Journal Article

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

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

2019

Overview

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.

• Publisher: Nature Publishing Group
• Subject: Artificial neural networks / Confocal microscopy / Deep learning / Defocusing / Depth of field / Drift / Fluorescence / Fluorescence microscopy / Focal plane / Image acquisition / Image resolution / Microscopes / Microscopy / Neural networks