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.
Keywords: Deep learning; Image-guided surgery; Laminectomy; Surgical planning.