Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network
Michigan Technological University（密歇根理工大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Chang Bing Show Chwan Memorial Hospital（彰滨秀传纪念医院）
Coronary artery disease (CAD) is one of the primary causes leading deaths worldwide. The presence of atherosclerotic lesions in coronary arteries is the underlying pathophysiological basis of CAD, and accurate extraction of individual arterial branches using invasive coronary angiography (ICA) is crucial for stenosis detection and CAD diagnosis. We propose an innovative approach called the Edge Attention Graph Matching Network (EAGMN) for coronary artery semantic labeling. By converting the coronary artery semantic segmentation task into a graph node similarity comparison task, identifying the node-to-node correspondence would assign semantic labels for each arterial branch. More specifically, The EAGMN utilizes the association graph constructed from the two individual graphs as input. Experimental results indicate the EAGMN achieved a weighted accuracy of 0.8653, a weighted precision of 0.8656, a weighted recall of 0.8653 and a weighted F1-score of 0.8643. Furthermore, we employ ZORRO to provide interpretability and explainability of the graph matching for artery semantic labeling. These findings highlight the potential of the EAGMN for accurate and efficient coronary artery semantic labeling using ICAs. By leveraging the inherent characteristics of ICAs and incorporating graph matching techniques, our proposed model provides a promising solution for improving CAD diagnosis and treatment
Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling
Michigan Technological University（密歇根理工大学）；Medical University of South Carolina（南卡罗来纳医科大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）
ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis
Nepal Applied Mathematics and Informatics Institute for research（尼泊尔应用数学与信息学研究所）；Fogsphere(Redev AI Ltd.)（Fogsphere（Redev AI有限公司））；University of Lausanne（洛桑大学）；University of Aberdeen（阿伯丁大学）
Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation
Nepal Applied Mathematics and Informatics Institute for research(NAAMII)（尼泊尔应用数学与信息学研究所）；Fogsphere(Redev AI Ltd.)（Fogsphere（Redev AI有限公司））；University of Lausanne（洛桑大学）；School of Natural and Computing Sciences, University of Aberdeen（阿伯丁大学自然与计算科学学院）
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht University（乌得勒支大学）
Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with coronary artery disease. Therefore, it is crucial to detect and classify the type of coronary artery plaque, as well as to detect and determine the degree of coronary artery stenosis. This study includes retrospectively collected clinically obtained coronary CT angiography (CCTA) scans of 163 patients. To perform automatic analysis for coronary artery plaque and stenosis classification, a multi-task recurrent convolutional neural network is applied on multi-planar reformat…
University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht University（乌得勒支大学）
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN). A 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Starting from a single seed point placed manually or automatically anywhere in a coronary artery, a tracker follows the vessel centerline in two directions using the predictions…
University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht University（乌得勒支大学）
In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with …
CORA: Generalizable coronary artery disease assessment and risk stratification from coronary CT angiography using pathology-centric representation learning
Department of Radiology, Northwestern University（西北大学放射学系）；Department of Biomedical Engineering, Northwestern University（西北大学生物医学工程系）；Department of Cardiology, Northwestern University（西北大学心脏病学系）；Department of Preventive Medicine, Northwestern University（西北大学预防医学系）
Multi-graph Graph Matching for Coronary Artery Semantic Labeling
Kennesaw State University（肯尼索州立大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Michigan Technological University（密歇根理工大学）；Medical University of South Carolina（南卡罗来纳医科大学）
LWT-ARTERY-LABEL: A Lightweight Framework for Automated Coronary Artery Identification
School of Mechanical and Manufacturing Engineering, University of New South Wales（机械与制造工程学院，新南威尔士大学）；School of Computer Science and Engineering, University of New South Wales（计算机科学与工程学院，新南威尔士大学）；Department of Radiology, Prince of Wales Hospital, Sydney, Australia（放射科，威尔士王子医院，悉尼，澳大利亚）；School of Medicine, University of New South Wales（医学院，新南威尔士大学）
Image Sciences Institute（影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Department of Radiology（放射科）
Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly personalized surface meshes enclosing the coronary lumen. In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. Predictions for individual vertex locations are based on local image features as well as on features of neighboring vertices in the mesh graph. The method was trained and evaluated using the publicly available Coronary Artery Stenoses Detection and Quantification Evaluation …
4D-CAT: Synthesis of 4D Coronary Artery Trees from Systole and Diastole
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属仁济医院）；Punan Branch of Renji Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属仁济医院浦南分院）
University of Pisa（比萨大学）；Fondazione Gabriele Monasterio（加布里埃莱·莫纳斯特里奥基金会）
Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the mo…
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；National Heart & Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University（安徽大学智能计算与信号处理教育部重点实验室）；Cardiovascular Research Centre, Royal Brompton Hospital（皇家布朗普顿医院心血管研究中心）；School of Biomedical Engineering, Sun Yat-Sen University（中山大学生物医学工程学院）；Western University（韦仕敦大学）
