scholarly journals Deep learning-based super-resolution fluorescence microscopy on small datasets

2021 ◽  
Author(s):  
Varun Mannam ◽  
Yide Zhang ◽  
Xiaotong Yuan ◽  
Scott Howard
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Super Resolution

scholarly journals Deep learning enables cross-modality super-resolution in fluorescence microscopy

2018 ◽  
Vol 16 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Hongda Wang ◽  
Yair Rivenson ◽  
Yiyin Jin ◽  
Zhensong Wei ◽  
Ronald Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Super Resolution

scholarly journals Design Principles for Autonomous Illumination Control in Localization Microscopy

2018 ◽  
Author(s):  
Marcel Štefko ◽  
Baptiste Ottino ◽  
Kyle M. Douglass ◽  
Suliana Manley
Keyword(s):  
Deep Learning ◽  
Control System ◽  
Fluorescence Microscopy ◽  
A Priori ◽  
Super Resolution ◽  
Density Estimator ◽  
Localization Microscopy ◽  
Extended Range ◽  
Self Tuning ◽  
Fine Tune

Super-resolution fluorescence microscopy improves spatial resolution, but this comes at a loss of image throughput and presents unique challenges in identifying optimal acquisition parameters. Microscope automation routines can offset these drawbacks, but thus far have required user inputs that presume a priori knowledge about the sample. Here, we develop a flexible illumination control system for localization microscopy comprised of two interacting components that require no sample-specific inputs: a self-tuning controller and a deep learning molecule density estimator that is accurate over an extended range. This system obviates the need to fine-tune parameters and demonstrates the design of modular illumination control for localization microscopy.

Cross-Modality Deep Learning Achieves Super-Resolution in Fluorescence Microscopy

2019 ◽  
Author(s):  
Hongda Wang ◽  
Yair Rivenson ◽  
Yiyin Jin ◽  
Zhensong Wei ◽  
Ronald Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Super Resolution

Deep-learning enables cross-modality super-resolution in fluorescence microscopy (Conference Presentation)

2019 ◽  
Author(s):  
Hongda Wang ◽  
Yair Rivenson ◽  
Yiyin Jin ◽  
Zhensong Wei ◽  
Ronald Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Super Resolution

scholarly journals DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy

2018 ◽  
Vol 34 (13) ◽  
pp. i284-i294 ◽  
Author(s):  
Yu Li ◽  
Fan Xu ◽  
Fa Zhang ◽  
Pingyong Xu ◽  
Mingshu Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bayesian Inference ◽  
Fluorescence Microscopy ◽  
Super Resolution ◽  
Structure Reconstruction

scholarly journals Deep learning achieves super-resolution in fluorescence microscopy

2018 ◽  
Author(s):  
Hongda Wang ◽  
Yair Rivenson ◽  
Yiyin Jin ◽  
Zhensong Wei ◽  
Ronald Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Numerical Models ◽  
Numerical Aperture ◽  
Super Resolution ◽  
Image Resolution ◽  
Stimulated Emission ◽  
Wide Field ◽  
Generative Adversarial Network ◽  
Adversarial Network

AbtsractWe present a deep learning-based method for achieving super-resolution in fluorescence microscopy. This data-driven approach does not require any numerical models of the imaging process or the estimation of a point spread function, and is solely based on training a generative adversarial network, which statistically learns to transform low resolution input images into super-resolved ones. Using this method, we super-resolve wide-field images acquired with low numerical aperture objective lenses, matching the resolution that is acquired using high numerical aperture objectives. We also demonstrate that diffraction-limited confocal microscopy images can be transformed by the same framework into super-resolved fluorescence images, matching the image resolution acquired with a stimulated emission depletion (STED) microscope. The deep network rapidly outputs these super-resolution images, without any iterations or parameter search, and even works for types of samples that it was not trained for.

scholarly journals Deep‐learning based super‐resolution for 3D isotropic coronary MR angiography in less than a minute

2021 ◽  
Author(s):  
Thomas Küstner ◽  
Camila Munoz ◽  
Alina Psenicny ◽  
Aurelien Bustin ◽  
Niccolo Fuin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mr Angiography ◽  
Super Resolution ◽  
Coronary Mr Angiography
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