Automated anatomical labeling plays a vital role in coronary artery disease diagnosing procedure. The main challenge in this problem is the large individual variability inherited in human anatomy. Existing methods usually rely on the position information and the prior knowledge of the topology of the coronary artery tree, which may lead to unsatisfactory performance when the main branches are confusing. Motivated by the wide application of the graph neural network in structured data, in this paper, we propose a conditional partial-residual graph convolutional network (CPR-GCN), which takes both position and CT image into consideration, since CT image contains abundant information such as branch size and spanning direction. Two majority parts, a Partial-Residual GCN and a conditions extractor, are included in CPR-GCN. The conditions extractor is a hybrid model containing the 3D CNN and the LSTM, which can extract 3D spatial image features along the branches. On the technical side, the Partial-Residual GCN takes the position features of the branches, with the 3D spatial image features as conditions, to predict the label for each branches. While on the mathematical side, our approach twists the partial differential equation (PDE) into the graph modeling. A dataset with 511 subjects is collected from the clinic and annotated by two experts with a two-phase annotation process. According to the five-fold cross-validation, our CPR-GCN yields 95.8% meanRecall, 95.4% meanPrecision and 0.955 meanF1, which outperforms state-of-the-art approaches.
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…
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…
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）
Peking University（北京大学）；Peking University First Hospital（北京大学第一医院）；MGH/BWH Center for Clinical Data Science（麻省总医院/布莱根妇女医院临床数据科学中心）
Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible. To solve this problem, we propose a knowledge transfer based shape-consistent generative adversarial network (SC-GAN), which is an annotation-free approach that uses the knowledge from publicly available annotated fundus dataset to segment coronary arteries. The proposed network is trained in an end-to-end fashion, generating and segmenting synthe…
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,…
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 …
Gulbarga University（古尔伯加大学）；S. V. College（S. V.学院）
At present, coronary angiography is the well known standard for the diagnosis of coronary artery disease. Conventional coronary angiography is an invasive procedure with a small, yet inherent risk of myocardial infarction, stroke, potential arrhythmias, and death. Other noninvasive diagnostic tools, such as electrocardiography, echocardiography, and nuclear imaging are now widely available but are limited by their inability to directly visualize and quantify coronary artery stenoses and predict the stability of plaques. Coronary magnetic resonance angiography (MRA) is a technique that allows v…
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…
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…
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）
Hamburg University of Technology（汉堡工业大学）；Universitätsklinikum Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）
Advanced atherosclerosis in the coronary arteries is one of the leading causes of deaths worldwide while being preventable and treatable. In order to image atherosclerotic lesions (plaque), intravascular optical coherence tomography (IVOCT) can be used. The technique provides high-resolution images of arterial walls which allows for early plaque detection by experts. Due to the vast amount of IVOCT images acquired in clinical routines, automatic plaque detection has been addressed. For example, attenuation profiles in single A-Scans of IVOCT images are examined to detect plaque. We address aut…
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…
A Diffusion Model for Simulation Ready Coronary Anatomy with Morpho-skeletal Control
Institute of Medical Engineering, MIT（麻省理工学院医学工程研究所）；Brigham and Women's Hospital, Harvard（哈佛大学布莱尔妇女医院）；HeartFlow（HeartFlow公司）；Showa University（昭和大学）；OLV Aalst
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）
Case Western Reserve University（凯斯西储大学）；The Hashemite University（哈希姆大学）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医学中心）；Florida Institute of Technology（佛罗里达理工学院）；University of South Florida（南佛罗里达大学）
BACKGROUND Careful evaluation of the risk of stent under-expansions before the intervention will aid treatment planning, including the application of a pre-stent plaque modification strategy. OBJECTIVES It remains challenging to achieve a proper stent expansion in the presence of severely calcified coronary lesions. Building on our work in deep learning segmentation, we created an automated machine learning approach that uses lesion attributes to predict stent under-expansion from pre-stent images, suggesting the need for plaque modification. METHODS Pre- and post-stent intravascular optical c…
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）