The quantification of the coronary artery stenosis is of significant clinical importance in coronary artery disease diagnosis and intervention treatment. It aims to quantify the morphological indices of the coronary artery lesions such as minimum lumen diameter, reference vessel diameter, lesion length, and these indices are the reference of the interventional stent placement. In this study, we propose a direct multiview quantitative coronary angiography (DMQCA) model as an automatic clinical tool to quantify the coronary artery stenosis from X-ray coronary angiography images. The proposed DMQ…
PPCAR-Net: Projection-Refined Parametric 3D Coronary Artery Reconstruction from Sparse X-ray Angiographic Views
Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR)（生物信息学研究所，科技研究局（A*STAR））；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；National Heart Centre Singapore（新加坡国家心脏中心）
Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography
School of Computer Science and Technology, Zhengzhou University of Light Industry（郑州轻工业大学计算机与通信工程学院）；School of Information Management and Engineering, Shanghai University of Finance and Economics（上海财经大学信息管理与工程学院）；Department of Applied Computing, Michigan Technological University（密歇根理工大学应用计算系）；Health Research Institute, Michigan Technological University（密歇根理工大学健康研究所）；Department of Computer Science, Kennesaw State University（肯尼索州立大学计算机科学系）
Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting Coronary Artery Hemodynamics
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Centre for Perceptual and Interactive Intelligence, the Chinese University of Hong Kong（香港中文大学感知与交互智能研究中心）；SenseTime Research（商汤科技研究院）；the Chinese University of Hong Kong（香港中文大学）
Henan University of Chinese Medicine（河南中医药大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
In recent years, deep convolution neural networks (DCNNs) have achieved great prospects in coronary artery vessel segmentation. However, it is difficult to deploy complicated models in clinical scenarios since high-performance approaches have excessive parameters and high computation costs. To tackle this problem, we propose \textbf{LightVessel}, a Similarity Knowledge Distillation Framework, for lightweight coronary artery vessel segmentation. Primarily, we propose a Feature-wise Similarity Distillation (FSD) module for semantic-shift modeling. Specifically, we calculate the feature similarit…
3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)（中国科学技术大学生命科学与医学部生物医学工程学院）；Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advance Research, USTC（中国科学技术大学苏州研究院医学影像、机器人、分析计算与学习中心）
AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms
Michigan Technological University（密歇根理工大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Medical University of South Carolina（南卡罗来纳医科大学）；Chang Bing Show Chwan Memorial Hospital（彰滨秀传纪念医院）
SFD-Mamba2Net: Structure-Guided Frequency-Enhanced Dual-Stream Mamba2 Network for Coronary Artery Segmentation
Sichuan Normal University（四川师范大学）；Education Big Data Collaborative Innovation Center of Sichuan 2011（四川省教育大数据协同创新中心）；The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院）；Michigan Technological University（密歇根理工大学）；Kennesaw State University（肯尼索州立大学）
Daegu Gyeongbuk Institute of Science & Technology（大邱庆北科学技术院）；Yonsei University College of Medicine（延世大学医学院）
Background and Objective: Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying coronary artery lesions by echocardiography is important for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to KD, coronavirus disease 2019, currently causing a worldwide pandemic, also manifests with fever; therefore, it is crucial at this moment that KD should be distinguished clearly among the febrile diseases in children. …
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht University（乌得勒支大学）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）
Treatment of patients with obstructive coronary artery disease is guided by the functional significance of a coronary artery stenosis. Fractional flow reserve (FFR), measured during invasive coronary angiography (ICA), is considered the gold standard to define the functional significance of a coronary stenosis. Here, we present a method for non-invasive detection of patients with functionally significant coronary artery stenosis, combining analysis of the coronary artery tree and the left ventricular (LV) myocardium in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA sca…
Machine-Learning Based Detection of Coronary Artery Calcification Using Synthetic Chest X-Rays
School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Mechanical and Manufacturing Engineering, University of New South Wales（新南威尔士大学机械与制造工程学院）；Independent Researcher, Sydney, Australia（独立研究者）
Tsinghua University（清华大学）；Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院）；The First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院）
Coronary angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiography videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels. This framework combines a 3D convolutional layer to extract spati…
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…
CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram
Military Institute of Science and Technology（军事科学技术学院）；University of Dhaka（达卡大学）；Qatar University（卡塔尔大学）；Kuwait College of Science and Technology（科威特科学技术学院）；Vels Institute of Sciences, Technology, and Advanced Studies（韦尔斯科学技术与高级研究学院）；Majmaah University（马杰马阿大学）；Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹亲王大学）；Manipal Academy of Higher Education（马尼帕尔高等教育学院）；Hamad Medical Corporation（哈马德医疗公司）
ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；Cardiovascular Research Unit, Copenhagen University Hospital – Rigshospitalet（哥本哈根大学医院——国立医院心血管研究科）
A topology-preserving three-stage framework for fully-connected coronary artery extraction
Center for Digital Media Computing, School of Film, School of Informatics, Xiamen University（厦门大学电影学院、信息学院数字媒体计算中心）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；Peking Union Medical College Hospital（北京协和医院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；City University of Hong Kong（香港城市大学）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
DeepCA: Deep Learning-based 3D Coronary Artery Tree Reconstruction from Two 2D Non-simultaneous X-ray Angiography Projections
University of Oxford（牛津大学）；Institute of Biomedical Engineering（生物医学工程研究所）；Division of Cardiovascular Medicine（心血管医学部）；Radcliffe Department of Medicine（拉德克利夫医学系）；Oxford Acute Vascular Imaging Centre（牛津急性血管成像中心）
Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning
Birmingham City University（伯明翰城市大学）；University of the West of England (UWE)（西英格兰大学）；Institute of Accounting and Administration, University of Aveiro（阿威罗大学会计与行政学院）；NUS High School of Math and Science（新加坡国立大学数学与科学高中）；Qatar University（卡塔尔大学）；Information Technology University（信息技术大学）；Bristol Heart Institute, University of Bristol（布里斯托大学布里斯托心脏研究所）
University of New South Wales（新南威尔士大学）；UNSW Sydney（新南威尔士大学悉尼分校）；Prince of Wales Hospital（威尔士亲王医院）；Auckland City Hospital（奥克兰市医院）
Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to create virtual models of coronary arteries. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. We provide anonymized CTCA images, voxel-wise annotations and associated data in the form of centrelines, calcification scores and meshes of the coronary lumen in 20 normal and 20 diseased cases. Images were obtained along with patient information with informed, written cons…
CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography
University of Málaga（马拉加大学）；Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND（马拉加生物医学与纳米医学平台研究所-IBIMA BIONAND平台）；Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV)（心血管疾病网络生物医学研究中心）；Instituto de Salud Carlos III (ISCIII)（卡洛斯三世卫生研究所）
Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局生物信息学研究所）；Hwa Chong Institution (College)（华侨中学（高中部））；National Heart Centre Singapore（新加坡国家心脏中心）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；School of Computing, National University of Singapore（新加坡国立大学计算学院）；Singapore Eye Research Institute (SERI)（新加坡眼科研究所）；Rehabilitation Research Institute of Singapore（新加坡康复研究所）；Institute of Bioengineering and Nanotechnology, A*STAR（新加坡科技研究局生物工程与纳米技术研究所）
The evaluation of obstructions (stenosis) in coronary arteries is currently done by a physician's visual assessment of coronary angiography video sequences. It is laborious, and can be susceptible to interobserver variation. Prior studies have attempted to automate this process, but few have demonstrated an integrated suite of algorithms for the end-to-end analysis of angiograms. We report an automated analysis pipeline based on deep learning to rapidly and objectively assess coronary angiograms, highlight coronary vessels of interest, and quantify potential stenosis. We propose a 3-stage auto…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；Utrecht University（乌得勒支大学）
In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 dif…
INESC-ID（里斯本系统与计算机工程研究所）；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）；Lisbon School of Medicine, University of Lisbon（里斯本大学里斯本医学院）
Coronary X-ray angiography is a crucial clinical procedure for the diagnosis and treatment of coronary artery disease, which accounts for roughly 16% of global deaths every year. However, the images acquired in these procedures have low resolution and poor contrast, making lesion detection and assessment challenging. Accurate coronary artery segmentation not only helps mitigate these problems, but also allows the extraction of relevant anatomical features for further analysis by quantitative methods. Although automated segmentation of coronary arteries has been proposed before, previous approa…
Predicting Coronary Artery Calcium Severity based on Non-Contrast Cardiac CT images using Deep Learning
School of Clinical Medicine, Faculty of Medicine \& Health, UNSW Sydney, Australia Rural Clinical School, Faculty of Medicine \& Health, UNSW, Port Macquarie, Australia School of Computer Science；Engineering, Faculty of Engineering, UNSW Sydney, Australia Bankstown-Lidcombe Hospital, South Western Sydney Local Health District, Bankstown, Australia
To decrease patient waiting time for diagnosis of the Coronary Artery Disease, automatic methods are applied to identify its severity using Coronary Computed Tomography Angiography scans or extracted Multiplanar Reconstruction (MPR) images, giving doctors a second-opinion on the priority of each case. The main disadvantage of previous studies is the lack of large set of data that could guarantee their reliability. Another limitation is the usage of handcrafted features requiring manual preprocessing, such as centerline extraction. We overcome both limitations by applying a different automated …
Computationally synthesized blood vessels can be used for training and evaluation of medical image analysis applications. We propose a deep generative model to synthesize blood vessel geometries, with an application to coronary arteries in cardiac CT angiography (CCTA). In the proposed method, a Wasserstein generative adversarial network (GAN) consisting of a generator and a discriminator network is trained. While the generator tries to synthesize realistic blood vessel geometries, the discriminator tries to distinguish synthesized geometries from those of real blood vessels. Both real and syn…
