AI RESEARCH
End2Reg: Learning Task-Specific Segmentation for Markerless Registration in Spine Surgery
arXiv CS.CV
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ArXi:2512.13402v2 Announce Type: replace Intraoperative navigation in spine surgery demands millimeter-level accuracy. Currently, this is achieved through radiation-intensive intraoperative imaging and bone-anchored markers that are invasive and disrupt surgical workflow. Markerless RGB-D registration methods offer a promising alternative. However, existing approaches rely on weak segmentation labels to isolate relevant anatomical structures, potentially propagating errors through the registration process.