A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä
Live-cell imaging in the deep learning era
Tekijät: Pylvänäinen Joanna W., Gómez-de-Mariscal Estibaliz, Henriques Ricardo, Jacquemet Guillaume
Julkaisuvuosi: 2023
Journal: Current Opinion in Cell Biology
Tietokannassa oleva lehden nimi: Current opinion in cell biology
Lehden akronyymi: Curr Opin Cell Biol
Vuosikerta: 85
ISSN: 0955-0674
eISSN: 1879-0410
DOI: https://doi.org/10.1016/j.ceb.2023.102271
Verkko-osoite: https://doi.org/10.1016/j.ceb.2023.102271
Rinnakkaistallenteen osoite: 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
