Saturday, August 13, 2022
SCIENMAG: Latest Science and Health News
No Result
View All Result
  • Login
  • HOME PAGE
  • BIOLOGY
  • CHEMISTRY AND PHYSICS
  • MEDICINE
    • Cancer
    • Infectious Emerging Diseases
  • SPACE
  • TECHNOLOGY
  • CONTACT US
  • HOME PAGE
  • BIOLOGY
  • CHEMISTRY AND PHYSICS
  • MEDICINE
    • Cancer
    • Infectious Emerging Diseases
  • SPACE
  • TECHNOLOGY
  • CONTACT US
No Result
View All Result
Scienmag - Latest science news from science magazine
No Result
View All Result
Home SCIENCE NEWS Chemistry AND Physics

Recurrent neural network advances 3D fluorescence imaging

March 23, 2021
in Chemistry AND Physics
0
Share on FacebookShare on Twitter

IMAGE

Credit: by Ozcan Lab, UCLA

Rapid 3D microscopic imaging of fluorescent samples has gained increasing importance in numerous applications in physical and biomedical sciences. Given the limited axial range that a single 2D image can provide, 3D fluorescence imaging often requires time-consuming mechanical scanning of samples using a dense sampling grid. In addition to being slow and tedious, this approach also introduces additional light exposure on the sample, which might be toxic and cause unwanted damage, such as photo-bleaching.

By devising a new recurrent neural network, UCLA researchers have demonstrated a deep learning-enabled volumetric microscopy framework for 3D imaging of fluorescent samples. This new method only requires a few 2D images of the sample to be acquired for reconstructing its 3D image, providing ~30-fold reduction in the number of scans required to image a fluorescent volume. The convolutional recurrent neural network that is at the heart of this 3D fluorescence imaging method intuitively mimics the human brain in processing information and storing memories, by consolidating frequently appearing and important object information and features, while forgetting or ignoring some of the redundant information. Using this recurrent neural network scheme, UCLA researchers successfully incorporated spatial features from multiple 2D images of a sample to rapidly reconstruct its 3D fluorescence image.

Published in Light: Science and Applications, the UCLA team demonstrated the success of this volumetric imaging framework using fluorescent C. Elegans samples, which are widely used as a model organism in biology and bioengineering. Compared with standard wide-field volumetric imaging that involves densely scanning of samples, this recurrent neural network-based image reconstruction approach provides a significant reduction in the number of required image scans, which also lowers the total light exposure on the sample. These advances offer much higher imaging speed for observing 3D specimen, while also mitigating photo-bleaching and phototoxicity related challenges that are frequently observed in 3D fluorescence imaging experiments of live samples.

###

This research is led by Professor Aydogan Ozcan, an associate director of the UCLA California NanoSystems Institute (CNSI) and the Volgenau Chair for Engineering Innovation at the electrical and computer engineering department at UCLA. The other authors include graduate students Luzhe Huang, Hanlong Chen, Yilin Luo and Professor Yair Rivenson, all from electrical and computer engineering department at UCLA. Ozcan also has UCLA faculty appointments in bioengineering and surgery, and is an HHMI professor.

Media Contact
Aydogan Ozcan
[email protected]

Related Journal Article

http://dx.doi.org/10.1038/s41377-021-00506-9

Tags: Chemistry/Physics/Materials SciencesOptics
Share25Tweet16Share4ShareSendShare
  • Amanda Poholek, Ph.D.

    Reinvigorating ‘lost cause’ exhausted T cells could improve cancer immunotherapy

    122 shares
    Share 49 Tweet 31
  • A new method boosts wind farms’ energy output, without new equipment

    75 shares
    Share 30 Tweet 19
  • Researchers fabricate cobalt copper catalysts for methane on metal-organic framework Contributes to goal of methane production from carbon dioxide emissions

    67 shares
    Share 27 Tweet 17
  • New insights on how some individuals with obesity can lose weight – and keep it off

    66 shares
    Share 26 Tweet 17
  • Brightest stars in the night sky can strip Neptune-sized planets to their rocky cores

    65 shares
    Share 26 Tweet 16
  • The North American Menopause Society releases its 2022 Hormone Therapy Position Statement

    145 shares
    Share 58 Tweet 36
ADVERTISEMENT

About us

We bring you the latest science news from best research centers and universities around the world. Check our website.

Latest NEWS

Reinvigorating ‘lost cause’ exhausted T cells could improve cancer immunotherapy

Experts optimistic about converting coal plants to production of clean geothermal energy

A role for cell ‘antennae’ in managing dopamine signals in the brain

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 193 other subscribers

© 2022 Scienmag- Science Magazine: Latest Science News.

No Result
View All Result
  • HOME PAGE
  • BIOLOGY
  • CHEMISTRY AND PHYSICS
  • MEDICINE
    • Cancer
    • Infectious Emerging Diseases
  • SPACE
  • TECHNOLOGY
  • CONTACT US

© 2022 Scienmag- Science Magazine: Latest Science News.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In