Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/53098
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Pereira, Sergio | por |
dc.contributor.author | Oliveira, Americo | por |
dc.contributor.author | Alves, Victor | por |
dc.contributor.author | Silva, Carlos A. | por |
dc.date.accessioned | 2018-03-21T15:04:37Z | - |
dc.date.issued | 2017 | - |
dc.identifier.isbn | 9781509048014 | por |
dc.identifier.uri | https://hdl.handle.net/1822/53098 | - |
dc.description.abstract | Magnetic Resonance Imaging is the preferred imaging modality for assessing brain tumors, and segmentation is necessary for diagnosis and treatment planning. Thus, robust automatic segmentation methods are required. Machine learning proposals where the model is learned from data are quite successful. Hierarchical segmentation approaches firstly segment the whole tumor, followed by intra-tumor tissue identification. However, results comparing it with single stages approaches are needed, as state of the art results are also achieved by all-at-once strategies. Currently, fully convolutional networks approaches for segmentation are very efficient. In this paper, a hierarchical approach for brain tumor segmentation using a fully convolutional network is studied. The evaluation is performed on the Brain Tumor Segmentation Challenge 2013 dataset, and we report the metrics Dice Score Coefficient, Positive Predictive Value, and Sensitivity. Results show benefits from segmenting the complete tumor first, over all tissues in one stage. Moreover, the tumor core also benefits from such approach. This behavior may be justified by the high data imbalance observed between tumor and normal tissues, which is mitigated by considering the tumor as a whole. | 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 Internacionalizacao (POCI) with the reference project POCI-01-0145-FEDER-006941. Sergio Pereira was supported by a scholarship from the Fundacao para a Ciencia 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 Atlas Archive and the BRATS 2012 challenge. | por |
dc.language.iso | eng | por |
dc.publisher | IEEE | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147325/PT | por |
dc.relation | PD/BD/105803/2014 | por |
dc.rights | restrictedAccess | por |
dc.subject | Convolutional neural network | por |
dc.subject | Brain tumor segmentation | por |
dc.subject | Magnetic Resonance Imaging | por |
dc.title | On hierarchical brain tumor segmentation in MRI using fully convolutional neural networks: a preliminary study | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.date.updated | 2018-03-12T19:53:37Z | - |
dc.identifier.doi | 10.1109/ENBENG.2017.7889452 | por |
dc.description.publicationversion | info:eu-repo/semantics/publishedVersion | por |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 4407 | - |
sdum.bookTitle | 2017 IEEE 5TH PORTUGUESE MEETING ON BIOENGINEERING (ENBENG) | - |
Aparece nas coleções: | DEI - Artigos em atas de congressos internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
ENBENG-SP-2017.pdf Acesso restrito! | 550,67 kB | Adobe PDF | Ver/Abrir |