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

Abstract

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.

Keywords

• 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