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Related Experiment Video

Updated: Dec 22, 2025

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
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A simple segmentation and quantification method for numerical quantitative analysis of cells and tissues.

Hyun-Kyu Kang1, Ki-Han Kim2, Jin-Su Ahn2

  • 1Department of Software Technology, College of Science and Technology, Konkuk University, Chungju, Korea.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|May 5, 2020
PubMed
Summary

This study presents a new image processing algorithm for quantitative decellularization analysis. The method enables accurate cell and tissue segmentation and quantification in diverse microscopic images, overcoming limitations of existing techniques.

Keywords:
Segmentationdecellularizationimage processmicroscope image

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Area of Science:

  • Biomedical Engineering
  • Microscopy
  • Image Analysis

Background:

  • Quantitative evaluation of decellularization requires microscopic image analysis.
  • Current methods are limited by expensive software or the need for extensive data in machine learning approaches.
  • A need exists for a general, accessible algorithm for decellularization assessment.

Purpose of the Study:

  • To develop a versatile image processing algorithm for quantitative analysis of cells and tissues in general microscopic images.
  • To provide an accessible alternative to existing decellularization evaluation methods.

Main Methods:

  • The algorithm processes color microscopic images (RGB) into binary images.
  • It separates cells and tissues using noise elimination, logical operations, and labeling.
  • The method was validated using decellularized porcine aortic valve samples.

Main Results:

  • Successful segmentation of cells and tissues was achieved.
  • Quantitative analysis of cell numbers and tissue area changes during decellularization was demonstrated.
  • The algorithm proved effective across varying microscopic image brightness.

Conclusions:

  • The developed algorithm enables robust cell and tissue extraction from microscopic images.
  • Quantitative numerical analysis of decellularization is feasible with this method.
  • The approach offers a general solution for microscopic image analysis in decellularization studies.