# 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] & 0.8446 & 0.9158 & 89.20 & 94.08 \\ R2 UNet [17] & 0.8554 & 0.9221 & 91.92 & 92.50 \\ UNet++ [40] & 0.8658 & 0.9281 & 90.87 & 94.82 \\ PointUNet++ 1 & 0.8942 & 0.9442 & 92.66 & **96.24** \\ UNet3+ [21] & 0.8449 & 0.9155 & 89.91 & 93.44 \\ AttentionUNet [24] & 0.8577 & 0.9234 & 90.97 & 93.76 \\ AttentionR2 UNet 2 & 0.8734 & 0.9324 & 91.41 & 95.15 \\ ANU-Net [20] & 0.9010 & 0.9479 & 94.00 & 95.60 \\
**Render U-Net** & **0.9038** & **0.949** & **94.14** & 95.77 \\ \hline \hline \end{tabular} \({}^{1}\) 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 5: Performance of kidney segmentation for MRI images. Figure 12: 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 UNet++. Some areas with incorrect predictions are indicated with red arrows in the figure. percentage points. It is also worth noting that U-Net also performs well in nuclei segmentation tasks. In particular, because U-Net has a simpler model, it is more suitable for such task. Obviously, the proposed network outperformed other networks in the five segmentation experiments above. The improvement is attributed to the nested structure, attention mechanism, point-sampling method, and deep supervision. ### Attention Learning Results

According to the results in Table 2 through Table 6, networks based on the attention mechanism consistently outperform original networks in the five tasks. Moreover, the proposed Render U-Net outperforms any other attention-aware models. Thus, we can conclude that the introduction of the attention gate is attributed to the improvement on the segmentation performance. Figure 14 is two groups of changes of the attention coefficient \(\alpha\) when segmenting liver from the CT image on the LiTS dataset. We can get the following conclusions of the attention coefficient \(\alpha\) from the figure:

* The attention coefficient will gradually change with model training. At the same time, the attention coefficient changes the weights of the focused area during training. In addition, then the attention coefficient is multiplying the feature pixel by pixel. * The weight around the target organ will gradually increase to enhance the learning of this area, as shown in the red area in the above figure. * Similarly, the weight of the confused tissue area that is not related to the segmentation target will gradually decrease, which is used to inhibit the learning of this area, as shown in the blue area in the figure above. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **mIoU** & **Dice** & **Precision (\%)** & **Recall (\%)** \\ \hline UNet [10] & 0.7909 & 0.8833 & 86.18 & 90.59 \\ R2 UNet [17] & 0.6987 & 0.8199 & 81.23 & 82.77 \\ UNet++ [40] & 0.7028 & 0.8229 & 92.36 & 74.36 \\ PointUNet++ 1 & 0.7507 & 0.8569 & 92.22 & 80.06 \\ UNet3+ [21] & 0.6853 & 0.8121 & 81.20 & 81.28 \\ AttentionUNet [24] & 0.7660 & 0.8675 & 85.81 & 87.71 \\ AttentionR2 UNet & 0.7025 & 0.8235 & 89.85 & 76.16 \\ ANU-Net [20] & 0.8259 & 0.9003 & 91.42 & **89.77** \\
**Render U-Net** & **0.8360** & **0.9072** & **92.88** & 89.46 \\ \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 6: Performance of nuclei segmentation by the models in the DSB test dataset. Figure 13: 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.