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相关概念视频

Spinal Cord: Cross-sectional Anatomy01:16

Spinal Cord: Cross-sectional Anatomy

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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...
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Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
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基于深度学习的重力加载人类脊椎的细分.

Hanyu Li1, Zejun Liang2, Jianrong He2

  • 1Department of Procurement and Supply, West China Hospital, Sichuan University.

Journal of visualized experiments : JoVE
|June 30, 2025
PubMed
概括

本研究介绍了一种深度学习方法,用于在承载条件下对脊椎的3D图像进行细分. 这提高了诊断脊柱疾病的准确性,例如脊柱结症.

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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科学领域:

  • 生物医学工程 生物医学工程
  • 放射学 放射学是一门学科.
  • 人工智能的人工智能

背景情况:

  • 准确的脊柱对齐分析对于肌肉骨疾病至关重要.
  • 传统的成像缺乏3D负载支脊柱数据.
  • 承重性CBCT提供了改善的脊柱成像.

研究的目的:

  • 开发一种深度学习协议,用于对负载CBCT图像进行细分.
  • 为了使脊柱在重力下对齐的精确3D分析.
  • 为了促进脊柱状况的准确诊断和管理.

主要方法:

  • 使用了一个U-Net卷积神经网络 (CNN),具有3D卷积层和剩余连接.
  • 开发了一种包含图像采集,手动注释,预处理和模型训练的协议.
  • 专注于从负载CBCT扫描中对脊椎体,骨盆和股骨头进行细分.

主要成果:

  • 在重力加载的脊柱图像中成功细分了关键的解剖结构.
  • 能够准确测量临床参数,如科布角和脊椎旋转.
  • 生成适合3D打印的3D模型,用于外科手术规划和教育.

结论:

  • 深度学习协议提供了一种可靠的方法,用于在承重条件下对脊柱结构进行细分.
  • 这种方法可以提高脊椎疾病的诊断准确性.
  • 该协议可适应在负载下对其他解剖结构进行细分,具有广泛的临床和研究应用.