Segmentation of the outer vessel wall of the common carotid artery in CTA

3-D Contrast-Enhanced Fusion Ultrasound for Accurate Volume Assessment of Vessel Lumen and Plaque in Carotid Artery Disease as Compared With Computed Tomography Angiography

OBJECTIVE

Three-dimensional contrast-enhanced fusion ultrasound (CEFUS) of atherosclerotic carotid arteries provides spatial visualization of the vessel lumen, creating a lumenography. As in 3-D computed tomography angiography (CTA), 3-D CEFUS outlines the contrast-filled lumen. Plaque and vessel contours are distinguished in 3-D CEFUS, allowing plaque volume quantification as a valid estimate of carotid plaque burden. Three-dimensional CEFUS is unproven in intermodality studies, vindicating the assessment of 3-D CEFUS applicability and comparing 3-D CEFUS and 3-D CTA lumenography as a proof-of-concept study.

METHODS

Using an ultrasound system with magnetic tracking, a linear array transducer and SonoVue contrast agent, 3-D CEFUS acquisitions were generated by spatial stitching of serial 2-D images. From 3-D CEFUS and 3-D CTA imaging, the atherosclerotic carotid arteries were reconstructed with lumenography in an offline software program for lumen and plaque volume quantification. Bland-Altman analysis was used for inter-image modality agreement.

RESULTS

The study included 39 carotid arteries. Mean lumen and plaque volume in 3-D CEFUS were 0.63 cm3 (standard deviation [SD]: 0.26) and 0.62 cm3 (SD: 0.26), respectively. Lumen volume differences between 3-D CEFUS and 3-D CTA were non-significant, with a mean difference of 0.01 cm3 (SD: 0.02, p = 0.26) and limits of agreement (LoA) range of ±0.11 cm3. Mean plaque volume difference was -0.12 cm3 (SD: 0.19, p = 0.006) with a LoA range of ±0.39 cm3.

CONCLUSION

There was strong agreement in lumenography between 3-D CEFUS and 3-D CTA. The interimage modality difference in plaque volumes was substantial because of challenging vessel wall definition in 3-D CTA. Three-dimensional CEFUS is viable in quantifying carotid plaque volume burden and can potentially monitor plaque development over time.

Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions

The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima-media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research.

Learning-based algorithms for vessel tracking: A review

Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.

Automated Artery Localization and Vessel Wall Segmentation using Tracklet Refinement and Polar Conversion

Quantitative analysis of blood vessel wall structures is important to study atherosclerotic diseases and assess cardiovascular event risks. To achieve this, accurate identification of vessel luminal and outer wall contours is needed. Computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and/or boundary initialization, are still needed. In addition, prior knowledge of the ring shape of vessel walls has not been fully explored in designing segmentation methods. In this work, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm was adapted to robustly identify the artery of interest from a neural network-based artery centerline identification architecture. Image patches were extracted from the centerlines and converted in a polar coordinate system for vessel wall segmentation. The segmentation method used 3D polar information and overcame problems such as contour discontinuity, complex vessel geometry, and interference from neighboring vessels. Verified by a large (>32000 images) carotid artery dataset collected from multiple sites, the proposed system was shown to better automatically segment the vessel wall than traditional vessel wall segmentation methods or standard convolutional neural network approaches. In addition, a segmentation uncertainty score was estimated to effectively identify slices likely to have errors and prompt manual confirmation of the segmentation. This robust vessel wall segmentation system has applications in different vascular beds and will facilitate vessel wall feature extraction and cardiovascular risk assessment.

3D Active Vessel Tracking Using an Elliptical Prior

In this paper, we propose a novel vessel tracking method, called active vessel tracking (AVT). The proposed method retains the major advantages that most 2D segmentation methods have demonstrated for 3D tracking while overcoming the drawbacks of previous 3D vessel tracking methods. Under the assumption that the vessel is cylindrical, thereby making its cross-section elliptical, the AVT finds a plane perpendicular to the vessel axis while tracking the vessel along its length. Also, We propose a method for vessel branch detection to automatically track complete vascular networks from a single starting point, whereas the previously proposed solutions have usually been limited in handling vessel bifurcations precisely on 3D or have required considerable user interaction. Our results show that the method is robust and accurate in both synthetic and clinical cases. In an experiment on synthetic data sets, the proposed method achieved a tracking accuracy of 96.1±0.5, detecting 99.1% of the branches. In an experiment on abdominal CTA data sets, it achieved a tracking accuracy of 98.4±0.5 for six target vessels, detecting 98.3% of the branches. These results show that the proposed method can outperform previous methods for vessel tracking.

