  Statistical shape models (SSMs) are an established way to represent the
anatomy of a population with various clinically relevant applications. However,
they typically require domain expertise, and labor-intensive landmark
annotations to construct. We address these shortcomings by proposing an
unsupervised method that leverages deep geometric features and functional
correspondences to simultaneously learn local and global shape structures
across population anatomies. Our pipeline significantly improves unsupervised
correspondence estimation for SSMs compared to baseline methods, even on highly
irregular surface topologies. We demonstrate this for two different anatomical
structures: the thyroid and a multi-chamber heart dataset. Furthermore, our
method is robust enough to learn from noisy neural network predictions,
potentially enabling scaling SSMs to larger patient populations without manual
segmentation annotation.
