Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Flow Cytometry01:23

Flow Cytometry

12.5K
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.
In...
12.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Retraction of "Label-Free Light Scattering Imaging with Purified Brownian Motion Differentiates Small Extracellular Vesicles in Cell Microenvironments".

Analytical chemistry·2025
Same author

Label-Free Light Scattering Imaging with Purified Brownian Motion Differentiates Small Extracellular Vesicles in Cell Microenvironments.

Analytical chemistry·2024
Same author

High-content video flow cytometry with digital cell filtering for label-free cell classification by machine learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2022
Same author

Differentiating single cervical cells by mitochondrial fluorescence imaging and deep learning-based label-free light scattering with multi-modal static cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2022
Same author

RAD51 gene is associated with advanced age-related macular degeneration in Chinese population.

Clinical biochemistry·2013
Same author

Immunization against recombinant GnRH-I alters ultrastructure of gonadotropin cell in an experimental boar model.

Reproductive biology and endocrinology : RB&E·2013

相关实验视频

Updated: Jun 18, 2025

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma
09:52

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

126

单探测器多重成像流细胞计用于癌细胞分类,使用深度学习.

Zhiwen Wang1,2, Qiao Liu3, Jie Zhou1

  • 1School of Integrated Circuits, Shandong University, Jinan, China.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
|August 5, 2024
PubMed
概括
此摘要是机器生成的。

多重成像流细胞计 (mIFC) 能够同时对细胞进行亮场和多色成像. 一种深度学习方法实现了97.1%的准确性,使用mIFC数据对卵巢细胞系进行分类.

关键词:
癌症检测 癌症检测深度学习是一种深度学习.图像流动细胞计量 图像流动细胞计多重成像成像多重成像一个单细胞的单细胞.波长划分多重复合 波长划分多重复合

更多相关视频

Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells
08:56

Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells

Published on: July 12, 2022

2.9K
Discrimination of Seven Immune Cell Subsets by Two-fluorochrome Flow Cytometry
10:58

Discrimination of Seven Immune Cell Subsets by Two-fluorochrome Flow Cytometry

Published on: March 5, 2019

13.8K

相关实验视频

Last Updated: Jun 18, 2025

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma
09:52

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

126
Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells
08:56

Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells

Published on: July 12, 2022

2.9K
Discrimination of Seven Immune Cell Subsets by Two-fluorochrome Flow Cytometry
10:58

Discrimination of Seven Immune Cell Subsets by Two-fluorochrome Flow Cytometry

Published on: March 5, 2019

13.8K

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
  • 光学成像技术的成像

背景情况:

  • 图像流动细胞计整合了流动细胞计和显微镜,用于先进的细胞分析.
  • 现有的方法在同时进行多道成像和自动化数据处理方面存在局限性.
  • 检测卵巢癌需要敏感和准确的单细胞分析技术.

研究的目的:

  • 开发一种新的多重成像流细胞计 (mIFC) 系统,使用空间波长分割多重复合.
  • 实施深度学习框架,用于自动处理和分析mIFC数据.
  • 评估mIFC在区分正常和癌症卵巢细胞系方面的表现.

主要方法:

  • 开发了一个由金属化物灯激发的单探测器mIFC系统.
  • 利用空间波长分割多重复合用于同时亮场和多色光成像.
  • 设计了一个包括U-net,VDSR和VGG19网络的深度学习模型,用于图像处理和细胞分类.

主要成果:

  • 通过分辨率和放大测试镜头验证了mIFC性能,证明了微米级差异化能力.
  • 与亮场,核或癌症抗原125 (CA125) 通道相比,分化24 (CD24) 通道的集群在分类卵巢细胞系方面表现出更高的灵敏度.
  • 通过在所有四个成像通道中使用深度学习分析,对三种卵巢细胞系类型实现了97.1%的平均分类准确度.

结论:

  • 开发的单探测器mIFC系统提供一致的成像通道和高差异化能力.
  • 深度学习分析显著提高了从mIFC数据中单细胞分类的准确性.
  • 这种mIFC技术有望在癌症检测和其他生物医学应用中推进自动化单细胞分析.