Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy.
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.
Chen
Xiaowei
X
HCNR Center for Bioinformatics, Harvard Medical School, Boston, MA 02115, USA.
Zhou
Xiaobo
X
Wong
Stephen T C
ST
D001185:Artificial Intelligence; D002453:Cell Cycle; D002465:Cell Movement; D002467:Cell Nucleus / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods; D018715:Microscopy, Video / Q000379:methods*; D009369:Neoplasms / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014407:Tumor Cells, Cultured; D014463:Ultrasonography