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

• Main Author: Rodrigues, Pedro
• Publication Date: 2019
• Other Authors: Antunes, Michel, Raposo, Carolina, Marques, Pedro, Fonseca, Fernando, Barreto, João P.
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://hdl.handle.net/10316/107007; https://doi.org/10.1049/htl.2019.0078
• topic: RGB cameras; bone; bone surface; computed tomography scan; computer-aided system; computer-aided total knee arthroplasty; deep learning approach; deep segmentation; depth cameras; diseases; geometric pose estimation; image registration; image segmentation; joint disease; knee arthritis; learning (artificial intelligence); magnetic resonance imaging; medical image processing; navigation sensor; navigation system; neural nets; orthopaedics; pose estimation; preoperative 3D model; prosthetics; surgery; surgical flow

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.