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.

统计形状建模（SSM）是一种用于分析解剖结构形态变异的定量方法。我们提出了一个扩展粒子形状建模（PSM）的方法，允许形状建模于任意感兴趣的解剖区域，并使用网格场来定义任意形状表面上的感兴趣区域。此方法通过加入二次惩罚方法实现了任意多种限制条件的计算机高效执行。我们通过在一个合成数据集和两个医学数据集上的实验证明了该方法的有效性。