Segmentation of carotid artery in ultrasound images: method development and evaluation technique

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.

Fully automatic carotid arterial stiffness assessment from ultrasound videos based on machine learning

Arterial stiffness (AS) refers to the loss of arterial compliance and alterations in vessel wall properties. The study of local carotid stiffness (CS) is particularly important since carotid artery stiffening raises the risk of stroke, cognitive impairment, and dementia. So, stiffness measurement as a screening tool approach is crucial because it can reduce mortality and facilitate therapy planning. This study aims to evaluate the stiffness of the CCA using machine learning (ML) through the features of diameter change (ΔD) and stiffness parameters. This study was conducted in seven stages: data collection, preprocessing, CCA segmentation, CCA lumen diameter (DCCA) computing during cardiac cycles, denoising signals of DCCA, computational of AS parameters, and stiffness assessment using ML. The 51 videos (with 25 s) of CCA B-mode ultrasound (US) were used and analyzed. Each US video yielded approximately 750 sequential frames spanning about 24 cardiac cycles. Firstly, US preset settings with time gain compensation with a U-pattern were employed to enhance CCA segmentations. The study showed that auto region-growing, employed three times, is appropriate for segmenting walls with a short running time (4.56 s/frame). The diameter computed for frames constructs a signal (diameter signal) with noisy parts in the shape of peak variance and an un-smooth side. Among the 12 employed smoothing methods, spline fitting with a mean peak difference per cycle (MPDCY) of 0.58 pixels was the most effective for the diameter signal. The authors propose the MPDCY as a new selection criterion for smoothing methods with highly preserved peaks. The ΔD (D_sys-D_dia) determined in this study was validated by statistical analysis as a viable replacement for manual ΔD measurement. Statistical analysis was carried out by Mann-Whitney t-test with a p-value of 0.81, regression line R² = 0.907, and there was no difference in means between the two groups for box plots. The stiffness parameters of the carotid arteries were calculated based on auto-ΔD and pulse pressure. Five ML models, including K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and random forest (RF), fed by distension (ΔD) and five stiffness parameters, were used to distinguish between the stiffened and un-stiffened CCA. Except for SVM, all models performed excellently in terms of specificity, sensitivity, precision, and area under the curve (AUC). In addition, the scatterplot and statistical analysis of the fed features confirm these remarkable outcomes. The scatter plot demonstrates that a linear hyperline can easily distinguish between the two classes. The statistical analysis shows that the stiffness parameters computed from the database of this work were statistically (p < 0.05) distributed into the non-stiffness and stiffness groups. The presented models are validated by applying them to additional datasets. Applying models to other datasets reveals a model performance of 100%. The proposed ML models could be applied in clinical practice to detect CS early, which is essential for preventing stroke.

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.

Unsupervised domain adaptation method for segmenting cross-sectional CCA images

BACKGROUND AND OBJECTIVES

Automatic vessel segmentation in ultrasound is challenging due to the quality of the ultrasound images, which is affected by attenuation, high level of speckle noise and acoustic shadowing. Recently, deep convolutional neural networks are increasing in popularity due to their great performance on image segmentation problems, including vessel segmentation. Traditionally, large labeled datasets are required to train a network that achieves high performance, and is able to generalize well to different orientations, transducers and ultrasound scanners. However, these large datasets are rare, given that it is challenging and time-consuming to acquire and manually annotate in-vivo data.

METHODS

In this work, we present a model-based, unsupervised domain adaptation method that consists of two stages. In the first stage, the network is trained on simulated ultrasound images, which have an accurate ground truth. In the second stage, the network continues training on in-vivo data in an unsupervised way, therefore not requiring the data to be labelled. Rather than using an adversarial neural network, prior knowledge on the elliptical shape of the segmentation mask is used to detect unexpected outputs.

RESULTS

The segmentation performance was quantified using manually segmented images as ground truth. Due to the proposed domain adaptation method, the median Dice similarity coefficient increased from 0 to 0.951, outperforming a domain adversarial neural network (median Dice 0.922) and a state-of-the-art Star-Kalman algorithm that was specifically designed for this dataset (median Dice 0.942).

CONCLUSIONS

The results show that it is feasible to first train a neural network on simulated data, and then apply model-based domain adaptation to further improve segmentation performance by training on unlabeled in-vivo data. This overcomes the limitation of conventional deep learning approaches to require large amounts of manually labeled in-vivo data. Since the proposed domain adaptation method only requires prior knowledge on the shape of the segmentation mask, performance can be explored in various domains and applications in future research.

Stratification of risk of atherosclerotic plaque using Hu’s moment invariants of segmented ultrasonic images

Myocardial infarction is one of the major life-threatening diseases. The cause is atherosclerosis i.e. the occlusion of the coronary artery by deposition of plaque on its walls. The severity of plaque deposition in the artery depends on the characteristics of the plaque. Hence, the classification of the type of plaque is crucial for assessing the risk of atherosclerosis and predicting the chances of myocardial infarction. This paper proposes prediction of atherosclerotic risk by non-invasive ultrasound image segmentation and textural feature extraction. The intima-media complex is segmented using a snakes-based segmentation algorithm on the arterial wall in the ultrasound images. Then, the plaque is extracted from the segmented intima-media complex. The features of the plaque are obtained by computing Hu’s moment invariants. Visual pattern recognition independent of position, size, orientation and parallel projection could be done using these moment invariants. For the classification of the features of the plaque, an SVM classifier is used. The performance shows improvement in accuracy using lesser number of features than previous works. The reduction in feature size is achieved by incorporating segmentation in the pre-processing stage. Tenfold cross-validation protocol is used for training and testing the classifier. An accuracy of 97.9% is obtained with only two features. This proposed technique could work as an adjunct tool in quick decision-making for cardiologists and radiologists. The segmentation step introduced in the preprocessing stage improved the feature extraction technique. An improvement in performance is achieved with much less number of features.

Evaluation of carotid intima media thickness measurement from ultrasound images

A third of deaths in the world are due to cardiovascular diseases [1]. Atherosclerosis is the major cause of myocardial infarction, which occurs by deposition of plaque in the coronary artery. The chance of stroke rises with the thickening of carotid artery due to the plaque. Hence, accurate measurement of the intima-media thickness is necessary for predicting the chance of stroke. The stopping criterion and active resampling are incorporated in greedy snake segmentation technique. This modified algorithm segmented and extracted the intima-media complex in the ultrasound images. The snake control points obtained from the boundary of the region of interest forms the contour and demarcates the boundary of intima-media complex. The thickness ± standard deviation and the intra-observer error values obtained by modified algorithm are in conformity with the measurements by expert. The intra-observer error values for greedy snake segmentation methods were 0.10 and 0.09 for manual snake initialization and automatic snake initialization, respectively. Shapiro-Wilk test and One-way ANOVA test explains there is no statistical difference between group means obtained from these segmentation techniques and the expert measurement. The statistical analysis proves values of the intima-media thickness obtained from both snake segmentation techniques are very close to expert measurements.

