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CellPhePy: A python implementation of the CellPhe toolkit for automated cell phenotyping from microscopy time-lapse

Laura Wiggins1, Stuart Lacy2, Graeme Park3,4

  • 1Department of Materials Science and Engineering, University of Sheffield, Sheffield, UK.

Journal of Microscopy
|April 25, 2025
PubMed
Summary

CellPhePy, a new Python tool, automates cell phenotyping from microscopy images. It enhances cell analysis and classification, offering a user-friendly, flexible solution for biological research.

Keywords:
cell phenotypingimage analysismachine learningmicroscopyopen‐sourcesegmentationtimelapsetracking

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Area of Science:

  • * Cell biology
  • * Image analysis
  • * Bioinformatics

Background:

  • * The CellPhe toolkit, an R package, previously enabled automated cell phenotyping from ptychography time-lapse videos.
  • * There is a growing need for Python-based tools to improve interoperability with existing image analysis software.

Purpose of the Study:

  • * To develop a Python implementation of the CellPhe toolkit, named CellPhePy.
  • * To enhance cell phenotyping capabilities and broaden analytical applications in biological research.

Main Methods:

  • * CellPhePy preserves core functionalities: phenotypic feature extraction, time-series analysis, feature selection, and cell type classification.
  • * Integrates with CellPose for segmentation and TrackMate for tracking, enabling fully automated analysis from microscopy images.
  • * Offers an improved method for identifying differentiating features and extended multiclass classification support.

Main Results:

  • * CellPhePy provides automated, end-to-end cell segmentation, tracking, and feature extraction.
  • * Enhanced feature selection and multiclass classification capabilities improve analytical power.
  • * The package is flexible, modular, and customizable for various imaging modalities and research questions.

Conclusions:

  • * CellPhePy offers a powerful, accessible, and automated solution for cell phenotyping.
  • * Its Python-based design enhances interoperability and usability in biological image analysis.
  • * The toolkit is readily available via PyPi or GitHub, with an added GUI for user convenience.