Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/71342
Título: | Convolutional neural network-based regression for quantification of brain characteristics using MRI |
Autor(es): | Fernandes, João Vieira Alves, Victor Khalili, Nadieh Benders, Manon J. N. L. Išgum, Ivana Pluim, Josien Moeskops, Pim |
Palavras-chave: | Brain quantification Convolutional neural networks Deep learning Magnetic resonance imaging Preterm infants Rat brain Regression |
Data: | 2019 |
Editora: | Springer Verlag |
Revista: | Advances in Intelligent Systems and Computing |
Citação: | 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 |
Resumo(s): | 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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/71342 |
ISBN: | 978-3-030-16183-5 |
e-ISBN: | 978-3-030-16184-2 |
DOI: | 10.1007/978-3-030-16184-2_55 |
ISSN: | 2194-5357 |
Versão da editora: | https://link.springer.com/chapter/10.1007%2F978-3-030-16184-2_55 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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18.pdf Acesso restrito! | 737,56 kB | Adobe PDF | Ver/Abrir |