Automated 3D geometry segmentation of the healthy and diseased carotid artery in free-hand, probe tracked ultrasound images

PURPOSE

Rupture of an arterosclerotic plaque in the carotid artery is a major cause of stroke. Biomechanical analysis of plaques is under development aiming to aid the clinician in the assessment of plaque vulnerability. Patient-specific three-dimensional (3D) geometry assessment of the carotid artery, including the bifurcation, is required as input for these biomechanical models. This requires a high-resolution, 3D, noninvasive imaging modality such as ultrasound (US). In this study, a high-resolution two-dimensional (2D) linear array in combination with a magnetic probe tracking device and automatic segmentation method was used to assess the geometry of the carotid artery. The advantages of using this system over a 3D ultrasound probe are its higher resolution (spatial and temporal) and its larger field of view.

METHODS

A slow sweep (v = ± 5 mm/s) was made over the subject's neck so that the full geometry of the bifurcated geometry of the carotid artery is captured. An automated segmentation pipeline was developed. First, the Star-Kalman method was used to approximate the center and size of the vessels for every frame. Images were filtered with a Gaussian high-pass filter before conversion into the 2D monogenic signals, and multiscale asymmetry features were extracted from these data, enhancing low lateral wall-lumen contrast. These images, in combination with the initial ellipse contours, were used for an active deformable contour model to segment the vessel lumen. To segment the lumen-plaque boundary, Otsu's automatic thresholding method was used. Distension of the wall due to the change in blood pressure was removed using a filter approach. Finally, the contours were converted into a 3D hexahedral mesh for a patient-specific solid mechanics model of the complete arterial wall.

RESULTS

The method was tested on 19 healthy volunteers and on 3 patients. The results were compared to manual segmentation performed by three experienced observers. Results showed an average Hausdorff distance of 0.86 mm and an average similarity index of 0.91 for the common carotid artery (CCA) and 0.88 for the internal and external carotid artery. For the total algorithm, the success rate was 89%, in 4 out of 38 datasets the ICA and ECA were not sufficient visible in the US images. Accurate 3D hexahedral meshes were successfully generated from the segmented images .

CONCLUSIONS

With this method, a subject-specific biomechanical model can be constructed directly from a hand-held 2D US measurement, within 10 min, with a minimal user input. The performance of the proposed segmentation algorithm is comparable to or better than algorithms previously described in literature. Moreover, the algorithm is able to segment the CCA, ICA, and ECA including the carotid bifurcation in transverse B-mode images in both healthy and diseased arteries.

Carotid artery ultrasound image analysis: A review of the literature

Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima-media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima-media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning-based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks

For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential part in this monitoring process. In this paper, we propose a novel Deep Neural Network as a fully automated segmentation tool, and its application in delineating both the media-adventitia boundary and the lumen-intima boundary. We develop a new geometrically constrained objective function as part of the Network's Stochastic Gradient Descent optimisation, thus tuning it to the problem at hand. Furthermore, we also apply a bimodal fusion of amplitude and phase congruency data proposed by us in previous work, as an input to the network, as the latter provides an intensity-invariant data source to the network. We finally report the segmentation performance of the network on transverse sections of the carotid. Tests are carried out on an augmented dataset of 81,000 images, and the results are compared to other studies by reporting the DICE coefficient of similarity, modified Hausdorff Distance, sensitivity and specificity. Our proposed modification is shown to yield improved results on the standard network over this larger dataset, with the advantage of it being fully automated. We conclude that Deep Neural Networks provide a reliable trained manner in which carotid ultrasound images may be automatically segmented, using amplitude data and intensity invariant phase congruency maps as a data source.

Freehand three-dimensional ultrasound imaging of carotid artery using motion tracking technology

Ultrasound imaging has been extensively used for determining the severity of carotid atherosclerotic stenosis. In particular, the morphological characterization of carotid plaques can be performed for risk stratification of patients. However, using 2D ultrasound imaging for detecting morphological changes in plaques has several limitations. Due to the scan was performed on a single longitudinal cross-section, the selected 2D image is difficult to represent the entire morphology and volume of plaque and vessel lumen. In addition, the precise positions of 2D ultrasound images highly depend on the radiologists' experience, it makes the serial long-term exams of anti-atherosclerotic therapies are difficult to relocate the same corresponding planes by using 2D B-mode images. This has led to the recent development of three-dimensional (3D) ultrasound imaging, which offers improved visualization and quantification of complex morphologies of carotid plaques. In the present study, a freehand 3D ultrasound imaging technique based on optical motion tracking technology is proposed. Unlike other optical tracking systems, the marker is a small rigid body that is attached to the ultrasound probe and is tracked by eight high-performance digital cameras. The probe positions in 3D space coordinates are then calibrated at spatial and temporal resolutions of 10μm and 0.01s, respectively. The image segmentation procedure involves Otsu's and the active contour model algorithms and accurately detects the contours of the carotid arteries. The proposed imaging technique was verified using normal artery and atherosclerotic stenosis phantoms. Human experiments involving freehand scanning of the carotid artery of a volunteer were also performed. The results indicated that compared with manual segmentation, the lowest percentage errors of the proposed segmentation procedure were 7.8% and 9.1% for the external and internal carotid arteries, respectively. Finally, the effect of handshaking was calibrated using the optical tracking system for reconstructing a 3D image.

