Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111346
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dc.contributor.authorMaicas, G.en
dc.contributor.authorCarneiro, G.en
dc.contributor.authorBradley, A.en
dc.date.issued2017en
dc.identifier.citationProceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017 / pp.305-309en
dc.identifier.isbn9781509011711en
dc.identifier.issn1945-7928en
dc.identifier.issn1945-8452en
dc.identifier.urihttp://hdl.handle.net/2440/111346-
dc.description.abstractWe introduce a new fully automated breast mass segmentation method from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The method is based on globally optimal inference in a continuous space (GOCS) using a shape prior computed from a semantic segmentation produced by a deep learning (DL) model. We propose this approach because the limited amount of annotated training samples does not allow the implementation of a robust DL model that could produce accurate segmentation results on its own. Furthermore, GOCS does not need precise initialisation compared to locally optimal methods on a continuous space (e.g., Mumford-Shah based level set methods); also, GOCS has smaller memory complexity compared to globally optimal inference on a discrete space (e.g., graph cuts). Experimental results show that the proposed method produces the current state-of-the-art mass segmentation (from DCEMRI) results, achieving a mean Dice coefficient of 0.77 for the test set.en
dc.description.statementofresponsibilityGabriel Maicas, Gustavo Carneiro, Andrew P. Bradleyen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE International Symposium on Biomedical Imagingen
dc.rights©2017 IEEEen
dc.source.urihttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7944115en
dc.subjectBreast cancer; deep learning; energy-based segmentation; shape prior; breast mass segmentation; breast MRI; global optimizationen
dc.titleGlobally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prioren
dc.typeConference paperen
dc.identifier.rmid0030073409en
dc.contributor.conferenceIEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (18 Apr 2017 - 21 Apr 2017 : Melbourne, AUSTRALIA)en
dc.identifier.doi10.1109/ISBI.2017.7950525en
dc.publisher.placeOnlineen
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140102794en
dc.relation.granthttp://purl.org/au-research/grants/arc/FT110100623en
dc.identifier.pubid365543-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS03en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]en
Appears in Collections:Computer Science publications

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