Automatic Localization of Intimal and Adventitial Carotid Artery Layers with Noninvasive Ultrasound: A Novel Algorithm Providing Scan Quality Control

Transcutaneous ultrasound measurements of common carotid artery (CCA) diameter and intima-media thickness (IMT) give insight on arterial dynamics and anatomy, both correlating well with atherosclerosis and risk of cardiovascular disease. We propose a novel automatic algorithm to estimate CCA diameter and IMT in ultrasound (US) images, based on separate analysis of anterior and posterior CCA walls and able to distinguish internal (intima-intima) and external (adventitia-adventitia) diameter. The method combines off-line signal- and image-processing techniques to accommodate echo images acquired at a frame rate of 30 Hz and composed directly from RF data, circumventing digital video-grabbing. Segmentation consists of automatic CCA recognition, followed by adventitial delineation performed with a sustain-attack filter with exponentially decaying reference functions. Intimal delineation is then based on the multiscale anisotropic barycenter (MAB), which is an extension of a known delineation method involving the “first order absolute central moment” of the echo amplitude. An automatic measure of the quality of the US beam incidence for each wall is superimosed on the CCA contour overlays for visual feedback. Validation is carried out on 36 US CCA acquisitions from 12 healthy volunteers, as well as on synthetic US images. Results indicate good accuracy on synthetic US images (within 1.3% for diameter and 3% for IMT). The in vivo intra-recording beat-to-beat variations are on average lower than 50 μm for external diameter and IMT, and lower than 100 μm for internal diameter. A comparison with a commercial device (ART.LAB system) shows that the proposed algorithm performs better in terms of inter-recording precision. The beam incidence control significantly improves the repeatability of IMT estimates, and motivates sonographers actively to maintain a proper scan plane throughout the acquisition to minimize the incidence of confounding factors. The method is clinically viable, providing robust estimates of CCA internal and external diameter and IMT waveforms for both CCA walls, even at a low B-mode update rate of 30 Hz. (E-mail: peter.brands@esaote.nl)     

Nonlinear Processing in B-Mode Ultrasound Affects Carotid Diameter Assessment

Noninvasive diameter assessment in the common carotid artery (CCA) by means of ultrasound is a useful technique for estimation of arterial mechanical and dynamic properties, clinical screening and treatment monitoring. Before presentation on screen, ultrasound images are subjected to nonlinear processing, e.g., logarithmic compression and noise-level thresholding, to improve visualization. In addition, signal saturation may occur, either in the received radiofrequency (RF) signals or in their envelopes. The objective of this study is to evaluate the effect of signal nonlinearities on CCA diameter measurements by means of noninvasive B-mode ultrasound, comparing the performance of two different edge detectors. In 14 healthy subjects, three repeated ultrasonic acquisitions (6 s) without saturation were performed. The acquired RF signals were subjected off-line to envelope detection, logarithmic compression and various degrees of saturation applied to the signals before or after envelope detection. For the purpose of CCA diameter estimation, artery walls were automatically outlined frame by frame. As automatic edge detectors, we considered the sustain attack filter (SAF), based on exponentially decaying reference functions, and a derivative approach (DER), relying on the positions of first derivative maxima. Both methods are applied within a region-of-interest located on the CCA. No regularization of the detected wall positions by means of pre- or postprocessing is presently applied to directly relate the outcome of the edge detectors to the applied nonlinear processing. Diameter values assessed with SAF are unaffected by logarithmic compression because of the possibility to integrate the compression characteristic of the ultrasound system into the method. The estimated diameters values obtained with DER instead show differences in the order of 10% because of compression. Saturation affects DER more than SAF; DER exhibits larger intrarecording and intrasubject variations in the estimated diameter values. Therefore, SAF gives more precise and robust CCA diameter estimates than DER, and is more suited for integration in algorithms meant for vascular ultrasound image segmentation. This study demonstrates the relevant effects of nonlinearities such as saturation and logarithmic compression on the quality of noninvasive US CCA diameter measurements. (E-mail: peter.brands@esaote.nl)     

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.     

