Pseudo-nuclear staining of cells by deep learning improves the accuracy of automated cell counting in a label-free cellular population

• Published in: Journal of bioscience and bioengineering Vol. 131; no. 2; pp. 213 - 218
• Main Authors: Tsuzuki, Yuji, Sanami, Sho, Sugimoto, Kenji, Fujita, Satoshi
• Format: Journal Article
• Language: English
• Published: Japan Elsevier B.V 01.02.2021
• Subjects: Automatic process; Automation; Cell Count - methods; Cell counting; Deep Learning; Deep neural network; Humans; Microscopy; Nuclei detection; Stain-free cells; Staining and Labeling; Cell counting; Stain-free cells; Deep neural network; Nuclei detection; Automatic process
• ISSN: 1389-1723; 1347-4421
• DOI: 10.1016/j.jbiosc.2020.09.014

Deep learning has emerged as a breakthrough tool for the segmentation of images without supporting human experts. Here, we propose an automated approach that uses deep learning to generate pseudo-nuclear staining of cells from phase contrast images. Our proposed approach, which has the feature to generate pseudo-nuclear stained images by simple DNN, showed remarkable advantages over existing approaches in the cell-detection and the detection of the relative position of cells for various cell densities, as well as in counting the exact cell numbers. Pseudo-nuclear staining of cells by deep learning will improve the accuracy of automated cell counting in a label-free cellular population on phase contrast images.