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Development of image analysis software for quantification of viable cells in microchips
Maximilian Georg1, Tamara Fernández-Cabada2,3, Natalia Bourguignon2,3
1Department of Hematology and Oncology, University of Freiburg Medical Center, Freiburg, Germany.
Plos One
|March 2, 2018
Summary
A new Python-based image analysis software (PIACG) efficiently quantifies cell growth in transmission light microscopy images. This tool addresses limitations of existing software for analyzing cell morphology and area in microfluidic chips.
Area of Science:
- Cell Biology
- Microscopy Image Analysis
- Bioinformatics
Background:
- Automated image analysis is crucial for handling large datasets in cell biology.
- Existing software often struggles with transmission light microscopy images due to acquisition variability.
- There is a need for robust tools to analyze cell growth parameters from diverse microscopy techniques.
Purpose of the Study:
- To present Python-based Image Analysis for Cell Growth (PIACG) software.
- To enable efficient quantification of cell area and morphology from transmission light microscopy images.
- To analyze cell behavior in microfluidic chips under varying conditions.
Main Methods:
- Development of a novel Python-based image processing software (PIACG).
- Application of PIACG to analyze microscopy images from microfluidic chips.
- Quantification of total cell area for fusiform and rounded cell morphologies.
Main Results:
- PIACG successfully calculates the total area occupied by cells.
- The software demonstrates high efficiency in analyzing transmission light microscopy images.
- Cell growth was analyzed in response to different concentrations of fetal bovine serum.
Conclusions:
- PIACG offers an efficient solution for analyzing cell growth from transmission light microscopy.
- The software overcomes limitations of existing tools for variable image acquisition.
- PIACG is valuable for studying cell morphology and area in microfluidic cell culture systems.
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