Multicolor localization microscopy and point-spread-function engineering by deep learning
Multicolor localization microscopy and point-spread-function engineering by deep learning
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DOI：
10.1364/oe.27.006158
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发表时间：
2019-03-04
期刊：
影响因子：
3.8
通讯作者：
Shechtman, Yoav
中科院分区：
文献类型：
--
作者：
Hershko, Eran;Weiss, Lucien E.;Shechtman, Yoav
深度学习已经成为图像分类和图像恢复问题的一种非常有效的工具。在这里，我们将深度学习应用于显微镜，并演示了神经网络如何利用点扩散函数的颜色依赖性来对灰度相机上成像的单个发射器的颜色进行分类。虽然现有的用于光谱分类的定位显微镜方法在发射路径中需要附加的光学元件，例如，光谱滤波器、棱镜或相位掩模，我们的神经网络使用标准的、未修改的单通道配置以高效率正确地识别静态和移动的发射器。此外，我们还展示了如何使用深度学习来设计新的相位调制元件，当这些元件被应用到成像路径中时，可以进一步改善物种之间的颜色区分，包括在单个图像中同时区分四个物种。(C)根据OSA开放获取出版协议的条款，2019年美国光学学会
Deep learning has become an extremely effective tool for image classification and image restoration problems. Here, we apply deep learning to microscopy and demonstrate how neural networks can exploit the chromatic dependence of the point-spread function to classify the colors of single emitters imaged on a grayscale camera. While existing localization microscopy methods for spectral classification require additional optical elements in the emission path, e.g., spectral filters, prisms, or phase masks, our neural net correctly identifies static and mobile emitters with high efficiency using a standard, unmodified single-channel configuration. Furthermore, we show how deep learning can be used to design new phase-modulating elements that, when implemented into the imaging path, result in further improved color differentiation between species, including simultaneously differentiating four species in a single image. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement