  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.
