In The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND : Image registration is a crucial technology in robot-assisted knee arthroplasty, which provides real-time patient information by registering the pre-operative image data with data acquired during the operation. The existing registration method requires surgeons to manually pick up medical feature points (i.e. anatomical points) in pre-operative images, which is time-consuming and relied on surgeons experience. Moreover, different doctors have different preferences in preoperative planning, which may influence the consistency of surgical results.
METHODS : A medical feature points automatic extraction method based on PointNet++ named Point_RegNet is proposed to improve the efficiency of preoperative preparation and ensure the consistency of surgical results. The proposed method replaces the classification and segmentation layer of PointNet++ with a regression layer to predict the position of feature points. The comparative experiment is adopted to determine the optimal set of abstraction layers in PointNet++.
RESULTS : The proposed network with 3 set abstraction layers is more suitable for extracting feature points. The feature points predictions mean error of our method is less than 5mm, which is 1mm less than the manual marking method. Ultimately, our method only requires less than 3 seconds to extract all medical feature points in practical application. It is much faster than the manual extraction way which usually requires more than half an hour to mark all necessary feature points.
CONCLUSION : Our deep learning-based method can improve the surgery accuracy and reduce the preoperative preparation time. Moreover, this method can also be applied to other surgical navigation systems. This article is protected by copyright. All rights reserved.
Wang Weiya, Zhou Haifeng, Yan Yuxin, Cheng Xiao, Yang Peng, Gan Liangzhi, Kuang Shaolong
PointNet++, medical feature points extraction, registration, robot-assisted knee arthroplasty