  The paper demonstrates the use of the fully convolutional neural network for
glioma segmentation on the BraTS 2019 dataset. Three-layers deep
encoder-decoder architecture is used along with dense connection at encoder
part to propagate the information from coarse layer to deep layers. This
architecture is used to train three tumor sub-components separately.
Subcomponent training weights are initialized with whole tumor weights to get
the localization of the tumor within the brain. At the end, three segmentation
results were merged to get the entire tumor segmentation. Dice Similarity of
training dataset with focal loss implementation for whole tumor, tumor core and
enhancing tumor is 0.92, 0.90 and 0.79 respectively. Radiomic features along
with segmentation results and age are used to predict the overall survival of
patients using random forest regressor to classify survival of patients in
long, medium and short survival classes. 55.4% of classification accuracy is
reported for training dataset with the scans whose resection status is
gross-total resection.
