# Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation
## 3 Methodology
### Attention Gate
Thirdly, in order to calculate the similarity between the segmentation results and the real results, the dice coefficient is introduced as a measurement, which is defined as \(\frac{2\times Y_{i}\times\vec{Y}+\text{s}}{Y_{i}^{2}+\vec{Y}^{2}+\text{s}}\). The higher the degree of similarity, the bigger the value of the dice coefficient. ### Model Pruning

We have pruned the Render U-Net at four different depths and obtained four pruned sub-networks, which are shown in Figure 7. We all know that there are both input forward propagation and loss back propagation during model training. The components in the gray region can transmit errors during the loss back propagating. These components are essential for weight updates and cannot be removed. The reason why we can only remove the gray components during inference is that the network only has input forward propagation in the test phase, and the subsequent components will not affect the previous output and can therefore be removed. As shown in the figures, all convolution blocks, upsampling, downsampling, skip connections, and attention gates in the gray area have been pruned, which are retained during training but deleted during prediction. We rename the pruned networks as Render U-Net L1, L2, L3, and L4. In addition, LN also denotes that the final output of the network comes from \(X_{0\_N}\). The Render U-Net selects the pruned model by evaluating the four sub-networks' segmentation performance on the validation dataset. If the performance of one sub-network on the validation dataset has reached expectations, we can use the shallower Render U-Net for segmentation during inference. For example, in the most ideal case, if the most pruned architecture L1 can have satisfactory segmentation performance when validated, then the final result of the network comes from \(X_{0\_1}\). On the contrary, in the least ideal case, if only the minimally pruned architecture L4 can output good segmentation result, the final result will come from \(X_{0\_4}\). In summary, in this section, we introduced five methodologies used in Render U-Net. Section 3.1 analyzed the nested architecture to collect full semantic information from different levels. Section 3.2 analyzed the Attention gate to enhance the learning of the target organ. Section 3.3 analyzed the proposed point-sampling method to render high-quality boundaries of the target organ. Section 3.4 analyzed the deep supervision and the redesigned hybrid loss function to provide complete semantic information. Section 3.5 analyzed the model pruning to speed up prediction and decrease parameters. For the next section, we designed experiments to validate our improvements. ## 4 Experiments and Results

Four experiments are designed to prove our improvement due to the introduced methodologies, including segmentation experiment, attention learning experiment, point-sampling experiment, and model pruning experiment. ### Datasets

The above four experiments are all carried out based on five public medical image datasets. The detailed information is summarized in Figure 3.0 The target organs cover human liver, spleen, kidney and cell nuclei, and the image data come from common CT, MRI, and cell scans. The detailed introductions of the datasets are as follows:

The LiTS [31] dataset comes from the 2017 MICCAI challenge. The train set has 130 labeled CT image data and the test set has 70 unlabeled CT image data. There are three types of objects in the LiTS dataset: liver, tumor, and background. In this segmentation experiment, we only focus on the liver and regard it as a positive object, and the others as a negative object. The CHAOS [32] dataset comes from the 2019 ISBI challenge. Only the MRI image data are used in this article, including 120 labeled DICOM datasets in the train set. There are four types of objects

Figure 3.1 Deep supervision helps the trained Render U-Net pruning at different depths to obtain four sub-networks, which are represented by Render U-Net L1, L2, L3, and L4.