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Book chapter
Open access
English

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

PublisherCham : Springer
Collection
  • Advances in Intelligent Systems and Computing; 1252
Publication date2021
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.

Citation (ISO format)
MECHETER, Imene et al. Brain MR Imaging Segmentation Using Convolutional Auto Encoder Network for PET Attenuation Correction. In: Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys). Cham : Springer, 2021. p. 430–440. (Advances in Intelligent Systems and Computing) doi: 10.1007/978-3-030-55190-2_32
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Book chapter (Published version)
accessLevelPublic
Identifiers
ISBN978-3-030-55189-6
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Technical informations

Creation08/29/2020 12:54:00 PM
First validation08/29/2020 12:54:00 PM
Update time03/15/2023 10:48:38 PM
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