# HNCcorr: A Novel Combinatorial Approach for Cell Identification in Calcium-Imaging Movies

arXiv: https://arxiv.org/abs/1703.01999 · HTML: https://mlsanity.com/paper/1703.01999/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2017.

## Built on by (most cited first, 3 of 3)

- [Confidence HNC: A Network Flow Technique for Binary Classification with Noisy Labels](https://mlsanity.com/paper/2503.02352/) (2025)
- [An Effective Flow-based Method for Positive-Unlabeled Learning: 2-HNC](https://mlsanity.com/paper/2505.08212/) (2025)
- [Automatic Neuronal Activity Segmentation in Fast Four Dimensional Spatio-Temporal Fluorescence Imaging using Bayesian Approach](https://mlsanity.com/paper/2512.19032/) (2025)

## Abstract (arXiv)

Calcium imaging has emerged as a workhorse method in neuroscience to investigate patterns of neuronal activity. Instrumentation to acquire calcium imaging movies has rapidly progressed and has become standard across labs. Still, algorithms to automatically detect and extract activity signals from calcium imaging movies are highly variable from~lab~to~lab and more advanced algorithms are continuously being developed. Here we present HNCcorr, a novel algorithm for cell identification in calcium imaging movies based on combinatorial optimization. The algorithm identifies cells by finding distinct groups of highly similar pixels in correlation space, where a pixel is represented by the vector of correlations to a set of other pixels. The HNCcorr algorithm achieves the best known results for the cell identification benchmark of Neurofinder, and guarantees an optimal solution to the underlying deterministic optimization model resulting in a transparent mapping from input data to outcome.