ZFIN ID: ZDB-PUB-190920-6
Machine Learning Methods for Automated Quantification of Ventricular Dimensions
Schutera, M., Just, S., Gierten, J., Mikut, R., Reischl, M., Pylatiuk, C.
Date: 2019
Source: Zebrafish   16(6): 542-545 (Other)
Registered Authors: Just, Steffen, Mikut, Ralf, Pylatiuk, Christian
Keywords: biomedical imaging, deep learning, fractional shortening, medaka, segmentation, zebrafish
MeSH Terms:
  • Animals
  • Heart Ventricles/anatomy & histology*
  • Machine Learning*
  • Oryzias/anatomy & histology*
PubMed: 31536467 Full text @ Zebrafish
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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