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  • Received: Jul. 8, 2020

    Accepted: Sep. 15, 2020

    Posted: Dec. 1, 2020

    Published Online: Nov. 23, 2020

    The Author Email: Hu Haofeng (haofeng_hu@tju.edu.cn), Pan Leiting (plt@nankai.edu.cn)

    DOI: 10.3788/AOS202040.2410001

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    Fen Hu, Yang Lin, Mengdi Hou, Haofeng Hu, Leiting Pan, Tiegen Liu, Jingjun Xu. Super-Resolution Reconstruction of Cytoskeleton Image Based on Deep Learning[J]. Acta Optica Sinica, 2020, 40(24): 2410001

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Acta Optica Sinica, Vol. 40, Issue 24, 2410001 (2020)

Super-Resolution Reconstruction of Cytoskeleton Image Based on Deep Learning

Hu Fen1, Lin Yang2, Hou Mengdi1, Hu Haofeng2,*, Pan Leiting1,3,4,**, Liu Tiegen2, and Xu Jingjun1

Author Affiliations

  • 1Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics, TEDA Applied Physics School, Nankai University, Tianjin 300071, China
  • 2Key Laboratory of Opto-Electronics Information Technology, Ministry of Education, School of Precision Instrument & Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China;
  • 3State Key Laboratory of Medicinal Chemical Biology, College of Life Sciences, Nankai University, Tianjin 300071, China
  • 4Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 0 30006, China

Abstract

Super-resolution microscopy techniques invented at the beginning of the 21 st century provide unprecedented access to life science researches owing to its impressive ability of studying subcellular structures at the micrometer and nanometer scales. However, these techniques often require high cost of time and money. Recently, many researchers work on super-resolution image reconstruction algorithms based on deep learning. Herein, we obtained the super-resolution images of cell microtubule cytoskeletons by the self-built stochastic optical reconstruction microscopy (STORM), and then the bilinear interpolation down-sampling method was used to obtain the low-resolution input atlas. The traditional cubic spline interpolation method and the enhanced depth super-resolution neural network were used for learning and training to realize the super-resolution reconstruction of the low-resolution image. Results show that the effects of all kinds of down-sampling images reconstructed by deep learning are better than those obtained by traditional interpolation method; the super-resolution images of microtubule skeletons obtained by double down-sampling and experiments are comparable in subjective and objective evaluation indexes. Based on the enhanced depth super-resolution neural network, the super-resolution reconstruction of cytoskeleton images is expected to provide a simple, effective, and cost-effective imaging method, which can be applied to the rapid prediction of cytoskeleton super-microstructures.

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