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An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy
Felix Buggenthin1, Carsten Marr, Michael Schwarzfischer
1Institute of Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany. fabian.theis@helmholtz-muenchen.de.
BMC Bioinformatics
|October 5, 2013
Summary
We developed an automated pipeline for analyzing bright field microscopy images, enabling high-throughput cell segmentation and analysis. This method accurately processes diverse cell types and densities, outperforming standard approaches for stem cell research.
Area of Science:
- Cellular dynamics analysis
- High-throughput microscopy
- Image processing
Background:
- High-throughput microscopy generates vast datasets requiring automated analysis.
- Existing software excels with fluorescence but struggles with variable bright field data.
- Automated bright field image analysis is crucial for cellular studies.
Purpose of the Study:
- To present a fully automated image processing pipeline for robust cell segmentation and analysis.
- To enable high-throughput and time-efficient processing of bright field microscopy data.
- To address limitations of current software with variable bright field images.
Main Methods:
- Developed a two-step automated pipeline: optimized image acquisition and robust cell identification.
- Utilized fast image processing algorithms for single-cell detection.
- Applied the pipeline to a 6-day time-lapse movie of differentiating hematopoietic stem cells.
Main Results:
- Successfully segmented and analyzed cells with ellipsoid morphology from bright field microscopy.
- Achieved high accuracy in cell identification, outperforming standard methods.
- Identified three distinct growth phases in the stem cell population through population doubling time analysis.
Conclusions:
- The developed method enables fully automated processing and analysis of high-throughput bright field microscopy data.
- Robust cell detection and fast computation support high-content screening and on-line analysis.
- Facilitates the development of automated single-cell genealogy tracking.
