Residual Spatial Attention Network for Retinal Vessel Segmentation
- URL: http://arxiv.org/abs/2009.08829v1
- Date: Fri, 18 Sep 2020 13:17:13 GMT
- Title: Residual Spatial Attention Network for Retinal Vessel Segmentation
- Authors: Changlu Guo, M\'arton Szemenyei, Yugen Yi, Wei Zhou, Haodong Bian
- Abstract summary: We propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation.
RSAN employs a modified residual block structure that integrates DropBlock.
In order to further improve the representation capability of the network, we introduce the spatial attention (SA) and propose the Residual Spatial Attention Block (RSAB)
- Score: 6.513112974264861
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reliable segmentation of retinal vessels can be employed as a way of
monitoring and diagnosing certain diseases, such as diabetes and hypertension,
as they affect the retinal vascular structure. In this work, we propose the
Residual Spatial Attention Network (RSAN) for retinal vessel segmentation. RSAN
employs a modified residual block structure that integrates DropBlock, which
can not only be utilized to construct deep networks to extract more complex
vascular features, but can also effectively alleviate the overfitting.
Moreover, in order to further improve the representation capability of the
network, based on this modified residual block, we introduce the spatial
attention (SA) and propose the Residual Spatial Attention Block (RSAB) to build
RSAN. We adopt the public DRIVE and CHASE DB1 color fundus image datasets to
evaluate the proposed RSAN. Experiments show that the modified residual
structure and the spatial attention are effective in this work, and our
proposed RSAN achieves the state-of-the-art performance.
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