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
Citation
Schutera, M., Just, S., Gierten, J., Mikut, R., Reischl, M., Pylatiuk, C. (2019) Machine Learning Methods for Automated Quantification of Ventricular Dimensions. Zebrafish. 16(6):542-545.
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
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping