期刊
MATHEMATICAL PROBLEMS IN ENGINEERING
卷 2020, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2020/8273173
关键词
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资金
- Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program [IITP-2020-2018-0-01405]
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [NRF-2017M3C4A7068189]
Due to institutional and privacy issues, medical imaging researches are confronted with serious data scarcity. Image synthesis using generative adversarial networks provides a generic solution to the lack of medical imaging data. We synthesize high-quality brain tumor-segmented MR images, which consists of two tasks: synthesis and segmentation. We performed experiments with two different generative networks, the first using the ResNet model, which has significant advantages of style transfer, and the second, the U-Net model, one of the most powerful models for segmentation. We compare the performance of each model and propose a more robust model for synthesizing brain tumor-segmented MR images. Although ResNet produced better-quality images than did U-Net for the same samples, it used a great deal of memory and took much longer to train. U-Net, meanwhile, segmented the brain tumors more accurately than did ResNet.
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