Improving pedicle screw path planning by vertebral posture estimation

Robot-assisted pedicle screw placement in spine surgery can reduce the complications associated with the screw placement and reduce the hospital return counts due to malfunctions. However, it requires accurate planning for high quality procedures. The state-of-the-art technologies reported in the literature either ignore the anatomical variations across vertebrae or require substantial human interactions. We present an improved approach that achieves the pedicle screw path planning through multiple projections of a numerically re-oriented vertebra with the estimated posture. Approach: We proposed an improved YOLO-type neural network model (YOLOPOSE3D) to estimate the posture of a vertebra before the pedicle path planning. In YOLOPOSE3D, the vertebral posture is given as a rotation quaternion and 3D location coordinates by optimizing the intersection over union (IoU) of the vertebra with the predicted posture and the actual posture. Then, a new local coordinate system is established for the vertebra based on the estimated posture. Finally, the optimal pedicle screw path trajectory is determined from the multiple projections of the vertebra in the local coordinates. Main results:The experimental results in difficult cases of scoliosis showed that the new YOLOPOSE3D network could detect the location and posture of the vertebra accurately with average translation and orientation errors as small as 1.55 mm and 2.55. The screw path planning achieved 83.1% success rate without breaking the pedicle cortex for the lumbar vertebral L1-L5, which is better than that of doctor's manual planning, 82.4%. With the clinical class A requirement of allowing less than 2mm out of the pedicle cortex, the success rate achieved nearly 100%. Significance: The proposed YOLOPOSED3D method can accurately determine the vertebral postures. With the improved posture prior, better clinical outcomes can be achieved for pedicle screw placement in spine internal fixation procedures. &#xD.