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

Abstract EN

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.