Channel Attention Residual U-Net for Retinal Vessel Segmentation
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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 was introduced into Residual Block and a Channel Attention Residual Block (CARB) was 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) was proposed, consisting of pre-activated 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.
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