Activereg improves bone registration with fewer probing points
ActiveReg: Information-Driven Active Regional Probing for Partial-to-Full Bone Registration
Robotics
Summary
Surgical procedures often require pinpointing spots on bones to help guide navigation and robotic tools, but this can be slow and tricky when there’s limited access. The authors offer ActiveReg, a system that suggests regions instead of single points for probing, making it easier and more flexible for surgeons. It smartly picks where to probe next based on what’s already known, reducing how many points need to be touched while still staying accurate. Tests on simulated models and real setups showed it works well with much fewer points than earlier methods.
What this means in practice
- •For orthopedic surgeons: Guide probing during surgery to quickly gather bone surface data and improve surgical navigation accuracy with fewer contact points.
- •For surgical robotics teams: Integrate adaptive probing region recommendations to speed up bone registration tasks and reduce robot operation time.
Authors
Tiancheng Li, Yingyu Wang, Peter Walker, Liang Zhao, Shoudong Huang
Abstract
Accurate bone registration is essential for orthopedic surgical navigation and robotic assistance. In a common workflow, the surgeon needs to identify and probe a number of prescribed locations on the exposed bone surface to obtain data points for registration, which can be difficult and time-consuming under limited surgical exposure. Reducing the number of required points while providing clear probing guidance would ease the surgeon's acquisition task intra-operatively. We present ActiveReg, a closed-loop framework that recommends probing regions rather than individual points, allowing flexibility in the exact contact location. A D-optimal planner that takes into account the already acquired points and the uncertainty of current registration information helps achieve accurate registration with fewer points. An online assessment combines pre-update innovation consistency with uncertainty at predefined surgical task locations improves the reliability of completion decision, without requiring ground truth. Simulations across four anatomical scenarios demonstrated that ActiveReg can achieve competitive accuracy compared with baseline registration methods while using substantially fewer acquired points. Real phantom experiments with optical and electromagnetic tracking demonstrated comparable mean target registration error to the Gradient-SDF framework, using only 24-28 acquired points instead of 771-1263.