Automated Extraction of Surgical Needles from Tissue Phantoms

Priya Sundaresan, Brijen Thananjeyan, Johnathan Chiu, Danyal Fer, Ken Goldberg
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引用次数: 25

Abstract

We consider the surgical subtask of automated extraction of embedded suturing needles from silicone phantoms and propose a four-step algorithm consisting of calibration, needle segmentation, grasp planning, and path planning. We implement autonomous extraction of needles using the da Vinci Research Kit (dVRK). The proposed calibration method yields an average of 1.3mm transformation error between the dVRK end-effector and its overhead endoscopic stereo camera compared to 2.0mm transformation error using a standard rigid body transformation. In 143/160 images where a needle was detected, the needle segmentation algorithm planned appropriate grasp points with an accuracy of 97.20% and planned an appropriate pull trajectory to achieve extraction in 85.31% of images. For images segmented with $\gt50$% confidence, no errors in grasp or pull prediction occurred. In images segmented with 25-50% confidence, no erroneous grasps were planned, but a misdirected pull was planned in 6.45% of cases. In 100 physical trials, the dVRK successfully grasped needles in 75% of cases, and fully extracted needles in 70.7% of cases where a grasp was secured.
从组织幻影中自动提取手术针头
我们考虑了从硅胶假体中自动提取嵌入缝合针的手术子任务,并提出了一种四步算法,包括校准、针分割、抓取规划和路径规划。我们使用达芬奇研究工具包(dVRK)实现针头的自动提取。所提出的校准方法在dVRK末端执行器与其顶置内窥镜立体摄像机之间产生平均1.3mm的变换误差,而使用标准刚体变换的变换误差为2.0mm。在检测到针头的143/160张图像中,针头分割算法规划了合适的抓取点,准确率为97.20%,规划了合适的拉取轨迹,实现了85.31%的图像提取。对于以$ $ gt50$ $%置信度分割的图像,抓取或拉预测没有发生错误。在25-50%置信度分割的图像中,没有计划错误的抓取,但在6.45%的情况下计划错误的拉。在100次物理试验中,dVRK在75%的病例中成功地抓住了针头,在抓住的情况下,70.7%的病例完全拔出了针头。
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