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Multi-level deep supervised networks for retinal vessel segmentation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation.

Methods

A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors.

Results

We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set.

Conclusions

The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

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Acknowledgements

This work was supported by Fok Ying Tung Education Foundation (Grant 151068); National Natural Science Foundation of China (Grants 61332002); and Foundation for Youth Science and Technology Innovation Research Team of Sichuan Province (Grants 2016TD0018).

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, 610065, China

    Juan Mo & Lei Zhang

  2. School of Science, Inner Mongolia University of Science and Technology, Baotou, 014010, China

    Juan Mo

Authors
  1. Juan Mo
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  2. Lei Zhang
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Corresponding author

Correspondence to Lei Zhang.

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The authors declare that they have no conflict of interest.

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No animal or human experiments were conducted as part of this research.

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Informed consent was obtained from all individual participants included in the study.

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Cite this article

Mo, J., Zhang, L. Multi-level deep supervised networks for retinal vessel segmentation. Int J CARS 12, 2181–2193 (2017). https://doi.org/10.1007/s11548-017-1619-0

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  • DOI: https://doi.org/10.1007/s11548-017-1619-0

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