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

Gal Rosoff, Shir Elkabetz, Levi A. Gheber

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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).

Original languageEnglish
Pages (from-to)1712-1719
Number of pages8
JournalMicroscopy and Microanalysis
Issue number5
StatePublished - 29 Oct 2022


  • cell coverage
  • machine learning
  • phase contrast
  • proliferation

ASJC Scopus subject areas

  • Instrumentation


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