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基于深度学习的对大动脉形态的分析,来自三维MRI
Jia Guo1,2, Kevin Bouaou1,2, Sophia Houriez-Gombaud-Saintonge1,2,3
1Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale (LIB), Paris, France.
Journal of magnetic resonance imaging : JMRI
|January 12, 2024
概括
本研究介绍了一种深度学习 (DL) 方法,用于使用3DMRI进行自动化大动脉形态分析. DL方法准确地检测着标志和分段的大动脉,提高了患者评估的效率和可重复性.
科学领域:
- 医疗成像医学成像
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
背景情况:
- 准确量化大动脉形态对于诊断和监测大动脉疾病至关重要.
- 目前的手动测量方法耗时且容易变化.
- 需要自动化解决方案来提高大动脉评估的质量和可重复性.
研究的目的:
- 开发一种基于深度学习 (DL) 的自动化方法,用于从3DMRI中检测大动脉的地标和光线.
- 为了使大动脉形态的有效和可重复量化.
主要方法:
- 这是一项回顾性研究,涉及391名个人 (健康人和有大动脉疾病的患者),使用3DMRI数据.
- 一个两阶段的深度学习网络,通过强化学习训练用于地标检测和细分.
- 使用子相似系数 (DSC),豪斯多夫距离 (HD),平均对称表面距离 (ASSD) 和欧几里德距离 (ED) 的评估.
主要成果:
- 该DL方法实现了高分段精度 (DSC:0.90±0.05) 和精确的地标检测 (ED:5.0±6.1mm).
- 在DL衍生和参考大动脉指数 (r>0.95,平均偏差<7%) 之间观察到很强的一致性.
- 性能指标与观察者之间的变化相比较,除了突管结的里程碑.
结论:
- 成功开发了一种基于DL的新方法,用于自动化大动脉细分和从3DMRI检测里程碑.
- 这种方法为临床应用提供了客观和可重复的大动脉形态评估.
- 该自动化系统显示了提高对大动脉疾病患者评估的潜力.
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