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Deep learning enables fast, gentle STED microscopy.

Vahid EbrahimiTill StephanJiah KimPablo CarravillaChristian EggelingStefan JakobsKyu Young Han
Published in: bioRxiv : the preprint server for biology (2023)
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.
Keyphrases
  • deep learning
  • high resolution
  • convolutional neural network
  • artificial intelligence
  • single molecule
  • machine learning
  • high speed
  • optical coherence tomography
  • high throughput
  • oxidative stress
  • fluorescence imaging