How To Make Your Cell Tracker Say "I dunno!"
This work addresses the need for reliable, uncertainty-aware tools in high-throughput cell imaging, which is crucial for biologists analyzing large datasets, though it is incremental as it builds on existing tracking methods.
The paper tackles the problem of uncertainty quantification in automated cell tracking for live-cell microscopy by proposing methods that treat tracking as Bayesian inference or classification, and demonstrates that these methods produce well-calibrated uncertainties when applied to existing algorithms, including Transformer-based trackers.
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.