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Overview of Deep Learning Methods for Retinal Vessel Segmentation

arXiv 2306.06116 ↗First posted 1 Jun 2023eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Methods for automated retinal vessel segmentation play an important role in the treatment and diagnosis of many eye and systemic diseases. With the fast development of deep learning methods, more and more retinal vessel segmentation methods are implemented as deep neural networks. In this paper, we provide a brief review of recent deep learning methods from highly influential journals and conferences. The review objectives are: (1) to assess the design characteristics of the latest methods, (2) to report and analyze quantitative values of performance evaluation metrics, and (3) to analyze the advantages and disadvantages of the recent solutions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 46 references that are in the map. First posted on arXiv 1 Jun 2023.

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References · 13

  1. 2017Attention Is All You Needcited by 60,361
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  5. 2019Selective Kernel Networkscited by 403
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  7. 2020SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentationcited by 71
  8. 2019IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networkscited by 57
  9. 2018Deep Vessel Segmentation By Learning Graphical Connectivitycited by 43
  10. 2019Attention Guided Network for Retinal Image Segmentationcited by 32
  11. 2019Deep Retinal Image Segmentation with Regularization Under Geometric Priorscited by 13
  12. 2017A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus imagescited by 10
1 more
  1. 2023Non-adversarial Robustness of Deep Learning Methods for Computer Visioncited by 6

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