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A Novel Bayesian Model Incorporating Deep Neural Network and Statistical Shape Model for Pancreas Segmentation

  • Jingting MaEmail author
  • Feng Lin
  • Stefan Wesarg
  • Marius Erdt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11073)

Abstract

Deep neural networks have achieved significant success in medical image segmentation in recent years. However, poor contrast to surrounding tissues and high flexibility of anatomical structure of the interest object are still challenges. On the other hand, statistical shape model based approaches have demonstrated promising performance on exploiting complex shape variabilities but they are sensitive to localization and initialization. This motivates us to leverage the rich shape priors learned from statistical shape models to improve the segmentation of deep neural networks. In this work, we propose a novel Bayesian model incorporating the segmentation results from both deep neural network and statistical shape model for segmentation. In evaluation, experiments are performed on 82 CT datasets of the challenging public NIH pancreas dataset. We report 85.32 % of the mean DSC that outperforms the state-of-the-art and approximately 12 % improvement from the predicted segment of deep neural network.

Keywords

Bayesian model Deep neural networks Statistical shape model Pancreas segmentation 

Notes

Acknowledgments

This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative. This work is partially supported by a grant AcRF RGC 2017-T1-001-053 by Ministry of Education, Singapore.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jingting Ma
    • 1
    Email author
  • Feng Lin
    • 1
  • Stefan Wesarg
    • 3
  • Marius Erdt
    • 1
    • 2
  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Fraunhofer SingaporeNanyang Technological UniversitySingaporeSingapore
  3. 3.Visual Healthcare TechnologiesFraunhofer IGDDarmstadtGermany

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