We propose a new deep learning algorithm for multiple microtubule (MT) segmentation in time-lapse images using the recurrent attention.  Segmentation results from each pair of succeeding frames are being fed into a Hungarian algorithm to assign correspondences among MTs to generate a distinct path  through the frames. Based on the obtained trajectories, we calculate MT velocities. Results of this work is expected to help biologists to characterize MT behaviors as well as their potential interactions. To validate our  technique, we first use the statistics derived from the real time-lapse series of MT gliding assays to produce a large set of simulated data. We employ this dataset to train our network and optimize its hyperparameters. Then, we utilize the trained model to initialize the network while learning about the real data. Our experimental results show that the proposed  algorithm improves the precision for MT instance velocity estimation  to 71.3\% from the baseline result (29.3\%). We also demonstrate how the injection of temporal information into our  network can reduce the false negative rates from 67.8\% (baseline) down to 28.7\% (proposed)  .
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 .
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deep learning algorithm for multiple
method of instance-level
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segmentation
tracking
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images using the
image series using
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Our novel deep learning algorithm segments individual MTs at each frame.
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each pair of succeeding frames are being fed into a Hungarian algorithm
successive frames are used
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to generate
. This ultimately generates
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trajectory for each MT
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the obtained
these
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calculate
estimate
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Results of this work is expected to help biologists to characterize MT behaviors as well as their potential interactions.
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proposed
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first use the
conduct experiments using real and simulated data. We use
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the
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produce a large set of
simulate realistic MT time-lapse image series in our
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We employ this dataset to train our network and optimize its hyperparameters. Then, we utilize the trained model to initialize the network while learning about the
This dataset is employed as pre-training and hyperparameter optimization for our network before training on the
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supervised learning
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drastically
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injection
inclusion
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deep
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. Our findings in this work are expected to help biologists characterize the spatial arrangement of MTs, specifically the effects of MT-MT interactions