Machine Learning-Supported Analyses Improve Quantitative Histological Assessments of Amyloid-β Deposits and Activated Microglia.

Journal: Journal of Alzheimer's disease : JAD

PMID: 33337377

Abstract

BACKGROUND: Detailed pathology analysis and morphological quantification is tedious and prone to errors. Automatic image analysis can help to increase objectivity and reduce time. Here, we present the evaluation of the DeePathology STUDIO™ for automatic analysis of histological whole-slide images using machine learning/artificial intelligence.

Authors

Pablo Bascuñana

Department of Neuro-/Pathology, Translational Neurodegeneration Research and Neuropathology Lab, University of Oslo (UiO) and Oslo University Hospital (OUS), Oslo, Norway.
Mirjam Brackhan

Department of Neuro-/Pathology, Translational Neurodegeneration Research and Neuropathology Lab, University of Oslo (UiO) and Oslo University Hospital (OUS), Oslo, Norway.
Jens Pahnke

Department of Neuro-/Pathology, Translational Neurodegeneration Research and Neuropathology Lab, University of Oslo (UiO) and Oslo University Hospital (OUS), Oslo, Norway.

Keywords

Alzheimer Disease Animals Disease Models, Animal Image Processing, Computer-Assisted Machine Learning Mice Mice, Transgenic Microglia Plaque, Amyloid

External Resources

View on PubMed Access via DOI PubMed (33337377)

Machine Learning-Supported Analyses Improve Quantitative Histological Assessments of Amyloid-β Deposits and Activated Microglia.

Abstract

Authors

Keywords

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