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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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深度学习辅助等离子暗场显微镜用于超分辨率的无标签成像.

Ming Lei1, Junxiang Zhao1, Ayse Z Sahan2,3

  • 1Department of Electrical and Computer Engineering, University of California-San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States.

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概括

深度学习辅助的等离子暗场显微镜 (DAPD) 提高了无标签标本的成像分辨率. 这种新的技术为传统暗场显微镜 (DFM) 提供了一个紧的,超高分辨率的替代方案.

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超级分辨率的超级分辨率是什么暗场显微镜 (dark field microscopy) 是一种暗场显微镜.深度学习是一种深度学习.表面等离子体 polaritonsons 极光子

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科学领域:

  • 光学和光子学 在光学和光子学.
  • 生物医学成像技术 生物医学成像技术
  • 材料科学 材料科学 材料科学

背景情况:

  • 暗场显微镜 (DFM) 为没有标签的标本提供了高对比度的成像.
  • 传统的DFM受 difraktion 的限制,需要复杂的光学对齐.
  • 在无标签显微镜技术中需要提高分辨率.

研究的目的:

  • 开发一种深度学习辅助等离子体暗场显微镜 (DAPD) 技术.
  • 为了实现无标签标本的超高分辨率成像.
  • 为传统的DFM提供一个紧和改进的替代品.

主要方法:

  • 使用表面等离子体极子 (SPPs) 制造等离子体暗场 (PDF) 基板.
  • 在设计的基板上使用SPP照明样本的照明.
  • 在模拟数据上使用预训练的卷积神经网络 (CNN) 进行深度学习图像重建.

主要成果:

  • 在各种无标签对象上显示了2.8倍的分辨率增强.
  • 实现了单超高分辨率成像.
  • 展示了进一步解决方案改进的潜力.

结论:

  • 与传统的DFM相比,DAPD在空间分辨率上提供了显著的增强.
  • 该技术是高分辨率无标签成像的紧而有效的替代方案.
  • 深度学习与等离子学相结合,为先进显微镜开辟了新的途径.