The objective of this study is the segmentation of the intima-media complex of the common carotid artery, on longitudinal ultrasound images, to measure its thickness. We propose a fully automatic region-based segmentation method, involving a supervised region-based deep-learning approach based on a dilated U-net network. It was trained and evaluated using a 5-fold cross-validation on a multicenter database composed of 2176 images annotated by two experts. The resulting mean absolute difference (<120 um) compared to reference annotations was less than the inter-observer variability (180 um). With a 98.7% success rate, i.e., only 1.3% cases requiring manual correction, the proposed method has been shown to be robust and thus may be recommended for use in clinical practice.
GAPNet: Granularity Attention Network with Anatomy-Prior-Constraint for Carotid Artery Segmentation
Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院）山东省人工智能研究院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；School of Computer, Electronics and Information, Guangxi University（广西大学计算机与电子信息学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；University Paris Dauphine, PSL Research University, CNRS, UMR 7534, CEREMADE（巴黎多芬大学（PSL研究大学）、法国国家科学研究中心UMR 7534、CEREMADE）
Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis. This requires accurate segmentation of the vessel wall, i.e., the region between an artery's lumen and outer wall, in black-blood magnetic resonance (MR) images. Commonly used convolutional neural networks (CNNs) for semantic segmentation are suboptimal for this task as their use does not guarantee a contiguous ring-shaped segmentation. Instead, in this work, we cast vessel wall segmentation as a multi-task regression problem in a polar coordinate system. For each carotid a…
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…
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）
Erasmus MC（伊拉斯谟大学医学中心）；Stanford University（斯坦福大学）；Radboud University（拉德堡德大学）；University of Copenhagen（哥本哈根大学）
Purpose: To evaluate a fully-automated deep-learning-based method for assessment of intracranial carotid artery calcification (ICAC). Methods: Two observers manually delineated ICAC in non-contrast CT scans of 2,319 participants (mean age 69 (SD 7) years; 1154 women) of the Rotterdam Study, prospectively collected between 2003 and 2006. These data were used to retrospectively develop and validate a deep-learning-based method for automated ICAC delineation and volume measurement. To evaluate the method, we compared manual and automatic assessment (computed using ten-fold cross-validation) with …
In this report we want to present our method and results for the Carotid Artery Vessel Wall Segmentation Challenge. We propose an image-based pipeline utilizing the U-Net architecture and location priors to solve the segmentation problem at hand.
Segmentation of carotid vessel wall is required in vessel wall volume (VWV) and local vessel-wall-plus-plaque thickness (VWT) quantification of the carotid artery. Manual segmentation of the vessel wall is time-consuming and prone to interobserver variability. In this paper, we proposed a convolution neural network to segment the common carotid artery (CCA) from 3D carotid ultrasound images. The proposed CNN involves three U-Nets that segmented the 3D ultrasound (3DUS) images in the axial, lateral and frontal orientations. The segmentation maps generated by three U-Nets were consolidated by a …
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…
A CUBS-Compatible Ultrasound Morphology and Uncertainty-Aware Baseline for Carotid Intima-Media Segmentation and Preliminary Risk Prediction
School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology, Thammasat University（沙恭达国际技术学院，泰国朱拉隆功大学）
Force Sensing Guided Artery-Vein Segmentation via Sequential Ultrasound Images
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Centre for Artificial Intelligence and Robotics, HK Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；City University of Hong Kong（香港城市大学）
Northwestern Polytechnical University（西北工业大学）；Guangxi University for Nationalities（广西民族大学）；Shenzhen Delica Medical Equipement Company Limited（深圳德力凯医疗设备有限公司）；Hospital of Northwestern Polytechnical University（西北工业大学医院）
Ultrasound imaging plays an important role in the diagnosis of vascular lesions. Accurate segmentation of the vascular wall is important for the prevention, diagnosis and treatment of vascular diseases. However, existing methods have inaccurate localization of the vascular wall boundary. Segmentation errors occur in discontinuous vascular wall boundaries and dark boundaries. To overcome these problems, we propose a new boundary-delineation network (BDNet). We use the boundary refinement module to re-delineate the boundary of the vascular wall to obtain the correct boundary location. We designe…
Objective: Vessel-wall-volume (VWV) and localized vessel-wall-thickness (VWT) measured from 3D ultrasound (US) carotid images are sensitive to anti-atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen-intima (LIB) and media-adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of automating only CCA segmentation. An approach capable of segmenting the MAB and LIB from the CCA and ICA was required to accelerate VWV and VWT quantification. Methods: Segmentation…
