| Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. | |
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MedLine Citation:
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PMID: 16602586 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Quantitative measurement of cell cycle progression in individual cells over time is important in understanding drug treatment effects on cancer cells. Recent advances in time-lapse fluorescence microscopy imaging have provided an important tool to study the cell cycle process under different conditions of perturbation. However, existing computational imaging methods are rather limited in analyzing and tracking such time-lapse datasets, and manual analysis is unreasonably time-consuming and subject to observer variances. This paper presents an automated system that integrates a series of advanced analysis methods to fill this gap. The cellular image analysis methods can be used to segment, classify, and track individual cells in a living cell population over a few days. Experimental results show that the proposed method is efficient and effective in cell tracking and phase identification. |
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Authors:
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Xiaowei Chen; Xiaobo Zhou; Stephen T C Wong |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: IEEE transactions on bio-medical engineering Volume: 53 ISSN: 0018-9294 ISO Abbreviation: IEEE Trans Biomed Eng Publication Date: 2006 Apr |
Date Detail:
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Created Date: 2006-04-10 Completed Date: 2006-05-02 Revised Date: 2009-11-11 |
Medline Journal Info:
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Nlm Unique ID: 0012737 Medline TA: IEEE Trans Biomed Eng Country: United States |
Other Details:
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Languages: eng Pagination: 762-6 Citation Subset: IM |
Affiliation:
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HCNR Center for Bioinformatics, Harvard Medical School, Boston, MA 02115, USA. |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Artificial Intelligence* Cell Cycle Cell Movement* Cell Nucleus / ultrasonography* Humans Image Interpretation, Computer-Assisted / methods* Microscopy, Fluorescence / methods Microscopy, Video / methods* Neoplasms / pathology* Pattern Recognition, Automated / methods* Tumor Cells, Cultured |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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