DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field

  • Huazhu FuEmail author
  • Yanwu Xu
  • Stephen Lin
  • Damon Wing Kee Wong
  • Jiang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)


Retinal vessel segmentation is a fundamental step for various ocular imaging applications. In this paper, we formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture. Our method is based on two key ideas: (1) applying a multi-scale and multi-level Convolutional Neural Network (CNN) with a side-output layer to learn a rich hierarchical representation, and (2) utilizing a Conditional Random Field (CRF) to model the long-range interactions between pixels. We combine the CNN and CRF layers into an integrated deep network called DeepVessel. Our experiments show that the DeepVessel system achieves state-of-the-art retinal vessel segmentation performance on the DRIVE, STARE, and CHASE_DB1 datasets with an efficient running time.


Recurrent Neural Network Retinal Vessel Convolutional Neural Network Conditional Random Field Deep Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Huazhu Fu
    • 1
    Email author
  • Yanwu Xu
    • 1
  • Stephen Lin
    • 2
  • Damon Wing Kee Wong
    • 1
  • Jiang Liu
    • 1
    • 3
  1. 1.Institute for Infocomm Research, A*STARSingaporeSingapore
  2. 2.Microsoft ResearchBeijingChina
  3. 3.Cixi Institute of Biomedical EngineeringNingbo Institute of Materials Technology and Engineering, Chinese Academy of SciencesNingboChina

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