Partial Matching in the Space of Varifolds

TL;DR

This paper introduces a new asymmetric dissimilarity measure based on Varifold representations for partial shape matching, enabling meaningful registration despite topological differences in 3D structures.

Contribution

The paper proposes a novel Varifold-based dissimilarity term integrated into the LDDMM framework for partial shape matching across different modalities.

Findings

Effective partial matching of complex 3D shapes.

Robust registration across different imaging modalities.

Coherent results despite topological differences.

Abstract

In computer vision and medical imaging, the problem of matching structures finds numerous applications from automatic annotation to data reconstruction. The data however, while corresponding to the same anatomy, are often very different in topology or shape and might only partially match each other. We introduce a new asymmetric data dissimilarity term for various geometric shapes like sets of curves or surfaces. This term is based on the Varifold shape representation and assesses the embedding of a shape into another one without relying on correspondences between points. It is designed as data attachment for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, allowing to compute meaningful deformation of one shape onto a subset of the other. Registrations are illustrated on sets of synthetic 3D curves, real vascular trees and livers' surfaces from two different modalities: Computed Tomography (CT) and Cone Beam Computed Tomography (CBCT). All experiments show that this data dissimilarity term leads to coherent partial matching despite the topological differences.