A Novel Stereo Tracking Technology for Intraoperative Intervertebral Motion Compensation in Robot-Assisted Laminectomy

Robot-Assisted Laminectomy (RAL) has become an emerging approach for treatment of lumbar spinal stenosis. However, the spine is an articulated structure that can be deformed under contact force during grinding. Patient-image registration performed at the start of RAL cannot account for intraoperative intervertebral motion, resulting in degraded surgical accuracy and compromised safety. To tackle this issue, we propose a novel stereo tracking technology for intraoperative intervertebral motion compensation in RAL. Our tracking technology is a marker-based method, consisting of customized markers, a coding system, and a labeling and position tracking framework. Specifically, we first propose a new type of markers featured by binary-coded spatial patterns, referred to as SurgTag marker. Each marker in the codebook has a unique digital IDentification (ID) code and a fixed set of keypoints. By rigidly attaching a marker with a unique ID to each vertebra during RAL, we can simultaneously track multiple vertebrae. We then introduce a two-stage SurgTag-marker-based tracking framework consisting of a marker labeling stage followed by a keypoint detection stage. In theory, we formulate both marker labeling and keypoint detection as one-to-one set prediction problems and further propose respectively two different types of DEtection-TRansformer (DETR)-based detector: Marker-DETR and Keypoint-DETR. Marker poses are then estimated based on detected keypoints, enabling tracking and compensation for intervertebral motion. Comprehensive experiments were conducted for evaluation. The proposed method achieved on average a submillimeter translation error and a sub-degree orientation error on both a synthetic dataset and an ex-vivo dataset. Additionally, surgical robot with the proposed technology achieved a submillimeter grinding error, demonstrating its effectiveness for improving surgical accuracy. The code will be publicly available at https://github.com/Wenyuan-Sun/StereoTracking.