Tracking Single-Cells in Overcrowded Bacterial Colonies

TL;DR

This paper presents an automated cell tracking method for overcrowded bacterial colonies, achieving over 97% accuracy by leveraging dynamic neighborhood matching and divide-and-conquer strategies.

Contribution

A novel computational approach for accurate, fully automated tracking of cells in overcrowded bacterial colonies, inspired by video motion estimation techniques.

Findings

Achieves over 97% tracking accuracy in crowded colonies

Effective in diverse experimental conditions across different labs

Handles large-scale microbial colony data efficiently

Abstract

Cell tracking enables data extraction from time-lapse "cell movies" and promotes modeling biological processes at the single-cell level. We introduce a new fully automated computational strategy to track accurately cells across frames in time-lapse movies. Our method is based on a dynamic neighborhoods formation and matching approach, inspired by motion estimation algorithms for video compression. Moreover, it exploits "divide and conquer" opportunities to solve effectively the challenging cells tracking problem in overcrowded bacterial colonies. Using cell movies generated by different labs we demonstrate that the accuracy of the proposed method remains very high (exceeds 97%) even when analyzing large overcrowded microbial colonies.