基于深度学习的全自动主动脉说明书和计算机断层扫描图像的根测量 - - 一项可行性研究
Haruo Yamauchi1, Gakuto Aoyama2, Hiroyuki Tsukihara1,3
1Department of Cardiovascular Surgery, Graduate School of Medicine, The University of Tokyo.
概括
一个新的深度学习算法自动化了CT扫描中的大动脉和根测量,大大缩短了分析时间. 这种工具有助于规划治疗大动脉扩张,狭窄和吐的治疗方法.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 医学图像分析 医学图像分析
背景情况:
- 大动脉根扩张 (RD) 需要准确的解剖评估.
- 现有的自动测量算法需要针对特定条件进行重新训练.
研究的目的:
- 用CT数据重新训练深度学习算法,用于自动化大动脉/根测量.
- 评估重新训练算法的临床可行性和准确性.
主要方法:
- 重新训练了一种深度学习算法,使用了来自RD患者的67个ECG-gated心脏CT扫描.
- 在100个额外的CT数据集 (大动脉狭窄,大动脉吐) 上评估了算法.
- 将自动测量与专家手动测量进行比较,并评估测量时间.
主要成果:
- 该算法提供了3D可视化和大动脉/根的自动测量.
- 在自动和手动测量之间发现了中等至高的相关性.
- 自动测量比手动测量 (618-1126秒) 快得多 (122秒).
结论:
- 自动算法有助于评估大动脉/根动脉的解剖学.
- 它有助于规划手术和透导管治疗.
- 该算法节省了时间,并最大限度地减少了临床医生的工作量.
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