  We propose a new method of instance-level microtubule (MT) tracking in
time-lapse image series using recurrent attention. Our novel deep learning
algorithm segments individual MTs at each frame. Segmentation results from
successive frames are used to assign correspondences among MTs. This ultimately
generates a distinct path trajectory for each MT through the frames. Based on
these trajectories, we estimate MT velocities. To validate our proposed
technique, we conduct experiments using real and simulated data. We use
statistics derived from real time-lapse series of MT gliding assays to simulate
realistic MT time-lapse image series in our simulated data. This dataset is
employed as pre-training and hyperparameter optimization for our network before
training on the real data. Our experimental results show that the proposed
supervised learning algorithm improves the precision for MT instance velocity
estimation drastically to 71.3% from the baseline result (29.3%). We also
demonstrate how the inclusion of temporal information into our deep network can
reduce the false negative rates from 67.8% (baseline) down to 28.7% (proposed).
Our findings in this work are expected to help biologists characterize the
spatial arrangement of MTs, specifically the effects of MT-MT interactions.
