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

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.