# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 3 Results
### Related Work on Organ
To refine the network and enable the network to focus on effective information during decoding, the SE attention mechanism [49], the Cbam attention mechanism [50], and the Polarized Self-Attention mechanism are selected for comparison in this paper. The results of the ablation of different trunk Attention UNet structures are shown in Table 4.  
As seen in Table 4, the mIoU and mPA of the Attention UNet using the VGG16 backbone and PSA attention mechanism were 81.22% and 88.75%, respectively, while the mIoU and mPA metrics of the UNet neural network using the SE and Cbam attention mechanism under the same conditions were 79.75% and 87.02%, and 81.13% and 88.60%, respectively. Similarly, the mIoU and mPA of Attention UNet using ResNet50 backbone with PSA attention mechanism are 80.03% and 90.42%, respectively. In contrast, the mIoU and mPA metrics of neural networks using SE and Cbam attention mechanisms with the same backbone are 78.94% and 87.40%, and 79.47% and 88.63%, respectively. Table 4 shows that the Polarized Self-Attention mechanism improves the UNet segmentation network more than SE, and the Cbam attention mechanism in both ResNet50 or VGG16 backbone cases and the PSA attention mechanism improves better in refining the targets. In this paper, the information aggregation module is added based on Attention UNet, and the experimental results of the ablation of Attention UNet and CI-UNet are shown in Table 5.  
The algorithm in this paper uses the VGG16 network to replace the double convolutional backbone and uses the PSA attention mechanism also adds the information aggregation module for fusing the image information between the layers. Table 5 shows that the experimental results of mIoU and mPA are 82.33% and 90.10%, respectively. Compared with UNet without adding the information aggregation module under the same conditions, the improvement in accuracy is 1.11%, and the improvement in mPA is 1.35%. The algorithm in this paper is also compared with the PSPNet algorithm and DeepLabv3+ algorithm. It is also compared with dedicated algorithms developed for MRI, such as M-FCN [51] and U-Net_based [52]. The ablation results of each algorithm are shown in Table 6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Backbone** & **Attention** & **mIoU (\%)** & **mPA (\%)** & **PA (\%)** \\ \hline \multirow{6}{*}{Attention} & VGG16 & SE & 79.75 & 87.02 & 98.67 \\  & ResNet50 & SE & 78.94 & 87.40 & 98.45 \\ \cline{1-1}  & VGG16 & Cbam & 81.13 & 88.60 & 98.68 \\ \cline{1-1}  & ResNet50 & Cbam & 79.47 & 88.63 & 98.67 \\ \cline{1-1}  & VGG16 & PSA & 81.22 & 88.75 & 98.60 \\ \cline{1-1}  & ResNet50 & PSA & 80.03 & 90.42 & 98.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of the ablation of different trunk Attention UNet structures. Figure 10: Circumstance of mPA metrics for each organ. As can be seen in Table 6, the mIoU and mPA values of the ResNet50 backbone PSPNet network are 81.04% and 89.66%, respectively, lower than the algorithm in this paper by 1.28% and 0.44%, respectively. While the mIoU values of PSPNet and Deeplabv3+ networks using MobileNetV2 [53] backbones are 76.41% and 75.92%, which were lower than the algorithm in this paper by 5.92% and 6.41%, respectively. The mPA values were 87.02% and 86.04%, lower than the algorithm in this paper by 3.08% and 4.06%, respectively. We also found some dedicated algorithms for MRI 2d images for comparison. mIoU and mPA values for M-FCN are 77.21 and 86.40, respectively--5.12% and 3.7% lower than the algorithm in this paper. mIoU and mPA values for U-Net_based are 77.86 and 85.14, respectively, 4.47% and 4.96%, lower than the algorithm in this paper, respectively. The prediction plots of organ segmentation for each algorithm are shown in Figure 11.  
As shown in Figure 11 above, based on visual observation, the algorithm in this paper is closest to the gold standard in terms of organ edge structure, and there are no organ prediction errors and more well-defined angles.