## 4 Experiments And Results
### Segmentation Results  
Render 3D U-Net competes against the other seven popular models (3D UNet [18], 3D R2 UNet [41], 3D UNet++ [42], Attention 3D UNet [43], Attention 3D R2 UNet, and 3D UNet3+ [40]) on medical CT image segmentation tasks. Besides, we combine the proposed point-sampling method with UNet++ and added this Point UNet++ to the comparison. The experiments results are summarized in the following table, where DS means deep supervision, HD means Hausdorff distance. Note that the prediction time is the mean time it takes to predict each 3D volume in the test dataset. The GPU used in the test is a single NVIDIA GeForce GTX 1080 TI. Table 2 shows the experimental results of liver CT volume segmentation, the performance of Render 3D U-Net is the best. We can get the conclusion that the introduction of FPC can help the network obtain a better performance on liver CT volume segmentation. The output of the model and the manually annotated result are compared in Figure 8. Render 3D U-Net increased the IoU ratio by 2.85 percentage points over Attention 3D UNet, increased the dice coefficient by 1.62 percentage points, increased the recall rate by 0.0269, and decreased the Hausdorff distance by 3.6357. Table 3 shows the experimental results of kidney CT volume segmentation; the performance of Render 3D U-Net is the best. We can get the conclusion that the introduction of FPC can help the network obtain a better performance on kidney CT volume segmentation. The output of the model and the manually annotated result are compared in Figure 9. Render 3D U-Net increased the IoU ratio by 5.12 percentage points over 3D UNet3+, increased the dice coefficient by 3.06 percentage points, increased the recall rate by 0.0333, and decreased the Hausdorff distance by 1.0914. Table 4 shows the experimental results of heart CT volume segmentation; the performance of Render 3D U-Net is the best. We can get the conclusion that the introduction of FPC can help network obtain a better performance on heart CT volume segmentation. The output of model and the manually annotated result are compared in Figure 10. Render 3D U-Net increased the IoU ratio by 4.17 percentage points over Attention 3D R2 UNet, increased the dice coefficient by 2.34 percentage points, increased the recall rate by 0.0223, and decreased the Hausdorff distance by 1.8036. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **DS** & **FPC** & **mIoU (\%)** & **Dice** & **Recall** & **HD** & **Prediction Time(s)** \\ \hline
3D UNet [18] & x & x & 78.94 & 88.11 & 0.8641 & 24.5915 & 0.5943 \\
3D R2 UNet [41] & x & x & 80.26 & 88.42 & 0.9627 & 24.9202 & 1.8978 \\
3D UNet++ [42] & ✓ & x & 90.18 & 94.79 & 0.9540 & 15.3714 & 1.0944 \\ Point 3D UNet++ 1 & ✓ & x & 90.40 & 94.88 & 0.9459 & 12.8446 & 1.7755 \\
3D UNet3+ [40] & ✓ & x & 90.46 & 94.91 & 0.9538 & 11.8363 & 1.2836 \\ Attention 3D UNet [43] & x & x & 89.67 & 94.46 & 0.9329 & 14.5455 & 0.9939 \\ Attention 3D R2 UNet 2 & x & x & 83.99 & 91.01 & 0.9340 & 18.6025 & 1.9949 \\ Render 3D U-Net & x & x & 90.03 & 94.66 & 0.9466 & 14.3275 & 1.7634 \\ Render 3D U-Net & ✓ & x & 92.48 & 96.06 & **0.9641** & 12.5195 & 1.8577 \\ Render 3D U-Net & ✓ & ✓ & **92.52** & **96.08** & 0.9598 & **10.9098** & 1.9946 \\ \hline \hline \end{tabular}  
* Point 3D UNet++ is the integration of 3D UNet++ and the point-sampling method; 2 Attention 3D R2U-Net is the integration of 3D R2U-Net and Attention 3D U-Net.  
\end{table} 
Table 2: The liver segmentation experiment results of the LiTS test dataset. Figure 8: Comparison of model outputs and ground truth, where the prediction-missed areas are highlighted with red arrows: the prediction of Render 3D U-Net is generated under the point-sampling method and false positive classification module. Figure 9: Comparison of model outputs and ground truth, where the prediction-missed areas are highlighted with red arrows: the prediction of Render 3D U-Net is generated under the point-sampling method and false positive classification module.