Automatic Lumbar Vertebra Landmark Localization and Segmentation for Pedicle Screw Placement

Pedicle screw placement is a standard but technically demanding operation. Improper screw placement can cause nerve damage and postoperative complications. Usually, the computed tomography (CT) image of the patient’s spine is analyzed in joint surgical planning, and next the surgeons complete the path planning manually for screw placement, which is an error-prone, time-consuming, and labour-intensive process. This article aims to realize the automatic lumbar landmarks localization and segmentation for pedicle screw placement. For this purpose, we propose a coarse-to-fine framework based on deep learning. First, the lumbar part is automatically extracted from the 3D CT image of the patient’s spine, by using a CNN to predict the lumbar vertebrae centroid landmarks. Then another network is used to predict for each lumbar vertebra the midsagittal and pedicle landmarks critical to screw planning. These landmarks are used to construct bounding boxes for the five lumbar vertebrae, which are then cropped separately and rotated horizontally to train a segmentation network. Using the landmarks and vertebral body segmentation mask, we can localize the plane and initial path of screw placement, as well as the screw geometry parameters. Experimental results show that our framework can locate the lumbar vertebrae landmarks and segment the body with high accuracy, and plan an initial screw path as critical assistant information for orthopaedic clinicians. Moreover, this approach also facilitates automatic optimization of screw paths and has potential application value in preoperative planning automation.