Speaker
Description
Cell tracking is a key computational task in live single-cell microscopy. With the advent of automated high-throughput microscopy platforms, the amount of data quickly exceeds what humans are able to overlook. Thus, reliable and uncertainty-aware data analysis pipelines to process the collected amounts of data become crucial. In this work, we investigate the problem of quantifying uncertainty in cell tracking. We present a statistical framework for cell tracking which accommodates many existing tracking methods from the tracking-by-detection paradigm. Based on this framework, we discuss methods inspired from common classification problems for considering the calibration of tracking uncertainties and leveraging estimates thereof for more robust and improved tracking. We benchmark the different approaches on data from the Cell Tracking Challenge and a large-scale microbial dataset using various tracking methods, including the very recently presented Transformer-based tracking.
| Authors | Richard D. Paul*, Johannes Seiffarth, Katharina Nöh, Hanno Scharr |
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| Keywords | Cell tracking, uncertainty quantification, Bayesian statistics, calibration |
