Media-adventitia Boundaries Research Articles

Unsupervised shape-and-texture-based generative adversarial tuning of pre-trained networks for carotid segmentation from 3D ultrasound images.

Vessel-wall volume and localized three-dimensional ultrasound (3DUS) metrics are sensitive to the change of carotid atherosclerosis in response to medical/dietary interventions. Manual segmentation of the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) required to obtain these metrics is time-consuming and prone to observer variability. Although supervised deep-learning segmentation models have been proposed, training of these models requires a sizeable manually segmented training set, making larger clinical studiesprohibitive. We aim to develop a method to optimize pre-trained segmentation models without requiring manual segmentation to supervise the fine-tuningprocess. We developed an adversarial framework called the unsupervised shape-and-texture generative adversarial network (USTGAN) to fine-tune a convolutional neural network (CNN) pre-trained on a source dataset for accurate segmentation of a target dataset. The network integrates a novel texture-based discriminator with a shape-based discriminator, which together provide feedback for the CNN to segment the target images in a similar way as the source images. The texture-based discriminator increases the accuracy of the CNN in locating the artery, thereby lowering the number of failed segmentations. Failed segmentation was further reduced by a self-checking mechanism to flag longitudinal discontinuity of the artery and by self-correction strategies involving surface interpolation followed by a case-specific tuning of the CNN. The U-Net was pre-trained by the source dataset involving 224 3DUS volumes with 136, 44, and 44 volumes in the training, validation and testing sets. The training of USTGAN involved the same training group of 136 volumes in the source dataset and 533 volumes in the target dataset. No segmented boundaries for the target cohort were available for training USTGAN. The validation and testing of USTGAN involved 118 and 104 volumes from the target cohort, respectively. The segmentation accuracy was quantified by Dice Similarity Coefficient (DSC), and incorrect localization rate (ILR). Tukey's Honestly Significant Difference multiple comparison test was employed to quantify the difference of DSCs between models and settings, where was considered statisticallysignificant. USTGAN attained a DSC of % in LIB and % in MAB, improving from the baseline performance of % in LIB (p ) and % in MAB (p ). Our approach outperformed six state-of-the-art domain-adaptation models (MAB: , LIB: ). The proposed USTGAN also had the lowest ILR among the methods compared (LIB: 2.5%, MAB: 1.7%). Our framework improves segmentation generalizability, thereby facilitating efficient carotid disease monitoring in multicenter trials and in clinics with less expertise in 3DUSimaging.

Development of a Three-Dimensional Carotid Ultrasound Image Segmentation Workflow for Improved Efficiency, Reproducibility and Accuracy in Measuring Vessel Wall and Plaque Volume and Thickness.

Automated segmentation of carotid lumen-intima boundary (LIB) and media-adventitia boundary (MAB) by deep convolutional neural networks (CNN) from three-dimensional ultrasound (3DUS) images has made assessment and monitoring of carotid atherosclerosis more efficient than manual segmentation. However, training of CNN still requires manual segmentation of LIB and MAB. Therefore, there is a need to improve the efficiency of manual segmentation and develop strategies to improve segmentation accuracy by the CNN for serial monitoring of carotid atherosclerosis. One strategy to reduce segmentation time is to increase the interslice distance (ISD) between segmented axial slices of a 3DUS image while maintaining the segmentation reliability. We, for the first time, investigated the effect of ISD on the reproducibility of MAB and LIB segmentations. The intra-observer reproducibility of LIB and MAB segmentations at ISDs of 1 mm and 2 mm was not statistically significantly different, whereas the reproducibility at ISD = 3 mm was statistically lower. Therefore, we conclude that segmentation with an ISD of 2 mm provides sufficient reliability for CNN training. We further proposed training the CNN by the baseline images of the entire cohort of patients for automatic segmentation of the follow-up images acquired for the same cohort. We validated that segmentation with this time-based partitioning approach is more accurate than that produced by patient-based partitioning, especially at the carotid bifurcation. This study forms the basis for an efficient, reproducible, and accurate 3DUS workflow for serial monitoring of carotid atherosclerosis useful in risk stratification of cardiovascular events and in evaluating the efficacy of new treatments.

A Dual-Stream Centerline-Guided Network for Segmentation of the Common and Internal Carotid Arteries From 3D Ultrasound Images.

