Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery

Abstract—The robust identification and measurement of the intima media thickness (IMT) has a high clinical relevance because it represents one of the most precise predictors used in the assessment of potential future cardiovascular events. To facilitate the analysis of arterial wall thickening in serial clinical investigations, in this paper we have developed a novel fully automatic algorithm for the segmentation, measurement, and tracking of the intima media complex (IMC) in B-mode ultrasound video sequences. The proposed algorithm entails a two-stage image analysis process that initially addresses the segmentation of the IMC in the first frame of the ultrasound video sequence using a model-based approach; in the second step, a novel customized tracking procedure is applied to robustly detect the IMC in the subsequent frames. For the video tracking procedure, we introduce a spatially coherent algorithm called adaptive normalized correlation that prevents the tracking process from converging to wrong arterial interfaces. This represents the main contribution of this paper and was developed to deal with inconsistencies in the appearance of the IMC over the cardiac cycle. The quantitative evaluation has been carried out on 40 ultrasound video sequences of the common carotid artery (CCA) by comparing the results returned by the developed algorithm with respect to ground truth data that has been manually annotated by clinical experts. The measured IMTmean ± standard deviation recorded by the proposed algorithm is 0.60 mm ± 0.10, with a mean coefficient of variation (CV) of 2.05%, whereas the corresponding result obtained for the manually annotated ground truth data is 0.60 mm ± 0.11 with a mean CV equal to 5.60%. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment and track the IMC in ultrasound CCA video sequences, and we were encouraged by the stability of our technique when applied to data captured under different imaging conditions. Future clinical studies will focus on the evaluation of patients that are affected by advanced cardiovascular conditions such as focal thickening and arterial plaques

Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang. Automating carotid intima-media thickness video interpretation with convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y. Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of CIMT videos using convolutional neural networks. Deep Learning for Medical Image Analysis, Academic Press, 201

Association entre l'élastographie vasculaire non invasive et l'indice de masse corporelle chez les enfants

