Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/129377
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dc.contributor.authorNgo, T.A.en
dc.contributor.authorLu, Z.en
dc.contributor.authorCarneiro, G.en
dc.date.issued2017en
dc.identifier.citationMedical Image Analysis, 2017; 35:159-171en
dc.identifier.issn1361-8415en
dc.identifier.issn1361-8423en
dc.identifier.urihttp://hdl.handle.net/2440/129377-
dc.description.abstractWe introduce a new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data. This combination is relevant for segmentation problems, where the visual object of interest presents large shape and appearance variations, but the annotated training set is small, which is the case for various medical image analysis applications, including the one considered in this paper. In particular, level set methods are based on shape and appearance terms that use small training sets, but present limitations for modelling the visual object variations. Deep learning methods can model such variations using relatively small amounts of annotated training, but they often need to be regularised to produce good generalisation. Therefore, the combination of these methods brings together the advantages of both approaches, producing a methodology that needs small training sets and produces accurate segmentation results. We test our methodology on the MICCAI 2009 left ventricle segmentation challenge database (containing 15 sequences for training, 15 for validation and 15 for testing), where our approach achieves the most accurate results in the semi-automated problem and state-of-the-art results for the fully automated challenge.en
dc.description.statementofresponsibilityTuan Anh Ngo, Zhi Lu, Gustavo Carneiroen
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCrown Copyright ©2016 Published by Elsevier B.V. All rights reserved.en
dc.subjectCardiac cine magnetic resonance; Deep learning; Level set method; Segmentation of the left ventricle of the hearten
dc.titleCombining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonanceen
dc.typeJournal articleen
dc.identifier.rmid0030051348en
dc.identifier.doi10.1016/j.media.2016.05.009en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140102794en
dc.identifier.pubid260090-
pubs.library.collectionMedicine publicationsen
pubs.library.teamDS10en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]en
Appears in Collections:Medicine publications

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