Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy.
BACKGROUND
Automated identification of cell cycle phases captured via fluorescent microscopy is very important for understanding cell cycle and for drug discovery. In this article, 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 three 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.


BACKGROUND
The software and test datasets are available from the authors.
Wang
Meng
M
Center for Bioinformatics, Harvard Center for Neurodegeneration and Repair, Harvard Medical School, 3rd floor, 1249 Boylston, Boston, MA 02215, USA.
Zhou
Xiaobo
X
Li
Fuhai
F
Huckins
Jeremy
J
King
Randall W
RW
Wong
Stephen T C
ST
D000465:Algorithms; D001185:Artificial Intelligence; D002453:Cell Cycle; D002467:Cell Nucleus / Q000502:physiology / Q000648:ultrastructure*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*