  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 Github1.
