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https://hdl.handle.net/1822/71246
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Campo DC | Valor | Idioma |
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dc.contributor.author | Pinto, Adriano | por |
dc.contributor.author | Pereira, Sérgio | por |
dc.contributor.author | Rasteiro, Deolinda | por |
dc.contributor.author | Silva, Carlos A. | por |
dc.date.accessioned | 2021-04-03T14:17:48Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Pinto, A., Pereira, S., Rasteiro, D., & Silva, C. A. (2018). Hierarchical brain tumour segmentation using extremely randomized trees. Pattern Recognition, 82, 105-117. doi: https://doi.org/10.1016/j.patcog.2018.05.006 | por |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | https://hdl.handle.net/1822/71246 | - |
dc.description | Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.patcog.2018.05.006 . | por |
dc.description.abstract | Gliomas are the most common and aggressive primary brain tumours, with a short-life expectancy in their highest grade. Magnetic Resonance Imaging is the most common imaging technique to assess brain tumours. However, performing manual segmentation is a difficult and tedious task, mainly due to the large amount of information to be analysed. Therefore, there is a need for automatic and robust segmentation methods. We propose an automatic hierarchical brain tumour segmentation pipeline using Extremely Randomized Trees with appearance- and context-based features. Some of these features are computed over non-linear transformations of the Magnetic Resonance Imaging images. Our proposal was evaluated using the publicly available 2013 Brain Tumour Segmentation Challenge database, BRATS 2013. In the Challenge dataset, the proposed approach obtained a Dice Similarity Coefficient of 0.85, 0.79, and 0.75 for the complete, core, and enhancing regions, respectively. | por |
dc.description.sponsorship | This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 Programa Operacional Competitividade e Internacionalizao (POCI) with the reference project POCI-01-0145-FEDER-006941. Adriano Pinto was supported by a scholarship from the Fundação para a CieÍ;ncia e Tecnologia (FCT), Portugal (scholarship number PD/BD/113968/2015). Brain tumour image data used in this arti- cle were obtained from the MICCAI 2013 Challenge on Multimodal Brain Tumor Segmentation. The challenge database contains fully anonymized images from the Cancer Imaging Atlas Archive and the BRATS 2012 challenge. | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier 1 | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147325/PT | por |
dc.relation | PD/BD/113968/2015 | por |
dc.rights | restrictedAccess | por |
dc.subject | Brain tumour | por |
dc.subject | Magnetic resonance imaging | por |
dc.subject | Image segmentation | por |
dc.subject | Hierarchy of classifiers | por |
dc.subject | Extremely randomized trees | por |
dc.subject | Machine learning | por |
dc.title | Hierarchical brain tumour segmentation using extremely randomized trees | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0031320318301699 | por |
oaire.citationStartPage | 105 | por |
oaire.citationEndPage | 117 | por |
oaire.citationVolume | 82 | por |
dc.identifier.doi | 10.1016/j.patcog.2018.05.006 | por |
dc.date.embargo | 10000-01-01 | - |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Médica | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Pattern Recognition | por |
oaire.version | VoR | por |
dc.subject.ods | Saúde de qualidade | por |
Aparece nas coleções: | CMEMS - Artigos em revistas internacionais/Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Pinto_Pereira_Silva@2018.pdf Acesso restrito! | 2,28 MB | Adobe PDF | Ver/Abrir |