  Brain cancer can be very fatal, but chances of survival increase through
early detection and treatment. Doctors use Magnetic Resonance Imaging (MRI) to
detect and locate tumors in the brain, and very carefully analyze scans to
segment brain tumors. Manual segmentation is time consuming and tiring for
doctors, and it can be difficult for them to notice extremely small
abnormalities. Automated segmentations performed by computers offer quicker
diagnoses, the ability to notice small details, and more accurate
segmentations. Advances in deep learning and computer hardware have allowed for
high-performing automated segmentation approaches. However, several problems
persist in practice: increased training time, class imbalance, and low
performance. In this paper, I propose applying V-Net, a volumetric, fully
convolutional neural network, to segment brain tumors in MRI scans from the
BraTS Challenges. With this approach, I achieve a whole tumor dice score of
0.89 and train the network in a short time while addressing class imbalance
with the use of a dice loss layer. Then, I propose applying an existing
technique to improve automated segmentation performance in practice.
