Instance-Level Microtubule Segmentation Using Recurrent Attention

@article{Masoudi2019InstanceLevelMS,
  title={Instance-Level Microtubule Segmentation Using Recurrent Attention},
  author={Samira Masoudi and Afsaneh Razi and Cameron H. G. Wright and Jay C. Gatlin and Ulas Bagci},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.06006}
}
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… CONTINUE READING
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Key Quantitative Results

  • 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%).

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