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001     1047299
005     20251023202112.0
024 7 _ |a 10.48550/ARXIV.2503.09244
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037 _ _ |a FZJ-2025-04214
100 1 _ |a Paul, Richard D.
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245 _ _ |a How To Make Your Cell Tracker Say 'I dunno!'
260 _ _ |c 2025
|b arXiv
336 7 _ |a Preprint
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336 7 _ |a Electronic Article
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336 7 _ |a preprint
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336 7 _ |a ARTICLE
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336 7 _ |a Output Types/Working Paper
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500 _ _ |a 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].
520 _ _ |a 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.
536 _ _ |a 2171 - Biological and environmental resources for sustainable use (POF4-217)
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588 _ _ |a Dataset connected to DataCite
650 _ 7 |a Computer Vision and Pattern Recognition (cs.CV)
|2 Other
650 _ 7 |a Quantitative Methods (q-bio.QM)
|2 Other
650 _ 7 |a Applications (stat.AP)
|2 Other
650 _ 7 |a FOS: Computer and information sciences
|2 Other
650 _ 7 |a FOS: Biological sciences
|2 Other
700 1 _ |a Seiffarth, Johannes
|0 P:(DE-Juel1)176923
|b 1
700 1 _ |a Rügamer, David
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700 1 _ |a Scharr, Hanno
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700 1 _ |a Nöh, Katharina
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700 1 _ |a Scharr, Hanno
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773 _ _ |a 10.48550/ARXIV.2503.09244
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913 1 _ |a DE-HGF
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914 1 _ |y 2025
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