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Proceedings Paper

Deep-learning based three-dimensional virtual refocusing of fluorescence microscopy images (Conference Presentation)
Author(s): Yichen Wu; Yair Rivenson; Hongda Wang; Yilin Luo; Eyal Ben-David; Laurent A. Bentolila; Christian Pritz; Aydogan Ozcan

Paper 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.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11245, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII, 112450P (9 March 2020); doi: 10.1117/12.2545249
Show Author Affiliations
Yichen Wu, Univ. of California, Los Angeles (United States)
Yair Rivenson, Univ. of California, Los Angeles (United States)
Hongda Wang, Univ. of California, Los Angeles (United States)
Yilin Luo, Univ. of California, Los Angeles (United States)
Eyal Ben-David, Univ. of California, Los Angeles (United States)
Laurent A. Bentolila, Univ. of California, Los Angeles (United States)
Christian Pritz, The Hebrew Univ. of Jerusalem (Israel)
Aydogan Ozcan, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 11245:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII
Thomas G. Brown; Tony Wilson; Laura Waller, Editor(s)

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