Quantitative analysis of vessel wall structures by automated vessel wall segmentation provides useful imaging biomarkers in evaluating atherosclerotic lesions and plaque progression time-efficiently. To quantify vessel wall features, drawing lumen and outer wall contours of the artery of interest is required. To alleviate manual labor in contour drawing, some computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and boundary initialization are needed. In addition, the prior knowledge of the ring shape of vessel wall is not taken into considera…
Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高等研究院）；University of Chinese Academy of Sciences（中国科学院大学）；College of Computer Science, Sichuan University（四川大学计算机学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；The Chinese University of Hong Kong（香港中文大学）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）
University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；University of Lübeck（吕贝克大学）
The first choice in diagnostic imaging for patients suffering from peripheral arterial disease is 2D ultrasound (US). However, for a proper imaging process, a skilled and experienced sonographer is required. Additionally, it is a highly user-dependent operation. A robotized US system that autonomously scans the peripheral arteries has the potential to overcome these limitations. In this work, we extend a previously proposed system by a hierarchical image analysis pipeline based on convolutional neural networks in order to control the robot. The system was evaluated by checking its feasibility …
Philips Research North America（飞利浦北美研究院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Accurate, real-time segmentation of vessel structures in ultrasound image sequences can aid in the measurement of lumen diameters and assessment of vascular diseases. This, however, remains a challenging task, particularly for extremely small vessels that are difficult to visualize. We propose to leverage the rich spatiotemporal context available in ultrasound to improve segmentation of small-scale lower-extremity arterial vasculature. We describe efficient deep learning methods that incorporate temporal, spatial, and feature-aware contextual embeddings at multiple resolution scales while join…
Semantic Scene Graph for Ultrasound Image Explanation and Scanning Guidance
Computer Aided Medical Procedures (CAMP)（计算机辅助医疗程序）；TU Munich, Germany（慕尼黑工业大学，德国）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The Chinese University of Hong Kong, Hong Kong, China（香港中文大学，中国香港）
Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data
Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany（技术大学慕尼黑计算机辅助医疗程序）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心）
University of Alberta（阿尔伯塔大学）；CHU Hospital（里昂大学医疗中心）
Intravascular Ultrasound (IVUS) is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. Segmentation of arterial wall boundaries from the IVUS images is not only crucial for quantitative analysis of the vessel walls and plaque characteristics, but is also necessary for generating 3D reconstructed models of the artery. The aim of this study is twofold. Firstly, we investigate the feasibility of using a recently proposed region detector, namely Extremal Region of Extremum Level (EREL) to delineate the luminal and …
Cornell University（康奈尔大学）；Weill Cornell Medical College（威尔康奈尔医学院）
Segmentation of anatomical regions of interest such as vessels or small lesions in medical images is still a difficult problem that is often tackled with manual input by an expert. One of the major challenges for this task is that the appearance of foreground (positive) regions can be similar to background (negative) regions. As a result, many automatic segmentation algorithms tend to exhibit asymmetric errors, typically producing more false positives than false negatives. In this paper, we aim to leverage this asymmetry and train a diverse ensemble of models with very high recall, while sacri…
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images
Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany（技术大学慕尼黑计算机辅助医疗程序）；Munich Center of Machine Learning, Munich, Germany（慕尼黑机器学习中心）；Department for Vascular and Endovascular Surgery, rechts der Isar University Hospital, Technical University of Munich, Munich, Germany（技术大学慕尼黑血管和内血管外科部门）
Cornell University（康奈尔大学）；Weill Cornell Medical College（威尔康奈尔医学院）
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection. A major challenge for accurate anatomical landmark detection in volumetric images such as clinical CT scans is that large-scale data often constrain the capacity of the employed neural network architecture due to GPU memory limitations, which in turn can limit the precision of the output. We propose a multi-scale, end-to-end deep learning method that achieves fast and memory-efficient landmark detection in 3D images. Our architecture consists of blocks of shift-equivarian…
IntraVascular UltraSound (IVUS) is one of the most effective imaging modalities that provides assistance to experts in order to diagnose and treat cardiovascular diseases. We address a central problem in IVUS image analysis with Fully Convolutional Network (FCN): automatically delineate the lumen and media-adventitia borders in IVUS images, which is crucial to shorten the diagnosis process or benefits a faster and more accurate 3D reconstruction of the artery. Particularly, we propose an FCN architecture, called IVUS-Net, followed by a post-processing contour extraction step, in order to autom…
A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
Erasmus MC（伊拉斯姆斯医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Academic Medical Center University of Amsterdam（阿姆斯特丹大学学术医学中心）；Maastricht University Medical Center（马斯特里赫特大学医学中心）；University of Copenhagen（哥本哈根大学）
