Automated Detection and Tracking of Cell Clusters in Time-Lapse Fluorescence Microscopy Images

Abstract

Fluorescence microscopy imaging of live cells has drawn the attention of many researchers in the past decade. Because of the large amount of image data produced by a fluorescence microscope, automated cell detection and tracking algorithms have become an emerging need to help cell biologists visualize and analyze cell kinematics such as changes in cell population caused by proliferation, attachment, and differentiation. This study uses the adaptive background subtraction technique to detect cell clusters in time-lapse fluorescence microscopy images and the discrete Kalman filter to track the position, motion, merging and splitting of these clusters. A set of two-dimensional (2D) time-series images from mouse embryonic stem cells obtained using wide-field microscopy and a set of three-dimensional (3D) time-series from HeLa cells obtained using confocal microscopy are used to demonstrate that biologists can visualize various quantitative measures such as motion, merging and splitting, centroids, areas, and growth rates of cell clusters to acquire useful information through 2D and 3D interfaces.