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Guidewire Tracking based on Visual Algorithm for Endovascular Interventional Robotic System

机译:基于视觉算法的血管内介入机器人系统导丝跟踪

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During the Minimally invasive vascular intervention surgery, deformable guidewire tracking is still a challenging task due to background clutter of the image and the complex motion of the target. However, existing researches about guidewire tracking for robot-assisted endovascular catheterization system are still limited. In this paper, scale-adaptive mean-shift method is adopted in endovascular interventional robotic system to detect the position of the guidewire tip. To evaluate the performance of this algorithm in guidewire tracking, two interventional experiment using the rigid model of cerebral vascular were designed. The experimental results show that the ratio of frames with the center location error less than 5 pixels is 97.6% in these two tasks, and the average processing speed for each frame is 1.24ms. The result shows that this algorithm with high precision and real-time has the potential to apply in endovascular interventional robotic system.
机译:在微创血管介入手术期间,由于图像的背景杂乱和目标的复杂运动,可变形的导丝跟踪仍然是一项艰巨的任务。然而,关于机器人辅助血管内导管插入系统的导丝跟踪的现有研究仍然是有限的。本文在血管内介入机器人系统中采用尺度自适应均值漂移方法来检测导丝尖端的位置。为了评估该算法在导丝跟踪中的性能,设计了两个使用脑血管刚性模型的介入实验。实验结果表明,在这两个任务中,中心位置误差小于5个像素的帧的比例为97.6%,每帧的平均处理速度为1.24ms。结果表明,该算法具有较高的实时性,可应用于血管内介入机器人系统。

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