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001047302 1001_ $$0P:(DE-Juel1)176923$$aSeiffarth, Johannes$$b0$$ufzj
001047302 245__ $$aacia-workflows: Automated Single-cell Imaging Analysis for Scalable and Deep Learning-based Live-cell Imaging Analysis Workflows
001047302 260__ $$barXiv$$c2025
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001047302 500__ $$aJ.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.
001047302 520__ $$aLive-cell imaging (LCI) technology enables the detailed spatio-temporal characterization of living cells at the single-cell level, which is critical for advancing research in the life sciences, from biomedical applications to bioprocessing. High-throughput setups with tens to hundreds of parallel cell cultivations offer the potential for robust and reproducible insights. However, these insights are obscured by the large amount of LCI data recorded per experiment. Recent advances in state-of-the-art deep learning methods for cell segmentation and tracking now enable the automated analysis of such large data volumes, offering unprecedented opportunities to systematically study single-cell dynamics. The next key challenge lies in integrating these powerful tools into accessible, flexible, and user-friendly workflows that support routine application in biological research. In this work, we present acia-workflows, a platform that combines three key components: (1) the Automated live-Cell Imaging Analysis (acia) Python library, which supports the modular design of image analysis pipelines offering eight deep learning segmentation and tracking approaches; (2) workflows that assemble the image analysis pipeline, its software dependencies, documentation, and visualizations into a single Jupyter Notebook, leading to accessible, reproducible and scalable analysis workflows; and (3) a collection of application workflows showcasing the analysis and customization capabilities in real-world applications. Specifically, we present three workflows to investigate various types of microfluidic LCI experiments ranging from growth rate comparisons to precise, minute-resolution quantitative analyses of individual dynamic cells responses to changing oxygen conditions. Our collection of more than ten application workflows is open source and publicly available at https://github.com/JuBiotech/acia-workflows.
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001047302 536__ $$0G:(GEPRIS)458090666$$aDFG project 458090666 - SFB 1535: Mikrobielle Netzwerke – von Organellen bis hin zu Reich-übergreifenden Lebensgemeinschaften (458090666)$$c458090666$$x1
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001047302 650_7 $$2Other$$aComputer Vision and Pattern Recognition (cs.CV)
001047302 650_7 $$2Other$$aQuantitative Methods (q-bio.QM)
001047302 650_7 $$2Other$$aFOS: Computer and information sciences
001047302 650_7 $$2Other$$aFOS: Biological sciences
001047302 7001_ $$0P:(DE-Juel1)191491$$aKasahara, Keitaro$$b1$$ufzj
001047302 7001_ $$0P:(DE-Juel1)200387$$aBund, Michelle$$b2$$ufzj
001047302 7001_ $$0P:(DE-Juel1)194991$$aLückel, Benita$$b3$$ufzj
001047302 7001_ $$0P:(DE-Juel1)175101$$aPaul, Richard D.$$b4$$ufzj
001047302 7001_ $$0P:(DE-Juel1)199011$$aPesch, Matthias$$b5$$ufzj
001047302 7001_ $$0P:(DE-Juel1)194407$$aWitting, Lennart$$b6$$ufzj
001047302 7001_ $$0P:(DE-Juel1)128943$$aBott, Michael$$b7$$ufzj
001047302 7001_ $$0P:(DE-Juel1)140195$$aKohlheyer, Dietrich$$b8$$ufzj
001047302 7001_ $$0P:(DE-Juel1)129051$$aNöh, Katharina$$b9$$ufzj
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