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Semantic Segmentation of White Matter in FDG-PET Using Generative Adversarial Network

Authors
 Kyeong Taek Oh  ;  Sangwon Lee  ;  Haeun Lee  ;  Mijin Yun  ;  Sun K Yoo 
Citation
 JOURNAL OF DIGITAL IMAGING, Vol.33(4) : 816-825, 2020-08 
Journal Title
 JOURNAL OF DIGITAL IMAGING 
ISSN
 0897-1889 
Issue Date
2020-08
Keywords
ANDI ; Deep learning ; FDG-PET ; GAN ; White matter segmentation
Abstract
In the diagnosis of neurodegenerative disorders, F-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is used for its ability to detect functional changes at early stages of disease process. However, anatomical information from another modality (CT or MRI) is still needed to properly interpret and localize the radiotracer uptake due to its low spatial resolution. Lack of structural information limits segmentation and accurate quantification of the 18F-FDG PET/CT. The correct segmentation of the brain compartment in 18F-FDG PET/CT will enable the quantitative analysis of the 18F-FDG PET/CT scan alone. In this paper, we propose a method to segment white matter in 18F-FDG PET/CT images using generative adversarial network (GAN). The segmentation result of GAN model was evaluated using evaluation parameters such as dice, AUC-PR, precision, and recall. It was also compared with other deep learning methods. As a result, the proposed method achieves superior segmentation accuracy and reliability compared with other deep learning methods.
Full Text
https://link.springer.com/article/10.1007/s10278-020-00321-5
DOI
10.1007/s10278-020-00321-5
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
Yun, Mi Jin(윤미진) ORCID logo https://orcid.org/0000-0002-1712-163X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/180429
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