Deep-STORM: super-resolution single-molecule microscopy by deep learning

By Elias Nehme, Lucien E. Weiss, Tomer Michaeli, Yoav Shechtman Department of Electrical Engineering, Technion, Haifa, Israel Department of Biomedical Engineering, Israel. 2018

531 citations recorded by the source corpus.

We present an ultra-fast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically-blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities, and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking data-set. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data.

Subfields: Biophysics, Media Technology, Structural Biology

View Paper: https://www.semanticscholar.org/paper/1fb42852a47f77d26efdbacbf75cddcb294b5c3d

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Citations in the corpus

Democratising deep learning for microscopy with ZeroCostDL4Mic (2021, methodology): “Within ZeroCostDL4Mic, we implemented Deep-STORM 29 , a DL network capable of reconstructing single-molecule localisation microscopy (SMLM) data from dense Training Deep-STORM requires raw SMLM data accompanied by ground-truth localisation coordinates.”