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Fast catheter segmentation From echocardiographic sequences based on segmentation from corresponding x-ray fluoroscopy for cardiac catheterization interventions

机译:基于超声心动图序列的快速导管分割,该分割基于相应的X射线透视的分割,用于心脏导管干预

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摘要

Echocardiography is a potential alternative to X-ray fluoroscopy in cardiac catheterization given its richness in soft tissue information and its lack of ionizing radiation. However, its small field of view and acoustic artifacts make direct automatic segmentation of the catheters very challenging. In this study, a fast catheter segmentation framework for echocardiographic imaging guided by the segmentation of corresponding X-ray fluo- roscopic imaging is proposed. The complete framework consists of: 1) catheter initialization in the first X-ray frame; 2) catheter tracking in the rest of the X-ray sequence; 3) fast registration of corresponding X-ray and ultrasound frames; and 4) catheter segmentation in ultrasound images guided by the results of both X-ray tracking and fast registration. The main contributions include: 1) a Kalman filter-based growing strategy with more clin- ical data evalution; 2) a SURF detector applied in a constrained search space for catheter segmentation in ultrasound images; 3) a two layer hierarchical graph model to integrate and smooth catheter fragments into a complete catheter; and 4) the integration of these components into a system for clinical applications. This framework is evaluated on five sequences of porcine data and four sequences of patient data comprising more than 3000 X-ray frames and more than 1000 ultrasound frames. The results show that our algorithm is able to track the catheter in ultrasound im- ages at 1.3 s per frame, with an error of less than 2 mm. However, although this may satisfy the accuracy for visualization purposes and is also fast, the algorithm still needs to be further accelerated for real-time clinical applications.
机译:超声心动图由于其丰富的软组织信息且缺乏电离辐射,因此在心脏导管检查中是X射线荧光透视术的潜在替代方法。然而,其较小的视野和声学伪影使导管的直接自动分割非常具有挑战性。在这项研究中,提出了一种由相应的X射线荧光透视成像分割指导的超声心动图成像快速导管分割框架。完整的框架包括:1)在第一个X射线框架中初始化导管; 2)在其余的X射线序列中跟踪导管; 3)快速注册相应的X射线和超声帧; 4)由X射线跟踪和快速配准的结果指导的超声图像中的导管分割。主要贡献包括:1)具有更多临床数据评估的基于卡尔曼滤波器的增长策略; 2)将SURF检测器应用在受限的搜索空间中以对超声图像中的导管进行分割; 3)两层分层图模型,将导管碎片整合并平滑成一个完整的导管;和4)将这些组件集成到临床应用系统中。该框架是根据五个猪数据序列和四个患者数据序列(包括3000多个X射线框架和1000多个超声框架)进行评估的。结果表明,我们的算法能够在每帧1.3 s的超声图像中跟踪导管,误差小于2 mm。然而,尽管这可以满足可视化目的的准确性并且也很快,但是对于实时临床应用仍然需要进一步加速该算法。

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