Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy
This addresses the problem of modeling non-linear anatomical variability for medical imaging researchers, offering an incremental improvement over existing deep learning methods by eliminating the need for pre-optimized shape models.
The paper tackles the challenge of creating statistical shape models from medical images by introducing Mesh2SSM, an unsupervised deep learning method that deforms a template point cloud to subject-specific meshes, resulting in a computationally efficient approach that learns a population-specific template to reduce bias.
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.