Fundus image segmentation network based on skipping connection and multiattention fusion

The segmentation of retinal vessels is of paramount importance for the diagnosis of diseases and the performance of surgical procedures. With the advent de novo of in silico technologies, the field of in vivo vascular segmentation has witnessed a veritable explosion of innovation. In traditional segmentation networks, the challenge lies in the unclear delineation of vessels, which frequently results in a loss of important data. In response to this quandary, we present an innovative technique for vessel segmentation as part of our investigative work—the Lightweight Fully Connected MultiScale Attention Network (SCB-UNet). The use of data augmentation allows for the acquisition of a substantial quantity of annotated data without the necessity of utilizing thousands of datasets. This network combines the Full-Resolution UNet (FR-UNET) and the Full-Scale Cooperative Network (UNET3+) while integrating spatial and channel attention modules (CBAM). The network expands through high-resolution convolution interactions while preserving image resolution. Within the network, neighboring features are fused, and features from previous images are incorporated to gather contextual information. To enhance feature maps, attention fusion modules are introduced, in order to enhance the expressiveness of feature maps, it is necessary to concurrently strengthen them across spatial as well as channel perspectives. The efficacy in vivo of a novel network was evaluated on retinal vessel datasets (DRIVE, CHAIN_DB_1, & STARE). The results demonstrate in vivo efficacy superior to that of the aforementioned methods, thereby showcasing the network’s status as a highly efficacious and state-of-the-art approach.