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A Learning Approach for Suture Thread Detection With Feature Enhancement and Segmentation for 3-D Shape Reconstruction

机译:三维形状重建具有特征增强和分割的缝合线程检测的学习方法

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A vision-based system presents one of the most reliable methods for achieving an automated robot-assisted manipulation associated with surgical knot tying. However, some challenges in suture thread detection and automated suture thread grasping significantly hinder the realization of a fully automated surgical knot tying. In this article, we propose a novel algorithm that can be used for computing the 3-D coordinates of a suture thread in knot tying. After proper training with our data set, we built a deep-learning model for accurately locating the suture's tip. By applying a Hessian-based filter with multiscale parameters, the environmental noises can be eliminated while preserving the suture thread information. A multistencils fast marching method was then employed to segment the suture thread, and a precise stereomatching algorithm was implemented to compute the 3-D coordinates of this thread. Experiments associated with the precision of the deep-learning model, the robustness of the 2-D segmentation approach, and the overall accuracy of 3-D coordinate computation of the suture thread were conducted in various scenarios, and the results quantitatively validate the feasibility and reliability of the entire scheme for automated 3-D shape reconstruction. Note to Practitioners-This article was motivated by the challenges of suture thread detection and 3-D coordinate evaluation in a calibrated stereovision system. To precisely detect the suture thread with no distinctive feature in an image, additional information, such as the two ends of the suture thread or its total length, is usually required. This article suggests a new method utilizing a deep-learning model to automate the tip detection process, eliminating the need of manual click in the initial stage. After feature enhancements with image filters, a multistencils fast marching method was incorporated to compute the arrival time from the detected tip to other points on the suture contour. By finding the point that takes the maximal time to travel in a closed contour, the other end of the suture thread can be identified, thereby allowing suture threads of any length to be segmented out from an image. A precise stereomatching method was then proposed to generate matched key points of the suture thread on the image pair, thereby enabling the reconstruction of its 3-D coordinates. The accuracy and robustness of the entire suture detection scheme were validated through experiments with different backgrounds and lengths. This proposed scheme offers a new solution for detecting curvilinear objects and their 3-D coordinates, which shows potential in realizing automated suture grasping with robot manipulators.
机译:基于视觉的系统提供了实现与外科结捆绑相关的自动机器人辅助操作的最可靠的方法之一。然而,缝合线程检测和自动缝线螺纹的一些挑战显着地阻碍了完全自动外科结捆绑的实现。在本文中,我们提出了一种新颖的算法,该算法可用于计算缝合线的缝合线的三维坐标。通过我们的数据集进行适当的培训后,我们建立了一个深度学习模型,可准确定位缝合线的尖端。通过使用多尺度参数应用Hessian的过滤器,可以在保留缝合线程信息的同时消除环境噪声。然后采用多频者快速行进方法来分割缝合线程,并实现了精确的立体化算法以计算该线程的3-D坐标。与深学习模型的精度相关的实验,在各种场景中进行了二维分割方法的鲁棒性,以及缝合线程的3-D坐标计算的整体精度,结果定量验证了可行性和自动三维重建的整个方案的可靠性。向从业者注意 - 本文受到缝合线程检测的挑战和校准的立体系统中的3-D坐标评估的挑战。为了精确地检测在图像中没有明显特征的缝合线程,通常需要附加信息,例如缝合线的两端或其总长度。本文建议采用深学习模型自动化尖端检测过程的新方法,从而消除了在初始阶段中单击手动的需求。在具有图像过滤器的功能增强之后,并入了多频带的快速行进方法,以将来自检测到的尖端到缝合轮廓上的其他点计算到达时间。通过找到在闭轮廓中行进的最大时间的点,可以识别缝合线的另一端,从而允许从图像中分割任何长度的缝合线程。然后提出了一种精确的立体化方法以在图像对上产生缝合线的匹配关键点,从而能够重建其3-D坐标。通过不同背景和长度的实验验证整个缝合检测方案的准确性和稳健性。该提出的方案提供了一种用于检测曲线对象及其三维坐标的新解决方案,其显示了实现用机器人操纵器抓住自动缝合线的潜力。

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