Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 135, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2021.104565
Keywords
Zebrafish; Heart disease; Cardiomyopathy; Deep learning; Ejection fraction
Categories
Funding
- NSF CAREER Award [1917105]
- NSF [1936519]
- NIH SBIR [R44OD024874]
- NIH [HL107304, HL081753]
- Dept. of Education [P200A180052]
- China Scholarship Council [CSC201906370239]
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1936519] Funding Source: National Science Foundation
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The study introduces a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, achieving accuracy above 90% and wide applicability with regular resources. It promises efficient processing and analysis capacity for large amounts of videos generated by diverse teams.
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.
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