Ultrasound Images Of Carotid Artery Research Articles

MultiNet 2.0: A lightweight attention-based deep learning network for stenosis measurement in carotid ultrasound scans and cardiovascular risk assessment

BackgroundCardiovascular diseases (CVD) cause 19 million fatalities each year and cost nations billions of dollars. Surrogate biomarkers are established methods for CVD risk stratification; however, manual inspection is costly, cumbersome, and error-prone. The contemporary artificial intelligence (AI) tools for segmentation and risk prediction, including older deep learning (DL) networks employ simple merge connections which may result in semantic loss of information and hence low in accuracy. MethodologyWe hypothesize that DL networks enhanced with attention mechanisms can do better segmentation than older DL models. The attention mechanism can concentrate on relevant features aiding the model in better understanding and interpreting images. This study proposes MultiNet 2.0 (AtheroPoint, Roseville, CA, USA), two attention networks have been used to segment the lumen from common carotid artery (CCA) ultrasound images and predict CVD risks. ResultsThe database consisted of 407 ultrasound CCA images of both the left and right sides taken from 204 patients. Two experts were hired to delineate borders on the 407 images, generating two ground truths (GT1 and GT2). The results were far better than contemporary models. The lumen dimension (LD) error for GT1 and GT2 were 0.13±0.08 and 0.16±0.07 mm, respectively, the best in market. The AUC for low, moderate and high-risk patients’ detection from stenosis data for GT1 were 0.88, 0.98, and 1.00 respectively. Similarly, for GT2, the AUC values for low, moderate, and high-risk patient detection were 0.93, 0.97, and 1.00, respectively.The system can be fully adopted for clinical practice in AtheroEdge™ model by AtheroPoint, Roseville, CA, USA.

WAL-Net: Weakly supervised auxiliary task learning network for carotid plaques classification

The classification of carotid artery ultrasound images is a crucial step for diagnosing carotid plaques, holding significant clinical relevance for predicting the risk of stroke. Recent research suggests that utilizing supervised plaque segmentation as an auxiliary task for classification can enhance performance by leveraging the correlation between segmentation and classification tasks. However, this approach relies on obtaining a substantial amount of challenging-to-acquire segmentation annotations. This paper proposes a novel weakly supervised auxiliary task learning network model (WAL-Net) to explore the interdependence between carotid plaque classification and segmentation tasks. The plaque classification task is the primary one, while the plaque segmentation task serves as the auxiliary one, providing valuable information to enhance the performance of the primary task. Weakly supervised learning is adopted in the auxiliary task in which the segmentation masks are not provided. Experiments and evaluations are conducted on a dataset comprising 1270 carotid plaque ultrasound images from Wuhan University Zhongnan Hospital. Results indicate that WAL-Net achieved an approximately 1.3% improvement in carotid plaque classification accuracy compared to the baseline network. Specifically, the accuracy of mixed-echoic plaques classification increased by approximately 3.3%, demonstrating the effectiveness of WAL-Net.

Automated Lumen Segmentation in Carotid Artery Ultrasound Images Based on Adaptive Generated Shape Prior.

Ultrasound imaging is vital for diagnosing carotid artery vascular lesions, highlighting the importance of accurately segmenting lumens in ultrasound images to prevent, diagnose and treat vascular diseases. However, noise artifacts, blood residue and discontinuous lumens significantly affect segmentation accuracy. To achieve accurate lumen segmentation in low-quality images, we propose a novel segmentation algorithm which is guided by an adaptively generated shape prior. To tackle the above challenges, we introduce a shape-prior-based segmentation method for carotid artery lumen walls. The shape prior in this study is adaptively generated based on the evolutionary trend of vessel growth. Shape priors guide and constrain the active contour, resulting in precise segmentation. The efficacy of the proposed model was confirmed using 247 carotid artery ultrasound images, with experimental results showing an average Dice coefficient of 92.38%, demonstrating superior segmentation performance compared to existing mathematical models. Our method can quickly and effectively perform accurate lumen segmentation on low-quality carotid artery ultrasound images, which is of great significance for the diagnosis of cardiovascular and cerebrovascular diseases.

