Journal
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
Volume -, Issue -, Pages 1236-1242Publisher
IEEE COMPUTER SOC
DOI: 10.1109/ICPR48806.2021.9413346
Keywords
Segmentation; retinal blood vessel; SA-UNet; U-Net; spatial attention
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Funding
- China Scholarship Council
- Stipendium Hungaricum Scholarship
- National Natural Science Foundation of China [62062040]
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The proposed lightweight network, SA-UNet, achieves state-of-the-art performance in retinal blood vessel segmentation by introducing a spatial attention module and employing structured dropout convolutional blocks.
The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension. In this work, we propose a lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently. SA-UNet introduces a spatial attention module which infers the attention map along the spatial dimension, and multiplies the attention map by the input feature map for adaptive feature refinement. In addition, the proposed network employs structured dropout convolutional blocks instead of the original convolutional blocks of U-Net to prevent the network from overfitting. We evaluate SA-UNet based on two benchmark retinal datasets: the Vascular Extraction (DRIVE) dataset and the Child Heart and Health Study (CHASE_DB1) dataset. The results show that the proposed SA-UNet achieves state-of-the-art performance on both datasets. The implementation and the trained networks are available on Github(1).
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