美国加州大学洛杉矶分校的Aydogan Ozcan及其研究组使用深度学习实现对荧光显微镜图像进行三维虚拟重聚焦。2019年11月4日，国际学术期刊《自然—方法学》在线发表了该研究成果。

研究人员证明了可以训练一个深度神经网络来将二维荧光图像虚拟地重新聚焦到样本内用户定义的三维（3D）表面上。使用称为Deep-Z的这种方法，研究人员使用在单个焦平面上采集的荧光图像的时间序列，在3D中对秀丽隐杆线虫的神经元活动进行了成像，将视场深度数字化地增加了20倍，而没有任何轴向扫描、其他硬件或成像分辨率和速度之间的权衡。

此外，研究人员证明了这种方法可以校正采样漂移、倾斜和其他像差，所有这些都在获取单个荧光图像后以数字方式执行。该框架还可以将不同的成像方式相互交叉连接，从而可以对单个宽视野荧光图像进行3D重新聚焦，以匹配在不同样本平面上获取的共聚焦显微镜图像。 Deep-Z具有提高体积成像速度的潜力，同时减少了与标准3D荧光显微镜相关的与样品漂移、像差和散焦有关的问题。

附：英文原文

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

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

Issue&Volume: 2019-11-04

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.

DOI: 10.1038/s41592-019-0622-5

Source: https://www.nature.com/articles/s41592-019-0622-5