Semi-automated carotid lumen segmentation in computed tomography angiography images

Carotid artery stenosis causes narrowing of carotid lumens and may lead to brain infarction. The purpose of this study was to develop a semi-automated method of segmenting vessel walls, surrounding tissues, and more importantly, the carotid artery lumen by contrast computed tomography angiography (CTA) images and to define the severity of stenosis and present a three-dimensional model of the carotid for visual inspection. In vivo contrast CTA images of 14 patients (7 normal subjects and 7 patients undergoing endarterectomy) were analyzed using a multi-step segmentation algorithm. This method uses graph cut followed by watershed and Hessian based shortest path method in order to extract lumen boundary correctly without being corrupted in the presence of surrounding tissues. Quantitative measurements of the proposed method were compared with those of manual delineation by independent board-certified radiologists. The results were quantitatively evaluated using spatial overlap surface distance indices. A slightly strong match was shown in terms of dice similarity coefficient (DSC) = 0.87±0.08; mean surface distance (Dmsd) = 0.32±0.32; root mean squared surface distance (Drmssd) = 0.49±0.54 and maximum surface distance (Dmax) = 2.14±2.08 between manual and automated segmentation of common, internal and external carotid arteries, carotid bifurcation and stenotic artery, respectively. Quantitative measurements showed that the proposed method has high potential to segment the carotid lumen and is robust to the changes of the lumen diameter and the shape of the stenosis area at the bifurcation site. The proposed method for CTA images provides a fast and reliable tool to quantify the severity of carotid artery stenosis.

Medical image segmentation based on level set and isoperimetric constraint

Level set based methods are being increasingly used in image segmentation. In these methods, various shape constraints can be incorporated into the energy functionals to obtain the desired shapes of the contours represented by their zero level sets of functions. Motivated by the isoperimetric inequality in differential geometry, we propose a segmentation method in which the isoperimetric constrain is integrated into a level set framework to penalize the ratio of its squared perimeter to its enclosed area of an active contour. The new model can ensure the compactness of segmenting objects and complete missing or/and blurred parts of their boundaries simultaneously. The isoperimetric shape constraint is free of explicit expressions of shapes and scale-invariant. As a result, the proposed method can handle various objects with different scales and does not need to estimate parameters of shapes. Our method can segment lesions with blurred or/and partially missing boundaries in ultrasound, Computed Tomography (CT) and Magnetic Resonance (MR) images efficiently. Quantitative evaluation also confirms that the proposed method can provide more accurate segmentation than two well-known level set methods. Therefore, our proposed method shows potential of accurate segmentation of lesions for applying in diagnoses and surgical planning.

Feasibility of a Priori Numerical Assessment of Plaque Scaffolding after Carotid Artery Stenting in Clinical Routine: Proof of Concept

Purpose

Carotid artery stenting (CAS) is an alternative procedure for the treatment of severely stenosed carotid artery lesions in high-risk patients. Appropriate patient selection and stent design are paramount to achieve a low stroke and death rate in these complex high-risk procedures. This study introduces and evaluates a novel virtual, patient-specific, pre-operative environment to quantify scaffolding parameters based on routine imaging techniques.

Methods

Two patients who underwent CAS with two different sizes of the Acculink stent (Abbott Vascular, Santa Clara, CA, USA) were studied. Pre-operative data were used to build the numerical models for the virtual procedure. Numerical results were validated with post-operative angiography. Using novel virtual geometrical tools, incomplete stent apposition, free cell area and largest fitting sphere in the stent cell were evaluated in situ as quantitative measures of successful stent placement and to assess potential risk factors for CAS complications.

Results

A quantitative validation of the numerical outcome with post-operative images noted differences in lumen diameter of 5.31 ± 8.05% and 4.12 ± 9.84%, demonstrating the reliability of the proposed methodology. The quantitative measurements of the scaffolding parameters on the virtually deployed stent geometry highlight the variability of the device behavior in relation to the target lesion. The free cell area depends on the target diameter and oversizing, while the largest fitting spheres and apposition values are influenced by the local concavity and convexity of the vessel.

Conclusions

The proposed virtual environment may be an additional tool for endovascular specialists especially in complex anatomical cases where stent design and positioning may have a higher impact on procedural success and outcome.

A semi-automatic segmentation method for the structural analysis of carotid atherosclerotic plaques by computed tomography angiography

AIM

Computed tomography angiography (CTA) is currently the most reliable imaging technique for evaluating and planning the treatment of atherosclerosis. The drawbacks of the technique are its low spatial resolution and challenging manual measurements. The purpose of this study was to develop a semi-automatic method to segment vessel walls, surrounding tissue, and the carotid artery lumen to measure the severity of stenosis.