An automated, interactive analysis system for ultrasound sequences of the common carotid artery

Structural parameters of the common carotid artery (CCA) have shown to correlate with the risk of cardiovascular disease, but their precise measurement is challenging. We developed an automatic detection system with manual interaction capabilities that can reliably analyze B-mode ultrasound sequences of the CCA over several heart cycles. We evaluated 3824 frames from 40 sequences in two data qualities. Two readers measured the intima media thickness (IMT) and the lumen diameter at two evaluation times (T1/T2). A Bland-Altman analysis of the average IMT showed a bias ± SD of 0.002 ± 0.010 mm (T1), -0.004 ± 0.008 mm (T2) for completely automatic detections and -0.004 ± 0.010 mm (T1), -0.003 ± 0.010 mm (T2) for clips with manual corrections. The combination of automated analysis and manual intervention provides precise parameters as biomarkers for the atherosclerotic process and makes the system suitable for large scale epidemiological research, diagnostic and clinical practice.     

Automatic segmentation of the lumen region in intravascular images of the coronary artery

Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen. An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two experts in 326 IVUS images of the coronary artery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference and Dice coefficient were 0.88 ± 0.06, 0.29 ± 0.17  mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324 IVUS images successfully segmented. Additionally, a comparison with the studies found in the literature showed that the proposed method is slight better than the majority of the related methods that have been proposed. Hence, the new automatic segmentation method is shown to be effective in detecting the lumen in IVUS images without using complex solutions and user interaction.     

超声图像中基于经验模态分解自动提取内-中膜厚度

目的为改善传统人工标记测量血管内-中膜厚度(IMT)的准确性和稳定性,提出基于图像分割技术的经验模态分解(EMD)改进算法。方法采用EMD改进算法去噪,根据血管壁的特点,在其中的极值点插值步骤使用非均匀的二维B样条函数,在水平和垂直方向上控制网格的密度不同,分别满足不同的分辨精度和平滑程度要求,改进了原始的二维EMD算法;然后通过K均值方法从图像中分离出血管腔、血管壁和其他组织,使用数学形态学算法逐步得到最终的内-中膜组织分割结果。结果改进EMD算法取得了较好的重建和滤波效果,有效克服了超声图像的强噪声和低分辨力对图像分割的限制,整个算法分割比较准确,算法复杂度相对较小。结论改进EMD算法是在超声图像中自动提取内-中膜的较有潜力的方法,能有效去除超声噪声,同时保留条纹结构的细节和边缘信息,有望于其他强噪声环境下提取条纹结构。     

Orthogonal B-Mode Evaluation of Common Carotid Artery Plaques Reveals the Absence of Outward Remodeling

To properly assess morphologic and dynamic parameters of arteries and plaques, we propose the concept of orthogonal distance measurements, that is, measurements made perpendicular to the local lumen axis rather than along the ultrasound beam (vertical direction for a linear array). The aim of this study was to compare orthogonal and vertical artery and lumen diameters at the site of a plaque in the common carotid artery (CCA). Moreover, we investigated the interrelationship of orthogonal diameters and plaque size and the association of artery parameters with plaque echogenicity. In 29 patients, we acquired a longitudinal B-mode ultrasound recording of plaques at the posterior CCA wall. After semi-automatic segmentation of end-diastolic frames, diameters were extracted orthogonally along the lumen axis. To establish inter-observer variability of diameters obtained at the location of maximal plaque thickness, a second observer repeated the analysis (subset N?=?21). Orthogonal adventitia–adventitia and lumen diameters could be determined with good precision (coefficient of variation: 1%–5%. However, the precision of the change in lumen diameter from diastole to systole (distension) at the site of the plaque was poor (21%–50%). The orthogonal lumen diameter was significantly smaller than the vertical lumen diameter (p?<0.001). Surprisingly, the plaques did not cause outward remodeling, that is, a local increase in adventitia–adventitia distance at the site of the plaque. The intra- and inter-observer precision of diastolic–systolic plaque compression was poor and of the same order as the standard deviation of plaque compression. The orthogonal relative lumen distension was significantly lower for echogenic plaques, indicating a higher stiffness, than for echolucent plaques (p?<0.01). In conclusion, we illustrated the feasibility of extracting orthogonal CCA and plaque dimensions, albeit that the proposed approach is inadequate to quantify plaque compression.     

An integrated system for the non-invasive assessment of vessel wall and hemodynamic properties of large arteries by means of ultrasound.

OBJECTIVES: To integrate methods for non-invasive assessment of vessel wall properties (diastolic diameter, distension waveform and intima-media thickness) and hemodynamic properties (blood flow velocity and shear rate distribution) of large arteries by means of dedicated ultrasound signal processing. METHODS: we have developed an arterial laboratory (ART-lab) system. ART-lab consists of software running on a standard personal computer, equipped with a data acquisition card for the acquisition of radio frequency (RF) ultrasound signals obtained with a conventional echo scanner. It operates either (1) off-line or (2) in real-time. Real-time operation is restricted to the assessment of vessel wall properties because of limitations in computational power. RESULTS: This paper provides an overview of ART-lab ultrasound radio frequency data acquisition and dedicated RF-signal processing methods. The capabilities of the system are illustrated with some typical applications. CONCLUSIONS: ART-lab in real-time mode is a useful tool for monitoring arterial vessel wall dynamics, while off-line it can be employed to investigate the elastic vessel wall properties in combination with hemodynamics, such as blood flow velocity and shear rate distribution.     

