%0 Electronic Article
%A Paul, Richard D.
%A Seiffarth, Johannes
%A Rügamer, David
%A Scharr, Hanno
%A Nöh, Katharina
%A Scharr, Hanno
%T How To Make Your Cell Tracker Say 'I dunno!'
%I arXiv
%M FZJ-2025-04214
%D 2025
%Z RDP is funded by the Helmholtz School for Data Science in Life, Earth, and Energy (HDS-LEE). DR’s research is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 548823575. This work was supported by the President’s Initiative and Networking Funds of the Helmholtz Association of German Research Centres [EMSIG ZT-I-PF-04-044].
%X Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analysis tools, as the amount of data recorded within a single experiment exceeds what humans are able to overlook. We here propose and benchmark various methods to reason about and quantify uncertainty in linear assignment-based cell tracking algorithms. Our methods take inspiration from statistics and machine learning, leveraging two perspectives on the cell tracking problem explored throughout this work: Considering it as a Bayesian inference problem and as a classification problem. Our methods admit a framework-like character in that they equip any frame-to-frame tracking method with uncertainty quantification. We demonstrate this by applying it to various existing tracking algorithms including the recently presented Transformer-based trackers. We demonstrate empirically that our methods yield useful and well-calibrated tracking uncertainties.
%K Computer Vision and Pattern Recognition (cs.CV) (Other)
%K Quantitative Methods (q-bio.QM) (Other)
%K Applications (stat.AP) (Other)
%K FOS: Computer and information sciences (Other)
%K FOS: Biological sciences (Other)
%F PUB:(DE-HGF)25
%9 Preprint
%R 10.48550/ARXIV.2503.09244
%U https://juser.fz-juelich.de/record/1047299