Application of image-recognition techniques to automated micronucleus detection in the in vitro micronucleus assay

Abstract

Background An in vitro micronucleus assay is a standard genotoxicity test. Although the technique and interpretation of the results are simple, manual counting of the total and micronucleus-containing cells in a microscopic field is tedious. To address this issue, several systems have been developed for quick and efficient micronucleus counting, including flow cytometry and automated detection based on specialized software and detection systems that analyze images. Results Here, we present a simple and effective method for automated micronucleus counting using image recognition technology. Our process involves separating the RGB channels in a color micrograph of cells stained with acridine orange. The cell nuclei and micronuclei were detected by scaling the G image, whereas the cytoplasm was recognized from a composite image of the R and G images. Finally, we identified cells with overlapping cytoplasm and micronuclei as micronucleated cells, and the application displayed the number of micronucleated cells and the total number of cells. Our method yielded results that were comparable to manually measured values. Conclusions Our micronucleus detection (MN/cell detection software) system can accurately detect the total number of cells and micronucleus-forming cells in microscopic images with the same level of precision as achieved through manual counting. The accuracy of micronucleus numbers depends on the cell staining conditions; however, the software has options by which users can easily manually optimize parameters such as threshold, denoise, and binary to achieve the best results. The optimization process is easy to handle and requires less effort, making it an efficient way to obtain accurate results.