3D Segmentation,Visualization and Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells using Time-Lapse Fluorescence Microscopy Images

This paper explores the feasibility of automatic 3D segmentation, visualization and quantitative analysis for differentiation activities of mouse embryonic stem cells using time-lapse confocal fluorescence microscopy images. Technical approaches include bilateral filtering, mean-shift segmentation, adaptive thresholding, watershed segmentation, connected component labeling, and video tracking. Our method processes simultaneously two image channels, one for cytoplasm and the other for nuclei. The nucleus images are used to segment 2D and then 3D nuclei and to track each nucleus and calculate velocities of the 3D nucleus. The cytoplasm images are used to help nucleus segmentation and calculate the S/V (surface to volume) ratio of cytoplasm surrounding a nucleus. Volume rendering on the time-lapse fluorescence images generates time-series 3D images for visualizing the dynamic changes of cell velocity and S/V ratios. Using our prototype system, cells with different amount of EGFP fluorescent protein possesses different differentiation activity (velocity and S/V ratio) can be visualized and quantitatively analyzed.