  This paper presents a method for time-lapse 3D cell analysis. Specifically,
we consider the problem of accurately localizing and quantitatively analyzing
sub-cellular features, and for tracking individual cells from time-lapse 3D
confocal cell image stacks. The heterogeneity of cells and the volume of
multi-dimensional images presents a major challenge for fully automated
analysis of morphogenesis and development of cells. This paper is motivated by
the pavement cell growth process, and building a quantitative morphogenesis
model. We propose a deep feature based segmentation method to accurately detect
and label each cell region. An adjacency graph based method is used to extract
sub-cellular features of the segmented cells. Finally, the robust graph based
tracking algorithm using multiple cell features is proposed for associating
cells at different time instances. Extensive experiment results are provided
and demonstrate the robustness of the proposed method. The code is available on
Github and the method is available as a service through the BisQue portal.
