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My Corporis Fabrica: A Unified Ontological, Geometrical and Mechanical View of Human Anatomy

  • Olivier Palombi
  • Guillaume Bousquet
  • David Jospin
  • Sahar Hassan
  • Lionel Revéret
  • François Faure
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5903)

Abstract

A new anatomical database, My Corporis Fabrica (MyCF), is presented. It’s based on the reference anatomical ontology FMA (the Foundational Model of Anatomy) with the possibility to coherently integrate 3D geometrical data, as well as biomechanical parameters. The purpose of this extension is to allow the user to intuitively create a patient-specific 3D representation from a formal description of anatomical entities and also to automatically export this description to test a physical simulation. The main contribution of MyCF consists in the formalization of a comprehensive database structure implemented in MySQL, linking canonical description of anatomical entities with reality-grounded instances of such description in terms of geometrical data and physical attributes. The principle of ontology modeling inherited from FMA is maintained in order to guarantee a close consistency between the two databases. A detailed illustration is given for the knee. In this example, the canonical description of anatomical entities is developed, including the location of attachment of ligaments as well as their stiffness, using an intuitive 3D GUI. Finally, an instance of the knee based on this description is automatically augmented with the canonical part information and then exported to a physical simulator. This example shows the benefit of coupling an exhaustive description of the anatomical structures with a physical simulation to study the dynamic effect of ligaments on the stability of the knee articulation.

Keywords

Physical Simulation Deformable Model Anatomical Knowledge Instance Attribute Anatomical Entity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Olivier Palombi
    • 1
    • 2
  • Guillaume Bousquet
    • 1
  • David Jospin
    • 1
    • 2
  • Sahar Hassan
    • 1
  • Lionel Revéret
    • 1
  • François Faure
    • 1
  1. 1.LJK LabGrenoble Universités, CNRS, INRIA Rhône-AlpesFrance
  2. 2.Laboratory of AnatomyGrenoble UniversitésFrance

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