An improved method for retinal vessel segmentation in U-Net

The utilization of retinal vessel images is prevalent in the diagnosis of numerous diseases, such as chronic vascular diseases, diabetic retinopathy, and glaucoma. Despite the U-Net model's effective performance in retinal vessel imaging, it has been observed that the U-Net model fails to adequately detect dense blood vessels and vascular bifurcation. This research proposes a retinal vessel segmentation model that is both efficient and straightforward. The study introduces two significant contributions. Firstly, the multi-scale structure is improved, and a feature circulation module is added to enhance the detection of blood vessels. Secondly, an AGM module is incorporated into the jump connection to boost the transmission of context characteristics and suppress background noise. The presented model is assessed using publicly available DRIVE and STARE datasets. The proposed method and U-Net evaluation results are quantitatively and qualitatively analyzed, alongside a comparison with current state-of-the-art algorithms. By conducting a comparative analysis, certain algorithmic indices have exhibited state-of-the-art performance. The AttMSFCU-Net model, as introduced in this study, achieves advanced or comparable accuracy levels when evaluated on both the DRIVE and STARE datasets. Of particular note is the enhanced segmentation performance of dense and bifurcation vessels. The evaluation of the proposed model on the DRIVE and STARE datasets highlights its robustness and accuracy, affirming its utility in practical applications.