Researchers have combined AI algorithms with two cutting-edge microscopy techniques to drastically shorten image processing time from days to mere seconds, while ensuring that the resulting images are crisp and accurate.
When observing the swift neuronal signals in a fish brain for example (pictured below), scientists have started to use a technique called light-field microscopy, which makes it possible to image such fast biological processes in 3D.
But the images are often lacking in quality, and it takes hours or days for massive amounts of data to be converted into 3D volumes and movies.
Light-field microscopy captures large 3D images that allow researchers to track and measure remarkably fine movements, such as a fish larva’s beating heart, at very high speeds. However, this technique produces massive amounts of data, which can take days to process, and the final images usually lack resolution.
To combat this, the team from the European Molecular Biology Laboratory (EMBL) have started using a similarly named technique, light-sheet microscopy, which homes in on a single 2D plane of a given sample at one time, to quickly train AI algorithms to more easily understand the 3D images developed using the more data-intensive technique.
“Ultimately, we were able to take ‘the best of both worlds’ in this approach,” said Nils Wagner, one of the paper’s two lead authors.
“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.”
“If you build algorithms that produce an image, you need to check that these algorithms are constructing the right image,” said Anna Kreshuk, the EMBL group leader. She explained that their new study used light-sheet microscopy to make sure the AI algorithms were working properly.
The researchers believe their approach could potentially be modified to work with different types of microscopes too, eventually allowing biologists to look at dozens of different specimens and see much more, much faster.
For example, it could help to find genes that are involved in heart development, or could measure the activity of thousands of neurons at the same time.
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