CAS-UNet: A Retinal Segmentation Method Based on Attention

Abstract

Retinal vessel segmentation is an important task in medical image analysis that can aid doctors in diagnosing various eye diseases. However, due to the complexity and blurred boundaries of retinal vessel structures, existing methods face many challenges in practical applications. To overcome these challenges, this paper proposes a retina vessel segmentation algorithm based on an attention mechanism, called CAS-UNet. Firstly, the Cross-Fusion Channel Attention mechanism is introduced, and the Structured Convolutional Attention block is used to replace the original convolutional block of U-Net to achieve channel enhancement for retinal blood vessels. Secondly, an Additive Attention Gate is added to the skip-connection layer of the network to achieve spatial enhancement for retinal blood vessels. Finally, the SoftPool pooling method is used to reduce information loss. Experimental results using the CHASEDB1 and DRIVE datasets show that the proposed algorithm achieves an accuracy of 96.68% and 95.86%, and a sensitivity of 83.21% and 83.75%, respectively. The proposed CAS-UNet thus outperforms the existing U-Net-based classic algorithms.