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Computational Approach to Measuring Myocyte Disarray in Animal Models of Heart Disease
William Wan1, Leslie Leinwand1
1Biofrontiers Institute, University of Colorado at Boulder, Boulder, Colorado.
Insights
This study introduces an automated image analysis method to quantify myocyte disarray in heart tissue. This approach offers an unbiased, scalable alternative to traditional manual scoring in cardiovascular disease research.
Area of Science:
- Cardiovascular disease research
- Histopathology
- Image analysis
Background:
- Cardiovascular disease research frequently involves assessing cardiac function and heart tissue histology.
- Traditional methods for quantifying myocyte disarray rely on manual image assessment, which is subjective and not scalable.
- Automated techniques are needed to handle the large datasets generated by modern experimental methods.
Purpose of the Study:
- To develop an automated image analysis approach for unbiased numerical measurement of myocyte disarray.
- To provide a scalable solution for quantifying cardiac tissue morphology.
- To offer an alternative to subjective manual scoring in cardiovascular research.
Main Methods:
- Development of an automated image analysis pipeline.
- Utilizing image processing techniques for unbiased measurement of myocyte disarray.
- Providing step-by-step instructions and a basic Matlab script for implementation.
Main Results:
- An automated method for unbiased numerical measurement of myocyte disarray was established.
- The approach allows for scalable quantification of cardiac tissue morphology.
- This method overcomes limitations of traditional manual assessment.
Conclusions:
- The automated image analysis approach provides an objective and scalable tool for assessing myocyte disarray.
- This method enhances the efficiency and reliability of cardiovascular disease research.
- The developed Matlab script facilitates the implementation of this automated analysis.
Abstract:
In cardiovascular disease research, studies often include measuring cardiac function and performing histological examination of heart tissue. After measuring contractility, hearts from animals such as mice and rats are often frozen or fixed, sliced, and stained to quantify the morphology of various structures such as extracellular matrix proteins, cell nuclei, and F-actin. Traditional scoring methods have largely consisted of assessing sections of images for the presence or absence of myocyte disarray. These approaches require unbiased manual assessment, which can require extra personnel, and are not scalable to the quantity of data that can be generated by modern automated experimental techniques. Here, we describe an automated image analysis approach for unbiased numerical measurement of myocyte disarray. We provide step-by-step instructions for image preparation as well as a basic Matlab script for measurements. © 2017 by John Wiley & Sons, Inc.