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@INPROCEEDINGS{Paul:1049900,
      author       = {Paul, Richard Dominik and Seiffarth, Johannes and Rügamer,
                      David and Scharr, Hanno and Nöh, Katharina},
      title        = {{H}ow {T}o {M}ake {Y}our {C}ell {T}racker {S}ay "{I}
                      dunno!"},
      reportid     = {FZJ-2025-05660},
      pages        = {6914-6923},
      year         = {2025},
      comment      = {Proceedings of the IEEE/CVF International Conference on
                      Computer Vision (ICCV)},
      booktitle     = {Proceedings of the IEEE/CVF
                       International Conference on Computer
                       Vision (ICCV)},
      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.},
      month         = {Oct},
      date          = {2025-10-19},
      organization  = {International Conference on Computer
                       Vision, ICCV 2025, Honolulu (USA), 19
                       Oct 2025 - 23 Oct 2025},
      cin          = {IAS-8 / IBG-1},
      cid          = {I:(DE-Juel1)IAS-8-20210421 / I:(DE-Juel1)IBG-1-20101118},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/1049900},
}