Related Experiment Video
Updated: May 7, 2026
08:59
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
Published on: October 28, 2018
3D time series analysis of cell shape using Laplacian approaches
Cheng-Jin Du1, Phillip T Hawkins, Len R Stephens
1Warwick Systems Biology Centre, University of Warwick, Coventry CV4 7AL, UK. c.du@warwick.ac.uk.
BMC Bioinformatics
|October 5, 2013
Summary
This study introduces a new framework for analyzing 3D cell shape changes over time. The method improves cell segmentation and topology fixing, offering a faster and more accurate way to study cell dynamics.
Area of Science:
- Biophysics
- Cell Biology
- Image Analysis
Background:
- Cell shape dynamics are crucial for fundamental cellular processes like movement and division.
- Advancements in 3D+time imaging necessitate efficient tools for analyzing complex cell shape deformations.
Purpose of the Study:
- To develop a robust framework for 3D+time cell shape analysis.
- To improve the speed and accuracy of cell segmentation and topology fixing in 3D+time image data.
Main Methods:
- A fast, automatic random walker method for cell segmentation.
- A novel topology fixing algorithm for binary volumes.
- Demonstration of Laplacian operator's relevance across segmentation, topology fixing, and shape representation.
Main Results:
- The proposed cell segmentation is faster and more accurate than traditional methods on noisy 3D time-series of neutrophil cells.
- The topology fixing method shows superior success rates compared to existing SPHARM tools.
- All pipeline steps utilize Laplacian-based approaches, suggesting potential for integration.
Conclusions:
- The developed framework provides efficient tools for 3D+time cell shape analysis.
- Laplacian-based methods offer a unified approach to various computational tasks in cell shape analysis.
- This work facilitates a deeper understanding of cell shape dynamics in biological processes.
