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相关概念视频

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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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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相关实验视频

Updated: Jul 9, 2025

Autofluorescence Imaging to Evaluate Red Algae Physiology
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植物浮游生物图像细分和注释方法基于微观光学光学.

Renqing Jia1, Gaofang Yin2, Nanjing Zhao3

  • 1Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.

Journal of fluorescence
|December 6, 2023
PubMed
概括

这项研究引入了一种新的方法,用于使用化光和明亮场景图像对微观浮游生物进行细分,从而显著减少了手动注释的需求. 该方法实现了高精度,可与手动标签相提并论,用于水质评估.

关键词:
深度学习是一种深度学习.图像细分 图像细分 图像细分微观的光微观的光摄影生物学 摄影生物学植物浮游生物是植物浮游生物.

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Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
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科学领域:

  • 海洋生物学 海洋生物学
  • 环境科学 环境科学
  • 计算机视觉 计算机视觉

背景情况:

  • 精确的微观浮游植物细分对于水质评估至关重要.
  • 当前的计算机视觉方法面临的挑战是背景杂质和大量的手动注释.
  • 植物浮游生物的光特性为改善细分提供了潜在的途径.

研究的目的:

  • 开发一种用于细分和注释植物浮游生物轮的自动化方法.
  • 为了融合光和明亮场的显微镜图像,以提高细分精度.
  • 为了减少植物浮游生物图像分析中的大量手动注释工作量.

主要方法:

  • 在激发光下利用植物浮游生物光,与明亮场图像相结合.
  • 在光图像上应用形态操作,用于初始的轮检测.
  • 在明亮的场景图像上使用 Active Contour 模型进行轮细化.

主要成果:

  • 拟议的方法实现了回忆,精度,F1得分和IOU分别为85.3%,84.5%,84.7%和74.6%,在初步测试中表现优于手动标签.
  • 当用于训练Mask-RCNN时,自动注释的数据比手动注释 (95.3%的回忆,86.1%的精度,82.8%的IOU) 产生了更好的结果 (97.0%的回忆,86.5%的精度,91.1%的F1,84.2%的IOU).
  • 证明了七种不同藻类物种的准确细分.

结论:

  • 拟议的图像融合和活性轮方法准确地细分了微观植物浮游生物.
  • 自动注释的轮数据与手动注释的数据一样有效,用于训练像Mask-RCNN这样的深度学习模型.
  • 这种方法大大减少了植物浮游生物图像分析所需的手动注释工作.