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={CoRR},
  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
12
Twitter Mentions

Figures, Tables, Results, and Topics from this paper.

Key Quantitative Results

  • 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).

References

Publications referenced by this paper.
SHOWING 1-10 OF 45 REFERENCES

End-to-End Instance Segmentation with Recurrent Attention

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
VIEW 2 EXCERPTS

Medical news today.

E. Harrison
  • [Online]. Available: http: //www.prezi.com/xz88yonibenc/the-malfunction-of-the-microtubules. [Accessed:
  • 2017
VIEW 1 EXCERPT

Similar Papers

Loading similar papers…