A Fully-Automatic Method to Segment the Carotid Artery Layers in Ultrasound Imaging: Application to Quantify the Compression-Decompression Pattern of the Intima-Media Complex During the Cardiac Cycle

The aim of this study was to introduce and evaluate a contour segmentation method to extract the interfaces of the intima-media complex in carotid B-mode ultrasound images. The method was applied to assess the temporal variation of intima-media thickness during the cardiac cycle. The main methodological contribution of the proposed approach is the introduction of an augmented dimension to process 2-D images in a 3-D space. The third dimension, which is added to the two spatial dimensions of the image, corresponds to the tentative local thickness of the intima-media complex. The method is based on a dynamic programming scheme that runs in a 3-D space generated with a shape-adapted filter bank. The optimal solution corresponds to a single medial axis representation that fully describes the two anatomical interfaces of the arterial wall. The method is fully automatic and does not require any input from the user. The method was trained on 60 subjects and validated on 184 other subjects from six different cohorts and four different medical centers. The arterial wall was successfully segmented in all analyzed images (average pixel size = 57 ± 20 mm), with average segmentation errors of 47 ± 70 mm for the lumen-intima interface, 55 ± 68 mm for the media-adventitia interface and 66 ± 90 mm for the intima-media thickness. The amplitude of the temporal variations in IMT during the cardiac cycle was significantly higher in the diseased population than in healthy volunteers (106 ± 48 vs. 86 ± 34 mm, p = 0.001). The introduced framework is a promising approach to investigate an emerging functional parameter of the arterial wall by assessing the cyclic compression-decompression pattern of the tissues.

Ultrasound intima-media thickness measurement of the carotid artery using ant colony optimization combined with a curvelet-based orientation-selective filter

PURPOSE

Automatic measurement of the intima-media thickness (IMT) from ultrasound carotid images is an important task in clinical diagnosis. Many computer-based techniques for IMT measurement have been proposed to overcome the limits of manual segmentation. However, the robustness of the algorithms would be influenced by the inherent speckle noise of ultrasound image. This paper proposed a curvelet guided ant colony optimization (CGACO) strategy that could achieve satisfied accuracy for IMT measurement with improved robustness to noise.

METHODS

The curvelet-based orientation-selective (CBOS) filter was first introduced for speckle removal and edge enhancement. Different from conventional methods, CBOS filter processes the curvelet coefficients by orientations rather than by magnitude. Then, a specially designed two-leg ant colony optimization technique, combined with Otsu thresholding and Sobel edge detector, was proposed as a novel segmentation method to extract the media-adventitia (MA) and the lumen-intima (LI) boundaries. Finally, a coupled snake model was employed to further smooth the contours of MA and LI.

RESULTS

In addition to 224 carotid artery images acquired from 34 participants, simulated speckled images with nine levels of noise were also included in the database. The mean absolute distance errors of CGACO for LI interface tracings, MA interface tracings, and IMT measurements were 0.030 ± 0.027, 0.039 ± 0.036, and 0.041 ± 0.036 mm, respectively. Besides, CGACO had a correlation coefficient as high as 0.992 and a bias as low as -0.008. All these measures were comparable to or better than a previous technique and the manual segmentation. On the other hand, CGACO had the highest success rate of 98.7% in the segmentation of real data. It also maintained a much higher success rate in the segmentation of simulated images with different levels of speckle noise.

CONCLUSIONS

The proposed technique showed accurate IMT measurement results. Furthermore, benefiting from the CBOS filter, the robustness to noise of the algorithm was substantially improved. Therefore, CGACO could provide a reliable way to segment the carotid artery from ultrasound images and could be used in clinical practice of IMT measurement, particularly in early atherosclerotic stages.

A framework for quantification and visualization of segmentation accuracy and variability in 3D lateral ventricle ultrasound images of preterm neonates

PURPOSE

Intraventricular hemorrhage (IVH) is a major cause of brain injury in preterm neonates. Three dimensional ultrasound (US) imaging systems have been developed to visualize 3D anatomical structure of preterm neonatal intracranial ventricular system with IVH and ventricular dilation. To allow quantitative analysis, the ventricle system is required to be segmented accurately and efficiently from 3D US images. Although semiautomatic segmentation algorithms have been developed, local segmentation accuracy and variability associated with these algorithms should be evaluated statistically before they can be applied in clinical settings. This work proposes a statistical framework to quantify the local accuracy and variability and performs statistical tests to identify locations where the semiautomatically segmented surfaces are significantly different from manually segmented surfaces.

METHODS

Three dimensional lateral ventricle US images of preterm neonates were each segmented six times manually and using a semiautomated segmentation algorithm. The local difference between manually and algorithmically segmented surfaces as well as the segmentation variability for each method was computed and superimposed on the ventricular surface of each subject. To summarize the segmentation performance for a whole group of subjects, the subject-specific local difference and standard deviation maps were registered onto a 3D template ventricular surface using a nonrigid registration algorithm. Pointwise, intersubject average accuracy and pooled variability for the whole group of subjects can be computed and visualized on the template surface, providing a summary of performance of the segmentation algorithm for the whole group of ventricles with highly variable geometry. In addition to pointwise statistical analysis performed on the template surface, statistical conclusion regarding the accuracy of the segmentation algorithm was made for subregions and the whole ventricle with the spatial correlation of pointwise accuracy taken into account.

RESULTS

Ten 3D US images were involved in this study. Pointwise local difference, ΔS, its absolute value |ΔS| as well as the standard deviations of the manual and algorithm segmentations were computed and superimposed on the each ventricle surface. Regions with lower segmentation accuracy and higher segmentation variability can be identified from these maps, and the localized information was applied to improve the accuracy of the algorithm. Intersubject average ΔS and |ΔS| as well as pooled standard deviations was computed on the template surface. Intersubject average ΔS and |ΔS| indicated that the algorithm underestimated regions in the neighborhood of the tips of anterior, inferior, and posterior horns. Intersubject pooled standard deviations indicated that manual segmentation had a higher segmentation variability than algorithm segmentation over the whole ventricle. Statistical analysis on the template surface showed that there was significant difference between algorithm and manual methods for segmenting the right lateral ventricle but not for the left lateral ventricle.

CONCLUSIONS

A framework was proposed for evaluating, visualizing, and summarizing the local accuracy and variability of a segmentation algorithm. This framework can be used for improving the accuracy of segmentation algorithms, as well as providing useful feedback to improve the manual segmentation performance. More importantly, this framework can be applied for longitudinal monitoring of local ventricular changes of neonates with IVH.

