Enhancing Cell Tracking with a Time-Symmetric Deep Learning Approach

TL;DR

This paper introduces a novel deep learning approach for cell tracking in microscopy videos that leverages non-consecutive frame information and learns motion patterns without prior assumptions, improving robustness and generalization.

Contribution

A new time-symmetric deep learning method for cell tracking that does not rely on consecutive frames and learns motion patterns directly from data.

Findings

Outperforms existing state-of-the-art cell tracking methods.

Handles large datasets with artifacts effectively.

Learns cell motion patterns without prior assumptions.

Abstract

The accurate tracking of live cells using video microscopy recordings remains a challenging task for popular state-of-the-art image processing based object tracking methods. In recent years, several existing and new applications have attempted to integrate deep-learning based frameworks for this task, but most of them still heavily rely on consecutive frame based tracking embedded in their architecture or other premises that hinder generalized learning. To address this issue, we aimed to develop a new deep-learning based tracking method that relies solely on the assumption that cells can be tracked based on their spatio-temporal neighborhood, without restricting it to consecutive frames. The proposed method has the additional benefit that the motion patterns of the cells can be learned completely by the predictor without any prior assumptions, and it has the potential to handle a large number of video frames with heavy artifacts. The efficacy of the proposed method is demonstrated through biologically motivated validation strategies and compared against multiple state-of-the-art cell tracking methods.