Tracking Single-Cells in Overcrowded Bacterial Colonies
This work addresses the challenge of single-cell analysis in dense microbial environments, which is incremental as it builds on existing tracking approaches.
The paper tackles the problem of tracking individual cells in overcrowded bacterial colonies from time-lapse movies, achieving over 97% accuracy with a fully automated method based on dynamic neighborhoods and motion estimation.
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.