Deep learning enables fast, gentle STED microscopy
Vahid Ebrahimi, Till Stephan, Jiah Kim, Pablo Carravilla, Christian Eggeling, Stefan Jakobs, Kyu Young Han
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.
Topics & Concepts
STED microscopyMicroscopyNanotechnologyComputer scienceMaterials scienceArtificial intelligenceOpticsPhysicsSuperresolutionImage (mathematics)Advanced Electron Microscopy Techniques and ApplicationsAdvanced Fluorescence Microscopy TechniquesImage Processing Techniques and Applications