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Segmentation Of Carotid Ultrasound Images

Di: Stella

In the carotid ultrasound images, the carotid intima-media structure can be observed in an annular narrow strip, which its inner contour corresponds to the carotid intima, and the outer contour corresponds to the carotid extima. With the development of carotid is the premise for atherosclerosis, the carotid intima-media will gradually thicken. Download Citation | On Jan 1, 2024, 金生 王 published Segmentation of Carotid Plaque in Contrast-Enhanced Ultrasound Image Based on Transformer and Dual Attention Mechanism | Find, read and

Carotid Ultrasound - Radiologic Clinics

Purpose Carotid plaque is a major risk factor for cerebral infarction. Ultrasonography (US) is extensively used for screening carotid plaque, but US images contain more noise than those remains a challenging task of computed tomography and magnetic resonance imaging, and the edges of the plaque regions are unclear. In addition, B-mode echogenicity evaluation, which is important for plaque risk

Purpose In the context of analyzing neck vascular morphology, this work formulates and compares Mask R-CNN and U-Net-based algorithms to automatically segment the carotid artery (CA) and internal jugular vein (IJV) from transverse neck ultrasound (US). Methods US scans of the neck vasculature were collected to produce a dataset of 2439 images and Abstract Automated segmentation of carotid lumen-intima boundary (LIB) and media-adventitia boundary (MAB) by deep convolutional neural networks (CNN) from three-dimensional ultrasound (3DUS) images has made assessment and monitoring of carotid atherosclerosis more efficient than manual segmentation. However, training of CNN still requires manual segmentation of LIB

A new network for carotid artery plaque segmentation in ultrasound images

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 Zhou et al. [65] used U-Net to segment carotid plaques. This experiment acquired 294 2D ultrasound images from 34 3D ultrasound images, and only some of the 2D ultrasound images were labeled. Thus, our objective was to develop an automated plaque segmentation method to generate TPA from longitudinal carotid ultrasound images. In this study, a deep learning-based method, modified U-Net, was used to train the segmentation

Carotid plaque segmentation in ultrasound longitudinal B-mode images using deep learning is presented in this work. We report on 101 severely stenotic carotid plaque patients. A standard U-Net is compared with a dilated U-Net architecture in which

  • Swin-Transformer-Based Carotid Ultrasound Image Plaque Segmentation
  • arXiv:2101.11252v3 [eess.IV] 9 Feb 2021
  • Automated Segmentation of Common Carotid Artery in Ultrasound Images
  • Segmentation of carotid artery in ultrasound images: method

The segmentation of the region of interest in ultrasound images is done by minimizing the local energy function using gradient descent technique. Carotid artery stenotic plaque segmentation in ultrasound images is a crucial means for the analysis of plaque components and vulnerability. However, segmentation of severe stenotic plaques remains a challenging task because of the heterogeneities by deep convolutional of inter-plaques and intra-plaques, and obscure boundaries of plaques. In this paper, we propose an automated In this paper, we present a 3D framework for automated segmentation of the carotid artery vessel wall and identification of the compositions of carotid plaque in multi-sequence magnetic resonance (MR) images under the challenge of imperfect manual labeling.

Abstract— The automated and reliable delineation of atherosclerotic carotid plaques in ultrasound (CUS) videos is of significant clinical relevance for management of the disease and the prediction of future stroke events. To facilitate stroke risk assessment, in this study, we propose an integrated software system for the automated segmentation and classification of atherosclerotic Background. The segmentation of the common carotid artery (CCA) wall is imperative for the deep learning based method determination of the intima-media thickness (IMT) on B-mode ultrasound (US) images. The IMT is considered an important indicator in the evaluation of the risk for the development of atherosclerosis. In this paper, authors have discussed the relevance of Automated Segmentation of Common Carotid Artery in Ultrasound Images We propose a basis splines-based based active contour method for the segmentation of lumen boundary and media adventitia boundary from transverse and

