Selected Grantee Publications

Deep Learning-Based Framework for Cardiac Function Assessment in Embryonic Zebrafish from Heart Beating Videos

Naderi et al., Computers in Biology and Medicine. 2021.

https://www.sciencedirect.com/science/article/pii/S0010482521003590

Zebrafish is a powerful model system for a host of biological investigations, cardiovascular studies, and genetic screening. However, the current methods for quantifying and monitoring zebrafish cardiac functions involve tedious manual work and inconsistent estimations. Naderi et al. developed 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. The framework could be widely applicable with any laboratory resources, and the automatic feature holds promise to enable efficient, consistent, and reliable processing and analysis capacity. Supported by ORIP (R44OD024874)