Lumen segmentation and stenosis quantification of atherosclerotic carotid arteries in CTA utilizing a centerline intensity prior

Purpose: The degree of stenosis is an important biomarker in assessing the severity of cardiovascular disease. The purpose of our work is to develop and evaluate a semiautomatic method for carotid lumen segmentation and subsequent carotid artery stenosis quantification in CTA images. Methods: The authors present a semiautomatic stenosis detection and quantification method following lumen segmentation. The lumen of the carotid arteries is segmented in three steps. First, centerlines of the internal and external carotid arteries are extracted with an iterative minimum cost path approach in which the costs are based on a measure of medialness and intensity similarity to lumen. Second, the lumen boundary is delineated using a level set procedure which is steered by gradient info Results: The method is trained and tested on a publicly available database from the cls2009 challenge. For the segmentation, the authors obtain a Dice similarity coefficient of 90.2% and a mean absolute surface distance of 0.34 mm. For the stenosis quantification, the authors obtain an average error of 15.7% for cross-sectional diameter-based stenosis and 19.2% for cross-sectional area-based stenosis quantification. Conclusions: With these results, the method ranks second in terms of carotid lumen segmentation accuracy, and first in terms of carotid artery stenosis quantification. (C) 2013 American Association of Physicists in Medicine

Semi-automatic MRI segmentation and volume quantification of intra-plaque hemorrhage

Intra-plaque hemorrhage (IPH) is associated with plaque instability. Therefore, the presence and volume of IPH in carotid arteries may be relevant in predicting the progression of atherosclerotic disease and the occurrence of clinical events. The aim of our work was to develop and evaluate a method for semi-automatic IPH segmentation in T1-weighted (T1w)-magnetic resonance imaging (MRI). IPH segmentation is performed by a regional level set method that models the intensity of the IPH and the background in T1w-MRI to be smoothly varying. The method only requires minimal user interaction, i.e., one or more mouse clicks inside the hemorrhage serve as initialization. The parameters of the method are optimized using a leave-one-out strategy by maximizing the Dice similarity coefficient (DSC) between manual and semi-automatic segmentations. We evaluated the IPH segmentation method on 22 carotid arteries; 10 of which were annotated by two observers and 12 were scanned twice within a 2 week period. We obtained a DSC of 0.52 between the manual and level set segmentations on all 22 carotids. The inter-observer DSC on 10 arteries is 0.57, which is comparable to the DSC between the method and the manual segmentation (0.55). The correlation between the IPH volumes extracted from the level set segmentation and the manual segmentation is 0.88, which is close to the inter-observer volume correlation of 0.92. The reproducibility after rescanning 12 carotids yield an IPH volume correlation of 0.97. The robustness with respect to the initialization by manually clicking two sets of seed points in these 12 carotid artery pairs yields a volume correlation of 0.99. Semi-automatic segmentation and quantification of IPHs are feasible with an accuracy in the range of the inter-observer variability. The method has excellent reproducibility with respect to rescanning and manual initialization