Towards Markerless Intraoperative Tracking of Deformable Spine Tissue
This work addresses the need for less invasive and time-consuming tracking methods in orthopedic spine surgery, though it appears incremental as it builds on existing markerless tracking concepts with new data and methods.
The paper tackled the problem of markerless intraoperative tracking of deformable spine tissue by introducing the first real-world clinical RGB-D dataset for spine surgery and developing SpineAlign for capturing deformation, along with an intraoperative segmentation network and CorrespondNet for predicting key registration regions.
Consumer-grade RGB-D imaging for intraoperative orthopedic tissue tracking is a promising method with high translational potential. Unlike bone-mounted tracking devices, markerless tracking can reduce operating time and complexity. However, its use has been limited to cadaveric studies. This paper introduces the first real-world clinical RGB-D dataset for spine surgery and develops SpineAlign, a system for capturing deformation between preoperative and intraoperative spine states. We also present an intraoperative segmentation network trained on this data and introduce CorrespondNet, a multi-task framework for predicting key regions for registration in both intraoperative and preoperative scenes.