we propose a novel vessel-mixing based consistency regularization framework, for cross-domain learning in retinal A/V classification. Specially, to alleviate the severe bias to source domain, based on the labe…
University College London（伦敦大学学院）；UCL（伦敦大学学院）；NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology（英国国家健康研究所摩尔菲尔德眼科医院NHS基金会信托与伦敦大学学院眼科研究所生物医学研究中心）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences（惠康/英国工程与物理科学研究委员会介入与外科科学中心）
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease. In this work, we integrate these clinical features into a novel vascular feature optimised loss function (VAFO-Loss), in order to regularise networks to produce segmentation maps, with which more accurate vascular features can be derived. Two common vascular features, vessel density and fractal dimension, are identified to be sensitive to intra-segment misclassification, wh…
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）
University of Maryland, College Park（马里兰大学帕克分校）；Intuitive Surgical, Inc（直观外科公司）
Semantic image segmentation plays an important role in modeling patient-specific anatomy. We propose a convolution neural network, called Kid-Net, along with a training schema to segment kidney vessels: artery, vein and collecting system. Such segmentation is vital during the surgical planning phase in which medical decisions are made before surgical incision. Our main contribution is developing a training schema that handles unbalanced data, reduces false positives and enables high-resolution segmentation with a limited memory budget. These objectives are attained using dynamic weighting, ran…
Peripancreatic vessel segmentation and anatomical labeling play extremely important roles to assist the early diagnosis, surgery planning and prognosis for patients with pancreatic tumors. However, most current techniques cannot achieve satisfactory segmentation performance for peripancreatic veins and usually make predictions with poor integrity and connectivity. Besides, unsupervised labeling algorithms cannot deal with complex anatomical variation while fully supervised methods require a large number of voxel-wise annotations for training, which is very labor-intensive and time-consuming. T…
Shanghai Jiao Tong University（上海交通大学）；Peking Union Medical College Hospital（北京协和医院）
The segmentation of coronary arteries by convolutional neural network is promising yet requires a large amount of labor-intensive manual annotations. Transferring knowledge from retinal vessels in widely-available public labeled fundus images (FIs) has a potential to reduce the annotation requirement for coronary artery segmentation in X-ray angiograms (XAs) due to their common tubular structures. However, it is challenged by the cross-anatomy domain shift due to the intrinsically different vesselness characteristics in different anatomical regions under even different imaging protocols. To so…
Nanjing University of Science and Technology（南京理工大学）
Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age / gender / eye / d…
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）
Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Department of Medical Physics and Biomedical Engineering, UCL（伦敦大学学院医学物理与生物医学工程系）；NIHR Biomedical Research Centre at Moorfields Eye Hospital（莫菲尔德眼科医院NIHR生物医学研究中心）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences（惠康/EPSRC介入与外科科学中心）；Research Center for Healthcare Data Science, Zhejiang Lab（之江实验室医疗健康数据科学研究中心）；UCL Respiratory, University College London（伦敦大学学院呼吸病学中心）；UCL Institute of Ophthalmology, University College London（伦敦大学学院眼科学研究所）
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-cla…
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…
Xiamen University（厦门大学）；Chinese PLA General Hospital（中国人民解放军总医院）；Deepwise AI Laboratory（深睿智慧人工智能实验室）
The analysis of organ vessels is essential for computer-aided diagnosis and surgical planning. But it is not a easy task since the fine-detailed connected regions of organ vessel bring a lot of ambiguity in vessel segmentation and sub-type recognition, especially for the low-contrast capillary regions. Furthermore, recent two-staged approaches would accumulate and even amplify these inaccuracies from the first-stage whole vessel segmentation into the second-stage sub-type vessel pixel-wise classification. Moreover, the scarcity of manual annotation in organ vessels poses another challenge. In …
Deepwise AI Lab（深度智耀人工智能实验室）；Northwestern Polytechnical University（西北工业大学）；Peking University（北京大学）
Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases. Although being extensively studied, segmentation of blood vessels, particularly thin vessels and capillaries, remains challenging mainly due to the lack of an effective interaction between local and global features. In this paper, we propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans, respectively. In PC-Net, the pyramid …
Anatomy-constrained modelling of image-derived input functions in dynamic PET using multi-organ segmentation
