Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/71394

TitleSpatial normalization of MRI brain studies using a U-Net based neural network
Author(s)Jesus, Tiago Rafael Andrade
Magalhães, Ricardo José Silva
Alves, Victor
KeywordsDeep Learning
Neuroimaging
Spatial normalization
Issue date2020
PublisherSpringer, Cham
JournalAdvances in Intelligent Systems and Computing
CitationJesus T., Magalhães R., Alves V. (2020) Spatial Normalization of MRI Brain Studies Using a U-Net Based Neural Network. In: Rocha Á., Adeli H., Reis L., Costanzo S., Orovic I., Moreira F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1161. Springer, Cham. https://doi.org/10.1007/978-3-030-45697-9_48
Abstract(s)Over recent years, Deep Learning has proven to be an excellent technology to solve problems that would otherwise be too complex. Furthermore, it has seen great success in the area of medical imaging, especially when applied to the segmentation of brain tissues. As such, this work explores a possible new approach, using Deep Learning to perform spatial normalization on Magnetic Resonance Imaging brain studies. Spatial normalization of Magnetic Resonance images by tools like FSL, or SPM can be inefficient for researches as they require too many resources to achieve good results. These resources include, for example, wasted human and computer time when executing the commands to normalize and waiting for the process to finish. This can take up to several hours just for one study. Therefore, to enable a faster and easier method to normalize the data, a U-Net based Deep Neural Network was developed using Keras and TensorFlow. This approach should free the researchers’ time for other more relevant tasks and help reach conclusions faster in their studies when trying to find patterns between the analyzed brains. The results obtained have shown potential by predicting the correct brain shape in less than 10 s per exam instead of hours even though the model did not yet accomplish a fully usable spatial normalized brain.
TypeConference paper
URIhttp://hdl.handle.net/1822/71394
ISBN978-3-030-45696-2
e-ISBN978-3-030-45697-9
DOI10.1007/978-3-030-45697-9_48
ISSN2194-5357
Publisher versionhttps://link.springer.com/chapter/10.1007%2F978-3-030-45697-9_48
Peer-Reviewedyes
AccessRestricted access (Author)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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