Segmentation of the carotid section encompassing the common carotid artery (CCA), the bifurcation and the internal carotid artery (ICA) from three-dimensional ultrasound (3DUS) is required to measure the vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT), shown to be sensitive to treatment effect. We proposed an approach to combine a centerline extraction network (CHG-Net) and a dual-stream centerline-guided network (DSCG-Net) to segment the lumen-intima (LIB) and media-adventitia boundaries (MAB) from 3DUS images. Correct arterial location is essential for successful segmentation of the carotid section encompassing the bifurcation. We addressed this challenge by using the arterial centerline to enhance the localization accuracy of the segmentation network. The CHG-Net was developed to generate a heatmap indicating high probability regions for the centerline location, which was then integrated with the 3DUS image by the DSCG-Net to generate the MAB and LIB. The DSCG-Net includes a scale-based and a spatial attention mechanism to fuse multi-level features extracted by the encoder, and a centerline heatmap reconstruction side-branch connected to the end of the encoder to increase the generalization ability of the network. Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.8±1.9% and 92.3±5.4% for CCA MAB and LIB, respectively, and 93.2±4.4% and 89.0±10.0% for ICA MAB and LIB, respectively. Our approach outperformed four state-of-the-art 3D CNN models, even after their performances were boosted by centerline guidance. The efficiency afforded by the framework would allow it to be incorporated into the clinical workflow for improved quantification of plaque change.

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

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

Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss.

Vessel wall volume (VWV) and localized vessel-wall-plus-plaque thickness (VWT) measured from three-dimensional (3D) ultrasound (US) carotid images are sensitive to anti-atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen-intima (LIB) and media-adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of segmenting the CCA only. An approach capable of segmenting the MAB and LIB from the CCA and ICA was required to accelerate VWV and VWT quantification. Segmentation for CCA and ICA was performed independently using the proposed two-channel U-Net, which was driven by a novel loss function known as the adaptive triple Dice loss (ADTL) function. The training set was augmented by interpolating manual segmentation along the longitudinal direction, thereby taking continuity of the artery into account. A test-time augmentation (TTA) approach was applied, in which segmentation was performed three times based on the input axial images and its flipped versions; the final segmentation was generated by pixel-wise majority voting. Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.1%±4.1% and 91.6%±6.6% for the MAB and LIB, in the CCA, respectively, and 94.2%±3.3% and 89.0%±8.1% for the MAB and LIB, in the ICA, respectively. TTA and ATDL independently contributed to a statistically significant improvement to all boundaries except the LIB in ICA. The proposed two-channel U-Net with ADTL and TTA can segment the CCA and ICA accurately and efficiently from the 3DUS volume. Our approach has the potential to accelerate the transition of 3DUS measurements of carotid atherosclerosis to clinical research.

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.

Convolutional Neural Network for Segmentation and Measurement of Intima Media Thickness.

The measurement of Carotid Intima Media Thickness (IMT) on Common Carotid Artery (CCA) is a principle marker of risk of cardiovascular disease. This paper presents a novel method of using deep Convolutional Neural Network (CNN) for identification and measurement of IMT on the far wall of the artery. The Region of Interest (ROI) is extracted using CNN architecture with 8 layers. 110 subjects are taken for the study. Each subject is recorded with one Right Common Carotid Artery (RCCA) and Left Common Carotid Artery (LCCA) frame resulting in 220 recordings. Patch based segmentation with 2640 patches are given to the training network for ROI localization. Intima Media Complex (IMC) is the area where IMT is measured. This region is extracted after defining the ROI. Keeping in mind the end objective of measurement of IMT values binary threshold with snake algorithm is applied to extract the lumen-intima and media-adventitia boundary. IMT values are measured for 20 cases and mean difference is found to be 0.08mm.

Segmentation of arterial walls in intravascular ultrasound cross-sectional images using extremal region selection

