Deep learning microscopy
Deep learning microscopy
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DOI：
10.1364/optica.4.001437
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发表时间：
2017-11-20
期刊：
影响因子：
10.4
通讯作者：
Ozcan, Aydogan
中科院分区：
文献类型：
--
作者：
Rivenson, Yair;Gorocs, Zoltan;Ozcan, Aydogan
我们证明了深度神经网络可以显着改善光学显微镜，提高其在大视场和景深上的空间分辨率。在训练之后，该网络的唯一输入是使用常规光学显微镜获取的图像，而无需对其设计进行任何更改。我们使用低分辨率和宽视场系统成像的各种组织样本对这种深度学习方法进行了盲测试，其中网络快速输出具有更好分辨率的图像，与更高数值孔径镜头的性能相匹配，并显着超过其有限的视场和景深。这些结果对于使用显微镜工具的各种领域都是重要的，包括，例如，生命科学，其中光学显微镜被认为是最广泛使用和部署的技术之一。除了这些应用之外，所提出的方法可能适用于其他成像模式，也跨越电磁频谱的不同部分，并且可以用于设计计算成像器，这些计算成像器在继续对标本进行成像并在不同成像模式之间建立新的转换时变得更好。(C)2017美国光学学会。
We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with better resolution, matching the performance of higher numerical aperture lenses and also significantly surpassing their limited field of view and depth of field. These results are significant for various fields that use microscopy tools, including, e.g., life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, the presented approach might be applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better as they continue to image specimens and establish new transformations among different modes of imaging. (C) 2017 Optical Society of America.