基于深度学习辅助细分的正常上腺的年龄相关形态学
Yuanchong Chen1, Jiejin Yang1, Yaofeng Zhang2
1Department of Radiology, Peking University First Hospital, Beijing, 100034, China.
Heliyon
|June 22, 2023
概括
这项研究使用深度学习来测量成人正常上腺体大小. 男性上腺的体积在38-47岁之间达到顶峰,根据年龄和性别存在显著差异.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 人类解剖学和生理学 人类解剖学和生理学
背景情况:
- 精确的上腺体形态分析对于诊断各种内分泌和瘤疾病至关重要.
- 传统的手动测量方法耗时,容易引起观察者之间的变化.
- 深度学习为自动化和精确的解剖细分提供了一个有希望的方法.
研究的目的:
- 用半自动深度学习细分模型评估成年患者正常上腺的形态测量.
- 为确定上腺体体体积,CT值和直径的规范性数据.
- 为了研究年龄和性别对上腺体形态学的影响.
主要方法:
- 追溯收集520个腹部CT系列用于模型训练和1043个用于分析.
- 开发和应用基于3D U-Net的深度学习模型,用于上腺细分.
- 手动细化细分,然后对体积,CT值和直径进行定量分析.
主要成果:
- 在所有患者中,左上腺明显大于右.
- 与所有年龄组的女性相比,男性患者表现出更大的双边上腺体体积.
- 男性的上腺体体积呈现了年龄相关的趋势,在38-47岁之间达到峰值.
结论:
- 半自动深度学习细分提供可靠的上腺体形态测量.
- 上腺体体积显示出与年龄和性别的显著差异.
- 在38-47岁的男性中确定了上腺体体积的峰值,为规范性数据做出了贡献.
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