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Proceedings Paper

Segmentation of common carotid artery with active appearance models from ultrasound images
Author(s): Xin Yang; Wanji He; Aaron Fenster; Ming Yuchi; Mingyue Ding
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Paper Abstract

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.

Paper Details

Date Published: 28 February 2013
PDF: 7 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86703H (28 February 2013); doi: 10.1117/12.2006232
Show Author Affiliations
Xin Yang, Huazhong Univ. of Science and Technology (China)
Wanji He, Shanghai Jiao Tong Univ. (China)
Aaron Fenster, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Ming Yuchi, Huazhong Univ. of Science and Technology (China)
Mingyue Ding, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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