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The Mitotic Spindle

The Mitotic Spindle

The mitotic spindle—or spindle apparatus—is a eukaryotic, cytoskeletal structure made up of long protein fibers called microtubules. Formed during cell division, the spindle separates sister chromatids and moves them to opposite ends of a parental cell, where the now individual chromosomes are distributed to two daughter cell nuclei.
The bipolar configuration of the mitotic spindle facilitates chromosomal segregation, preparing the cell for division. One mechanism that ensures...

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Updated: May 24, 2025

Automated Detection and Analysis of Exocytosis

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

Explainable handcrafted features for mitotic event detection and classification.

Panason Manorost¹, Thomas Deckers^2,3, Veerle Bloemen^3,4

¹M3-BIORES Group, Division Animal and Human Health Engineering, Department of Biosystems, KU Leuven, Leuven, Belgium. manorost.panason@kuleuven.be.

Scientific Reports

|March 2, 2025

Summary

This summary is machine-generated.

A new machine learning method automates mitotic event detection, improving cell proliferation analysis accuracy and speed. This approach reduces false positives common in traditional imaging, enhancing research in fields like cancer and tissue engineering.

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Live Cell Imaging to Assess the Dynamics of Metaphase Timing and Cell Fate Following Mitotic Spindle Perturbations

Published on: September 20, 2019

Related Experiment Videos

Last Updated: May 24, 2025

Automated Detection and Analysis of Exocytosis

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

Live Imaging of Mitosis in the Developing Mouse Embryonic Cortex

Live Imaging of Mitosis in the Developing Mouse Embryonic Cortex

Published on: June 4, 2014

Live Cell Imaging to Assess the Dynamics of Metaphase Timing and Cell Fate Following Mitotic Spindle Perturbations

Live Cell Imaging to Assess the Dynamics of Metaphase Timing and Cell Fate Following Mitotic Spindle Perturbations

Published on: September 20, 2019

Area of Science:

Cell biology
Biomedical imaging
Machine learning

Background:

Accurate cell population analysis is crucial for biological and medical research, including tissue engineering and cancer studies.
Manual cell observation via microscopy is time-consuming and prone to inaccuracies.
Traditional image processing methods struggle with noise, artifacts, and high cell densities, leading to false positives and reduced precision.

Purpose of the Study:

To develop a fully automated method for mitotic event detection.
To reduce processing time and improve the accuracy of cell proliferation rate estimation.
To minimize false positive detections in cell imaging analysis.

Main Methods:

A machine learning-based approach combining traditional image processing (thresholding, cell tracking) with feature extraction.
Feature selection using mutual information and ANOVA testing.
Classification of mitotic events using tree and random forest classifiers to reject false positives.

Main Results:

The machine learning method achieved high processing performance and explainable feature contributions.
Mean accuracy of 85.12% with 88.01% precision and 92.70% recall on a public phase contrast dataset.
Higher accuracy (87.66%), precision (88.01%), and recall (91.78%) on a lens-free image dataset using fewer features.

Conclusions:

The developed methodology offers a robust and explainable alternative to traditional cell imaging analysis.
It achieves comparable performance to deep learning approaches while providing interpretable features.
This automated method enhances the efficiency and accuracy of proliferation rate estimation in cell populations.