# MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation
## 4 Discussion  
In this paper, the application of a full convolution neural network in fundus blood vessel segmentation is studied. Traditional machine learning methods cannot effectively segment low-contrast microvessels, and there are problems of low segmentation accuracy and sensitivity. Based on this, an improved U-Net model is proposed, which not only realizes the information fusion of high-dimensional features and low-dimensional features but also improves the efficiency of feature use. In this study, experiments were conducted on DRIVE and STARE fundus image databases, and image blocks were randomly extracted to increase the amount of network training data to avoid network overfitting. In terms of network structure design and parameter setting, based on the traditional U-shaped network, the network is defined as a structure block connected by upsampling or downsampling operations. According to the characteristics of small amount of experimental data, this study randomly extracts image blocks to reduce the dimension of image data, so as to reduce the complexity of the network model. Only three downsampling and three upsampling operations are used. In order to make full use of the feature information extracted from the network and to minimize the loss of feature information, the convolution operation with step size of 2 is used to replace the traditional pooling operation and each structural block is changed to Res-Block. In order to enhance the generalization ability of the network and to better identify the small vessel structure, dropout, batch normalization layer, and relu activation function were added before each convolution layer. Then, in order to better extract the context semantic information of the image and to effectively segment the vascular structure of different scales, we propose a multi-scale dilation convolution fusion method based on dilation convolution. In addition, in order to solve the semantic gap between the encoder and decoder, we propose a Res-Dilated block to replace the skip connection in the traditional U-Net model. As shown in Figures 10-13, the traditional U-shaped network can be improved to better segment the blood vessels with central line reflection and the microvessels with low contrast, so that the sensitivity, F1 value, accuracy, and area under ROC curve (AUC) index of this research algorithm are improved. In order to more intuitively compare the improvement effect of the U-Net model and the segmentation performance of blood vessels, this study compared a variety of deep learning methods including U-Net model. In general, compared with the original U-Net model, the MRU-Net model has less noise points in the vessel probability map. From the details, the results of this study can better segment the small vessels and retain the integrity of the vessels, and the continuity of the vessels is better. In order to observe the influence of lesions on vascular segmentation more intuitively, by comparing with the experimental results of the original U-Net model, it can be seen from Figure 10 that the MRU-Net network proposed in this study can better segment blood vessels and is less affected by the lesion area. Compared with other methods, it can be seen from the detection indicators that our \begin{table}
\begin{tabular}{c c c} \hline
**Methods** & **Time** & **GPU** \\ \hline
[30] & 92 s & Tesla K20c \\
[31] & 0.64 s & - \\
[32] & 1.3 s & Tesla K40 \\
[33] & 0.5 s & - \\
[24] & 0.063 & GTX1070 \\
[34] & 70 s & - \\ Our & 2.2 s & GTX 1080 Ti \\ \hline \end{tabular}
\end{table} 
Table 4: Computation time for processing one image. \begin{table}
\begin{tabular}{c c c c c} \hline
**Data Set** & **AUC** & **F1** & **ACC** & **SE** \\ \hline DRIVE & 0.0060 & 0.0002 & 0.0956 & \(5.14\times 10^{-6}\) \\ STARE & 0.0216 & 0.0454 & 0.1787 & 0.0173 \\ \hline \end{tabular}
\end{table} 
Table 3: \(t\)-test result of each index (\(p<0.05\) significant).