Segmenting tumors and their subregions is a challenging task as demonstrated
by the annual BraTS challenge. Moreover, predicting the survival of the patient
using mainly imaging features, while being a desirable outcome to evaluate the
treatment of the patient, it is also a difficult task. In this paper, we
present a cascaded pipeline to segment the tumor and its subregions and then we
use these results and other clinical features together with image features
coming from a pretrained VGG-16 network to predict the survival of the patient.
Preliminary results with the training and validation dataset show a promising
start in terms of segmentation, while the prediction values could be improved
with further testing on the feature extraction part of the network.