Deep learning method could improve optical microscopy

Dec. 4, 2017
The holographic imaging technique produces better images than current methods that use multiple holograms.

A team of researchers at the University of California Los Angeles (UCLA) used a form of machine learning called deep learning, which uses multi-layered artificial neural networks to automate data analysis and reconstruct a hologram to form a microscopic image of an object, thereby improving optical microscopy.

Related: Holographic microscope tracks sperm motion in 3D

The holographic imaging technique produces better images than current methods that use multiple holograms, the researchers say, and it is easier to implement because it requires fewer measurements and performs computations faster.

The research was led by Aydogan Ozcan, an associate director of the UCLA California NanoSystems Institute and the Chancellor's Professor of Electrical and Computer Engineering at the UCLA Henry Samueli School of Engineering and Applied Science; and by postdoctoral scholar Yair Rivenson and graduate student Yibo Zhang, both of UCLA's electrical and computer engineering department.

An artificial neural network is used to transform low-resolution microscopic images of samples into high-resolution images, revealing more details of the sample, which could be crucial for pathology and medical diagnostics. (Image credit: Ozcan Research Group/UCLA)

In one study, the researchers produced holograms of Pap smears, which are used to screen for cervical cancer, as well as blood and breast tissue samples. In each case, the neural network learned to extract and separate the features of the true image of the object from undesired light interference and from other physical byproducts of the image reconstruction process.

"These results are broadly applicable to any phase recovery and holographic imaging problem, and this deep-learning-based framework opens up myriad opportunities to design fundamentally new coherent imaging systems, spanning different parts of the electromagnetic spectrum, including visible wavelengths and even x-rays," Ozcan says.

Another advantage of the new approach was that it was achieved without any modeling of light-matter interaction or a solution of the wave equation, which can be challenging and time-consuming to model and calculate for each individual sample and form of light.

In a second study, the researchers used the same deep-learning framework to improve the resolution and quality of optical microscopic images.

That advance could help diagnosticians or pathologists looking for very small-scale abnormalities in a large blood or tissue sample, and Ozcan says it represents the powerful opportunities for deep learning to improve optical microscopy for medical diagnostics and other fields in engineering and the sciences.

Full details of the work appear in the journals Light: Science and Applications and Optica, respectively.

About the Author

BioOptics World Editors

We edited the content of this article, which was contributed by outside sources, to fit our style and substance requirements. (Editor’s Note: BioOptics World has folded as a brand and is now part of Laser Focus World, effective in 2022.)

Sponsored Recommendations

Ask the Expert: Building Better Laser Micromachining Systems

Dec. 8, 2023
Dr. Cliff Jolliffe, Head of Strategic Marketing, Industrial Automation, Physik Instrumente (PI), fields questions about integrating controls for different motion systems and lasers...

Video: December 8, 2023 Photonics Hot List

Dec. 8, 2023
In this episode, we cover a microscopy method that hits uncharted cell territory, drone-based imaging for solar farm inspection, soliton microcombs that boost conversion efficiency...

China’s industrial laser market shows steady growth in turbulent times

Dec. 8, 2023
This in-depth market update focuses on trends in laser processing and industrial lasers while touching on what to expect in the ultrafast laser, fiber laser, LiDAR, and handheld...

What does it take to land venture capital for photonics-driven startups?

Dec. 7, 2023
Capital to grow a startup company can come from many sources: contract and non-recurring engineering (NRE) funding, angels and friends, customer upfront payments, and venture ...

Voice your opinion!

To join the conversation, and become an exclusive member of Laser Focus World, create an account today!