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Automated mesenchymal stem cell segmentation and machine learning-based phenotype classification using morphometric

Sakina M Mota1, Robert E Rogers2, Andrew W Haskell2

  • 1Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|February 5, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an image analysis method to objectively assess mesenchymal stem cell (MSC) morphology for predicting culture efficacy. The algorithm accurately segments and classifies MSCs, aiding in the development of cell-based therapies.

Keywords:
cell phenotype classificationimage segmentationmachine learningmonolayer cell culturestem cellviability assessment

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Area of Science:

  • Biomedical Engineering
  • Cell Biology
  • Computational Biology

Background:

  • Mesenchymal stem cells (MSCs) show therapeutic potential for various diseases.
  • Assessing MSC culture quality is crucial but current methods are limited.
  • MSC morphology reflects their proliferative and immunomodulatory properties.

Purpose of the Study:

  • To develop an objective image analysis approach for evaluating mesenchymal stem cell (MSC) morphology.
  • To predict the efficacy of MSC cultures using morphological phenotype.
  • To overcome limitations of subjective, destructive, or time-consuming traditional assessment methods.

Main Methods:

  • Training an algorithm on phase-contrast micrographs of MSCs during early and mid-logarithmic expansion.
  • Utilizing edge detection, thresholding, morphological operations, and H-minima transform for cell localization and differentiation.
  • Employing marker-controlled watershed segmentation for isolating single cells and extracting morphometric/textural features for machine learning classification.

Main Results:

  • Achieved 88% sensitivity and 86% precision for overall cell detection.
  • Demonstrated high accuracy in segmenting cells with a mean Sorensen-Dice coefficient.
  • Obtained an AUC of 0.816 and 0.787 for classifying MSC phenotypes at different expansion stages.

Conclusions:

  • The developed image analysis method accurately segments and classifies MSCs based on morphology.
  • This approach offers a quantifiable and robust quality assessment for MSC cultures.
  • Facilitates the development of cytotherapies by enabling consistent, morphology-based quality control.