Efficient cell segmentation and tracking of developing plant meristem
Analysis of Confocal Laser Scanning Microscopy (CLSM) images is gaining popularity in developmental biology for understanding growth dynamics. The automated analysis of such images is highly desirable for efficiency and accuracy. The first step in this process is segmentation and tracking leading to computation of cell lineages. In this paper, we present efficient, accurate, and robust segmentation and tracking algorithms for cells and detection of cell divisions in a 4D spatio-temporal image stack of a growing plant meristem. We show how to optimally choose the parameters in the watershed algorithm for high quality segmentation results. This yields high quality tracking results using cell correspondence evaluation functions. We show segmentation and tracking results on Confocal laser scanning microscopy data captured for 72 hours at every 3 hour intervals. Compared to recent results in this area, the proposed algorithms provide significantly longer cell lineages and more comprehensive identification of cell divisions.