An Automatic Path Planning Method of Pedicle Screw Placement Based on Preoperative CT Images

Abstract

Due to good fixation and obvious clinical advantages in the treatment of spinal diseases, pedicle screw technology is widely employed in spinal surgery. However, most screw placement paths are manually selected, which is a complicated and time-consuming process. Therefore, various kinds of spine surgery robots have been invented to assist during surgeries. An automatic path planning method of pedicle screw placement is proposed in this article. First, based on preoperative CT scans, the spine of the patient can be segmented and each individual vertebra is classified by using a trained deep learning network model. Additionally, a local coordinate system in every single vertebra is established according to its anatomical characteristics. Second, after analyzing the images to recognize the feature points, the pedicle region and route of screw placement are identified, which renders completion of the surgical path planning automatic. The experimental results show that 93% of the planning results can be directly applied in surgeries after surgeon evaluation. In addition to the improvement in accuracy, the whole algorithm does not rely on any human assistance. This fully automatic process greatly reduces the time required for surgical planning.