Intravascular Ultrasound (IVUS) is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. Segmentation of arterial wall boundaries from the IVUS images is not only crucial for quantitative analysis of the vessel walls and plaque characteristics, but is also necessary for generating 3D reconstructed models of the artery. The aim of this study is twofold. Firstly, we investigate the feasibility of using a recently proposed region detector, namely Extremal Region of Extremum Level (EREL) to delineate the luminal and media-adventitia borders in IVUS frames acquired by 20 MHz probes. Secondly, we propose a region selection strategy to label two ERELs as lumen and media based on the stability of their textural information. We extensively evaluated our selection strategy on the test set of a standard publicly available dataset containing 326 IVUS B-mode images. We showed that in the best case, the average Hausdorff Distances (HD) between the extracted ERELs and the actual lumen and media were 0.22 mm and 0.45 mm, respectively. The results of our experiments revealed that our selection strategy was able to segment the lumen with ⩽0.3 mm HD to the gold standard even though the images contained major artifacts such as bifurcations, shadows, and side branches. Moreover, when there was no artifact, our proposed method was able to delineate media-adventitia boundaries with 0.31 mm HD to the gold standard. Furthermore, our proposed segmentation method runs in time that is linear in the number of pixels in each frame. Based on the results of this work, by using a 20 MHz IVUS probe with controlled pullback, not only can we now analyze the internal structure of human arteries more accurately, but also segment each frame during the pullback procedure because of the low run time of our proposed segmentation method.

A Novel Segmentation Approach for Intravascular Ultrasound Images

Intravascular ultrasound (IVUS) is an important imaging technique to study the architecture of vascular wall for diagnosis and assessment of vascular diseases. Segmentation of lumen and media-adventitia boundaries from IVUS images is the foundation for quantitative assessment of the vascular walls. In this paper, a novel and fully automated segmentation approach is proposed for IVUS images. The proposed approach utilizes different segmentation strategies for lumen and media-adventitia boundaries respectively according to their different region and boundary features on IVUS images. For lumen, the segmentation is carried out by combining image gradient with a fuzzy connectedness model. For media-adventitia boundary segmentation, minimal path based on fast marching model is used. The effectiveness of the proposed IVUS image segmentation approach was validated by segmenting 180 IVUS images from nine patients, in which the results were compared to the corresponding gold standard. The approach achieved high mean overlap area ratios of (86.49 ± 16.93%)/(92.73 ± 5.47%) and small average boundary distances of (0.09 ± 0.10) mm/(0.07 ± 0.06) mm for lumen and media-adventitia, respectively. The obtained results have shown the potential of the proposed approach to effectively segment lumen and media-adventitia boundaries from IVUS images.

Left Ventricular Function Assessed by One-Point Carotid Wave Intensity in Newly Diagnosed Untreated Hypertensive Patients.

To investigate whether newly diagnosed untreated hypertensive patients show higher left ventricular (LV) contractility, as assessed by traditional echocardiographic indices and carotid wave intensity (WI) parameters, including amplitude of the peak during early (W1 ) and late systole (W2 ). A total of 145 untreated hypertensive patients were compared with 145 age- and sex-matched normotensive subjects. They underwent comprehensive echocardiography and WI analysis. WI analysis was performed at the level of the common carotid artery. The diameter changes were the difference between the displacement of the anterior and posterior walls, with the cursors set to track the media-adventitia boundaries 2 cm proximal to the carotid bulb and calibrated by systolic and diastolic BP. Peak acceleration was derived from blood flow velocity measured by Doppler sonography with the range-gate positioned at the center of the vessel diameter. WI was based on the calculation of (dP/dt)×(dU/dt), where dP/dt and dU/dt were the derivatives of BP (P) and velocity (U) with respect to time. One-point pulse wave velocity (PWVβ) and the interval between the R wave on ECG and the first peak of WI (R-W1 ), using a high definition echo-tracking system implemented in the ultrasound machine (Aloka), were also derived. After adjustment for body weight, heart rate, and physical activity, the two groups had similar general characteristics and diastolic function. However, hypertensives showed significantly higher LV mass, LV ejection fraction (LVEF), circumferential and LV end-systolic stress, and one-point PWV as well as W1 (13.646 ± 7.368 vs 9.308 ± 4.675 mmHg m/s3 , P =.001) and W2 (4.289 ± 2.017 vs 2.995 ± 1.868 mmHg m/s3 , P =.001). Hypertensives were divided into tertiles according to LVEF: W1 (11.934 ± 5.836 vs 11.576 ± 5.857 vs 17.227 ± 8.889 mmHg m/s3 , P <.0001) was higher in the highest LVEF tertile along with relative wall thickness, midwall fractional shortening, endocardial fractional shortening, and R-W1 . Newly diagnosed hypertensives show increased LVM and LV contractility, including carotid WI parameters and R-W1 values, as compared with normotensive subjects, but no differences in LV diastolic function.

