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Grinding trajectory generator in robot-assisted laminectomy surgery
Qian Li1, Zhijiang Du1, Hongjian Yu2
1State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China.
Purpose:
Grinding trajectory planning for robot-assisted laminectomy is a complicated and cumbersome task. The purpose of this research is to automatically obtain the surgical target area from the CT image, and based on this, formulate a reasonable robotic grinding trajectory.
Methods:
We propose a deep neural network for laminae positioning, a trajectory generation strategy, and a grinding speed adjusting strategy. These algorithms can obtain surgical information from CT images and automatically complete grinding trajectory planning.
Results:
The proposed laminae positioning network can reach a recognition accuracy of 95.7%, and the positioning error is only 1.12 mm in the desired direction. The simulated surgical planning on the public dataset has achieved the expected results. In a set of comparative robotic grinding experiments, those using the speed adjustment algorithm obtained a smoother grinding force.
Conclusion:
Our work can automatically extract laminar centers from the CT image precisely to formulate a reasonable surgical trajectory plan. It simplifies the surgical planning process and reduces the time needed for surgeons to perform such a cumbersome operation manually.
