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相关概念视频

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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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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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Updated: May 10, 2025

Conventional BODIPY Conjugates for Live-Cell Super-Resolution Microscopy and Single-Molecule Tracking
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单分子定位超分辨率数据集用于深度学习,配对低分辨率图像.

Xian'ao Zhao1,2, Tianjie Yang1,2, Tianying Pan1,3

  • 1National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.

Scientific data
|April 23, 2025
PubMed
概括
此摘要是机器生成的。

一个新的数据集,DL-SMLM,提供配对的低分辨率和超高分辨率显微镜图像. 本资源有助于深度学习模型开发超分辨率显微镜,解决当前的数据稀缺问题.

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科学领域:

  • 生物物理学的生物物理.
  • 细胞生物学 细胞生物学
  • 显微镜的使用方法

背景情况:

  • 深度学习已经迅速推进了超分辨率显微镜.
  • 单分子局部化显微镜 (SMLM) 提供了高质量的数据,但公共数据集很少,限制了深度学习的发展.

研究的目的:

  • 引入DL-SMLM,这是一个新的生物图像数据集,用于训练超分辨率显微镜深度学习模型.
  • 为各种亚细胞结构提供配对的低分辨率和超分辨率SMLM数据.

主要方法:

  • DL-SMLM数据集包含原始的SMLM数据和6个亚细胞结构的相应的低分辨率图像.
  • 数据细分允许生成成千上万的训练对.
  • 图像系统的性能使用DNA原木样本进行了验证.

主要成果:

  • DL-SMLM包括188组原始SMLM数据,每个低分辨率图像有100个信号级别.
  • 通过训练超分辨率模型来证明DL-SMLM的有效性.
  • 该数据集有助于开发基于深度学习的超高分辨率显微镜.

结论:

  • DL-SMLM有效地解决了超高分辨率显微镜深度学习公共数据集的稀缺问题.
  • 该数据集通过可访问的培训数据支持深度学习超分辨率技术的进步.