DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy.

Journal: Bioinformatics (Oxford, England)
Published Date: Jul 1, 2018

Abstract

MOTIVATION: Super-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures.

Authors

  • Yu Li
    Department of Public Health, Shihezi University School of Medicine, 832000, China.
  • Fan Xu
    Department of Public Health, Chengdu Medical College, Sichuan, China.
  • Fa Zhang
    High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Pingyong Xu
    Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
  • Mingshu Zhang
    Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
  • Ming Fan
    Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Lihua Li
    College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China. Electronic address: lilh@hdu.edu.cn.
  • Xin Gao
    Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA.
  • Renmin Han
    King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia.

Keywords

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