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Artificial-intelligence-driven segmentation and analysis of microbial cells.

Shuang Zhang1, Carleton Coffin2, Karyn L Rogers3,4

  • 1Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States.

Journal of Biomedical Optics
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an AI system for automated microbial cell analysis, improving efficiency and accuracy in microscopy. The framework enables advanced research on microbial adaptations and extremophiles.

Keywords:
cell segmentationmicrobial morphologyquantitative cell analysis

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Area of Science:

  • Microbiology
  • Biotechnology
  • Computational Biology

Background:

  • Single-cell microbial analysis is crucial for physiology and biotechnology.
  • Current manual annotation methods are inefficient and limit scalability.
  • Automated quantitative analysis of cell growth and morphology is highly desirable.

Purpose of the Study:

  • To develop an AI-driven system for automated microbial cell segmentation and quantitative feature analysis.
  • To enable efficient monitoring of cell counts and morphological characteristics without manual annotation.
  • To provide a scalable solution for high-throughput microbial cell analysis.

Main Methods:

  • A four-component modular system: denoising, zero-shot segmentation (Segment Anything Model - SAM), post-processing, and feature extraction.
  • Ablation experiments were conducted to evaluate individual component performance.
  • The Segment Anything Model (SAM) with the SAM-H variant was optimized for microbial cell segmentation.

Main Results:

  • Denoising and post-processing enhanced segmentation accuracy by 12.10% and 2.30%, respectively.
  • The SAM-H model achieved a low average error rate of 3.0% on 1162 annotated *Escherichia coli* cells.
  • The system successfully extracted morphometric and intensity features from *E. coli*, yeast, and cancer cell lines.

Conclusions:

  • The developed framework automates quantitative analysis of microbial cells in high-resolution microscopy.
  • This automation facilitates advanced research into microbial adaptations.
  • The system has potential applications in studying extremophiles in challenging environments.