Super-resolution microscopy demystified

Nature Cell Biology, Journal Year: 2018, Volume and Issue: 21(1), P. 72 - 84

Published: Dec. 17, 2018

Language: Английский

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Content-aware image restoration: pushing the limits of fluorescence microscopy

Nature Methods, Journal Year: 2018, Volume and Issue: 15(12), P. 1090 - 1097

Published: Nov. 19, 2018

Language: Английский

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Inference in artificial intelligence with deep optics and photonics

Nature, Journal Year: 2020, Volume and Issue: 588(7836), P. 39 - 47

Published: Dec. 2, 2020

Language: Английский

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Single-molecule localization microscopy

Nature Reviews Methods Primers, Journal Year: 2021, Volume and Issue: 1(1)

Published: June 3, 2021

Single-molecule localization microscopy (SMLM) describes a family of powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited and can image biological structures at the molecular scale. In SMLM, individual fluorescent molecules are computationally localized from sequences localizations used to generate super-resolution or time course images, define trajectories. this Primer, we introduce basic principles SMLM before describing main experimental considerations when performing including labelling, sample preparation, hardware requirements acquisition in fixed live cells. We then explain how low-resolution processed reconstruct images and/or extract quantitative information, highlight selection discoveries enabled by closely related methods. discuss some limitations potential artefacts as well ways alleviate them. Finally, present an outlook on advanced promising new developments fast-evolving field SMLM. hope Primer will be useful reference for both newcomers practitioners

Language: Английский

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Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction

Nature Methods, Journal Year: 2019, Volume and Issue: 16(12), P. 1215 - 1225

Published: July 8, 2019

Language: Английский

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Deep learning in nano-photonics: inverse design and beyond

Photonics Research, Journal Year: 2021, Volume and Issue: 9(5), P. B182 - B182

Published: Jan. 29, 2021

Deep learning in the context of nano-photonics is mostly discussed terms its potential for inverse design photonic devices or nanostructures. Many recent works on machine-learning are highly specific, and drawbacks respective approaches often not immediately clear. In this review we want therefore to provide a critical capabilities deep progress which has been made so far. We classify different learning-based at higher level as well by their applications critically discuss strengths weaknesses. While significant part community's attention lies nano-photonic design, evolved tool large variety applications. The second will focus machine research "beyond design". This spans from physics informed neural networks tremendous acceleration photonics simulations, over sparse data reconstruction, imaging "knowledge discovery" experimental

Language: Английский

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PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning

Light Science & Applications, Journal Year: 2019, Volume and Issue: 8(1)

Published: Feb. 6, 2019

Abstract Using a deep neural network, we demonstrate digital staining technique, which term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into that are equivalent brightfield microscopy same samples histologically stained. Through pairs image data (QPI and corresponding images, acquired after staining), train generative adversarial network effectiveness this virtual-staining approach using human skin, kidney, liver tissue, matching stained with Hematoxylin Eosin, Jones’ stain, Masson’s trichrome respectively. This digital-staining framework may further strengthen various uses QPI techniques in pathology applications biomedical research general, by eliminating need for histological staining, reducing sample preparation related costs saving time. Our results provide powerful example some unique opportunities created data-driven transformations enabled learning.

Language: Английский

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Faster, sharper, and deeper: structured illumination microscopy for biological imaging

Nature Methods, Journal Year: 2018, Volume and Issue: 15(12), P. 1011 - 1019

Published: Nov. 15, 2018

Language: Английский

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Machine learning and applications in ultrafast photonics

Nature Photonics, Journal Year: 2020, Volume and Issue: 15(2), P. 91 - 101

Published: Nov. 30, 2020

Language: Английский

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Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software

Nature Methods, Journal Year: 2019, Volume and Issue: 16(5), P. 387 - 395

Published: April 8, 2019

Language: Английский

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