A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning

Abstract

Abstract Virological plaque assay is the major method of detecting and quantifying infectious viruses in research and diagnostic samples. Furthermore, viral plaque phenotypes contain information about the life cycle and spreading mechanism of the virus forming them. While some modernisations have been proposed, the conventional assay typically involves manual quantification of plaque phenotypes, which is both laborious and time-consuming. Here, we present an annotated dataset of digital photographs of plaque assay plates of Vaccinia virus - a prototypic propoxvirus. We demonstrate how analysis of these plates can be performed using deep learning by training models based on the leading architecture for biomedical instance segmentation - StarDist. Finally, we show that the entire analysis can be achieved in a single step by HydraStarDist - the modified architecture we propose.