Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/123156
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dc.contributor.authorDunnhofer, M.en
dc.contributor.authorAntico, M.en
dc.contributor.authorSasazawa, F.en
dc.contributor.authorTakeda, Y.en
dc.contributor.authorCamps, S.en
dc.contributor.authorMartinel, N.en
dc.contributor.authorMicheloni, C.en
dc.contributor.authorCarneiro, G.en
dc.contributor.authorFontanarosa, D.en
dc.date.issued2020en
dc.identifier.citationMedical Image Analysis, 2020; 60:101631-1-101631-17en
dc.identifier.issn1361-8415en
dc.identifier.issn1361-8423en
dc.identifier.urihttp://hdl.handle.net/2440/123156-
dc.description.abstractThe tracking of the knee femoral condyle cartilage during ultrasound-guided minimally invasive procedures is important to avoid damaging this structure during such interventions. In this study, we propose a new deep learning method to track, accurately and efficiently, the femoral condyle cartilage in ultrasound sequences, which were acquired under several clinical conditions, mimicking realistic surgical setups. Our solution, that we name Siam-U-Net, requires minimal user initialization and combines a deep learning segmentation method with a siamese framework for tracking the cartilage in temporal and spatio-temporal sequences of 2D ultrasound images. Through extensive performance validation given by the Dice Similarity Coefficient, we demonstrate that our algorithm is able to track the femoral condyle cartilage with an accuracy which is comparable to experienced surgeons. It is additionally shown that the proposed method outperforms state-of-the-art segmentation models and trackers in the localization of the cartilage. We claim that the proposed solution has the potential for ultrasound guidance in minimally invasive knee procedures.en
dc.description.statementofresponsibilityMatteo Dunnhofer, Maria Antico, Fumio Sasazawa, Yu Takeda, Saskia Camps, Niki Martinel, Christian Micheloni, Gustavo Carneiro, Davide Fontanarosaen
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCrown Copyright © 2019 Published by Elsevier B.V. All rights reserved.en
dc.subjectKnee arthroscopy; knee cartilage; ultrasound; ultrasound guidance; visual tracking; fully convolutional siamese networks; deep learningen
dc.titleSiam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound imagesen
dc.typeJournal articleen
dc.identifier.doi10.1016/j.media.2019.101631en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP180103232en
pubs.publication-statusPublisheden
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]en
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