Medical image segmentation is generally an ill-posed problem that can only be solved by incorporating prior
knowledge. The ambiguities arise due to the presence of noise, weak edges, imaging artifacts, inhomogeneous
interior and adjacent anatomical structures having similar intensity profile as the target structure. In this paper
we propose a novel approach to segment the masseter muscle using the graph-cut incorporating additional 3D
shape priors in CT datasets, which is robust to noise; artifacts; and shape deformations. The main contribution
of this paper is in translating the 3D shape knowledge into both unary and pairwise potentials of the Markov
Random Field (MRF). The segmentation task is casted as a Maximum-A-Posteriori (MAP) estimation of the
MRF. Graph-cut is then used to obtain the global minimum which results in the segmentation of the masseter
muscle. The method is tested on 21 CT datasets of the masseter muscle, which are noisy with almost all
possessing mild to severe imaging artifacts such as high-density artifacts caused by e.g. the very common dental
fillings and dental implants. We show that the proposed technique produces clinically acceptable results to the
challenging problem of muscle segmentation, and further provide a quantitative and qualitative comparison with
other methods. We statistically show that adding additional shape prior into both unary and pairwise potentials
can increase the robustness of the proposed method in noisy datasets.
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