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An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images
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Three-dimensional topology-based analysis segments volumetric and spatiotemporal fluorescence microscopy.

Luca Panconi1,2,3, Amy Tansell2,4, Alexander J Collins5

  • 1Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK.

Biological Imaging
|March 22, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel 3D segmentation algorithm using topological data analysis (TDA) for microscopy images. The method accurately segments biological structures in 3D and tracks cellular dynamics in spatiotemporal data.

Keywords:
Rcell segmentationcell trackingfluorescence microscopytopological data analysis

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Area of Science:

  • * Computational biology
  • * Image analysis
  • * Topological data analysis

Background:

  • * Interpreting microscopy data requires objective and reproducible statistics.
  • * High-dimensional (3D volumetric and spatiotemporal) data offer richer biological insights but pose segmentation challenges.
  • * Existing 3D segmentation techniques are often basic or overly specialized.

Purpose of the Study:

  • * To develop a generalizable 3D segmentation algorithm for microscopy images.
  • * To adapt topological data analysis (TDA) principles for segmenting complex 3D and spatiotemporal biological data.
  • * To provide sensitive and specific analysis for identifying biological structures and tracking cellular dynamics.

Main Methods:

  • * Formulation of a 3D segmentation algorithm based on 2D topological data analysis (TDA) principles.
  • * Implementation of persistent homology to detect variations in image intensity.
  • * Derivation of spatial and spatiotemporal variants of the TDA-based segmentation algorithm.

Main Results:

  • * The algorithm demonstrates sensitive and specific segmentation of simulated data.
  • * It successfully distinguishes diverse biological structures in fluorescence microscopy images, irrespective of shape.
  • * Temporal TDA effectively tracks cell lineage and quantifies cell/organelle replication frequency.

Conclusions:

  • * The developed TDA-based segmentation algorithm offers a robust and versatile tool for 3D and spatiotemporal image analysis.
  • * This approach overcomes limitations of existing methods, enabling detailed interpretation of complex biological data.
  • * The method facilitates advanced applications such as lineage tracing and replication analysis in microscopy.