  In this study, an automated three dimensional (3D) deep segmentation approach
for detecting gliomas in 3D pre-operative MRI scans is proposed. Then, a
classi-fication algorithm based on random forests, for survival prediction is
presented. The objective is to segment the glioma area and produce segmentation
labels for its different sub-regions, i.e. necrotic and the non-enhancing tumor
core, the peri-tumoral edema, and enhancing tumor. The proposed deep
architecture for the segmentation task encompasses two parallel streamlines
with two different reso-lutions. One deep convolutional neural network is to
learn local features of the input data while the other one is set to have a
global observation on whole image. Deemed to be complementary, the outputs of
each stream are then merged to pro-vide an ensemble complete learning of the
input image. The proposed network takes the whole image as input instead of
patch-based approaches in order to con-sider the semantic features throughout
the whole volume. The algorithm is trained on BraTS 2019 which included 335
training cases, and validated on 127 unseen cases from the validation dataset
using a blind testing approach. The proposed method was also evaluated on the
BraTS 2019 challenge test dataset of 166 cases. The results show that the
proposed methods provide promising segmentations as well as survival
prediction. The mean Dice overlap measures of automatic brain tumor
segmentation for validation set were 0.84, 0.74 and 0.71 for the whole tu-mor,
core and enhancing tumor, respectively. The corresponding results for the
challenge test dataset were 0.82, 0.72, and 0.70, respectively. The overall
accura-cy of the proposed model for the survival prediction task is %52 for the
valida-tion and %49 for the test dataset.
