AI
algorithms, light-field microscopy and light-sheet microscopy have been
combined by researchers to image biological processes in 3D.
Scientists
at the European Molecular Biology Laboratory (EMBL) have combined artificial
intelligence (AI) algorithms with two cutting-edge microscopy techniques
to shorten the time for image processing from days to seconds, while ensuring
that the resulting images are crisp and accurate.
To observe
the swift neuronal signals in a fish brain, scientists can use a technique
called light-field microscopy, which makes it possible to image fast biological
processes in three-dimensions (3D). However, 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 – highlighting the need for the new AI methodology.
“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.”
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.
Light-sheet
microscopy homes in on a single two-dimensional (2D) plane of a given sample at
one time, so researchers can image samples at higher resolution. Compared with
light-field microscopy, light-sheet microscopy produces images that are quicker
to process, but the data are not as comprehensive, since they only capture
information from a single 2D plane at a time.
To take
advantage of the benefits of each technique, the researchers developed an approach
that uses light-field microscopy to image large 3D samples and light-sheet
microscopy to train the AI algorithms, which then create an accurate 3D picture
of the sample.
“If you
build algorithms that produce an image, you need to check that these algorithms
are constructing the right image,” explained Anna Kreshuk, group leader whose
team brought machine learning expertise to the project.
In the new
study, the researchers used light-sheet microscopy to make sure the AI
algorithms were working.
The researchers
say this approach could potentially be modified to work with different types of
microscopes, 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.
Next, the
researchers plan to explore whether the method can be applied to larger
species, including mammals.
The findings
are published in Nature Methods.
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