Multimodal Brain Tumor Segmentation and Survival Prediction Using a 3D Self-ensemble ResUNet

Abstract

AbstractIn this paper, we propose a 3D self-ensemble ResUNet (srUNet) deep neural network architecture for brain tumor segmentation and machine learning-based method for overall survival prediction of patients with gliomas. UNet architecture has been using for semantic image segmentation. It also been used for medical imaging segmentation, including brain tumor segmentation. In this work, we utilize the srUNet to differentiate brain tumors, then the segmented tumors are used for survival prediction. We apply the proposed method to the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 validation dataset for both tumor segmentation and survival prediction. The tumor segmentation result shows dice score coefficient (DSC) of 0.7634, 0.899, and 0.816 for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively. For the survival prediction method, we achieve 56.4% classification accuracy with mean square error (MSE) 101697, and 55.2% accuracy with MSE 56169 for training and validation, respectively. In the testing phase, the proposed method offers the DSC of 0.786, 0.881, and 0.823, for ET, WT, and TC, respectively. It also achieves an accuracy of 0.43 for overall survival prediction.KeywordsDeep neural networkTumor segmentationSurvival predictionFeature fusion