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

Healthcare Technology Letters

article

Pedro Rodrigues¹  Michel Antunes²  Carolina Raposo²  Pedro Marques³  Fernando Fonseca³  Joao P. Barreto¹ 

[1] Institute of Systems and Robotics, University of Coimbra;Perceive 3D;Faculty of Medicine, Coimbra Hospital and University Centre

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;   

DOI  :  10.1049/htl.2019.0078

学科分类：肠胃与肝脏病学

来源: Wiley

【 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.

【 授权许可】

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