Live-cell imaging in the deep learning era

Joanna W. Pylvänäinen, Estibaliz Gómez-de-Mariscal, Ricardo Henriques, Guillaume Jacquemet

Research output: Contribution to journalReview Article or Literature Reviewpeer-review

5 Citations (Scopus)
34 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number102271
Number of pages13
JournalCurrent Opinion in Cell Biology
Volume85
DOIs
Publication statusPublished - Dec 2023
MoE publication typeA2 Review article in a scientific journal

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