METHODS

In vivo contrast CTA images from eight patients undergoing endarterectomy were analyzed using a tailored five-step process involving an adaptive segmentation algorithm and region growing to measure the maximum percent stenosis in the cross-sectional area of the carotid artery. The accuracy of this method was compared with that of manual measurements made by physicians.

RESULTS

There were no significant differences between the maximum percent stenosis value obtained using the semi-automatic tool and that obtained using manual measurements (6%; p=0.31). The data acquisition and analysis required an average of 145 seconds.

CONCLUSION

This new semi-automatic segmentation method for CTA provides a fast and reliable tool to quantify the severity of carotid artery stenosis.

Automatic motion analysis system for pyloric flow in ultrasonic videos

Ultrasonography has been widely used to evaluate duodenogastric reflux (DGR). But to the best of our knowledge, no automatic analysis system was developed to realize the quantitative computer-aided analysis. In this paper, we propose a system to perform the automatic detection of DGR in the ultrasonic image sequences by applying the automatic motion analysis. The motion field is estimated based on image velocimetry. Then, an intelligent motion analysis is applied. For the DGR detection, the motion and structural information is combined to analyze the transploric motion of the fluid. In order to test the performance of the proposed system, we designed the experiment with the real and synthetic ultrasonic data. The proposed system achieved a good performance in the DGR detection. The automatic results were accordant with the gold standard in analyzing the fluid motion. The proposed system is supposed to be a promising tool for the study and evaluation of DGR.

Dual-pass feature extraction on human vessel images

We present a novel algorithm for the extraction of cavity features on images of human vessels. Fat deposits in the inner wall of such structure introduce artifacts, and regions in the images captured invalidating the usual assumption of an elliptical model which makes the process of extracting the central passage effectively more difficult. Our approach was designed to cope with these challenges and extract the required image features in a fully automated, accurate, and efficient way using two stages: the first allows to determine a bounding segmentation mask to prevent major leakages from pixels of the cavity area by using a circular region fill that operates as a paint brush followed by Principal Component Analysis with auto correction; the second allows to extract a precise cavity enclosure using a micro-dilation filter and an edge-walking scheme. The accuracy of the algorithm has been tested using 30 computed tomography angiography scans of the lower part of the body containing different degrees of inner wall distortion. The results were compared to manual annotations from a specialist resulting in sensitivity around 98 %, false positive rate around 8 %, and positive predictive value around 93 %. The average execution time was 24 and 18 ms on two types of commodity hardware over sections of 15 cm of length (approx. 1 ms per contour) which makes it more than suitable for use in interactive software applications. Reproducibility tests were also carried out with synthetic images showing no variation for the computed diameters against the theoretical measure.

Automatic coronary calcium scoring in low-dose chest computed tomography

The calcium burden as estimated from non-ECG-synchronized computed tomography (CT) exams acquired in screening of heavy smokers has been shown to be a strong predictor of cardiovascular events. We present a method for automatic coronary calcium scoring with low-dose, non-contrast-enhanced, non-ECG-synchronized chest CT. First, a probabilistic coronary calcium map was created using multi-atlas segmentation. This map assigned an a priori probability for the presence of coronary calcifications at every location in a scan. Subsequently, a statistical pattern recognition system was designed to identify coronary calcifications by texture, size, and spatial features; the spatial features were computed using the coronary calcium map. The detected calcifications were quantified in terms of volume and Agatston score. The best results were obtained by merging the results of three different supervised classification systems, namely direct classification with a nearest neighbor classifier, and two-stage classification with nearest neighbor and support vector machine classifiers.We used a total of 231 test scans containing 45,674 mm³ of coronary calcifications. The presented method detected on average 157/198 mm³ (sensitivity 79.2%) of coronary calcium volume with on average 4 mm false positive volume. Calcium scoring can be performed automatically in low-dose, non-contrast enhanced, non-ECG-synchronized chest CT in screening of heavy smokers to identify subjects who might benefit from preventive treatment.

Analysis of carotid artery plaque and wall boundaries on CT images by using a semi-automatic method based on level set model

INTRODUCTION

The purpose of this study was to evaluate the potentialities of a semi-automated technique in the detection and measurement of the carotid artery plaque.

