Highly accurate and non-invasive cell counts utilizing machine learning
Although quantification of cell numbers is an important method in biological and medical research, measurement of fluorescent stained cell nuclei etc. involves many problems in terms of measurement accuracy and phototoxicity. The NIS.ai functions built into the NIS-Elements software can utilize machine learning to measure cells. In this application note, we demonstrate that the number of cells can be measured with high inference accuracy from diascopic phase contrast images using the NIS.ai function, avoiding the effects of fluorescent reagents and excitation light irradiation.