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TR-GAN: Multi-session future MRI prediction with temporal recurrent generative adversarial network

文献类型：期刊论文

作者Fan, Chen-Chen4,5; Peng, Liang4; Wang, Tian2; Yang, Hongjun4; Zhou, Xiao-Hu4; Ni, Zhen-Liang4,5; Wang, Guan’an4,5; Chen, Sheng4,5; Zhou, Yan-Jie4,5; Hou, Zeng-Guang1,3,4,5
刊名IEEE Transactions on Medical Imaging
出版日期2022
卷号vol. 41期号:no. 8页码:1925-1937
关键词Alzheimer’s disease magnetic resonance imaging generative adversarial network
DOI10.1109/TMI.2022.3151118
英文摘要
Magnetic Resonance Imaging (MRI) has been proven to be an efficient way to diagnose Alzheimer's disease (AD). Recent dramatic progress on deep learning greatly promotes the MRI analysis based on data-driven CNN methods using a large-scale longitudinal MRI dataset. However, most of the existing MRI datasets are fragmented due to unexpected quits of volunteers. To tackle this problem, we propose a novel Temporal Recurrent Generative Adversarial Network (TR-GAN) to complete missing sessions of MRI datasets. Unlike existing GAN-based methods, which either fail to generate future sessions or only generate fixed-length sessions, TR-GAN takes all past sessions to recurrently and smoothly generate future ones with variant length. Specifically, TR-GAN adopts recurrent connection to deal with variant input sequence length and flexibly generate future variant sessions. Besides, we also design a multiple scale & location (MSL) module and a SWAP module to encourage the model to better focus on detailed information, which helps to generate high-quality MRI data. Compared with other popular GAN architectures, TR-GAN achieved the best performance in all evaluation metrics of two datasets. After expanding the Whole MRI dataset, the balanced accuracy of AD vs. cognitively normal (CN) vs. mild cognitive impairment (MCI) and stable MCI vs. progressive MCI classification can be increased by 3.61% and 4.00%, respectively.
语种英语
源URL[http://ir.ia.ac.cn/handle/173211/51864]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Hou, Zeng-Guang
作者单位1.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing 100190, China
2.Neuroscience and Intelligent Media Institute, Communication University of China, Beijing 100024, China
3.CASIA-MUST Joint Laboratory of Intelligence Sci ence and Technology, Institute of Systems Engineering, Macau Uni versity of Science and Technology, Macau 999078, China
4.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
5.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
推荐引用方式
GB/T 7714		 Fan, Chen-Chen,Peng, Liang,Wang, Tian,et al. TR-GAN: Multi-session future MRI prediction with temporal recurrent generative adversarial network[J]. IEEE Transactions on Medical Imaging,2022,vol. 41(no. 8):1925-1937.
APA Fan, Chen-Chen.,Peng, Liang.,Wang, Tian.,Yang, Hongjun.,Zhou, Xiao-Hu.,...&Hou, Zeng-Guang.(2022).TR-GAN: Multi-session future MRI prediction with temporal recurrent generative adversarial network.IEEE Transactions on Medical Imaging,vol. 41(no. 8),1925-1937.
MLA Fan, Chen-Chen,et al."TR-GAN: Multi-session future MRI prediction with temporal recurrent generative adversarial network".IEEE Transactions on Medical Imaging vol. 41.no. 8(2022):1925-1937.

入库方式： OAI收割

来源：自动化研究所

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