An improved U-Net model with multiscale fusion for retinal vessel segmentation

The condition of the retinal vessels is involved in various ocular diseases, such as diabetes, cardiovascular and cerebrovascular diseases. Accurate and early diagnosis of eye diseases is important to human health. Recently, deep learning has been widely used in retinal vessel segmentation. However, problems such as complex vessel structures, low contrast, and blurred boundaries affect the accuracy of segmentation. To address these problems, this paper proposes an improved model based on U-Net. In the proposed model, pyramid pooling structure is introduced to help the network capture the contextual information of the images at different levels, thus enhancing the receptive field. In the decoder, a dual attention block module is designed to improve the perception and selection of fine vessel features while reducing the interference of redundant information. In addition, an optimization method for morphological processing in image pre-processing is proposed, which can enhance segmentation details while removing some background noise. Experiments are conducted on three recognized datasets, namely DRIVE, CHASE-DB1 and STARE. The results show that our model has excellent performance in retinal vessel segmentation tasks.