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通过对视图之间的空间关系进行监督,用于心脏磁共振成像的自动视图平面处方
Dong Wei1, Yawen Huang1, Donghuan Lu1
1Tencent Jarvis Lab, Shenzhen, China.
Medical physics
|September 6, 2023
概括
这项研究引入了一种用于心脏磁共振 (CMR) 视图规划的自动化系统,消除了手动注释的需要. 深度学习方法准确地规定CMR成像平面,提高临床实践的效率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 心脏磁共振 (CMR) 图像查看计划在临床环境中是复杂和耗时的.
- 目前的自动化方法需要额外的体积数据或手动地标注释.
研究的目的:
- 开发一个与诊所兼容的,无注释的系统,用于自动化CMR视图规划.
- 为了消除需要额外的体积扫描或手动输入.
主要方法:
- 利用基于U-Net的深度网络,根据目标平面和源视图之间的空间关系回归热图.
- 采用堆叠的沙钟架构和多视图规划策略,以提高准确性.
- 利用DICOM数据固有的空间信息,消除了手动注释要求.
主要成果:
- 在一组181次CMR检查的测试中,获得了5.68°的平均角差和3.12mm的点对平面距离.
- 与标准CMR平面的现有以图谱为基础和深度学习方法相比,证明了更高的准确性.
- 成功地从面向身体的侦察员处分心脏-解剖面向的平面.
结论:
- 开发的深度学习系统使用存档数据实现了准确的,自动的CMR视图平面处方.
- 该系统消除了对手动注释的需求,提供了显著的进步.
- 这种方法为在CMR之外的其他面向解剖学的医学成像模式中自动视图规划铺平了道路.
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