# Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation
## 3 Methodology
### Attention Gate
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 UNet3+. Some areas with incorrect predictions are indicated with red arrows in the figure. ### Point-Sampling Results

We used the parameters (\(k=3\), \(\beta=0.75\), \(n=8096\)) for uncertain points selection strategy. Considering that all the segmentation tasks in the experiments were binary classification tasks, we used the distance between the probability of coarse prediction mask and 0.5 as a measure of point uncertainty. Table 7 compares the boundary segmentation performance measured by Hausdorff distance, where Render U-Net had the best performance. Figure 15 shows the example of the point-sampling process for nuclei segmentation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Liver (CT)** & **Spleen** & **Kidney** & **Liver (MRI)** & **Nuclei** \\ \hline UNet [10] & 14.95 & 11.94 & 11.87 & 30.98 & 35.54 \\ R2 UNet [17] & 17.76 & 9.05 & 12.71 & 28.05 & 41.19 \\ UNet++ [40] & 9.31 & 7.82 & 11.97 & 25.59 & 34.05 \\ PointUNet++ 1  & 7.62 & 6.32 & 10.53 & 23.12 & 32.32 \\ UNet3+ [21] & 6.36 & 7.25 & 13.13 & 23.97 & 37.21 \\ AttentionUNet [24] & 7.21 & 6.23 & 13.63 & 23.09 & 34.85 \\ AttentionR2 UNet 2 & 12.26 & 8.20 & 13.87 & 20.33 & 35.51 \\ ANU-Net [20] & 5.48 & 5.97 & 10.64 & 18.06 & 32.04 \\
**Render U-Net** & **4.41** & **5.39** & **10.49** & **16.96** & **31.65** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Hausdorff distance of five segmentation tasks. Figure 14: The graph of the attention coefficient \(\alpha\) changing with training. The color change in the figures represents the change in the weight of attention learning, where red represents enhancement and blue represents inhibition. (**a1–f1**) is the first group of changes. (**a2–f2**) is the second group of changes. (**a1,a2**) are the input CT images. (**b1,b2**) are the ground truth of liver, respectively. (**c1–f1**) and Figure (**c2–f2**) are two groups of the attention coefficients \(\alpha\) in different training phases. As we can see, during point-sampling, our network selected a set of uncertain points and calculated masks from low-resolution (32 \(\times\) 32) to high-resolution (256 \(\times\) 256). Compared with the traditional upsampling method that calculates full-resolution points (256 \(\times\) 256 = 64 K), the point-sampling method can extract boundary information by using only a few points for prediction (8096 \(\times\) 3 = 24 K), thereby assisting the overall segmentation task and reducing computational overhead. ### Model Pruning Results

Table 8 quantifies the segmentation performance and inference time on four tasks, including spleen segmentation, kidney segmentation, liver segmentation, and nuclei segmentation. We can get conclusions that model pruning can be used to obtain a model with fewer model parameters, which should have a faster prediction speed. More specifically, in the spleen segmentation task, the performances of Render U-Net L3 and L4 are very close, but the parameter amount of L3 is only 2.91 M. Therefore, in this task, we can use a shallow sub-network Render U-Net L3 to get segmentation performance similar to L4, which owns 12.0 M parameters. Through this pruning operation, we can save 75.75% of the parameter, and reduce the prediction time by 22.93%, while Dice only reduces 0.0178 and mIoU reduces 0.0306. In the kidney MRI image task and liver MRI image segmentation task, pruned models achieved the similar improvement on prediction speed at the expense of a small amount of performance. In particular, it is also worth noting that Render U-Net L3 performed better than L4 in the nuclei segmentation task. After analysis, we believe that this is because the semantic information extracted by L3 is enough to restore the characteristics of the nuclei, while the features extracted by L4 are more complex and not suitable for such a task.