Automatic brain structures segmentation using deep residual dilated U-Net

Abstract

Brain image segmentation is used for visualizing and quantifying anatomical structures of the brain. We present an automated approach 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 (http://mrbrains18.isi.uu.nl/ ) and ranked 9 th 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, 4.35 mm vs 11.59 mm in averaged robust Hausdorff distance) and is computationally efficient.