Journal
NATURE METHODS
Volume 16, Issue 12, Pages 1323-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41592-019-0622-5
Keywords
-
Categories
Funding
- Koc Group
- National Science Foundation
- Howard Hughes Medical Institute
- SPIE John Kiel scholarship
- National Institutes of Health Shared Instrumentation grant [S10OD025017]
- National Science Foundation Major Research Instrumentation grant [CHE-0722519]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available