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

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
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
Fixation and Sectioning01:03

Fixation and Sectioning

4.2K
Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...
4.2K

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

Targeting metabolic dependencies to reverse chemoradiotherapy resistance in colorectal cancer.

Journal of experimental & clinical cancer research : CR·2026
Same author

VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data.

Genome medicine·2026
Same author

The Protein Phosphatase Inhibitor LB100 Targets the Mesenchymal Lineage of Pancreatic Ductal Adenocarcinoma.

MedComm·2026
Same author

Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels.

European journal of cancer (Oxford, England : 1990)·2026
Same author

Pretreatment MRI: One of the Key Components of Treatment Planning.

Deutsches Arzteblatt international·2026
Same author

Zentralblatt fur Chirurgie·2026
Same journal

A comprehensive benchmark of sequence-based subcellular localization predictors for human proteins.

Nature methods·2026
Same journal

Efficient evidence-based genome annotation with EviAnn.

Nature methods·2026
Same journal

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.

Nature methods·2026
Same journal

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Nature methods·2026
Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methods·2026
Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026
查看所有相关文章

相关实验视频

Updated: Jun 14, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.5K

细分任何用于显微镜的东西.

Anwai Archit1, Luca Freckmann1, Sushmita Nair1

  • 1Georg-August-University Göttingen, Institute of Computer Science, Goettingen, Germany.

Nature methods
|February 12, 2025
PubMed
概括
此摘要是机器生成的。

我们向您介绍了微观镜 (μSAM) 的任何部分,这是一种工具,可以改善显微镜研究人员的图像细分. 这种先进的深度学习模型在各种成像条件下提高了细分质量.

更多相关视频

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images
14:28

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

Published on: July 15, 2020

7.9K
A Method for Obtaining Serial Ultrathin Sections of Microorganisms in Transmission Electron Microscopy
09:46

A Method for Obtaining Serial Ultrathin Sections of Microorganisms in Transmission Electron Microscopy

Published on: January 17, 2018

14.2K

相关实验视频

Last Updated: Jun 14, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.5K
Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images
14:28

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

Published on: July 15, 2020

7.9K
A Method for Obtaining Serial Ultrathin Sections of Microorganisms in Transmission Electron Microscopy
09:46

A Method for Obtaining Serial Ultrathin Sections of Microorganisms in Transmission Electron Microscopy

Published on: January 17, 2018

14.2K

科学领域:

  • 显微镜的使用方法
  • 图像分析 图像分析
  • 人工智能的人工智能

背景情况:

  • 精确的对象细分在显微镜图像中至关重要,但具有挑战性.
  • 现有的工具经常与各种成像条件和模式作斗争.
  • 这就需要先进的解决方案来进行高效和可靠的图像分析.

研究的目的:

  • 介绍"微镜的任何部分" (μSAM),这是一个用于分割和跟踪多维显微镜数据的新工具.
  • 为了增强针对特定显微镜应用的通用分段任何模型的功能.
  • 为显微镜图像注释提供统一高效的解决方案.

主要方法:

  • 利用分段任何东西 (基础模型) 来进行图像分割.
  • 在光和电子显微镜数据集上微调通用主义模型.
  • 开发一个交互式和自动细分 napari插件.

主要成果:

  • μSAM 显著提高了在广泛的显微镜成像条件下的细分质量.
  • 该工具在光和电子显微镜方面表现出增强的性能.
  • 纳帕里插件加速了各种细分任务,并统一了跨模式的注释.

结论:

  • μSAM在将视觉基础模型应用于显微镜图像分析方面取得了重大进展.
  • 该工具提供了一种强大而通用的解决方案,用于细分和跟踪多维显微镜数据.
  • μSAM为未来基于深度学习的显微镜图像分析解决方案奠定了基础.