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Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images

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Biomedical Engineering Systems and Technologies (BIOSTEC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1814))

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Abstract

Micropatterning in living cells is used for the analysis of protein-protein interactions. The quantitative analysis of images produced within this process is time-consuming and non-trivial task. For the simplification and speedup of such analyses, we describe a method for fully automated analysis of micro-patterned cells in fluorescence microscopy images. An approach based on an evolution strategy allows the grid extraction of the assays to estimate the pattern on the cells. We outline a workflow for the segmentation of these patterned cells based on a Unet. We also show the efficiency of different data augmentations applied to different patterning setups. A Dice score of 0.89 with 3 µm patterns and 0.79 with 1 µm patterns could be achieved. As we demonstrate in this study, we can provide thorough micropatterning studies, by automating the cell segmentation process.

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Acknowledgements

The work described in this paper is supported by the Center of Excellence for Technical Innovation in Medicine (TIMED, project BIOsens), and the Christian-Doppler Forschungsgesellschaft (Josef Ressel Center for Phytogenic Drug Research). Special thanks to my supervisor Joseph Scharinger.

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Authors and Affiliations

  1. University of Applied Sciences Upper Austria, Bioinformatics, Hagenberg, Austria

    Jonas Schurr, Andreas Haghofer & Stephan Winkler

  2. Johannes Kepler University Linz, Linz, Austria

    Jonas Schurr, Andreas Haghofer & Stephan Winkler

  3. University of Applied Sciences Upper Austria, Wels, Austria

    Peter Lanzerstorfer

Authors
  1. Jonas Schurr
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  2. Andreas Haghofer
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  3. Peter Lanzerstorfer
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  4. Stephan Winkler
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Corresponding author

Correspondence to Jonas Schurr .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Caparica, Portugal

    Ana Cecília A. Roque

  2. Virginia Tech, Blacksburg, VA, USA

    Denis Gracanin

  3. University of Vienna, Vienna, Austria

    Ronny Lorenz

  4. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  5. Université de Montréal, Montréal, QC, Canada

    Nathalie Bier

  6. Instituto de Telecomunicações and Instituto Superior Técnico - Lisbon University, Lisbon, Portugal

    Ana Fred

  7. Nova School of Science and Technology and University of Lisbon, Caparica, Portugal

    Hugo Gamboa

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Schurr, J., Haghofer, A., Lanzerstorfer, P., Winkler, S. (2023). Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images. In: Roque, A.C.A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2022. Communications in Computer and Information Science, vol 1814. Springer, Cham. https://doi.org/10.1007/978-3-031-38854-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-38854-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-38853-8

  • Online ISBN: 978-3-031-38854-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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