Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
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DOI：
10.1038/s41592-019-0622-5
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发表时间：
2019-12-01
期刊：
影响因子：
48
通讯作者：
Ozcan, Aydogan
中科院分区：
文献类型：
--
作者：
Wu, Yichen;Rivenson, Yair;Ozcan, Aydogan
我们证明，深度神经网络可以被训练来虚拟地将二维荧光图像重新聚焦到样品中用户定义的三维(3D)表面上。使用这种名为Deep-Z的方法，我们使用在单个焦平面获取的荧光图像的时间序列，在3D中对线虫的神经元活动进行成像，以数字方式将景深增加20倍，而无需任何轴向扫描、额外的硬件或成像分辨率和速度的权衡。此外，我们证明这种方法可以纠正样品漂移、倾斜和其他像差，所有这些都是在获取单一荧光图像后以数字方式执行的。该框架还将不同的成像模式相互交叉连接，使单一的广域荧光图像能够进行3D重新聚焦，以匹配在不同样本平面上获取的共焦显微镜图像。Deep-Z有可能提高体积成像速度，同时减少与标准3D荧光显微镜相关的样品漂移、像差和散焦的挑战。
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.