Utility of Brain Parcellation in Enhancing Brain Tumor Segmentation and Survival Prediction

Abstract

In this paper, we proposed a UNet-based brain tumor segmentation method and a linear model-based survival prediction method. The effectiveness of UNet has been validated in automatically segmenting brain tumors from multimodal magnetic resonance (MR) images. Rather than network architecture, we focused more on making use of additional information (brain parcellation), training and testing strategy (coarse-to-fine), and ensemble technique to improve the segmentation performance. We then developed a linear classification model for survival prediction. Different from previous studies that mainly employ features from brain tumor segmentation, we also extracted features from brain parcellation, which further improved the prediction accuracy. On the challenge testing dataset, the proposed approach yielded average Dice scores of 88.43%, 84.51%, and 78.93% for the whole tumor, tumor core, and enhancing tumor in the segmentation task and an overall accuracy of 0.533 in the survival prediction task.