# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
## 1 Introduction
Another significant contribution of semantic segmentation is the incorporation of skip connections, which aggregate low-level features into high-level features, allowing for the recovery of reduced details. Building upon FCN and skip connections, U-Net [13] introduced a U-shaped encoder-decoder structure, which further enhanced and extended the FCN architecture. Another specific architecture, Deeplabv3+ [14], designed to solve image segmentation problems, has also demonstrated excellent performance. It utilizes a spatial pyramid pooling module and an encoder-decoder structure to achieve semantic segmentation. The spatial pyramid pooling module captures contextual information by pooling features of different resolutions, while the encoder-decoder structure aids in obtaining clear object boundaries. However, Deeplabv3+ usually has higher computational complexity and a larger number of parameters than U-Net. Inspired by the symmetrical structure and effective skip connections of U-Net, many variant methods based on U-shaped structures have been applied to medical image segmentation. [PERSON] et al. [15] realized that the accuracy of segmentation can be improved by embedding the inception-residual block into the U-shaped symmetric encoder-decoder architecture. Dense dilated convolutions [16] and deformable convolutions [17; 18] have also been introduced into the U-Net architecture to capture multi-scale local context information, which can improve the effectiveness of retinal vessel segmentation. Dilated convolution layers [19] can be applied to expand the receptive field and aggregate multi-scale features from different kernels, while selective kernels (SK) [20] evaluate information combinations from multiple kernels and select effective spatial scales. [PERSON] et al. [21] placed selective kernels at skip connections to locate receptive fields related to lesion size, which can improve the utilization of multi-scale features in dermoscopy images. In recent years, attention mechanisms have gained significant note in various tasks, including attention gate networks [22], SENet [23; 24], spatial attention mechanisms [25], etc. These methods have been introduced to efficiently capture important objects, locations, and channel information,thereby enhancing the representation ability. [PERSON] et al. [26] developed an attention module based on the U-Net architecture to incorporate global information during the feature fusion process. [PERSON] et al. [27] proposed Att U-Net (Attention U-Net), which utilizes an attention-gating mechanism at skip connections to minimize the impact of background pixels on segmentation. To enhance the network's segmentation ability for retinal blood vessels, [PERSON] et al. [28] adopted DANet (dual attention networks), which leverages both spatial attention and channel attention to capture contextual features. The integration of the convolutional neural network and the transformer model also has shown promising results in image segmentation. [PERSON] et al. [29] constructed a joint CNN-transformer structure as the encoder, utilizing the U-Net architecture. They also incorporated a cascade upsampling operation in the decoder to improve the accuracy of position information. In the context of medical images, [PERSON] et al. [30] designed the group transformer module and constructed the GT U-Net network. In addition, [PERSON] et al. [31] proposed an adaptive feature fusion cascade transformer retinal segmentation algorithm to address the issue of incomplete segmentation of small blood vessels. Inspired by the above, a modified U-shaped network called MU-Net, which focuses on extracting multi-scale features is proposed in this study. The encoding stage of MU-Net includes a multi-scale residual convolution module (MRCM), which is designed to incorporate hidden feature information from different scales. MRCM performs average pooling operations on the horizontal and vertical to generate attention maps, thereby achieving long-range context information aggregation.