Deep segmentation leverages geometric pose estimation in computer-aided total knee arthroplasty

[摘要] Knee arthritis is a common joint disease that usually requires a total knee arthroplasty. There are multiple surgical variables that have a direct impact on the correct positioning of the implants, and an optimal combination of all these variables is the most challenging aspect of the procedure. Usually, preoperative planning using a computed tomography scan or magnetic resonance imaging helps the surgeon in deciding the most suitable resections to be made. This work is a proof of concept for a navigation system that supports the surgeon in following a preoperative plan. Existing solutions require costly sensors and special markers, fixed to the bones using additional incisions, which can interfere with the normal surgical flow. In contrast, the authors propose a computer-aided system that uses consumer RGB and depth cameras and do not require additional markers or tools to be tracked. They combine a deep learning approach for segmenting the bone surface with a recent registration algorithm for computing the pose of the navigation sensor with respect to the preoperative 3D model. Experimental validation using ex-vivo data shows that the method enables contactless pose estimation of the navigation sensor with the preoperative model, providing valuable information for guiding the surgeon during the medical procedure.

[学科分类] 肠胃与肝脏病学

[关键词] orthopaedics;surgery;image registration;bone;medical image processing;diseases;pose estimation;prosthetics;image segmentation;learning (artificial intelligence);neural nets;knee arthritis;joint disease;computed tomography scan;magnetic resonance imaging;navigation system;surgical flow;computer-aided system;depth cameras;deep learning approach;bone surface;navigation sensor;preoperative 3D model;computer-aided total knee arthroplasty;deep segmentation;geometric pose estimation;RGB cameras