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

Spinal Cord: Cross-sectional Anatomy01:16

Spinal Cord: Cross-sectional Anatomy

The cross-sectional anatomy of the spinal cord offers a detailed view of its complex structure and function within the central nervous system. At the core of the spinal cord lies the gray matter, characterized by its butterfly or "H"-shaped appearance in cross-section. This central region is enveloped by white matter, with the overall structure divided into symmetrical halves by the dorsal median sulcus and the ventral median fissure.
Gray Matter and its Components
Central to the gray matter is...

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

Branch architecture and flowering time are separately regulated by GhFL and GhSP2 in cotton.

The Plant journal : for cell and molecular biology·2026
Same author

Genomic reconstruction of upland cotton domestication uncovers staged selection, gene flow, and flowering-time adaptation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Genome-wide identification and expression analysis of IDD gene family in Gossypium hirsutum L.

Functional & integrative genomics·2026
Same author

Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.

EJNMMI physics·2026
Same author

Enhanced Root Exudation as an Adaptation Mechanism to Facilitate Phosphorus Mobilization in a Primary Tropical Forest Under Chronic Nitrogen Deposition.

Global change biology·2026
Same author

Dose reduction for synaptic density PET imaging in Parkinson's disease.

NeuroImage·2026

相关实验视频

Updated: May 8, 2026

Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

1.9K

一个新的双分支细分算法,用于整体脊柱细分.

Tian Gao1,2, He Zhang3, Yuhan Ying1,2,4

  • 1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.

Quantitative imaging in medicine and surgery
|April 16, 2025
PubMed
概括

深度学习模型DBU-Net在计算机断层扫描 (CT) 图像中准确标记脊椎. 这种自动化方法提高了手术精度,并减少了脊柱手术期间的组织损伤.

关键词:
计算机断层扫描 (CT) 扫描语境注意力机制的注意力机制深度学习是一种深度学习.图像分割 图像细分 图像细分脊柱 脊柱 脊柱 脊柱 脊柱

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

321
Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites
07:45

Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites

Published on: September 27, 2024

2.0K

相关实验视频

Last Updated: May 8, 2026

Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

1.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

321
Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites
07:45

Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites

Published on: September 27, 2024

2.0K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 神经外科 神经外科

背景情况:

  • 准确识别骨结构对于手术规划和最小化损伤至关重要.
  • 在计算机断层扫描 (CT) 图像中自动标记脊椎是一个具有挑战性但重要的任务.
  • 深度学习为自动化复杂的医学图像分析任务提供了一个有希望的途径.

研究的目的:

  • 开发一种用于CT图像中标记脊椎的自动化方法.
  • 为了提高脊柱结构识别的效率和准确性,为外科指导提供指导.
  • 在nnUnet框架内引入DBU-Net深度学习细分网络.

主要方法:

  • DBU-Net包含一个多尺度的功能通道注意模块,以整合来自不同图像尺度的信息.
  • 一个双分支解码器架构,增强了一个上下文变压器模块,捕获全球上下文信息.
  • 两个分支的功能在每个解码阶段相互作用,将全球上下文与本地细节合并,以改善细分.

主要成果:

  • DBU-Net在脊椎细分 (VerSe) 数据集 (MICCAI 2019和2020) 上进行了评估.
  • 该网络实现了最先进的性能,平均子系数为94.59%.
  • 这些结果证明了DBU-Net在细分脊柱结构方面的有效性.

结论:

  • DBU-Net显示出有很大的潜力,可以帮助外科医生准确识别脊柱结构.
  • 自动化细分可以导致更精确的手术执行和更好的疾病诊断.
  • 这种深度学习方法为脊柱相关干预提供了强大的技术支持.