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| Home > Publications database > How To Make Your Cell Tracker Say 'I dunno!' |
| Preprint | FZJ-2025-04214 |
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2025
arXiv
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Please use a persistent id in citations: doi:10.48550/ARXIV.2503.09244
Abstract: 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.
Keyword(s): Computer Vision and Pattern Recognition (cs.CV) ; Quantitative Methods (q-bio.QM) ; Applications (stat.AP) ; FOS: Computer and information sciences ; FOS: Biological sciences
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