Abstract
The segmentation of teeth is of great importance for the computer aided planning of dental implants, orthodontic treatment, and orthognathic surgery. However, it is hampered by metallic streak artifacts present in Computed Tomography (CT) images in general, and the lack of contrast between the teeth and bone in Cone-Beam CT (CBCT) images particularly. Therefore, we propose a novel graph cut based algorithm that effectively integrates a statistical shape model based on a probabilistic shape representation. The statistical shape model is obtained from a set of training samples and imposes a Gaussian distribution on the shape space. The presented algorithm minimises an energy function that is formulated according to a maximum a posteriori criterion and consists of three terms: an image likelihood term, a segmentation likelihood term integrating the shape model into the graph cut framework, and a shape model term favoring shapes that are more likely according to the statistical shape model.
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Keustermans, J., Vandermeulen, D., Suetens, P. (2012). Integrating Statistical Shape Models into a Graph Cut Framework for Tooth Segmentation. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_30
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DOI: https://doi.org/10.1007/978-3-642-35428-1_30
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