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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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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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实时光成像流动细胞计是通过移动模糊和深度学习算法实现的.

Yiming Wang1,2, Ziwei Huang1,2, Xiaojie Wang1,2

  • 1Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China. bqli@ustc.edu.cn.

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概括

这项研究引入了光成像流细胞计 (IFC) 的实时系统,该系统使用深度学习直接从动作模糊图像中分类细胞类型. 该技术在识别细胞循环阶段方面实现了高精度,克服了高速细胞分析中的运动模糊挑战.

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科学领域:

  • 生物医学工程 生物医学工程
  • 细胞生物学 细胞生物学
  • 机器学习 机器学习

背景情况:

  • 光成像流细胞计 (IFC) 对于分析细胞亚群至关重要.
  • 高速IFC图像中的运动模糊阻碍了准确的细胞识别.
  • 开发克服运动模糊的方法对于推进IFC应用至关重要.

研究的目的:

  • 为光IFC开发一个实时单细胞成像和分类系统.
  • 通过深度学习,从动作模糊图像中直接识别细胞类型.
  • 为了应对高速细胞分析中运动模糊的挑战.

主要方法:

  • 使用了与深度学习算法集成的光显微镜.
  • 一个移动消除模糊的算法被开发用于重建无模糊图像.
  • 一个ResNet模型被训练在去模糊的HeLa细胞图像上进行分类.

主要成果:

  • 该系统成功地从动作模糊图像中直接识别了细胞类型.
  • 在不同细胞周期阶段获得了HeLa细胞的模糊图像.
  • 该ResNet模型在2个月内实现了96.6%的准确性,将HeLa细胞分类为三种线粒细胞阶段.

结论:

  • 开发的系统为实时单细胞光IFC和分类提供了一种新的方法.
  • 这项技术有效地克服了IFC中的运动模糊限制.
  • 该系统具有广泛的生物和医学应用的巨大潜力,需要快速的细胞分析.