Automated Registration of Freehand B-Mode Ultrasound and Magnetic Resonance Imaging of the Carotid Arteries Based on Geometric Features

An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual annotation of three seed points in US and MRI. The registration procedure starts with alignment of the lumen centerlines using a point-based registration algorithm. The resulting rigid transformation is used to initialize a rigid and subsequent non-rigid registration procedure that jointly aligns centerlines and segmentations by minimizing a weighted sum of the Euclidean distance between centerlines and the dissimilarity between segmentations. The method was evaluated in 28 carotid arteries from eight patients and six healthy volunteers. First, the automated US lumen segmentation method was validated and optimized in a cross-validation experiment. Next, the effect of the weighting parameter of the proposed registration dissimilarity metric and the control point spacing in the non-rigid registration was evaluated. Finally, the proposed registration method was evaluated in comparison to an existing intensity-and-point-based method, a registration using only the centerlines and a registration using manual US lumen segmentations. Registration accuracy was measured in terms of the mean surface distance between manual US segmentations and the registered MRI segmentations. The average mean surface distance was 0.78 ± 0.34 mm for all subjects, 0.65 ± 0.09 mm for healthy volunteers and 0.87 ± 0.42 mm for patients. The results for the complete set were significantly better (Wilcoxon test, p < 0.01) than the results for the intensity-and-point-based method and the centerline-based registration method. We conclude that the proposed method can robustly and accurately register US and MR images of the carotid artery, allowing multimodal analysis of the carotid plaque to improve plaque assessment.     

The feasibility of hybrid automatic segmentation of axillary lymph nodes from a 3-D sonogram

The use of manual segmentation of lymph nodes, within an ultrasound image, is challenging due to operator dependency and speckle. A group of 23 healthy female volunteers consented to a short imaging session to capture a maximum of three axillary lymph nodes. A feasibility study was completed using both automatic and manual segmentation techniques to analyze a sample of 45, three-dimensional (3-D) nodal volume sets. Level-set segmentation based on geodesic active contours and shape-space learning based on a level-set segmentation approach was used to capture global node shapes. Most of the image feature based segmentation methods failed; however, a more precise automatic segmentation algorithm was obtained using a superimposed shape model. Shape model based segmentation significantly improved the segmentation compared with standard level sets. The best segmentation results were achieved when an experienced sonographer assisted with setting seed surfaces. The initialization of seed surfaces improved the capture of the global shape and lymphatic vessels.     

Automatic real-time endocardial edge detection in two-dimensional echocardiography

Previous approaches to automatic border recognition in two-dimensional echocardiography have utilized off-line computer image analysis. In this paper, we describe a hardware technique for real-time endocardial edge detection during the echocardiographic examination. This technique is based on a new method of line-by-line compensation for ultrasound attenuation. The detected edges and composite edge images can be displayed alone or superimposed upon the usual echocardiographic display. To test the accuracy of our real-time technique, we obtained echocardiograms of excised hearts and compared real-time edges with edges manually traced by an experienced observer. Left ventricular cavity areas and endocardial perimeters derived from real-time edges correlated well with those derived from observer-defined edges.     

Fully Automatic Plaque Segmentation in 3-D Carotid Ultrasound Images

Automatic segmentation of the carotid plaques from ultrasound images has been shown to be an important task for monitoring progression and regression of carotid atherosclerosis. Considering the complex structure and heterogeneity of plaques, a fully automatic segmentation method based on media-adventitia and lumen-intima boundary priors is proposed. This method combines image intensity with structure information in both initialization and a level-set evolution process. Algorithm accuracy was examined on the common carotid artery part of 26 3-D carotid ultrasound images (34 plaques ranging in volume from 2.5 to 456 mm³) by comparing the results of our algorithm with manual segmentations of two experts. Evaluation results indicated that the algorithm yielded total plaque volume (TPV) differences of −5.3 ± 12.7 and −8.5 ± 13.8 mm³ and absolute TPV differences of 9.9 ± 9.5 and 11.8 ± 11.1 mm³. Moreover, high correlation coefficients in generating TPV (0.993 and 0.992) between algorithm results and both sets of manual results were obtained. The automatic method provides a reliable way to segment carotid plaque in 3-D ultrasound images and can be used in clinical practice to estimate plaque measurements for management of carotid atherosclerosis.     

