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Zebrafish is a powerful and widely-used model system for a host of biologicalinvestigations including cardiovascular studies and genetic screening.Zebrafish are readily assessable during developmental stages; however, thecurrent methods for quantification and monitoring of cardiac functions mostlyinvolve tedious manual work and inconsistent estimations. In this paper, wedeveloped and validated a Zebrafish Automatic Cardiovascular AssessmentFramework (ZACAF) based on a U-net deep learning model for automated assessmentof cardiovascular indices, such as ejection fraction (EF) and fractionalshortening (FS) from microscopic videos of wildtype and cardiomyopathy mutantzebrafish embryos. Our approach yielded favorable performance with accuracyabove 90% compared with manual processing. We used only black and white regularmicroscopic recordings with frame rates of 5-20 frames per second (fps); thus,the framework could be widely applicable with any laboratory resources andinfrastructure. Most importantly, the automatic feature holds promise to enableefficient, consistent and reliable processing and analysis capacity for largeamounts of videos, which can be generated by diverse collaborating teams.

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