# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 3 Results
### Related Work on Organ
However, at the same time, we observed segmentation errors in the above VGG prediction result maps and PSPNet, and MobileNetV2 backbone prediction images, which all showed incorrect predictions of liver images in the third set of image predictions, of which we analyzed the reasons as follows: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Backbone** & **Attention** & **mIoU (\%)** & **mPA (\%)** & **PA (\%)** \\ \hline \multirow{2}{*}{PSPNet} & MobileNetV2 & None & 76.41 & 87.02 & 98.49 \\  & ResNet50 & None & 81.04 & 89.66 & 98.71 \\ \hline Deeplabv3+ & MobileNetV2 & None & 75.92 & 86.04 & 98.42 \\ \hline M-FCN & None & None & 77.21 & 86.40 & 98.51 \\ \hline U-Net\_based & None & None & 77.86 & 85.14 & 98.55 \\ \hline CI-UNet & VGG16 & PSA & 82.33 & 90.10 & 98.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The ablation results of each algorithm. Figure 11: The prediction plots of organ segmentation for each algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Backbone** & **Attention** & **mIoU (\%)** & **mPA (\%)** & **PA (\%)** \\ \hline \multirow{4}{*}{Attention} & VGG16 & SE & 79.75 & 87.02 & 98.67 \\  & ResNet50 & SE & 78.94 & 87.40 & 98.45 \\ \multirow{2}{*}{UNet} & VGG16 & Cbam & 81.13 & 88.60 & 98.68 \\  & ResNet50 & Cbam & 79.47 & 88.63 & 98.67 \\ \multirow{2}{*}{USR} & VGG16 & PSA & 81.22 & 88.75 & 98.60 \\  & ResNet50 & PSA & 80.03 & 90.42 & 98.72 \\ \hline CI-UNet & VGG16 & PSA & 82.33 & 90.10 & 98.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The experimental results of the ablation of Attention UNet and CI-UNet. 1. Segmentation errors due to unbalanced datasets, the accuracy of the model may be affected when dealing with unbalanced datasets. Since the number of samples in specific categories in the dataset is small, misclassification may occur, leading to a decrease in accuracy;
2. Mislabeling of the dataset itself. If there is noise or outliers in the dataset, such as mislabeled data, it may also cause the neural network to learn the wrong patterns, leading to inaccurate predictions;
3. Overlap between categories, where multiple objects exist for that image feature point;
4. Overfitting, where the neural network is overtrained, resulting in features that need to be captured not being captured. The confusion matrix of the algorithm in this paper on the validation set, the mIoU segmentation index for each organ, and the mPA segmentation index for each organ segmentation are shown in Figure 12a-c. In this paper, the Chaos dataset was utilized to design a multi-organ segmentation task, and the weights were obtained after the network completed training in this paper. The CI-UNet designed in this paper can segment multiple organs in a single prediction. We have selected some representative images from the Chaos validation set for multi-organ segmentation image prediction. There are some typical features in our validation images, and the following six image structures are typical:  
Figure 12: (**a**) The confusion matrix of the algorithm in this paper on the validation set; (**b**) The mIoU segmentation index for each organ; (**c**) The mPA segmentation index for each organ segmentation. 1. No-organ images;
2. Single-organ liver images;
3. Left and right kidney images;
4. Liver, left, and right kidney images;
5. Liver, left kidney, and spleen images;
6. Liver, left and right kidney, spleen images. We use CI-UNet to make predictions for each of the above six typical images, and the prediction results are shown in Figure 13.  
Data imbalance is the main problem that causes segmentation errors and affects network accuracy. Network training is more effective when the datasets inside the training set are more balanced. In the Chaos dataset, the large sample class is background, liver. The problem of unbalanced data in this paper still needs to be solved. We balance the dataset by sampling most of the samples and augmenting a few samples.