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

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.

Results

Here, we propose a novel deep learning guided Bayesian inference (DLBI) approach, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of th

SUBMITTER: Li Y 

PROVIDER: S-EPMC6022599 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature