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
-
- Imaging, Three-Dimensional
- Embryo, Nonmammalian/diagnostic imaging
- Embryo, Nonmammalian/physiology
- Automation
- Zebrafish/embryology
- Zebrafish/physiology*
- Reproducibility of Results
- Heart/embryology
- Heart/physiology*
- Deep Learning*
- Animals
- Neural Networks, Computer
- PubMed
- 31548281 Full text @ Dis. Model. Mech.
Citation
Akerberg, A.A., Burns, C.E., Burns, C.G., Nguyen, C. (2019) Deep learning enables automated volumetric assessments of cardiac function in zebrafish. Disease models & mechanisms. 12(10):.
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
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping