Fully automated identification of brain abnormality from whole-body FDG-PET imaging using deep learning-based brain extraction and statistical parametric mapping

Fully automated identification of brain abnormality from whole-body FDG-PET imaging using deep learning-based brain extraction and statistical parametric mapping

Abstract Background The whole brain is often covered in [18F]Fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) in oncology patients, but the covered brain abnormality is typically screened by visual interpretation without quantitative analysis in clinical practice. In this study, we aim...

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Bibliographic Details
Main Authors: Wonseok Whi, Hongyoon Choi, Jin Chul Paeng, Gi Jeong Cheon, Keon Wook Kang, Dong Soo Lee
Format: Article
Language:English
Published: SpringerOpen 2021-11-01
Series:EJNMMI Physics
Subjects:
Brain segmentation
Quantitative PET analysis
Deep learning
Convolutional neural network
FDG-PET
Brain FDG-PET
Online Access:https://doi.org/10.1186/s40658-021-00424-0

Internet

https://doi.org/10.1186/s40658-021-00424-0

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