# Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation
## 3 Methodology
### Attention Gate
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **mIoU** & **Dice** & **Precision (\%)** & **Recall (\%)** \\ \hline UNet [10] & 0.7537 & 0.8596 & 87.31 & 84.65 \\ R2 UNet [17] & 0.7780 & 0.8750 & 92.11 & 83.39 \\ UNet++ [40] & 0.8423 & 0.9139 & 93.06 & 89.79 \\ PointUNet++ [1] & 0.8551 & 0.9218 & 94.01 & 90.42 \\ UNet3+ [21] & 0.8224 & 0.9005 & 93.00 & 88.02 \\ AttentionUNet [24] & 0.7600 & 0.8637 & 91.11 & 82.09 \\ AttentionR2 UNet [2] & 0.8244 & 0.9035 & 94.74 & 86.40 \\ ANU-Net [20] & 0.8789 & 0.9355 & 94.23 & 92.88 \\
**Render U-Net** & **0.9056** & **0.9504** & **95.53** & **94.56** \\ \hline \hline \end{tabular}

* PointUNet++ is the integration of UNet++ and the Point-sampling method. 2 AttentionR2 UNet is the integration of R2 UNet and the attention mechanism.

\end{table} 
Table 4: Performance of liver segmentation for MRI images. Figure 10: Comparison of model outputs and ground truth. (**a**) is composed of the other three figures above are stacked from top to bottom, where the predicted extra areas are clearly seen. (**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 U-Net. Some areas with incorrect predictions are indicated with the red arrows in the figure. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **mIoU** & **Dice** & **Precision (\%)** & **Recall (\%)** \\ \hline UNet [10] & 0.7618 & 0.8648 & 82.34 & 91.06 \\ R2 UNet [17] & 0.8150 & 0.8977 & 93.60 & 86.24 \\ UNet++ [40] & 0.8105 & 0.8953 & 86.37 & 92.93 \\ PointUNet++ [1] & 0.9057 & 0.9502 & **97.70** & 92.50 \\ UNet3+ [21] & 0.8582 & 0.9235 & 94.77 & 90.18 \\ AttentionUNet [24] & 0.8413 & 0.9138 & 91.54 & 91.22 \\ AttentionR2 UNet [2] & 0.8162 & 0.8979 & 90.88 & 88.73 \\ ANU-Net [20] & 0.8923 & 0.8979 & 95.19 & 93.44 \\
**Render U-Net** & **0.9290** & **0.9629** & 97.32 & **95.35** \\ \hline \hline \end{tabular}

* PointUNet++ is the integration of UNet++ and the Point-sampling method. 2 AttentionR2 UNet is the integration of R2 UNet and the attention mechanism.

\end{table} 
Table 3: Performance of spleen segmentation for MRI images. Table 5 compares the kidney 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 12. Our proposed network increased IoU by over 0.038 [40], and increased the Dice coefficient by 0.0213, increased precision by 4.23 percentage points, and increased the recall rate by 0.95 percentage points. Table 6 compares the nuclei segmentation performance, where Render U-Net had the best performance. The output of model and the manually annotated result are compared in Figure 13. Our proposed network achieves IoU that increased over 0.1504 [21], and the Dice coefficient increased by 0.0951, the precision decreased 11.68 percentage points, and the recall rate increased 8.18

Figure 11: Comparison of model outputs and ground truth. (**a**) is composed of the other three figures above 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 ANU-Net. Some areas with incorrect predictions are indicated with the red arrows in the figure.