Spine surgery is a therapeutic modality for treatment of spine disorders, including spinal deformity, degeneration, and trauma. Such procedures benefit from accurate localization of surgical targets, precise delivery of instrumentation, and reliable validation of surgical objectives – for example, confirming that the surgical implants are delivered as planned and desired changes to the global spinal alignment (GSA) are achieved. Recent advances in surgical navigation have helped to improve the accuracy and precision of spine surgery, including intraoperative imaging integrated with real-time tracking and surgical robotics. This thesis aims to develop two methods for improved image-guided surgery using image analytic techniques. The first provides a means for automatic detection of pedicle screws in intraoperative radiographs – for example, to streamline intraoperative assessment of implant placement. The algorithm achieves a precision and recall of 0.89 and 0.91, respectively, with localization accuracy within ~10 mm. The second develops two algorithms for automatic assessment of GSA in computed tomography (CT) or cone-beam CT (CBCT) images, providing a means to quantify changes in spinal curvature and reduce the variability in GSA measurement associated with manual methods. The algorithms demonstrate GSA estimates with 93.8% of measurements within a 95% confidence interval of manually defined truth. Such methods support the goals of safe, effective spine surgery and provide a means for more quantitative intraoperative quality assurance. In turn, the ability to quantitatively assess instrument placement and changes in GSA could represent important elements of retrospective analysis of large image datasets, improved clinical decision support, and improved patient outcomes
