Deep-STORM: super-resolution single-molecule microscopy by deep learning
Deep-STORM: super-resolution single-molecule microscopy by deep learning
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DOI：
10.1364/optica.5.000458
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发表时间：
2018-04-20
期刊：
影响因子：
10.4
通讯作者：
Shechtman, Yoav
中科院分区：
文献类型：
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作者：
Nehme, Elias;Weiss, Lucien E.;Shechtman, Yoav
我们提出了一种超快、精确、无参数的方法，我们称之为 Deep-STORM，用于从随机闪烁的发射器（例如用于定位显微镜的荧光分子）获取超分辨率图像。 Deep-STORM 使用深度卷积神经网络，可以根据模拟数据或实验测量进行训练，这两种数据都已得到演示。该方法在具有挑战性的信噪比条件和高发射器密度下实现了最先进的分辨率，并且比现有方法要快得多。此外，不需要有关底层结构形状的先验信息，使得该方法适用于任何闪烁数据集。我们通过模拟和实验获得的数据的超分辨率图像重建来验证我们的方法。 (C) 2018 年美国光学协会根据 OSA 开放获取出版协议条款
We present an ultrafast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking dataset. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement