Detalhes bibliográficos
| Resumo: | 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. |
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| Autores principais: | Fernandes, João Vieira |
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| Outros Autores: | Alves, Victor; Khalili, Nadieh; Benders, Manon J. N. L.; Išgum, Ivana; Pluim, Josien; Moeskops, Pim |
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| Assunto: | Brain quantification Convolutional neural networks Deep learning Magnetic resonance imaging Preterm infants Rat brain Regression |
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| Ano: | 2019 |
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| País: | Portugal |
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| Tipo de documento: | comunicação em conferência |
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| Tipo de acesso: | acesso restrito |
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| Instituição associada: | Universidade do Minho |
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| Idioma: | inglês |
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| Origem: | RepositóriUM - Universidade do Minho |
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