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Segmentation of brain tumor from magnetic resonance imaging (MRI) is a vitalprocess to improve diagnosis, treatment planning and to study the differencebetween subjects with tumor and healthy subjects. In this paper, we exploit aconvolutional neural network (CNN) with hypercolumn technique to segment tumorfrom healthy brain tissue. Hypercolumn is the concatenation of a set of vectorswhich form by extracting convolutional features from multiple layers. Proposedmodel integrates batch normalization (BN) approach with hypercolumn. BN layershelp to alleviate the internal covariate shift during stochastic gradientdescent (SGD) training by zero-mean and unit variance of each mini-batch.Survival Prediction is done by first extracting features(Geometric, Fractal,and Histogram) from the segmented brain tumor data. Then, the number of days ofoverall survival is predicted by implementing regression on the extractedfeatures using an artificial neural network (ANN). Our model achieves a meandice score of 89.78%, 82.53% and 76.54% for the whole tumor, tumor core andenhancing tumor respectively in segmentation task and 67.90% in overallsurvival prediction task with the validation set of BraTS 2018 challenge. Itobtains a mean dice accuracy of 87.315%, 77.04% and 70.22% for the whole tumor,tumor core and enhancing tumor respectively in the segmentation task and a46.80% in overall survival prediction task in the BraTS 2018 test data set.

Glioma Prognosis: Segmentation of the Tumor and Survival Prediction using Shape, Geometric and Clinical Information