Automated Skin Segmentation in Ultrasonic Evaluation of Skin Toxicity in Breast Cancer Radiotherapy

Skin toxicity is the most common side effect of breast cancer radiotherapy and impairs the quality of life of many breast cancer survivors. We, along with other researchers, have recently found quantitative ultrasound to be effective as a skin toxicity assessment tool. Although more reliable than standard clinical evaluations (visual observation and palpation), the current procedure for ultrasound-based skin toxicity measurements requires manual delineation of the skin layers (i.e., epidermis-dermis and dermis-hypodermis interfaces) on each ultrasound B-mode image. Manual skin segmentation is time consuming and subjective. Moreover, radiation-induced skin injury may decrease image contrast between the dermis and hypodermis, which increases the difficulty of delineation. Therefore, we have developed an automatic skin segmentation tool (ASST) based on the active contour model with two significant modifications: (i) The proposed algorithm introduces a novel dual-curve scheme for the double skin layer extraction, as opposed to the original single active contour method. (ii) The proposed algorithm is based on a geometric contour framework as opposed to the previous parametric algorithm. This ASST algorithm was tested on a breast cancer image database of 730 ultrasound breast images (73 ultrasound studies of 23 patients). We compared skin segmentation results obtained with the ASST with manual contours performed by two physicians. The average percentage differences in skin thickness between the ASST measurement and that of each physician were less than 5% (4.8 ± 17.8% and −3.8 ± 21.1%, respectively). In summary, we have developed an automatic skin segmentation method that ensures objective assessment of radiation-induced changes in skin thickness. Our ultrasound technology offers a unique opportunity to quantify tissue injury in a more meaningful and reproducible manner than the subjective assessments currently employed in the clinic.     

Registration-based segmentation of three-dimensional ultrasound images for quantitative measurement of fetal craniofacial structure

Segmentation of a fetal head from three-dimensional (3-D) ultrasound images is a critical step in the quantitative measurement of fetal craniofacial structure. However, two main issues complicate segmentation, including fuzzy boundaries and large variations in pose and shape among different ultrasound images. In this article, we propose a new registration-based method for automatically segmenting the fetal head from 3-D ultrasound images. The proposed method first detects the eyes based on Gabor features to identify the pose of the fetus image. Then, a reference model, which is constructed from a fetal phantom and contains prior knowledge of head shape, is aligned to the image via feature-based registration. Finally, 3-D snake deformation is utilized to improve the boundary fitness between the model and image. Four clinically useful parameters including inter-orbital diameter (IOD), bilateral orbital diameter (BOD), occipital frontal diameter (OFD) and bilateral parietal diameter (BPD) are measured based on the results of the eye detection and head segmentation. Ultrasound volumes from 11 subjects were used for validation of the method accuracy. Experimental results showed that the proposed method was able to overcome the aforementioned difficulties and achieve good agreement between automatic and manual measurements.     

Longitudinal wall motion of the common carotid artery can be assessed by velocity vector imaging

Velocity vector imaging (VVI) is novel ultrasound image analysis software, enabling simultaneous evaluation of longitudinal and radial tissue motion. This study aimed to investigate the possible usefulness of VVI in evaluating the longitudinal vessel wall movement of the common carotid artery (CCA). Sixteen healthy volunteers and 16 patients with established coronary artery disease (CAD) were included in the study. CCA was scanned and standard B-mode ultrasound images were analysed off-line with VVI. In healthy volunteers, total longitudinal displacements (tLoD) of the right and left CCA were similar, as were the movements of the near- and far wall of the right CCA. The CAD group showed significantly lower tLoD compared to the healthy volunteers (0·543 ± 0·394 versus 0·112 ± 0·074, P<0·0001). VVI is a highly feasible technique in assessing longitudinal CCA wall motion, which may be of potential relevance as a novel vascular biomarker.     

