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5 March 2021 Deep-learning-based volumetric imaging in fluorescence microscopy
Luzhe Huang, Yilin Luo, Yair Rivenson, Aydogan Ozcan
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Proceedings Volume 11649, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVIII; 116490G (2021) https://doi.org/10.1117/12.2580674
Event: SPIE BiOS, 2021, Online Only
Abstract
We report a deep learning-based volumetric imaging framework that uses sparse 2D-scans captured by standard wide-field fluorescence microscopy at arbitrary axial positions within the sample. Through the design of a recurrent neural network, the information from different input planes is blended, and virtually propagated in space to rapidly reconstruct the sample volume over an extended axial range. We validated this deep-learning-based volumetric imaging framework using C. Elegans and nanobead samples to demonstrate a 30-fold reduction in the number of required scans. This versatile and rapid volumetric imaging technique reduces the photon dose on the sample and improves the temporal resolution.
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Luzhe Huang, Yilin Luo, Yair Rivenson, and Aydogan Ozcan "Deep-learning-based volumetric imaging in fluorescence microscopy", Proc. SPIE 11649, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVIII, 116490G (5 March 2021); https://doi.org/10.1117/12.2580674
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KEYWORDS
Luminescence
Microscopy
Biomedical optics
Convolution
Image restoration
Microscopes
Neural networks
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