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…
Coronary heart disease is one of the top rank leading cause of mortality in the world which can be because of plaque burden inside the arteries. Intravascular Ultrasound (IVUS) has been recognized as power- ful imaging technology which captures the real time and high resolution images of the coronary arteries and can be used for the analysis of these plaques. The IVUS segmentation involves the extraction of two arterial walls components namely, lumen and media. In this paper, we investi- gate the effectiveness of Convolutional Neural Networks including U-Net to segment ultrasound scans of arte…
Siemens SRL（西门子SRL公司）；Transilvania University of Brasov（布拉索夫特兰西瓦尼亚大学）；Clinical Emergency Hospital（临床急诊医院）；Siemens Healthcare GmbH（西门子医疗保健有限公司）；Siemens Healthineers（西门子医疗）
Invasive coronary angiography (ICA) is the gold standard in Coronary Artery Disease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, in general, cardiac phase detection on each temporal frame of a coronary angiography acquisition is of significant importance for the anatomical and non-invasive functional assessment of CAD. This task is generally performed via manual frame selection or semi-automated selection based on simultaneously acquired ECG signals - thus introducing the requirement of simultaneous ECG recordings. We evaluate the performance of a purely image based workflow …
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…
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）
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…
Case Western Reserve University（凯斯西储大学）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医疗中心）；The Hashemite University（哈希姆大学）；University of South Florida（南佛罗里达大学）
Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4,360 IVOCT image frames of 77 lesions among 41 patients. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifti…
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））
McMaster University（麦克马斯特大学）；Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Coronary artery disease (CAD) is the leading causes of death around the world. One of the most common imaging methods for diagnosing this disease is X-ray angiography. Diagnosing using these images is usually challenging due to non-uniform illumination, low contrast, presence of other body tissues, presence of catheter etc. These challenges make the diagnoses task of cardiologists tougher and more prone to misdiagnosis. In this paper we propose a new automated framework for coronary arteries segmentation, catheter detection and center-line extraction in x-ray angiography images. Our proposed s…
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）
Deep learning based detection of collateral circulation in coronary angiographies
Siemens SRL（西门子有限责任公司）；Transilvania University of Brasov（布拉索夫特兰西瓦尼亚大学）；University Heart Center Lübeck（吕贝克大学心脏中心）；University Hospital（大学医院）；University Medical Centre Mannheim（曼海姆大学医学中心）；Heidelberg University（海德堡大学）
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…
Erasmus MC, University Medical Center Rotterdam（鹿特丹伊拉斯姆斯医学中心（鹿特丹大学医学中心）；Delft University of Technology（代尔夫特理工大学）
Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using non-contrast-enhanced fluoroscopic images, since higher use of contrast agents increases the risk of kidney failure. When using fluoroscopic images, the interventional cardiologist needs to rely on a mental anatomical reconstruction. This paper reports on the development of a novel dynamic coronary roadmapping approach for improving visual feedback …
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…
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 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）
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）
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）
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）
Coronary Artery Disease Classification with Different Lesion Degree Ranges based on Deep Learning
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 red 生物医学研究中心（CIBERCV））；Instituto de Salud Carlos III (ISCIII)（卡洛斯三世卫生研究所（ISCIII））；Hospital Universitario Virgen de la Victoria（维多利亚圣母大学医院）
Automated Plaque Detection and Agatston Score Estimation on Non-Contrast CT Scans: A Multicenter Study
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；National Institute of Arthritis and Musculoskeletal and Skin Diseases（美国国立关节炎、肌肉骨骼和皮肤病研究所）
Every year, thousands of innocent people die due to heart attacks. Often undiagnosed heart attacks can hit people by surprise since many current medical plans don't cover the costs to require the searching of calcification on these scans. Only if someone is suspected to have a heart problem, a gated CT scan is taken, otherwise, there's no way for the patient to be aware of a possible heart attack/disease. While nongated CT scans are more periodically taken, it is harder to detect calcification and is usually taken for a purpose other than locating calcification in arteries. In fact, in real ti…