Department of diagnostic and interventional Radiology and Neuroradiology, University Hospital Augsburg（诊断与介入放射学及神经放射学系，奥格斯堡大学医院）；Computer-Aided Medical Procedures and Augmented Reality, Technical University of Munich（计算机辅助医疗程序与增强现实，慕尼黑技术大学）；Digital Medicine, University Hospital Augsburg（数字医学，奥格斯堡大学医院）；Center of Advanced Analytics and Predictive Sciences, University of Augsburg（高级分析与预测科学中心，奥格斯堡大学）
Sun Yat-sen University（中山大学）；The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院）；Infervision Inc.（推想医疗科技股份有限公司）
Hepatocellular carcinoma (HCC) is the second most frequent cause of malignancy-related death and is one of the diseases with the highest incidence in the world. Because the liver is the only organ in the human body that is supplied by two major vessels: the hepatic artery and the portal vein, various types of malignant tumors can spread from other organs to the liver. And due to the liver masses' heterogeneous and diffusive shape, the tumor lesions are very difficult to be recognized, thus automatic lesion detection is necessary for the doctors with huge workloads. To assist doctors, this work…
Nagoya University（名古屋大学）；Aichi Cancer Center（爱知癌症中心）
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of full volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and pancreas) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training organ-specific models. To this end, we propose a two-stage, coarse-to-fine approach that trains an FCN model to roughly delineate the organs of interest in the first…
Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
Brigham and Women’s Hospital Harvard Medical School（哈佛医学院布莱尔妇女医院）；Khoury College of Computer Sciences Northeastern University（东北大学计算机科学学院）；Washington University in St. Louis（圣路易斯华盛顿大学）；Dana-Farber Cancer Institute Harvard Medical School（哈佛医学院丹娜-法伯癌症研究院）
Topologically Faithful Multi-class Segmentation in Medical Images
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学伊萨尔河畔 Klinikum 医学院与健康学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Munich Data Science Institute, Technical University of Munich（慕尼黑工业大学慕尼黑数据科学研究所）；Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）
Segmenting the retinal vasculature entails a trade-off between how much of the overall vascular structure we identify vs. how precisely we segment individual vessels. In particular, state-of-the-art methods tend to under-segment faint vessels, as well as pixels that lie on the edges of thicker vessels. Thus, they underestimate the width of individual vessels, as well as the ratio of large to small vessels. More generally, many crucial bio-markers---including the artery-vein (AV) ratio, branching angles, number of bifurcation, fractal dimension, tortuosity, vascular length-to-diameter ratio and…
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）
University of Bournemouth（伯恩茅斯大学）；IDIAP Research Institute（IDIAP研究所）；École de Technolgie Superieure de Montréal（蒙特利尔高等技术学院）；Diagnos Inc.（Diagnos公司）；CRCHUM University of Montreal Hospital Centre（蒙特利尔大学医院研究中心）
The segmentation of the retinal vasculature from eye fundus images represents one of the most fundamental tasks in retinal image analysis. Over recent years, increasingly complex approaches based on sophisticated Convolutional Neural Network architectures have been slowly pushing performance on well-established benchmark datasets. In this paper, we take a step back and analyze the real need of such complexity. Specifically, we demonstrate that a minimalistic version of a standard U-Net with several orders of magnitude less parameters, carefully trained and rigorously evaluated, closely approxi…
The status of retinal arteriovenous crossing is of great significance for clinical evaluation of arteriolosclerosis and systemic hypertension. As an ophthalmology diagnostic criteria, Scheie's classification has been used to grade the severity of arteriolosclerosis. In this paper, we propose a deep learning approach to support the diagnosis process, which, to the best of our knowledge, is one of the earliest attempts in medical imaging. The proposed pipeline is three-fold. First, we adopt segmentation and classification models to automatically obtain vessels in a retinal image with the corresp…
retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers
Department of Ophthalmology, Erasmus University Medical Center（埃因霍温大学医学中心眼科系）；Department of Epidemiology, Erasmus University Medical Center（埃因霍温大学医学中心流行病学系）；Department of Ophthalmology, Radboud University Medical Center（拉德堡德大学医学中心眼科系）；Institute of Molecular and Clinical Ophthalmology, University of Basel（巴塞尔大学分子与临床眼科研究所）；Dept. of Computational Biology, University of Lausanne（洛桑大学计算生物学系）；Swiss Institute of Bioinformatics, Lausanne, Switzerland（瑞士生物信息学研究所，洛桑，瑞士）；Dept. of Integrative Biomedical Sciences, University of Cape Town（开普敦大学整合生物医学科学系）
Robust Real-time Segmentation of Bio-Morphological Features in Human Cherenkov Imaging during Radiotherapy via Deep Learning
Dartmouth College（达特茅斯学院）；Dartmouth Health（达特茅斯医疗）；NVIDIA Corporation（英伟达公司）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Missouri-Columbia（密苏里大学哥伦比亚分校）
