Semantic Segmentation of White Matter in FDG-PET Using Generative Adversarial Network
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ORIGINAL PAPER
Semantic Segmentation of White Matter in FDG-PET Using Generative Adversarial Network Kyeong Taek Oh 1 & Sangwon Lee 2 & Haeun Lee 1 & Mijin Yun 2 & Sun K. Yoo 1
# Society for Imaging Informatics in Medicine 2020
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. Keywords GAN . Deep learning . FDG-PET . ADNI . White matter segmentation
Introduction Segmentation of the brain compartment such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) for the quantification of tissue volume and functional analysis of different structures is of great importance for research and clinical studies using magnetic resonance imaging (MRI) of the brain [1]. For MRI, various approaches and open source
* Mijin Yun [email protected] * Sun K. Yoo [email protected] Kyeong Taek Oh [email protected] Sangwon Lee [email protected] Haeun Lee [email protected] 1
Department of Medical Engineering, Yonsei University College of Medicine, Seoul, South Korea
2
Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
software packages have been used for brain segmentation and volumetric quantification. Recently, deep learning models have been used in developing algorithms for segmentation of brain structures in anatomical images [2–5]. Of the deep learning-related algorithms, generative adversarial network (GAN) model has revealed excellent performance in image generation tasks, including image-to-image translation, text-to-image synthesis, semantic segmentation, and low to high resolution translation [6]. GAN consists of two networks which has a generator and a discriminator. The generator learns a mapping function to create similar output to real data. The discriminator learns how to differentiate the generated data from the original data. After the concept of adversarial learning was introduced, various GAN models were applied for automatic segmentation of medical images with excellent results. Mondal et al. showed a higher performance for segmenting brain st
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