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

Computed Tomography01:10

Computed Tomography

4.5K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.5K

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

Utility of Pancreatic Perivascular Adipose Tissue as a CT Imaging Biomarker for Diagnosing Type 2 Diabetes.

Diabetes·2026
Same author

Ethical implications of high attrition in AI-based mental health interventions: a systematic review and meta-analysis.

BMC medical ethics·2026
Same author

Balloon-occluded hepatic arterial infusion for unresectable hepatocellular carcinoma: a phase II trial interim analysis.

Frontiers in oncology·2026
Same author

Self-Powered Smart Textiles for Accelerated Wound Healing through Band Alignment in Piezoelectric Heterojunctions.

ACS nano·2026
Same author

Drug-eluting beads transarterial chemoembolization improves tumor response and survival compared with conventional TACE in intermediate-stage hepatocellular carcinoma: a retrospective cohort study.

American journal of cancer research·2026
Same author

pH-responsive CDs-based nanoplatform for chemo-resistant esophageal cancer treatment via downregulation of HIF-1α related pathway.

Journal of nanobiotechnology·2026

相关实验视频

Updated: Jun 26, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

10.5K

增强肌肉和脂肪细分用于基于CT的身体组成分析:一项比较性研究.

Benjamin Hou1, Tejas Sudharshan Mathai2, Jianfei Liu2

  • 1National Institutes of Health (NIH) Clinical Center, Bethesda, MD, USA. benjamin.hou@nih.gov.

International journal of computer assisted radiology and surgery
|May 17, 2024
PubMed
概括

使用腹部CT扫描的内部身体组成分析工具在测量皮下脂肪和肌肉方面表现出比TotalSegmentator工具更高的准确性. 在内脏脂肪细分方面也发现了很高的一致性,这表明改善了临床风险评估潜力.

关键词:
身体组成 身体组成这就是为什么CTCTCTCTCTCT胖胖胖的 胖胖的 胖胖的肌肉 肌肉 肌肉 肌肉分段化 分段化 分段化 分段化皮下使用 皮下使用 皮下使用在内脏的内脏.

更多相关视频

Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
13:09

Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography

Published on: April 4, 2012

16.1K
Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
07:33

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

403

相关实验视频

Last Updated: Jun 26, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

10.5K
Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
13:09

Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography

Published on: April 4, 2012

16.1K
Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
07:33

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

403

科学领域:

  • 放射学和医学成像学 医学成像学
  • 生物医学工程 生物医学工程
  • 定量成像技术 定量成像技术

背景情况:

  • 常规的腹部CT扫描为身体组成分析提供了有价值的数据,包括肌肉和脂肪体积和衰减.
  • 这些指标与重要的临床结果有关,例如心血管事件,骨折和死亡率.
  • 这些组织的准确细分对于可靠的风险分层至关重要.

研究的目的:

  • 对肌肉,皮下脂肪和内脏脂肪的内部细分工具的可靠性进行评估.
  • 将内部工具的性能与已建立的公开 TotalSegmentator 工具进行比较.
  • 评估内部工具在增强身体成分分析方面的潜力.

主要方法:

  • 这项研究使用了来自SAROS数据集的900个CT系列.
  • 对皮下脂肪和肌肉的细分精度使用Dice分数进行了评估.
  • 对内脏脂肪的细分协议使用Cohen's Kappa进行了评估,因为缺乏地面真相数据.

主要成果:

  • 内部工具显示,皮下脂肪 (83.8%与80.8%) 和肌肉 (87.6%与83.2%) 的Dice得分有所改善,具有统计学上显著的差异 (p < 0.01).
  • 科恩的卡帕得分为0.856,表明这些工具之间的内脏脂肪细分几乎完全一致.
  • 在肌肉体积 (R2=0.99),肌肉衰减 (R2=0.93) 和皮下脂肪体积 (R2=0.99) 中观察到强烈的相关性.

结论:

  • 内部工具在细分皮下脂肪和肌肉方面表现优于TotalSegmentator.
  • 对内脏脂肪细分的高度一致性表明了内部工具的可靠性.
  • 这些发现凸显了内部工具在提升CT扫描身体组成分析精度方面的潜力.