An Exact Hypergraph Matching Algorithm for Nuclear Identification in Embryonic Caenorhabditis elegans

TL;DR

This paper presents an exact hypergraph matching algorithm tailored for identifying nuclei in embryonic C. elegans, improving accuracy over existing methods by modeling complex relationships as hypergraph matching.

Contribution

The paper introduces a novel hypergraph matching algorithm that guarantees optimal solutions for complex point set matching tasks in biological imaging.

Findings

More accurate identification of seam cells in C. elegans embryos.

Extends classical branch and bound to hypergraph matching.

Framework applicable to other complex point set matching problems.

Abstract

Finding an optimal correspondence between point sets is a common task in computer vision. Existing techniques assume relatively simple relationships among points and do not guarantee an optimal match. We introduce an algorithm capable of exactly solving point set matching by modeling the task as hypergraph matching. The algorithm extends the classical branch and bound paradigm to select and aggregate vertices under a proposed decomposition of the multilinear objective function. The methodology is motivated by Caenorhabditis elegans, a model organism used frequently in developmental biology and neurobiology. The embryonic C. elegans contains seam cells that can act as fiducial markers allowing the identification of other nuclei during embryo development. The proposed algorithm identifies seam cells more accurately than established point-set matching methods, while providing a framework to approach other similarly complex point set matching tasks.