Spatio-temporal cell cycle analysis using 3D level set segmentation of unstained nuclei in line scan confocal fluorescence images

Automated analysis of live cells over extended time periods requires both novel assays and automated image analysis algorithms. Among other applications, this is necessary for studying the effect of inhibitor compounds which are designed to block the replication of cancerous cells in a high-throughput environment. Due to their toxicity, fluorescent dyes cannot be used to mark nuclei. Instead, the cell cycle itself may be marked with a fluorescent protein. This paper describes a set of image analysis methods designed to automatically segment nuclei in 2D time-lapse images. Since each nucleus is unstained, it needs to be segmented from the surrounding stained cytoplasm, and since the appearance of each cell depends on its stage in the cell cycle, standard image processing techniques cannot be used for localization. This paper addresses these challenges by segmenting the spatio-temporal volume using level sets. Experimental results show the promise of this approach