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

• Main Authors: Pedro Rodrigues, Michel Antunes, Carolina Raposo, Pedro Marques, Fernando Fonseca, Joao P. Barreto
• Format: Article
• Language: English
• Published: Wiley 2019-10-01
• Series: Healthcare Technology Letters
• Subjects: 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
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/htl.2019.0078

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.