Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images
- Authors
- Lee, Hyunhee; Jo, Jaechoon; Lim, Heuiseok
- Issue Date
- 20-5월-2020
- Publisher
- HINDAWI LTD
- Citation
- MATHEMATICAL PROBLEMS IN ENGINEERING, v.2020
- Indexed
- SCIE
SCOPUS
- Journal Title
- MATHEMATICAL PROBLEMS IN ENGINEERING
- Volume
- 2020
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/55662
- DOI
- 10.1155/2020/8273173
- ISSN
- 1024-123X
- Abstract
- 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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Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
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