# A Multi-Scale Attention Fusion Network for Retinal Vessel Segmentation
## 1 Introduction  
The retinal vascular system is a crucial component of the visual system, playing a key role in maintaining intraocular homeostasis and ensuring visual function. It serves as a vital actor in sustaining the normal functionality of visual tissues by regulating blood flow and adjusting vascular tension, ensuring an ample supply of blood and oxygen to visual tissues. Additionally, the retinal vascular system is involved in the regulation of intraocular pressure, which is essential for maintaining the morphology and structure of the eyeball [1; 2].  
Under normal circumstances, the retinal vascular network exhibits a highly organized distribution, including major vessels, such as the central artery and central vein, along with various branches and capillaries. This structured arrangement ensures a stable blood supply to the retina, establishing optimal conditions for the transmission and processing of visual signals. Nevertheless, in pathological conditions, alterations to this structure may occur, leading to the formation of vascular abnormalities. These abnormalities can result in issues such as insufficient nutrient supply and hypoxia, ultimately affecting the normal functioning of the visual system [2; 3].  
In the field of ophthalmology, the structure and function of retinal vessels play a crucial role in diagnosing and treating various ocular and systemic diseases. The diameter, reflectivity, curvature, and branching characteristics of retinal blood vessels are crucial indicators for various retinal and systemic diseases [4; 5; 6]. A quantitative analysis of retinal vessels can assist ophthalmologists in detecting and diagnosing the early stages of certain severe conditions [7; 8].  
In recent years, methods utilizing artificial intelligence (AI) technology for medical image segmentation have garnered widespread attention [9]. In medical image analysis, AI technology has become a potent tool, assisting doctors in diagnosing diseases quickly and accurately. Retinal vessel segmentation is a crucial task in medical image analysis. The manual segmentation of retinal vessels is a laborious and time-consuming task prone to inter-observer variability. With the aid of AI technology, particularly machine learning and deep learning algorithms, automated retinal vessel segmentation can be achieved, aiding ophthalmologists in better understanding and analyzing the morphology and structure of retinal vessels, thereby providing superior clinical decision support for ophthalmologists.  
The advancements in medical image segmentation have primarily been driven by deep learning techniques. The well-known CNN architecture U-Net [10] has demonstrated excellent performance in medical image segmentation. However, with the increase in network depth, multiple pooling operations may lead to the problem of crucial information loss, and the handling of insufficient local contextual features caused by skip connections can affect the accurate segmentation of retinal vessels. To address these problems, we proposed a multi-scale attention fusion network (MsAF-Net) for retinal vessel segmentation. Our main contributions are as follows:  
1. We propose a multi-scale feature extraction (MsFE) block to capture diverse scale information from low-level features, providing the network with richer contextual information.
2. We propose a multi-scale attention fusion (MsAF) block, which combines channel attention from low-level features and spatial attention from high-level features, enabling the network to comprehensively understand the content of the image.
3. Combining the MsFE block and MsAF block, we propose a novel model for retinal vessel segmentation. Experimental results on three datasets demonstrated that our proposed model exhibited strong competitiveness compared with other state-of-the-art methods.