Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images

Jonas Schurr; Andreas Haghofer; Peter Lanzerstorfer; Stephan Winkler

doi:10.1007/978-3-031-38854-5_3

Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images

Jonas Schurr, Andreas Haghofer, Peter Lanzerstorfer, Stephan Winkler

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Biomedical Engineering Systems and Technologies - 15th International Joint Conference, BIOSTEC 2022, Revised Selected Papers
Editors	Ana Cecília A. Roque, Denis Gracanin, Ronny Lorenz, Athanasios Tsanas, Nathalie Bier, Ana Fred, Hugo Gamboa
Publisher	Springer
Pages	34-52
Number of pages	19
ISBN (Print)	9783031388538
DOIs	https://doi.org/10.1007/978-3-031-38854-5_3 https://doi.org/10.1007/978-3-031-38854-5_3
Publication status	Published - 2023
Event	Proceedings of the 15th International Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022 - Virtual, Online Duration: 9 Feb 2022 → 11 Feb 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1814 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	Proceedings of the 15th International Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022
City	Virtual, Online
Period	09.02.2022 → 11.02.2022

Keywords

Bioinformatics
Fluorescence microscopy
Image processing methods
Image segmentation
Imageanalysis

Access to Document

Cite this

Schurr, J., Haghofer, A., Lanzerstorfer, P., & Winkler, S. (2023). Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images. In A. C. A. Roque, D. Gracanin, R. Lorenz, A. Tsanas, N. Bier, A. Fred, & H. Gamboa (Eds.), Biomedical Engineering Systems and Technologies - 15th International Joint Conference, BIOSTEC 2022, Revised Selected Papers (pp. 34-52). (Communications in Computer and Information Science; Vol. 1814 CCIS). Springer. https://doi.org/10.1007/978-3-031-38854-5_3, https://doi.org/10.1007/978-3-031-38854-5_3

Schurr, Jonas ; Haghofer, Andreas ; Lanzerstorfer, Peter et al. / Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images. Biomedical Engineering Systems and Technologies - 15th International Joint Conference, BIOSTEC 2022, Revised Selected Papers. editor / Ana Cecília A. Roque ; Denis Gracanin ; Ronny Lorenz ; Athanasios Tsanas ; Nathalie Bier ; Ana Fred ; Hugo Gamboa. Springer, 2023. pp. 34-52 (Communications in Computer and Information Science).

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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.",

keywords = "Bioinformatics, Fluorescence microscopy, Image processing methods, Image segmentation, Imageanalysis",

author = "Jonas Schurr and Andreas Haghofer and Peter Lanzerstorfer and Stephan Winkler",

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Schurr, J, Haghofer, A, Lanzerstorfer, P & Winkler, S 2023, Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images. in ACA Roque, D Gracanin, R Lorenz, A Tsanas, N Bier, A Fred & H Gamboa (eds), Biomedical Engineering Systems and Technologies - 15th International Joint Conference, BIOSTEC 2022, Revised Selected Papers. Communications in Computer and Information Science, vol. 1814 CCIS, Springer, pp. 34-52, Proceedings of the 15th International Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022, Virtual, Online, 09.02.2022. https://doi.org/10.1007/978-3-031-38854-5_3, https://doi.org/10.1007/978-3-031-38854-5_3

Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images. / Schurr, Jonas; Haghofer, Andreas; Lanzerstorfer, Peter et al.
Biomedical Engineering Systems and Technologies - 15th International Joint Conference, BIOSTEC 2022, Revised Selected Papers. ed. / Ana Cecília A. Roque; Denis Gracanin; Ronny Lorenz; Athanasios Tsanas; Nathalie Bier; Ana Fred; Hugo Gamboa. Springer, 2023. p. 34-52 (Communications in Computer and Information Science; Vol. 1814 CCIS).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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T1 - Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images

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AU - Haghofer, Andreas

AU - Lanzerstorfer, Peter

AU - Winkler, Stephan

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N2 - 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.

AB - 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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T3 - Communications in Computer and Information Science

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BT - Biomedical Engineering Systems and Technologies - 15th International Joint Conference, BIOSTEC 2022, Revised Selected Papers

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A2 - Gracanin, Denis

A2 - Lorenz, Ronny

A2 - Tsanas, Athanasios

A2 - Bier, Nathalie

A2 - Fred, Ana

A2 - Gamboa, Hugo

PB - Springer

T2 - Proceedings of the 15th International Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022

Y2 - 9 February 2022 through 11 February 2022

ER -

Schurr J, Haghofer A, Lanzerstorfer P , Winkler S. Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images. In Roque ACA, Gracanin D, Lorenz R, Tsanas A, Bier N, Fred A, Gamboa H, editors, Biomedical Engineering Systems and Technologies - 15th International Joint Conference, BIOSTEC 2022, Revised Selected Papers. Springer. 2023. p. 34-52. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-38854-5_3, 10.1007/978-3-031-38854-5_3