Stroke is the second most frequent cause of death worldwide with a considerable economic burden on the health systems. In about 15% of strokes, atherosclerotic carotid plaques (ACPs) constitute the main etiological factor. Early detection of ACPs may have a key-role for preventing strokes by managing the patient a-priory to the occurrence of the damage. ACPs can be detected on panoramic images. As these are one of the most common images performed for routine dental practice, they can be used as a source of available data for computerized methods of automatic detection in order to significantly increase timely diagnosis of ACPs. Recently, there has been a definite breakthrough in the field of analysis of medical images due to the use of deep learning based on neural networks. These methods, however have been barely used in dentistry. In this study we used the Faster Region-based Convolutional Network (Faster R-CNN) for deep learning. We aimed to assess the operation of the algorithm on a small database of 65 panoramic images. Due to a small amount of available training data, we had to use data augmentation by changing the brightness and randomly flipping and rotating cropped regions of interest in multiple angles. Receiver Operating Characteristic (ROC) analysis was performed to calculate the accuracy of detection. ACP was detected with a sensitivity of 75%, specificity of 80% and an accuracy of 83%. The ROC analysis showed a significant Area Under Curve (AUC) difference from 0.5. Our novelty lies in that we have showed the efficiency of the Faster R-CNN algorithm in detecting ACPs on routine panoramic images based on a small database. There is a need to further improve the application of the algorithm to the level of introducing this methodology in routine dental practice in order to enable us to prevent stroke events.
Detection of non-calcified plaques in the coronary tree is a challenging problem due to the nature of comprising substances. Hard plaques are easily discernible in CTA data cloud due to apparent bright behaviour, therefore many approaches have been proposed for automatic segmentation of calcified plaques. In contrast soft plaques show very small difference in intensity with respect to surrounding heart tissues & blood voxels. This similarity in intensity makes the isolation and detection of soft plaques very difficult. This work aims to develop framework for segmentation of vulnerable plaques …
Carotid Artery Plaque Analysis in 3D Based on Distance Encoding in Mesh Representations
Institute of Computer-Assisted Cardiovascular Medicine（计算机辅助心血管医学研究所）；Deutsches Herzzentrum der Charité（Charité心脏中心）；Fraunhofer MEVIS（弗劳恩霍夫医学影像研究所）；DZHK (German Centre for Cardiovascular Research)（德国心血管研究中心）；Physikalisch-Technische Bundesanstalt (PTB)（物理技术联邦机构）
Beihang University（北京航空航天大学）；XuanWu Hospital, Capital Medical University（首都医科大学宣武医院）；Beijing Diagnostic Center of Vascular Ultrasound（北京血管超声诊断中心）
Carotid arteries vulnerable plaques are a crucial factor in the screening of atherosclerosis by ultrasound technique. However, the plaques are contaminated by various noises such as artifact, speckle noise, and manual segmentation may be time-consuming. This paper proposes an automatic convolutional neural network (CNN) method for plaque segmentation in carotid ultrasound images using a small dataset. First, a parallel network with three independent scale decoders is utilized as our base segmentation network, pyramid dilation convolutions are used to enlarge receptive fields in the three segme…
CRESTomics: Analyzing Carotid Plaques in the CREST-2 Trial with a New Additive Classification Model
University of Maryland Institute for Health Computing（马里兰大学健康计算研究所）；University of Maryland（马里兰大学）；University of Maryland School of Medicine（马里兰大学医学院）；Mayo Clinic（梅奥诊所）；Medical University of South Carolina（南卡罗来纳医学院）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Hierarchical Corpus-View-Category Refinement for Carotid Plaque Risk Grading in Ultrasound
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound（医学超声关键技术工程实验室）；School of Biomedical Engineering, Medical School, Shenzhen University（生物医学工程学院）；College of Computer Science and Software Engineering, Shenzhen University（计算机科学与软件工程学院）；Fuwai Shenzhen Hospital, Chinese Academy of Medical Sciences（阜外深圳医院，中国医学科学院）；Shenzhen RayShape Medical Technology Co., Ltd（深圳RayShape医疗科技有限公司）；Faculty of Applied Sciences, Macao Polytechnic University（应用科学学院）
Carotid atherosclerosis is the major cause of ischemic stroke resulting in significant rates of mortality and disability annually. Early diagnosis of such cases is of great importance, since it enables clinicians to apply a more effective treatment strategy. This paper introduces an interpretable classification approach of carotid ultrasound images for the risk assessment and stratification of patients with carotid atheromatous plaque. To address the highly imbalanced distribution of patients between the symptomatic and asymptomatic classes (16 vs 58, respectively), an ensemble learning scheme…
Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI
department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University（放射医学与核医学科，宣武医院，首都医科大学）；Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics（北京磁共振成像与脑信息学重点实验室）；Beijing United Imaging Research Institute of Intelligent Imaging（北京智能成像联合研究院）
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）
A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification
Hubei University of Technology（湖北工业大学）；Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属梨园医院）；Zhongnan Hospital, Wuhan University（武汉大学中南医院）；Robarts Research Institute, Western University（西安大略大学罗伯茨研究所）
An Intra- and Cross-frame Topological Consistency Scheme for Semi-supervised Atherosclerotic Coronary Plaque Segmentation
Xiamen University（厦门大学）；University of Washington（华盛顿大学）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；National Institute for Data Science in Health and Medicine, Xiamen University（厦门大学健康与医学数据科学国家研究院）
Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of carotid vessel wall on magnetic resonance (MR) images remains challenging, due to limited annotations and heterogeneous arteries. In this paper, we propose a semi-supervised label propagation framework to segment lumen, normal vessel walls, and atherosclerotic vessel wall on 3D MR images. By interpolating the provided annotations, we get 3D…
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…
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…
Advanced Technology Development Centre（先进技术发展中心）；Department of Computer Science and Engineering（计算机科学与工程系）；Department of Electrical Engineering（电气工程系）；Indian Institute of Technology Kharagpur（印度理工学院卡哈拉格普尔分校）；India（印度（注：根据上下文，此处“India”为国家，非机构，原数据可能存在格式问题，按要求不提取国家信息，但按输出要求保留英文机构名））
Coronary artery disease accounts for a large number of deaths across the world and clinicians generally prefer using x-ray computed tomography or magnetic resonance imaging for localizing vascular pathologies. Interventional imaging modalities like intravascular ultrasound (IVUS) are used to adjunct diagnosis of atherosclerotic plaques in vessels, and help assess morphological state of the vessel and plaque, which play a significant role for treatment planning. Since speckle intensity in IVUS images are inherently stochastic in nature and challenge clinicians with accurate visibility of the ve…
Two major causes of death in the United States and worldwide are stroke and myocardial infarction. The underlying cause of both is thrombi released from ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the heart (myocardial infarction) or the brain (stroke). Clinical studies show that plaque composition plays a more important role than lesion size in plaque rupture or erosion events. To determine the plaque composition, various cell types in 3D cardiovascular immunofluorescent images of plaque lesions are counted. However, counting these cells manually is expensive, …
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）
