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Deep Belief Network Based Vertebra Segmentation for CT Images

  • Syed Furqan Qadri
  • Mubashir Ahmad
  • Danni Ai
  • Jian Yang
  • Yongtian Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

Automatic vertebra segmentation is a challenging task from CT images due to anatomically complexity, shape variation and vertebrae articulation with each other. Deep Learning is a machine learning paradigm that focuses on deep hierarchical learning modeling of input data. In this paper, we propose a novel approach of automatic vertebrae segmentation from computed tomography (CT) images by using deep belief networks (BDNs) modeling. Using the DBN model, the contexture features of vertebra from CT images are extracted automatically by an unsupervised pattern called pre-training and followed by supervised training called back-propagation algorithm; then segmentation the vertebra from other abdominal structure. To evaluate the performance, we computed the overall accuracy (94.2%), sensitivity (83.2%), specificity (94.8%) and mean Dice coefficients (0.85 ± 0.03) for segmentation evaluation. Experimental results show that our proposed model provides a more accuracy in vertebra segmentation compared to the previous state of art methods.

Keywords

Segmentation Deep belief network (BDN) Pre-training Back-propagation algorithm 

Notes

Acknowledgments

This work was supported by the National Science Foundation Program of China (61527827).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Syed Furqan Qadri
    • 1
  • Mubashir Ahmad
    • 2
  • Danni Ai
    • 2
  • Jian Yang
    • 2
  • Yongtian Wang
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  2. 2.Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and ElectronicsBeijing Institute of TechnologyBeijingChina

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