Learning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images
This work addresses the challenging task of plant cell tracking for biological research, offering incremental improvements over state-of-the-art methods.
The paper tackled the problem of tracking plant cells in unregistered time-lapse microscope images by proposing a learning-based method that uses 3D graphs to exploit tightly packed cell structures, achieving improved tracking accuracy and inference-time on a benchmark dataset.
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.