A2 Refereed review article in a scientific journal
Live-cell imaging in the deep learning era
Authors: Pylvänäinen Joanna W., Gómez-de-Mariscal Estibaliz, Henriques Ricardo, Jacquemet Guillaume
Publication year: 2023
Journal: Current Opinion in Cell Biology
Journal name in source: Current opinion in cell biology
Journal acronym: Curr Opin Cell Biol
Volume: 85
ISSN: 0955-0674
eISSN: 1879-0410
DOI: https://doi.org/10.1016/j.ceb.2023.102271
Web address : https://doi.org/10.1016/j.ceb.2023.102271
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/181772641
Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity. Yet, due to critical challenges (i.e., drift, phototoxicity, dataset size), implementing live imaging and analyzing the resulting datasets is rarely straightforward. Over the past years, the development of bioimage analysis tools, including deep learning, is changing how we perform live imaging. Here we briefly cover important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis. We also cover recent advances in self-driving microscopy.
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