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A Statistical Shape Model for Multiple Organs Based on Synthesized-Based Learning

  • Atsushi Saito
  • Misaki Nakada
  • Elco Oost
  • Akinobu Shimizu
  • Hidefumi Watanabe
  • Shigeru Nawano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8198)

Abstract

This paper presents a statistical shape model for multiple abdominal organs using synthesized-based learning to compensate the lack of a large manually labeled training data set. Experiments on 23 non-contrast CT volumes showed that a model trained on both true and synthesized data, outperforms conventional shape models, in terms of generalization, specificity and overlap of neighboring organs.

Keywords

Synthesized-based learning level set morphing statistical shape model multiple abdominal organs CT volume 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Atsushi Saito
    • 1
  • Misaki Nakada
    • 1
  • Elco Oost
    • 1
  • Akinobu Shimizu
    • 1
  • Hidefumi Watanabe
    • 1
  • Shigeru Nawano
    • 2
  1. 1.Tokyo University of Agriculture and TechnologyKoganeiJapan
  2. 2.Center for Radiological Science, Mita HopitalInternational University of Health and WelfareMinato-kuJapan

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