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Enabled by novel molecular markers, fluorescence microscopy enables the monitoring of multiple cellular functions using live cell assays. Automated image analysis is necessary to monitor such model systems in a high-throughput and high-content environment. Here, we demonstrate the ability to simultaneously track cell cycle phase and cell motion at the single cell level. Using a recently introduced cell cycle marker, we present a set of image analysis tools for automated cell phase analysis of live cells over extended time periods. Our model-based approach enables the characterization of the four phases of the cell cycle G1, S, G2, and M, which enables the study of the effect of inhibitor compounds that are designed to block the replication of cancerous cells in any of the phases. We approach the tracking problem as a spatio-temporal volume segmentation task, where the 2D slices are stacked into a volume with time as the z dimension. The segmentation of the G2 and S phases is accomplished using level sets, and we designed a model-based shape/size constraint to control the evolution of the level set. Our main contribution is the design of a speed function coupled with a fast marching path planning approach for tracking cells across the G1 phase based on the appearance change of the nuclei. The viability of our approach is demonstrated by presenting quantitative results on both controls and cases in which cells are treated with a cell cycle inhibitor.

Original publication

DOI

10.1016/j.media.2008.06.018

Type

Journal article

Journal

Med Image Anal

Publication Date

02/2009

Volume

13

Pages

143 - 155

Keywords

Algorithms, Cell Cycle, Cells, Cultured, Image Enhancement, Image Interpretation, Computer-Assisted, Microscopy, Fluorescence, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Video Recording