超声波短轴视图中的U-Net及其变体基于辐射动脉的自动跟踪:一项试点研究
Yuan Tian1, Ruiyang Gao2, Xinran Shi2
1Department of Anesthesiology, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing 100730, China.
Diagnostics (Basel, Switzerland)
|November 9, 2024
概括
这项研究表明,深度学习模型,特别是Res-UNet,可以准确地自动化辐射动脉跟踪 (RAT) 超声指导程序. 这为更一致,更有效的导管注射铺平了道路.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 心血管干预 心血管干预
背景情况:
- 辐射动脉跟踪 (RAT) 对于超声导向的辐射动脉导管 (RAC) 非常重要.
- 当前的方法可能存在差异,需要自动化解决方案.
- 这项试点研究探讨了自动化RAT的深度学习.
研究的目的:
- 评估U-Net及其自动辐射动脉跟踪 (RAT) 变体的可行性.
- 为了确定最佳的深度学习架构,以获得准确的RAT.
- 建立自动超声导向放射性动脉导管 (RAC) 的基础.
主要方法:
- 包括U-Net和其变体 (注意U-Net,UNet++,Res-UNet,TransUNet,UNeXt) 在内的深度学习模型被训练在超声波图像上.
- 绩效使用子相似系数 (DSC) 和贾卡德相似系数 (JSC) 来评估.
- 克鲁斯卡尔-瓦利斯测试分析了模型之间的性能差异.
主要成果:
- Res-UNet获得了最高的DSC (93.14%) 和JSC (87.93%).
- Res-UNet表现出比U-Net和注意力U-Net显著改善 (p < 0.05).
- 大多数模型都显示出一致的损失函数收,除了注意力U-Net.
结论:
- U-Net及其变体可用于自动辐射动脉跟踪 (RAT).
- 在测试中,Res-UNet是自动化RAT的主要架构.
- 这项研究支持自动超声导向放射性动脉导管 (RAC) 的发展.
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