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

Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

3
Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
3
Two-Dimensional Microscopy in Microbiology01:29

Two-Dimensional Microscopy in Microbiology

6
Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
6
Overview of Microscopy Techniques01:22

Overview of Microscopy Techniques

9.8K
The early pioneers of microscopy opened a window into the invisible world of microorganisms. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes that leveraged nonvisible light, such as fluorescence microscopy that uses an ultraviolet light source and electron microscopy that uses short-wavelength electron beams. These advances significantly improved magnification, image resolution, and contrast. By comparison, the...
9.8K
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

13.1K
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,...
13.1K
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

6.9K
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...
6.9K
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

4.6K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
4.6K

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

Done EAZY: An Automated Procedure for <sup>89</sup>Zr-Radiolabeling and Size-Exclusion Chromatography Purification of Nanoliposomal Anticancer Therapeutics.

Molecular pharmaceutics·2026
Same author

SAMJ: fast image annotation on ImageJ/Fiji via segment anything model.

Nature communications·2026
Same author

Predicting Clinical Sensitivities of PDGFRA Exon 18 Mutations to Imatinib and Avapritinib to Optimize Gastrointestinal Stromal Tumor Treatment.

Cancer research communications·2026
Same author

HDAC7 controls anti-viral and anti-tumor immunity by CD8<sup>+</sup> T cells.

Frontiers in immunology·2026
Same author

Multi-layered molecular profiling informs the diagnosis and targeted therapy of desmoplastic small round cell tumor.

Nature communications·2026
Same author

MyCODE: a prospective evaluation of lay-language molecular tumor board protocols in precision oncology.

The oncologist·2026

相关实验视频

Updated: Jun 9, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.4K

显微镜中的机器学习 - - 洞察力,机遇和挑战

Inês Cunha1, Emma Latron1, Sebastian Bauer1

  • 1Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Tomtebodavägen 23, 171 65 Solna, Sweden.

Journal of cell science
|October 28, 2024
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 通过提供数据策划,探索和预测的新工具来增强显微镜图像分析. 本综述指导用户如何有效地利用ML,同时减轻生命科学研究中常见的挑战.

关键词:
分析 分析 分析生物信息学是一种生物信息学.数据 数据 数据 数据 数据图像分析 图像分析机器学习 机器学习显微镜的使用方法

更多相关视频

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

3.9K
In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses
07:26

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

Published on: December 5, 2019

7.9K

相关实验视频

Last Updated: Jun 9, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.4K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

3.9K
In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses
07:26

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

Published on: December 5, 2019

7.9K

科学领域:

  • 生命科学研究 生命科学研究
  • 显微镜 图像分析
  • 计算生物学 计算生物学

背景情况:

  • 机器学习 (ML) 正在彻底改变图像处理和分析.
  • 它在显微镜中的应用为生命科学研究提供了巨大的潜力.
  • 了解ML的作用对于推进图像驱动的生物研究至关重要.

研究的目的:

  • 审查将ML管道应用于显微镜数据集的机会和挑战.
  • 以指导用户根据数据特征选择合适的ML模型.
  • 讨论ML在显微镜中的实用性和潜在陷.

主要方法:

  • 在显微镜图像分析中对ML应用的审查.
  • 分析影响ML模型选择的数据特征 (数量,可转移性,内容).
  • 探索ML的实用范围:策划,探索,预测和解释.

主要成果:

  • 机器学习在显微镜领域提供了多样化的应用,包括自动化和模式发现.
  • 数据特征显著影响了ML模型的选择和结果.
  • ML实用程序涵盖了细胞生物学中的数据策划,探索,预测和解释.

结论:

  • 机器学习为显微镜提供了重大机遇,但也带来了挑战和风险.
  • 仔细考虑数据和模型选择对于成功实施ML至关重要.
  • 建议采取缓解策略,以解决常见的ML文物和显微镜中的陷.