  This article presents a multiscale patch based convolutional neural network
for the automatic segmentation of brain tumors in multi-modality 3D MR images.
We use multiscale deep supervision and inputs to train a convolutional network.
We evaluate the effectiveness of the proposed approach on the BRATS 2017
segmentation challenge where we obtained dice scores of 0.755, 0.900, 0.782 and
95% Hausdorff distance of 3.63mm, 4.10mm, and 6.81mm for enhanced tumor core,
whole tumor and tumor core respectively.
