Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

158 Scopus citations

Abstract

We demonstrate that a deep neural network can be trained to virtually refocus a two-dimensional fluorescence image onto user-defined three-dimensional (3D) surfaces within the sample. Using this method, termed Deep-Z, we imaged the neuronal activity of a Caenorhabditis elegans worm in 3D using a time sequence of fluorescence images acquired at a single focal plane, digitally increasing the depth-of-field by 20-fold without any axial scanning, additional hardware or a trade-off of imaging resolution and speed. Furthermore, we demonstrate that this approach can correct for sample drift, tilt and other aberrations, all digitally performed after the acquisition of a single fluorescence image. This framework also cross-connects different imaging modalities to each other, enabling 3D refocusing of a single wide-field fluorescence image to match confocal microscopy images acquired at different sample planes. Deep-Z has the potential to improve volumetric imaging speed while reducing challenges relating to sample drift, aberration and defocusing that are associated with standard 3D fluorescence microscopy.

Original languageAmerican English
Pages (from-to)1323-1331
Number of pages9
JournalNature Methods
Volume16
Issue number12
DOIs
StatePublished - 1 Dec 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.

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