PUBLICATION

Deep learning enables automated volumetric assessments of cardiac function in zebrafish

Authors
Akerberg, A.A., Burns, C.E., Burns, C.G., Nguyen, C.
ID
ZDB-PUB-190925-9
Date
2019
Source
Disease models & mechanisms   12(10): (Journal)
Registered Authors
Akerberg, Alex, Burns (Erter), Caroline
Keywords
CFIN, Cardiac function, Deep learning., Ejection fraction, Light sheet fluorescence microscopy (LSFM), Zebrafish embryos
MeSH Terms
  • Animals
  • Automation
  • Deep Learning*
  • Embryo, Nonmammalian/diagnostic imaging
  • Embryo, Nonmammalian/physiology
  • Heart/embryology
  • Heart/physiology*
  • Imaging, Three-Dimensional
  • Neural Networks, Computer
  • Reproducibility of Results
  • Zebrafish/embryology
  • Zebrafish/physiology*
PubMed
31548281 Full text @ Dis. Model. Mech.
Abstract
Although the zebrafish embryo is a powerful animal model of human heart failure, the methods routinely employed to monitor cardiac function produce rough approximations that are susceptible to bias and inaccuracies. We developed and validated CFIN (cardiac functional imaging network), a deep learning-based image analysis platform for automated extraction of volumetric parameters of cardiac function from dynamic light sheet fluorescence microscopy images of embryonic zebrafish hearts. CFIN automatically delivers rapid and accurate assessments of cardiac performance with greater sensitivity than current approaches.
Genes / Markers
Figures
Show all Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping