Deep-learning based three-dimensional virtual refocusing of fluorescence microscopy images (Conference Presentation)

Yichen Wu; Yair Rivenson; Hongda Wang; Yilin Luo; Eyal Ben-David; Laurent A. Bentolila; Christian Pritz; Aydogan Ozcan

doi:10.1117/12.2545249

9 March 2020 Deep-learning based three-dimensional virtual refocusing of fluorescence microscopy images (Conference Presentation)

Yichen Wu, Yair Rivenson, Hongda Wang, Yilin Luo, Eyal Ben-David, Laurent A. Bentolila, Christian Pritz, Aydogan Ozcan

Author Affiliations +

Proceedings Volume 11245, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII; 112450P (2020) https://doi.org/10.1117/12.2545249
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

We report a digital image refocusing framework in fluorescence microscopy (termed “Deep-Z”), where a deep neural network is trained to virtually-refocus a 2D fluorescence image onto user-defined 3D surfaces. Using Deep-Z, we demonstrated 3D reconstruction of C. elegans neuronal activity from a 2D movie, digitally increasing the depth-of-field by 20-fold. We also demonstrated digital correction of sample drift, tilt and other image aberrations, all performed after the acquisition of a single image. Deep-Z also permits cross-modality virtual refocusing, where a single 2D wide-field image can be digitally refocused to match a confocal microscopy image captured at a different sample plane.

Conference Presentation

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Yichen Wu, Yair Rivenson, Hongda Wang, Yilin Luo, Eyal Ben-David, Laurent A. Bentolila, Christian Pritz, and Aydogan Ozcan "Deep-learning based three-dimensional virtual refocusing of fluorescence microscopy images (Conference Presentation)", Proc. SPIE 11245, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII, 112450P (9 March 2020); https://doi.org/10.1117/12.2545249

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KEYWORDS

Luminescence

Microscopy

3D image processing

3D modeling

Biomedical optics

Digital holography

Holography

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