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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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Detalles Bibliográficos
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
Descripción
Sumario: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.