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Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network

  • Renzhen Wang
  • Shilei CaoEmail author
  • Kai Ma
  • Deyu Meng
  • Yefeng Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Semantic segmentation has been popularly addressed using fully convolutional networks (FCNs) with impressive results if the training set is diverse and large enough. However, FCNs often fail to achieve satisfactory results due to a limited number of manually labelled samples in medical imaging. In this paper, we propose a conjugate fully convolutional network (CFCN) to address this challenging problem. CFCN is a novel framework where pairwise samples are input and synergistically segmented in the network for capturing a rich context representation. To avoid overfitting introduced by appearance and shape changes in a small number of training samples, a fusion module is designed to provide proxy supervision for the network training process. Quantitative evaluation shows that the proposed method has a significant performance improvement on pathological liver segmentation.

Keywords

Semantic segmentation Pairwise segmentation Conjugate fully convolutional network Proxy supervision 

Notes

Acknowledgments

This work was supported by the China NSFC (11690011, 61661166011, 61721002, 81830053, U1811461) and the Key Area Research and Development Program of Guangdong Province, China (2018B010111001).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Renzhen Wang
    • 1
    • 2
  • Shilei Cao
    • 2
    Email author
  • Kai Ma
    • 2
  • Deyu Meng
    • 1
  • Yefeng Zheng
    • 2
  1. 1.School of Mathematics and StatisticsXi’an Jiaotong UniversityXi’anChina
  2. 2.Youtu Lab, TencentShenzhenChina

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