  Statistical shape modeling is the computational process of discovering
significant shape parameters from segmented anatomies captured by medical
images (such as MRI and CT scans), which can fully describe subject-specific
anatomy in the context of a population. The presence of substantial non-linear
variability in human anatomy often makes the traditional shape modeling process
challenging. Deep learning techniques can learn complex non-linear
representations of shapes and generate statistical shape models that are more
faithful to the underlying population-level variability. However, existing deep
learning models still have limitations and require established/optimized shape
models for training. We propose Mesh2SSM, a new approach that leverages
unsupervised, permutation-invariant representation learning to estimate how to
deform a template point cloud to subject-specific meshes, forming a
correspondence-based shape model. Mesh2SSM can also learn a population-specific
template, reducing any bias due to template selection. The proposed method
operates directly on meshes and is computationally efficient, making it an
attractive alternative to traditional and deep learning-based SSM approaches.