An ultrasonographic technique to assess the jugular venous pulse: a proof of concept

The purpose of the work described here was to investigate the feasibility of assessing the jugular venous pulse (JVP) using ultrasound (US) equipment. Three young healthy subjects underwent a B-mode US scan of the internal jugular vein (IJV) to acquire a sonogram sequence in the transverse plane. On each acquired sonogram, the IJV contour was manually traced, and both the cross-sectional area (CSA) and the perimeter were measured. The CSA data set represents the US jugular diagram (USJD). The arterial distension waveform of the subjects was compared with its USJD. The correlation between the CSA and the perimeter was assessed during the cardiac cycle to verify IJV distension. For each subject, a short sonogram sequence of a few seconds was recorded, and the USJD obtained exhibited periodic behavior. Furthermore, for all subjects, the CSA was found to be correlated with the perimeter (Pearson coefficient, R > 0.9), indicating that the IJV in supine position is distended. We compared 390 manually traced contours of the IJV cross-sectional area with corresponding values semi-automatically calculated by an algorithm developed in-house. For all subjects, the sensitivity, specificity and accuracy were around 95%, 85% and 90% respectively. We found that a diagram reflecting the JVP can be obtained by analyzing a B-mode sonogram sequence of the IJV; such a diagram can result in a new methodology to assess the IJV functionality.

Lumen segmentation and motion estimation in B-mode and contrast-enhanced ultrasound images of the carotid artery in patients with atherosclerotic plaque

In standard B-mode ultrasound (BMUS), segmentation of the lumen of atherosclerotic carotid arteries and studying the lumen geometry over time are difficult owing to irregular lumen shapes, noise, artifacts, and echolucent plaques. Contrast enhanced ultrasound (CEUS) improves lumen visualization, but lumen segmentation remains challenging owing to varying intensities, CEUS-specific artifacts and lack of tissue visualization. To overcome these challenges, we propose a novel method using simultaneously acquired BMUS&CEUS image sequences. Initially, the method estimates nonrigid motion (NME) from the image sequences, using intensity-based image registration. The motion-compensated image sequence is then averaged to obtain a single "epitome" image with improved signal-to-noise ratio. The lumen is segmented from the epitome image through an intensity joint-histogram classification and a graph-based segmentation. NME was validated by comparing displacements with manual annotations in 11 carotids. The average root mean square error (RMSE) was 112±73 μm . Segmentation results were validated against manual delineations in the epitome images of two different datasets, respectively containing 11 (RMSE 191±43 μm) and 10 (RMSE 351±176 μm ) carotids. From the deformation fields, we derived arterial distensibility with values comparable to the literature. The average errors in all experiments were in the inter-observer variability range. To the best of our knowledge, this is the first study exploiting combined BMUS&CEUS images for atherosclerotic carotid lumen segmentation.

An integrated system for the segmentation of atherosclerotic carotid plaque ultrasound video

The robust border identification of atherosclerotic carotid plaque, the corresponding degree of stenosis of the common carotid artery (CCA), and also the characteristics of the arterial wall, including plaque size, composition, and elasticity, have significant clinical relevance for the assessment of future cardiovascular events. To facilitate the follow-up and analysis of the carotid stenosis in serial clinical investigations, we propose and evaluate an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound videos of the CCA based on video frame normalization, speckle reduction filtering, M-mode state-based identification, parametric active contours, and snake segmentation. Initially, the cardiac cycle in each video is identified and the video M-mode is generated, thus identifying systolic and diastolic states. The video is then segmented for a time period of at least one full cardiac cycle. The algorithm is initialized in the first video frame of the cardiac cycle, with human assistance if needed, and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. Two different initialization methods are investigated in which initial contours are estimated every 20 video frames. In the first initialization method, the initial snake contour is estimated using morphology operators; in the second initialization method, the Chan-Vese active contour model is used. The performance of the algorithm is evaluated on 43 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, available every 20 frames in a time span of 3 to 5 s, covering, in general, 2 cardiac cycles. The segmentation results were very satisfactory, according to the expert objective evaluation, for the two different methods investigated, with true-negative fractions (TNF-specificity) of 83.7 ± 7.6% and 84.3 ± 7.5%; true-positive fractions (TPF-sensitivity) of 85.42 ± 8.1% and 86.1 ± 8.0%; and between the ground truth and the proposed segmentation method, kappa indices (KI) of 84.6% and 85.3% and overlap indices of 74.7% and 75.4%. The segmentation contours were also used to compute the cardiac state identification and radial, longitudinal, and shear strain indices for the CCA wall and plaque between the asymptomatic and symptomatic groups were investigated. The results of this study show that the integrated system investigated in this study can be successfully used for the automated video segmentation of the CCA plaque in ultrasound videos.

Three-dimensional segmentation of three-dimensional ultrasound carotid atherosclerosis using sparse field level sets

PURPOSE

Three-dimensional ultrasound (3DUS) vessel wall volume (VWV) provides a 3D measurement of carotid artery wall remodeling and atherosclerotic plaque and is sensitive to temporal changes of carotid plaque burden. Unfortunately, although 3DUS VWV provides many advantages compared to measurements of arterial wall thickening or plaque alone, it is still not widely used in research or clinical practice because of the inordinate amount of time required to train observers and to generate 3DUS VWV measurements. In this regard, semiautomated methods for segmentation of the carotid media-adventitia boundary (MAB) and the lumen-intima boundary (LIB) would greatly improve the time to train observers and for them to generate 3DUS VWV measurements with high reproducibility.

METHODS

The authors describe a 3D algorithm based on a modified sparse field level set method for segmenting the MAB and LIB of the common carotid artery (CCA) from 3DUS images. To the authors' knowledge, the proposed algorithm is the first direct 3D segmentation method, which has been validated for segmenting both the carotid MAB and the LIB from 3DUS images for the purpose of computing VWV. Initialization of the algorithm requires the observer to choose anchor points on each boundary on a set of transverse slices with a user-specified interslice distance (ISD), in which larger ISD requires fewer user interactions than smaller ISD. To address the challenges of the MAB and LIB segmentations from 3DUS images, the authors integrated regional- and boundary-based image statistics, expert initializations, and anatomically motivated boundary separation into the segmentation. The MAB is segmented by incorporating local region-based image information, image gradients, and the anchor points provided by the observer. Moreover, a local smoothness term is utilized to maintain the smooth surface of the MAB. The LIB is segmented by constraining its evolution using the already segmented surface of the MAB, in addition to the global region-based information and the anchor points. The algorithm-generated surfaces were sliced and evaluated with respect to manual segmentations on a slice-by-slice basis using 21 3DUS images.

RESULTS

The authors used ISD of 1, 2, 3, 4, and 10 mm for algorithm initialization to generate segmentation results. The algorithm-generated accuracy and intraobserver variability results are comparable to the previous methods, but with fewer user interactions. For example, for the ISD of 3 mm, the algorithm yielded an average Dice coefficient of 94.4% ± 2.2% and 90.6% ± 5.0% for the MAB and LIB and the coefficient of variation of 6.8% for computing the VWV of the CCA, while requiring only 1.72 min (vs 8.3 min for manual segmentation) for a 3DUS image.

