Mitosis cell identification with conditional random fields

Lichen Liang; Xiaobo Zhou; Fuhai Li; Stephen T. Wong; Jeremy Huckins; Randy W. King

doi:10.1109/LSSA.2007.4400872

Mitosis cell identification with conditional random fields

Lichen Liang, Xiaobo Zhou, Fuhai Li, Stephen T. Wong, Jeremy Huckins, Randy W. King

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

24 Scopus citations

Abstract

Correct identification of mitosis phase of individual cells in a large population imaged via time-lapse fluorescence microscopy is important for drug discovery and cell cycle study. The large amount of image data makes manually analysis unrealistic, which calls for automatic systems for mitosis cell identification. The automatic system has to be able to handle two challenges: small size of training samples and datasets obtained under different conditions. Existing methods are rather limited in dealing with these two challenges. The paper introduces a Conditional Random Fields (CRFs) model, which can well handle the two requirements.

Original language	English (US)
Title of host publication	2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	9-12
Number of pages	4
ISBN (Print)	9781424418138
DOIs	https://doi.org/10.1109/LSSA.2007.4400872
State	Published - Jan 1 2007
Event	2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA - Bethesda, MD, United States Duration: Nov 8 2007 → Nov 9 2007

Other

Other	2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA
Country/Territory	United States
City	Bethesda, MD
Period	11/8/07 → 11/9/07

ASJC Scopus subject areas

Computer Science Applications
Information Systems

Access to Document

10.1109/LSSA.2007.4400872

Cite this

Liang, L, Zhou, X, Li, F , Wong, ST, Huckins, J & King, RW 2007, Mitosis cell identification with conditional random fields. in 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA., 4400872, Institute of Electrical and Electronics Engineers Inc., pp. 9-12, 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA, Bethesda, MD, United States, 11/8/07. https://doi.org/10.1109/LSSA.2007.4400872

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abstract = "Correct identification of mitosis phase of individual cells in a large population imaged via time-lapse fluorescence microscopy is important for drug discovery and cell cycle study. The large amount of image data makes manually analysis unrealistic, which calls for automatic systems for mitosis cell identification. The automatic system has to be able to handle two challenges: small size of training samples and datasets obtained under different conditions. Existing methods are rather limited in dealing with these two challenges. The paper introduces a Conditional Random Fields (CRFs) model, which can well handle the two requirements.",

author = "Lichen Liang and Xiaobo Zhou and Fuhai Li and Wong, {Stephen T.} and Jeremy Huckins and King, {Randy W.}",

year = "2007",

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doi = "10.1109/LSSA.2007.4400872",

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booktitle = "2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA",

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AU - Liang, Lichen

AU - Zhou, Xiaobo

AU - Li, Fuhai

AU - Wong, Stephen T.

AU - Huckins, Jeremy

AU - King, Randy W.

PY - 2007/1/1

Y1 - 2007/1/1

N2 - Correct identification of mitosis phase of individual cells in a large population imaged via time-lapse fluorescence microscopy is important for drug discovery and cell cycle study. The large amount of image data makes manually analysis unrealistic, which calls for automatic systems for mitosis cell identification. The automatic system has to be able to handle two challenges: small size of training samples and datasets obtained under different conditions. Existing methods are rather limited in dealing with these two challenges. The paper introduces a Conditional Random Fields (CRFs) model, which can well handle the two requirements.

AB - Correct identification of mitosis phase of individual cells in a large population imaged via time-lapse fluorescence microscopy is important for drug discovery and cell cycle study. The large amount of image data makes manually analysis unrealistic, which calls for automatic systems for mitosis cell identification. The automatic system has to be able to handle two challenges: small size of training samples and datasets obtained under different conditions. Existing methods are rather limited in dealing with these two challenges. The paper introduces a Conditional Random Fields (CRFs) model, which can well handle the two requirements.

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DO - 10.1109/LSSA.2007.4400872

M3 - Conference contribution

SN - 9781424418138

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EP - 12

BT - 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA

Y2 - 8 November 2007 through 9 November 2007

ER -

Mitosis cell identification with conditional random fields

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