Brain MR Imaging Segmentation Using Convolutional Auto Encoder Network for PET Attenuation Correction
| Autor: | Abbes Amira, Habib Zaidi, Imene Mecheter, Maysam F. Abbod |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Deep learning Pattern recognition Magnetic resonance imaging Image segmentation Autoencoder ddc:616.0757 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Positron emission tomography medicine Brain segmentation Segmentation Artificial intelligence business Correction for attenuation 030217 neurology & neurosurgery |
| Zdroj: | Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) pp. 430-440 Advances in Intelligent Systems and Computing ISBN: 9783030551896 Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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