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^*

^*Corresponding author for this work

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

131 Scopus citations

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
State	Published - Jul 2018
Externally published	Yes

Keywords

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

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10.1016/j.tibtech.2017.12.008

Cite this

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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 = "Publisher Copyright: {\textcopyright} 2018 The Authors",

year = "2018",

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

language = "English",

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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

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

UR - https://www.scopus.com/pages/publications/85041216958

U2 - 10.1016/j.tibtech.2017.12.008

DO - 10.1016/j.tibtech.2017.12.008

M3 - Short survey

C2 - 29395345

AN - SCOPUS:85041216958

SN - 0167-7799

VL - 36

SP - 649

EP - 652

JO - Trends in Biotechnology

JF - Trends in Biotechnology

IS - 7

ER -

Diagnostic Potential of Imaging Flow Cytometry

Abstract

Keywords

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