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

Hyunhee Lee, Jaechoon Jo and Heuiseok Lim

Mathematical Problems in Engineering, 2020, vol. 2020, 1-12

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8273173

DOI: 10.1155/2020/8273173

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