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

Optica ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 458 ◽  
Author(s):  
Elias Nehme ◽  
Lucien E. Weiss ◽  
Tomer Michaeli ◽  
Yoav Shechtman
Keyword(s):  
Deep Learning ◽  
Single Molecule ◽  
Super Resolution ◽  
Single Molecule Microscopy ◽  
Resolution Single

scholarly journals Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors

GigaScience ◽  
2018 ◽  
Vol 7 (3) ◽  
Author(s):  
Tomáš Lukeš ◽  
Jakub Pospíšil ◽  
Karel Fliegel ◽  
Theo Lasser ◽  
Guy M Hagen
Keyword(s):  
Growth Factor ◽  
Single Molecule ◽  
Super Resolution ◽  
Growth Factor Receptors ◽  
Single Molecule Microscopy ◽  
Resolution Single

scholarly journals Cortical actin nodes: Their dynamics and recruitment of podosomal proteins as revealed by super-resolution and single-molecule microscopy

PLoS ONE ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. e0188778 ◽  
Author(s):  
Yuki M. Shirai ◽  
Taka A. Tsunoyama ◽  
Nao Hiramoto-Yamaki ◽  
Koichiro M. Hirosawa ◽  
Akihiro C. E. Shibata ◽  
...  
Keyword(s):  
Single Molecule ◽  
Super Resolution ◽  
Single Molecule Microscopy ◽  
Cortical Actin

scholarly journals Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy

2020 ◽  
Author(s):  
Gili Dardikman-Yoffe ◽  
Yonina C. Eldar
Keyword(s):  
Deep Learning ◽  
Single Molecule ◽  
Optical System ◽  
Domain Knowledge ◽  
Explicit Knowledge ◽  
Classical Method ◽  
Super Resolution ◽  
Parameter Determination ◽  
Learning Methods ◽  
Specific Distribution

AbstractThe use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization. However, this is achieved at the cost of lengthy imaging times, limiting temporal resolution. In recent years, a variety of approaches have been suggested to reduce imaging times, ranging from classical optimization and statistical algorithms to deep learning methods. Classical methods often rely on prior knowledge of the optical system and require heuristic adjustment of parameters or do not lead to good enough performance. Deep learning methods proposed to date tend to suffer from poor generalization ability outside the specific distribution they were trained on, and require learning of many parameters. They also tend to lead to black-box solutions that are hard to interpret. In this paper, we suggest combining a recent high-performing classical method, SPARCOM, with model-based deep learning, using the algorithm unfolding approach which relies on an iterative algorithm to design a compact neural network considering domain knowledge. We show that the resulting network, Learned SPARCOM (LSPARCOM), requires far fewer layers and parameters, and can be trained on a single field of view. Nonetheless it yields comparable or superior results to those obtained by SPARCOM with no heuristic parameter determination or explicit knowledge of the point spread function, and is able to generalize better than standard deep learning techniques. It even allows producing a high-quality reconstruction from as few as 25 frames. This is due to a significantly smaller network, which also contributes to fast performance - 5× improvement in execution time relative to SPARCOM, and a full order of magnitudes improvement relative to a leading competing deep learning method (Deep-STORM) when implemented serially. Our results show that we can obtain super-resolution imaging from a small number of high emitter density frames without knowledge of the optical system and across different test sets. Thus, we believe LSPARCOM will find broad use in single molecule localization microscopy of biological structures, and pave the way to interpretable, efficient live-cell imaging in a broad range of settings.

Revealing dynamically-organized receptor ion channel clusters in live cells by a correlated electric recording and super-resolution single-molecule imaging approach

2018 ◽  
Vol 20 (12) ◽  
pp. 8088-8098 ◽  
Author(s):  
Rajeev Yadav ◽  
H. Peter Lu
Keyword(s):  
Ion Channel ◽  
Single Molecule ◽  
Super Resolution ◽  
Live Cells ◽  
Imaging Approach ◽  
Resolution Imaging ◽  
Function Relationship ◽  
Bleaching Step ◽  
Relationship Of ◽  
Resolution Single

Correlating single-molecule fluorescence photo-bleaching step analysis and single-molecule super-resolution imaging, our findings for the clustering effect of the NMDA receptor ion channel on the live cell membranes provide a new and significant understanding of the structure–function relationship of NMDA receptors.

A literature review of localization methods and emergence of deep learning in single-molecule localization microscopy

2020 ◽  
Author(s):  
Anish Mukherjee
Keyword(s):  
Deep Learning ◽  
Single Molecule ◽  
Super Resolution ◽  
Point Sources ◽  
Learning Approaches ◽  
Localization Algorithm ◽  
Localization Microscopy ◽  
Trade Offs ◽  
Emitter Localization ◽  
Single Molecule Localization Microscopy

The quality of super-resolution images largely depends on the performance of the emitter localization algorithm used to localize point sources. In this article, an overview of the various techniques which are used to localize point sources in single-molecule localization microscopy are discussed and their performances are compared. This overview can help readers to select a localization technique for their application. Also, an overview is presented about the emergence of deep learning methods that are becoming popular in various stages of single-molecule localization microscopy. The state of the art deep learning approaches are compared to the traditional approaches and the trade-offs of selecting an algorithm for localization are discussed.

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