TY - GEN
T1 - Automated Evaluation of Cell Viability in Microfluidic Spheroid Arrays
AU - Schurr, Jonas
AU - Winkler, Stephan
AU - Ertl, Peter
AU - Eilenberger, Christoph
AU - Scharinger, Josef
N1 - Funding Information:
The Research described in this Paper was funded by the Christian-Doppler Forschungsgesellschaft (Josef Ressel Center for Phytogenic Drug Research).
Publisher Copyright:
© 2021 The Authors.
PY - 2021/9
Y1 - 2021/9
N2 - Three-dimensional (3D) spheroid arrays promise improved predictability due to their higher physiological relevance. They have the potential to improve drug screening outcomes in preclinical studies. Despite the advances, they can often lead to non-reproducible and unpredictable results. To support the development and subsequent analyses of spheroid arrays, we present a method for analyzing and evaluating cell viability in these. We provide a fast and easy-to-use fully automated workflow for the viability analysis in fluorescence images of cell aggregates within these arrays. The algorithm consists of multiple image processing algorithms for the segmentation and mapping of a priori knowledge about the array layout. The segmentation step is based on Otsu's thresholding followed by morphological filtering to obliterate the necessity of input parameters. No preprocessing is required. Besides, the algorithm offers the possibility of applying an additional flood fill algorithm. Subsequently, a k-means algorithm allows a fast image independent mapping of the grid to identify the cell aggregates. The complete workflow allows the extraction of essential metrics describing the viability of each cell aggregate. With our automated approach, we can show an increase in accuracy compared to simple manual segmentation. Additionally, the objectivity is increased by reducing human intervention. Furthermore, the needed analysis time is shortened and the information extraction and evaluation process is simplified.
AB - Three-dimensional (3D) spheroid arrays promise improved predictability due to their higher physiological relevance. They have the potential to improve drug screening outcomes in preclinical studies. Despite the advances, they can often lead to non-reproducible and unpredictable results. To support the development and subsequent analyses of spheroid arrays, we present a method for analyzing and evaluating cell viability in these. We provide a fast and easy-to-use fully automated workflow for the viability analysis in fluorescence images of cell aggregates within these arrays. The algorithm consists of multiple image processing algorithms for the segmentation and mapping of a priori knowledge about the array layout. The segmentation step is based on Otsu's thresholding followed by morphological filtering to obliterate the necessity of input parameters. No preprocessing is required. Besides, the algorithm offers the possibility of applying an additional flood fill algorithm. Subsequently, a k-means algorithm allows a fast image independent mapping of the grid to identify the cell aggregates. The complete workflow allows the extraction of essential metrics describing the viability of each cell aggregate. With our automated approach, we can show an increase in accuracy compared to simple manual segmentation. Additionally, the objectivity is increased by reducing human intervention. Furthermore, the needed analysis time is shortened and the information extraction and evaluation process is simplified.
KW - Image Processing Methods
KW - Fluorescence Microscopy
KW - Image Analysis
KW - Bioinformatics
UR - http://www.scopus.com/inward/record.url?scp=85142935877&partnerID=8YFLogxK
U2 - 10.46354/i3m.2021.iwish.005
DO - 10.46354/i3m.2021.iwish.005
M3 - Conference contribution
T3 - 10th International Workshop on Innovative Simulation for Health Care, IWISH 2021
SP - 27
EP - 35
BT - 10th International Workshop on Innovative Simulation for Health Care, IWISH 2021
A2 - Bruzzone, Agostino G.
A2 - Frascio, Marco
A2 - Longo, Francesco
A2 - Novak, Vera
ER -