TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation

Abstract

The high human labor demand involved in collecting paired medical imaging data severely impedes the application of deep learning methods to medical image processing tasks such as tumor segmentation. The situation is further worsened when collecting multi-modal image pairs. However, this issue can be resolved through the help of generative adversarial networks, which can be used to generate realistic images. In this work, we propose a novel framework, named TumorGAN, to generate image segmentation pairs based on unpaired adversarial training. To improve the quality of the generated images, we introduce a regional perceptual loss to enhance the performance of the discriminator. We also develop a regional L1 loss to constrain the color of the imaged brain tissue. Finally, we verify the performance of TumorGAN on a public brain tumor data set, BraTS 2017. The experimental results demonstrate that the synthetic data pairs generated by our proposed method can practically improve tumor segmentation performance when applied to segmentation network training.

Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

“…Since its introduction, there has been a surge of interest in the application of GAN frameworks related to the brain. Some of the applications include image generation with improved properties such as achieving super resolution or better quality [6][7][8][9][10][11], data augmentation [12][13][14], segmentation [9,[13][14][15][16], image reconstruction [17][18][19][20], image-to-image translation [21][22][23][24], and motion correction [25,26]. While these important studies have demonstrated the exciting prospect of using GAN architectures, there is a limited amount of work that has focused on utilizing the generated images for subsequent tasks such as disease classification [27].…”

Improvement of Multiparametric MR Image Segmentation by Augmenting the Data With Generative Adversarial Networks for Glioma Patients

“…“TumorGan” (Qingyun Li et al, 2020 ) and “ANT-GAN” (Sun et al, 2020 ) were different GAN methodologies to generate synMR. Direct quantitative comparison of synMR image quality is difficult among studies due to synMR/rMR structural differences.…”

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

“…We proposed the ensemble DL framework using 26 layers of the 3D residual neural network (ResNet) composed of 3D convolution blocks by stacking a 3 × 3 × 3 convolution operation, ReLU activation function (42), batch normalization layer (43), and dropout technique (44) to construct the brain age predictive models (Figure 1). Previous studies have indicated data augmentation approach might expand the diversity of data properties and further improve the prediction performance (45)(46)(47). Therefore, to achieve superior prediction performance of the constructed ensemble DL model, we also generated novel synthetic assisted T2-weighted fluid-attenuated inversion recovery (FLAIR) images as an additional input feature set for the proposed DL models.…”