We propose a fast method to automatically pick protein particles in cryo-EM micrographs, which is now completed manually in practice. Our method is based on Fast R-CNN, with sliding window as the regions proposal solution. To reduce the false positive detections, we set a single class for the major contaminant ice, and pick out all the ice particles in the whole datasets. Tests on the recently-published cryo-EM data of three proteins have demonstrated that our approach can automatically accomplish the human-level particle picking task, and we successfully reduce the test time from 1.5 minutes of previous deep learning method to 2 seconds without any recall or precision losses. Our program is available under the MIT License at https://github.com/xiao1fan/FastParticlePicker.
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5 June 2017
APPLIED MATHEMATICS AND COMPUTER SCIENCE: Proceedings of the 1st International Conference on Applied Mathematics and Computer Science
27–29 January 2017
Rome, Italy
Research Article|
June 05 2017
A fast method for particle picking in cryo-electron micrographs based on fast R-CNN
Yifan Xiao;
Yifan Xiao
a)
1School of Computer Science and Technology,
Tsinghua University
, Beijing, 100084, China
Search for other works by this author on:
Guangwen Yang
Guangwen Yang
b)
1School of Computer Science and Technology,
Tsinghua University
, Beijing, 100084, China
Search for other works by this author on:
AIP Conf. Proc. 1836, 020080 (2017)
Citation
Yifan Xiao, Guangwen Yang; A fast method for particle picking in cryo-electron micrographs based on fast R-CNN. AIP Conf. Proc. 5 June 2017; 1836 (1): 020080. https://doi.org/10.1063/1.4982020
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