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

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 signi...

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Autores principales: Gratz, Daniel, Winkle, Alexander J., Dalic, Alyssa, Unudurthi, Sathya D., Hund, Thomas J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6931069/
https://www.ncbi.nlm.nih.gov/pubmed/31890644
http://dx.doi.org/10.1016/j.mex.2019.11.028
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author Gratz, Daniel
Winkle, Alexander J.
Dalic, Alyssa
Unudurthi, Sathya D.
Hund, Thomas J.
author_facet Gratz, Daniel
Winkle, Alexander J.
Dalic, Alyssa
Unudurthi, Sathya D.
Hund, Thomas J.
author_sort Gratz, Daniel
collection PubMed
description 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.
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spelling pubmed-69310692019-12-30 Computational tools for automated histological image analysis and quantification in cardiac tissue Gratz, Daniel Winkle, Alexander J. Dalic, Alyssa Unudurthi, Sathya D. Hund, Thomas J. MethodsX Medicine and Dentistry 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. Elsevier 2019-12-07 /pmc/articles/PMC6931069/ /pubmed/31890644 http://dx.doi.org/10.1016/j.mex.2019.11.028 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Medicine and Dentistry
Gratz, Daniel
Winkle, Alexander J.
Dalic, Alyssa
Unudurthi, Sathya D.
Hund, Thomas J.
Computational tools for automated histological image analysis and quantification in cardiac tissue
title Computational tools for automated histological image analysis and quantification in cardiac tissue
title_full Computational tools for automated histological image analysis and quantification in cardiac tissue
title_fullStr Computational tools for automated histological image analysis and quantification in cardiac tissue
title_full_unstemmed Computational tools for automated histological image analysis and quantification in cardiac tissue
title_short Computational tools for automated histological image analysis and quantification in cardiac tissue
title_sort computational tools for automated histological image analysis and quantification in cardiac tissue
topic Medicine and Dentistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6931069/
https://www.ncbi.nlm.nih.gov/pubmed/31890644
http://dx.doi.org/10.1016/j.mex.2019.11.028
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