Jackson Russell C, Yuan Rick, Chow Der-Lin, Newman Wyatt, Çavuşoğlu M Cenk
Department of Electrical Engineering and Computer Science (EECS) at Case Western Reserve University in Cleveland, OH, USA.
IEEE Int Conf Robot Autom. 2015 May 26;2015:4710-4716. doi: 10.1109/ICRA.2015.7139853.
In order to realize many of the potential benefits associated with robotically assisted minimally invasive surgery, the robot must be more than a remote controlled device. Currently using a surgical robot can be challenging, fatiguing, and time consuming. Teaching the robot to actively assist surgical tasks, such as suturing, has the potential to vastly improve both patient outlook and the surgeon's efficiency. One obstacle to completing surgical sutures autonomously is the difficulty in tracking surgical suture threads. This paper proposes an algorithm which uses a Non-Uniform Rational B-Spline (NURBS) curve to model a suture thread. The NURBS model is initialized from a single selected point located on the thread. The NURBS curve is optimized by minimizing the image match energy between the projected stereo NURBS image and the segmented thread image. The algorithm is able to accurately track a suture thread as it translates, deforms, and changes length in real-time.
为了实现与机器人辅助微创手术相关的许多潜在益处,机器人必须不仅仅是一个遥控设备。目前使用手术机器人可能具有挑战性、令人疲劳且耗时。教导机器人主动协助诸如缝合等手术任务,有可能极大地改善患者的预后以及外科医生的效率。自主完成手术缝合的一个障碍是追踪手术缝线的困难。本文提出一种算法,该算法使用非均匀有理B样条曲线(NURBS)对缝线进行建模。NURBS模型从位于线上的单个选定的点开始初始化。通过最小化投影的立体NURBS图像与分割后的线图像之间的图像匹配能量来优化NURBS曲线。该算法能够实时准确地追踪缝线在平移、变形和长度变化时的情况。