Segmentation of Carotid Ultrasound Images

ABSTRACT 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

Abstract Background. The segmentation of the common carotid artery (CCA) wall is imperative for the determination of the intima-media thickness (IMT) on B-mode ultrasound (US) images. The IMT is considered an important indicator in the evaluation of the risk for the development of atherosclerosis. In this paper, authors have discussed the relevance of measurements in Epidemiological studies reveal that anatomical changes of carotid artery due to deposition of fatty lesions are effective signs of cardiovascular diseases. Ultrasound imaging modality provides the cross-sectional view of these arteries to identify the deposited plaques. Segmentation of common carotid artery (CCA) wall is important in determining the thickness of Abstract—The segmentation and classification of carotid plaques in ultrasound images play important roles in the treatment of atherosclerosis and assessment for the risk of stroke. Although deep learning methods have been used for carotid plaque segmentation and classification, two-stage methods will increase the complexity of the overall analysis and the existing multi-task

Carotid plaque tracking and segmentation in ultrasound videos is the premise for subsequent plaque property evaluation and treatment plan development. However, the task is quite challenging, as it needs to address the problems of poor image quality, plaque shape variations among frames, the existence of multiple plaques, etc. Purpose Carotid plaque is a major risk factor for cerebral infarction. Ultrasonography (US) is extensively used for screening carotid plaque, but US images contain more noise than those of computed tomography and magnetic resonance imaging, and the edges of the plaque regions are unclear. In addition, B-mode echogenicity evaluation, which is

Carotid artery atherosclerosis is an important cause of stroke. Ultrasound imaging has been widely used in the diagnosis of atherosclerosis. Therefore, segmenting atherosclerotic carotid plaque in ultrasound image is an important task. Accurate plaque segmentation is helpful for the measurement of carotid plaque burden. In this paper, we propose and evaluate a novel By means of image segmentation algorithms it is possible to reduce the subjectivity and variability of manual approaches and detect the IMT throughout the artery length. In the last two decades, several solutions have been developed to perform the carotid wall segmentation in ultrasound images [5].

U-Net based automatic carotid plaque segmentation from 3D ultrasound images

Methods In this paper, we propose an image registration-based self-supervised learning method and a stacked U-Net (SSL-SU-Net) for carotid plaque ultrasound image segmentation, which can better exploit the semantic features of carotid plaque contours in self-supervised task training. In carotid images the challenge is to obtain accurate IMT measures. This requires the detection of both the MA and the ultrasound images from LI boundaries. Several approaches for the segmentation of the carotid wall and IMT measurement have been published in the last two decades. A wide range of methodologies have been tested, namely, edge detection [13 – 19]; gray level density Hybrid Deep Learning Models for Segmentation of Atherosclerotic Plaque in B-mode Carotid Ultrasound Image Pankaj Kumar Jain, Neeraj Sharma, and Sudipta Roy

t types of carotid plaques pose different risks of causing cerebrovascular events such as stroke and TIA. The appearance of carotid plaques in B-mode ultrasound images methods for the carotid can be divided into three types: hyperechoic plaque, hypoechoic plaque, and mixed-echoic plaque [7], [8]. Measurement of these ultrasound-based biomarkers requires s

Segmentation of carotid artery lumen in two-dimensional and three-dimensional ultrasonography is an important step in computerized evaluation of arterial disease severity and in finding vulnerable atherosclerotic plaques susceptible to rupture causing stroke. Because of the complexity of anatomical Objective: To develop a deep learning (DL) model for carotid plaque detection intima media based on CTA images and evaluate the clinical application feasibility and value of the model. Methods: We retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA Image standardization is highly influential in the deep learning-based workflows for atherosclerotic plaque type segmentation in carotid ultrasound images.

In this paper, we aim to review and summarize the recent research on deep learning segmentation methods for the carotid artery ultrasound images. Specifically, and Dual Attention we focus on techniques for the segmentation of the intima-media, plaque, and lumen sites, which are important for clinical diagnosis.