{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T12:26:24Z","timestamp":1765974384116,"version":"build-2065373602"},"reference-count":16,"publisher":"Institution of Engineering and Technology (IET)","issue":"6","license":[{"start":{"date-parts":[[2019,12,6]],"date-time":"2019-12-06T00:00:00Z","timestamp":1575590400000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0\/"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Healthcare Tech Letters"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:p>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\u2010aided 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\u2010vivo 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.<\/jats:p>","DOI":"10.1049\/htl.2019.0078","type":"journal-article","created":{"date-parts":[[2019,10,7]],"date-time":"2019-10-07T22:37:15Z","timestamp":1570487835000},"page":"226-230","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Deep segmentation leverages geometric pose estimation in computer\u2010aided total knee arthroplasty"],"prefix":"10.1049","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1108-1796","authenticated-orcid":false,"given":"Pedro","family":"Rodrigues","sequence":"first","affiliation":[{"name":"Institute of Systems and Robotics University of Coimbra  Coimbra Portugal"}]},{"given":"Michel","family":"Antunes","sequence":"additional","affiliation":[{"name":"Perceive 3D  Coimbra Portugal"}]},{"given":"Carolina","family":"Raposo","sequence":"additional","affiliation":[{"name":"Perceive 3D  Coimbra Portugal"}]},{"given":"Pedro","family":"Marques","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Coimbra Hospital and University Centre  Coimbra Portugal"}]},{"given":"Fernando","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Coimbra Hospital and University Centre  Coimbra Portugal"}]},{"given":"Joao P.","family":"Barreto","sequence":"additional","affiliation":[{"name":"Institute of Systems and Robotics University of Coimbra  Coimbra Portugal"},{"name":"Perceive 3D  Coimbra Portugal"}]}],"member":"265","published-online":{"date-parts":[[2019,12,6]]},"reference":[{"volume-title":"Insall & Scott surgery of the knee","year":"2017","author":"Scott W.N.","key":"e_1_2_6_2_1"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.2106\/JBJS.F.00222"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11999\u2010009\u20101119\u20109"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00167\u2010016\u20104305\u20109"},{"key":"e_1_2_6_6_1","unstructured":"Smith & Nephew: \u2018Navio surgical system\u2019. Available athttp:\/\/smithnephewlivesurgery.com\/navio\u2010surgical\u2010system accessed 29 March 2019"},{"key":"e_1_2_6_7_1","doi-asserted-by":"crossref","unstructured":"Raposo C. Sousa C. Ribeiro L.L. et al. : \u2018Video\u2010based computer aided arthroscopy for patient specific reconstruction of the anterior cruciate ligament\u2019.Medical Image Computing and Computer Assisted Intervention (MICCAI) Granada Spain 2018","DOI":"10.1007\/978-3-030-00937-3_15"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_6_9_1","unstructured":"Iglovikov V. Shvets A.: \u2018Ternausnet: U\u2010net with VGG11 encoder pre\u2010trained on ImageNet for image segmentation\u2019 2018. Available athttp:\/\/arxiv.org\/abs\/1801.05746"},{"key":"e_1_2_6_10_1","doi-asserted-by":"crossref","unstructured":"Raposo C. Barreto J.P.: \u20183D registration of curves and surfaces using local differential information\u2019.The IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) Salt Lake City Utah 2018","DOI":"10.1109\/CVPR.2018.00969"},{"key":"e_1_2_6_11_1","doi-asserted-by":"crossref","unstructured":"Rusu R.B. Blodow N. Beetz M.: \u2018Fast point feature histograms (FPFH) for 3D registration\u2019.IEEE Int. Conf. on Robotics and Automation Amsterdam Netherlands May2009 pp.3212\u20133217","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"e_1_2_6_12_1","first-page":"766","article-title":"Fast global registration","volume":"9906","author":"Zhou Q.Y.","year":"2016","journal-title":"European Conf. on Computer Vision"},{"key":"e_1_2_6_13_1","doi-asserted-by":"crossref","unstructured":"Mohamad M. Ahmed M.T. Rappaport D. et al. : \u2018Super generalized 4PCS for 3D registration\u2019.Proc. \u2013 2015 Int. Conf. on 3D Vision 3DV 2015 Ecole Normale Superieure Lyon October2015 pp.598\u2013606","DOI":"10.1109\/3DV.2015.74"},{"key":"e_1_2_6_14_1","doi-asserted-by":"crossref","unstructured":"Aiger D. Mitra N.J. Cohen\u2010Or D.: \u20184\u2010points congruent sets for robust pairwise surface registration\u2019.SIGGRAPH'08: Int. Conf. on Computer Graphics and Interactive Techniques ACM SIGGRAPH 2008 Papers 2008 Los Angeles CA USA August2008","DOI":"10.1145\/1399504.1360684"},{"key":"e_1_2_6_15_1","doi-asserted-by":"crossref","unstructured":"Mellado N. Aiger D. Mitra N.J.: \u2018SUPER 4PCS fast global point cloud registration via smart indexing\u2019.Eurographics Symp. on Geometry Processing Airela\u2010Ville Switzerland 2005 vol. 33 (5) pp.205\u2013215","DOI":"10.1111\/cgf.12446"},{"key":"e_1_2_6_16_1","doi-asserted-by":"crossref","unstructured":"Raposo C. Barreto J.P.: \u2018Using 2 point+normal sets for fast registration of point clouds with small overlap\u2019.2017 IEEE Int. Conf. on Robotics and Automation (ICRA) Marina Bay Sands Singapore May2017 pp.5652\u20135658","DOI":"10.1109\/ICRA.2017.7989664"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.121791"}],"container-title":["Healthcare Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl.2019.0078","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/htl.2019.0078","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl.2019.0078","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T12:33:51Z","timestamp":1761654831000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/htl.2019.0078"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":16,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["10.1049\/htl.2019.0078"],"URL":"https:\/\/doi.org\/10.1049\/htl.2019.0078","archive":["Portico"],"relation":{},"ISSN":["2053-3713","2053-3713"],"issn-type":[{"type":"print","value":"2053-3713"},{"type":"electronic","value":"2053-3713"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"2019-09-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-10-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-12-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}