AI Enables Combination of Two Microscopy Methods to Improve Imaging

EMBL scientists have combined artificial intelligence (AI) algorithms with light-field as well as light-sheet microscopy to overcome each technique's limitations as well as shorten the time for image processing from days to seconds, while ensuring that the resulting images are crisp and accurate. The study was published today in Nature Methods.

"Ultimately, we were able to take the best of both worlds in this approach," says 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. But 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 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 team 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.