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Multi-scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification

  • Liying Peng
  • Lanfen LinEmail author
  • Hongjie Hu
  • Huali Li
  • Qingqing Chen
  • Dan Wang
  • Xian-Hua Han
  • Yutaro Iwamoto
  • Yen-Wei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11045)

Abstract

Automated tissue classification is an essential step for quantitative analysis and treatment of emphysema. Although many studies have been conducted in this area, there still remain two major challenges. First, different emphysematous tissue appears in different scales, which we call “inter-class variations”. Second, the intensities of CT images acquired from different patients, scanners or scanning protocols may vary, which we call “intra-class variations”. In this paper, we present a novel multi-scale residual network with two channels of raw CT image and its differential excitation component. We incorporate multi-scale information into our networks to address the challenge of inter-class variations. In addition to the conventional raw CT image, we use its differential excitation component as a pair of inputs to handle intra-class variations. Experimental results show that our approach has superior performance over the state-of-the-art methods, achieving a classification accuracy of 93.74% on our original emphysema database.

Keywords

Emphysema classification Multi-scale Differential excitation component 

Notes

Acknowledgements

This work was supported in part by the National Key R&D Program of China under the Grant No. 2017YFB0309800, in part by the Science and Technology Support Program of Hangzhou under the Grant No. 20172011A038, and in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant Nos. 18H03267 and No. 17H00754.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Liying Peng
    • 1
  • Lanfen Lin
    • 1
    Email author
  • Hongjie Hu
    • 2
  • Huali Li
    • 2
  • Qingqing Chen
    • 2
  • Dan Wang
    • 2
  • Xian-Hua Han
    • 3
  • Yutaro Iwamoto
    • 3
  • Yen-Wei Chen
    • 3
  1. 1.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
  2. 2.Department of RadiologySir Run Run Shaw HospitalHangzhouChina
  3. 3.College of Information Science and EngineeringRitsumeikan UniversityKusatsuJapan

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