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Title

Brain MR Imaging Segmentation Using Convolutional Auto Encoder Network for PET Attenuation Correction

Authors
Mecheter, Imene
Amira, Abbes
Abbod, Maysam
Published in Arai K., Kapoor S., Bhatia R. Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys). Cham: Springer. 2021, p. 430-440
Collection Advances in Intelligent Systems and Computing; 1252
Abstract Magnetic resonance (MR) image segmentation is one of the most robust MR based attenuation correction methods which have been adopted in clinical routine for positron emission tomography (PET) quantification. However, the segmentation of the brain into different tissue classes is a challenging process due to the similarity between bone and air signal intensity values. The aim of this work is to study the feasibility of deep learning to improve the brain segmentation with the application of data augmentation. A deep convolutional auto encoder network is applied to segment the brain into three tissue classes: air, soft tissue, and bone. The dice similarity coefficients of air, soft tissue, and bone tissues are 0.96 ± 0.01, 0.86 ± 0.02, and 0.63 ± 0.06 respectively. Despite the small datasets used in this work, the results are promising and show the feasibility of deep learning with data augmentation to perform accurate segmentation.
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ISBN: 978-3-030-55189-6
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Research group Imagerie Médicale (TEP et TEMP) (542)
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MECHETER, Imene et al. Brain MR Imaging Segmentation Using Convolutional Auto Encoder Network for PET Attenuation Correction. In: Arai K., Kapoor S., Bhatia R. (Ed.). Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys). Cham : Springer, 2021. p. 430-440. (Advances in Intelligent Systems and Computing; 1252) doi: 10.1007/978-3-030-55190-2_32 https://archive-ouverte.unige.ch/unige:143361

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Deposited on : 2020-10-20

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