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@ARTICLE{Ruzaeva:1005228,
      author       = {Ruzaeva, Karina and Cohrs, Jan-Christopher and Kasahara,
                      Keitaro and Kohlheyer, Dietrich and Nöh, Katharina and
                      Berkels, Benjamin},
      title        = {{C}ell tracking for live-cell microscopy using an
                      activity-prioritized assignment strategy},
      publisher    = {arXiv},
      reportid     = {FZJ-2023-01376},
      year         = {2022},
      abstract     = {Cell tracking is an essential tool in live-cell imaging to
                      determine single-cell features, such as division patterns or
                      elongation rates. Unlike in common multiple object tracking,
                      in microbial live-cell experiments cells are growing,
                      moving, and dividing over time, to form cell colonies that
                      are densely packed in mono-layer structures. With increasing
                      cell numbers, following the precise cell-cell associations
                      correctly over many generations becomes more and more
                      challenging, due to the massively increasing number of
                      possible associations. To tackle this challenge, we propose
                      a fast parameter-free cell tracking approach, which consists
                      of activity-prioritized nearest neighbor assignment of
                      growing cells and a combinatorial solver that assigns
                      splitting mother cells to their daughters. As input for the
                      tracking, Omnipose is utilized for instance segmentation.
                      Unlike conventional nearest-neighbor-based tracking
                      approaches, the assignment steps of our proposed method are
                      based on a Gaussian activity-based metric, predicting the
                      cell-specific migration probability, thereby limiting the
                      number of erroneous assignments. In addition to being a
                      building block for cell tracking, the proposed activity map
                      is a standalone tracking-free metric for indicating cell
                      activity. Finally, we perform a quantitative analysis of the
                      tracking accuracy for different frame rates, to inform life
                      scientists about a suitable (in terms of tracking
                      performance) choice of the frame rate for their cultivation
                      experiments, when cell tracks are the desired key outcome.},
      keywords     = {Computer Vision and Pattern Recognition (cs.CV) (Other) /
                      Quantitative Methods (q-bio.QM) (Other) / FOS: Computer and
                      information sciences (Other) / FOS: Biological 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.2210.11441},
      url          = {https://juser.fz-juelich.de/record/1005228},
}