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Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net

  • Hongwei LiEmail author
  • Andrii Zhygallo
  • Bjoern Menze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

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.

Keywords

Brain structure segmentation Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Technical University of MunichMunichGermany

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