Virtual refocusing of fluorescence images using an engineered point-spread function and deep learning

Yang Xilin; Luzhe Huang; Yilin Luo; Yichen Wu; Hongda Wang; Yair Rivenson; Aydogan Ozcan

doi:10.1117/12.2594416

1 August 2021 Virtual refocusing of fluorescence images using an engineered point-spread function and deep learning

Yang Xilin, Luzhe Huang, Yilin Luo, Yichen Wu, Hongda Wang, Yair Rivenson, Aydogan Ozcan

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180425 (2021) https://doi.org/10.1117/12.2594416
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

We report a deep learning-based virtual image refocusing method that utilizes double-helix point-spread-function (DH-PSF) engineering and a cascaded neural network model, termed W-Net. This method can virtually refocus a defocused fluorescence image onto an arbitrary axial plane within the sample volume, enhancing the imaging depth-of-field and lateral resolution at the same time. We demonstrated the efficacy of our method by imaging fluorescent nano-beads at various defocus distances, and also quantified the nano-particle localization performance achieved with our virtually-refocused images, demonstrating ~20-fold improvement in image depth-of-field over wide-field microscopy, enabled by the combination of DH-PSF and W-Net inference.

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Citation Download Citation

Yang Xilin, Luzhe Huang, Yilin Luo, Yichen Wu, Hongda Wang, Yair Rivenson, and Aydogan Ozcan "Virtual refocusing of fluorescence images using an engineered point-spread function and deep learning", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180425 (1 August 2021); https://doi.org/10.1117/12.2594416

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KEYWORDS

Luminescence

Image resolution

Point spread functions

Error analysis

Image enhancement

Microscopy

Neural networks

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