7436 2019 eng 58 64 conferenceobject 0 -- -- -- Statistical Shape Models (SSMs) are a proven means for model-based 3D anatomy reconstruction from medical image data. In orthopaedics and biomechanics, SSMs are increasingly employed to individualize measurement data or to create individualized anatomical models to which implants can be adapted to or functional tests can be performed on. For modeling and analysis of articulated structures, so called articulated SSMs (aSSMs) have been developed. However, a missing feature of aSSMs is the consideration of collisions in the course of individual fitting and articulation. The aim of our work was to develop aSSMs that handle collisions between components correctly. That way it becomes possible to adjust shape and articulation in view of a physically and geometrically plausible individualization. To be able to apply collision-aware aSSMs in simulation and optimisation, our approach is based on an e� cient collision detection method employing Graphics Processing Units (GPUs). Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC) yes 02.08.2019 Robert Joachimsky Stefan Zachow Lihong Ma Christian Icking Stefan Zachow Visual Data Analysis Therapy Planning Zachow, Stefan Spine Visual and Data-centric Computing