Advancing biological super-resolution microscopy through deep learning: a brief review

TL;DR

This review discusses how deep learning techniques are revolutionizing super-resolution microscopy by improving image reconstruction, addressing technical challenges, and enabling new biological insights at nanometer resolution.

Contribution

It provides a comprehensive overview of recent deep learning advancements in super-resolution microscopy, highlighting technical challenges and future prospects.

Findings

Deep learning significantly enhances super-resolution image reconstruction.

Deep learning addresses key technical challenges in microscopy.

Future developments will further integrate deep learning into microscopy.

Abstract

Super-resolution microscopy overcomes the diffraction limit of conventional light microscopy in spatial resolution. By providing novel spatial or spatio-temporal information on biological processes at nanometer resolution with molecular specificity, it plays an increasingly important role in life sciences. However, its technical limitations require trade-offs to balance its spatial resolution, temporal resolution, and light exposure of samples. Recently, deep learning has achieved breakthrough performance in many image processing and computer vision tasks. It has also shown great promise in pushing the performance envelope of super-resolution microscopy. In this brief Review, we survey recent advances in using deep learning to enhance performance of super-resolution microscopy. We focus primarily on how deep learning ad-vances reconstruction of super-resolution images. Related key technical challenges are discussed. Despite the challenges, deep learning is set to play an indispensable and transformative role in the development of super-resolution microscopy. We conclude with an outlook on how deep learning could shape the future of this new generation of light microscopy technology.