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Deep Neural Network for Pancreas Segmentation from CT Images

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Advances in Brain Inspired Cognitive Systems (BICS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11691))

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Abstract

Automatic pancreas segmentation from Computed Tomography (CT) images is a prerequisite of clinical practices such as cancer detection, yet challenging due to the variability in shape. To address this challenge, we propose a Hierarchical Convolutional Neural Network (H-CNN) to fuse multi-scale features, which could remedy the lost image details in progressive convolutional and pooling layers. In our proposed H-CNN, a hierarchical fusion block is designed to fuse low-level and high-level features across different layers. The H-CNN is evaluated on NIH pancreas dataset and outperforms the current state-of-art methods by achieving 86.59% ± 4.33% in terms of DSC. The experimental results confirm the effectiveness of the proposed H-CNN.

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Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, China

    Zhanlan Chen & Jiangbin Zheng

Authors
  1. Zhanlan Chen
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  2. Jiangbin Zheng
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Corresponding author

Correspondence to Jiangbin Zheng .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Jinchang Ren

  2. Edinburgh Napier University, Edinburgh, UK

    Amir Hussain

  3. Guangdong Polytechnic Normal University, Guangzhou, China

    Huimin Zhao

  4. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Kaizhu Huang

  5. Northwestern Polytechnical University, Xi'an, China

    Jiangbin Zheng

  6. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  7. Guangdong Polytechnic Normal University, Guangzhou, China

    Rongjun Chen

  8. Guangdong Polytechnic Normal University, Guangzhou, China

    Yinyin Xiao

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Cite this paper

Chen, Z., Zheng, J. (2020). Deep Neural Network for Pancreas Segmentation from CT Images. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_39

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  • DOI: https://doi.org/10.1007/978-3-030-39431-8_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39430-1

  • Online ISBN: 978-3-030-39431-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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