TY - GEN
T1 - Automated Segmentation of Patterned Cells in Micropatterning Microscopy Images
AU - Schurr, Jonas
AU - Haghofer, Andreas
AU - Lanzerstorfer, Peter
AU - Winkler, Stephan
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
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.
KW - Bioinformatics
KW - Fluorescence microscopy
KW - Image processing methods
KW - Image segmentation
KW - Imageanalysis
UR - http://www.scopus.com/inward/record.url?scp=85172217198&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-38854-5_3
DO - 10.1007/978-3-031-38854-5_3
M3 - Conference contribution
AN - SCOPUS:85172217198
SN - 9783031388538
T3 - Communications in Computer and Information Science
SP - 34
EP - 52
BT - Biomedical Engineering Systems and Technologies - 15th International Joint Conference, BIOSTEC 2022, Revised Selected Papers
A2 - Roque, Ana Cecília A.
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 -