  Tracking of plant cells in images obtained by microscope is a challenging
problem due to biological phenomena such as large number of cells, non-uniform
growth of different layers of the tightly packed plant cells and cell division.
Moreover, images in deeper layers of the tissue being noisy and unavoidable
systemic errors inherent in the imaging process further complicates the
problem. In this paper, we propose a novel learning-based method that exploits
the tightly packed three-dimensional cell structure of plant cells to create a
three-dimensional graph in order to perform accurate cell tracking. We further
propose novel algorithms for cell division detection and effective
three-dimensional registration, which improve upon the state-of-the-art
algorithms. We demonstrate the efficacy of our algorithm in terms of tracking
accuracy and inference-time on a benchmark dataset.
