# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 3 Results
### Related Work on Spine  
The spine segmentation experimental environment is the same as the multi-organ segmentation task, with experimental parameters of maximum learning rate of \(1\times 10^{-4}\) and the termination learning rate is 0.01 times the start learning rate, momentum parameter of the optimizer of 0.9 and second moment of 0.999, batch size of model training of 8, training generations as the sum of Cross-entropy loss function and Dice loss function, and total training generations of 100. We applied the CI-UNet network designed in this paper to the spine dataset of the Guizhou branch of Beijing Jishuitan Hospital. The geographical location of Guiyang City, Guizhou Province, is shown in Figure 15.  
Guizhou Province is the most affected area by coal-fired fluorosis. The coal-burning type of ground fluorosis, and coal-burning polluted endemic fluorosis is a type of ground fluorosis unique to China in the world, affecting about 15 million people, with about 10 million people suffering from dental fluorosis and about 1 million people suffering from bone fluorosis. Fluorosis severely affects residents' bone health and quality of life [54]. The spine dataset of the Guizhou branch of Beijing Jishuitan Hospital uses X-ray images of the spine medical images, which reflect the bone quality of the inhabitants of Guizhou province, China, between the ages of 18 and 70. Due to geological conditions and the level of economic development,  
Figure 14: The results of Grad-CAM used for the images. Figure 15: The geographical location of Guiyang City, Guizhou Province. middle-aged and elderly residents have poor height development and often suffer from a range of diseases, including scoliosis, which is particularly serious. This dataset was developed in collaboration with the Guizhou branch of Beijing Jishuitan Hospital. Many ortho X-ray medical images of residents in Guizhou province were retrieved to produce the dataset. Due to privacy and legal issues, this dataset cannot be made public, and this dataset is labeled by is manually labeled. The segmentation prediction results generated by CI-UNet are shown in Figure 16.  
Training datasets need to be increased, resulting in less-than-optimal predictive segmentation. In the future, we will invest more human and material resources to solve the problem of data volume labeling. At the same time, we will produce more datasets for the local high prevalence of diseases. Based on the predicted images obtained, we calculated the angle of curvature of the spine. We chose the internationally used Cobb [55; 56] index for spinal curvature assessment. The Cobb angle is a standard method used to measure the degree of scoliosis. It determines the degree of scoliosis by measuring the lateral bending angle of two adjacent vertebrae. Specifically, the Cobb angle is derived from the angle between two vertical lines drawn from the upper to lower surfaces of the two vertebrae. During the measurement, the physician selects the two most curved adjacent vertebrae, draws a vertical line on their upper and lower surfaces, and then measures the angle between these two lines. This angle is the Cobb angle. Usually, the greater the Cobb angle, the more severe the scoliosis. The Cobb angle is commonly used to diagnose scoliosis in adults, children, adolescents, and other spinal deformities. It is one of the most important indicators to assess the degree of scoliosis and the choice of treatment options. We calculated the prediction results based on the principle of Cobb calculation. The predicted image Cobb calculation results are shown in Figure 17.  
We have discussed with the spine doctors that CI-UNet predictive imaging can significantly help them improve their efficiency by producing semi-automatic calculations for scoliosis alone. The doctors are very receptive to our work, and we will follow up with more tasks for more body structures.