Bioinformatics Advance Access published online on November 7, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm530
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Novel Cell Segmentation and Online SVM for Cell Cycle Phase Identification in Automated Microscopy
1Center for Bioinformatics, Harvard Center for Neurodegeneration and Repair, Harvard Medical School, 3rd floor, 1249 Boylston, Boston, MA 02215, USA, 2Functional and Molecular Imaging Center, Department of Radiology, Brigham and Women's Hospital, One Brigham Circle, 1620 Tremont Street, Boston, MA 02121, USA and, 3Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
*To whom correspondence should be addressed. Stephen T.C. Wong, E-mail: wong{at}crystal.harvard.edu
| Abstract |
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Motivation: Automated identification of cell cycle phases captured via fluorescent microscopy is very important for understanding cell cycle and for drug discovery. In this paper, we propose a novel cell detection method that utilizes both the intensity and shape information of the cell for better segmentation quality. In contrast to conventional off-line learning algorithms, an Online Support Vector Classifier (OSVC) is thus proposed, which removes support vectors from the old model and assigns new training examples weighted according to their importance to accommodate the ever-changing experimental conditions.
Results: We image 3 cell lines using fluorescent microscopy under different experiment conditions, including one treated with taxol Then we segment and classify the cell types into interphase, prophase, metaphase and anaphase. Experimental results show the effectiveness of the proposed system in image segmentation and cell phase identification.
Availability: The software and test data sets are available from the authors.
Contact: wong{at}crystal.harvard.edu
Associate Editor: Dr. Trey Ideker
Received on April 8, 2007; revised on September 12, 2007; accepted on October 16, 2007
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