Researchers at the University of Gothenburg, Sweden, have developed an AI tool that offers new opportunities for analyzing images taken with microscopes. A study in Applied Physics Reviews, “Quantitative digital microscopy with deep learning” shows that the tool—DeepTrack 2.0—which has already received international recognition, can fundamentally change microscopy and pave the way for new discoveries and areas of use within both research and industry.
“Deep learning has taken the world by storm and has had a huge impact on many industries, sectors and scientific fields. We have now developed a tool that makes it possible to utilize the incredible potential of deep learning, with focus on images taken with microscopes,” says Benjamin Midtvedt, a doctoral student in physics and the main author of the study as well as a previous study in ACS Nano, “Fast and Accurate Nanoparticle Characterization using Deep-Learning-Enhanced Off-Axis Holography.”
Deep learning can be described as a mathematical model used to solve problems that are difficult to tackle using traditional algorithmic methods. In microscopy, the great challenge is to retrieve as much information as possible from the data-packed images, and this is where deep learning has proven to be very effective.
DeepTrack 2.0 is an integrated software environment to design, train, and validate deep-learning solutions for digital microscopy. The developers recommend all users start with the graphical user interface, which provides a visual approach to deep learning and an intuitive feel for how the various software components interact.
The DeepTrack 2.0 tool that Midtvedt and his research colleagues have developed involves neural networks learning to retrieve exactly the information that a researcher wants from an image by looking through a huge number of images, known as training data. The tool simplifies the process of producing training data compared with having to do so manually, so that tens of thousands of images can be generated in an hour instead of a hundred in a month.
Image – Image courtesy of ACS Nano, 15(2), p 2240-2250, 2021.
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