Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images

Hyunhee Lee, Jaechoon Jo, Heuiseok Lim, Sanghyuk Lee

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Article number8273173
JournalMathematical Problems in Engineering
Publication statusPublished - 2020

Bibliographical note

Publisher Copyright:
© 2020 Hyunhee Lee et al.

ASJC Scopus subject areas

  • General Mathematics
  • General Engineering


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