%0 Conference Paper
%A Paul, Richard Dominik
%A Seiffarth, Johannes
%A Rügamer, David
%A Scharr, Hanno
%A Nöh, Katharina
%T How To Make Your Cell Tracker Say "I dunno!"
%M FZJ-2025-05660
%P 6914-6923
%D 2025
%< Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
%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.
%B International Conference on Computer Vision, ICCV 2025
%C 19 Oct 2025 - 23 Oct 2025, Honolulu (USA)
Y2 19 Oct 2025 - 23 Oct 2025
M2 Honolulu, USA
%F PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
%9 Contribution to a conference proceedingsContribution to a book
%U https://juser.fz-juelich.de/record/1049900