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

TL;DR

This study demonstrates that synthetic MR images generated by a GAN can enhance the training of segmentation networks for glioma regions, potentially addressing data scarcity in medical imaging.

Contribution

The paper introduces a method to generate high-quality synthetic MR images with GANs and evaluates their impact on improving glioma segmentation accuracy.

Findings

Synthetic MR images achieved SSIM=0.79, indicating high quality.

Adding synthetic images correlates with improved overall validation metrics.

Synthetic images help delineate structural boundaries but struggle with high-gradient regions.

Abstract

Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. Physicians use MR images as a key tool in the diagnosis and treatment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigates the use of varying amounts of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) MR images created by a generative adversarial network to overcome the lack of annotated medical image data in training separate 2D U-Nets to segment enhancing tumor, peritumoral edema, and necrosis (non-enhancing tumor core) regions on gliomas. These synthetic MR images were assessed quantitively (SSIM=0.79) and qualitatively by a physician who found that the synthetic images seem stronger for delineation of structural boundaries but struggle more when gradient is significant, (e.g. edema signal in T2 modalities). Multiple 2D U-Nets were trained with original BraTS data and differing subsets of a quarter, half, three-quarters, and all synthetic MR images. There was not an obvious correlation between the improvement of values of the metrics in separate validation dataset for each structure and amount of synthetic data added, there is a strong correlation between the amount of synthetic data added and the number of best overall validation metrics. In summary, this study showed ability to generate high quality synthetic Flair, T2, T1, and T1CE MR images using the GAN. Using the synthetic MR images showed encouraging results to improve the U-Net segmentation performance which has the potential to address the scarcity of readily available medical images.