Segmentation of the lumen and media-adventitia boundaries of the common carotid artery from 3D ultrasound images

Abstract

Three-dimensional ultrasound (3D US) vessel wall volume (VWV) measurements provide high measurement sensitivity and reproducibility for the monitoring and assessment of carotid atherosclerosis. In this paper, we describe a semiautomated approach based on the level set method to delineate the media-adventitia and lumen boundaries of the common carotid artery from 3D US images to support the computation of VWV. Due to the presence of plaque and US image artifacts, the carotid arteries are challenging to segment using image information alone. Our segmentation framework combines several image cues with domain knowledge and limited user interaction. Our method was evaluated with respect to manually outlined boundaries on 430 2D US images extracted from 3D US images of 30 patients who have carotid stenosis of 60% or more. The VWV given by our method differed from that given by manual segmentation by 6.7% ± 5.0%. For the media-adventitia and lumen segmentations, respectively, our method yielded Dice coefficients of 95.2% ± 1.6%, 94.3% ± 2.6%, mean absolute distances of 0.3 ± 0.1 mm, 0.2 ± 0.1 mm, maximum absolute distances of 0.8 ± 0.4 mm, 0.6 ± 0.3 mm, and volume differences of 4.2% ± 3.1%, 3.4% ± 2.6%. The realization of a semi-automated segmentation method will accelerate the translation of 3D carotid US to clinical care for the rapid, non-invasive, and economical monitoring of atherosclerotic disease progression and regression during therapy.