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）
Coronary Artery Diseases although preventable are one of the leading cause of mortality worldwide. Due to the onerous nature of diagnosis, tackling CADs has proved challenging. This study addresses the automation of resource-intensive and time-consuming process of manually detecting stenotic lesions in coronary arteries in X-ray coronary angiography images. To overcome this challenge, we employ a specialized Convnext-V2 backbone based Mask RCNN model pre-trained for instance segmentation tasks. Our empirical findings affirm that the proposed model exhibits commendable performance in identifying stenotic lesions. Notably, our approach achieves a substantial F1 score of 0.5353 in this demanding task, underscoring its effectiveness in streamlining this intensive process.
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）
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）
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…
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）
The determination of a coronary stenosis and its severity in current clinical workflow is typically accomplished manually via physician visual assessment (PVA) during invasive coronary angiography. While PVA has shown large inter-rater variability, the more reliable and accurate alternative of Quantitative Coronary Angiography (QCA) is challenging to perform in real-time due to the busy workflow in cardiac catheterization laboratories. We propose a deep learning approach based on Convolutional Neural Networks (CNN) that automatically characterizes and analyzes coronary stenoses in real-time by…
Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital（人工智能在医疗与健康中的chair，慕尼黑技术大学及慕尼黑技术大学医院）；Department of Computing, Imperial College London, UK, and Munich Center for Machine Learning (MCML)（计算系，伦敦帝国学院，英国，以及慕尼黑机器学习中心）；Department of Internal Medicine, TUM University Hospital（内科系，慕尼黑技术大学医院）
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…
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…
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 …
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）
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）
Fudan University（复旦大学）；Shanghai East Hospital, Tongji University（同济大学附属东方医院）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）
We proposed a novel unsupervised methodology named Disarranged Zone Learning (DZL) to automatically recognize stenosis in coronary angiography. The methodology firstly disarranges the frames in a video, secondly it generates an effective zone and lastly trains an encoder-decoder GRU model to learn the capability to recover disarranged frames. The breakthrough of our study is to discover and validate the Sequence Intensity (Recover Difficulty) is a measure of Coronary Artery Stenosis Status. Hence, the prediction accuracy of DZL is used as an approximator of coronary stenosis indicator. DZL is …
Instituto Superior Técnico（高等技术学院）；University of Lisbon（里斯本大学）；CCUL（里斯本大学中心学院）；Lisbon School of Medicine（里斯本医学院）
Coronary artery disease leading up to stenosis, the partial or total blocking of coronary arteries, is a severe condition that affects millions of patients each year. Automated identification and classification of stenosis severity from minimally invasive procedures would be of great clinical value, but existing methods do not match the accuracy of experienced cardiologists, due to the complexity of the task. Although a number of computational approaches for quantitative assessment of stenosis have been proposed to date, the performance of these methods is still far from the required levels fo…
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 …
Coronary angiography is the "gold standard" for diagnosing coronary artery disease (CAD). At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice. Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this meth…
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 …
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）
School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；School of Material Science and Engineering, Ocean University of China（中国海洋大学材料科学与工程学院）；School of Mathematical Sciences, Ocean University of China（中国海洋大学数学科学学院）；Department of Cardiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University（山东大学齐鲁医学院齐鲁医院（青岛）心内科）
Coronary angiography is the "gold standard" for diagnosing coronary artery disease (CAD). At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice. Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this meth…
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…
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 …
Coronary heart disease (CHD) is the leading cause of adult death in the United States and worldwide, and for which the coronary angiography procedure is the primary gateway for diagnosis and clinical management decisions. The standard-of-care for interpretation of coronary angiograms depends upon ad-hoc visual assessment by the physician operator. However, ad-hoc visual interpretation of angiograms is poorly reproducible, highly variable and bias prone. Here we show for the first time that fully-automated angiogram interpretation to estimate coronary artery stenosis is possible using a sequenc…
Michigan Technological University（密歇根理工大学）；University of Southern Mississippi（南密西西比大学）；Tulane University（杜兰大学）；Ochsner Medical Center（奥克斯纳医疗中心）
In stable coronary artery disease (CAD), reduction in mortality and/or myocardial infarction with revascularization over medical therapy has not been reliably achieved. Coronary arteries are usually extracted to perform stenosis detection. We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs.In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. Incorporating features such as the Inception residual module with depth-wise separable convolutional layers,…
A Deep Learning Model for Coronary Artery Segmentation and Quantitative Stenosis Detection in Angiographic Images
School of Mathematical Sciences, Ocean University of China（中国海洋大学数学科学学院）；School of Haide, Ocean University of China（中国海洋大学海德学院）；School of Ocean and Atmosphere, Ocean University of China（中国海洋大学海洋与大气学院）；Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University（山东大学齐鲁医院（青岛））
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）
MAC-XA: Multi-view Anatomy-Correspondence Fusion for Coronary Stenosis Reporting from X-ray Angiography
Technical University of Munich（慕尼黑工业大学）；German Heart Center Munich（慕尼黑德国心脏中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）；German Centre for Cardiovascular Research, Munich Heart Alliance（德国心血管研究中心，慕尼黑心脏联盟）；HELIOS Hospital West of Munich（慕尼黑西部黑利奥斯医院）；relAI – Konrad Zuse School of Excellence in Reliable AI（relAI - 康拉德·楚泽可靠人工智能卓越学院）
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）
A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography
Center for Applied AI, University of Kentucky（应用人工智能中心，肯塔基大学）；EduceLab, University of Kentucky（EduceLab，肯塔基大学）；Department of Medicine, Division of Cardiovascular Medicine and the Gill Heart and Vascular Institute, University of Kentucky（医学部，心血管医学分会和吉尔心脏和血管研究所，肯塔基大学）；Department of Radiology, College of Medicine, University of Kentucky（放射学部，医学院，肯塔基大学）
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. …
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）
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…
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…
Coronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography
School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran（计算机科学学院，基础科学研究所（IPM），德黑兰，伊朗）；Department of Physics, Shahid Beheshti University, Tehran, Iran（物理系，肖德·贝赫什提大学，德黑兰，伊朗）；Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran（脑图谱研究中心，肖德·贝赫什提医学科学大学，德黑兰，伊朗）；Gilan University of Medical Sciences, Rasht, Gilan, Iran（加利良医学科学大学，拉什特，加利良，伊朗）；Department of Physics, Simon Fraser University, Burnaby, Canada（物理系，西蒙·弗雷泽大学，本比，加拿大）；Fatemeh Zahra Hospital, Alborz University of Medical Sciences, Eshtehard, Iran（法特梅·扎拉医院，阿尔博兹医学科学大学，埃什泰尔德，伊朗）；Rajaie Cardiovascular Medical and Research Institute, Iran University of Medical Sciences, Tehran, Iran（拉贾伊心血管医学与研究学院，伊朗医学科学大学，德黑兰，伊朗）
