  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 quantification and monitoring of cardiac functions mostly
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.