CONCLUSIONS

The proposed 3D semiautomated segmentation algorithm yielded high-accuracy and high-repeatability, while reducing the expert interaction required for initializing the algorithm than the previous 2D methods.

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 mm(3)) 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 mm(3) and absolute TPV differences of 9.9 ± 9.5 and 11.8 ± 11.1 mm(3). 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.

Ultrasound common carotid artery segmentation based on active shape model

Carotid atherosclerosis is a major reason of stroke, a leading cause of death and disability. In this paper, a segmentation method based on Active Shape Model (ASM) is developed and evaluated to outline common carotid artery (CCA) for carotid atherosclerosis computer-aided evaluation and diagnosis. The proposed method is used to segment both media-adventitia-boundary (MAB) and lumen-intima-boundary (LIB) on transverse views slices from three-dimensional ultrasound (3D US) images. The data set consists of sixty-eight, 17 × 2 × 2, 3D US volume data acquired from the left and right carotid arteries of seventeen patients (eight treated with 80 mg atorvastatin and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. Manually outlined boundaries by expert are adopted as the ground truth for evaluation. For the MAB and LIB segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 94.4% ± 3.2% and 92.8% ± 3.3%, mean absolute distances (MAD) of 0.26 ± 0.18 mm and 0.33 ± 0.21 mm, and maximum absolute distances (MAXD) of 0.75 ± 0.46 mm and 0.84 ± 0.39 mm. It took 4.3 ± 0.5 mins to segment single 3D US images, while it took 11.7 ± 1.2 mins for manual segmentation. The method would promote the translation of carotid 3D US to clinical care for the monitoring of the atherosclerotic disease progression and regression.

Comparative study on the performance of textural image features for active contour segmentation

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

Fully automated dual-snake formulation for carotid intima-media thickness measurement. A new approach

Automated computer-aided detection systems for measurement of the carotid intima-media thickness (IMT) are becoming popular. These systems yield lumen-intima (LI) and media-adventitia (MA) borders. In this work, we developed and validated a novel and patented completely automated IMT measurement system called carotid measurement using dual snakes (CMUDS): a class of AtheroEdge system (Global Biomedical Technologies, Inc, Roseville, CA). CMUDS is modeled as a dual parametric system corresponding to LI and MA borders with initialization from the far adventitia layer. The novelty of CMUDS is the first-order absolute moment-based external energy, which provides stable deformation. The dual snakes evolve simultaneously and are forced to maintain a regularized distance to prevent collapsing or bleeding. Two independent readers manually traced the LI/MA boundaries of a multi-institutional, multi-ethnic, and multi-scanner database of 665 longitudinal images for performance evaluation. CMUDS was also benchmarked against a previously developed automated technique. CMUDS correctly processed 660 images (99.2% success). The differences between the CMUDS and two manual IMT measurements (mean ± SD) were 0.013 ± 0.216 and -0.021 ± 0.197 mm, respectively. The corresponding figures of merit for CMUDS compared to reader tracings were 98.4% and 97.5%. Compared to the previous technique (IMT differences, 0.022 ± 0.276 and -0.012 ± 0.266 mm), CMUDS improved accuracy (Wilcoxon P < 0.009) and variability (Fisher P < 10(-8)). Among different resolution images from original equipment manufacturer ultrasound scanners, CMUDS performed best with high-resolution images corresponding to 0.0789 mm/pixel. Accuracy in IMT measurement with the proposed automated CMUDS technique makes this system adaptable to large multi-center studies, in which such an IMT measurement system would be very useful tool.

Segmentation of atherosclerotic carotid plaque in ultrasound video

The degree of stenosis of the common carotid artery (CCA) but also the characteristics of the arterial wall including plaque size, composition and elasticity represent important predictors used in the assessment of the risk for future cardiovascular events. This paper proposes and evaluates an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound video of the CCA based on normalization, speckle reduction filtering (with the hybrid median filter) and parametric active contours. The algorithm is initialized in the first video frame of the cardiac cycle with human assistance and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. The algorithm is evaluated on 10 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, for every 20 frames in a time span of 3-5 seconds, covering in general 2 cardiac cycles. The segmentation results are very satisfactory with a true negative fraction (TNF) of 79.3%, a true-positive fraction (TPF) of 78.12%, a false-positive fraction (FPF) of 6.7% and a false-negative fraction (FNF) of 19.6% between the ground truth and the presented plaque segmentations, a Williams index (KI) of 80.3%, an overlap index of 71.5%, a specificity of 0.88±0.09, a precision of 0.86±0.10 and an effectiveness measure of 0.77±0.09. The results show that integrated system investigated in this study could be successfully used for the automated video segmentation of the carotid plaque.

Segmentation of plaques in sequences of ultrasonic B-mode images of carotid arteries based on motion estimation and a Bayesian model

The goal of this work is to perform a segmentation of atherosclerotic plaques in view of evaluating their burden and to provide boundaries for computing properties such as the plaque deformation and elasticity distribution (elastogram and modulogram). The echogenicity of a region of interest comprising the plaque, the vessel lumen, and the adventitia of the artery wall in an ultrasonic B-mode image was modeled by mixtures of three Nakagami distributions, which yielded the likelihood of a Bayesian segmentation model. The main contribution of this paper is the estimation of the motion field and its integration into the prior of the Bayesian model that included a local geometrical smoothness constraint, as well as an original spatiotemporal cohesion constraint. The Maximum A Posteriori (MAP) of the proposed model was computed with a variant of the Exploration/Selection (ES) algorithm. The starting point is a manual segmentation of the first frame. The proposed method was quantitatively compared with manual segmentations of all frames by an expert technician. Various measures were used for this evaluation, including the mean point-to-point distance and the Hausdorff distance. Results were evaluated on 94 sequences of 33 patients (for a total of 8988 images). We report a mean point-to- point distance of 0.24 ± 0.08 mm and a Hausdorff distance of 1.24 ± 0.40 mm. Our tests showed that the algorithm was not sensitive to the degree of stenosis or calcification.

