Live-cell imaging in the deep learning era

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

Forskningsoutput: TidskriftsbidragÖversiktsartikelPeer review

5 Citeringar (Scopus)
34 Nedladdningar (Pure)

Sammanfattning

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.

OriginalspråkEngelska
Artikelnummer102271
Antal sidor13
TidskriftCurrent Opinion in Cell Biology
Volym85
DOI
StatusPublicerad - dec. 2023
MoE-publikationstypA2 Granska artikel i en vetenskaplig tidskrift

Fingeravtryck

Fördjupa i forskningsämnen för ”Live-cell imaging in the deep learning era”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här