A Model-Based Approach to the Segmentation of Nasal Cavity and Paranasal Sinus Boundaries

We present a model-driven approach to the segmentation of nasal cavity and paranasal sinus boundaries. Based on computed tomography data of a patients head, our approach aims to extract the border that separates the structures of interest from the rest of the head. This three-dimensional region information is useful in many clinical applications, e.g. diagnosis, surgical simulation, surgical planning and robot assisted surgery. The desired boundary can be made up of bone, mucosa or air what makes the segmentation process very difficult and brings traditional segmentation approaches, like e.g. region growing, to their limits. Motivated by the work of Tsai et al. [1] and Leventon et al. [2], we therefore show how a parametric level-set model can be generated from hand-segmented nasal cavity and paranasal sinus data that gives us the ability to transform the complex segmentation problem into a finitedimensional one. On this basis, we propose a processing chain for the automated segmentation of the endonasal structures that incorporates the model information and operates without any user interaction. Promising results are obtained by evaluating our approach on two-dimensional data slices of 50 patients with very diverse paranasal sinuses.