Diagnostic Potential of Imaging Flow Cytometry

Minh Doan, Ivan Vorobjev, Paul Rees, Andrew Filby, Olaf Wolkenhauer, Anne E. Goldfeld, Judy Lieberman, Natasha Barteneva, Anne E. Carpenter, Holger Hennig

Research output: Contribution to journalShort survey

31 Citations (Scopus)

Abstract

Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice.

Original languageEnglish
Pages (from-to)649-652
Number of pages4
JournalTrends in Biotechnology
Volume36
Issue number7
DOIs
Publication statusPublished - Jul 2018

Keywords

  • deep learning
  • disease diagnostics
  • high-content analysis
  • imaging flow cytometry
  • translational medicine

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering

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  • Cite this

    Doan, M., Vorobjev, I., Rees, P., Filby, A., Wolkenhauer, O., Goldfeld, A. E., Lieberman, J., Barteneva, N., Carpenter, A. E., & Hennig, H. (2018). Diagnostic Potential of Imaging Flow Cytometry. Trends in Biotechnology, 36(7), 649-652. https://doi.org/10.1016/j.tibtech.2017.12.008