Automated detection of the carotid artery wall in longitudinal B-mode images using active contours initialized by the Hough transform

In this paper, a fully automatic active-contour-based segmentation method is presented, for detecting the carotid artery wall in longitudinal B-mode ultrasound images. A Hough-transform-based methodology is used for the definition of the initial snake, followed by a gradient vector flow (GVF) snake deformation for the final contour detection. The GVF snake is based on the calculation of the image edge map and the calculation of GVF field which guides its deformation for the estimation of the real arterial wall boundaries. In twenty cases there was no significant difference between the automated segmentation and the manual diameter measurements. The sensitivity, specificity and accuracy were 0.97, 0.99 and 0.98, respectively, for both diastolic and systolic cases. In conclusion, the proposed methodology provides an accurate and reliable way to segment ultrasound images of the carotid artery.

A review of noninvasive ultrasound image processing methods in the analysis of carotid plaque morphology for the assessment of stroke risk

Noninvasive ultrasound imaging of carotid plaques allows for the development of plaque-image analysis methods associated with the risk of stroke. This paper presents several plaque-image analysis methods that have been developed over the past years. The paper begins with a review of clinical methods for visual classification that have led to standardized methods for image acquisition, describes methods for image segmentation and denoising, and provides an overview of the several texture-feature extraction and classification methods that have been applied. We provide a summary of emerging trends in 3-D imaging methods and plaque-motion analysis. Finally, we provide a discussion of the emerging trends and future directions in our concluding remarks.

Three-dimensional carotid ultrasound segmentation variability dependence on signal difference and boundary orientation

Quantitative measurements of the progression (or regression) of carotid plaque burden are important in monitoring patients and evaluating new treatment options. We previously developed a quantitative metric to analyze changes in carotid plaque morphology from 3-D ultrasound (US) on a point-by-point basis. This method requires multiple segmentations of the arterial wall and lumen boundaries to obtain the local standard deviation (SD) of vessel-wall-plus-plaque thickness (VWT) so that t-tests could be used to determine whether a change in VWT is statistically significant. However, the requirement for multiple segmentations makes clinical trials laborious and time-consuming. Therefore, this study was designed to establish the relationship between local segmentation SD and local signal difference on the arterial wall and lumen boundaries. We propose metrics to quantify segmentation SD and signal difference on a point-by-point basis, and studied whether the signal difference at arterial wall or lumen boundaries could be used to predict local segmentation SD. The ability to predict the local segmentation SD could eliminate the need of repeated segmentations of a 2-D transverse image to obtain the local segmentation standard deviation, thereby making clinical trials less laborious and saving time. Six subjects involved in this study were associated with different degrees of atherosclerosis: three carotid stenosis subjects with mean plaque area >3 cm(2) and >60% carotid stenosis were involved in a clinical study evaluating the effect of atorvastatin, a cholesterol-lowering and plaque-stabilizing drug; and three subjects with carotid plaque area >0.5 cm(2) were subjects with moderate atherosclerosis. Our results suggest that when local signal difference is higher than 8 greyscale value (GSV), the local segmentation SD stabilizes at 0.05 mm and is thus predictable. This information provides a target value of local signal difference on the arterial boundaries that should be achieved to obtain an accurate prediction of local segmentation SD. (E-mail: bcychiu@alumni.uwo.ca).

Three-dimensional ultrasound quantification of intensive statin treatment of carotid atherosclerosis

This study was designed to evaluate 3-D ultrasound (3DUS)-derived vessel wall volume (VWV), a 3-D measurement of the carotid artery intima and media, including atherosclerotic plaque, in patients enrolled in a randomized placebo-controlled three-month study of intensive atorvastatin treatment. Thirty-five subjects with carotid stenosis >60% who provided written informed consent and completed a randomized, double-blind, placebo-controlled study were evaluated at baseline and at three months after receiving either 80 mg atorvastatin (16 subjects, nine male, mean age 68+/-8.6 y) or placebo (19 subjects, 15 male, mean age 70+/-9.4 y) daily. 3DUS images were acquired and 3DUS VWV was manually segmented by a single observer. Individual lumen and wall segmentation contours were also used to generate carotid atherosclerosis thickness difference maps by establishing correspondence between points along the vessel wall and lumen segmentation contour surfaces, and digitally subtracting registered baseline and follow-up thickness maps. 3DUS VWV increased by 70+/-140 mm(3) (+4.9+/-10.3%) in the placebo group and decreased by 30+/-110 mm(3) (-1.4+/-7.7%) in the atorvastatin group (p<0.05). Two-dimensional maps generated from the VWV measurements show localized heterogeneity and vessel wall thickness changes for all subjects, mainly in the common carotid artery. Carotid 3DUS VWV is a quantitative measure of atherosclerosis burden including the intima, media and plaque, with sensitivity to detect changes over short periods of time. Quantitative VWV thickness difference maps provide visual evidence of the spatial and temporal dynamics of carotid artery changes.

Automated detection of the carotid artery wall in B-mode ultrasound images using active contours initialized by the Hough Transform

Automatic segmentation of the arterial lumen from ultrasound images is an important and often challenging task in clinical diagnosis. We previously used the Hough Transform (HT) to automatically extract circles from sequences of B-mode ultrasound images of transverse sections of the carotid artery. In this paper, an active-contour-based methodology is suggested, initialized by the HT circle, in an attempt to extend previous findings and to accurately detect the arterial wall boundary. The methodology is based on the generation of a gradient vector flow field, an approach attempting to overcome conventional active contours constraints. Contour estimation is then achieved by deforming the initial curve (circle) based on the gradient vector flow field. In ten normal subjects, the specificity and accuracy of the segmentation were on average higher than 0.98, whereas the sensitivity was higher than 0.82. The methodology was also applied to four subjects with atherosclerosis, in which sensitivity, specificity and accuracy were comparable to those of normal subjects. In conclusion, the HT-initialized active contours methodology provides a reliable tool to detect the carotid artery wall in ultrasound images and can be used in clinical practice.

Fully automated common carotid artery and internal jugular vein identification and tracking using B-mode ultrasound

We describe a fully automated ultrasound analysis system that tracks and identifies the common carotid artery (CCA) and the internal jugular vein (IJV). Our goal is to prevent inadvertent damage to the CCA when targeting the IJV for catheterization. The automated system starts by identifying and fitting ellipses to all the regions that look like major arteries or veins throughout each B-mode ultrasound image frame. The spokes ellipse algorithm described in this paper tracks these putative vessels and calculates their characteristics, which are then weighted and summed to identify the vessels. The optimum subset of characteristics and their weights were determined from a training set of 38 subjects, whose necks were scanned with a portable 10 MHz ultrasound system at 10 frames per second. Stepwise linear discriminant analysis (LDA) narrowed the characteristics to the five that best distinguish between the CCA and IJV. A paired version of Fisher's LDA was used to calculate the weights for each of the five parameters. Leave-one-out validation studies showed that the system could track and identify the CCA and IJV with 100% accuracy in this dataset.

