This paper presents ongoing research on a semi-automatic method for computing, from CT and MR data, patient-specific anatomical models used in surgical simulation. Surgical simulation is a software implementation enabling a user to interact, through virtual surgical tools, with an anatomical model representative of relevant tissues and endowed with realistic constitutive properties. Up to now, surgical simulators have generally been characterized by their reliance on a generic anatomical model, typically obtained at the cost of extensive user interaction, and by biomechanical computations based on mass-spring networks. We propose a minimally supervised procedure for extracting from a set of CT and MR scans a highly descriptive tissue classification, a set of triangulated surfaces coinciding with relevant tissue boundaries, and volumetric meshes bounded by these surfaces and comprised of tetrahedral elements of homogeneous tissue. In this manner, a series of models could be obtained with little user interaction, allowing surgeons to be trained on & large set of pathologies which are clinically representative of those they are likely to encounter. The application of this procedure to the simulation of pituitary surgery is described. Furthermore, the resolution of the surface and tissue meshes is explicitly controllable with a few simple parameters. In turn, the target mesh resolution can be expressed as a radially varying function from a central point, in this case coinciding with a point on the pituitary gland. A further objective is to produce anatomical models which can interact with a published finite element-based biomechanical simulation technique which partitions the volume into separate parent and child meshes: the former sparse and linearly elastic; the latter dense, centered on the region of clinical interest and possibly nonlinearly elastic.
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