Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans

TL;DR

This paper introduces a GAN-based method to synthesize high-contrast MRI images for improved brain tumor segmentation, reducing reliance on multiple real modalities and enhancing segmentation accuracy on the BraTS 2019 dataset.

Contribution

The study presents a novel GAN extension that generates synthetic high-contrast images to improve brain tumor segmentation and predict patient survival, reducing the need for multiple real MRI channels.

Findings

Significant segmentation improvement using synthetic images

Reduced number of real channels needed for segmentation

Effective survival prediction based on tumor features

Abstract

The magnetic resonance (MR) analysis of brain tumors is widely used for diagnosis and examination of tumor subregions. The overlapping area among the intensity distribution of healthy, enhancing, non-enhancing, and edema regions makes the automatic segmentation a challenging task. Here, we show that a convolutional neural network trained on high-contrast images can transform the intensity distribution of brain lesions in its internal subregions. Specifically, a generative adversarial network (GAN) is extended to synthesize high-contrast images. A comparison of these synthetic images and real images of brain tumor tissue in MR scans showed significant segmentation improvement and decreased the number of real channels for segmentation. The synthetic images are used as a substitute for real channels and can bypass real modalities in the multimodal brain tumor segmentation framework. Segmentation results on BraTS 2019 dataset demonstrate that our proposed approach can efficiently segment the tumor areas. In the end, we predict patient survival time based on volumetric features of the tumor subregions as well as the age of each case through several regression models.