Deep segmentation leverages geometric pose estimation in computer‐aided total knee arthroplasty. Issue 6 (6th December 2019)

• Record Type: Journal Article
• Authors: Rodrigues, Pedro; Antunes, Michel; Raposo, Carolina; Marques, Pedro; Fonseca, Fernando; Barreto, Joao P.
• Is Part Of: Healthcare technology letters. Volume 6:Issue 6(2019)
• Page Start: 226
• Page End: 230
• Publication Date: 2019-12-06
• 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; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2019.0078
• Languages: English
• ISSNs: 2053-3713

Abstract

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.