  Retinal vessel segmentation is a vital step for the diagnosis of many early
eye-related diseases. In this work, we propose a new deep learning model,
namely Channel Attention Residual U-Net (CAR-UNet), to accurately segment
retinal vascular and non-vascular pixels. In this model, we introduced a novel
Modified Efficient Channel Attention (MECA) to enhance the discriminative
ability of the network by considering the interdependence between feature maps.
On the one hand, we apply MECA to the "skip connections" in the traditional
U-shaped networks, instead of simply copying the feature maps of the
contracting path to the corresponding expansive path. On the other hand, we
propose a Channel Attention Double Residual Block (CADRB), which integrates
MECA into a residual structure as a core structure to construct the proposed
CAR-UNet. The results show that our proposed CAR-UNet has reached the
state-of-the-art performance on three publicly available retinal vessel
datasets: DRIVE, CHASE DB1 and STARE.
