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

ABSTRACT: 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 represe

SUBMITTER: Iyer K 

PROVIDER: S-EPMC11036176 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature