Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/52002

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dc.contributor.authorPereira, Sérgiopor
dc.contributor.authorPinto, Adrianopor
dc.contributor.authorAlves, Victorpor
dc.contributor.authorSilva, Carlos A.por
dc.date.accessioned2018-03-09T18:32:50Z-
dc.date.issued2016-
dc.identifier.isbn9783319308579por
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/1822/52002-
dc.description.abstractIn their most aggressive form, the mortality rate of gliomas is high. Accurate segmentation is important for surgery and treatment planning, as well as for follow-up evaluation. In this paper, we propose to segment brain tumors using a Deep Convolutional Neural Network. Neural Networks are known to suffer from overfitting. To address it, we use Dropout, Leaky Rectifier Linear Units and small convolutional kernels. To segment the High Grade Gliomas and Low Grade Gliomas we trained two different architectures, one for each grade. Using the proposed method it was possible to obtain promising results in the 2015 Multimodal Brain Tumor Segmentation (BraTS) data set, as well as the second position in the on-site challenge.por
dc.description.sponsorshipThis work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941. Sérgio Pereira was supported by a scholarship from Fundação para a Ciência e Tecnologia (FCT), Portugal (scholarship number PD/BD/105803/2014). Brain tumor image data used in this article were obtained from the MICCAI 2013 Challenge on Multimodal Brain Tumor Segmentation. The challenge database contain fully anonymized images from the Cancer Imaging Archive.por
dc.language.isoengpor
dc.publisherSpringer-Verlagpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147325/PTpor
dc.relationPD/BD/105803/2014por
dc.rightsclosedAccesspor
dc.subjectBrain tumorpor
dc.subjectDeep convolutional neural networkpor
dc.subjectDeep learningpor
dc.subjectGliomapor
dc.subjectMagnetic resonance imagingpor
dc.subjectSegmentationpor
dc.titleDeep convolutional neural networks for the segmentation of gliomas in multi-sequence MRIpor
dc.typeconferencePaperpor
dc.peerreviewedyespor
oaire.citationStartPage131por
oaire.citationEndPage143por
oaire.citationVolume9556-
dc.date.updated2018-03-01T19:28:30Z-
dc.identifier.doi10.1007/978-3-319-30858-6_12por
dc.description.publicationversioninfo:eu-repo/semantics/publishedVersionpor
sdum.export.identifier4137-
sdum.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
Appears in Collections:DEI - Artigos em atas de congressos internacionais

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