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

Thin-Film Engineering of Artificial Interphases for Lithium Batteries.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Epicardial adipose tissue signatures in Asian coronary artery disease: Insights from cardiac CT.

American journal of preventive cardiology·2026
Same author

Vertebral fracture caused by electric shock: case report and systematic review.

Frontiers in surgery·2026
Same author

2025 Singapore consensus statements on the management of osteoporosis.

Annals of the Academy of Medicine, Singapore·2026
Same author

Collaborative Dynamic Optimization Control for Municipal Solid Waste Incineration Process.

IEEE transactions on cybernetics·2026
Same author

Image-based prediction of residential building attributes with deep learning.

Journal of industrial ecology·2026

相关实验视频

Updated: May 24, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

一个三阶段的半监督学习方法来对脊柱图像细分.

Ruixiang Pan, Xiaohong Wang, Zhiping Lin

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 3, 2025
    PubMed
    概括

    这项研究引入了一种新型的半监督学习方法,用于细分脊柱CT图像,提高用有限的标记数据检测骨折的准确性. 这种方法提高了工作负载效率和模型性能,即使有资源限制.

    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

    348
    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 24, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.6K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    348
    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
    • 计算机视觉 计算机视觉

    背景情况:

    • 在计算机断层扫描 (CT) 图像中精确的脊柱细分对于自动化分析至关重要.
    • 现有的数据集往往缺乏标记的断裂数据,阻碍了预测模型的开发.
    • 脊椎相似性对精确的细分提出了挑战.

    研究的目的:

    • 开发一个半监督学习模型,用于脊柱细分,使用标记和未标记的CT数据.
    • 为了减少医疗成像分析的手动注释工作量.
    • 创建一个能够处理断裂数据的模型,而不需要特定的标记断裂数据集.

    主要方法:

    • 基于U-Net架构的三阶段2.5D半监督学习方法被采用.
    • 一个级联框架,模仿临床检查,被用于精确的脊椎细分.
    • 通过2.5D输入补充的2D网络培训被战略性地用于管理大型3DCT数据和GPU约束.

    主要成果:

    • 初步发现表明,该模型对脊柱区域的细分能力有了显著的改善.
    • 该方法显示出有效性,特别是在设备能力有限的环境中.
    • 该方法在提高脊柱细分精度和效率方面表现有前途.

    结论:

    • 拟议的2.5D半监督学习方法为脊柱细分提供了一个可行的解决方案,使用有限的标记数据进行细分.
    • 这种方法有效地解决了数据稀缺性和计算局限性带来的挑战.
    • 需要进一步的研究,以充分评估其在各种临床场景中的潜力,包括骨折检测.