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https://hdl.handle.net/1822/71342
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Campo DC | Valor | Idioma |
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dc.contributor.author | Fernandes, João Vieira | por |
dc.contributor.author | Alves, Victor | por |
dc.contributor.author | Khalili, Nadieh | por |
dc.contributor.author | Benders, Manon J. N. L. | por |
dc.contributor.author | Išgum, Ivana | por |
dc.contributor.author | Pluim, Josien | por |
dc.contributor.author | Moeskops, Pim | por |
dc.date.accessioned | 2021-04-06T19:56:05Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Fernandes J. et al. (2019) Convolutional Neural Network-Based Regression for Quantification of Brain Characteristics Using MRI. In: Rocha Á., Adeli H., Reis L., Costanzo S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 931. Springer, Cham. https://doi.org/10.1007/978-3-030-16184-2_55 | por |
dc.identifier.isbn | 978-3-030-16183-5 | - |
dc.identifier.issn | 2194-5357 | - |
dc.identifier.uri | https://hdl.handle.net/1822/71342 | - |
dc.description.abstract | Preterm birth is connected to impairments and altered brain growth. Compared to their term born peers, preterm infants have a higher risk of behavioral and cognitive problems since most part of their brain development is in extra-uterine conditions. This paper presents different deep learning approaches with the objective of quantifying the volumes of 8 brain tissues and 5 other image-based descriptors that quantify the state of brain development. Two datasets were used: one with 86 MR brain images of patients around 30 weeks PMA and the other with 153 patients around 40 weeks PMA. Two approaches were evaluated: (1) using the full image as 3D input and (2) using multiple image slices as 3D input, both achieving promising results. A second study, using a dataset of MR brain images of rats, was also performed to assess the performance of this method with other brains. A 2D approach was used to estimate the volumes of 3 rat brain tissues. | por |
dc.description.sponsorship | This work was supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. We gratefully acknowledge the support of the NVIDIA Corporation with their donation of a Quadro P6000 board used in this research. | por |
dc.language.iso | eng | por |
dc.publisher | Springer Verlag | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147280/PT | por |
dc.rights | restrictedAccess | por |
dc.subject | Brain quantification | por |
dc.subject | Convolutional neural networks | por |
dc.subject | Deep learning | por |
dc.subject | Magnetic resonance imaging | por |
dc.subject | Preterm infants | por |
dc.subject | Rat brain | por |
dc.subject | Regression | por |
dc.title | Convolutional neural network-based regression for quantification of brain characteristics using MRI | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007%2F978-3-030-16184-2_55 | por |
oaire.citationStartPage | 577 | por |
oaire.citationEndPage | 586 | por |
oaire.citationVolume | 931 | por |
dc.date.updated | 2021-04-06T17:24:34Z | - |
dc.identifier.doi | 10.1007/978-3-030-16184-2_55 | por |
dc.date.embargo | 10000-01-01 | - |
dc.identifier.eisbn | 978-3-030-16184-2 | - |
dc.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | por |
sdum.export.identifier | 10242 | - |
sdum.journal | Advances in Intelligent Systems and Computing | por |
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