Adaptive Particle-Based Shape Modeling for Anatomical Surface Correspondence

TL;DR

This paper presents an adaptive particle-based shape modeling method that improves surface correspondence accuracy by automatically adjusting to local geometric features, enhancing anatomical surface analysis.

Contribution

It introduces a neighborhood correspondence loss and a geodesic regularization algorithm to enhance adaptivity in particle configurations for anatomical shape modeling.

Findings

Improved surface correspondence accuracy on challenging datasets.

Enhanced adaptivity in particle configurations to local geometric features.

Benchmarking shows superior performance over existing methods.

Abstract

Particle-based shape modeling (PSM) is a family of approaches that automatically quantifies shape variability across anatomical cohorts by positioning particles (pseudo landmarks) on shape surfaces in a consistent configuration. Recent advances incorporate implicit radial basis function representations as self-supervised signals to better capture the complex geometric properties of anatomical structures. However, these methods still lack self-adaptivity -- that is, the ability to automatically adjust particle configurations to local geometric features of each surface, which is essential for accurately representing complex anatomical variability. This paper introduces two mechanisms to increase surface adaptivity while maintaining consistent particle configurations: (1) a novel neighborhood correspondence loss to enable high adaptivity and (2) a geodesic correspondence algorithm that regularizes optimization to enforce geodesic neighborhood consistency. We evaluate the efficacy and scalability of our approach on challenging datasets, providing a detailed analysis of the adaptivity-correspondence trade-off and benchmarking against existing methods on surface representation accuracy and correspondence metrics.