Learning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images

TL;DR

This paper introduces a learning-based 3D graph method for tracking plant cells in time-lapse microscopy images, addressing challenges like cell division, non-uniform growth, and noise to improve accuracy and efficiency.

Contribution

It presents a novel 3D graph-based approach with new algorithms for cell division detection and registration, advancing the state-of-the-art in plant cell tracking.

Findings

Improved tracking accuracy on benchmark datasets

Faster inference time compared to existing methods

Effective detection of cell division events

Abstract

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.