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Spatial Normalization of MRI Brain Studies Using a U-Net Based Neural Network

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Trends and Innovations in Information Systems and Technologies (WorldCIST 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1161))

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Abstract

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.

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Acknowledgements

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We gratefully acknowledge the support of the NVIDIA Corporation with their donation of a Quadro P6000 board used in this research.

Author information

Authors and Affiliations

  1. Department of Informatics, School of Engineering, University of Minho, Braga, Portugal

    Tiago Jesus

  2. Neurospin, Joliot, CEA, Gif-Sur-Yvette, France

    Ricardo Magalhães

  3. Université Paris-Saclay, Gif-Sur-Yvette, France

    Ricardo Magalhães

  4. Algoritmi Centre, University of Minho, Braga, Portugal

    Victor Alves

Authors
  1. Tiago Jesus
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  2. Ricardo Magalhães
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  3. Victor Alves
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Corresponding author

Correspondence to Tiago Jesus .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. FEUP, Universidade do Porto, Porto, Portugal

    Luís Paulo Reis

  4. DIMES, Università della Calabria, Arcavacata, Italy

    Sandra Costanzo

  5. Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro

    Irena Orovic

  6. Universidade Portucalense, Porto, Portugal

    Fernando Moreira

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Jesus, 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

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