Key result
An automated image analysis approach using a Matlab script enables unbiased, scalable numerical measurement of myocyte disarray in animal models of heart disease.
This automated image analysis method provides a scalable, unbiased approach to quantifying myocyte disarray in preclinical cardiovascular research.
Supports standardized quantification in animal models; leaves open clinical translation and validation.
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.
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Wan et al. (2017) studied Heart Disease (animal models). Automated image analysis approach vs. Traditional manual scoring methods was evaluated on Measurement of myocyte disarray. An automated image analysis approach using a Matlab script enables unbiased, scalable numerical measurement of myocyte disarray in animal models of heart disease.
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