Diego D. B. Carvalho

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.

Statistical segmentation of carotid plaque neovascularization

In several studies, intraplaque neovascularization (IPN) has been linked with plaque vulnerability. The recent development of contrast enhanced ultrasound enables IPN detection, but an accurate quantification of IPN is a big challenge due to noise, motion, subtle contrast response, blooming of contrast and artifacts. We present an algorithm that automatically estimates the location and amount of contrast within the plaque over time. Plaque pixels are initially labeled through an iterative expectation-maximization (EM) algorithm. The used algorithm avoids several drawbacks of standard EM. It is capable of selecting the best number of components in an unsupervised way, based on a minimum message length criterion. Next, neighborhood information using a 5×5 kernel and spatiotemporal behavior are combined with the known characteristics of contrast spots in order to group components, identify artifacts and finalize the classification. Image sequences are divided into 3-seconds subgroups. A pixel is relabeled as an artifact if it is labeled as contrast for more than 1.5 seconds in at least two subgroups. For 10 plaques, automated segmentation results were validated with manual segmentation of contrast in 10 frames per clip. Average Dice index and area ratio were 0.73±0.1 (mean±SD) and 98.5±29.6 (%) respectively. Next, 45 atherosclerotic plaques were analyzed. Time integrated IPN surface area was calculated. Average area of IPN was 3.73±3.51 mm2. Average area of 45 plaques was 11.6±8.6 mm2. This method based on EM contrast segmentation provides a new way of IPN quantification.

Carotid artery lumen segmentation in 3D free-hand ultrasound images using surface graph cuts.

We present a new approach for automated segmentation of the carotid lumen bifurcation from 3D free-hand ultrasound using a 3D surface graph cut method. The method requires only the manual selection of single seed points in the internal, external, and common carotid arteries. Subsequently, the centerline between these points is automatically traced, and the optimal lumen surface is found around the centerline using graph cuts. To refine the result, the latter process was iterated. The method was tested on twelve carotid arteries from six subjects including three patients with a moderate carotid artery stenosis. Our method successfully segmented the lumen in all cases. We obtained an average dice overlap with respect to a manual segmentation of 84% for healthy volunteers. For the patient data, we obtained a dice overlap of 66.7%.

Fully automated carotid plaque segmentation in combined contrast-enhanced and B-mode ultrasound

Carotid plaque segmentation in B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) is crucial to the assessment of plaque morphology and composition, which are linked to plaque vulnerability. Segmentation in BMUS is challenging because of noise, artifacts and echo-lucent plaques. CEUS allows better delineation of the lumen but contains artifacts and lacks tissue information. We describe a method that exploits the combined information from simultaneously acquired BMUS and CEUS images. Our method consists of non-rigid motion estimation, vessel detection, lumen-intima segmentation and media-adventitia segmentation. The evaluation was performed in training (n = 20 carotids) and test (n = 28) data sets by comparison with manually obtained ground truth. The average root-mean-square errors in the training and test data sets were comparable for media-adventitia (411 ± 224 and 393 ± 239 μm) and for lumen-intima (362 ± 192 and 388 ± 200 μm), and were comparable to inter-observer variability. To the best of our knowledge, this is the first method to perform fully automatic carotid plaque segmentation using combined BMUS and CEUS.

Atherosclerotic carotid lumen segmentation in combined B-mode and contrast enhanced ultrasound images

