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Glioma, the malignant brain tumor, requires immediate treatment to improvethe survival of patients. Gliomas heterogeneous nature makes the segmentationdifficult, especially for sub-regions like necrosis, enhancing tumor,non-enhancing tumor, and Edema. Deep neural networks like full convolutionneural networks and ensemble of fully convolution neural networks aresuccessful for Glioma segmentation. The paper demonstrates the use of a 3Dfully convolution neural network with a three layer encoder decoder approachfor layer arrangement. The encoder blocks include the dense modules, anddecoder blocks include convolution modules. The input to the network is 3Dpatches. The loss function combines dice loss and focal loss functions. Thevalidation set dice score of the network is 0.74, 0.88, and 0.73 for enhancingtumor, whole tumor, and tumor core, respectively. The Random Forest Regressoruses shape, volumetric, and age features extracted from ground truth foroverall survival prediction. The regressor achieves an accuracy of 44.8% on thevalidation set.

3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction