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相关概念视频

Abdominal Regions and Quadrants01:19

Abdominal Regions and Quadrants

To promote clear communication, for instance, about the location of a patient's abdominal pain or a suspicious mass, anatomists and clinicians typically use imaginary lines to categorize the abdominopelvic cavity into either four quadrants or nine regions to identify organs in the cavity.
The simpler quadrants approach, which is more commonly used in medicine, subdivides the cavity with one horizontal and one vertical line that intersects at the patient's umbilicus (navel). The four quadrants...

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相关实验视频

Updated: Jun 19, 2026

In situ Quantification of Pancreatic Beta-cell Mass in Mice
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在CT中基于里程碑的胰腺子区域细分.

Yan Zhuang1,2, Abhinav Suri3, Tejas Sudharshan Mathai3

  • 1Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Journal of imaging informatics in medicine
|April 30, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种自动化的3D工具,用于CT扫描上对胰腺子区域 (头部,身体,尾部) 进行细分. 这使得特定区域的成像生物标志物能够在胰腺病理中更好地预测疾病严重程度.

关键词:
这就是为什么CTCTCTCTCT胰腺是什么? 胰腺是什么?分段化 分段化 分段化 分段化亚区域 亚区域是指一个亚区域.

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Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
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Whole Body and Regional Quantification of Active Human Brown Adipose Tissue Using 18F-FDG PET/CT
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科学领域:

  • 医疗成像医学成像
  • 放射学 放射学是一门学科.
  • 计算解剖学的计算解剖学

背景情况:

  • 基于CT的成像生物标志物对于检测胰腺病理至关重要.
  • 目前的方法缺乏特定区域的生物标志物,这阻碍了对胰腺腺癌等疾病的准确疾病严重程度预测.
  • 胰腺亚区域细分对于开发精确的诊断工具至关重要.

研究的目的:

  • 在CT卷上开发一个自动化的3D工具来对胰腺子区域 (头部,身体,尾部) 进行细分.
  • 为了使区域特定的成像生物标志物的导出,以加强疾病检测和严重性评估.
  • 通过精确的解剖细分来提高胰腺病理的诊断能力.

主要方法:

  • 一项回顾性研究使用了来自TotalSegmentator数据集的549个CT卷,其中30个来自TCIA NIH胰腺-CT数据集,用于外部验证.
  • 一个全分辨率的3DnnUNet模型被训练有自定义的损失功能来检测胰腺的头部,身体和尾部的地标.
  • 一个后处理算法根据检测到的地标生成了子区域细分,使用子相似系数 (DSC) 和规范表面距离 (NSD) 进行评估.

主要成果:

  • 该模型在细分胰腺子区域方面取得了高准确性,平均DSC和NSD值在内部数据集上表现强.
  • 对TCIA NIH胰腺-CT数据集的外部验证证实了该模型的通用性,为所有子区域提供了可比的DSC和NSD分数.
  • 自动细分成功区分了胰腺的头部,体部和尾部,提供了准确的特定区域数据.

结论:

  • 开发的3D工具准确地在CT卷上细分胰腺的头部,身体和尾部.
  • 这使得能够推导出特定区域的成像生物标志物,这对于预测疾病严重程度至关重要.
  • 自动化方法增强了改善胰腺病理的诊断和管理的潜力.