基于FPFH-PointNet的术中膝关节软骨表面点云自动提取

Automatic extraction of point cloud on cartilage surface of intraoperative knee using FPFH-PointNet

目的机器人辅助膝关节置换术导航系统采用激光扫描仪获取术中软骨点云,并与术前模型配准,实现自动非接触空间注册。术中患者膝关节病变点云包含大量的肌肉、肌腱、韧带及手术器械等无关背景点云。手动去除无关点云会因人机交互而占据手术时间,因此,本研究提出一种新颖的膝关节软骨表面点云自动提取方法,以便快速精准实施术中注册。方法PointNet因缺乏软骨表面和几何局部信息的充分描述,不能高精度地提取软骨点云。本研究提出了一种结合快速点特征直方图FPFH-PointNet方法,该方法有效地描述了软骨点云表观,实现了软骨表面点云的自动高效分割。结果本研究以从不同角度扫描10例尸体膝关节标本和1例人腿模型的股骨远端软骨点云为数据集。PointNet和FPFH-PointNet分割软骨点云的准确率分别为0.94±0.003和0.98±0,平均交并比(mIOU)分别为0.83±0.015和0.93±0.005。FPFH-PointNet相比于PointNet,准确率和mIOU分别提高了4%和10%,而耗时仅约为1.37 s。结论FPFH-PointNet能够精准地自动提取术中膝关节软骨点云,满足了术中导航的性能需求。

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 humancomputer 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.