# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
## 1 Introduction  
Retinal vessel segmentation has great significance in the early diagnosis of eye-related diseases [1]. The presence of vessel leakage, obstruction, and swelling in the retina of the eye indicates that it is highly probable to be a complication of diabetes resulting from elevated blood sugar levels. Retinal vessel segmentation plays an important role in clinical medicine, which can extract vascular features from retinal fundus images for analyzing and detecting diseases. However, compared with other medical images, retinal blood vessels have intricate structures and smaller areas of interest, making manual segmentation time-consuming and limited by experience [2]. Therefore, it is essential to develop automatic retinal vessel segmentation methods, which not only assist doctors in diagnosis but also enable large-scale analysis of fundus images. Currently, retinal vessel segmentation methods can be categorized into two groups: unsupervised methods [3] and supervised methods [4]. The unsupervised method does not need manual annotation as a reference and only realizes the segmentation according to the characteristics of the retinal vessels themselves, including clustering-based methods, level sets, model-based algorithms, etc. [PERSON] et al. [5] used a matching filter to enhance image contrast and spatially weighted fuzzy C-means clustering to obtain segmentation results. [PERSON] et al. [6] designed a combination segmentation model based on density spatial clustering and morphological reconstruction to improve the performance of vessel segmentation. [PERSON] et al. [7] constructed an active contour model to learn different types ofregional feature information. The aforementioned unsupervised segmentation methods are straightforward and have made some advancements in retinal vascular image segmentation. However, owing to the unique nature of medical images and the intricacy of fundus images themselves, the extracted vascular information obtained using unsupervised methods is relatively coarse, with limited resistance to noise and segmentation accuracy. Consequently, it fails to fulfill the actual requirements of clinical assistance. As a result, researchers are increasingly focusing on and utilizing supervised methods in their studies. The core of the supervised method is to use prior knowledge to construct algorithms and discover hidden patterns in the data, so that the algorithm can automatically extract retinal vessel information and achieve vessel segmentation. The biggest difference between traditional machine learning and deep learning lies in the feature extraction process: the former requires manual construction of image features, while the latter can automatically extract and select features. [PERSON] et al. [8] built feature vectors based on the width of blood vessels and pixel grayscale values, and used support vector machines for classification. [PERSON] et al. [9] constructed a 7-D vector that consisted of gray-level and moment invariants-based features to represent each pixel, and employed a neural network architecture to classify pixels in retinal fundus images. Although these traditional machine learning methods perform well in some cases, handmade features rely too heavily on prior knowledge and therefore fail in many complex datasets [10]. Semantic segmentation is a fundamental topic in computer vision, aiming to assign semantic labels to each pixel in an image [11; 12]. With the improvement of computer hardware, especially the GPU technology, deep learning algorithms have shown tremendous advantages in the field of image segmentation. Many network structures designed for semantic segmentation tasks based on deep convolutional neural networks have demonstrated excellent performance. These structures possess rich representation capabilities. fully convolutional networks (FCN) play a crucial role in the development of semantic segmentation. FCN utilizes an encoder-decoder structure and employs a fully convolutional integrated class network to extract features across the entire backbone.