# MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation
## 1 Introduction  
Retinal blood vessel images are often used by doctors as a window to observe the response of various diseases, such as hypertension, coronary heart disease, and diabetes, and vessel abnormalities can reflect the severity of these diseases. Retinal blood vessels are the only blood vessels in the human body that can be obtained without trauma. The fundus camera can directly take images of fundus blood vessels and can respond clearly to microvessels and lesions. Therefore, the research of fundus images has important medical application value. In order to make an effective diagnosis of the disease, it is necessary to accurately segment blood vessels in the fundus. However, because the retinal blood vessel image may have problems such as lesions, uneven lighting, noise, and low contrast between small blood vessels and the background, it is difficult to completely segment the fundus blood vessel image. Therefore, fundus blood vessel image segmentation has become a hot topic at home and abroad. In recent years, many scholars have conducted extensive and in-depth research on automatic segmentation of fundus blood vessel images and have proposed many segmentation methods. These algorithms can be divided into supervised segmentation and unsupervised segmentationbased on whether a gold standard image is required. Unsupervised fundus vascular segmentation does not require a gold standard image as a segmentation standard. For example, [PERSON] et al. [1] proposed a morphological-based fundus image segmentation technology, which can use Kirsch edge detection method to identify retinal vein from retinal image. The advantage of the method is that it can identify the vasculature devoid of any tiny information, but the disadvantage is that the segmentation of microvascular is insufficient. A matching filtering method designed by [PERSON] et al. [2] used a multi-scale second-order Gaussian derivative filter and performs filtering in the direction score domain to obtain the maximum response. This method has a good effect in dealing with complex vessels, but there is a phenomenon of error segmentation. [PERSON] et al. [3] employed a multi-scale tracking method based on Heisson matrix feature vectors and gradient information. This method can extract blood vessels, especially small blood vessels, at different resolutions. Although many scholars have conducted in-depth research on the unsupervised segmentation method, its segmentation effect still needs to be improved. Compared with the unsupervised segmentation method, the supervised segmentation method can segment the fundus blood vessel images more effectively. For supervised fundus blood vessel image segmentation, before being able to use end-to-end deep learning methods for feature learning, researchers must manually extract image features based on prior knowledge of the image. [PERSON] et al. [4], for example, adopted multi-scale linear detector response and two-dimensional wavelet responses on the green channel as features, used structured output support vector machine to train parameters of a fully connected conditional random field model, and then used the trained conditional random field to segment blood vessels. This method can effectively improve the performance index, but there will be error segmentation in the bright central reflection area. [PERSON] et al. [5] proposed a supervised learning method based on multi-feature and multi-classifier fusion to segment retinal vessels. Four types of features were extracted, and the results of decision tree and AdaBoost classifier were fused together to make a joint decision. This method can effectively improve the accuracy and sensitivity of segmentation, but it needs to be improved compared with the deep learning method. Although the above method has achieved certain effects, the extraction of artificial features requires a wealth of prior knowledge, and it has a large subjectivity for feature extraction, so it may cause problems such as insufficient microvessel segmentation and incorrect segmentation.