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 multiple biologically motivated validation strategies and compared
against several state-of-the-art cell tracking methods.

基于深度学习的细胞追踪方法，通过细胞的时空领域实现准确追踪，并能学习细胞的运动模式，适用于处理大量带有重要特征的视频帧。