# MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation
## 1 Introduction
The core idea of deep learning is to extract the main representation features from the original data through a series of nonlinear transformations. This feature is multi-level and multi-angle, which also makes the features extracted by deep learning method have stronger generalization and expressive ability, which is exactly what the image segmentation process needs. Convolutional neural network, as an effective deep learning model of image segmentation, has been widely concerned by researchers. [PERSON] et al. [6] proposed a context encoder network (CE-Net) to capture more high-level information, to save spatial information, and to be used for 2D medical image segmentation. Although this method has certain effects on various medical data sets, its accuracy still needs to be improved. [PERSON] et al. [7] Proposed a neural network structure, UNET ++, for semantic and instance segmentation. By using nested structure and improved hop connection structure, the segmentation performance of the model is effectively improved. The disadvantage of the model is that the parameters increase greatly compared with the original model. [PERSON] et al. [8] employed a fundus vessel segmentation method based on deep convolutional neural network (CNN). Firstly, image enhancement was performed using fuzzy logic and image processing strategy, and then, an encoding and decoding CNN model with skip connection structure was proposed for retinal blood vessel segmentation.This method can effectively improve the contrast between blood vessel and background, but there is over-segmentation in the segmentation process. [PERSON] et al. [9] adopted an improved local phase unit (ReLU), which is an efficient and trainable convolution layer. When the ReLPU layer is used at the top of the U-Net segmentation network, it can effectively improve the segmentation performance of the U-Net model. The disadvantage of this method is that it cannot segment the microvessels effectively. [PERSON] et al. [10] proposed an improved Multi-Layer Perceptron (MLP) neural network that can automatically extract and distinguish blood vessels and background pixels in fundus images. This method has strong robustness, but the model is more complex. [PERSON] et al. [11] used an improved U-Net model, that is, adding dense block structures to the original model, which can effectively improve the accuracy, but the segmentation sensitivity needs to be improved. [PERSON] et al. [12] proposed a double U-Net model structure of fundus image segmentation method. This method uses two U-Net models; the second U-Net model adds residual structure. By adding the information flow extracted from the first U-Net model to the second model, information loss can be effectively avoided. [PERSON] et al. [13] developed an improved U-Net model for retinal vascular segmentation. This method adds the Resnet module to the original structure, which solves the problem that the traditional U-Net model cannot deepen. However, the segmentation results show that the method cannot effectively segment the peripheral blood vessels. [PERSON] et al. [14] used a new Resnet module to improve the U-Net. By adding more Resnet modules, the network layer can be deepened, so the model can extract features better. However, with the deepening of the network, the model becomes more complex. Based on the above problems, such as discontinuous segmentation, difficult segmentation of microvessels and complex segmentation models, we propose a MRU-Net deep learning model structure, i.e., a multi-scale residual U-shaped network model. The main work of our paper is as follows:  
* In MRU-Net, we add a multi-scale feature fusion module in the transition phase between the encoder and decoder and extract the feature information with different granularities using multi-scale dilated convolutions, so as to improve the model's understanding of context information and to improve the model's segmentation ability for microvessels.