ERegPose: An explicit regression based 6D pose estimation for snake-like wrist-type surgical instruments

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.