Carotid atherosclerosis is a major cause of stroke, a leading cause of death and disability. In this paper, a new
segmentation method is proposed and evaluated for outlining the common carotid artery (CCA) from transverse view
images, which were sliced from three-dimensional ultrasound (3D US) of 1mm inter-slice distance (ISD), to support the
monitoring and assessment of carotid atherosclerosis. The data set consists of forty-eight 3D US images acquired from both left and right carotid arteries of twelve patients in two time points who had carotid stenosis of 60% or more at the baseline. The 3D US data were collected at baseline and three-month follow-up, where seven treated with 80mg atorvastatin and five with placebo. The baseline manual boundaries were used for Active Appearance Models (AAM) training; while the treatment data for segmentation testing and evaluation. The segmentation results were compared with experts manually outlined boundaries, as a surrogate for ground truth, for further evaluation. For the adventitia and lumen segmentations, the algorithm yielded Dice Coefficients (DC) of 92.06%±2.73% and 89.67%±3.66%, mean absolute distances (MAD) of 0.28±0.18 mm and 0.22±0.16 mm, maximum absolute distances (MAXD) of 0.71±0.28 mm and 0.59±0.21 mm, respectively. The segmentation results were also evaluated via Pratt’s figure of merit (FOM) with the value of 0.61±0.06 and 0.66±0.05, which provides a quantitative measure for judging the similarity.
Experimental results indicate that the proposed method can promote the carotid 3D US usage for a fast, safe and
economical monitoring of the atherosclerotic disease progression and regression during therapy.