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  4. A novel Bayesian model incorporating deep neural network and statistical shape model for pancreas segmentation
 
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2018
Conference Paper
Title

A novel Bayesian model incorporating deep neural network and statistical shape model for pancreas segmentation

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.
Author(s)
Ma, Jingting
Nanyang Technological University, Singapore
Lin, Feng
Nanyang Technological University, Singapore
Wesarg, Stefan  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Erdt, Marius  
Fraunhofer Singapore  
Mainwork
Medical Image Computing and Computer Assisted Intervention, MICCAI 2018  
Conference
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018  
DOI
10.1007/978-3-030-00937-3_55
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Singapore  
Keyword(s)
  • neural network

  • statistical shape model (SSM)

  • Pancreas

  • segmentation

  • Lead Topic: Individual Health

  • Research Line: Computer graphics (CG)

  • Research Line: Computer vision (CV)

  • Research Line: Modeling (MOD)

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