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

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.