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

doi:10.1016/j.tibtech.2017.12.008

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

Department of Biology

Research output: Contribution to journal › Short survey › peer-review

34 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 language	English
Pages (from-to)	649-652
Number of pages	4
Journal	Trends in Biotechnology
Volume	36
Issue number	7
DOIs	https://doi.org/10.1016/j.tibtech.2017.12.008
Publication status	Published - 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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10.1016/j.tibtech.2017.12.008

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title = "Diagnostic Potential of Imaging Flow Cytometry",

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.",

keywords = "deep learning, disease diagnostics, high-content analysis, imaging flow cytometry, translational medicine",

author = "Minh Doan and Ivan Vorobjev and Paul Rees and Andrew Filby and Olaf Wolkenhauer and Goldfeld, {Anne E.} and Judy Lieberman and Natasha Barteneva and Carpenter, {Anne E.} and Holger Hennig",

note = "Funding Information: 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.).",

year = "2018",

month = jul,

doi = "10.1016/j.tibtech.2017.12.008",

language = "English",

volume = "36",

pages = "649--652",

journal = "Trends in Biotechnology",

issn = "0167-7799",

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TY - JOUR

T1 - Diagnostic Potential of Imaging Flow Cytometry

AU - Doan, Minh

AU - Vorobjev, Ivan

AU - Rees, Paul

AU - Filby, Andrew

AU - Wolkenhauer, Olaf

AU - Goldfeld, Anne E.

AU - Lieberman, Judy

AU - Barteneva, Natasha

AU - Carpenter, Anne E.

AU - Hennig, Holger

N1 - Funding Information: 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.).

PY - 2018/7

Y1 - 2018/7

N2 - 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.

AB - 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.

KW - deep learning

KW - disease diagnostics

KW - high-content analysis

KW - imaging flow cytometry

KW - translational medicine

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U2 - 10.1016/j.tibtech.2017.12.008

DO - 10.1016/j.tibtech.2017.12.008

M3 - Short survey

AN - SCOPUS:85041216958

VL - 36

SP - 649

EP - 652

JO - Trends in Biotechnology

JF - Trends in Biotechnology

SN - 0167-7799

IS - 7

ER -