Using DeepLabCut as a Real-Time and Markerless Tool for Cardiac Physiology Assessment in Zebrafish

• Published in: Biology (Basel, Switzerland) Vol. 11; no. 8; p. 1243
• Main Authors: Suryanto, Michael Edbert, Saputra, Ferry, Kurnia, Kevin Adi, Vasquez, Ross D., Roldan, Marri Jmelou M., Chen, Kelvin H.-C., Huang, Jong-Chin, Hsiao, Chung-Der
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21.08.2022; MDPI
• Subjects: Artificial intelligence; Automation; cardiac physiology; Cardiovascular system; Danio rerio; Deep learning; DeepLabCut; Edema; Ejection fraction; Electrocardiography; Embryos; Ethanol; Feasibility studies; heart; Heart rate; Labeling; Neural networks; Open source software; pericardium; Physiology; time series analysis; toxicology; Ventricle; Zebrafish; Taiwan; deep learning; zebrafish; DeepLabCut; cardiac physiology
• ISSN: 2079-7737; 2079-7737
• DOI: 10.3390/biology11081243

DeepLabCut (DLC) is a deep learning-based tool initially invented for markerless pose estimation in mammals. In this study, we explored the possibility of adopting this tool for conducting markerless cardiac physiology assessment in an important aquatic toxicology model of zebrafish (Danio rerio). I...