# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 3 Results  
### Related Work on Organ  
The multi-organ segmentation experimental environment is a computing platform with a Win11 operating system, INTEL-12900K processor, and RTX4090 24G \(\times\) 2 graphics card. The experimental code uses Python programming language, and the deep learning framework is Pytorch. CI-UNet model training uses the Adam optimization algorithm. The output image size is 256 \(\times\) 256. The training's start learning rate is 1 \(\times\) 10\({}^{-5}\), the termination learning rate is 0.01 times the start learning rate, and the cyclical learning rate can obtain better training results [43]. The momentum parameter of the optimizer is 0.9, the second moment is 0.999, and the batch size of the model training is 8. The training generations are the sum of the Cross-entropy loss function and Dice loss function, and the total training generations are 100. The ratio of the training set and validation set is 8:2. In this section, we experiment with the Chaos dataset, which has 623 original images. The 623 original data do not give good results, so proper data enhancement is needed. We use a rotation of 10 degrees for data augmentation, and we use data augmentation for two reasons:  
1. Some data enhancement methods cannot transform the original data image and label at the same time. In contrast, image rotation can make the image and label produce the same change, and the structure and change in the original image are small;As a proven method of image enhancement, image rotation is widely used in various fields and has strong versatility. In this paper, we conducted ablation experiments for the rotation angle of the image, and we selected 5\({}^{\circ}\),10\({}^{\circ}\), 20\({}^{\circ}\), and 30\({}^{\circ}\) for the angle transformation, which is used for VGG16 backbone UNet network for the experiments. The results of different rotation angles in the VGG16 network are shown in Table 1.  
Table 1 shows that the best mIoU and mPA evaluation metrics performance was achieved at a rotation angle of 10\({}^{\circ}\). A very likely reason for this situation is that the selection of the angular band has a tangible impact on the nature of the data. Medical images usually need to preserve spatial orientation, and a more significant rotation may make the images unrealistic from a clinical point of view. There is little difference in the evaluation metrics between the rotation angle of 5\({}^{\circ}\) and 10\({}^{\circ}\). However, we enhanced the data with a rotation angle of 10\({}^{\circ}\) to create a moderate differentiation in the images. We believe that setting an angle of 10\({}^{\circ}\) and being able to expand the dataset will not affect the image characteristics of the dataset significantly. Therefore, this paper selects the expanded dataset with a rotation angle of 10\({}^{\circ}\) as the experimental dataset. Cutout as a data enhancement method can cope with regularization well, but excessive Cutout can reduce the accuracy of the network. Here, we take three different degrees of Cutout data enhancement methods for ablation experiments. The results of ablation experiments with different degrees of Cutout are shown in Table 2.  
When validating the dataset with Cutout using the VGG16 backbone UNet, it was found that the accuracy of the evaluation metrics obtained when using a Cutout ratio of 30% was better than when using a Cutout ratio of 20% and 40%. We analyze the reason for this. As deep learning training is prone to overfitting, various regularization methods are needed. Cutout is a unique form of Dropout. The size of the feature map values of the shallow feature map has increased, and the distribution of the deep feature map is wider. We selected a combination of Rotation 10\({}^{\circ}\) and Cutout for data enhancement from the above experimental results. We used Cross-Validation [44; 45; 46] to select the dataset parameter. Cross-Validation helps to perform model parameter determination and, simultaneously, can avoid the limitations and specificity of fixed division datasets.