## 1 Introduction  
Medical image segmentation is of great significance in clinical applications. It plays a critical role in extracting and isolating regions of interest from medical images for further analysis and diagnosis. The retinal blood vessels in the fundus serve as a crucial indicator of ocular health and a biomarker for various systemic diseases, including diabetic retinopathy, glaucoma, and hypertension [1, 2]. Changes in vessel diameter, caliber, and leakage can aid in identifying conditions like retinal artery occlusion or central serous chorioretinopathy [3]. Retinal vessel segmentation plays a pivotal role in the analysis and evaluation of ophthalmologic images. Therefore, precise and efficient segmentation of retinal vessels holds significant clinical importance, enhancing the accuracy and efficiency of medical diagnosis and treatment. Manual segmentation by skilled medical professionals is a challenging and tedious task, with the possibility of incorrect segmentation due to the subjective nature of the process [4]. Automated segmentation for retinal vessels is more efficient and consistent than manual segmentation, as it reduces human error and bias. It holds practical applications in telemedicine and low-resource settings. Currently, automatic retinal vessel segmentation methods are mainly divided into traditional methods and deep learning-based methods. Traditional methods include matching filtering, wavelet transform, mathematical morphology, cluster analysis, and more [5]. These methods generally rely on handcrafted features for segmentation, necessitating rich prior knowledge and professional skills, resulting in poor model generalization and low segmentation accuracy [6]. The development of deep learning has significantly improved the accuracy and efficiency of medical image segmentation [7]. Deep learning techniques, such as convolutional neural networks, have shown great potential in accurately segmenting various types of medical images, including CT scans, MRI images, and X-rays. Segmentation methods based on deep learning, including fully convolutional networks (FCNs) [8], SegNet [9], and U-Net [10], can automatically learn the characteristics of retinal blood vessels, demonstrating strong generalization capabilities. Due to the superior performance in terms of accuracy and efficiency, most current research methods are based on U-Net, which employs an encoder-decoder structure [11]. The encoder extracts features through convolution and downsampling, while the decoder restores image resolution through upsampling [12]. Moreover, in the decoding stage, the skip connection enables the fusion of low-level features and high-level features at different resolutions. In retinal image analysis, the scale variation of retinal vessels is a common and challenging issue. This scale variation can be caused by various factors, including vessel diameter, curvature, and branching degree in different regions. During retinal vessel segmentation, due to this scale variation, models trained on a single scale often fail to fully capture the subtle features of vessels, resulting in decreased segmentation accuracy. To address this problem, we adopted U-Net as the base architecture and designed a multi-scale feature extraction module at the skip connections to enhance the ability of the network to capture multi-scale information. Specifically, we employed dilated convolutions with different dilation rates to extract multi-scale information, followed by channel attention modules to emphasize important features. Furthermore, fundus images often contain unrelated noise that can interfere with blood-vessel segmentation, thereby impacting segmentation accuracy. Separating redundant noise information from useful spatial information can significantly improve the segmentation performance of the task. Drawing inspiration from [13; 14], we designed an image reconstruction branch to assist in the segmentation task, which can suppress redundant information and reduce image noise.