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@ARTICLE{Seiffarth:1047302,
      author       = {Seiffarth, Johannes and Kasahara, Keitaro and Bund,
                      Michelle and Lückel, Benita and Paul, Richard D. and Pesch,
                      Matthias and Witting, Lennart and Bott, Michael and
                      Kohlheyer, Dietrich and Nöh, Katharina},
      title        = {acia-workflows: {A}utomated {S}ingle-cell {I}maging
                      {A}nalysis for {S}calable and {D}eep {L}earning-based
                      {L}ive-cell {I}maging {A}nalysis {W}orkflows},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-04217},
      year         = {2025},
      note         = {J.S. was supported by the President’s Initiative and
                      Networking Funds of the Helmholtz Association of German
                      Research Centres [EMSIG ZT-I-PF-04-44] and received funding
                      from the Helmholtz Association of German Research Centres
                      within the Helmholtz School for Data Science in Life, Earth,
                      and Energy (HDS-LEE). R.P. received funding from the
                      Helmholtz Association of German Research Centres within the
                      Helmholtz School for Data Science in Life, Earth, and Energy
                      (HDS-LEE). K.K., M.Bu., B.L., and L.W. were funded by the
                      Deutsche Forschungsgemeinschaft(DFG, German Research
                      Foundation) – SFB1535 - Project ID 458090666.},
      abstract     = {Live-cell imaging (LCI) technology enables the detailed
                      spatio-temporal characterization of living cells at the
                      single-cell level, which is critical for advancing research
                      in the life sciences, from biomedical applications to
                      bioprocessing. High-throughput setups with tens to hundreds
                      of parallel cell cultivations offer the potential for robust
                      and reproducible insights. However, these insights are
                      obscured by the large amount of LCI data recorded per
                      experiment. Recent advances in state-of-the-art deep
                      learning methods for cell segmentation and tracking now
                      enable the automated analysis of such large data volumes,
                      offering unprecedented opportunities to systematically study
                      single-cell dynamics. The next key challenge lies in
                      integrating these powerful tools into accessible, flexible,
                      and user-friendly workflows that support routine application
                      in biological research. In this work, we present
                      acia-workflows, a platform that combines three key
                      components: (1) the Automated live-Cell Imaging Analysis
                      (acia) Python library, which supports the modular design of
                      image analysis pipelines offering eight deep learning
                      segmentation and tracking approaches; (2) workflows that
                      assemble the image analysis pipeline, its software
                      dependencies, documentation, and visualizations into a
                      single Jupyter Notebook, leading to accessible, reproducible
                      and scalable analysis workflows; and (3) a collection of
                      application workflows showcasing the analysis and
                      customization capabilities in real-world applications.
                      Specifically, we present three workflows to investigate
                      various types of microfluidic LCI experiments ranging from
                      growth rate comparisons to precise, minute-resolution
                      quantitative analyses of individual dynamic cells responses
                      to changing oxygen conditions. Our collection of more than
                      ten application workflows is open source and publicly
                      available at https://github.com/JuBiotech/acia-workflows.},
      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 / 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) / DFG project 458090666 - SFB
                      1535: Mikrobielle Netzwerke – von Organellen bis hin zu
                      Reich-übergreifenden Lebensgemeinschaften (458090666)},
      pid          = {G:(DE-HGF)POF4-2171 / G:(GEPRIS)458090666},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2510.05886},
      url          = {https://juser.fz-juelich.de/record/1047302},
}