Abstract
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 finite-dimensional 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.
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References
Tsai, A., Yezzi Jr., A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging 22(2), 137–154 (2003)
Leventon, M., Grimson, W., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 316–323 (2000)
Rilk, M., Wahl, F., Eichhorn, K., Wagner, I., Bootz, F.: Path planning for robot-guided endoscopes in deformable environments. In: Kröger, T., Wahl, F. (eds.) Advances in Robotics Research, pp. 263–274. Springer, Heidelberg (2009)
Tingelhoff, K., Moral, A., Kunkel, M., Rilk, M., Wagner, I., Eichhorn, K., Wahl, F., Bootz, F.: Comparison between manual and semi-automatic segmentation of nasal cavity and paranasal sinuses from ct images. In: Proc. of 29th Annual International Conference of the IEEE EMBS, pp. 5505–5508 (2007)
Apelt, D., Preim, B., Hahn, H., Strauß, G.: Bildanalyse und Visualisierung für die Planung von Nasennebenhöhlen-Operationen. In: Tolxdorff, T., Braun, J., Handels, H., Horsch, A., Meinzer, H.P. (eds.) Bildverarbeitung für die Medizin 2004, pp. 194–198. Springer, Heidelberg (2004)
Salah, Z., Bartz, D., Dammann, F., Schwaderer, E., Maassen, M., Straßer, W.: A fast and accurate approach for the segmentation of the paranasal sinus. In: Meinzer, H.P., Handels, H., Horsch, A., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2005, pp. 93–97. Springer, Heidelberg (2005)
Pirner, S., Tingelhoff, K., Wagner, I., Westphal, R., Rilk, M., Wahl, F., Bootz, F., Eichhorn, K.: CT-based manual segmentation and evaluation of paranasal sinuses. European Archives of Oto-Rhino-Laryngology 266(4), 507–518 (2009)
Tingelhoff, K., Eichhorn, K., Wagner, I., Kunkel, M., Moral, A., Rilk, M., Wahl, F., Bootz, F.: Analysis of manual segmentation in paranasal ct images. European Archives of Oto-Rhino-Laryngology 265(9), 1061–1070 (2008)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)
Chen, Y., Thiruvenkadam, S., Huang, F., Wilson, D., Geiser, E.: On the incorporation of shape priors into geometric active contours. In: Proc. of IEEE Workshop on Variational and Level Set Methods, pp. 145–152 (2001)
Cremers, D., Tischhäuser, F., Weickert, J., Schnörr, C.: Diffusion snakes: Introducing statistical shape knowledge into the mumford-shah functional. International Journal of Computer Vision 50(3), 295–313 (2002)
Bresson, X., Vandergheynst, P., Thiran, J.: A priori information in image segmentation: Energy functional based on shape statistical model and image information. In: Proc. of IEEE International Conference on Image Processing, pp. 428–431 (2003)
Rousson, M., Paragios, N., Deriche, R.: Implicit active shape models for 3d segmentation in mr imaging. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 209–216. Springer, Heidelberg (2004)
Chan, T., Vese, L.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)
Dhawan, A.: Medical Image Analysis. Wiley-IEEE Press (2003)
Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations. Journal of Computational Physics 79(1), 12–49 (1988)
Breu, H., Gil, J., Kirkpatrick, D., Werman, M.: Linear time euclidean distance transform algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(5), 529–533 (1995)
Lagarias, J., Reeds, J., Wright, M., Wright, P.: Convergence properties of the nelder–mead simplex method in low dimensions. SIAM Journal on Optimization 9(1), 112–147 (1998)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. In: Proc. of 26th ACM International Conference on Computer Graphics and Interactive Techniques, pp. 187–194 (1999)
Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., Seidel, H.P.: A statistical model of human pose and body shape. Computer Graphics Forum (Proceedings of Eurographics 2009) 28(2), 337–346 (2009)
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Last, C., Winkelbach, S., Wahl, F.M., Eichhorn, K.W.G., Bootz, F. (2010). A Model-Based Approach to the Segmentation of Nasal Cavity and Paranasal Sinus Boundaries. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds) Pattern Recognition. DAGM 2010. Lecture Notes in Computer Science, vol 6376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15986-2_34
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DOI: https://doi.org/10.1007/978-3-642-15986-2_34
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