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.

翻译：在机器人手术过程中自动化操控与递送缝合线，是前沿手术机器人领域的关键难题。实现这一任务的自动化能显著减轻远程手术中医生的疲劳，使其能更专注于高级临床决策。要在现实世界中完成自主缝合与线操控，需要精确的缝合线定位与重建——即从二维立体相机手术图像对中创建缝合线的三维形状表征。由于缝合线可用的像素信息极其有限，且对光照和镜面反射敏感，这一问题极具挑战性。我们提出一种缝合线重建方法，通过可靠关键点与最小方差样条（MVS）平滑优化，从分割后的手术图像对中构建三维中心线。该方法与现有缝合线重建工作性能相当，且在抓取点估计精度上具有潜在优势。我们的代码与数据集将开源至：https://github.com/ucsdarclab/thread-reconstruction。