To observe the swift neuronal signals in a fish brain, 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. Now, EMBL scientists have combined artificial intelligence (AI) algorithms with two cutting-edge microscopy techniques - an advance that shortens the time for image processing from days to mere seconds, while ensuring that the resulting images are crisp and accurate. The findings are published 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 and now a PhD student at the Technical University of Munich.

A representation of a neural network provides a backdrop to a fish larva's beating heart. The advent of deep learning, a powerful form of machine learning, has led to rapid advancements in areas such as speech recognition, visual object recognition, genomics and drug discovery. These methods are characterized by multiple processing layers that can tease out intricate patterns and structures in very large, complex data sets. Now, a team of European researchers has incorporated deep learning algorithms into a light-field microscope to enhance both its reconstruction speed and image quality (Nat. Methods, doi: 10.1038/s41592-021-01136-0). The results significantly extend the capabilities of light-field microscopy for whole-brain or whole-animal imaging of living specimens for biomedical research.

To observe the swift neuronal signals in a fish brain, 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. Now, EMBL scientists have combined artificial intelligence (AI) algorithms with two cutting-edge microscopy techniques--an advance that shortens the time for image processing from days to mere seconds, while ensuring that the resulting images are crisp and accurate. The findings are published 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 and now a Ph.D. student at the Technical University of Munich.

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.

To observe the swift neuronal signals in a fish brain, 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. Now, EMBL scientists have combined artificial intelligence (AI) algorithms with two cutting-edge microscopy techniques - an advance that shortens the time for image processing from days to mere seconds, while ensuring that the resulting images are crisp and accurate. The findings are published 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 and now a PhD student at the Technical University of Munich.