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Session: Imaging - Cardiac System
Session starts: Thursday 24 January, 13:30
Presentation starts: 14:30
Room: Lecture room 559

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Hui Tang ()
Theo van Walsum ()
Reinhard Hameeteman ()
Michiel Schaap ()
Aad van der Lugt ()
Lucas van Vliet ()
Wiro Niessen ()

Abstract:
Atherosclerosis is a major vascular disease which is asymptomatic in the early stages of development[1]. Several studies have been performed to predict atherosclerosis progression using information on plaque composition [2, 3] and vessel geometry [4]. In these studies, accurate lumen segmentation of atherosclerotic vessels is required to determine the vessel geometry or to detect a region-of-interest for plaque composition analysis. As a result, lumen segmentation in CTA has received considerable interest. Compared to lumen segmentation in healthy vessels, lumen segmentation in atherosclerotic vessels is far more challenging due to the presence of stenoses and plaques. In this paper, we propose a semi-automatic lumen segmentation method for the segmentation of the lumen in CTA datasets of atherosclerotic carotid arteries. The proposed segmentation method includes two steps: centreline extraction followed by a levelset evolution initialized by the extracted centrelines. We extract the centerline using an iterative minimum cost path approach where the costs are defined by intensity and gradient information [5]. In the second step we utilize a levelset approach, which uses both boundary information and regional intensity information to accurately segment the lumen of atherosclerotic vessels. The method was trained and validated on a publicly available database of 56 carotid arteries. The average Dice similarity coefficient was 90.2%, the mean absolute surface distance was 0.23 mm. Compared to using only boundary information, this method yields better segmentation in severely atherosclerotic vessels. REFERENCES 1. R. Ross, “Atherosclerosis: an inflammatory disease,” New England Journal of Medicine, vol. 340, pp. 115–126, 1999. 2. J.M.A. Hofman, et al, “Quantification of atherosclerotic plaque components using in vivo mri and supervised classifiers,” Magn. Reson. Med., vol. 55, no. 4, pp. 790–799, 2006. 3. F Liu, et al, “Automated in vivo segmentation of carotid plaque MRI with morphology-enhanced probability maps,” Magn Reson Med, vol. 55, no. 3, pp. 659– 668, Mar. 2006. 4. J.B. Thomas, et al, “Variation in the carotid bifurcation geometry of young versus older adults: Implications for geometric risk of atherosclerosis,” Stroke, vol. 36, no. 11, pp. 2450–2456, Nov. 2005. 5. Hui Tang et al., Multispectral MRI centerline tracking in carotid arteries, Proc. SPIE 7962, 79621N (2011) , 2011.