Abstract
This paper presents an approach to the problem of intra-operative reconstruction of 3D anatomical surfaces. The method is based on the integration of intra-operatively available shape and image data of different dimensionality such as 3D scattered point data, 2.5D ultra sound data, X-ray images etc. by matching them to a statistical shape model, thus providing the surgeon with a complete surface representation of the object of interest. Previous papers of the authors describe the matching of either 3D or 2D data to a statistical model and clinical applications. The here presented work combines former published ideas with a new approach for the complex task of shape analysis required for the computation of the statistical model, thus providing a generic approach for intra-operative surface reconstruction based on statistical models. The method for shape extraction/analysis is based on a generic model of the object and is used to segment training shapes and to establish point to point correspondence simultaneously in a set of CT images. Reconstruction experiments are performed on a statistical model of lumbar vertebrae. Results are provided for 3D/3D, 2D/3D and hybrid matching with simulated data and for 3D/2D matching for a cadaveric spine.
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References
P.J. Besl and N.D. McKay. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239–256, 1992.
J. Lotjonen et al. Model extraction from magnetic resonance volume data using the deformable pyramid. Mecial Image Analysis, 3(4):387–406, 1999.
J. Montagnat et al. Surface simplex meshes for 3d medical image segmentation. In ICRA, 2000.
A.F. Frangi et al. Automatic 3d asm construction via atlas-based landmarking and volumetric elastic registration. In Proc. Information Processing in Medical imaging (IPMI’01), pages 78–91, 2001.
C. Huberson et al. Surgical navigation for spine: Ct virtual imagery versus virtual fluoroscopy about 223 pedicle screws, in 88 patients. In CAOSUSA, pages 203–205, 2001.
M. Fleute and Stephane Lavallee. Building a Complete Surface Model from Sparse Data Using Statistical Shape Models: Application to Computer Assisted Knee Surgery. In W. M. Wells, A. Colchester, and S. Delp, editors, Medical Image Computing and Computer-Assisted Intervention-MICCAI’98, pages 880–887. Springer Verlag, October 1998.
M. Fleute and S. Lavallee. Nonrigid 3-D/2-D Registration of Images Using Statistical Models. In MICCAI’99, pages 138–147, 1999.
W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge, England, second edition, 1992.
R. Pichumani. Construction of a Three-dimensional Geometric Model for Segmentation and Visualization of Cervical Spine Images. PhD thesis, Stanford University School of Medicine, 1997.
R. Szeliski and S. Lavallee. Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines. Int. J. of Computer Vision (IJCV), (18)(2):171–186, 1996.
T.W. Sederberg and S.R. Parry. Free-form deformations of solid geometric models. Computer Graphics (SIGGRAPH’86), 20(4):151–160, 1986.
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© 2002 Springer-Verlag Berlin Heidelberg
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Fleute, M., Lavallée, S., Desbat, L. (2002). Integrated Approach for Matching Statistical Shape Models with Intra-operative 2D and 3D Data. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45787-9_46
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DOI: https://doi.org/10.1007/3-540-45787-9_46
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