Improving Computer-Aided Medical Diagnosis Using Generative Adversarial Networks for Carotid Artery Ultrasound Image Data Augmentation and Classification

Cardiovascular diseases are a leading cause of death globally, making early detection of atherosclerosis critical for prevention. Carotid artery ultrasound imaging is a common diagnostic tool; however, the limited availability of labelled medical images hinders the training of deep learning models. This study examines generative adversarial networks (GANs) for data augmentation and classification of carotid artery Doppler images to improve computer-aided medical diagnosis. Four convolutional neural networks (CNNs) – AlexNet, VGGNet, GoogleNet, and CifarNet – are evaluated for their classification performance on original and extended datasets. AlexNet outperforms the other models, achieving a classification accuracy of 94.18% on the extended dataset. The GAN implementation for data augmentation and overfitting reduction demonstrates the potential of generative models in enhancing the performance of deep learning models in medical image analysis, particularly in the "common artery carotid" class. This research contributes to understanding GANs as a valuable tool for data augmentation and classification in the context of carotid artery ultrasound images.

Carotid wave analysis in young adults with a history of adolescent anorexia nervosa: a case control study

BackgroundAnorexia nervosa (AN) is associated with abnormalities that may increase the risk of future cardiovascular disease. This study assessed the cardiovascular health of individuals who recovered from AN during adolescence by conducting wave power analysis.MethodsFormer AN patients discharged from the Royal Children’s and Monash Children’s Hospitals (N = 17) in Melbourne, Australia underwent ultrasound imaging of the right carotid artery. Wave power analysis was conducted to assess biomechanical interactions of the cardiovascular system. Patient measures were compared to healthy controls (N = 51).ResultsEighty-eight percent of the former AN patients and controls were female, aged approximately 25 years, with a healthy body mass index. Mean carotid flow and pulsatility index were not different between groups. Carotid arterial strain and distensibility were lower, and the wave speed and beta stiffness index higher in the former AN patients. Characteristic impedance was not different nor were the forward and backward wave amplitudes. However, wave reflection indices (ratios of backward-to-forward compression wave area, and wave-related effect on pressure and hydraulic power) were 12–18% lower in the former AN patients (p < 0.05).ConclusionsIncreased carotid artery stiffness and reduced wave reflection are evident in young adults who recovered from adolescent AN. This may relate to an adaptive process that helps to maintain or restore flow and characteristic impedance despite increased vessel stiffness, with this warranting future investigation.

Prior-knowledge Embedded U-Net based Fully Automatic Vessel Wall Volume Measurement of the Carotid Artery in 3D Ultrasound Image.

The vessel-wall-volume (VWV) measured based on three-dimensional (3D) carotid artery (CA) ultrasound (US) images can help to assess carotid atherosclerosis and manage patients at risk for stroke. Manual involvement for measurement work is subjective and requires well-trained operators, and fully automatic measurement tools are not yet available. Thereby, we proposed a fully automatic VWV measurement framework (Auto-VWV) using a CA prior-knowledge embedded U-Net (CAP-UNet) to measure the VWV from 3D CA US images without manual intervention. The Auto-VWV framework is designed to improve the repeated VWV measuring consistency, which resulted in the first fully automatic framework for VWV measurement. CAP-UNet is developed to improve segmentation accuracy on the whole CA, which composed of a U-Net type backbone and three additional prior-knowledge learning modules. Specifically, a continuity learning module is used to learn the spatial continuity of the arteries in a sequence of image slices. A voxel evolution learning module was designed to learn the evolution of the artery in adjacent slices, and a topology learning module was used to learn the unique topology of the carotid artery. In two 3D CA US datasets, CAP-UNet architecture achieved state-of-the-art performance compared to eight competing models. Furthermore, CAP-UNet-based Auto-VWV achieved better accuracy and consistency than Auto-VWV based on competing models in the simulated repeated measurement. Finally, using 10 pairs of real repeatedly scanned samples, Auto-VWV achieved better VWV measurement reproducibility than intra- and inter-operator manual measurements.

