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Segmentation and Classification of Cell Cycle Phases in Fluorescence Imaging.

Ersoy, Ilker and Bunyak, Filiz and Chagin, Vadim and Cardoso, M Cristina and Palaniappan, Kannappan (2009):
Segmentation and Classification of Cell Cycle Phases in Fluorescence Imaging.
In: Lecture notes in computer science, 5762pp. 617-624, ISSN 0302-9743,
[Online-Edition: http://www.cardoso-lab.org/publications/Ersoy_2009.pdf],
[Article]

Abstract

Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

Item Type: Article
Erschienen: 2009
Creators: Ersoy, Ilker and Bunyak, Filiz and Chagin, Vadim and Cardoso, M Cristina and Palaniappan, Kannappan
Title: Segmentation and Classification of Cell Cycle Phases in Fluorescence Imaging.
Language: English
Abstract:

Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

Journal or Publication Title: Lecture notes in computer science
Volume: 5762
Divisions: 10 Department of Biology > Cell Biology and Epigenetics
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10 Department of Biology
Date Deposited: 06 Mar 2010 16:22
Official URL: http://www.cardoso-lab.org/publications/Ersoy_2009.pdf
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