eng URI http://hdl.handle.net/10795/2663 βιολογία Cell tracking enables data extraction from timelapse "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. 4 pp. LOMv1.0 creator LOMv1.0 Project Deliverable Number Π24-Δ5.1 LOMv1.0 Project Final Beneficiary LOMv1.0 Project Executing Organisation 2015 Digital Library of the Operational Programme "Education and Lifelong Learning" abstract types Text Single-cells Bacterial colonies 3944159 application/pdf http://repository.edulll.gr/edulll/bitstream/10795/2663/2/2663_Deliverable%20%28%ce%a024_d%20-%20%ce%9451%29%20THALIS%20Biofilm%20MIS-380229%20Balomenos%20etal%20.pdf URI http://hdl.handle.net/10795/2663 LOMv1.0creator 2016-04-15T07:23:16Z LOMv1.0validator 2016-04-15T07:23:16Z LOMv1.0 gre no no Copyright EYD-EPEDBM (Operational Programme "Education and Lifelong Learning")