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A minimally supervised image processing pipeline for computing FE-ready anatomical models for neurosurgical simulation
International Congress Series 1281 (2005) 1369
A minimally supervised image processing pipeline for computing FE-ready anatomical models for neurosurgical simulation M.A. Audettea,T, M. Descoˆteauxb, H. Delingetteb, K. Chinzeia a
AIST, Namiki 1-2, Tsukuba, 305-8564, Japan b INRIA, Sophia-Antipolis, France
Keywords: Surgery simulation; Patient-specific; Anatomical meshing; Bayesian level sets
1. Introduction Our goal is a minimally supervised method for producing a patient-specific anatomical models for endoscopic pituitary surgery simulation. 2. Methods The tissue classification resolves a seemingly intractable problem in feature space, from coregistered MR and CT data. We exploit the sheet-like structure of cranial bones to identify them from CT. Soft tissue classification benefits from considering the tubular structure of critical tissues and the embedded structure in relation to dilated air and bone voxels. Also, we use high-confidence points to resolve ambiguous points. Next, surface meshing, that is topology-preserving and spatially optimal, uses the result of tissue-guided marching cubes [1] to initialize, by duality, a simplex surface model [2] that produces dense triangular faces near the surgical target and coarse faces away from it, coincident with boundaries. Lastly, we employ a tetrahedralization with optimal internal vertex placement and also featuring spatially varying mesh size strategy. 3. Results Ongoing work on validation includes producing patient-specific models for pituitary surgery simulation and the use of a digital head phantom. 4. Conclusion This paper presented an image processing pipeline that can produce detailed anatomical models for the simulation of neurosurgery. References [1] W. Lorensen, H. Cline, Marching cubes: a high resolution 3D surface construction algorithm, Comput. Graph. 21 (4) (1987 July) 163 – 170. [2] H. Delingette, General object reconstruction based on simplex meshes, Int. J. Comput. Vis. 32 (1999) 111 – 142.
T Corresponding author. E-mail address: [email protected] (M.A. Audette). 0531-5131/ D 2005 CARS & Elsevier B.V. All rights reserved. doi:10.1016/j.ics.2005.03.085
A minimally supervised image processing pipeline for computing FE-ready anatomical models for neurosurgical simulation M.A. Audettea,T, M. Descoˆteauxb, H. Delingetteb, K. Chinzeia a
AIST, Namiki 1-2, Tsukuba, 305-8564, Japan b INRIA, Sophia-Antipolis, France
Keywords: Surgery simulation; Patient-specific; Anatomical meshing; Bayesian level sets
1. Introduction Our goal is a minimally supervised method for producing a patient-specific anatomical models for endoscopic pituitary surgery simulation. 2. Methods The tissue classification resolves a seemingly intractable problem in feature space, from coregistered MR and CT data. We exploit the sheet-like structure of cranial bones to identify them from CT. Soft tissue classification benefits from considering the tubular structure of critical tissues and the embedded structure in relation to dilated air and bone voxels. Also, we use high-confidence points to resolve ambiguous points. Next, surface meshing, that is topology-preserving and spatially optimal, uses the result of tissue-guided marching cubes [1] to initialize, by duality, a simplex surface model [2] that produces dense triangular faces near the surgical target and coarse faces away from it, coincident with boundaries. Lastly, we employ a tetrahedralization with optimal internal vertex placement and also featuring spatially varying mesh size strategy. 3. Results Ongoing work on validation includes producing patient-specific models for pituitary surgery simulation and the use of a digital head phantom. 4. Conclusion This paper presented an image processing pipeline that can produce detailed anatomical models for the simulation of neurosurgery. References [1] W. Lorensen, H. Cline, Marching cubes: a high resolution 3D surface construction algorithm, Comput. Graph. 21 (4) (1987 July) 163 – 170. [2] H. Delingette, General object reconstruction based on simplex meshes, Int. J. Comput. Vis. 32 (1999) 111 – 142.
T Corresponding author. E-mail address: [email protected] (M.A. Audette). 0531-5131/ D 2005 CARS & Elsevier B.V. All rights reserved. doi:10.1016/j.ics.2005.03.085