Computational tools for automated histological image analysis and quantification in cardiac tissue

[Display omitted] Image processing and quantification is a routine and important task across disciplines in biomedical research. Understanding the effects of disease on the tissue and organ level often requires the use of images, however the process of interpreting those images into data which can b...

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Bibliographic Details
Published in:MethodsX 2020-01, Vol.7, p.22-34, Article 100755
Main Authors: Gratz, Daniel, Winkle, Alexander J., Dalic, Alyssa, Unudurthi, Sathya D., Hund, Thomas J.
Format: Article
Language:English
Subjects:
Fibrosis
Heart failure
Histology
Image processing
Macrophage
Medicine and Dentistry
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Summary:[Display omitted] Image processing and quantification is a routine and important task across disciplines in biomedical research. Understanding the effects of disease on the tissue and organ level often requires the use of images, however the process of interpreting those images into data which can be tested for significance is often time intensive, tedious and prone to inaccuracy or bias. When working within resource constraints, these different issues often present a trade-off between time invested in analysis and accuracy. To address these issues, we present two novel open source and publically available tools for automated analysis of histological cardiac tissue samples: •Automated Fibrosis Analysis Tool (AFAT) for quantifying fibrosis; and•Macrophage Analysis Tool (MAT) for quantifying infiltrating macrophages.
ISSN:2215-0161
2215-0161
DOI:10.1016/j.mex.2019.11.028