Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net

被引:4
|
作者
Li, Hongwei [1 ]
Zhygallo, Andrii [1 ]
Menze, Bjoern [1 ]
机构
[1] Tech Univ Munich, Munich, Germany
关键词
Brain structure segmentation; Deep learning;
D O I
10.1007/978-3-030-11723-8_39
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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 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.
引用
收藏
页码:385 / 393
页数:9
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