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Journal Article

Deep learning enables fast, gentle STED microscopy

MPS-Authors
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Stephan,  Till
Department of NanoBiophotonics, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society;

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Jakobs,  Stefan       
Department of NanoBiophotonics, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society;

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s42003-023-05054-z.pdf
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Author_Correction.pdf
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Citation

Ebrahimi, V., Stephan, T., Kim, J., Carravilla, P., Eggeling, C., Jakobs, S., et al. (2023). Deep learning enables fast, gentle STED microscopy. Communications Biology, 6(1): 674. doi:10.1038/s42003-023-05054-z.


Cite as: https://hdl.handle.net/21.11116/0000-000D-B491-3
Abstract
STED microscopy is widely used to image subcellular structures with super-resolution. Here, we report that restoring 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.