The rise of data‐driven microscopy powered by machine learning

• Published in: Journal of microscopy (Oxford) Vol. 295; no. 2; pp. 85 - 92
• Main Authors: Morgado, Leonor, Gómez‐de‐Mariscal, Estibaliz, Heil, Hannah S., Henriques, Ricardo
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.08.2024
• Subjects: Data acquisition; Data analysis; data‐driven; Feedback loops; Image acquisition; Image analysis; Image processing; Learning algorithms; Light microscopy; Machine learning; Microscopes; Optical microscopy; Optimization; Phototoxicity; reactive microscopy; Target acquisition; data‐driven; machine learning; reactive microscopy; image analysis
• ISSN: 0022-2720; 1365-2818; 1365-2818
• DOI: 10.1111/jmi.13282

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.