Deep adversarial network for super stimulated emission depletion imaging
Deep adversarial network for super stimulated emission depletion imaging
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DOI：
10.1117/1.jnp.14.016009
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发表时间：
2020-01
影响因子：
1.5
通讯作者：
Mengzhou Li;Hongming Shan;Sergey Pryshchep;M. M. Lopez-M.;Ge Wang
中科院分区：
文献类型：
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作者：
Mengzhou Li;Hongming Shan;Sergey Pryshchep;M. M. Lopez-M.;Ge Wang
抽象的。受激辐射耗尽(STED)作为一种新兴的超分辨技术，定义了一种最新的图像分辨方法。在过去的几年里，它已经发展成为一种通用的荧光成像工具。目前STED提供的最佳横向分辨率约为20 nm，但在实际的活细胞成像应用中，通过该机制提供的常规分辨率约为100 nm，受到光毒性的限制。许多关键的生物结构低于这一分辨率水平。因此，通过后处理技术提高STED的分辨率将是非常有价值的。为了显著提高STED图像的分辨率，我们提出了一种深度对抗网络，该网络以STED图像为输入，依靠物理建模来获得训练数据，并在更高的分辨率水平上输出“自我细化”的对应图像。换言之，我们利用STED点扩散函数的先验知识和细胞的结构信息来生成用于网络训练的模拟标签数据对。我们的结果表明，从60 nm分辨率的STED图像可以获得30 nm的分辨率，在我们的模拟和实验中，标签和输出结果之间的结构相似指数值达到了0.98左右，显著高于使用Lucy-Richardson去卷积方法和基于最先进的NET的超分辨率网络所获得的结果。
Abstract. Stimulated emission depletion (STED), as one of the emerging super-resolution techniques, defines a state-of-the-art image resolution method. It has been developed into a universal fluorescent imaging tool over the past several years. The currently best available lateral resolution offered by STED is around 20 nm, but in real live cell imaging applications, the regular resolution offered through this mechanism is around 100 nm, limited by phototoxicity. Many critical biological structures are below this resolution level. Hence, it will be invaluable to improve the STED resolution through postprocessing techniques. We propose a deep adversarial network for improving the STED resolution significantly, which takes an STED image as an input, relies on physical modeling to obtain training data, and outputs a “self-refined” counterpart image at a higher resolution level. In other words, we use the prior knowledge on the STED point spread function and the structural information about the cells to generate simulated labeled data pairs for network training. Our results suggest that 30-nm resolution can be achieved from a 60-nm resolution STED image, and in our simulation and experiments, the structural similarity index values between the label and output result reached around 0.98, significantly higher than those obtained using the Lucy–Richardson deconvolution method and a state-of-the-art UNet-based super-resolution network.