Adaptive optics for structured illumination microscopy based on deep learning

2021 ◽  
Author(s):  
Yao Zheng ◽  
Jiajia Chen ◽  
Chenxue Wu ◽  
Wei Gong ◽  
Ke Si
Keyword(s):  
Deep Learning ◽  
Adaptive Optics ◽  
Structured Illumination ◽  
Structured Illumination Microscopy

Fast structured illumination microscopy via deep learning

2020 ◽  
Vol 8 (8) ◽  
pp. 1350 ◽  
Author(s):  
Chang Ling ◽  
Chonglei Zhang ◽  
Mingqun Wang ◽  
Fanfei Meng ◽  
Luping Du ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structured Illumination ◽  
Structured Illumination Microscopy

scholarly journals Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images

2021 ◽  
Author(s):  
ZAFRAN HUSSAIN SHAH ◽  
Marcel Müller ◽  
TUNG-CHENG WANG ◽  
Philip Scheidig ◽  
Axel Schneider ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Microscopy Images

scholarly journals Full three-dimensional imaging deep through multicellular thick samples with subcellular resolution by structured illumination microscopy and adaptive optics

2020 ◽  
Author(s):  
Ruizhe Lin ◽  
Edward T. Kipreos ◽  
Jie Zhu ◽  
Chang Hyun Khang ◽  
Peter Kner
Keyword(s):  
Adaptive Optics ◽  
Three Dimensional ◽  
Structured Illumination ◽  
Axial Resolution ◽  
Structured Illumination Microscopy ◽  
Optical Aberrations ◽  
C Elegans ◽  
Culture Cells ◽  
Tissue Culture Cells ◽  
Subcellular Resolution

AbstractStructured Illumination Microscopy enables live imaging with resolutions of ~120 nm. Unfortunately, optical aberrations can lead to loss of resolution and artifacts in Structured Illumination Microscopy rendering the technique unusable in samples thicker than a single cell. Here we report on the combination of Adaptive Optics and Structured Illumination Microscopy enabling imaging with 140 nm lateral and 585 nm axial resolution in tissue culture cells, C. elegans, and rice blast fungus. We demonstrate that AO improves resolution and reduces artifacts, making full 3D SIM possible in thicker samples.

scholarly journals Improving axial resolution in Structured Illumination Microscopy using deep learning

2021 ◽  
Vol 379 (2199) ◽  
Author(s):  
Miguel A. Boland ◽  
Edward A. K. Cohen ◽  
Seth R. Flaxman ◽  
Mark A. A. Neil
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Super Resolution ◽  
Structured Illumination ◽  
Learning Models ◽  
Axial Resolution ◽  
Structured Illumination Microscopy ◽  
Recent Advances ◽  
Robust To Noise ◽  
Up Scaling

Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution. This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 1)’.

scholarly journals Dynamic super-resolution structured illumination imaging in the living brain

2019 ◽  
Vol 116 (19) ◽  
pp. 9586-9591 ◽  
Author(s):  
Raphaël Turcotte ◽  
Yajie Liang ◽  
Masashi Tanimoto ◽  
Qinrong Zhang ◽  
Ziwei Li ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Super Resolution ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Optical Aberrations ◽  
Sample Motion ◽  
Heterogeneous Tissue ◽  
Conventional Microscopy ◽  
Super Resolution Microscopy

Cells in the brain act as components of extended networks. Therefore, to understand neurobiological processes in a physiological context, it is essential to study them in vivo. Super-resolution microscopy has spatial resolution beyond the diffraction limit, thus promising to provide structural and functional insights that are not accessible with conventional microscopy. However, to apply it to in vivo brain imaging, we must address the challenges of 3D imaging in an optically heterogeneous tissue that is constantly in motion. We optimized image acquisition and reconstruction to combat sample motion and applied adaptive optics to correcting sample-induced optical aberrations in super-resolution structured illumination microscopy (SIM) in vivo. We imaged the brains of live zebrafish larvae and mice and observed the dynamics of dendrites and dendritic spines at nanoscale resolution.

scholarly journals Deep learning enables structured illumination microscopy with low light levels and enhanced speed

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Luhong Jin ◽  
Bei Liu ◽  
Fenqiang Zhao ◽  
Stephen Hahn ◽  
Bowei Dong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Low Light ◽  
Light Levels

scholarly journals Adaptive optics for structured illumination microscopy

2008 ◽  
Vol 16 (13) ◽  
pp. 9290 ◽  
Author(s):  
Delphine Débarre ◽  
Edward J. Botcherby ◽  
Martin J. Booth ◽  
Tony Wilson
Keyword(s):  
Adaptive Optics ◽  
Structured Illumination ◽  
Structured Illumination Microscopy

scholarly journals Deep Learning Based Image Enhancement using Super Resolution

2021 ◽  
pp. 190-194
Author(s):  
Nihar Das ◽  
Nisarg Sharma ◽  
Vaishnavi Shebare ◽  
Parth Dawda ◽  
Prajakta Gourkhede ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Input Image ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Research Areas ◽  
Adversarial Network ◽  
Biomedical Microscopy ◽  
Microscopy Images

With the ever-growing field of microscopy there is pretty much a necessity of high - resolution microscopic images. A microscope may have powerful magnifying lenses, but if the resolution is poor, the magnified image is just blur and no useful insights can be gained from such images. Traditional techniques like Structured Illumination Microscopy (SIM) are not feasible enough for proper use and current solutions based on deep learning assume that the input image is noise free. Based on our research and existing applications related to deep learning-based image enhancement, our proposed solution of deep learning based General Adversarial Network (GAN), will help jointly denoise and super-resolved microscopy images. Thus, this project has competitive applications in different research areas including biomedical microscopy, medical diagnosis, astronomical research, surveillance or investigation, etc., and many other areas as well.

Sign in / Sign up

Export Citation Format

Share Document