Automatic image-based pedicle screw planning

The use of pedicle screws for spinal stabilization has become a common technique in spine surgery. Due to the proximity of the inserted implant to the spinal column, high accuracy is required. In the current surgical routine, the screw trajectory is planned manually on a CT scan, which is prone to error and time-consuming. We propose an automatic pedicle screw placement approach that can simplify and speed up the screw planning suitable for intra-operative use. For automatic screw positioning in the CT volume, we designed a vertebra instance patch-based approach, employing a state-of-the-art U-Net framework, which estimates a mask for the screw pair location. From the predicted screw masks, we derive the desired screw parameters. The method was trained in a 5-fold cross-validation for the lumbar spine on a large training set of 155 patients (1052 screws) and evaluated on an external test set with 30 patients (198 screws). The automatically obtained and manually defined plans for screw trajectories showed a relatively high agreement and were clinically accepted by a spinal neurosurgeon based on the Gertzbein-Robbins classification. The mean absolute difference (MAD) and corresponding standard deviation (SD) was 0.4 ± 0.3 mm for the screw diameter, 4.6 ± 3.1 mm for the screw length, 4.3 ± 2.1 mm and 4.2 ± 2.4 mm for the head and axis point and 6.0 ± 3.7° for screw insertion directions with an average inference time per vertebra of 2.9 sec. For patch initialization, we propose a manual and automatic vertebra center localization technique, facilitating the seamless integration of the method into the clinical workflow.