The Rise of Data-Driven Microscopy powered by Machine Learning

TL;DR

This paper reviews how machine learning enhances data-driven microscopy by enabling real-time image analysis and adaptive imaging, leading to more efficient and versatile optical microscopy techniques.

Contribution

It provides a comprehensive overview of machine learning methods applied to microscopy, highlighting recent advances and future challenges in the field.

Findings

Machine learning enables real-time image analysis in microscopy.

Adaptive imaging techniques improve resolution and reduce phototoxicity.

Integration of ML into microscopy workflows opens new experimental possibilities.

Abstract

Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field-of-view, and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real-time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse, and adapt promise to transform optical imaging by opening new experimental possibilities.