# Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation
## 3 Methodology
### Attention Gate  
To help the model better strengthen the learning of the target region, an attention-aware module was added to the network. This module was designed by [PERSON] [24] and named Attention gate. The details of the Attention gate are analyzed in Algorithm 1. ``` Input: g, the upsampling feature; f, the corresponding depth feature. Output:\(F_{3}\), Attention selected feature. 1\(Result=BN_{g}\left(W_{g}^{T}\times g+b_{g}\right)+BN_{f}\left(W_{f}^{T}\times f +b_{f}\right)\). 2 Convolutional operation \((W_{g},b_{g})(W_{f},b_{f})\) extracts contextual information in the gate signal (\(g\)) and then determines the focus regions in upsampling features (\(f\)). BatchNorm \((BN_{g},BN_{f})\) normalizes the data and reduces the jitter of the data between batches, thereby improving the training speed. These two results are added, pixel by pixel, to merge the features. \(F_{1}=\sigma_{1}\left(Result\right)\). 3 ReLU activation function \((\sigma_{1}(x)=max\left(0,x\right))\) is used to add nonlinear factors. \(F_{2}=BN_{\theta}\left(W_{\theta}^{T}\times F_{1}+b_{\theta}\right)\). 4 Convolutional operation \((W_{\theta},b_{\theta})\) extracts features in the target region. BatchNorm \((BN_{\theta})\) normalises the data. \(\alpha=\sigma_{2}\left(F_{2}\right)\). 5 Sigmoid activation function \((\sigma_{2}(x)=\frac{1}{1+e^{(-x)}})\) converts the Attention coefficient \(\alpha\) value into the range [\(-1\), \(1\)]. \(F_{3}=f\times\alpha\). 6 The upsampling features (\(f\)) are multiplied, pixel by pixel, by the coefficients (\(\alpha\)) to change the value of certain regions, so as to achieve the purpose of paying attention to or suppressing different task regions. return\(F_{3}\);
7

Figure 2: Structure of Render U-Net. Every convolutional block captures features through two convolutional layers, which are activated by ReLU functions. From a horizontal perspective, all blocks are connected by dense skip connections with attention gates. Each block in the decoders concatenates multi-scale features from its all preceding blocks. Each block in the encoder transfers features from top to bottom through the maxpooling method from a vertical perspective, and each block in the decoder integrates multi-scale features across different resolutions through the point-sampling method. In addition, the block in deeper layer is sent to the attention gate as a gate signal. The diagram of the Attention gate is shown in Figure 3. As seen, Attention gate is placed on the dense skip connection between convolution blocks. The upsampling feature from deeper block is used as the gate signal to focus on the target from prior block. The Attention gate performs well in selection by producing the attention coefficient \(\alpha\). Multiplying \(\alpha\) and the feature from the encoder pixel by pixel can change the value and distribution of the latter. To strengthen the learning of the target organ, the weight of the target area has been enhanced. Similarly, the weight of the irrelevant region is reduced, thereby suppressing the learning of the area. Therefore, Attention gate can improve the efficiency of propagating semantic information. ### Point-Sampling Method

Images can be regarded as regular grids of pixels (points). Their feature vectors are hidden representations on the regular grid. Pixels upsampled uniformly on the hidden representations are mapped to a set of labels. The rendering method does not calculate the labels uniformly on the hidden representations. Rather, it adaptively selects uncertain points from all pixels of the image to calculate the labels. We adopted the idea of the classic subdivision [14] strategy. This strategy calculates only those locations where the values are significantly different from their neighbors. The values at other locations are obtained by interpolating the existing results. This process, which we call the **point-sampling** method, computes masks from low resolution to high resolution by selecting uncertain points. We used the proposed point-sampling method to solve the boundary blur problem.