Markerless robotic pedicle screw placement based on structured light tracking

Applications of Augmented Reality in Orthopaedic Spine Surgery

The application of augmented reality (AR) in surgical settings has primarily been as a navigation tool in the operating room because of its ease of use and minimal effect on surgical procedures. The surgeon can directly face the surgical field while viewing 3D anatomy virtually, thus reducing the need to look at an external display, such as a navigation system. Applications of AR are being explored in spine surgery. The basic principles of AR include data preparation, registration, tracking, and visualization. Current literature provides sufficient preclinical and clinical data evidence for the use of AR technology in spine surgery. AR systems are efficient assistive devices, providing greater accuracy for insertion points, more comfort for surgeons, and reduced operating time. AR technology also has beneficial applications in surgical training, education, and telementorship for spine surgery. However, costs associated with specially designed imaging equipment and physicians' comfort in using this technology continue to remain barriers to its adoption. As this technology evolves to a more widespread use, future applications will be directed by the cost-effectiveness of AR-assisted surgeries.

Multi-view 3D skin feature recognition and localization for patient tracking in spinal surgery applications

BACKGROUND

Minimally invasive spine surgery is dependent on accurate navigation. Computer-assisted navigation is increasingly used in minimally invasive surgery (MIS), but current solutions require the use of reference markers in the surgical field for both patient and instruments tracking.

PURPOSE

To improve reliability and facilitate clinical workflow, this study proposes a new marker-free tracking framework based on skin feature recognition.

METHODS

Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Feature (SURF) algorithms are applied for skin feature detection. The proposed tracking framework is based on a multi-camera setup for obtaining multi-view acquisitions of the surgical area. Features can then be accurately detected using MSER and SURF and afterward localized by triangulation. The triangulation error is used for assessing the localization quality in 3D.

RESULTS

The framework was tested on a cadaver dataset and in eight clinical cases. The detected features for the entire patient datasets were found to have an overall triangulation error of 0.207 mm for MSER and 0.204 mm for SURF. The localization accuracy was compared to a system with conventional markers, serving as a ground truth. An average accuracy of 0.627 and 0.622 mm was achieved for MSER and SURF, respectively.

CONCLUSIONS

This study demonstrates that skin feature localization for patient tracking in a surgical setting is feasible. The technology shows promising results in terms of detected features and localization accuracy. In the future, the framework may be further improved by exploiting extended feature processing using modern optical imaging techniques for clinical applications where patient tracking is crucial.