Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images. (20th May 2020)

• Record Type: Journal Article
• Authors: Lee, Hyunhee; Jo, Jaechoon; Lim, Heuiseok
• Other Names: Lee Sanghyuk Guest Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-05-20
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/8273173
• Languages: English
• ISSNs: 1024-123X

Abstract

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.