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International Workshop on Deep Learning in Medical Image Analysis

International Workshop on Multimodal Learning for Clinical Decision Support

DLMIA 2017, ML-CDS 2017: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support pp 126–134Cite as

A Deep Level Set Method for Image Segmentation

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10553)

Abstract

This paper proposes a novel image segmentation approach that integrates fully convolutional networks (FCNs) with a level set model. Compared with a FCN, the integrated method can incorporate smoothing and prior information to achieve an accurate segmentation. Furthermore, different than using the level set model as a post-processing tool, we integrate it into the training phase to fine-tune the FCN. This allows the use of unlabeled data during training in a semi-supervised setting. Using two types of medical imaging data (liver CT and left ventricle MRI data), we show that the integrated method achieves good performance even when little training data is available, outperforming the FCN or the level set model alone.

Keywords

  • Image segmentation
  • Level set
  • Deep learning
  • FCN
  • Semi-supervised learning
  • Shape prior

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Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, Canada

    Min Tang, Sepehr Valipour, Zichen Zhang, Dana Cobzas & Martin Jagersand

Authors
  1. Min Tang
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  2. Sepehr Valipour
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  3. Zichen Zhang
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  4. Dana Cobzas
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  5. Martin Jagersand
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Corresponding author

Correspondence to Min Tang .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Québec, Canada

    Tal Arbel

  3. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  4. IBM Research - Almaden, San Jose, California, USA

    Tanveer Syeda-Mahmood

  5. Universidade do Porto, Porto, Portugal

    Prof. Dr. João Manuel R.S. Tavares

  6. IBM Research - Almaden, San Jose, USA

    Mehdi Moradi

  7. University of Queensland, Brisbane, Queensland, Australia

    Andrew Bradley

  8. Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  9. Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  10. Case Western Reserve University, Cleveland, Ohio, USA

    Anant Madabhushi

  11. Instituto Superior Técnico, Lisboa, Portugal

    Jacinto C. Nascimento

  12. Universidade do Porto, Porto, Portugal

    Jaime S. Cardoso

  13. University of Oxford, Oxford, United Kingdom

    Dr. Vasileios Belagiannis

  14. University of South Australia, Adelaide, South Australia, Australia

    Zhi Lu

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Tang, M., Valipour, S., Zhang, Z., Cobzas, D., Jagersand, M. (2017). A Deep Level Set Method for Image Segmentation. In: , et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-67558-9_15

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