# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 3 Results
### Related Work on Organ
The primary purpose of conducting crossover experiments is to determine the appropriate learning rate, optimizer, and second moment. We choose the generic 0.9 and 0.999 to conduct the experiments. The Cross-Validation results are shown in Figure 9a,b. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Backbone** & **Cutout Ratio (\%)** & **mIoU (\%)** & **mPA (\%)** & **PA (\%)** \\ \hline \multirow{3}{*}{UNet} & & 20\({}^{\circ}\) & 79.18 & 86.98 & 98.68 \\  & VGG16 & 30\({}^{\circ}\) & 79.37 & 86.48 & 98.64 \\ \cline{1-1}  & & 40\({}^{\circ}\) & 79.16 & 86.54 & 98.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of ablation experiments with different degrees of Cutout. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Backbone** & **Cutout Ratio (\%)** & **mIoU (\%)** & **mPA (\%)** & **PA (\%)** \\ \hline \multirow{3}{*}{UNet} & & 20\({}^{\circ}\) & 79.18 & 86.98 & 98.68 \\  & VGG16 & 30\({}^{\circ}\) & 79.37 & 86.48 & 98.64 \\ \cline{1-1}  & & 40\({}^{\circ}\) & 79.16 & 86.54 & 98.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of different rotation angles in VGG16 network. As can be seen from Figure 9 above, in each cross-validation, the starting learning rate is \(1\times 10^{-5}\) when each evaluation index is the best, the training set for Cross-Validation is 1495 sets, and the validation set is 374 sets. In the following experiments, we will determine the use of \(1\times 10^{-5}\) as the learning rate parameter since the experimental results of the first group in the above crossover experiments were much higher than those of the other groups. To eliminate fluctuations, in this section, this experiment selects the average of 5 sets of experiments, the third set of experimental data for the experiment. The following will outline and explains the results of our experimental analysis of predictive tomographic abdominal organ segmentation. By comparing the different structures of UNet, PSPNet [47], DeepLabv3+, MRI-specific algorithms, and our algorithm CI-UNet. The ablation results of different backbone UNet networks are shown in Table 3.  
The PA index reflects the global degree of correct pixel point classification, which is relatively unconvincing because the background pixel points of tomographic organ images account for the vast majority of the background pixel points, resulting in small changes in the PA index. Therefore, the paper mainly uses mIoU and mPA index data for experimental ablation illustration. From the control of mIoU and mPA index data, it can be found that the extraction effect of double convolution backbone UNet is the worst. The mIoU metric of the double convolutional backbone UNet is 73.25%, and the mPA metric is 83.32%. The monotonic structure of the UNet backbone network makes the network perform poorly in image feature information extraction, which makes the mIoU and mPA metrics much lower than the VGG16 backbone UNet, and the ResNet50 [48] backbone UNet mIoU metrics of VGG16 backbone UNet and ResNet50 backbone UNet are relatively close, 79.37% and 78.71%, respectively, and mPA metrics are 86.88% and 88.08%, respectively. mPA metrics have a 1.2% difference, which is caused by the fact that the ResNet50 backbone UNet has more pixels correctly predicted category than the VGG backbone UNet during the prediction process. The circumstance of mPA metrics for each organ of the VGG16 backbone UNet network and ResNet50 backbone UNet are shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Backbone** & **Attention** & **mIoU (\%)** & **mPA (\%)** & **PA (\%)** \\ \hline \multirow{3}{*}{UNet} & Double Conv & None & 73.25 & 83.32 & 98.21 \\  & VGG16 & None & 79.37 & 86.88 & 98.64 \\ \cline{1-1}  & ResNet50 & None & 78.71 & 88.08 & 98.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The ablation results of different backbone UNet networks. Figure 9: (a) Cross-Validation mIoU index results; (b) Cross-Validation mPA index results.