Sachant que l’Athérosclérose commence durant l’enfance par des marqueurs subcliniques, cette étude explore l’association entre l’indice de masse corporelle (IMC) et l’élastographie vasculaire non-invasive (NIVE) des artères carotides communes chez les enfants. On compare aussi les techniques de mesure de l’intima-média (IMT) des artères carotides en se basant sur le mode-B et la radiofréquence (RF) chez les enfants avec IMC normal et élevé. Il s’agit d’une étude prospective effectuée entre 2005 et 2011. Les paramètres de « NIVE » ont été comparés pour deux groupes d’IMC (normal et élevé) de 60 enfants respectivement, faisant tous partie de la cohorte de l’étude QUebec Adipose and Lifestyle Investigation in Youth (QUALITY). Les paramètres de NIVE incluent la contrainte axiale cumulative (CAS) en %, la translation axiale cumulative (CAT) en mm. L’épaisseur de l’intima-média est calculée selon trois méthodes : logiciel «M’ath-Std» (mode-B), « echotracking » des signaux de RF et probabilité de distribution des signaux de RF sur la plateforme NIVE. Une analyse ANOVA et corrélation Pearson ont été effectuées sur le logiciel SAS version 9.3. Une corrélation intra-class (ICC) a été effectuée sur un logiciel MedCalc version 17.2. L’âge moyen était 11,4 ans pour le groupe IMC normal et 12 pour le groupe IMC élevé. Cinquante-huit pourcent étaient des garçons dans le groupe IMC normal et 63% dans le groupe IMC élevé. Les deux groupes étaient différents selon l’âge, stade de Tanner, tension artérielle (systolique et diastolique), et LDL mais similaire pour le sexe. En contrôlant pour les variables confondantes, la CAS n’est pas différente entre les deux groupes. La CAT est plus basse chez les enfants avec IMC normal (CAT=0.51 +/-0.17 mm pour le groupe « IMC normal » et 0.67+/-0.24 mm pour le groupe « IMC élevé » (p<0.001)). Il y a une très faible corrélation entre les trois techniques de mesure d’IMT ICC=0,34 (95% intervalle de confiance 0,27-0,39). L’IMT est significativement plus élevé dans le groupe d’enfants « IMC élevé ». Mode-B (0.55 mm « IMC normal » vs. 0.57 mm « IMC élevé »; p=0.02); IMT RF (0.45 mm « IMC normal » vs. 0.48 mm « IMC élevé »; p=0.03) et IMT probabilité de distribution des signaux RF (0.32 mm « IMC normal » vs. 0.35 mm « IMC élevé »; p=0.010). La NIVE montre une différence significative dans la CAT de l'artère carotide commune des enfants avec un IMC normal par rapport à l'IMC élevé. Des variations significatives de la mesure des IMT ont été observées entre les différentes techniques. Cependant, les enfants avec IMC élevé ont des valeurs IMT plus élevées, indépendamment de la méthode utilisée. Les deux marqueurs subcliniques peuvent être utilisés pour la stratification des enfants à risque de maladies cardiovasculaires. La même méthode devrait toujours être utilisée.Knowing that cardiovascular disease risk factors are present in asymptomatic children, this study explores the association between non-invasive vascular elastography (NIVE) as a subclinical marker of atherosclerosis and obesity in children. In the absence of a gold standard, we also compare B-mode and Radiofrequency (RF) based ultrasound measurements of intima-media thickness (IMT) in children with normal and increased body mass index (BMI). This is a prospective study between 2005 and 2011. NIVE parameters and IMT of the common carotid artery were compared between 60 children with normal BMI and 60 children with increased BMI enrolled in the QUebec Adipose and Lifestyle Investigation in Youth cohort (QUALITY). NIVE parameters included cumulated axial strain (CAS) (%) and cumulated axial translation (CAT) in mm. The three methods of IMT measurements included M’ath Std (B-mode), RF echotracking system and RF probability distribution using NIVE platform. ANOVA analysis and Pearson correlation were calculated using SAS version 9.3. Intra-class correlation coefficient (ICC) and regression analysis was done on MedCalc software version 17.2. The mean age was 11.4 years for the normal BMI group and 12 years for the increased BMI group. Fifty-eight percent were boys in the normal BMI group and 63% in the increased BMI group. The two groups were significantly different with respect to age, Tanner stage, systolic and diastolic blood pressure and were similar with respect to sex. After controlling for confounders, the results show no difference in CAS between the two groups and a significantly lower CAT in the normal BMI group (CAT=0.51+/-0.17 mm for the normal BMI group and 0.67+/-0.24 mm for the increased BMI group (p<0.001)). There is a weak correlation among the three techniques. ICC=0.34 (95% confidence interval (CI): 0.27-0.39). There is however significantly increased IMT in children with increased BMI according to all three techniques. The results were as follow: for B-mode IMT (0.55 mm (normal BMI group) vs. 0.57 mm (increased BMI group); p=0.02); for RF echotracking IMT (0.45 mm (normal BMI group) vs. 0.48 mm (increased BMI group); p=0.03) and for RF probability distribution IMT (0.32 mm (normal BMI group) vs. 0.35 mm (increased BMI group); p=0.010).NIVE is a one-step technique for IMT and CAT measurement in children at risk. Significant IMT measurement variation is observed between the three techniques. However, children with increased BMI tend to have higher IMT values regardless of the technique. Both subclinical markers can be used for optimal stratification of children with cardiovascular disease risk factors. The same technique should be used throughout

Applications of artificial intelligence-based models in vulnerable carotid plaque

Carotid atherosclerotic disease is a widely acknowledged risk factor for ischemic stroke, making it a major concern on a global scale. To alleviate the socio-economic impact of carotid atherosclerotic disease, crucial objectives include prioritizing prevention efforts and early detection. So far, the degree of carotid stenosis has been regarded as the primary parameter for risk assessment and determining appropriate therapeutic interventions. Histopathological and imaging-based studies demonstrated important differences in the risk of cardiovascular events given a similar degree of luminal stenosis, identifying plaque structure and composition as key determinants of either plaque vulnerability or stability. The application of Artificial Intelligence (AI)-based techniques to carotid imaging can offer several solutions for tissue characterization and classification. This review aims to present a comprehensive overview of the main concepts related to AI. Additionally, we review the existing literature on AI-based models in ultrasound (US), computed tomography (CT), and Magnetic Resonance Imaging (MRI) for vulnerable plaque detection, and we finally examine the advantages and limitations of these AI approaches

Computer assisted analysis of contrast enhanced ultrasound images for quantification in vascular diseases

