Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos

Amir Mohammad Naderi, Haisong Bu, Jingcheng Su, Mao Hsiang Huang, Khuong Vo, Ramses Seferino Trigo Torres, J. C. Chiao, Juhyun Lee, Michael P.H. Lau, Xiaolei Xu, Hung Cao

Research output: Contribution to journalArticlepeer-review


Zebrafish is a powerful and widely-used model system for a host of biological investigations, including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages; however, the current methods for quantifying and monitoring cardiac functions mainly involve tedious manual work and inconsistent estimations. In this paper, we developed and validated a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, such as ejection fraction (EF) and fractional shortening (FS) from microscopic videos of wildtype and cardiomyopathy mutant zebrafish embryos. Our approach yielded favorable performance with accuracy above 90% compared with manual processing. We used only black and white regular microscopic recordings with frame rates of 5–20 frames per second (fps); thus, the framework could be widely applicable with any laboratory resources and infrastructure. Most importantly, the automatic feature holds promise to enable efficient, consistent, and reliable processing and analysis capacity for large amounts of videos, which can be generated by diverse collaborating teams.

Original languageEnglish (US)
Article number104565
JournalComputers in Biology and Medicine
StatePublished - Aug 2021


  • Cardiomyopathy
  • Deep learning
  • Ejection fraction
  • Heart disease
  • Zebrafish

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


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