Machine Learning Methods for Automated Quantification of Ventricular Dimensions.

Journal: Zebrafish
Published Date: Dec 1, 2019

Abstract

Medaka () and zebrafish () 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.

Authors

  • 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.
  • Ralf Mikut
    Institute for Applied Computer Science, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.
  • Markus Reischl
    Institut für Automation und angewandte Informatik, Karlsruher Institut für Technologie, Eggenstein-Leopoldshafen.
  • Christian Pylatiuk
    Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein, Germany.

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