PUBLICATION

Machine Learning Methods for Automated Quantification of Ventricular Dimensions

Authors
Schutera, M., Just, S., Gierten, J., Mikut, R., Reischl, M., Pylatiuk, C.
ID
ZDB-PUB-190920-6
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.
Genes / Markers
Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping