Abstract:

 ^{Objective  The navigation system of robot-assisted knee arthroplasty uses a laser scanner to acquire intraoperative cartilage point clouds and align them with the preoperative model for automatic non contact space registration. The intraoperative patient knee lesion point cloud contains a large number of irrelevant background point clouds of muscles, tendons, ligaments and surgical instruments. Manual removal of irrelevant point clouds takes up surgery time due to human-computer interaction, so in this study we proposed a novel method  for automatic extraction of point clouds from the knee cartilage surface for fast and accurate intraoperative registration.    Methods  Due to the lack of adequate description of cartilage surface and geometric local information, PointNet cannot extract cartilage point clouds with high precision. In this paper, a fast point feature histogram(FPFH)-PointNet method  combined with fast point feature histogram was proposed, which effectively described the appearance of cartilage point cloud and achieved the automatic and efficient segmentation of cartilage point cloud.    Results  The point clouds of distal femoral cartilage of 10 cadaveric knee specimens and 1 human leg model were scanned from different directions as data sets. The accuracy of cartilage point cloud segmentation by PointNet and FPFH-PointNet were 0.94 ±0.003 and 0.98 ±0, and mean intersection over union(mIOU) were 0.83 ±0.015 and 0.93 ±0.005, respectively. Compared with PointNet, FPFH-PointNet improved accuracy and mIOU by 4% and 10% respectively, while the elapsed time was only about 1.37 s.    Conclusion  FPFH-PointNet can accurately and automatically extract the knee cartilage point cloud, which meets the performance requirement for intraoperative navigation.}

Key words:  Knee arthroplasty, Surgical navigation, Point cloud segmentation, PointNet, Fast point feature histogram, Human