Semiautomatic quantification of carotid plaque volume with three-dimensional ultrasound imaging

Abstract

ObjectiveVessel wall volume (VWV) assessed by three-dimensional duplex ultrasound (3DUS) imaging provides a more comprehensive measure of plaque burden than conventional two-dimensional measures of diameter stenosis. We previously demonstrated that manual outlining of the arterial lumen-intima boundary and outer wall boundary can be performed reliably on images obtained with a commercially available 3D-DUS transducer. Manual segmentation, however, is time consuming (∼45 minutes), limiting its clinical translation. We have developed a semiautomatic algorithm (manual selection of the carotid bifurcation image with subsequent automatic plaque outlining) to outline carotid plaques on 3DUS data sets. In this study, we investigated the accuracy, reproducibility, reliability, and time taken by this algorithm. Methods3DUS data sets from 30 patients with asymptomatic ≥50% carotid stenosis underwent manual outlining of lumen-intima boundary and outer wall boundary to measure VWV. Two observers implemented a semiautomatic segmentation algorithm. The algorithm's accuracy was compared with manual outlining using the Pearson correlation coefficient. The Dice similarity coefficient (DSC) and modified-Hausdorff distance (MHD) were used to quantify the geometric similarity of the outlines. We also compared results after an intermediate stage of the algorithm vs the complete algorithm. Reproducibility and the least amount of detectable change in plaque volume were computed for each method. Intraobserver and interobserver metrics for each method were computed using the intraclass correlation coefficient (ICC), coefficient of variability (CV), minimum detectable change (MDC), and standard error of measurement (SEM) of the VWV. ResultsPlaque volume estimates obtained from the semiautomatic algorithm were accurate compared with manual outlining. The Pearson correlation coefficient was 0.76 (P < .001), and measurements were geometrically similar (DSC, 0.85; MHD, 0.48 mm). The algorithm was more reproducible and reliable and could detect smaller changes in plaque volume on repeat imaging (low interobserver variability: ICC, 0.9; CV, 8.22%; MDC, 5.57%; SEM, 1.45%; DSC, 0.88; MHD, 0.43 mm). Intraobserver variability was even lower (ICC, 0.9; CV, 8%; MDC, 3.62%; SEM, 1.31%; DSC, 0.89; MHD, 0.37 mm). Plaque volume estimates at the intermediate stage of the algorithm matched results from the full algorithm (Pearson correlation coefficient, 0.76; DSC, 0.84; MHD, 0.52 mm). The intermediate approach, however, was less reliable than the full algorithm (interobserver: ICC, 0.81; CV, 11.7%; MDC, 9.58%; SEM, 3.46%; DSC, 0.88; MHD, 0.42 mm; intraobserver: ICC, 0.87; CV, 8.6%; MDC, 4.55%; SEM, 1.64%; DSC, 0.89; MHD, 0.38 mm). The full algorithm required ∼14 minutes to implement. However, a quick (7 minutes) and accurate assessment of VWV can be obtained by running only the intermediate stage of the algorithm, although with a loss in repeatability and reliability. ConclusionsWe present a unique algorithm to perform semiautomatic quantification of carotid plaque volume using 3DUS imaging. It is quick (mean time, 14 minutes), accurate, repeatable, and implementable in a clinical environment and in longitudinal studies tracking plaque progression. It reliably detects plaque volume changes as low as 4% to 6% with 95% confidence.