A deep learning workflow for quantification of Micronuclei in DNA damage studies in cultured cancer cell lines: a proof of principle investigation

Abstract

The cytokinesis block micronucleus assay is widely used for measuring/scoring/counting micronuclei, a marker of genome instability in cultured and primary cells. Though a gold standard method, this is a laborious and time-consuming process with person-to-person variation observed in quantification of micronuclei. We report in this study the utilisation of a new deep learning workflow for detection of micronuclei in DAPI stained nuclear images. The proposed deep learning framework achieved an average precision of >90% in detection of micronuclei. This proof of principle investigation in a DNA damage studies laboratory supports the idea of deploying AI powered tools in a cost-effective manner for repetitive and laborious tasks with relevant computational expertise. These systems will also help improving the quality of data and wellbeing of researchers. Simple SummaryThis study aims to test a suitable deep learning method for micronucleus detection in images acquired for cytokinesis block micronucleus assay. This study has reached a mean average precision of >90%.