2024/01/10 by Leonor Morgado, Estibaliz Gómez‐de‐Mariscal, Estibaliz Gómez-de-Mariscal +2 · 3 voices · 6 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Physics and Astronomy · #Advanced Fluorescence Microscopy Techniques #Cell Image Analysis Techniques #Image Processing Techniques and Applications #physics.bio-ph #q-bio.QM
paper · pdf · 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.