  Segmentation of brain tumor from magnetic resonance imaging (MRI) is a vital
process to improve diagnosis, treatment planning and to study the difference
between subjects with tumor and healthy subjects. In this paper, we exploit a
convolutional neural network (CNN) with hypercolumn technique to segment tumor
from healthy brain tissue. Hypercolumn is the concatenation of a set of vectors
which form by extracting convolutional features from multiple layers. Proposed
model integrates batch normalization (BN) approach with hypercolumn. BN layers
help to alleviate the internal covariate shift during stochastic gradient
descent (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 of
overall survival is predicted by implementing regression on the extracted
features using an artificial neural network (ANN). Our model achieves a mean
dice score of 89.78%, 82.53% and 76.54% for the whole tumor, tumor core and
enhancing tumor respectively in segmentation task and 67.90% in overall
survival prediction task with the validation set of BraTS 2018 challenge. It
obtains 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 a
46.80% in overall survival prediction task in the BraTS 2018 test data set.
