Channel Attention Residual U-Net for Retinal Vessel Segmentation

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-U-Net), to accurately segment retinal vascular and non-vascular pixels. In this model, the channel attention mechanism is introduced into Residual Block and a Channel Attention Residual Block (CARB) is proposed to enhance the discriminative ability of the network by considering the interdependence between the feature channels. Moreover, to prevent the convolutional networks from overfitting, a Structured Dropout Residual Block (SDRB) is proposed, consisting of pre-activation residual block and DropBlock. The results show that our proposed CAR-U-Net has reached the state-of-the-art performance on two publicly available retinal vessel datasets: DRIVE and CHASE DB1.