  Automated brain tumour segmentation has the potential of making a massive
improvement in disease diagnosis, surgery, monitoring and surveillance.
However, this task is extremely challenging. Here, we describe our automated
segmentation method using 2D CNNs that are based on U-Net. To deal with class
imbalance effectively, we have used a weighted Dice loss function. We found
that increasing the depth of the 'U' shape beyond a certain level results in a
decrease in performance, so it is essential to choose an optimum depth. We also
found that 3D contextual information cannot be captured by a single 2D network
that is trained with patches extracted from multiple views whereas an ensemble
of three 2D networks trained in multiple views can effectively capture the
information and deliver much better performance. We obtained Dice scores of
0.79 for enhancing tumour, 0.90 for whole tumour, and 0.82 for tumour core on
the BraTS 2018 validation set. Our method using 2D network consumes very less
time and memory, and is much simpler and easier to implement compared to the
state-of-the-art methods that used 3D networks; still, it manages to achieve
comparable performance to those methods.

Image and Video Processing;Computer Vision and Pattern Recognition