Graph-based motion-driven segmentation of the carotid atherosclerotique plaque in 2D ultrasound sequences

Carotid plaque segmentation in ultrasound images is a crucial step for carotid atherosclerosis. However, image quality, important deformations and lack of texture are prohibiting factors towards manual or accurate carotid segmentation. We propose a novel fully automated methodology to identify the plaque region by exploiting kinematic dependencies between the atherosclerotic and the normal arterial wall. The proposed methodology exploits group-wise image registration towards recovering the deformation field, on which information theory criteria are used to determine dominant motion classes and a map reflecting kinematic dependencies, which is then segmented using Markov random fields. The algorithm was evaluated on 120 cases, for which manually-traced plaque contours by an experienced physician were available. Promising evaluation results showed the enhanced performance of the algorithm in automatically segmenting the plaque region, while future experiments on additional datasets are expected to further elucidate its potential.