Authors:
Jonas Schurr
1
;
2
;
Andreas Haghofer
1
;
2
;
Marian Fürsatz
3
;
4
;
Hannah Janout
1
;
2
;
Sylvia Nürnberger
3
;
4
;
5
and
Stephan Winkler
1
;
2
Affiliations:
1
Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11–13, Hagenberg, Austria
;
2
Department of Computer Science, Johannes Kepler University, Altenberger Straße 69, Linz, Austria
;
3
Department of Orthopedics and Trauma-Surgery, Division of Trauma-Surgery, Medical University of Vienna, Austria
;
4
Ludwig Boltzmann Institute for Traumatology, The Research Center in Cooperation with the AUVA, Vienna, Austria
;
5
Austrian Cluster for Tissue Regeneration, Vienna, Austria
Keyword(s):
Heuristic Optimization, Machine Learning, Image Processing, Spheroids, High Throughput Screening.
Abstract:
Cell Spheroids are of high interest for clinical cell applications and cell screening. To allow the extraction of early readout parameters a high amount of image data of petri dishes is created. To support automated analyses of spheroids in petri dish images we present a method for analysing and quantification of spheroids in its development stages. The algorithm is based on multiple image processing algorithms and neural networks. With an evolutionary strategy, engraved grid cells on petri dish are extracted and on top a Unet is used for the segmentation and quantification of different cell compartment states. The measured f1-scores for the different states are 0.77 for monolayer grid cells, 0.86 for starting formation grid cells and 0.85 for spheroids. As we describe in this study we can provide thorough analyses of cell spheroid in petri dishes, by automating the quantification process.