Patients with carotid atherosclerotic plaques carry an increased risk of cardiovascular events such as stroke. Ultrasound has been employed as a standard for diagnosis of carotid atherosclerosis. To assess atherosclerosis, the intima contour of the carotid artery lumen should be accurately outlined. For this purpose, we use simultaneously acquired side-by-side longitudinal contrast enhanced ultrasound (CEUS) and B-mode ultrasound (BMUS) images and exploit the information in the two imaging modalities for accurate lumen segmentation. First, nonrigid motion compensation is performed on both BMUS and CEUS image sequences, followed by averaging over the 150 time frames to produce an image with improved signal-to-noise ratio (SNR). After that, we segment the lumen from these images using a novel method based on dynamic programming which uses the joint histogram of the CEUS and BMUS pair of images to distinguish between background, lumen, tissue and artifacts. Finally, the obtained lumen contour in the improved-SNR mean image is transformed back to each time frame of the original image sequence. Validation was done by comparing manual lumen segmentations of two independent observers with automated lumen segmentations in the improved-SNR images of 9 carotid arteries from 7 patients. The root mean square error between the two observers was 0.17±0.10mm and between automated and average of manual segmentation of two observers was 0.19±0.06mm. In conclusion, we present a robust and accurate carotid lumen segmentation method which overcomes the complexity of anatomical structures, noise in the lumen, artifacts and echolucent plaques by exploiting the information in this combined imaging modality.

Registration of free-hand ultrasound and MRI of carotid arteries through combination of point-based and intensity-based algorithms

We propose a methodology to register medical images of carotid arteries from tracked freehand sweep B-Mode ultrasound (US) and magnetic resonance imaging (MRI) acquisitions. Successful registration of US and MR images will allow a multimodal analysis of atherosclerotic plaque in the carotid artery. The main challenge is the difference in the positions of the patients neck during the examinations. While in MRI the patients neck remains in a natural position, in US the neck is slightly bent and rotated. Moreover, the image characteristics of US and MRI around the carotid artery are very different. Our technique uses the estimated centerlines of the common, internal and external carotid arteries in each modality as landmarks for registration. For US, we used an algorithm based on a rough lumen segmentation obtained by robust ellipse fitting to estimate the lumen centerline. In MRI, we extract the centerline using a minimum cost path approach in which the cost is defined by medialness and an intensity based similarity term. The two centerlines are aligned by an iterative closest point (ICP) algorithm, using rigid and thin-plate spline transformation models. The resulting point correspondences are used as a soft constraint in a subsequent intensity-based registration, optimizing a weighted sum of mutual information between the US and MRI and the Euclidean distance between corresponding points. Rigid and B-spline transformation models were used in this stage. Experiments were performed on datasets from five healthy volunteers. We compared different registration approaches, in order to evaluate the necessity of each step, and to establish the optimum algorithm configuration. For the validation, we used the Dice similarity index to measure the overlap between lumen segmentations in US and MRI.

Nonrigid motion compensation in B-mode and contrast enhanced ultrasound image sequences of the carotid artery

In this work, we investigate nonrigid motion compensation in simultaneously acquired (side-by-side) B-mode ultrasound (BMUS) and contrast enhanced ultrasound (CEUS) image sequences of the carotid artery. These images are acquired to study the presence of intraplaque neovascularization (IPN), which is a marker of plaque vulnerability. IPN quantification is visualized by performing the maximum intensity projection (MIP) on the CEUS image sequence over time. As carotid images contain considerable motion, accurate global nonrigid motion compensation (GNMC) is required prior to the MIP. Moreover, we demonstrate that an improved lumen and plaque differentiation can be obtained by averaging the motion compensated BMUS images over time. We propose to use a previously published 2D+t nonrigid registration method, which is based on minimization of pixel intensity variance over time, using a spatially and temporally smooth B-spline deformation model. The validation compares displacements of plaque points with manual trackings by 3 experts in 11 carotids. The average (± standard deviation) root mean square error (RMSE) was 99±74μm for longitudinal and 47±18μm for radial displacements. These results were comparable with the interobserver variability, and with results of a local rigid registration technique based on speckle tracking, which estimates motion in a single point, whereas our approach applies motion compensation to the entire image. In conclusion, we evaluated that the GNMC technique produces reliable results. Since this technique tracks global deformations, it can aid in the quantification of IPN and the delineation of lumen and plaque contours.