Atherosclerotic plaque classification in carotid ultrasound images using machine learning and explainable deep learning

ObjectiveThe incidence of cardiovascular diseases (CVD) is rising rapidly worldwide. Some forms of CVD, such as stroke and heart attack, are more common among patients with certain conditions. Atherosclerosis development is a major factor underlying cardiovascular events, such as heart attack and stroke, and its early detection may prevent such events. Ultrasound imaging of carotid arteries is a useful method for diagnosis of atherosclerotic plaques; however, an automated method to classify atherosclerotic plaques for evaluation of early-stage CVD is needed. Here, we propose an automated method for classification of high-risk atherosclerotic plaque ultrasound images. MethodsFive deep learning (DL) models (VGG16, ResNet-50, GoogLeNet, XceptionNet, and SqueezeNet) were used for automated classification and the results compared with those of a machine learning (ML)-based technique, involving extraction of 23 texture features from ultrasound images and classification using a Support Vector Machine classifier. To enhance model interpretability, output gradient-weighted convolutional activation maps (GradCAMs) were generated and overlayed on original images. ResultsA series of indices, including accuracy, sensitivity, specificity, F1-score, Cohen-kappa index, and area under the curve values, were calculated to evaluate model performance. GradCAM output images allowed visualization of the most significant ultrasound image regions. The GoogLeNet model yielded the highest accuracy (98.20%). ConclusionML models may be also suitable for applications requiring low computational resource. Further, DL models could be more completely automated than ML models.

Total carotene plasma concentrations are inversely associated with atherosclerotic plaque burden: A post-hoc analysis of the DIABIMCAP cohort

Atherosclerosis is the major risk factor for cardiovascular disease (CVD), the first cause of death worldwide. Chronic low-grade inflammation and a sustained oxidative milieu are causatively related to atherosclerosis onset and progression, and therefore, dietary patterns rich in bioactive compounds with anti-inflammatory and antioxidant activities might likely contribute to revert or slowing the progression of atherosclerosis. The aim of this study is to analyse the association between fruit and vegetables intake, quantitatively measured through carotene plasma concentrations, and atherosclerotic burden, as a surrogate biomarker of CVD, in free-living subjects from the DIABIMCAP cohort study. The 204 participants of the DIABIMCAP Study cohort (Carotid Atherosclerosis in Newly Diagnosed Type 2 Diabetic Individuals, ClinicalTrials.gov Identifier: NCT01898572), were included in this cross-sectional study. Total, α-, and β-carotenes were quantified by HPLC-MS/MS. Lipoprotein analysis in serum was performed by 2D- 1H NMR- DOSY, and atherosclerosis and intima media thickness (IMT) were measured through standardized bilateral carotid artery ultrasound imaging. Subjects with atherosclerosis (n=134) had lower levels of large HDL particles than subjects without atherosclerosis. Positive associations were found between α-carotene and both large and medium HDL particles, and inverse associations were found between β- and total carotene, and VLDL and its medium/small particles. Subjects with atherosclerosis presented significantly lower plasma concentrations of total carotene compared with subjects without atherosclerosis. Plasma concentrations of carotene decreased as the number of atherosclerotic plaques increased, although after multivariate adjustment, the inverse association between β- and total carotene with plaque burden remained significant only in women. A diet rich in fruit and vegetables results in higher plasmatic carotene concentrations, which are associated with a lesser atherosclerotic plaque burden.

A review of deep learning segmentation methods for carotid artery ultrasound images

The carotid artery is a critical blood vessel that supplies blood to the brain, and its health and function are essential for preventing cardiovascular diseases such as stroke. Ultrasound imaging is commonly used to diagnose the carotid artery and monitor its health, but traditional methods have limitations in terms of accuracy and efficiency. In recent years, deep learning segmentation methods have been developed to improve the diagnosis of the carotid artery, which have shown great potential for improving the accuracy and efficiency of cardiovascular diagnosis. In this paper, we aim to review and summarize the recent research on deep learning segmentation methods for the carotid artery ultrasound images. Specifically, we focus on techniques for the segmentation of the intima-media, plaque, and lumen sites, which are important for clinical diagnosis. Through our analysis of the literature, we seek to identify the key trends and challenges in this field, and to provide insights into the opportunities and challenges for future research and development in this area.

Preprocessing techniques with medical ultrasound common carotid artery images

Preprocessing techniques with medical ultrasound common carotid artery images

Method for Carotid Artery 3-D Ultrasound Image Segmentation Based on CSWin Transformer.

