TY  - CONF
AU  - Paul, Richard Dominik
AU  - Seiffarth, Johannes
AU  - Rügamer, David
AU  - Scharr, Hanno
AU  - Nöh, Katharina
TI  - How To Make Your Cell Tracker Say "I dunno!"
M1  - FZJ-2025-05660
SP  - 6914-6923
PY  - 2025
AB  - 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.
T2  - International Conference on Computer Vision, ICCV 2025
CY  - 19 Oct 2025 - 23 Oct 2025, Honolulu (USA)
Y2  - 19 Oct 2025 - 23 Oct 2025
M2  - Honolulu, USA
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - https://juser.fz-juelich.de/record/1049900
ER  -