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Proceedings Paper

Three-dimensional semi-automated segmentation of carotid atherosclerosis from three-dimensional ultrasound images
Author(s): E. Ukwatta; J. Awad; D. Buchanan; G. Parraga; A. Fenster
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Paper Abstract

Three-dimensional ultrasound (3DUS) provides non-invasive and precise measurements of carotid atherosclerosis that directly reflects arterial wall abnormalities that are thought to be related to stroke risk. Here we describe a threedimensional segmentation method based on the sparse field level set method to automate the segmentation of the mediaadventitia (MAB) and lumen-intima (LIB) boundaries of the common carotid artery from 3DUS images. To initiate the process, an expert chooses four anchor points on each boundary on a subset of transverse slices that are orthogonal to the axis of the artery. An initial surface is generated using the anchor points as initial guess for the segmentation. The MAB is segmented first using five energies: length minimization energy, local region-based energy, edge-based energy, anchor point-based energy, and local smoothness energy. Five energies are also used for the LIB segmentation: length minimization energy, local region-based energy, global region-based energy, anchor point-based energy, and boundary separation-based energy. The algorithm was evaluated with respect to manual segmentations on a slice-by-slice basis using 15 3DUS images. To generate results in this paper, inter-slice distance of 2 mm is used for the initialization. For the MAB and LIB segmentations, our method yielded Dice coefficients of more than 92% and sub-millimeter values for mean and maximum absolute distance errors. Our method also yielded a vessel wall volume error of 7.1% ± 3.4%. The realization of a semi-automated algorithm will aid in the translation of 3DUS measurements to clinical research for the rapid, non-invasive, and economical monitoring of atherosclerotic disease.

Paper Details

Date Published: 23 February 2012
PDF: 6 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150O (23 February 2012); doi: 10.1117/12.912365
Show Author Affiliations
E. Ukwatta, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
J. Awad, Robarts Research Institute (Canada)
D. Buchanan, Robarts Research Institute (Canada)
G. Parraga, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
A. Fenster, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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