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Related Experiment Video

Updated: May 23, 2026

Measurement of Microtubule Dynamics by Spinning Disk Microscopy in Monopolar Mitotic Spindles
08:31

Measurement of Microtubule Dynamics by Spinning Disk Microscopy in Monopolar Mitotic Spindles

Published on: November 15, 2019

A deterministic method for quantifying spindle-shaped cells in noisy bright-field microscopy.

Martin Radvanský1, Markéta Vašinková2, Miloš Kudělka1

  • 1Department of Computer Science, FEECS, VSB - Technical University of Ostrava, 17. listopadu 2172/15, 70800, Ostrava, Czech Republic.

Scientific Reports
|May 21, 2026
PubMed
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A new image analysis method accurately quantifies spindle-shaped cells in bright-field microscopy without fluorescent stains or deep learning. This deterministic approach offers a practical solution for analyzing cell populations in challenging imaging conditions.

Area of Science:

  • Cell Biology
  • Biomedical Imaging
  • Computational Biology

Background:

  • Accurate quantification of spindle-shaped cells in bright-field microscopy is difficult due to low contrast, noise, and variable morphology.
  • Conventional methods like fluorescent staining or deep learning have limitations, including phototoxicity, extensive data requirements, and lack of interpretability.

Purpose of the Study:

  • To develop a deterministic image analysis method for accurate spindle-shaped cell quantification directly from bright-field microscopy images.
  • To provide a practical alternative to fluorescent labeling and deep learning for analyzing cell populations.

Main Methods:

  • The workflow integrates contrast enhancement, adaptive thresholding, contour filtering, and shape-guided refinement.
  • The method is designed to be robust against irregular cell shapes and heterogeneous backgrounds in time-lapse microscopy.
Keywords:
Bright-field microscopyCell countingContour detectionFibroblast-like cellsGaussian-based modelSpindle-shaped cells

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  • No fluorescent labeling or supervised training is required.
  • Main Results:

    • The method reliably quantifies spindle-shaped cells under challenging imaging conditions, including noise and low contrast.
    • Consistent performance was observed across varying noise levels and imaging parameters.
    • The approach achieved competitive accuracy with high interpretability and low computational cost.

    Conclusions:

    • This deterministic image analysis method offers a practical and interpretable solution for large-scale spindle-shaped cell quantification in bright-field microscopy.
    • It avoids the drawbacks of fluorescent staining and complex deep learning models.
    • Enables straightforward integration into existing biomedical imaging workflows.