Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/107007
DC Field | Value | Language |
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dc.contributor.author | Rodrigues, Pedro | - |
dc.contributor.author | Antunes, Michel | - |
dc.contributor.author | Raposo, Carolina | - |
dc.contributor.author | Marques, Pedro | - |
dc.contributor.author | Fonseca, Fernando | - |
dc.contributor.author | Barreto, João P. | - |
dc.date.accessioned | 2023-05-09T08:48:43Z | - |
dc.date.available | 2023-05-09T08:48:43Z | - |
dc.date.issued | 2019-12 | - |
dc.identifier.issn | 2053-3713 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/107007 | - |
dc.description.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. | pt |
dc.language.iso | eng | pt |
dc.publisher | Wiley-Blackwell | pt |
dc.relation | project VisArthro (ref.: PTDC/ EEIAUT/3024/2014) | pt |
dc.relation | European Union’s Horizon 2020 research and innovation programmes under grant agreement no 766850 | pt |
dc.relation | PhD scholarship SFRH/ BD/113315/2015 | pt |
dc.relation | OE – national funds of FCT/MCTES (PIDDAC) under project UID/EEA/00048/2019 | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt |
dc.subject | RGB cameras | pt |
dc.subject | bone | pt |
dc.subject | bone surface | pt |
dc.subject | computed tomography scan | pt |
dc.subject | computer-aided system | pt |
dc.subject | computer-aided total knee arthroplasty | pt |
dc.subject | deep learning approach | pt |
dc.subject | deep segmentation | pt |
dc.subject | depth cameras | pt |
dc.subject | diseases; geometric pose estimation | pt |
dc.subject | image registration | pt |
dc.subject | image segmentation | pt |
dc.subject | joint disease | pt |
dc.subject | knee arthritis | pt |
dc.subject | learning (artificial intelligence) | pt |
dc.subject | magnetic resonance imaging | pt |
dc.subject | medical image processing | pt |
dc.subject | navigation sensor | pt |
dc.subject | navigation system | pt |
dc.subject | neural nets | pt |
dc.subject | orthopaedics | pt |
dc.subject | pose estimation | pt |
dc.subject | preoperative 3D model | pt |
dc.subject | prosthetics | pt |
dc.subject | surgery | pt |
dc.subject | surgical flow | pt |
dc.title | Deep segmentation leverages geometric pose estimation in computer-aided total knee arthroplasty | pt |
dc.type | article | - |
degois.publication.firstPage | 226 | pt |
degois.publication.lastPage | 230 | pt |
degois.publication.issue | 6 | pt |
degois.publication.title | Healthcare Technology Letters | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1049/htl.2019.0078 | pt |
degois.publication.volume | 6 | pt |
dc.date.embargo | 2019-12-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.researchunit | ISR - Institute of Systems and Robotics | - |
crisitem.author.researchunit | Centre for Mechanical Technology and Automation | - |
crisitem.author.researchunit | ISR - Institute of Systems and Robotics | - |
crisitem.author.parentresearchunit | University of Coimbra | - |
crisitem.author.parentresearchunit | University of Coimbra | - |
crisitem.author.orcid | 0000-0002-1108-1796 | - |
crisitem.author.orcid | 0000-0003-3572-2225 | - |
crisitem.author.orcid | 0000-0001-5220-9170 | - |
Appears in Collections: | I&D ISR - Artigos em Revistas Internacionais FMUC Medicina - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
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Deep segmentation leverages geometric pose estimation in computer-aided totalknee arthroplasty.pdf | 1.61 MB | Adobe PDF | View/Open |
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