Enhancing Cell Tracking with a Time-Symmetric Deep Learning Approach
This addresses the problem of generalized cell tracking for biologists, but it appears incremental as it builds on existing deep learning frameworks with a specific architectural change.
The paper tackled the challenge of accurately tracking live cells in video microscopy by developing a deep learning method that uses spatio-temporal neighborhoods without relying on consecutive frames, and demonstrated its efficacy through validation against state-of-the-art methods.
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.