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

TL;DR

DLBI introduces a novel deep learning guided Bayesian inference method that significantly improves the speed and accuracy of super-resolution fluorescence microscopy image reconstruction, enabling detailed visualization of biological structures.

Contribution

The paper presents a new approach combining deep learning and Bayesian inference for faster, more accurate super-resolution image reconstruction in fluorescence microscopy.

Findings

Outperforms the state-of-the-art 3B analysis in accuracy and realism.

Achieves over 100 times faster processing speed.

Effective on both real and simulated datasets.

Abstract

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. Here we propose a novel deep learning guided Bayesian inference approach, DLBI, 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 the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster. The main program is available at https://github.com/lykaust15/DLBI