ERegPose：一种基于显式回归的蛇形腕式手术器械 6D 位姿估计方法。
ERegPose: An explicit regression based 6D pose estimation for snake-like wrist-type surgical instruments.
机构信息
Key Lab for Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, China.
School of Mechanical Engineering, Tianjin University, Tianjin, China.
出版信息
Int J Med Robot. 2024 Jun;20(3):e2640. doi: 10.1002/rcs.2640.
BACKGROUND
Accurately estimating the 6D pose of snake-like wrist-type surgical instruments is challenging due to their complex kinematics and flexible design.
METHODS
We propose ERegPose, a comprehensive strategy for precise 6D pose estimation. The strategy consists of two components: ERegPoseNet, an original deep neural network model designed for explicit regression of the instrument's 6D pose, and an annotated in-house dataset of simulated surgical operations. To capture rotational features, we employ an Single Shot multibox Detector (SSD)-like detector to generate bounding boxes of the instrument tip.
RESULTS
ERegPoseNet achieves an error of 1.056 mm in 3D translation, 0.073 rad in 3D rotation, and an average distance (ADD) metric of 3.974 mm, indicating an overall spatial transformation error. The necessity of the SSD-like detector and L1 loss is validated through experiments.
CONCLUSIONS
ERegPose outperforms existing approaches, providing accurate 6D pose estimation for snake-like wrist-type surgical instruments. Its practical applications in various surgical tasks hold great promise.
背景
由于蛇形腕式手术器械的复杂运动学和灵活设计，准确估计其 6D 姿态具有挑战性。
方法
我们提出了 ERegPose，这是一种用于精确 6D 姿态估计的综合策略。该策略由两部分组成：ERegPoseNet，这是一个专门用于明确回归器械 6D 姿态的原始深度神经网络模型，以及一个内部注释的模拟手术操作数据集。为了捕捉旋转特征，我们采用了类似于 SSD 的检测器来生成器械尖端的边界框。
结果
ERegPoseNet 在 3D 平移中误差为 1.056mm，在 3D 旋转中误差为 0.073rad，平均距离（ADD）指标为 3.974mm，这表明整体空间变换误差。通过实验验证了类似于 SSD 的检测器和 L1 损失的必要性。
结论
ERegPose 优于现有的方法，为蛇形腕式手术器械提供了准确的 6D 姿态估计。它在各种手术任务中的实际应用具有很大的潜力。
Zotero 插件