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

arXiv: https://arxiv.org/abs/2104.10003 · HTML: https://mlsanity.com/paper/2104.10003/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 47 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [KerGM: Kernelized Graph Matching](https://mlsanity.com/paper/1911.11120/) (2019)
- [A Flexible Tensor Block Coordinate Ascent Scheme for Hypergraph Matching](https://mlsanity.com/paper/1504.07907/) (2015)
- [An Efficient Multilinear Optimization Framework for Hypergraph Matching](https://mlsanity.com/paper/1511.02667/) (2015)

## Built on by (most cited first, 2 of 2)

- [A Semi-automatic Cell Tracking Process Towards Completing the 4D Atlas of C. elegans Development](https://mlsanity.com/paper/2207.13611/) (2022)
- [Multiple Hypothesis Hypergraph Tracking for Posture Identification in Embryonic Caenorhabditis elegans](https://mlsanity.com/paper/2111.06425/) (2021)

## Abstract (arXiv)

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.