  Quantifying cardiovascular parameters like ejection fraction in zebrafish as
a host of biological investigations has been extensively studied. Since current
manual monitoring techniques are time-consuming and fallible, several image
processing frameworks have been proposed to automate the process. Most of these
works rely on supervised deep-learning architectures. However, supervised
methods tend to be overfitted on their training dataset. This means that
applying the same framework to new data with different imaging setups and
mutant types can severely decrease performance. We have developed a Zebrafish
Automatic Cardiovascular Assessment Framework (ZACAF) to quantify the cardiac
function in zebrafish. In this work, we further applied data augmentation,
Transfer Learning (TL), and Test Time Augmentation (TTA) to ZACAF to improve
the performance for the quantification of cardiovascular function
quantification in zebrafish. This strategy can be integrated with the available
frameworks to aid other researchers. We demonstrate that using TL, even with a
constrained dataset, the model can be refined to accommodate a novel microscope
setup, encompassing diverse mutant types and accommodating various video
recording protocols. Additionally, as users engage in successive rounds of TL,
the model is anticipated to undergo substantial enhancements in both
generalizability and accuracy. Finally, we applied this approach to assess the
cardiovascular function in nrap mutant zebrafish, a model of cardiomyopathy.