Contrast enhanced ultrasound (CEUS) with microbubble contrast agents has shown great potential in imaging microvasculature, quantifying perfusion and hence detecting vascular diseases. However, most existing perfusion quantification methods based on image intensity, and are susceptible to confounding factors such as attenuation artefacts. Improving reproducibility is also a key challenge to clinical translation. Therefore, this thesis aims at developing attenuation correction and quantification techniques in CEUS with applications for detection and quantification of microvascular flow / perfusion. Firstly, a technique for automatic correction of attenuation effects in vascular imaging was developed and validated on a tissue mimicking phantom. The application of this technique to studying contrast enhancement of carotid adventitial vasa vasorum as a biomarker of radiation-induced atherosclerosis was demonstrated. The results showed great potential in reducing attenuation artefact and improve quantification in CEUS of carotid arteries. Furthermore, contrast intensity was shown to significantly increase in irradiated carotid arteries and could be a useful imaging biomarker for radiation-induced atherosclerosis. Secondly, a robust and automated tool for quantification of microbubble identification in CEUS image sequences using a temporal and spatial analysis was developed and validated on a flow phantom. The application of this technique to evaluate human musculoskeletal microcirculation with contrast enhanced ultrasound was demonstrated. The results showed an excellent accuracy and repeatability in quantifying active vascular density. It has great potential for clinical translation in the assessment of lower limb perfusion. Finally, a new bubble activity identification and quantification technique based on differential intensity projection in CEUS was developed and demonstrated with an in-vivo study, and applied to the quantification of intraplaque neovascularisation in an irradiated carotid artery of patients who were previously treated for head and neck cancer. The results showed a significantly more specific identification of bubble signals and had good agreement between the differential intensity-based technique and clinical visual assessment. This technique has potential to assist clinicians to diagnose and monitor intraplque neovascularisation.Open Acces

Exploring the Impact of Learning Paradigms on Network Generalization: A Multi-Center IMT Study

The intima-media thickness (IMT) is an important parameter for evaluating cardiovascular disease risk and progression and can be extracted from B-mode longitudinal ultrasound images of the carotid artery. Despite its clinical significance, inter- and intra-operator variability in IMT measurement is a challenge due to subjective factors. Therefore, automatic and semi-automatic approaches based on heuristic methods and deep neural networks have been proposed to reduce the variability in IMT measurement. However, the inter- and intra- operator variability still remains an issue as it affects the quality and diversity of ground truth (GT) data used for training deep learning models. In this study, the authors evaluate the performance of different learning paradigms using different GTs on a multi-center IMT dataset. A recent segmentation network, ConvNeXt, is trained on a dataset of 2576 B-mode longitudinal ultrasound images of the carotid artery, using different GT annotations and learning paradigms. The method is then tested on an external dataset of 448 images from four different centers for which three manual segmentations were available. The results show how the use of different GT annotations and learning paradigms can enhance the generalization ability of deep learning models, demonstrating the importance of selecting appropriate GT data and learning strategies in achieving robust and reliable solutions. The study highlights the significance of incorporating heuristic methods in the training process of deep learning models to enhance the accuracy and consistency of IMT measurement, thus enabling more precise cardiovascular disease risk assessment

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Deep learning for the detection and characterization of the carotid artery in ultrasound imaging

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2018, Tutor: Laura Igual Muñoz[en] Atherosclerosis is the main process causing most Cardio Vascular (CV) diseases. The measurement of Intima Media Thickness (IMT) in artery ultrasound images can be used to detect the presence of atherosclerotic plaques, which may appear in several territories of the artery. Moreover, it is well known that disruption of atherosclerotic plaque plays a crucial role in the pathogenesis of CV events. Several works have tried to automatize the detection of the IMT and the classification of the plaque by its composition. Traditionally, the methods used in the literature are semi-automatic. Furthermore, very little work has been done using Deep Learning approaches in order to solve this problems. In this thesis, we explore the effectiveness of Deep Learning techniques in attempting to automatize and improve the diagnosis of atheroma plaques. To achieve so we tackle the following problems: ultrasound image segmentation and plaque tissue classification. The techniques applied in this work are the following. For the segmentation of the common carotid artery IMT we replicate a state of the art Fully Convolutional Network approach and explore the implementation of a trained network to another dataset. Regarding the plaque classification problem, we explore the performance of Convolutional Neural Networks as well with two baseline methods. These techniques are applied on two datasets: REGICOR and NEFRONA. These datasets are provided by two research groups of IMIM and IRBLleida in collaboration in a larger project with the UB. A data exploration analysis is also presented on the patient’s data of NEFRONA to justify the importance of detecting the atherosclerotic plaques and thus the techniques we explore

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm

Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment