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Related Experiment Video

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Osteoclast Derivation from Mouse Bone Marrow
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A Machine Learning-Based Image Segmentation Method to Quantify In Vitro Osteoclast Culture Endpoints.

Bethan K Davies1,2, Andrew P Hibbert1, Scott J Roberts1

  • 1Department of Comparative Biomedical Sciences, Royal Veterinary College, Royal College Street, London, NW1 0TU, UK.

Calcified Tissue International
|August 11, 2023
PubMed
Summary
This summary is machine-generated.

This study developed an automated workflow for quantifying osteoclasts, significantly reducing variability and time. The machine learning approach accurately measures osteoclast numbers in vitro, aiding research on bone-related diseases.

Keywords:
FormationIlastikMachine learningOsteoclastResorption

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Area of Science:

  • Biomedical Engineering
  • Cell Biology
  • Image Analysis

Background:

  • Manual quantification of in vitro osteoclast cultures is labor-intensive and prone to user variability.
  • Accurate osteoclast quantification is crucial for research into bone metabolism and diseases.

Purpose of the Study:

  • To develop and validate an automated workflow for robust quantification of in vitro osteoclast cultures.
  • To reduce inter- and intra-user variability and save time in osteoclast analysis.

Main Methods:

  • Utilized ilastik, a machine learning software, to train an algorithm on tartrate-resistant acid phosphatase-stained mouse osteoclast images.
  • Validated the automated method by assessing its ability to detect treatment-induced changes in osteoclast number.

Main Results:

  • Osteoclast counts from the automated method strongly correlated with manual counts (r=0.87).
  • The workflow demonstrated significant reductions in user variability (93%) and analysis time (80%).
  • Successfully detected treatment effects, including a 70% reduction with zoledronate and a dose-dependent decrease with ticagrelor.

Conclusions:

  • An automated, machine learning-based image analysis workflow for in vitro osteoclast quantification has been successfully developed and validated.
  • This method offers a consistent, sensitive, and user-friendly alternative to manual counting, applicable across different species and substrates.
  • The workflow significantly improves efficiency and reliability in osteoclast research, with readily available resources for implementation.