A Method for Automatic Feature Points Extraction of Pelvic Surface Based on PointMLP_RegNet

Abstract

ABSTRACT The success of robot‐assisted pelvic fracture reduction surgery heavily relies on the accuracy of 3D/3D feature‐based registration. This process involves extracting anatomical feature points from pre‐operative 3D images which can be challenging because of the complex and variable structure of the pelvis. PointMLP_RegNet, a modified PointMLP, was introduced to address this issue. It retains the feature extraction module of PointMLP but replaces the classification layer with a regression layer to predict the coordinates of feature points instead of conducting regular classification. A flowchart for an automatic feature points extraction method was presented, and a series of experiments was conducted on a clinical pelvic dataset to confirm the accuracy and effectiveness of the method. PointMLP_RegNet extracted feature points more accurately, with 8 out of 10 points showing less than 4 mm errors and the remaining two less than 5 mm. Compared to PointNet++ and PointNet, it exhibited higher accuracy, robustness and space efficiency. The proposed method will improve the accuracy of anatomical feature points extraction, enhance intra‐operative registration precision and facilitate the widespread clinical application of robot‐assisted pelvic fracture reduction.