Does a Bayesian segmentation model with motion estimation accurately segment atherosclerotic plaques in ultrasonic B-mode images of carotid arteries compared to expert manual segmentation?
A novel Bayesian segmentation model incorporating motion estimation accurately segments carotid artery plaques in ultrasound sequences, showing low sensitivity to stenosis or calcification degree.
The goal of this work is to perform a segmentation of atherosclerotic plaques in view of evaluating their burden and to provide boundaries for computing properties such as the plaque deformation and elasticity distribution (elastogram and modulogram). The echogenicity of a region of interest comprising the plaque, the vessel lumen, and the adventitia of the artery wall in an ultrasonic B-mode image was modeled by mixtures of three Nakagami distributions, which yielded the likelihood of a Bayesian segmentation model. The main contribution of this paper is the estimation of the motion field and its integration into the prior of the Bayesian model that included a local geometrical smoothness constraint, as well as an original spatiotemporal cohesion constraint. The Maximum A Posteriori (MAP) of the proposed model was computed with a variant of the Exploration/Selection (ES) algorithm. The starting point is a manual segmentation of the first frame. The proposed method was quantitatively compared with manual segmentations of all frames by an expert technician. Various measures were used for this evaluation, including the mean point-to-point distance and the Hausdorff distance. Results were evaluated on 94 sequences of 33 patients (for a total of 8988 images). We report a mean point-to- point distance of 0.24 ± 0.08 mm and a Hausdorff distance of 1.24 ± 0.40 mm. Our tests showed that the algorithm was not sensitive to the degree of stenosis or calcification.
Destrempes et al. (Tue,) studied this question.
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