Automated Detection and Diameter Estimation for Mouse Mesenteric Artery Using Semantic Segmentation.

Journal: Journal of vascular research

Published Date: Jan 1, 2021

Abstract

BACKGROUND: Pressurized myography is useful for the assessment of small artery structures and function. However, this procedure requires technical expertise for sample preparation and effort to choose an appropriate sized artery. In this study, we developed an automatic artery/vein differentiation and a size measurement system utilizing machine learning algorithms.

Authors

Akinori Higaki

Department of Molecular Cardiovascular Biology and Pharmacology, Ehime University, Graduate School of Medicine, Toon, Ehime, Japan.
Ahmad U M Mahmoud

Hypertension and Vascular Research Unit, Lady Davis Institute for Medical Research, Montreal, Québec, Canada.
Pierre Paradis

Hypertension and Vascular Research Unit, Lady Davis Institute for Medical Research, Montreal, Québec, Canada.
Ernesto L Schiffrin

Hypertension and Vascular Research Unit, Lady Davis Institute for Medical Research, Montreal, Québec, Canada.

Keywords

Animals Arterial Pressure Automation Female Genotype Image Interpretation, Computer-Assisted Machine Learning Male Mesenteric Arteries Mesenteric Veins Mice Mice, Inbred C57BL Mice, Transgenic Microscopy Myography Neural Networks, Computer Phenotype Predictive Value of Tests

External Resources

View on PubMed Access via DOI PubMed (34182554)

Automated Detection and Diameter Estimation for Mouse Mesenteric Artery Using Semantic Segmentation.

Abstract

Authors

Keywords

External Resources

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