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

Super-Resolution Reconstruction of Cytoskeleton Image Based on Deep Learning

Fen Hu1, Yang Lin2, Mengdi Hou1, Haofeng Hu2、*, Leiting Pan1,3,4、**, Tiegen Liu2, and Jingjun Xu1
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
  • show less
    References(35)

    [1] Klar T A, Jakobs S, Dyba M et al. Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission[J]. Proceedings of the National Academy of Sciences of the United States of America, 97, 8206-8210(2000).

    [2] Betzig E, Patterson G H, Sougrat R et al. Imaging intracellular fluorescent proteins at nanometer resolution[J]. Science, 313, 1642-1645(2006).

    [3] Rust M J, Bates M, Zhuang X W. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)[J]. Nature Methods, 3, 793-795(2006).

    [4] Fletcher D A, Mullins R D. Cell mechanics and the cytoskeleton[J]. Nature, 463, 485-492(2010).

    [6] Xu K, Zhong G, Zhuang X. Actin, spectrin, and associated proteins form a periodic cytoskeletal structure in axons[J]. Science, 339, 452-456(2013).

    [7] Pan L T, Yan R, Li W et al. Super-resolution microscopy reveals the native ultrastructure of the erythrocyte cytoskeleton[J]. Cell Reports, 22, 1151-1158(2018).

    [8] Guo Y T, Li D, Zhang S W et al. Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales[J]. Cell, 175, 1430-1442(2018).

    [9] Pan L T, Zhang P, Hu F et al. Hypotonic stress induces fast, reversible degradation of the vimentin cytoskeleton via intracellular calcium release[J]. Advanced Science, 6, 1900865(2019).

    [11] Obermeyer Z, Emanuel E J. Predicting the future-big data, machine learning, and clinical medicine[J]. The New England Journal of Medicine, 375, 1216-1219(2016).

    [12] Camacho D M, Collins K M, Powers R K et al. Next-generation machine learning for biological networks[J]. Cell, 173, 1581-1592(2018).

    [13] Carleo G, Cirac I, Cranmer K et al. Machine learning and the physical sciences[J]. Reviews of Modern Physics, 91, 045002(2019).

    [14] Ramprasad R, Batra R, Pilania G et al. Machine learning in materials informatics: Recent applications and prospects[J]. Npj Computational Materials, 3, 54(2017).

    [15] Guei A C, Akhloufi M. Deep learning enhancement of infrared face images using generative adversarial networks[J]. Applied Optics, 57, D98-D107(2018).

    [17] Guirado E, Tabik S, Rivas M L et al. Whale counting in satellite and aerial images with deep learning[J]. Scientific Reports, 9, 14259(2019).

    [18] Shi J, Liu Q, Wang C et al. Super-resolution reconstruction of MR image with a novel residual learning network algorithm[J]. Physics in Medicine and Biology, 63, 085011(2018).

    [19] Song T A, Chowdhury S R, Yang F et al. Super-resolution PET imaging using convolutional neural networks[J]. IEEE Transactions on Computational Imaging, 6, 518-528(2020).

    [20] Dong C, Loy C C, He K M et al[M]. Learning a deep convolutional network for image super-resolution, 184-199(2014).

    [21] Lai W S, Huang J B, Ahuja N et al. Fast and accurate image super-resolution with deep Laplacian pyramid networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 2599-2613(2019).

    [22] Du X F, Qu X B, He Y F et al. Single image super-resolution based on multi-scale competitive convolutional neural network[J]. Sensors, 18, 789(2018).

    [26] Christiansen E M, Yang S J, Ando D M et al. In silico labeling: Predicting fluorescent labels in unlabeled images[J]. Cell, 173, 792-803(2018).

    [27] Ouyang W, Aristov A, Lelek M et al. Deep learning massively accelerates super-resolution localization microscopy[J]. Nature Biotechnology, 36, 460-468(2018).

    [28] Wu Y, Rivenson Y, Wang H et al. Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning[J]. Nature Methods, 16, 1323-1331(2019).

    [29] Newby J M, Schaefer A M, Lee P T et al. Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D[J]. Proceedings of the National Academy of Sciences, 115, 9026-9031(2018).

    [33] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA., 1132-1140(2017).

    [34] Huang B, Wang W Q, Bates M et al. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy[J]. Science, 319, 810-813(2008).

    [35] Zontak M, Irani M. Internal statistics of a single natural image[C]//IEEE Conference on Computer Vision and Pattern Recognition., 977-984(2011).

    Tools

    Get Citation

    Copy Citation Text

    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

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Jul. 8, 2020

    Accepted: Sep. 15, 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

    Topics