# Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation
## 3 Methodology
### Attention Gate
This result is mutually confirmed with the results in Table 6.

Figure 16 directly compares the relationship between performance and inference time of four pruned models with different parameters. As we can see, the parameters of the four sub-networks are various, where the parameters of the shallowest sub-network is 0.1 M, while the deepest one has 12.0 M. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Spleen**} & \multicolumn{3}{c}{**Kidney**} & \multicolumn{3}{c}{**Liver**} & \multicolumn{3}{c}{**Nuclei**} \\ \cline{2-13}  & **Dice** & **mIoU** & **Time** & **Dice** & **mIoU** & **Time** & **Dice** & **mIoU** & **Time** & **Dice** & **mIoU** & **Time** \\ \hline L1 & 0.8258 & 0.7203 & 28.54 & 0.7588 & 0.6198 & 28.37 & 0.6624 & 0.5332 & 18.10 & 0.8490 & 0.7529 & 8.74 \\ L2 & 0.9272 & 0.8655 & 44.95 & 0.8519 & 0.7450 & 41.77 & 0.7546 & 0.6480 & 34.63 & 0.8516 & 0.7603 & 22.74 \\ L3 & 0.9451 & 0.8984 & 59.88 & 0.9353 & 0.8793 & 59.09 & 0.8797 & 0.8170 & 50.28 & 0.9072 & 0.8360 & 40.35 \\ L4 & 0.9629 & 0.9290 & 77.69 & 0.9494 & 0.9038 & 80.32 & 0.9056 & 0.9504 & 64.79 & 0.8864 & 0.8065 & 61.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Segmentation results of four pruned models. The inference time (counted by seconds) is calculated by segmenting 1K images. Figure 15: Diagram of the Point-sampling process for rendering a higher resolution boundary when inferring nuclei. The red dots in different resolution are the selected uncertain points to calculate. In the most ideal case, if the segmentation result of Render U-Net L1 has reached expectations, then we can save 99.9% of the parameters. Unfortunately, according to the four segmentation experimental results on CHAOS and DSB, the segmentation performance of L1 cannot meet the basic requirement. We analyze the reason because it is too shallow for extracting effective features. ## 5 Conclusions

In this paper, we provided a unique perspective on render, and we viewed the medical image segmentation task as a render problem. We adapted a subdivision-based point-sampling method to replace the original upsampling method for \"rendering\" high-quality boundaries. The proposed model, called Render U-Net in this paper, performed segmentation experiments on LiTS, CHAOS, and DSB datasets together with other models, including U-Net, R2U-Net, UNet++, Point UNet++, UNet3+, Attention U-Net, Attention R2U-Net, and ANU-Net. The experiment's results demonstrated that the proposed network outperforms in overall and boundary segmentation. We make the following analysis: the improvement in accuracy is due to the integration of the point sampling method, supervised dense skip connection, and attention mechanism in the nested architecture. The point-sampling method has been proven to render higher-quality boundaries than the traditional upsampling method. C.L. : Methodology and Writing--original draft; W.C.: Supervision and Writing--review and editing; Y.T. : Project administration. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (No. 2018 YFB0204301). The authors declare no conflict of interest. ## References

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Figure 16: Diagram of the relationships between the Dice coefficients and inference times of the four pruned Render U-Nets with segmentation of four kinds of objects.