Precise segmentation of carotid artery (CA) structure is an important prerequisite for the medical assessment and detection of carotid plaques. For automatic segmentation of the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) in 3-D ultrasound images of the CA, a U-shaped CSWin transformer (U-CSWT) is proposed. Both the encoder and decoder of the U-CSWT are composed of hierarchical CSWT modules, which can capture rich global context information in the 3-D image. Experiments were performed on a 3-D ultrasound image data set of the CA, and the results indicate that the U-CSWT performs better than other convolutional neural network (CNN)-based and CNN-transformer hybrid methods. The model yields Dice coefficients of 94.6 ± 3.0% and 90.8 ± 5.1% for the MAB and LIB in the common carotid artery (CCA) and 92.9 ± 4.9% and 89.6 ± 6.2% for MAB and LIB in the bifurcation, respectively. Our U-CSWT is expected to become an effective method for automatic segmentation of 3-D ultrasound images of CA.

Carotid intima media thickness and white matter hyperintensity volume among midlife women.

Carotid atherosclerosis may be associated with brain white matter hyperintensities (WMH). Few studies consider women at midlife, a critical time for women's cardiovascular and brain health. We tested the hypothesis that higher carotid intima media thickness (IMT) would be associated with greater WMH volume (WMHV) among midlife women. We explored interactions by apolipoprotein E (APOE) ε4 status. Two hundred thirty-nine women aged 45 to 67 underwent carotid artery ultrasound, phlebotomy, and magnetic resonance imaging (MRI). One hundred seventy participants had undergone an ultrasound 5 years earlier. Higher IMT was associated with greater whole brain (B[standard error (SE)]=0.77 [.31], P=0.01; multivariable) and periventricular (B[SE]=0.80 [.30], P=0.008; multivariable) WMHV. Associations were observed for IMT assessed contemporaneously with the MRI and 5 years prior to the MRI. Associations were strongest for APOE ε4-positive women. Among midlife women, higher IMT was associated with greater WMHV. Vascular risk is critical to midlife brain health, particularly for APOE ε4-positive women.

FDADE: Flow direction algorithm with differential evolution for measurement of intima-media thickness of the carotid artery in ultrasound images

The third leading cause of death globally is cardiovascular disease. Atherosclerosis, the build-up of plaque on the arterial wall, is the earliest sign of cardiovascular disease. The common carotid artery's Intima-Media Thickness (IMT) is primarily used as an early indicator of cardiovascular disease. This paper proposes a new method called FDADE by integrating the Flow Direction Algorithm (FDA) with Differential Evolution (DE) to segment images using multilevel thresholding. Otsu's interclass variance function is used in FDADE to establish optimal threshold values for measuring IMT in Carotid Artery Ultrasound Images. Carotid ultrasound images are subjected to a quantitative evaluation, and the results are compared to those obtained using other, more recent techniques. This method is reliable in measuring the IMT in carotid ultrasound pictures by the experimental investigation. Also, the proposed FDADE's performance is tested using a variety of benchmark issues of varying levels of complexity. Various tests are undertaken, such as the Wilcoxon rank-sum test for statistical implication, convergence curvatures, and the detachment amid the resolution earlier and later applying alteration approaches. The data are then compared and analyzed to demonstrate the superiority of the proposed FDADE over alternative algorithms.

Morbid obesity is associated with hypertrophic outward remodeling and increased stiffness of small conduit arteries: An ultra-high frequency ultrasound study

Background and aimsAlthough many studies have been published on the effect of obesity on large and small arteries, there are no data in the literature regarding the effect of obesity on medium-sized arteries, and in particular of small conduit arteries. The aim of the present study was to investigate whether patients with severe obesity presented structural or functional alterations in different arterial segments. Methods and results34 patients with severe obesity (BMI≥35 kg/m2) and 34 age-and sex-matched normal weight patients were recruited as controls. Aortic stiffness (carotid-femoral pulse wave velocity) and wave reflection (augmentation index) were recorded. Ultrasound images of common carotid, radial and interdigital arteries were acquired for the assessment of wall-to-lumen ratio, wall cross-sectional area (WCSA), compliance, distensibility coefficient (DC) and Young's elastic modulus (Einc). Insulin sensitivity was calculated by oral glucose sensitivity index (OGIS). No differences between groups in carotid artery remodeling were found, while WCSA of the radial and interdigital arteries were higher in obese group than in controls. As regard the parameters of vascular elasticity, the DC of radial and interdigital arteries were lower (p = 0.025 and p = 0.001, respectively), as well as the Einc of radial arteries was higher (p = 0.021), in subject with obesity compared to controls. All these correlations were consistent after adjustment for the main covariates. Finally, in a multiple regression analysis OGIS was and independent determinant of interdigital artery DC (R2 = 0.29, p = 0.001). ConclusionsFor the first time, we describe an outward remodeling and increased stiffness in small conduit arteries in severe obesity.

