Particle-Based Shape Modeling for Arbitrary Regions-of-Interest
This work addresses a domain-specific problem in medical imaging for researchers analyzing morphological variations, but it is incremental as it extends an existing framework.
The paper tackles the problem of building statistical shape models for arbitrary anatomical regions of interest, which existing methods handle inefficiently and with topological limitations, by proposing an extension to particle-based shape modeling using mesh fields and a quadratic penalty method, resulting in computationally efficient enforcement of constraints as demonstrated on synthetic and medical datasets.
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.