Research Article
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Published Online: 2 December 2019

Machine Learning Methods for Automated Quantification of Ventricular Dimensions

Publication: Zebrafish
Volume 16, Issue Number 6

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.

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References

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Information & Authors

Information

Published In

cover image Zebrafish
Zebrafish
Volume 16Issue Number 6December 2019
Pages: 542 - 545
PubMed: 31536467

History

Published online: 2 December 2019
Published in print: December 2019
Published ahead of print: 3 October 2019

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Authors

Affiliations

Mark Schutera
Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein, Germany.
Steffen Just
Department of Internal Medicine II, University of Ulm, Ulm, Germany.
Jakob Gierten
Department of Pediatric Cardiology, University Hospital Heidelberg, Heidelberg, Germany.
Centre for Organismal Studies Heidelberg, Heidelberg University, Heidelberg, Germany.
Ralf Mikut
Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein, Germany.
Markus Reischl
Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein, Germany.
Christian Pylatiuk [email protected]
Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein, Germany.

Notes

Address correspondence to: Christian Pylatiuk, MD, Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, Eggenstein 76344, Germany [email protected]

Disclosure Statement

No competing financial interests exist.

Funding Information

This work was supported by the Helmholtz Association program “BioInterfaces in Technology and Medicine - BIFTM”. We thank Joachim Wittbrodt [Centre for Organismal Studies (COS), Heidelberg University] for generous support. Jakob Gehrig is a member of the Heidelberg Biosciences International Graduate School (HBIGS) and was supported by a Research Center for Molecular Medicine (HRCMM) Career Development Fellowship (CDF), the MD/PhD program of the Medical Faculty Heidelberg, the Deutsche Herzstiftung e.V. (S/02/17), and by an Add-On Fellowship for Interdisciplinary Science of the Joachim Herz Stiftung.

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