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

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

HYPER-Net: Physics-Conditioned Self-Supervised Reconstruction for Fourier Light-Field Microscopy.

bioRxiv : the preprint server for biology·2026
Same author

Dimorphic neural network architecture prioritizes sexual-related behaviors in male <i>Caenorhabditis elegans</i>.

eLife·2026
Same author

A Facile Method Based on Faster R-CNN for Cell Detection in Microfluidic Devices.

Analytical chemistry·2026
Same author

Hungry for Knowledge: Octopamine Signaling Regulates Hunger-Enhanced Olfactory Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Global trends in Alzheimer's disease and other dementias: A comprehensive analysis of incidence, socio-demographic variations, and future projections.

PloS one·2025
Same author

Microfluidics-Enabled Simultaneous Imaging of Neural Activity and Behavior in Chemically Stimulated, Head-Fixed <i>C. elegans</i>.

bioRxiv : the preprint server for biology·2025
Same journal

Non-canonical amino acid incorporation enables minimally disruptive labeling of stress granule and TDP-43 proteinopathy.

eLife·2026
Same journal

Analysis of dendritic input currents during place field dynamics.

eLife·2026
Same journal

TopoMetry systematically learns and evaluates the latent geometry of single-cell data.

eLife·2026
Same journal

Navigating the path: Advice to physician-scientists on choosing a clinical specialty.

eLife·2026
Same journal

Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex.

eLife·2026
Same journal

The exquisite mechanics of a tsetse bite.

eLife·2026
查看所有相关文章

相关实验视频

Updated: May 31, 2025

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.5K

在多细胞图像中使用改进的CRF_ID算法进行自动化的单元格注释.

Hyun Jee Lee1, Jingting Liang2, Shivesh Chaudhary1

  • 1School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, United States.

eLife
|January 24, 2025
PubMed
概括
此摘要是机器生成的。

我们先进的自动化细胞识别用于生物图像. 新的CRF_ID 2.0方法提高了Caenorhabditis elegans多细胞成像的准确性,减少了主观性并加快了分析速度.

关键词:
这里是C. elegans.细胞识别 细胞识别影像成像技术 影像成像技术神经基因表达神经基因表达神经科学 神经科学

更多相关视频

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images
10:00

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

Published on: August 31, 2012

14.5K
Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States
06:25

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

Published on: January 19, 2024

918

相关实验视频

Last Updated: May 31, 2025

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.5K
An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images
10:00

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

Published on: August 31, 2012

14.5K
Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States
06:25

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

Published on: January 19, 2024

918

科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 神经科学是一个神经科学.

背景情况:

  • 自动细胞识别对于生物图像数据分析至关重要.
  • 以前的CRF_ID方法在Caenorhabditis elegans全脑图像中显示出高性能.
  • 没有保证CRF_ID在显示细胞亚群的多细胞图像上具有相似的性能.

研究的目的:

  • 为了介绍CRF_ID 2.0,这是一个进步,将自动细胞识别的普遍性扩展到多细胞成像.
  • 为了说明CRF_ID 2.0在C. elegans多细胞成像中的应用和特征.
  • 为了证明细胞特异性基因表达分析的实用性.

主要方法:

  • 开发CRF_ID 2.0,一个增强的自动化细胞识别算法.
  • 在C. elegans多细胞图像上应用和验证CRF_ID 2.0.
  • 与细胞特异性基因表达分析工作流程的整合.

主要成果:

  • CRF_ID 2.0证明了超越全脑应用的多细胞成像的改进概括性.
  • 在C. elegans多细胞图像中实现了高精度的自动细胞注释.
  • 该方法有效支持细胞特异性基因表达分析.

结论:

  • CRF_ID 2.0显著加快了C. elegans多细胞成像中的细胞识别.
  • 这种进步减少了生物图像分析中的主观性.
  • CRF_ID 2.0 具有各种生物图像数据集的潜力.