一个全新的框架,用于对血管内超声波和光学连贯性断层扫描成像数据的全自动联合注册
Xingwei He1,2, Kit Mills Bransby3, Ahmet Emir Ulutas4
1Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.
一个新的深度学习框架自动化了血管内超声波 (IVUS) 和光学连贯性断层扫描 (OCT) 图像的对齐. 这种人工智能方法实现了专家级准确性和速度,用于多式联络心血管成像研究.
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:
- 血管内超声波 (IVUS) 和光学连贯性断层扫描 (OCT) 的准确联合注册对于全面的心血管评估至关重要.
- 手动调整IVUS和OCT数据是耗时的,容易引起观察者之间的变化.
研究的目的:
- 开发一个深度学习 (DL) 框架,用于完全自动的IVUS和OCT图像的纵向和环形联合注册.
- 与专家分析相比,评估DL框架的准确性和效率.
主要方法:
- 一个DL模型被训练使用专家注释的IVUS和OCT数据从230名急性心肌梗塞患者.
- 纵向联合注册使用了动态时间扭曲算法,而环形注册使用了旋转成本矩阵和动态编程.
- 框架提取了光膜边界,侧枝和状组织特征以进行对齐.
主要成果:
- 对于纵向 (CCC>0.99) 和周边 (CCC>0.90) 联合注册,DL框架与专家分析师达成高度一致.
- 威廉姆斯指数值为0.96 (纵向) 和0.97 (周边) 表示性能与专家可比.
- 对于DL管道的处理时间为每艘船不到90秒.
结论:
- 一个完全自动化的基于DL的IVUS-OCT联合注册框架提供了速度和准确性.
- 该框架的性能与专家分析师相当,使其适合大规模的多式联络成像研究.
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