论文标题
部分可观测时空混沌系统的无模型预测
Suture Thread Spline Reconstruction from Endoscopic Images for Robotic Surgery with Reliability-driven Keypoint Detection
论文作者
论文摘要
在手术机器人技术的前沿，自动化操纵和交付缝合线的过程是一个突出的问题，因为自动执行此任务可以大大减轻远程手术手术期间的外科医生的疲劳，并让他们花费更多时间来解决更高水平的临床决策。在现实世界中完成自主缝合和缝合操作需要准确的缝合线定位和重建，这是从2D立体声相机外科手术图像对创建缝合线的3D形状表示的过程。这是一个非常具有挑战性的问题，因为如何有限的像素信息可用于线程以及它们对照明和镜面反射的敏感性。我们提出了使用可靠的关键点和最小变化样条（MVS）平滑优化的缝合线重建工作，以从分段的外科手术图像对构造3D中心线。该方法可与以前的缝合线重建作品相媲美，其可能提高了抓地点估计的准确性。我们的代码和数据集将在以下网址提供：https：//github.com/ucsdarclab/thread-reconstruction。
Automating the process of manipulating and delivering sutures during robotic surgery is a prominent problem at the frontier of surgical robotics, as automating this task can significantly reduce surgeons' fatigue during tele-operated surgery and allow them to spend more time addressing higher-level clinical decision making. Accomplishing autonomous suturing and suture manipulation in the real world requires accurate suture thread localization and reconstruction, the process of creating a 3D shape representation of suture thread from 2D stereo camera surgical image pairs. This is a very challenging problem due to how limited pixel information is available for the threads, as well as their sensitivity to lighting and specular reflection. We present a suture thread reconstruction work that uses reliable keypoints and a Minimum Variation Spline (MVS) smoothing optimization to construct a 3D centerline from a segmented surgical image pair. This method is comparable to previous suture thread reconstruction works, with the possible benefit of increased accuracy of grasping point estimation. Our code and datasets will be available at: https://github.com/ucsdarclab/thread-reconstruction.
