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Related Experiment Video

Updated: Apr 12, 2026

Quantitative Measurement of Invadopodia-mediated Extracellular Matrix Proteolysis in Single and Multicellular Contexts
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FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron

Nicholas T Gigliotti1, Justin Lee2, Emily H Mang1

  • 1Department of Materials Science and Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.

Plos One
|February 6, 2025
PubMed
Summary

A new machine learning workflow, FIRM, accurately identifies extracellular matrix features in microscopy images. This automated method is faster and less biased than manual analysis for tissue remodeling studies.

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Area of Science:

  • Biomaterials Science
  • Cell Biology
  • Biophysics

Background:

  • The extracellular matrix (ECM) is crucial for tissue structure and function.
  • Quantifying ECM changes is vital for understanding tissue remodeling but challenging due to imaging complexities.
  • Existing image analysis tools struggle with edge ambiguity in electron microscopy.

Purpose of the Study:

  • To develop a novel machine learning-based workflow for analyzing microscopy images of the ECM.
  • To address the challenge of feature edge ambiguity in electron microscopy image analysis.
  • To improve the efficiency and accuracy of quantifying ECM features.

Main Methods:

  • A machine learning workflow named FIRM (Feature Identification from Raw Microscopy) was developed.
  • FIRM utilizes a random forest classifier to identify ECM features.
  • Binary segmentation masks are generated for quantification using ImageJ-FIJI.

Main Results:

  • FIRM achieved an F1 score of 0.794 and over 80% accuracy in detecting feature number and size.
  • The deviation of FIRM from ground truth in fibril number, size, and distribution was comparable to human analysis.
  • FIRM demonstrated similar performance to manual analysis but required significantly less time.

Conclusions:

  • FIRM offers an efficient, unbiased, and automatable solution for ECM image analysis.
  • The workflow can be optimized for various features, benefiting diverse scientific disciplines.
  • This technique enhances the accuracy and speed of quantifying critical tissue remodeling events.