METHODS

Twenty-two consecutive patients (18 males, 4 females; mean age 62 years) examined with MDCTA from January 2011 to March 2011 were included in this retrospective study. Carotid arteries are examined with a 16-multi-detector-row CT system, and for each patient, the most diseased carotid was selected. In the first phase, the carotid plaque was identified and one experienced radiologist manually traced the inner and outer boundaries by using polyline and radial distance method (PDM and RDM, respectively). In the second phase, the carotid inner and outer boundaries were traced with an automated algorithm: level-set-method (LSM). Data were compared by using Pearson rho correlation, Bland-Altman, and regression.

RESULTS

A total of 715 slices were analyzed. The mean thickness of the plaque using the reference PDM was 1.86 mm whereas using the LSM-PDM was 1.96 mm; using the reference RDM was 2.06 mm whereas using the LSM-RDM was 2.03 mm. The correlation values between the references, the LSM, the PDM and the RDM were 0.8428, 0.9921, 0.745 and 0.6425. Bland-Altman demonstrated a very good agreement in particular with the RDM method.

CONCLUSION

Results of our study indicate that LSM method can automatically measure the thickness of the plaque and that the best results are obtained with the RDM. Our results suggest that advanced computer-based algorithms can identify and trace the plaque boundaries like an experienced human reader.

Carotid atherosclerotic plaque progression and change in plaque composition over time: a 5-year follow-up study using serial CT angiography

BACKGROUND AND PURPOSE

Serial in vivo imaging of atherosclerosis is important for understanding plaque progression and is potentially useful in predicting cardiovascular events and monitoring treatment efficacy. This prospective study aims to quantify temporal changes in carotid atherosclerotic plaque volume and plaque composition using MDCTA.

MATERIALS AND METHODS

In 109 patients with TIA or ischemic stroke, serial MDCTA of the carotid arteries was performed after 5.3 ± 0.7 years. The carotid bifurcation was semiautomatically registered for paired baseline follow-up datasets. Outer vessel wall and lumen boundaries were defined using semiautomated segmentation tools. Plaque component volumes were measured using HU thresholds. Annual changes in plaque volume and plaque component proportions were calculated.

RESULTS

One-hundred-ninety-three carotid arteries were analyzed. Plaque volume decreased in 31% and increased in 69% of vessels (range -5.6-10.1%/year). Overall, plaque volume increased 1.2% per year (95% CI, 0.8-1.6, P ≤ .001). Plaque composition changed significantly from BL (fibrous 66.4%, lipid 28.8%, calcifications 4.8%): fibrous tissue decreased by 1.5%, lipid decreased by 1.8%, and calcification increased by 3.3% (P < .001). Intraobserver reproducibility of all volume and proportion measurements was good (ICC 0.78-1.00) and interobserver reproducibility was moderate (ICC 0.76-0.99).

CONCLUSIONS

Changes in carotid plaque burden and plaque composition can be quantified by using serial MDCTA. Plaque burden development is a heterogeneous and slow process.

Automated versus manual segmentation of atherosclerotic carotid plaque volume and components in CTA: associations with cardiovascular risk factors

The purpose of this study was to validate automated atherosclerotic plaque measurements in carotid arteries from CT angiography (CTA). We present an automated method (three initialization points are required) to measure plaque components within the carotid vessel wall in CTA. Plaque components (calcifications, fibrous tissue, lipids) are determined by different ranges of Hounsfield Unit values within the vessel wall. On CTA scans of 40 symptomatic patients with atherosclerotic plaque in the carotid artery automatically segmented plaque volume, calcified, fibrous and lipid percentages were 0.97 ± 0.51 cm(3), 10 ± 11%, 63 ± 10% and 25 ± 5%; while manual measurements by first observer were 0.95 ± 0.60 cm(3), 14 ± 16%, 63 ± 13% and 21 ± 9%, respectively and manual measurement by second observer were 1.05 ± 0.75 cm(3), 11 ± 12%, 61 ± 11% and 27 ± 10%. In 90 datasets, significant associations were found between age, gender, hypercholesterolemia, diabetes, smoking and previous cerebrovascular disease and plaque features. For both automated and manual measurements, significant associations were found between: age and calcium and fibrous tissue percentage; gender and plaque volume and lipid percentage; diabetes and calcium, smoking and plaque volume; previous cerebrovascular disease and plaque volume. Significant associations found only by the automated method were between age and plaque volume, hypercholesterolemia and plaque volume and diabetes and fibrous tissue percentage. Significant association found only by the manual method was between previous cerebrovascular disease and percentage of fibrous tissue. Automated analysis of plaque composition in the carotid arteries is comparable with the manual analysis and has the potential to replace it.