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Published in

Cambridge University Press, Microscopy and Microanalysis, 5(28), p. 1712-1719, 2022

DOI: 10.1017/s1431927622000794

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Machine-Learning-Aided Quantification of Area Coverage of Adherent Cells from Phase-Contrast Images

Journal article published in 2022 by Gal Rosoff, Shir Elkabetz, Levi A. Gheber ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

Abstract The advances in machine learning (ML) software availability, efficiency, and friendliness, combined with the increase in the computation power of personal computers, are harnessed to rapidly and (relatively) effortlessly analyze time-lapse image series of adherent cell cultures, taken with phase-contrast microscopy (PCM). Since PCM is arguably the most widely used technique to visualize adherent cells in a label-free, noninvasive, and nondisruptive manner, the ability to easily extract quantitative information on the area covered by cells, should provide a valuable tool for investigation. We demonstrate two cases, in one we monitor the shrinking of cells in response to a toxicant, and in the second we measure the proliferation curve of mesenchymal stem cells (MSCs).