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Zebrafish. 2019 Oct 3. doi: 10.1089/zeb.2019.1754. [Epub ahead of print]

Machine Learning Methods for Automated Quantification of Ventricular Dimensions.

Author information

1
Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein, Germany.
2
Department of Internal Medicine II, University of Ulm, Ulm, Germany.
3
Department of Pediatric Cardiology, University Hospital Heidelberg, Heidelberg, Germany.
4
Centre for Organismal Studies Heidelberg, Heidelberg University, Heidelberg, Germany.

Abstract

Medaka (Oryzias latipes) and zebrafish (Danio rerio) contribute substantially to our understanding of the genetic and molecular etiology of human cardiovascular diseases. In this context, the quantification of important cardiac functional parameters is fundamental. We have developed a framework that segments the ventricle of a medaka hatchling from image sequences and subsequently quantifies ventricular dimensions.

KEYWORDS:

biomedical imaging; deep learning; fractional shortening; medaka; segmentation; zebrafish

PMID:
31536467
DOI:
10.1089/zeb.2019.1754

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