arXiv:2512.13402v2 Announce Type: replace-cross Abstract: 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. We present End2Reg, an end-to-end deep learning framework that jointly
The continuous drive for less invasive and more accurate surgical methods, coupled with advancements in deep learning, makes this improvement timely.
This development addresses a critical need for precision in spine surgery, potentially democratizing advanced surgical navigation by reducing reliance on costly and invasive methods.
The reliance on weak segmentation labels and bone-anchored markers for surgical registration can be replaced with more accurate, markerless, and radiation-free alternatives.
- · Surgical robotics companies
- · Medical AI developers
- · Hospitals and surgical centers
- · Patients undergoing spine surgery
- · Manufacturers of bone-anchored markers
- · Older intraoperative imaging technology providers
Surgical navigation systems become more accurate and less invasive.
Reduced surgical complications and faster patient recovery times lead to increased adoption of advanced surgical techniques.
The methodology could be extended to other complex surgical procedures, enhancing overall surgical precision across medicine.
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Read at arXiv cs.AI