High speed laser tomography system

A high speed laser tomography system was developed capable of acquiring three-dimensional (3D) images of optically thin clouds of moving micron-sized particles. It operates by parallel-shifting an illuminating laser sheet with a pair of galvanometer-driven mirrors and synchronously recording two-dimensional (2D) images of thin slices of the imaged volume. The maximum scanning speed achieved was 120,000 slices/s, sequences of 24 volume scans (up to 256 slices each) have been obtained. The 2D slices were stacked to form 3D images of the volume, then the positions of the particles were identified and followed in the consecutive scans. The system was used to image a complex plasma with particles moving at speeds up to cm/s.

Using the Hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery

Automatic segmentation of the arterial lumen from ultrasound images is an important task in clinical diagnosis. In this paper, the Hough transform (HT) was used to automatically extract straight lines and circles from sequences of B-mode ultrasound images of longitudinal and transverse sections, respectively, of the carotid artery. In 10 normal subjects, the specificity and accuracy of HT-based segmentation were on average higher than 0.96 for both sections, whereas the sensitivity was higher than 0.96 in longitudinal and higher than 0.82 in transverse sections. The intima-media thickness (IMT) was also estimated from images of longitudinal sections; the corresponding validation parameters were generally higher than 0.90. To further validate the results, arterial distension waveforms (ADW) were estimated from sequences of images using the HT technique as well as motion analysis using block matching (BM). In longitudinal sections, diastolic and systolic diameters and relative diameter changes using HT and BM were not significantly different. In transverse sections, diastolic and systolic diameters were significantly lower using the HT technique; the differences were <7%. Relative diameter changes in transverse sections were not significantly different from BM-estimated ones. The HT technique was also applied to four subjects with atherosclerosis, in which sensitivity, specificity and accuracy were comparable to those of normal subjects; the low values of sensitivity in transverse sections may reflect departure from the circular model because of the presence of plaque. In conclusion, the HT technique provides a reliable way to segment ultrasound images of the carotid artery and can be used in clinical practice to estimate indices of arterial wall physiology, such as the IMT and the ADW.

Segmentation of the heart muscle in 3-D pediatric echocardiographic images

This study aimed to show segmentation of the heart muscle in pediatric echocardiographic images as a preprocessing step for tissue analysis. Transthoracic image sequences (2-D and 3-D volume data, both derived in radiofrequency format, directly after beam forming) were registered in real time from four healthy children over three heart cycles. Three preprocessing methods, based on adaptive filtering, were used to reduce the speckle noise for optimizing the distinction between blood and myocardium, while preserving the sharpness of edges between anatomical structures. The filtering kernel size was linked to the local speckle size and the speckle noise characteristics were considered to define the optimal filter in one of the methods. The filtered 2-D images were thresholded automatically as a first step of segmentation of the endocardial wall. The final segmentation step was achieved by applying a deformable contour algorithm. This segmentation of each 2-D image of the 3-D+time (i.e., 4-D) datasets was related to that of the neighboring images in both time and space. By thus incorporating spatial and temporal information of 3-D ultrasound image sequences, an automated method using image statistics was developed to perform 3-D segmentation of the heart muscle.

Real-time vessel segmentation and tracking for ultrasound imaging applications

A method for vessel segmentation and tracking in ultrasound images using Kalman filters is presented. A modified Star-Kalman algorithm is used to determine vessel contours and ellipse parameters using an extended Kalman filter with an elliptical model. The parameters can be used to easily calculate the transverse vessel area which is of clinical use. A temporal Kalman filter is used for tracking the vessel center over several frames, using location measurements from a handheld sensorized ultrasound probe. The segmentation and tracking have been implemented in real-time and validated using simulated ultrasound data with known features and real data, for which expert segmentation was performed. Results indicate that mean errors between segmented contours and expert tracings are on the order of 1%-2% of the maximum feature dimension, and that the transverse cross-sectional vessel area as computed from estimated ellipse parameters a, b as determined by our algorithm is within 10% of that determined by experts. The location of the vessel center was tracked accurately for a range of speeds from 1.4 to 11.2 mm/s.

3D reconstruction of carotid atherosclerotic plaque: comparison between spatial compound ultrasound models and anatomical models

This study deals with the creation of 3D models that can work as a tool for discriminating between tissue and background in the development of tissue classification methods. Ten formalin-fixed atherosclerotic carotid plaques removed by endarterectomy were scanned with 3D multi-angle spatial compound ultrasound (US) and subsequently sliced and photographed to produce a 3D anatomical data set. Outlines in the ultrasound data were found by means of active contours and combined into 10 3D ultrasound models. The plaque regions of the anatomical photographs were outlined manually and then combined into 10 3D anatomical models. The volumes of the anatomical models correlated with the volume found by a water displacement method (r = 0.95), except for an offset. The models were compared in three ways. Visual inspection showed quite good agreement between the models. The volumes of the ultrasound models correlated with the volumes of the anatomical models (r = 0.93), again with an offset. Finally, the overlap between the anatomical models and the ultrasound models showed, on average, that the intersection comprised 90%(vol) of the anatomical models and 73%(vol) of the ultrasound models.

Manual planimetric measurement of carotid plaque volume using three-dimensional ultrasound imaging

We investigated the utility of three manual planimetric methods to quantify carotid plaque volume. A single observer measured 15 individual plaques from 15 three-dimensional (3D) ultrasound (3D US) images of patients ten times each using three different planimetric approaches. Individual plaque volumes were measured (range: 32.6-597.1 mm3) using a standard planimetric approach (M1) whereby a plaque end was identified and sequential contours were measured. The same plaques were measured using a second approach (M2), whereby plaque ends were first identified and the 3D US image of the plaque was then subdivided into equal intervals. A third method (M3) was used to measure total plaque burden (range: 165.1-1080.0 mm3) in a region (+/- 1.5 cm) relative to the carotid bifurcation. M1 systematically underestimated individual plaque volume compared to M2 (V2 = V1 + 14.0 mm3, r = 0.99, p = 0.006) due to a difference in the mean plaque length measured. Coefficients of variance (CV) for M1 and M2 decrease with increasing plaque volume, with M2 results less than M1. Root mean square difference between experimental and theoretical CV for M2 was 3.2%. The standard deviation in the identification of the transverse location of the carotid bifurcation was 0.56 mm. CVs for plaque burden measured using M3 ranged from 1.2% to 7.6% and were less than CVs determined for individual plaque volumes of the same volume. The utility of M3 was demonstrated by measuring carotid plaque burden and volume change over a period of 3 months in three patients. In conclusion, M2 was determined to be a more superior measurement technique than M1 to measure individual plaque volume. Furthermore, we demonstrated the utility of M3 to quantify regional plaque burden and to quantify change in plaque volume.

