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Related Concept Videos

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Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
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Related Experiment Video

Updated: Mar 7, 2026

Live Imaging of Mitosis in the Developing Mouse Embryonic Cortex
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Published on: June 4, 2014

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Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy.

Joana Sarah Grah1, Jennifer Alison Harrington2, Siang Boon Koh2

  • 1University of Cambridge, Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, United Kingdom.

Methods (San Diego, Calif.)
|February 13, 2017
PubMed
Summary

This study introduces a new workflow for detecting and tracking mitotic cells using phase contrast microscopy. The automated method analyzes cell division, fate, and morphology, overcoming limitations of manual analysis.

Keywords:
Cell trackingCircular Hough transformLevel-set methodsMitosis analysisPhase contrast microscopyVariational methods

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Area of Science:

  • Cell Biology
  • Microscopy
  • Image Analysis

Background:

  • Live-cell imaging often uses phase contrast microscopy to avoid phototoxicity associated with fluorescent markers.
  • Phase contrast microscopy presents unique image characteristics that challenge standard image processing techniques.
  • Manual analysis of cell division is subjective, time-consuming, and biased, necessitating automated solutions.

Purpose of the Study:

  • To develop and present an automated workflow for detecting and tracking mitotic cells in time-lapse microscopy image sequences.
  • To overcome the limitations of manual analysis and the challenges posed by phase contrast microscopy in live-cell imaging.
  • To enable quantitative measurements of mitosis duration, cell fate ratios, and cell morphology statistics.

Main Methods:

  • A workflow employing mathematical imaging methods for mitosis detection and tracking.
  • Mitosis detection using the circular Hough transform to identify cell contours.
  • A variational methods-based tracking algorithm, including backward tracking to find the mitosis start and forward tracking to the end.

Main Results:

  • Successful detection and tracking of mitotic cells in time-lapse sequences.
  • Quantitative data on average mitosis duration and ratios of different cell fates (death, no division, multiple daughter cells).
  • Acquisition of statistics on cell morphologies throughout the mitosis process.

Conclusions:

  • The proposed workflow provides an automated and objective method for analyzing mitotic events in live-cell imaging.
  • The system, integrated into the MitosisAnalyser MATLAB® Graphical User Interface, facilitates efficient analysis of large datasets.
  • This approach enhances the study of cell division dynamics and outcomes without requiring fluorescent markers.