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@ARTICLE{Seiffarth:1047300,
      author       = {Seiffarth, Johannes and Nöh, Katharina},
      title        = {{P}y{UAT}: {O}pen-source {P}ython framework for efficient
                      and scalable cell tracking},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-04215},
      year         = {2025},
      note         = {We acknowledge the inspiring scientific environment
                      provided by the Helmholtz School for Data Science in Life,
                      Earth and Energy (HDS-LEE), thank Axel Theorell for
                      insightful discussions, and Wolfgang Wiechert for continuous
                      support. This work was supported by the President’s
                      Initiative and Networking Funds of the Helmholtz Association
                      of German Research Centres [SATOMI ZT-I-PF-04-011, EMSIG
                      ZT-I-PF-04-44].},
      abstract     = {Tracking individual cells in live-cell imaging provides
                      fundamental insights, inevitable for studying causes and
                      consequences of phenotypic heterogeneity, responses to
                      changing environmental conditions or stressors. Microbial
                      cell tracking, characterized by stochastic cell movements
                      and frequent cell divisions, remains a challenging task when
                      imaging frame rates must be limited to avoid counterfactual
                      results. A promising way to overcome this limitation is
                      uncertainty-aware tracking (UAT), which uses statistical
                      models, calibrated to empirically observed cell behavior, to
                      predict likely cell associations. We present PyUAT, an
                      efficient and modular Python implementation of UAT for
                      tracking microbial cells in time-lapse imaging. We
                      demonstrate its performance on a large 2D+t data set and
                      investigate the influence of modular biological models and
                      imaging intervals on the tracking performance. The
                      open-source PyUAT software is available at
                      https://github.com/JuBiotech/PyUAT, including example
                      notebooks for immediate use in Google Colab.},
      keywords     = {Quantitative Methods (q-bio.QM) (Other) / Computer Vision
                      and Pattern Recognition (cs.CV) (Other) / FOS: Biological
                      sciences (Other) / FOS: Computer and information sciences
                      (Other)},
      cin          = {IBG-1},
      cid          = {I:(DE-Juel1)IBG-1-20101118},
      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.2503.21914},
      url          = {https://juser.fz-juelich.de/record/1047300},
}