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@ARTICLE{Paul:1047299,
      author       = {Paul, Richard D. and Seiffarth, Johannes and Rügamer,
                      David and Scharr, Hanno and Nöh, Katharina and Scharr,
                      Hanno},
      title        = {{H}ow {T}o {M}ake {Y}our {C}ell {T}racker {S}ay '{I}
                      dunno!'},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-04214},
      year         = {2025},
      note         = {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].},
      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.},
      keywords     = {Computer Vision and Pattern Recognition (cs.CV) (Other) /
                      Quantitative Methods (q-bio.QM) (Other) / Applications
                      (stat.AP) (Other) / FOS: Computer and information sciences
                      (Other) / FOS: Biological sciences (Other)},
      cin          = {IBG-1 / IAS-8},
      cid          = {I:(DE-Juel1)IBG-1-20101118 / I:(DE-Juel1)IAS-8-20210421},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2171},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2503.09244},
      url          = {https://juser.fz-juelich.de/record/1047299},
}