Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fusion

Abstract

AbstractAutomatic segmentation of gliomas from brain Magnetic Resonance Imaging (MRI) volumes is an essential step for tumor detection. Various 2D Convolutional Neural Network (2D-CNN) and its 3D variant, known as 3D-CNN based architectures, have been proposed in previous studies, which are used to capture contextual information. The 3D models capture depth information, making them an automatic choice for glioma segmentation from 3D MRI images. However, the 2D models can be trained in a relatively shorter time, making their parameter tuning relatively easier. Considering these facts, we tried to propose an ensemble of 2D and 3D models to utilize their respective benefits better. After segmentation, prediction of Overall Survival (OS) time was performed on segmented tumor sub-regions. For this task, multiple radiomic and image-based features were extracted from MRI volumes and segmented sub-regions. In this study, radiomic and image-based features were fused to predict the OS time of patients. Experimental results on BraTS 2020 testing dataset achieved a dice score of 0.79 on Enhancing Tumor (ET), 0.87 on Whole Tumor (WT), and 0.83 on Tumor Core (TC). For OS prediction task, results on BraTS 2020 testing leaderboard achieved an accuracy of 0.57, Mean Square Error (MSE) of 392,963.189, Median SE of 162,006.3, and Spearman R correlation score of −0.084. KeywordsBrain tumor segmentationOverall survival predictionEnsembleU-Net