Development and Evaluation of an Automated Computational Approach for the Precise Placement of Pedicle Screws in Spinal Surgery Leveraging Three-Dimensional Point Cloud Registration Methods

This study focuses on enhancing the precision and efficacy of pedicle screw placement in spinal surgeries, particularly for patients with osteoporosis. It emphasizes the importance of accurate screw positioning to maximize pullout strength and biomechanical efficacy. The research highlights the relationship between CT values, Bone Mineral Density (BMD), and the Young’s modulus of bone tissue, suggesting that higher CT values, indicative of denser bone, lead to stronger mechanical properties. This understanding is crucial for assessing bone health, especially in osteoporosis and fracture risk analysis. The study introduces an innovative automated planning method for pedicle screw insertion in spinal vertebrae, beneficial for osteoporotic patients. This method uses PointNet++ combined with a Siamese network for semi-supervised segmentation of vertebrae in CT images, converting them into point clouds for individualized planning. The approach aims to improve accuracy and efficiency in pedicle screw placement, especially in complex or osteoporotic vertebrae. The method was validated using osteoporotic in vitro models, demonstrating its potential effectiveness for surgeons facing challenges in pedicle screw placement in osteoporotic patients. The complete workflow begins with semi-supervised vertebra segmentation using a Siamese PointNet++ architecture. The extracted vertebra point cloud is then aligned with a predefined pedicle screw model via an adaptively tuned Super4PCS registration algorithm to generate patient-specific trajectories. The paper also reviews traditional surgical path planning, which relied on surgeons’ experience and intuition, and the shift towards Computer-Aided Design (CAD) and Virtual Reality (VR) technologies for pre-operative planning. Despite these advancements, challenges remain in accurately simulating tissue properties and managing physiological variations. The study proposes a bifurcated approach to automated trajectory planning: segmenting individual vertebrae and planning pedicle screw implantation for each segmented vertebra. The method involves converting CT scans into 3D point clouds, using PointNet++ and the Siamese method for vertebra segmentation, and registering the pedicle screw point cloud with the vertebra model for precise surgical planning. The study demonstrates the method’s feasibility through clinical data from Shengjing Hospital, showing high applicability and consistency in surgical path planning, particularly in the lumbar and lower thoracic regions. The planning results were consistent with surgeons’ experience, indicating the algorithm’s adaptability and stability.