We submitted the paper due to an operational error. This paper is a modified version of the original paper Two New Stenoses Detection Methods of Coronary Angiograms (arXiv:2108.01516). And we will update the revised paper to the original paper later
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 method, the user can choose any vessel segment to do further analysis of its stenoses. Experiments show that the proposed methods are robust for angiograms with various vessel structures. The precision, sensitivity, and $F_1$ score of the automatic stenosis detection method are 0.821, 0.757, and 0.788, respectively. Further investigation proves that the interactive method can provide a more precise outcome of stenosis detection, and our quantitative analysis is closer to reality. The proposed automatic method and interactive method are effective and can complement each other in clinical practice. The first method can be used for preliminary screening, and the second method can be used for further quantitative analysis. We believe the proposed solution is more suitable for the clinical diagnosis of CAD.
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…
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…
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 …
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…
Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms
Zhengzhou University of Light Industry（郑州轻工业大学）；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）
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 …
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 …
X-ray coronary angiography (XCA) is a principal approach employed for identifying coronary disorders. Deep learning-based networks have recently shown tremendous promise in the diagnosis of coronary disorder from XCA scans. A deep learning-based edge adaptive instance normalization style transfer technique for segmenting the coronary arteries, is presented in this paper. The proposed technique combines adaptive instance normalization style transfer with the dense extreme inception network and convolution block attention module to get the best artery segmentation performance. We tested the prop…
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…
A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms
Radiology, Department of Surgical Sciences, Uppsala University（乌普萨拉大学放射科）；Antaros Medical（Antaros医疗）；Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg（哥德堡大学医学院分子与临床医学系）
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））
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…
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）
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…
the Second Affiliated Hospital Zhengzhou University（郑州大学第二附属医院）；School of Information Engineering Zhengzhou University（郑州大学信息工程学院）
How to accurately classify and diagnose whether an individual has Coronary Stenosis (CS) without invasive physical examination? This problem has not been solved satisfactorily. To this end, the four machine learning (ML) algorithms, i.e., Boosted Tree (BT), Decision Tree (DT), Logistic Regression (LR) and Random Forest (RF) are employed in this paper. First, eleven features including basic information of an individual, symptoms and results of routine physical examination are selected, as well as one label is specified, indicating whether an individual suffers from different severity of coronar…
Background. Percutaneous coronary intervention(PCI) in stable coronary artery disease(CAD) is commonly triggered by abnormal myocardial perfusion imaging(MPI). However, due to the possibilities of multivessel disease and variability of coronary artery perfusion distribution, opportunity exists to better align anatomic stenosis with perfusion abnormalities to improve revascularization decisions. This study aims to develop a 3D multi-modality fusion approach to assist decision-making for PCI. Methods. Coronary arteries from fluoroscopic angiography(FA) were reconstructed into 3D artery anatomy. …
In recent years, coronary artery disease is escalating and is likely to assume an epidemic proportion by 2030. Currently the reliable methods for detection of coronary arterial lesions are either conventional coronary angiogram (CAG) or MDCT (Multiple Detector Computed Tomography) coronary angiogram. Conventional CAG is an invasive procedure. Conventional CAG and CT (Computed Tomography) angiogram, both require expert supervision of either an interventional cardiologist or a radiologist. In this work, we have proposed a novel design and method for non-invasive detection and localization of cor…
Vessel stenosis is a major risk factor in cardiovascular diseases (CVD). To analyze the degree of vessel stenosis for supporting the treatment management, extraction of coronary artery area from Computed Tomographic Angiography (CTA) is regarded as a key procedure. However, manual segmentation by cardiologists may be a time-consuming task, and present a significant inter-observer variation. Although various computer-aided approaches have been developed to support segmentation of coronary arteries in CTA, the results remain unreliable due to complex attenuation appearance of plaques, which are …
Babol Noshirvani University of Technology（巴博勒努希尔瓦尼技术大学）；Italian Institute of Technology（意大利技术研究院）；Royan Institute for Stem Cell Biology and Technology（罗扬干细胞生物学与技术研究所）；Sharif University of Technology（谢里夫理工大学）
Prediction of the blood flow characteristics is of utmost importance for understanding the behavior of the blood arterial network, especially in the presence of vascular diseases such as stenosis. Computational fluid dynamics (CFD) has provided a powerful and efficient tool to determine these characteristics including the pressure and velocity fields within the network. Despite numerous studies in the field, the extremely high computational cost of CFD has led the researchers to develop new platforms including Machine Learning approaches that instead provide faster analyses at a much lower cos…
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…
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 …
Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
Chair for AI in Healthcare and Medicine, Technical University of Munich and TUM University Hospital（人工智能在医疗与医学中的研究所，慕尼黑技术大学及慕尼黑大学医院）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML））；Department of Internal Medicine, TUM University Hospital（慕尼黑大学医院内科学系）
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）
Zhengzhou University of Light Industry（郑州轻工业大学）；University of Southern Mississippi（南密西西比大学）；Michigan Technological University（密歇根理工大学）；New York Presbyterian Queens Hospital（纽约长老会皇后医院）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）
Automatic identification of proper image frames at the end-diastolic (ED) and end-systolic (ES) frames during the review of invasive coronary angiograms (ICA) is important to assess blood flow during a cardiac cycle, reconstruct the 3D arterial anatomy from bi-planar views, and generate the complementary fusion map with myocardial images. The current identification method primarily relies on visual interpretation, making it not only time-consuming but also less reproducible. In this paper, we propose a new method to automatically identify angiographic image frames associated with the ED and ES…
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）
A Federated Learning Framework for Stenosis Detection
Università Politecnica delle Marche（马尔凯理工大学）；Università degli Studi di Macerata（马切拉塔大学）；Azienda Ospedaliero Universitaria delle Marche（马尔凯大学医院）；Ospedale del Mare, ASL NA 1（ASL NA 1 海洋医院）；University of Macerata（马切拉塔大学）；Scuola Superiore Sant’Anna（圣安娜高等研究学院）
CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: a clinically-inspired deep learning pipeline
University of Pavia（帕维亚大学）；Centro Cardiologico Monzino IRCCS（蒙齐诺心脏病学中心IRCCS）；University of Milan（米兰大学）；IRCCS Istituti Clinici Scientifici Maugeri（IRCCS莫杰里科学临床研究所）
Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Siemens Healthcare GmbH（西门子医疗有限公司）；Das Radiologische Zentrum - Radiology Center（放射中心）
Coronary CT angiography (CCTA) has established its role as a non-invasive modality for the diagnosis of coronary artery disease (CAD). The CAD-Reporting and Data System (CAD-RADS) has been developed to standardize communication and aid in decision making based on CCTA findings. The CAD-RADS score is determined by manual assessment of all coronary vessels and the grading of lesions within the coronary artery tree. We propose a bottom-up approach for fully-automated prediction of this score using deep-learning operating on a segment-wise representation of the coronary arteries. The method relies…
ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images
Guangdong University of Technology（广东工业大学）；Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences)（广东省人民医院（广东省医学科学院））；Southern Medical University（南方医科大学）；Missouri University of Science and Technology（密苏里科技大学）
