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Automated cell segmentation for reproducibility in bioimage analysis.

Michael C Robitaille1, Jeff M Byers1, Joseph A Christodoulides1

  • 1Materials Science and Technology Division, U.S. Naval Research Laboratory, Washington, DC, USA.

Synthetic Biology (Oxford, England)
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

We developed a novel self-supervised learning (SSL) method for automated cell segmentation in live-cell imaging. This approach enhances reproducibility in synthetic biology research by removing human bias and manual input.

Keywords:
automated image analysiscell segmentationreproducibilityself-supervised learning

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Area of Science:

  • Synthetic Biology
  • Bioimage Analysis
  • Cellular Imaging

Background:

  • Live-cell imaging is crucial in synthetic biology but lacks standardized analysis, hindering reproducibility.
  • Manual image analysis requires significant user input, introducing bias and challenges.
  • Reproducible characterization of engineered cells necessitates objective and automated analysis methods.

Purpose of the Study:

  • To introduce a novel self-supervised learning (SSL) method for automated cell segmentation.
  • To address the challenges of standardization and reproducibility in live-cell image analysis.
  • To apply the SSL method for characterizing engineered *Dictyostelium* cells.

Main Methods:

  • Developed a self-supervised learning (SSL) algorithm for image analysis.
  • The SSL method recursively trains on motion within live-cell microscopy images.
  • No end-user input, parameter optimization, or manual training is required.

Main Results:

  • Achieved objective cell segmentation through automated image analysis.
  • Demonstrated the SSL method's application in characterizing engineered *Dictyostelium* cells.
  • The method proved highly generalizable across cell types and optical modalities.

Conclusions:

  • The SSL method offers an automated and objective solution for live-cell image analysis.
  • This approach significantly enhances reproducibility in synthetic biology research.
  • Represents a step towards accessible bioimage analysis software and reproducible measurement technologies.