Prostate contouring uncertainty in megavoltage computed tomography images acquired with a helical tomotherapy unit during image-guided radiation therapy

PURPOSE

To evaluate the image-guidance capabilities of megavoltage computed tomography (MVCT), this article compares the interobserver and intraobserver contouring uncertainty in kilovoltage computed tomography (KVCT) used for radiotherapy planning with MVCT acquired with helical tomotherapy.

METHODS AND MATERIALS

Five prostate-cancer patients were evaluated. Each patient underwent a KVCT and an MVCT study, a total of 10 CT studies. For interobserver variability analysis, four radiation oncologists, one physicist, and two radiation therapists (seven observers in total) contoured the prostate and seminal vesicles (SV) in the 10 studies. The intraobserver variability was assessed by asking all observers to repeat the contouring of 1 patient's KVCT and MVCT studies. Quantitative analysis of contour variations was performed by use of volumes and radial distances.

RESULTS

The interobserver and intraobserver contouring uncertainty was larger in MVCT compared with KVCT. Observers consistently segmented larger volumes on MVCT where the ratio of average prostate and SV volumes was 1.1 and 1.2, respectively. On average (interobserver and intraobserver), the local delineation variability, in terms of standard deviations [Deltasigma = radical(sigma2MVCT-sigma2KVCT)], increased by 0.32 cm from KVCT to MVCT.

CONCLUSIONS

Although MVCT was inferior to KVCT for prostate delineation, the application of MVCT in prostate radiotherapy remains useful.

Quantification of carotid plaque volume measurements using 3D ultrasound imaging

An accurate and reliable technique used to quantify carotid plaque volume has practical importance in research and patient management. In this study, we develop and investigate a theoretical description of carotid plaque volume measurements made using three-dimensional (3D) ultrasound (US) images and compare it with experimental results. Multiple observers measured 48 3D US patient images of carotid plaque (13.2 to 544.0 mm(3)) by manual planimetry. Coefficients of variation in the measurement of plaque volume were found to decrease with increasing plaque size for both inter- (90.8 to 3.9%) and intraobserver (70.2 to 3.1%) measurements. Plaque volume measurement variability was found to increase with interslice distance (ISD), while the relative measurement accuracy remained constant for ISDs between 1.0 and 3.0 mm and then decreased. Root-mean-square (RMS) difference between our theoretical description of plaque volume measurement variance and the experimental results was 5.7%. Thus, our results support the clinical utility of measuring carotid plaque volume by manual planimetry noninvasively using 3D US.

Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour

Knowing the location and the volume of the prostate is important for ultrasound-guided prostate brachytherapy, a commonly used prostate cancer treatment method. The prostate boundary must be segmented before a dose plan can be obtained. However, manual segmentation is arduous and time consuming. This paper introduces a semi-automatic segmentation algorithm based on the dyadic wavelet transform (DWT) and the discrete dynamic contour (DDC). A spline interpolation method is used to determine the initial contour based on four user-defined initial points. The DDC model then refines the initial contour based on the approximate coefficients and the wavelet coefficients generated using the DWT. The DDC model is executed under two settings. The coefficients used in these two settings are derived using smoothing functions with different sizes. A selection rule is used to choose the best contour based on the contours produced in these two settings. The accuracy of the final contour produced by the proposed algorithm is evaluated by comparing it with the manual contour outlined by an expert observer. A total of 114 2D TRUS images taken for six different patients scheduled for brachytherapy were segmented using the proposed algorithm. The average difference between the contour segmented using the proposed algorithm and the manually outlined contour is less than 3 pixels.

Measurement of carotid plaque volume by 3-dimensional ultrasound

BACKGROUND AND PURPOSE

Measurement of carotid plaque volume and its progression are important tools for research and patient management. In this study, we investigate the observer variability in the measurement of plaque volume as determined by 3-dimensional (3D) ultrasound (US). We also investigate the effect of interslice distances (ISD) and repeated 3D US scans on measurement variability. Materials and Methods Forty 3D US patient images of plaques (range, 37.43 to 604.1 mm3) were measured by manual planimetry. We applied ANOVA to determine plaque volume measurement variability and reliability. Plaque volumes were measured with 9 ISDs to determine the effect of ISD on measurement variability. Additional plaque volumes were also measured from multiple 3D US scans to investigate repeated scan acquisition variability. Results Intraobserver and interobserver measurement reliabilities were 94% and 93.2%, respectively. Plaque volume measurement variability decreased with increasing plaque volume (range, 27.1% to 2.2%). Measurement precision was constant for ISDs between 1.0 and 3.0 mm, whereas plaque volume measurement variability increased with ISD. Repeated 3D US scan measurements were not different from single-scan measurements (P=0.867). Conclusions The coefficient of variation in the measurement of plaque volume decreased with plaque size. The volumetric change that must be observed to establish with 95% confidence that a plaque has undergone change is approximately 20% to 35% for plaques <100 mm3 and approximately 10% to 20% for plaques >100 mm3. Measurement precision was unchanged for ISDs <3.0 mm, whereas measurement variability increased with ISD. Repeated 3D US scans did not affect plaque volume measurement variability.

Carotid geometry reconstruction: a comparison between MRI and ultrasound

Image-based Computational Fluid Dynamics (CFD) has become a popular tool for the prediction of in vivo flow profiles and hemodynamic wall parameters. Currently, Magnetic Resonance Imaging (MRI) is most widely used for in vivo geometry acquisition. For superficial arteries such as the carotids and the femoral artery, three-dimensional (3-D) extravascular ultrasound (3-DUS) could be a cost-effective alternative to MRI. In this study, nine healthy subjects were scanned both with MRI and 3-DUS. The reconstructed carotid artery geometries for each subject were compared by evaluating cross-sectional areas, centerlines, and carotid nonplanarity. Lumen areas agreed very well between the two different acquisition techniques, whereas centerlines and nonplanarity parameters showed measurable disagreement, possibly due to the different neck and head positions adopted for 3-DUS versus MRI. With the current level of agreement achieved, 3-DUS has the potential to become an inexpensive and fast alternative to MRI for image-based CFD modeling of superficial arteries.