Estimation of viscoelasticity of a carotid artery from ultrasound cine images and brachial pressure waveforms: Viscous parameters as a new index of detecting low plaque burden.

Viscoelasticity may be an important physical index for diagnosing vascular diseases, but wall viscosity has received less attention than elasticity due to difficulties in measurement in clinical scenarios. In this study, viscoelastic parameters were estimated from the pressure diameter relationship using carotid artery ultrasound images and brachial artery pressure waveforms of the patients. Carotid artery diameter waveforms were obtained by analyzing wall motion in ultrasound cine images, and carotid pressure waveforms were estimated from brachial waveforms using a transfer function. The estimated viscoelastic parameters quantitatively agreed with the published data, and three viscous parameters (viscous index, energy dissipation ratio, and phase lag between pressure and diameter waveforms) showed good positive correlations with each other. No significant difference in wall elasticity was found between the no plaque (NP) and low plaque (LP) groups, whereas viscous parameters were lower in the NP group than the LP group. This result suggests that the viscous parameters may be a new mechanical index for detecting early atherosclerosis.

CSM-Net: Automatic joint segmentation of intima-media complex and lumen in carotid artery ultrasound images

The intima-media thickness (IMT) is an effective biomarker for atherosclerosis, which is commonly measured by ultrasound technique. However, the intima-media complex (IMC) segmentation for the IMT is challenging due to confused IMC boundaries and various noises. In this paper, we propose a flexible method CSM-Net for the joint segmentation of IMC and Lumen in carotid ultrasound images. Firstly, the cascaded dilated convolutions combined with the squeeze-excitation module are introduced for exploiting more contextual features on the highest-level layer of the encoder. Furthermore, a triple spatial attention module is utilized for emphasizing serviceable features on each decoder layer. Besides, a multi-scale weighted hybrid loss function is employed to resolve the class-imbalance issues. The experiments are conducted on a private dataset of 100 images for IMC and Lumen segmentation, as well as on two public datasets of 1600 images for IMC segmentation. For the private dataset, our method obtain the IMC Dice, Lumen Dice, Precision, Recall, and F1 score of 0.814 ± 0.061, 0.941 ± 0.024, 0.911 ± 0.044, 0.916 ± 0.039, and 0.913 ± 0.027, respectively. For the public datasets, we obtain the IMC Dice, Precision, Recall, and F1 score of 0.885 ± 0.067, 0.885 ± 0.070, 0.894 ± 0.089, and 0.885 ± 0.067, respectively. The results demonstrate that the proposed method precedes some cutting-edge methods, and the ablation experiments show the validity of each module. The proposed method may be useful for the IMC segmentation of carotid ultrasound images in the clinic. Our code is publicly available at https://github.com/yuanyc798/US-IMC-code.

Speckle Reduction in Ultrasound Images of the Common Carotid Artery Based on Integer and Fractional-Order Total Variation.

Designing a technique with higher speckle noise suppressing capability, better edge preserving performance, and lower time complexity is a research objective for the common carotid artery (CCA) ultrasound despeckling. Total variation based techniques have been widely used in the image denoising and have good performance in preserving the edges in the images. However, the total variation based filters can produce the staircase artifacts. To address this issue, second-order total variation based techniques have been proposed for the image denoising. However, the previous study has been proved that the fractional differential model has better performance in reducing the speckles in ultrasound despeckling compared with the second-order model. Thus, to improve the performance of ultrasound despeckling and edge preserving, a novel despeckling model based on integer and fractional-order total variation (IFOTV) is proposed for CCA ultrasound images. Moreover, the minimization problems in our despeckling model are solved by the alternating direction method of multiplier (ADMM). In results with synthetic images, the edge preservation index (EPI) values of proposed method are 0.9524, 0.8797, and 0.7351 as well as 0.9137, 0.8253, and 0.6847 under three different levels of noise, which are the highest among four advanced methods. In results with simulated CCA ultrasound images, the speckle suppression and mean preservation indices of proposed method are 0.5596, 0.6571, and 0.8106 under three different levels of noise, which are the best among four advanced methods. In results with clinical images, the average absolute error of intima-media thickness measurements of proposed method is 0.0660 ± 0.0679 (mean ± std in mm), which is the lowest among four advanced methods. In conclusion, the IFOTV method has improved performance in suppressing the speckle noise and preserving the edge, and is thus a potential alternative to the current filters for the CCA ultrasound despeckling.

Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images.

Plaque deposits in the carotid artery are the major cause of stroke and atherosclerosis. Ultrasound imaging is used as an early indicator of disease progression. Classification of the images to identify plaque presence and intima-media thickness (IMT) by machine learning algorithms requires features extracted from the images. A total of 361 images were used for feature extraction, which will assist in further classification of the carotid artery. This study presents the extraction of 65 features, which constitute of shape, texture, histogram, correlogram, and morphology features. Principal component analysis (PCA)-based feature selection is performed, and the 22 most significant features, which will improve the classification accuracy, are selected. Naive Bayes algorithm and dynamic learning vector quantization (DLVQ)-based machine learning classifications are performed with the extracted and selected features, and analysis is performed.

Abstract 347: Using Deep Convolutional Neural Networks To Automate Classification Of Carotid Plaques From Ultrasound Imaging

Background: Stroke is a devastating consequence of plaque rupture from the carotid arteries. Current management of carotid plaques involves waiting for symptoms (e.g., stroke or mini-stroke), as intervention itself has risk of stroke and not all plaques are vulnerable to rupture. There is a need to better risk-stratify plaque that causes stroke. Carotid ultrasound (US) is a non-invasive and inexpensive visualization of plaques but is limited by human interpretation. We hypothesize that convolutional neural networks (CNNs) will identify unique features of carotid plaques for automated risk stratification. Methods: Our workflow is illustrated in Fig. a. A total of 141 B-mode US images of carotid arteries were included; 64 high-risk with symptomatic carotid plaques and 75 low-risk with no significant plaque. Data was cropped and divided into training (70%) and holdout test (30%) subsets. During model training, an ensemble of ResNet-18 CNNs learned classification of low-risk and high-risk cases using five-fold stratified cross validation and was used to predict on the holdout test set. The model was evaluated using ROC-AUC and sensitivity. Saliency maps were used for model interpretability to highlight relevant pixels for model decisions. Results: The cross-validation AUC was 0.995 ± 0.010. The testing AUC was 0.909 and class-wise sensitivities were 0.88 (low-risk) and 0.79 (high-risk). The density plot (Fig. b) shows that the classifier correctly identifies both classes with confidence. Model interpretability using saliency maps (Fig. c) shows pixels corresponding to carotid artery vessel edges and in high-risk cases, carotid plaques. Conclusions: Using this proof-of-concept model, carotid US long axis images are sufficient to identify high-risk plaques in symptomatic patients - we now need to determine whether we can identify high-risk plaques before symptoms to prevent devastating stroke caused by carotid disease.

Intelligent Segmentation of Intima-Media and Plaque Recognition in Carotid Artery Ultrasound Images.

Ultrasound imaging has been established as an effective method for measuring the thickness of the intima-media, the thickening of which, along with carotid plaque, is an indicator of cerebrovascular diseases. Here, a 2-D V-Net model that can automatically segment the intima-media in carotid artery ultrasound images is proposed. Moreover, a plaque recognition algorithm that automatically identifies plaque-affected areas is described. Performance tests to determine the average accuracy of the intima-media segmentation yielded the following results (expressed as lumen-intima boundary/media-adventitia boundary): intersection over union (IOU) of 0.752/0.813, pixel accuracy of 0.813/0.885 and Dice loss of 0.858/0.897. Finally, average IOU of 0.785, pixel accuracy of 0.825 and Dice loss of 0.866 were obtained for plaque recognition. These results satisfy the threshold for clinical application and indicate that the proposed model can assist doctors in making more efficient and accurate diagnoses.