Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery

Featured Application Current spinal navigation systems rely on optical reference markers to detect the patient's position. To bypass the use of current reference marker solutions, our work aims at a reliable and simple skin feature detection technology for improving clinical workflow during spine surgery. Unfortunately, reference markers or reference frames can be displaced or obscured during the surgical procedures. We present a solution by applying hyperspectral imaging (HSI) to directly detect skin features for navigation. The initial results demonstrate that HSI has the potential to replace marker-based solutions and can serve as a platform for the further development of markerless tracking. In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers.