PUB - Publikationen an der Universität Bielefeld

Motivation Microfluidic platforms for live-cell analysis are in dire need of automated image analysis pipelines. In this context, producing reliable tracks of single cells in colonies has proven to be notoriously difficult without manual assistance, especially when image sequences experience low frame rates. Results With Uncertainty-Aware Tracking (UAT), we propose a novel probabilistic tracking paradigm for simultaneous tracking and estimation of tracking-induced errors in biological quantities derived from live-cell experiments. To boost tracking accuracy, UAT relies on a Bayesian approach which exploits temporal information on growth patterns to guide the formation of lineage hypotheses. A biological study is presented, in which UAT demonstrates its ability to track cells, with comparable to better accuracy than state-of-the-art trackers, while simultaneously estimating tracking-induced errors. Availability and implementation Image sequences and Java executables for reproducing the results are available at https://doi.org/10.5281/zenodo.1299526. Supplementary information Supplementary data are available at Bioinformatics online.