Shanghai Jiao Tong University（上海交通大学）；University of Electronic Science and Technology of China（电子科技大学）；SenseTime Research（商汤科技研究院）；Peking Union Medical College Hospital（北京协和医院）
The segmentation of coronary arteries in X-ray angiograms by convolutional neural networks (CNNs) is promising yet limited by the requirement of precisely annotating all pixels in a large number of training images, which is extremely labor-intensive especially for complex coronary trees. To alleviate the burden on the annotator, we propose a novel weakly supervised training framework that learns from noisy pseudo labels generated from automatic vessel enhancement, rather than accurate labels obtained by fully manual annotation. A typical self-paced learning scheme is used to make the training …
Dynamic reconstructions (3D+T) of coronary arteries could give important perfusion details to clinicians. Temporal matching of the different views, which may not be acquired simultaneously, is a prerequisite for an accurate stereo-matching of the coronary segments. In this paper, we show how a neural network can be trained from angiographic sequences to synchronize different views during the cardiac cycle using raw x-ray angiography videos exclusively. First, we train a neural network model with angiographic sequences to extract features describing the progression of the cardiac cycle. Then, w…
Deep Learning-Based Automated Quantification of TIMI Myocardial Perfusion Frame Count (DL-TMPFC) from Coronary Angiography: A Novel Framework for Rapid Assessment of Microvascular Dysfunction
School of Artificial Intelligence and Digital Economy Industry, Guangzhou Institute of Science and Technology（人工智能与数字经济发展学院，广州科学研究院）；Department of Cardiology, Second Affiliated Hospital, Jiangxi Medical College, Nanchang University（南华大学江西医学院心内科，第二附属医院）；Department of Cardiology, First Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第一医院心内科）；Department of Cardiology, National University Heart Centre（国立大学心脏中心心内科）
Korea Advanced Institute of Science and Technology（韩国科学技术院）；University of Ulsan College of Medicine（蔚山大学医学院）；Asan Medical Center（峨山医学中心）；Asan Institute for Life Sciences（峨山生命科学研究院）
Acute Coronary Syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morphologically to a plaque rupture. In this paper, we propose methods to classify TCFA using various machine learning classifiers including Feed-forward Neural Network (FNN), K-Nearest Neighbor (KNN), Random Forest (RF) and Convolutional Neural Network (CNN) to figure out a classifie…
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）；Cardiovascular Research Foundation（心血管研究基金会）
Optical coherence tomography (OCT) can provide high-resolution cross-sectional images for analyzing superficial plaques in coronary arteries. Commonly, plaque characterization using intra-coronary OCT images is performed manually by expert observers. This manual analysis is time consuming and its accuracy heavily relies on the experience of human observers. Traditional machine learning based methods, such as the least squares support vector machine and random forest methods, have been recently employed to automatically characterize plaque regions in OCT images. Several processing steps, includ…
Insight Lifetech（因赛特生命科技）；Southern Medical University（南方医科大学）
Intravascular ultrasound (IVUS) is the preferred modality for capturing real-time and high resolution cross-sectional images of the coronary arteries, and evaluating the stenosis. Accurate and real-time segmentation of IVUS images involves the delineation of lumen and external elastic membrane borders. In this paper, we propose a two-stream framework for efficient segmentation of 60 MHz high resolution IVUS images. It combines shallow and deep networks, namely, CSDN. The shallow network with thick channels focuses to extract low-level details. The deep network with thin channels takes charge o…
Shanghai Jiao Tong University（上海交通大学）；University of Sydney（悉尼大学）；Ruijin Hospital, Shanghai Jiaotong University School of Medicine（上海交通大学医学院附属瑞金医院）
Implantation of stents into coronary arteries is a common treatment option for patients with cardiovascular disease. Assessment of safety and efficacy of the stent implantation occurs via manual visual inspection of the neointimal coverage from intravascular optical coherence tomography (IVOCT) images. However, such manual assessment requires the detection of thousands of strut points within the stent. This is a challenging, tedious, and time-consuming task because the strut points usually appear as small, irregular shaped objects with inhomogeneous textures, and are often occluded by shadows,…
Imperial College London（帝国理工学院）；HeartFlow, Inc.（HeartFlow公司）
Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typically involves computing the minimal cost path through intermediate representations derived from segmentation masks. However, this methodology has significant limitations in the context of projective imaging of tree-structured 3D anatomical data such as coronary arteries, since there are often overlapping branches in the 2D projection. In this work, we propose a novel approach to predicting tree connectivity structur…
