# Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation
## 3 Methodology
### Attention Gate
We can find that the feature representation of U-Net is not clear enough, and the semantic information is not completely extracted. This is because the original skip connections in U-Net are relatively simple, where the encoder features are directly connected to the decoder features after upsampling. In comparison, the features in UNet++ and Render U-Net are connected by more complex skip connections, which merged more semantic information, and gradually formed a clearer feature representation. In addition, we find that, because of the introduction of deep supervision (Section 3.4), the loss of each output node could be calculated. This helps Render U-Net extract semantic information better. Figure 8: Visualization and comparison of feature maps along skip connections for liver MRI images. ### Segmentation Results

The proposed model has completed the organ segmentation experiments under the mentioned datasets. The experimental results are compared with seven other popular models. In addition, we also combined the proposed point sampling method with UNet++ to obtain Point UNet++. This experimental result was added to the comparison to prove that the point sampling method improves the segmentation performance. Tables 2-6 collect the experimental results based on the above nine models. Table 2 compares the liver segmentation performance on CT images, where Render U-Net had the best performance. The output of model and the manually annotated result are compared in Figure 9. Our proposed network increased IoU by over 0.0484 [24], and increased the Dice coefficient by 0.0253, increased precision by 2.1 percentage points, and increased the recall rate by 2.96 percentage points. Similarly, Table 3 compares the spleen segmentation performance on MRI images, where Render U-Net had the best performance. The output of the model and the manually annotated result are compared in Figure 10. Our proposed network increased mIoU by over 0.1672 [10], and increased the Dice coefficient by 0.0981, increased precision by 14.98 percentage points, and increased the recall rate by 4.29 percentage points. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **mIoU** & **Dice** & **Precision (\%)** & **Recall (\%)** \\ \hline UNet [10] & 0.8949 & 0.9445 & 93.24 & 95.70 \\ R2 UNet [17] & 0.9069 & 0.9511 & 93.80 & 96.48 \\ UNet++ [40] & 0.9446 & 0.9715 & 98.16 & 96.17 \\ PointUNet++ 1  & 0.9655 & 0.9825 & 98.04 & 98.45 \\ UNet3+ [21] & 0.9784 & 0.9891 & 98.94 & 98.87 \\ AttentionUNet [24] & 0.9339 & 0.9658 & 96.79 & 96.37 \\ AttentionR2 UNet 2 & 0.9238 & 0.9603 & 97.12 & 94.98 \\ ANU-Net [20] & 0.9748 & 0.9815 & 98.15 & 99.31 \\
**Render U-Net** & **0.9823** & **0.9911** & **98.89** & **99.33** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The experiment results of the LiTS test dataset. Figure 9: Comparison of model outputs and ground truth. (**a**) shows the other three figures stacked from top to bottom, where the predicted extra areas are clearly seen. The red area in (**b**) is the manually annotated result. The blue region in (**c**) is the output of Render U-Net. The green region in (**d**) is the output of Attention U-Net. Some areas with incorrect predictions are indicated with red arrows in the figure. Table 4 compares the liver segmentation performance on MRI images, where Render U-Net had the best performance. The output of model and the manually annotated result are compared in Figure 11. Our proposed network increased IoU by over 0.0267 [20], increased the Dice coefficient by 0.0149, increased the precision by 1.3 percentage points, and increased the recall rate by 1.68 percentage points.