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

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 cons...

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Veröffentlicht in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-12
Hauptverfasser: Lee, Hyunhee, Lim, Heuiseok, Jo, Jaechoon
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container_title Mathematical problems in engineering
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creator Lee, Hyunhee
Lim, Heuiseok
Jo, Jaechoon
description 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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subjects Brain
Brain cancer
Engineering
Image quality
Image segmentation
International conferences
Magnetic resonance imaging
Medical imaging
Medical research
Multimedia
Synthesis
Tumors
title Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images
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