Segmentation and classification of cell cycle phases in fluorescence imaging.
Med Image Comput Comput Assist Interv
; 12(Pt 2): 617-24, 2009.
Article
em En
| MEDLINE
| ID: mdl-20426163
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Reconhecimento Automatizado de Padrão
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Interpretação de Imagem Assistida por Computador
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Aumento da Imagem
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Ciclo Celular
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Núcleo Celular
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Microscopia de Fluorescência
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2009
Tipo de documento:
Article