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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

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Automatic cell segmentation in strongly agglomerated cell networks for different cell types.

S Buhl¹, B Neumann¹, S C Schäfer¹

¹Institute for Computer Science, Vision and Computational Intelligence, South Westphalia University of Applied Sciences, Frauenstuhlweg 31, 58644 Iserlohn, Germany.

International Journal of Computational Biology and Drug Design

|June 1, 2014

Summary

This study introduces a universal cell separation method for clustered cells with varied morphology. The technique uses nucleus-based growth simulation and histogram backprojection for accurate segmentation in histological and fluorescent images.

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Area of Science:

Cell biology
Image analysis
Biotechnology

Background:

Cell clusters with diverse morphologies pose challenges for automated separation.
Existing methods often lack universality across different cell types.

Purpose of the Study:

To develop a universally applicable method for separating connected cells in clusters.
To address the challenge of varying cell morphologies in cluster segmentation.

Main Methods:

Cell segmentation based on a growth simulation initiated from nuclei.
Utilizing histological staining (May-Grünwald solution) for cell visualization.
Employing histogram backprojection to differentiate nuclei from other cell areas.

Main Results:

The method successfully segments cells in clusters with highly varying morphologies.
Demonstrated applicability to both histological and fluorescent-stained cells.
The nucleus-based growth simulation provides a robust segmentation approach.

Conclusions:

The proposed method offers a universal solution for cell cluster separation.
The technique is adaptable for various cell types and staining methods.
This approach enhances automated analysis of cellular structures.