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Related Experiment Video

Updated: Jun 3, 2026

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

Published on: September 14, 2016

Digital image processing of live/dead staining.

Pieter Spaepen¹, Sebastian De Boodt, Jean-Marie Aerts

¹Katholieke Hogeschool Kempen, Geel, Belgium. pieter.spaepen@khk.be

Methods in Molecular Biology (Clifton, N.J.)

|April 7, 2011

Summary

Automating cell counting using computer algorithms enhances efficiency and data quality in cell biology. This guide helps researchers assess the feasibility of developing custom software for live and dead cell quantification.

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Area of Science:

Cell Biology
Digital Imaging
Bioinformatics

Background:

Manual quantification of live and dead cells is labor-intensive and unreliable for large sample sizes.
Microscopic cell counting often overlooks valuable data such as cell size, shape, and distribution.
Current methods are qualitative or manual, limiting efficiency and data richness.

Purpose of the Study:

To present an outline for developing custom computer algorithms for automated cell quantification.
To guide researchers in assessing the feasibility and cost-effectiveness of creating dedicated software solutions.
To improve the efficiency and quality of data gathering in cell biology research.

Main Methods:

Explaining basic concepts of digital imaging in a step-by-step approach.
Providing a framework for estimating the difficulty and cost of algorithm development.
Utilizing a series of questions to assess the potential of creating a computer algorithm versus expected costs.

Main Results:

Developing software routines can significantly increase the efficiency and quality of cell counting.
The feasibility of creating a computer algorithm depends on sample-specific factors and available technical expertise.
In many cases, developing a custom algorithm is more straightforward than anticipated.

Conclusions:

Automated cell quantification offers a more efficient and reliable alternative to manual counting.
Researchers can leverage digital imaging principles and a structured approach to develop custom algorithms.
Careful assessment of project-specific factors is crucial for successful implementation of automated cell analysis.