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9 March 2022 Three-dimensional virtual refocusing of point-spread function engineered images using cascaded neural networks
Xilin Yang, Luzhe Huang, Yilin Luo, Yichen Wu, Hongda Wang, Yair Rivenson, Aydogan Ozcan
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Proceedings Volume PC12019, AI and Optical Data Sciences III; PC1201906 (2022) https://doi.org/10.1117/12.2608278
Event: SPIE OPTO, 2022, San Francisco, California, United States
Abstract
We report a virtual image refocusing framework for fluorescence microscopy, which extends the imaging depth-of-field by ~20-fold and provides improved lateral resolution. This method utilizes point-spread function (PSF) engineering and a cascaded convolutional neural network model, which we termed as W-Net. We tested this W-Net architecture by imaging 50 nm fluorescent nanobeads at various defocus distances using a double-helix PSF, demonstrating ~20-fold improvement in image depth-of-field over conventional wide-field microscopy. W-Net architecture can be used to develop deep-learning-based image reconstruction and computational microscopy techniques that utilize engineered PSFs and can significantly improve the spatial resolution and throughput of fluorescence microscopy.
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Xilin Yang, Luzhe Huang, Yilin Luo, Yichen Wu, Hongda Wang, Yair Rivenson, and Aydogan Ozcan "Three-dimensional virtual refocusing of point-spread function engineered images using cascaded neural networks", Proc. SPIE PC12019, AI and Optical Data Sciences III, PC1201906 (9 March 2022); https://doi.org/10.1117/12.2608278
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KEYWORDS
3D image processing
Image resolution
Microscopy
Neural networks
Luminescence
Point spread functions
Convolutional neural networks
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