Detection of Arterial Wall Boundaries Using an Echo Model Composed of Multiple Ultrasonic Pulses

The assessment of the intima-media thickness (IMT) of the carotid arterial wall, which is the most frequently used indicator to diagnose atherosclerosis by ultrasound, involves the measurement of the lumen-intima boundary (LIB) and media-adventitia boundary (MAB). In this study, using the mean squared error (MSE) method and by applying the template matching technique, an adaptive model of an ultrasonic echo, which is obtained from an ultrasonic pulse measured with a hydrophone, was fitted with the measured in vivo RF echo to estimate the boundaries of the carotid arterial wall. In the present study, the frequency and phase of the adaptive model were considered to improve the accuracy in the determination of the LIB and MAB. For a 7.5-mm-long short segment of the carotid artery in the longitudinal direction, the average IMTs estimated by the improved technique and the previous method were 502±61 and 558±120 µm, respectively, showing a decrease in the standard deviation by the proposed method. Moreover, the result obtained by the improved technique presented only 0.4% difference between the automatically detected boundary and the manually detected boundary, which is smaller than that obtained by the previous method (10.7% difference). These results verified that the boundary detected by the improved technique was more accurate than that detected by the previous method.

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

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. 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. 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. 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.

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.

Detection of Boundaries of Carotid Arterial Wall by Analyzing Ultrasonic RF Signals

In line with the fact that the intima–media thickness (IMT) of the carotid arterial wall is the most frequently used indicator to diagnose atherosclerosis by ultrasound, it is essential to accurately estimate the thickness of the intima–media complex (IMC) boundaries, i.e., the lumen–intima boundary (LIB) and media–adventitia boundary (MAB). In this study, an improved adaptive model of an ultrasonic echo was developed for the model to realize better fitting to the reference RF echo, which is measured from a glass plate, using a Gaussian window for the envelope function of the adaptive model. Using the mean squared error (MSE) method, the envelope of the improved adaptive model (multiply the sinusoidal wave with the Gaussian window) was fitted with the envelope of an RF echo measured in vivo to estimate the boundaries of the carotid arterial wall. Firstly, a computer simulation of the carotid arterial wall was conducted to evaluate the accuracy of boundary detection using the envelope of the improved adaptive model. In the simulation, IMT was set at 0.50 mm in a 7.2-mm-long short segment in the longitudinal direction. The IMT estimated by the proposed method was 0.54 mm. The 8% error between the true and detected IMTs showed the high accuracy of the envelope of the improved adaptive model in boundary detection. In the in vivo measurement, for the 4.8-mm-long short segment in the longitudinal direction, the average IMT automatically estimated by the proposed method was 0.57 mm. The result was compared with those obtained by the previous method and manually. The IMT estimated by the previous method, which uses an RF adaptive model, was 0.59 mm and the manually determined IMT was 0.56 mm. The smaller difference between the results obtained by the proposed method and manually verified that boundary detection by the proposed method was better than that by the previous method.

An automated approach for segmentation of intravascular ultrasound images based on parametric active contour models

This paper presents a fully automated approach to detect the intima and media-adventitia borders in intravascular ultrasound images based on parametric active contour models. To detect the intima border, we compute a new image feature applying a combination of short-term autocorrelations calculated for the contour pixels. These feature values are employed to define an energy function of the active contour called normalized cumulative short-term autocorrelation. Exploiting this energy function, the intima border is separated accurately from the blood region contaminated by high speckle noise. To extract media-adventitia boundary, we define a new form of energy function based on edge, texture and spring forces for the active contour. Utilizing this active contour, the media-adventitia border is identified correctly even in presence of branch openings and calcifications. Experimental results indicate accuracy of the proposed methods. In addition, statistical analysis demonstrates high conformity between manual tracing and the results obtained by the proposed approaches.

Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images

Intravascular ultrasound (IVUS) imaging is a catheter-based medical methodology establishing itself as a useful modality for studying atherosclerosis. The detection of lumen and media-adventitia boundaries in IVUS images constitutes an essential step towards the reliable quantitative diagnosis of atherosclerosis. In this paper, a novel scheme is proposed to automatically detect lumen and media-adventitia borders. This segmentation method is based on the level-set model and the contourlet multiresolution analysis. The contourlet transform decomposes the original image into low-pass components and band-pass directional bands. The circular hough transform (CHT) is adopted in low-pass bands to yield the initial lumen and media-adventitia contours. The anisotropic diffusion filtering is then used in band-pass subbands to suppress noise and preserve arterial edges. Finally, the curve evolution in the level-set functions is used to obtain final contours. The proposed method is experimentally evaluated via 20 simulated images and 30 real images from human coronary arteries. It is demonstrated that the mean distance error and the relative mean distance error have increased by 5.30 pixels and 7.45%, respectively, as compared with those of a recently traditional level-set model. These results reveal that the proposed method can automatically and accurately extract two vascular boundaries.

