Deep learning enables cross-modality super-resolution in fluorescence microscopy
Deep learning enables cross-modality super-resolution in fluorescence microscopy
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DOI：
10.1038/s41592-018-0239-0
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发表时间：
2019-01-01
期刊：
影响因子：
48
通讯作者：
Ozcan, Aydogan
中科院分区：
文献类型：
--
作者：
Wang, Hongda;Rivenson, Yair;Ozcan, Aydogan
我们提出了在不同的荧光显微镜模式下实现深度学习的超分辨率。这种数据驱动的方法不需要成像过程的数值建模或点扩展函数的估计，并且基于训练生成对抗网络（GAN）将衍射限制的输入图像转换为超分辨率图像。利用该框架，我们提高了用低数值孔径物镜获得的宽视场图像的分辨率，与使用高数值孔径物镜获得的分辨率相匹配。我们还展示了跨模超分辨率，转换共聚焦显微镜图像，以匹配与受激发射耗尽（STED）显微镜获得的分辨率。我们进一步证明，细胞和组织内的亚细胞结构的全内反射荧光（TIRF）显微镜图像可以转换，以匹配基于TIRF的结构照明显微镜获得的结果。深度网络快速输出这些超分辨率图像，不需要任何迭代或参数搜索，可以为超分辨率成像的民主化服务。
We present deep-learning-enabled super-resolution across different fluorescence microscopy modalities. This data-driven approach does not require numerical modeling of the imaging process or the estimation of a point-spread-function, and is based on training a generative adversarial network (GAN) to transform diffraction-limited input images into super-resolved ones. Using this framework, we improve the resolution of wide-field images acquired with low-numerical-aperture objectives, matching the resolution that is acquired using high-numerical-aperture objectives. We also demonstrate cross-modality super-resolution, transforming confocal microscopy images to match the resolution acquired with a stimulated emission depletion (STED) microscope. We further demonstrate that total internal reflection fluorescence (TIRF) microscopy images of subcellular structures within cells and tissues can be transformed to match the results obtained with a TIRF-based structured illumination microscope. The deep network rapidly outputs these super-resolved images, without any iterations or parameter search, and could serve to democratize super-resolution imaging.