Accurate coronary artery segmentation from coronary computed tomography angiography is essential for quantitative coronary analysis and clinical decision support. Nevertheless, reliable segmentation remains challenging because of small vessel calibers, complex branching, blurred boundaries, and myocardial interference. We propose a coronary artery segmentation framework that integrates myocardial anatomical priors, structure aware feature encoding, and three dimensional wavelet inverse wavelet transformations. Myocardial priors and residual attention based feature enhancement are incorporated during encoding to strengthen coronary structure representation. Wavelet inverse wavelet based downsampling and upsampling enable joint spatial frequency modeling and preserve multi scale structural consistency, while a multi scale feature fusion module integrates semantic and geometric information in the decoding stage. The model is trained and evaluated on the public ImageCAS dataset using a 3D overlapping patch based strategy with a 7:1:2 split for training, validation, and testing. Experimental results demonstrate that the proposed method achieves a Dice coefficient of 0.8082, Sensitivity of 0.7946, Precision of 0.8471, and an HD95 of 9.77 mm, outperforming several mainstream segmentation models. Ablation studies further confirm the complementary contributions of individual components. The proposed method enables more stable and consistent coronary artery segmentation under complex geometric conditions, providing reliable segmentation results for subsequent coronary structure analysis tasks.
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…
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）
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…
A multicenter benchmark and clinically structured metric for coronary CTA report generation
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Pengcheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（北京协和医学院附属北京协和医院（中国医学科学院北京协和医学院））
University of Science and Technology of China（中国科学技术大学）；The First Affiliated Hospital of Dalian Medical University（大连医科大学附属第一医院）
Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. The primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL backbone (termed as T-MIL) to aggregate the mu…
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 …
The University of Tokyo（东京大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Sakurabashi Watanabe Hospital（樱桥渡边医院）
Providing closed and well-connected boundaries of coronary artery is essential to assist cardiologists in the diagnosis of coronary artery disease (CAD). Recently, several deep learning-based methods have been proposed for boundary detection and segmentation in a medical image. However, when applied to coronary wall detection, they tend to produce disconnected and inaccurate boundaries. In this paper, we propose a novel boundary detection method for coronary arteries that focuses on the continuity and connectivity of the boundaries. In order to model the spatial continuity of consecutive image…
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）
O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis
Dushu Lake Hospital Affiliated to Soochow University（苏州大学附属独墅湖医院）；Institution for Hypertension of Soochow University（苏州大学高血压研究所）；University of Science and Technology of China（中国科学技术大学）
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) has posed a leading threat to the lives of cardiovascular disease patients worldwide for a long time. Therefore, automated diagnosis of CAD has indispensable significance in clinical medicine. However, the complexity of coronary artery plaques that cause CAD makes the automatic detection of coronary artery stenosis in Coronary CT angiography (CCTA) a difficult task. In this paper, we propose a Transformer network (TR-Net) for the automatic detection of significant stenosis (i.e. luminal narrowing > 50%) while practically completing the computer-assisted diagnosis …
Philips Medical Systems Technologies Ltd.（飞利浦医疗系统技术有限公司）
Purpose: The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm from coronary computed tomography angiography (CCTA). Materials and methods: We assessed the potential added value of PVE integration as a part of the automatic coronary lumen segmentation algorithm by means of segmentation accuracy using the MICCAI 2012 challenge framework and by means of flow simulation overall accuracy, sensitivity, specificity, negative and positive predictive values and the receiver operated characte…
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）
Mayo Clinic（梅奥诊所）；University of Iowa（爱荷华大学）；Siemens Healthineers（西门子医疗）；NVIDIA（英伟达）
Coronary Computed Tomography Angiography (CCTA) evaluation of chest-pain patients in an Emergency Department (ED) is considered appropriate. While a negative CCTA interpretation supports direct patient discharge from an ED, labor-intensive analyses are required, with accuracy in jeopardy from distractions. We describe the development of an Artificial Intelligence (AI) algorithm and workflow for assisting interpreting physicians in CCTA screening for the absence of coronary atherosclerosis. The two-phase approach consisted of (1) Phase 1 - focused on the development and preliminary testing of a…
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…
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）
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…
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）
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）
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）
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…
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. …
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）
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）
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Siemens Healthcare GmbH（西门子医疗有限公司）；University Clinic Frankfurt（法兰克福大学诊所）；Cardioangiological Centrum Bethanien（贝塔尼恩心血管中心）
Assessing coronary artery plaque segments in coronary CT angiography scans is an important task to improve patient management and clinical outcomes, as it can help to decide whether invasive investigation and treatment are necessary. In this work, we present three machine learning approaches capable of performing this task. The first approach is based on radiomics, where a plaque segmentation is used to calculate various shape-, intensity- and texture-based features under different image transformations. A second approach is based on deep learning and relies on centerline extraction as sole pr…
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）
