# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
## 2 Methods
### Multi-Scale Residual Convolution Module  
The architecture of inception networks enhances the utilization of computing resources by increasing both their depth and width, while maintaining the same computing budget. It achieves this by incorporating filters of different sizes on the same layer to process feature information from multiple scales. The features are then aggregated in the next layer to extract fused features from multiple scales in the subsequent inception module [32]. Additionally, the use of residual connections has been proven to be advantageous in training deep learning models [33]. Taking inspiration from the inception module and residual connections, a multi-scale residual convolution module was designed in the encoder section, as depicted in Figure 2. This module consists of convolutional layers, batch normalization (BN), DropBlock, corrected linear units (ReLU), and Softmax layers. To extract blood vascular feature information comprehensively, three types of convolution blocks with different scales are utilized to achieve multi-scale extraction of retinal blood vascular features. Different convolution kernels have varying receptive fields. Convolution kernels with smaller receptive fields are designed to capture small targets and local detailed information. On the other hand, convolution kernels with larger receptive fields can provide larger targets and global semantic information. Inspired by the successful application of DropBlock in recent computer vision research [34; 35], this study employed DropBlock as a regularization technique in the network at MRCM. DropBlock is a structured version of DropOut, with the key distinction that it removes contiguous regions in a layer's feature map instead of randomly discarding independent units. By eliminating  
Figure 1: Architecture of MU-Net. features in continuous regions, DropBlock forces the network to rely on evidence from other regions that are relevant to the data. Input \(F_{int}^{l}\in R^{B\times C^{\prime}\times H\times W}\) is first fed into a \(3\times 3\) convolution layer for feature extraction, resulting in a new feature map \(A^{l}\in R^{B\times C\times H\times W}\). Then, feature map \(A^{l}\) is passed through three parallel convolution layers with kernels of 1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5, generating three new feature maps \(A^{l}_{1}\), \(A^{l}_{2}\), and \(A^{l}_{3}\). To preserve multi-scale feature information, feature maps \(B^{l}\in R^{B\times C\times H\times W}\) are obtained by element-wise addition, which is calculated as follows:  
\[B^{l}=f_{1}(A^{l})\oplus f_{2}(A^{l})\oplus f_{3}(A^{l}) \tag{1}\]  
where \(f_{1}\), \(f_{2}\), and \(f_{3}\) represents the \(1\times 1\), \(3\times 3\), and \(5\times 5\) convolution operations, respectively, and \(\oplus\) denotes the element-wise addition. In order to capture the entire scene relationship through the attention of a spatial attention mechanism, adaptive average pooling operations are performed to \(B^{l}\) on the H and W dimensions respectively. The resulting outputs \(C^{l}\in R^{B\times C\times 1\times W}\) and \(D^{l}\in R^{B\times C\times H\times 1}\) are fused adopting matrix multiplication to generate a spatial attention feature map \(E\) using sigmoid. The feature map \(F^{l}\) is calculated as follows:  
\[F^{l}=\sigma(pool_{c}(B^{l})\otimes pool_{r}(B^{l})) \tag{2}\]  
where \(\sigma\) is a sigmoid activation, \(pool_{c}\) and \(pool_{r}\) present the average pooling of columns and rows, respectively, and \(\otimes\) denotes the element-wise multiplication. Next, the elements of \(A^{l}\) and \(F^{l}\) are multiplied to obtain the spatial weight map \(G^{l}\in R^{B\times C\times H\times W}\) for each pixel. This operation can selectively aggregate contexts based on the attention map, and contain abundant contextual information. Then, the spatial weight map G is added to the input image \(F_{in}^{l}\) through a residual connection.