Deep Learning Approach for Quantification of Fluorescently Labeled Blood Cells in Danio rerio (Zebrafish).

ABSTRACT: Neutrophils are a type of white blood cell essential for the function of the innate immune system. To elucidate mechanisms of neutrophil biology, many studies are performed in vertebrate animal model systems. In Danio rerio (zebrafish), in vivo imaging of neutrophils is possible due to transgenic strains that possess fluorescently labeled leukocytes. However, due to the relative abundance of neutrophils, the counting process is laborious and subjective. In this article, we propose the use of a custom trained "you only look once" (YOLO) machine learning algorithm to automate the identification and counting of fluorescently labeled neutrophils in zebrafish. Using this model, we found the correlation coefficient between human counting and the model equals r = 0.8207 with an 8.65

SUBMITTER: Thapa S 

PROVIDER: S-EPMC8369963 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature