Deep learning enables fast, gentle STED microscopy

2023-01-27 | preprint

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​Deep learning enables fast, gentle STED microscopy​
Ebrahimi, V.; Stephan, T.; Kim, J.; Carravilla, P.; Eggeling, C.; Jakobs, S.  & Han, K. Y.​ (2023). DOI: https://doi.org/10.1101/2023.01.26.525571 

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Authors
Ebrahimi, Vahid; Stephan, Till; Kim, Jiah; Carravilla, Pablo; Eggeling, Christian; Jakobs, Stefan ; Han, Kyu Young
Abstract
STED microscopy is widely used to image subcellular structures with super-resolution. Here, we report that denoising STED images with deep learning can mitigate photobleaching and photodamage by reducing the pixel dwell time by one or two orders of magnitude. Our method allows for efficient and robust restoration of noisy 2D and 3D STED images with multiple targets and facilitates long-term imaging of mitochondrial dynamics.
Issue Date
27-January-2023
Project
SFB 1286: Quantitative Synaptologie 
SFB 1286 | A05: Mitochondriale Heterogenität in Synapsen 
Working Group
RG Jakobs (Structure and Dynamics of Mitochondria) 
External URL
https://sfb1286.uni-goettingen.de/literature/publications/188
Language
English

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