超声造影对正常动脉检测能力的实验研究

目的研究生理状态下彩色多普勒血流成像(CDFI)对不同深度血管血流的显示能力以及彩色多普勒超声造影(以下简称彩超造影)与实时灰阶谐波超声造影(以下简称谐波造影)的表现。方法动物选择普通家犬5只。使用意大利百胜Technos DU8超声诊断仪及SonoVue超声造影剂。二维超声分别显示犬的髂总动脉、髂外动脉、髂内动脉、股动脉及腋动脉,并测量内径,脉冲多普勒测量收缩期峰值流速(PSV)。人为增加血管深度,CDFI检查记录该深度状态的血流强度。至CDFI不能清晰显示血流时,分别利用彩超造影与谐波造影两种方法再次检测。彩超造影检测时记录血流强度及PSV。结果随着深度增加CDFI观察到的血流信号减弱,造影后血流信号均明显增强;造影后在同一部位检测到的PSV增加36.1%,两组数据比较有显著性差异;谐波造影显示注射造影剂后动脉管腔内回声迅速增强,能够清晰显示血管管壁与管腔的分界。结论造影剂的应用可明显提高CDFI对深部血流信号的检出,而谐波造影能更直观、准确地显示血管壁及流道的轮廓。     

Ultrasound image segmentation using spectral clustering

Segmentation of ultrasound images is necessary in a variety of clinical applications, but the development of automatic techniques is still an open problem. Spectral clustering techniques have recently become popular for data and image analysis. In particular, image segmentation has been proposed via the normalized cut (NCut) criterion. This article describes an initial investigation to determine the suitability of such segmentation techniques for ultrasound images. The adaptation of the NCut technique to ultrasound is described first. Segmentation is then performed on simulated ultrasound images. Tests are also performed on abdominal and fetal images with the segmentation results compared to manual segmentation. The success of the segmentation on these test cases warrants further research into NCut-based segmentation of ultrasound images.     

An Integrated Approach to Computer‐Based Automated Tracing and Its Validation for 200 Common Carotid Arterial Wall Ultrasound Images

Objective. Most of the algorithms for the segmentation of the common carotid artery (CCA) wall require human interaction to locate the vessel in the ultrasound image. The aim of this article is to show an accurate algorithm for the computer‐based automated tracing of the CCA in longitudinal B‐mode ultrasound images. Methods. Two hundred images (100 normal CCAs, 50 CCAs with an increased intima‐media thickness, 30 with fibrous plaques, and 20 with anechoic plaques) were processed to delineate the region of interest containing the CCA. The strategy is an integrated approach (carotid artery layer extraction using an integrated approach [CALEXia]) consisting of geometric feature extraction, line fitting, and classification. The output of the algorithm is the tracings of the proximal and distal adventitia layers. Performance of the algorithm was validated against human tracings considered the ground truth. Results. The mean distance errors ± SD using this integrated approach were 1.05 ± 1.04 pixels (0.07 ± 0.07 mm) for proximal or near adventitia and 2.68 ± 3.94 pixels (0.17 ± 0.24 mm) for distal or far adventitia. Sixteen of 200 images were not perfectly traced because of the presence of both plaques and blood backscattering. The computational cost ensures the possibility for near real‐time detection. Conclusions . Although the CALEXia algorithm automatically detects the CCA, it is also robust and validated over a large database. This can constitute a general basis for a completely automated segmentation procedure widely applicable to other anatomies.     

Automated aorta segmentation in low-dose chest CT images

Purpose

   Abnormalities of aortic surface and aortic diameter can be related to cardiovascular disease and aortic aneurysm. Computer-based aortic segmentation and measurement may aid physicians in related disease diagnosis. This paper presents a fully automated algorithm for aorta segmentation in low-dose non-contrast CT images.

Methods

   The original non-contrast CT scan images as well as their pre-computed anatomy label maps are used to locate the aorta and identify its surface. First a seed point is located inside the aortic lumen. Then, a cylindrical model is progressively fitted to the 3D image space to track the aorta centerline. Finally, the aortic surface is located based on image intensity information. This algorithm has been trained and tested on 359 low-dose non-contrast CT images from VIA-ELCAP and LIDC public image databases. Twenty images were used for training to obtain the optimal set of parameters, while the remaining images were used for testing. The segmentation result has been evaluated both qualitatively and quantitatively. Sixty representative testing images were used to establish a partial ground truth by manual marking on several axial image slices.

Results

   Compared to ground truth marking, the segmentation result had a mean Dice Similarity Coefficient of 0.933 (maximum 0.963 and minimum 0.907). The average boundary distance between manual segmentation and automatic segmentation was 1.39 mm with a maximum of 1.79 mm and a minimum of 0.83 mm.

Conclusion

   Both qualitative and quantitative evaluations have shown that the presented algorithm is able to accurately segment the aorta in low-dose non-contrast CT images.