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Multipath Densely Connected Convolutional Neural Network for Brain Tumor Segmentation

  • Cong Liu
  • Weixin SiEmail author
  • Yinling Qian
  • Xiangyun Liao
  • Qiong Wang
  • Yong Guo
  • Pheng-Ann Heng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

This paper presents a novel Multipath Densely Connected Convolutional Neural Network (MDCNN) for automatically segmenting glioma with unknown sizes, shapes and positions. Our network architecture is based on the Multipath Convolutional Neural Network [21], which considers both local and contextual patches of segmentation information, including original MRI images, symmetry information and spatial information. Motivated to reduce the feature loss induced by under-utility of feature maps, we propose to fuse feature maps from original local and contextual paths at three different units and introduce three more densely connected paths. Consequently, three auxiliary segmentation paths together with original local and contextural paths forms the complete segmentation network. The model’s training and validation are performed on the BraTS2017 dataset. Experimental results demonstrate that the proposed network is capable to effectively extract more accurate tumor locations and contours with improved stability.

Notes

Acknowledgment

This work was supported in part by the Shenzhen Science and Technology Program (No. JCYJ20160429190300857 and No. JCYJ20170413162617606), Research Grants Council of the Hong Kong Special Administrative Region (Project No. GRF 14203115) and the National Natural Science Foundation of China (Grant No. 61802385). Yinling Qian and Weixin Si are the corresponding authors.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Cong Liu
    • 1
  • Weixin Si
    • 2
    • 3
    Email author
  • Yinling Qian
    • 2
  • Xiangyun Liao
    • 2
  • Qiong Wang
    • 2
  • Yong Guo
    • 1
  • Pheng-Ann Heng
    • 2
    • 3
  1. 1.College of Mechanical and Electrical EngineeringCentral South UniversityChangshaChina
  2. 2.Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality TechnologyShenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
  3. 3.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina

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