Patient specific surface models of the jaw are beneficial for pre-operative planning and manufacturing of
customized prosthesis. Such models can be generated on the basis of dental cone-beam CT images, but those suffer from a
comparatively bad image quality with regard to the signal-to-noise ratio. Therefore, in this work, a statistical shape model
(SSM) is used for robust segmentation of the mandible bone. While previous works with that application require manual
interaction during SSM construction, we establish correspondence fully automatic by minimizing the description length of
the model. Subsequently, the mandible bone is automatically localized and segmented using the SSM as shape constraint.
The standard SSM constraint is known to be inherently limited insofar as patient specific anatomical details can often not
be represented. To overcome this limitation, a new, mathematically sound, computationally fast, and intuitively
interpretable, relaxed SSM constraint is derived, which can be applied without any user-provided parameter. Evaluation
on clinical cone beam CT images yields an improvement of the Jaccard coefficient up to 45% compared to the standard
SSM constraint. Our results are similar to that of alternative methods in the literature, indicating the general potential of
the proposed relaxed SSM constraint for medical image segmentation.
Keywords: Active shape model, Cone beam CT, Dental imaging, Image segmentation, Mandible bone, Optimization, Shape
constraint, Statistical shape model.
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