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Researchers used AI to turn two-dimensional (2D) images into stacks of virtual three-dimensional (3D) slices showing activity inside organisms. Pixabay

A team of researchers has used Artificial Intelligence (AI) to turn two-dimensional (2D) images into stacks of virtual three-dimensional (3D) slices showing activity inside organisms.

Using deep learning techniques, the team from University of California, Los Angeles (UCLA) devised a technique that extends the capabilities of fluorescence microscopy, which allows scientists to precisely label parts of living cells and tissue with dyes that glow under special lighting.


In a study published in the journal Nature Methods, the scientists also reported that their framework, called “Deep-Z,” was able to fix errors or aberrations in images, such as when a sample is tilted or curved.

Further, they demonstrated that the system could take 2D images from one type of microscope and virtually create 3D images of the sample as if they were obtained by another, more advanced microscope.

“This is a very powerful new method that is enabled by deep learning to perform 3D imaging of live specimens, with the least exposure to light, which can be toxic to samples,” said senior author Aydogan Ozcan, UCLA chancellor’s professor of electrical and computer engineering.

In addition to sparing specimens from potentially damaging doses of light, this system could offer biologists and life science researchers a new tool for 3D imaging that is simpler, faster and much less expensive than current methods.


3D imaging that shows the activity inside organisms has been made easier with the use of AI. Pixabay

The opportunity to correct for aberrations may allow scientists studying live organisms to collect data from images that otherwise would be unusable.

Investigators could also gain virtual access to expensive and complicated equipment, said researchers.

“Deep-Z” was taught using experimental images from a scanning fluorescence microscope, which takes pictures focused at multiple depths to achieve 3D imaging of samples.

In thousands of training runs, the neural network learned how to take a 2D image and infer accurate 3D slices at different depths within a sample.

Then, the framework was tested blindly – fed with images that were not part of its training, with the virtual images compared to the actual 3D slices obtained from a scanning microscope, providing an excellent match.

The researchers also found that Deep-Z could produce 3D images from 2D surfaces where samples were tilted or curved.

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“This feature was actually very surprising,” said Yichen Wu, a UCLA graduate student who is co-first author of the publication. “With it, you can see through curvature or other complex topology that is very challenging to image.” (IANS)


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Tokyo Olympics 2020 Indian wrestler Ravi Kumar enter semi finals.

Indian wrestler Ravi Kumar (57kg) and Deepak Punia (86kg) enjoyed fruitful outings at the Tokyo Olympic Games as they secured semifinal berths in their respective weight categories at the Makuhari Messe on Wednesday.

On the opening day of the wrestling competition, Ravi Kumar defeated Bulgaria's Georgi Vangelov 14-4 on technical superiority to reach the last-four in the men's 57kg category, while compatriot Deepak Punia overcame China's Zushen Lin 6-3 on points to advance to the semifinals.

Ravi Kumar will take on Nurislam Sanayev of Kazakhstan in the last-four, while Punia will be up against David Morris Taylor of the USA.

Earlier, Ravi Kumar had won his opening-round bout by technical superiority against Colombia's Oscar Tigreros to secure a quarterfinal spot. Competing in the Round-of-16 bout against the Colombian wrestler, the 23-year-old Ravi Kumar, who is making his Olympic debut, showed no nerves as he dominated the bout to win by technical superiority (13-2).

Ravi Kumar landed attack after attack and went 13-2 up, winning the bout by technical superiority with minutes to spare. In wrestling, building up a 10-point lead over the opponent results in a victory by technical superiority.

India's 86kg freestyle wrestler Deepak Punia showed no signs of the niggle that had forced him to pull out of the Poland Open Ranking Series in Warsaw in June, as he defeated Nigeria's Ekerekeme Agiomor on technical superiority to secure a quarterfinal berth.

He got his Olympic campaign to a fine start as he was in control from the start of the bout and hardly ever allowed his Nigerian opponent any room to maneuver his moves, finally winning with a 12-1 on technical superiority.

Punia, who had also suffered an elbow injury just before the Games, was slow at the start but came into his own as the bout progressed, inflicting takedowns at regular intervals to earn points.

The Indian wrestler eased into a 4-1 lead at the break and extended his lead comfortably in the second period.

Punia, the silver medallist from the 2019 world wrestling championships, then set up a clash with China's Lin Zushen in the quarterfinals and defeated him 6-3.

(IANS/HP)