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https://hdl.handle.net/2440/111346
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dc.contributor.author | Maicas, G. | - |
dc.contributor.author | Carneiro, G. | - |
dc.contributor.author | Bradley, A. | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2017, pp.305-309 | - |
dc.identifier.isbn | 9781509011711 | - |
dc.identifier.issn | 1945-7928 | - |
dc.identifier.issn | 1945-8452 | - |
dc.identifier.uri | http://hdl.handle.net/2440/111346 | - |
dc.description.abstract | We 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. | - |
dc.description.statementofresponsibility | Gabriel Maicas, Gustavo Carneiro, Andrew P. Bradley | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.relation.ispartofseries | IEEE International Symposium on Biomedical Imaging | - |
dc.rights | ©2017 IEEE | - |
dc.source.uri | http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7944115 | - |
dc.subject | Breast cancer; deep learning; energy-based segmentation; shape prior; breast mass segmentation; breast MRI; global optimization | - |
dc.title | Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior | - |
dc.type | Conference paper | - |
dc.contributor.conference | IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (18 Apr 2017 - 21 Apr 2017 : Melbourne, AUSTRALIA) | - |
dc.identifier.doi | 10.1109/ISBI.2017.7950525 | - |
dc.publisher.place | Online | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP140102794 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/FT110100623 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Carneiro, G. [0000-0002-5571-6220] | - |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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