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Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
Published on: November 17, 2016
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An automated cell viability quantification method for low-resolution confocal images of closely packed cells based on
R Kaviani1,2, P Merat3, F Moldovan2,4
1Department of Mechanical Engineering, Ecole Polytechnique of Montreal, Montreal, Canada.
Journal of Microscopy
|November 10, 2015
Summary
This study introduces a new method for automatically counting live/dead cells in low-resolution fluorescent images. The novel algorithm accurately quantifies closely packed cells of varying sizes, improving cell viability assessment.
Area of Science:
- Cell Biology
- Biomedical Imaging
- Computational Biology
Background:
- Fluorescent live/dead labeling and microscopy are standard for cell viability assessment but lack quantitative accuracy.
- Existing image-processing methods struggle with quantifying closely packed cells in low-resolution images of variable sizes.
Purpose of the Study:
- To develop a novel, automatic method for quantifying live/dead cells in challenging low-resolution 2D fluorescent images.
- To address the limitations of current methods in handling closely packed cells with variable sizes.
Main Methods:
- A new algorithm employing mean shift-based gradient flow tracking for automatic cell quantification.
- Validation performed on growth plate confocal images to assess accuracy and performance.
Main Results:
- The developed algorithm demonstrates superior performance compared to existing methods under similar low-resolution, closely packed cell conditions.
- Accurate quantification of live/dead cells was achieved even with variable cell sizes.
Conclusions:
- The novel mean shift-based gradient flow tracking method offers an effective solution for quantitative cell counting in low-resolution fluorescent microscopy.
- This advancement improves the reliability of cell viability assessment in complex biological samples.
