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1 August 2021 ZEUS: Zernike based nEUral network for light Sheet microscopy
Laura Pérez-García, Benjamin Midtvedt, Daniel Midtvedt, Gustavo Castro-Olvera, Jordi Andilla, Pablo Loza-Alvarez, Giovanni Volpe
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Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118040Y (2021) https://doi.org/10.1117/12.2594022
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States
Abstract
In recent years, deep learning has been used widely to solve a variety of digital microscopy problems. We present ZEUS as a method to correct out of focus aberrations and denoise light-sheet microscopy images. First, a convolutional neural network is used to estimate the aberrations in terms of Zernike coefficients. Then those values are used to train a UNET that outputs corrected images from noisy and aberrated ones. With this approach, we can access scanning frequencies and image qualities equivalent to the most advanced LSM systems without the need for costly equipment and complex optical setups.
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Laura Pérez-García, Benjamin Midtvedt, Daniel Midtvedt, Gustavo Castro-Olvera, Jordi Andilla, Pablo Loza-Alvarez, and Giovanni Volpe "ZEUS: Zernike based nEUral network for light Sheet microscopy", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118040Y (1 August 2021); https://doi.org/10.1117/12.2594022
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KEYWORDS
Microscopy
Neural networks
Optical components
High speed imaging
Image quality
Image restoration
Luminescence
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