A Deep Level Set Method for Image Segmentation

  • Min Tang
  • Sepehr Valipour
  • Zichen Zhang
  • Dana Cobzas
  • Martin Jagersand
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)


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.


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


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Min Tang
    • 1
  • Sepehr Valipour
    • 1
  • Zichen Zhang
    • 1
  • Dana Cobzas
    • 1
  • Martin Jagersand
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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