Particle-Based Shape Modeling for Arbitrary Regions-of-Interest
Hong Xu Alan Morris Shireen Y. Elhabian
Statistical Shape Modeling (SSM) is a quantitative method for analyzing
morphological variations in anatomical structures. These analyses often
necessitate building models on targeted anatomical regions of interest to focus
on specific morphological features. We propose an extension to \particle-based
shape modeling (PSM), a widely used SSM framework, to allow shape modeling to
arbitrary regions of interest. Existing methods to define regions of interest
are computationally expensive and have topological limitations. To address
these shortcomings, we use mesh fields to define free-form constraints, which
allow for delimiting arbitrary regions of interest on shape surfaces.
Furthermore, we add a quadratic penalty method to the model optimization to
enable computationally efficient enforcement of any combination of
cutting-plane and free-form constraints. We demonstrate the effectiveness of
this method on a challenging synthetic dataset and two medical datasets.