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B-spline based Motion Tracking of Cardiac Surface in Minimally Invasive Surgery

机译:微创手术中基于B样条的心脏表面运动跟踪

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摘要

This paper investigates the problem of tracking heart motion of patients when performing minimally invasive cardiac surgeries. In the existing literature, most algorithms for tracking the heart motion are based on a region or feature of the heart surface. A disadvantage of the former method is consuming time, and hence difficult to meet the demand of clinical surgery. The focus of this paper is on the latter, i.e. a feature of the heart surface. First, we employ the BRIEF feature for describing and matching the binary features on the cardiac surface. Based on the matching result, we then propose a new, fast and stable tracking algorithm via the Bspline interpolation. The sparse motion field of the heart image is obtained by matching the binary features, and the motion of the target point is built by using the B-spline interpolation method. In this way, positions of the target points on the subsequent heart images can be obtained. Finally, we use the triangulation algorithm to compute the 3D coordinates of the target point. The novelty of our method includes the binary features for describing the heart surface and an efficient Bspline algorithm for 3D tracking of the beating heart using stereo images from a calibrated stereo endoscope. The proposed tracking method is evaluated on in vivo images acquired by a DaVinci surgical robotic platform. Many different regions of the heart surface over 600 images have been tracked. The data is analyzed by Fourier transformation to reveal the heart motion. The experimental results demonstrate the effectiveness of the presented method.
机译:本文研究了在执行微创心脏手术时跟踪患者心脏运动的问题。在现有文献中,大多数用于追踪心脏运动的算法都是基于心脏表面的区域或特征。前一种方法的缺点是耗时,因此难以满足临床手术的需求。本文的重点是后者,即心脏表面的特征。首先,我们采用“简要”特征来描述和匹配心脏表面的二元特征。基于匹配结果,我们然后通过Bsp​​line插值提出一种新的,快速且稳定的跟踪算法。通过匹配二值特征获得心脏图像的稀疏运动场,并使用B样条插值方法建立目标点的运动。以这种方式,可以在随后的心脏图像上获得目标点的位置。最后,我们使用三角剖分算法来计算目标点的3D坐标。我们方法的新颖性包括用于描述心脏表面的二进制特征,以及使用来自校准立体内窥镜的立体图像对跳动的心脏进行3D跟踪的有效Bspline算法。拟议的跟踪方法是在达芬奇外科手术机器人平台获取的体内图像上进行评估的。心脏表面的许多不同区域已被跟踪超过600张图像。通过傅立叶变换对数据进行分析以揭示心脏运动。实验结果证明了该方法的有效性。

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