Micro-Simulation of Concrete Based on the Zero-Thickness Interface Element

Intravascular Ultrasound (IVUS) is one of interventional imaging modalities widely used in clinical diagnosis of vascular diseases, especially coronary artery diseases. Segmentation of IVUS images to extract vessel wall boundaries is of importance for quantitative analysis and 3D vessel reconstruction. A 3D parallel method for segmenting IVUS image sequence is proposed in this paper. Firstly, original images are preprocessed to reduce possible noises and eliminate ring-down artifacts. Then, several longitudinal cuts are obtained and intima-lumen and media-adventitia boundaries are detected. Once these boundaries are mapped onto each cross-sectional slice, initial plan of vessel wall boundaries in each frame is obtained. Finally, these initial contours evolve continuously until stop at target contours. Consequently, segmentation of each IVUS tomographic frame isimplemented simultaneously and the efficiency is greatly raised compared with 2D sequential approaches.

A Parallel Method for Segmenting Intravascular Ultrasound Image Sequence

Intravascular Ultrasound (IVUS) is one of interventional imaging modalities widely used in clinical diagnosis of vascular diseases, especially coronary artery diseases. Segmentation of IVUS images to extract vessel wall boundaries is of importance for quantitative analysis and 3D vessel reconstruction. A 3D parallel method for segmenting IVUS image sequence is proposed in this paper. Firstly, original images are preprocessed to reduce possible noises and eliminate ring-down artifacts. Then, several longitudinal cuts are obtained and intima-lumen and media-adventitia boundaries are detected. Once these boundaries are mapped onto each cross-sectional slice, initial plan of vessel wall boundaries in each frame is obtained. Finally, these initial contours evolve continuously until stop at target contours. Consequently, segmentation of each IVUS tomographic frame is implemented simultaneously and the efficiency is greatly raised compared with 2D sequential approaches.

Noninvasive Measurement of Transient Change in Viscoelasticity Due to Flow-Mediated Dilation Using Automated Detection of Arterial Wall Boundaries

We measured the stress–strain relationship of the radial arterial wall during a heartbeat noninvasively. In our previous study, the viscoelasticity of the intima–media region was estimated from the stress–strain relationship, and the transient change in viscoelasticity due to flow-mediated dilation (FMD) was estimated. In this estimation, it is necessary to detect the lumen–intima boundary (LIB) and the media–adventitia boundary (MAB). To decrease the operator dependence, in the present study, a method is proposed for automatic and objective boundary detection based on template matching between the measured and adaptive model ultrasonic signals. Using this method, arterial wall boundaries were appropriately detected in in vivo experiments. Furthermore, the transient change in viscoelasticity estimated from the stress–strain relationship was similar to that obtained manually. These results show the feasibility of the proposed method for automatic boundary detection enabling an objective and appropriate analysis of the transient change in viscoelasticity due to FMD.

Noninvasive Measurement of Transient Change in Viscoelasticity Due to Flow-Mediated Dilation Using Automated Detection of Arterial Wall Boundaries

We measured the stress–strain relationship of the radial arterial wall during a heartbeat noninvasively. In our previous study, the viscoelasticity of the intima–media region was estimated from the stress–strain relationship, and the transient change in viscoelasticity due to flow-mediated dilation (FMD) was estimated. In this estimation, it is necessary to detect the lumen–intima boundary (LIB) and the media–adventitia boundary (MAB). To decrease the operator dependence, in the present study, a method is proposed for automatic and objective boundary detection based on template matching between the measured and adaptive model ultrasonic signals. Using this method, arterial wall boundaries were appropriately detected in in vivo experiments. Furthermore, the transient change in viscoelasticity estimated from the stress–strain relationship was similar to that obtained manually. These results show the feasibility of the proposed method for automatic boundary detection enabling an objective and appropriate analysis of the transient change in viscoelasticity due to FMD.