Markerless AR Registration Framework Using Multi-Modal Imaging for Orthopedic Surgical Guidance

Orthopedic surgery demands precise bone alignment, yet marker-based navigation adds cost and complexity. We propose a markerless registration framework fusing depth imaging with preoperative CT/MRI via mutual information optimization. Anatomical features are extracted with a U-Net segmentation model, and alignment is performed using multi-modal ICP enhanced by adaptive weight learning. In 15 phantom surgeries, mean TRE was 1.4 mm, compared to 2.6 mm with marker-based setups. Preparation time decreased by 35%, and frame rates reached 25 fps. The framework reduces invasiveness while preserving surgical accuracy, offering clinical scalability.