With the help of artificial intelligence, even already powerful microscopes can see better, faster and process more data.

In a new study, published Friday in the journal Nature Methods, researchers used new machine learning algorithms to combine a pair of novel microscopy techniques.

The marriage dramatically accelerated image processing and yielded crisp, accurate results.

To capture speedy biological processes in 3D, like the beating heart of a fish larva, researchers rely on a method called light-field microscopy.

The technique involves the collection of massive amounts data, and as a result, image processing can take days. Often, the end result is lacking in resolution.

Another technique, called light-sheet microscopy, focuses on a single 2D plane from a sample. The method yields high-resolution images in a much shorter time frame, but captures less comprehensive data.

With the help of artificial intelligence, scientists were able to marry the two techniques.

"Ultimately, we were able to take 'the best of both worlds' in this approach," Nils Wagner, one of the paper's two lead authors, said in a press release.

"AI enabled us to combine different microscopy techniques, so that we could image as fast as light-field microscopy allows and get close to the image resolution of light-sheet microscopy," said Wagner, a doctoral student at the Technical University of Munich Germany.

The new hybrid method utilizes light-field microscopy to capture detailed images of large 3D samples, while light-sheet microscopy helps train the machine learning algorithms to process the image data more efficiently.

"If you build algorithms that produce an image, you need to check that these algorithms are constructing the right image," said co-author Anna Kreshuk, group leader at EMBL and an expert in biomedical image analysis.

The new algorithms used the high-resolution images captured through light-sheet microscopy to ensure they were accurately processing the light-field microscopy data.

"This makes our research stand out from what has been done in the past," Kreshuk said.

Researchers suggest the architecture of their novel algorithms can be adapted for a variety of different types of microscope technologies.

Scientists said they expect their breakthrough to help biologists study a variety of important larval and embryonic processes.

"Our method will be really key for people who want to study how brains compute. Our method can image an entire brain of a fish larva, in real time," said co-author Robert Prevedel, another EMBL group leader.

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