Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy ​

Substantial non-linear variability in human anatomy often makes the traditional shape modeling process challenging. Deep learning techniques have the potential to learn complex nonlinear representations of shapes and generate statistical shape models more faithful to the underlying population-level variability. This work aims to predict correspondences from meshes in an unsupervised manner. This approach seeks to overcome the limitations associated with linearity assumption and computationally intensive inference pipelines.