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

Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...
Bones of the Upper Limb: Humerus01:19

Bones of the Upper Limb: Humerus

The upper limb consists of the arm, forearm, wrist, and hand bones. The humerus is the single bone of the upper arm region. Proximally, it has a large, spherical, smooth head that articulates with the glenoid cavity of the scapula to form the glenohumeral or shoulder joint. The margin of the head is the anatomical neck, a residual epiphyseal plate. Laterally it extends to form bony projections called the greater tubercle and the lesser tubercle. Next to the tubercles is the surgical neck, a...
Bones of the Upper Limb: Ulna01:15

Bones of the Upper Limb: Ulna

The ulna and radius are parallel bones of the antebrachium or the forearm. The ulna lies medially and consists of a bony tip called the olecranon process at its proximal end. This hook-like projection articulates with the olecranon fossa of the humerus and forms the "hinged" ulnohumeral part of the elbow joint. This joint facilitates forearm extension and flexion while preventing its hyperextension. Similarly, the coronoid process, another bony projection on the proximal/anterior side of the...

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

Correlation between microbial source tracking markers and pathogens at beaches and estuaries in southern California.

The Science of the total environment·2026
Same author

Zero echo time MRI with deep learning reconstruction and chemical shift correction for detecting osteolytic myeloma lesions.

European radiology experimental·2026
Same author

Quantitative Ultrashort Echo-Time MRI to Assess In Vivo Rotator Cuff Tendon Quality.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society·2026
Same author

Fat-suppressed quantitative ultrashort echo time magnetization transfer (UTE-MT) imaging of the knee joint.

Magma (New York, N.Y.)·2026
Same author

A Deep Dive into the Molecular and Immune Landscape of Undifferentiated Carcinomas with Osteoclast-like Giant Cells.

Cells·2026
Same author

Applications of a 32-channel ultra-flexible phased-array prototype coil design optimized for small joint 3-Tesla MRI.

Physics in medicine and biology·2026
查看所有相关文章

相关实验视频

Updated: May 11, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.6K

使用DeepLab和U-Net进行肩骨细分.

Michael Carl1, Kaustubh Lall2, Darren Pai3

  • 1General Electric Healthcare, Menlo Park, CA.

Osteology (Basel, Switzerland)
|October 30, 2024
PubMed
概括
此摘要是机器生成的。

这项研究表明,与DeepLab相比,U-Net深度学习模型在零回声时间MRI扫描上实现了更高的准确性,帮助进行手术前规划.

关键词:
深度实验室 (DeepLab) 是一个深度实验室.这就是为什么MRI是MRI.这就是U-Net.中兴通讯公司 ZTE膜部的膜部是如何形成的?状腺体是什么 状腺体部头部 部头部图像处理是图像处理的过程.

更多相关视频

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

2.7K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.4K

相关实验视频

Last Updated: May 11, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.6K
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

2.7K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.4K

科学领域:

  • 放射学 放射学是一门学科.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 精确的3D骨形态评估关节关节对于手术前的规划至关重要.
  • 零回声时间 (ZTE) 磁共振成像 (MRI) 提供了出色的骨对比度,可能取代计算机断层扫描.
  • 肩部解剖学的自动细分是需要进行详细的评估和手术准备的.

研究的目的:

  • 为了比较DeepLab和U-Net深度学习模型的性能,用于自动对ZTEMRI上的手臂骨和腹骨进行细分.
  • 评估深度学习在改善肩膀MRI分析方面的潜力.

主要方法:

  • 两个深度学习模型,DeepLab和2D U-Net,在正常肩膀的轴向ZTEMRI扫描上进行了训练和验证,用于细分.
  • 模型在31个肩膀上接受训练,并在13个肩膀上进行测试.
  • 使用子得分量化表现.

主要成果:

  • 这两种模型都提供了视觉上令人满意的部骨的细分.
  • 在部细分精度 (p<0.05) 中,U-Net (88%的子得分) 显著超过了DeepLab (81%的子得分).
  • 与地面真相相比,U-Net显示略高估值,而DeepLab显示低估值.

结论:

  • 在ZTEMRI上,U-Net在自动化部骨分割方面表现出卓越的性能.
  • 在MRI控制台上实现U-Net允许按键深度学习细分处理.
  • 这种方法有可能通过减少手工后处理和帮助可视化膜关节来提高肩膀MR评估的潜力.