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The precise segmentation of retinal blood vessels is of great significancefor 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 canbe utilized in a data augmentation manner to use the available annotatedsamples more efficiently. SA-UNet introduces a spatial attention module whichinfers the attention map along the spatial dimension, and multiplies theattention map by the input feature map for adaptive feature refinement. Inaddition, the proposed network employs structured dropout convolutional blocksinstead of the original convolutional blocks of U-Net to prevent the networkfrom 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 achievesstate-of-the-art performance on both datasets.The implementation and thetrained networks are available on Github1.

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation