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 journalArticlepeer-review

126 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

Funding

This work was supported in part by the US National Science Foundation/UK Biotechnology and Biological Sciences Research Council under a joint grant NSF DBI 1458626 and BB/N005163 (A.E.C. and P.R.).

Keywords

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

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering

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