  Brain image segmentation is used for visualizing and quantifying anatomical
structures of the brain. We present an automated ap-proach using 2D deep
residual dilated networks which captures rich context information of different
tissues for the segmentation of eight brain structures. The proposed system was
evaluated in the MICCAI Brain Segmentation Challenge and ranked 9th out of 22
teams. We further compared the method with traditional U-Net using
leave-one-subject-out cross-validation setting on the public dataset.
Experimental results shows that the proposed method outperforms traditional
U-Net (i.e. 80.9% vs 78.3% in averaged Dice score, 4.35mm vs 11.59mm in
averaged robust Hausdorff distance) and is computationally efficient.
