Overview of Deep Learning Methods for Retinal Vessel Segmentation
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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.
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References · 13
- 2017Attention Is All You Needcited by 60,361
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- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
- 2019Selective Kernel Networkscited by 403
- 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
- 2020SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentationcited by 71
- 2019IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networkscited by 57
- 2018Deep Vessel Segmentation By Learning Graphical Connectivitycited by 43
- 2019Attention Guided Network for Retinal Image Segmentationcited by 32
- 2019Deep Retinal Image Segmentation with Regularization Under Geometric Priorscited by 13
- 2017A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus imagescited by 10
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