# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 4 Discussion
### Model Advantages  
The UNet network is symmetrical and beautiful in morphology, and this paper integrates the UNet idea and feature fusion idea to design a CI-UNet network for the features of blurred boundaries, different shapes, and different sizes of tomography abdominal organ images. A method for organ shape segmentation of MRI images based on the CI-UNet network is proposed through extensive experiments. The main contributions and innovations are as follows:  
1. In the multi-organ segmentation task, image rotation and Cutout data enhancement were performed for the case of an insufficient dataset, and the most suitable enhancement method for the multi-organ organ segmentation task was finally obtained through ablation experiments, which was both rotations by ten degrees and Cutout by 30 percent of the image.
2. The UNet dual convolutional feature extraction network is replaced by the VGG16 network equipped with Transfer Learning in the encoding network. The information aggregation module is designed to fuse the feature maps of the network networks of two adjacent layers to diversify the information of the feature maps entering the jump link part to solve the problems of low accuracy of organ edge segmentation and failure to detect some organs due to monotonic transfer.
3. Adding a Polarized Self-Attention mechanism to the decoding network can enhance the feature representation of the output information of the jump connection layer so that the network can focus on conveying organ image shape features and improve the accuracy of organ shape segmentation.
4. The hybrid loss function is constructed by using the Cross-entropy loss function and the Dice loss function, which can not only effectively evaluate the similarity between the target and the gold standard by the Dice loss function, but also can make the network model converge quickly by Cross-entropy stable backpropagation of the corresponding gradient of each category. To verify the method's effectiveness in this paper, extensive experiments are conducted on the Chaos dataset. The results show that the proposed model can separate the image features and avoid redundant, repetitive transmission of irrelevant information. Furthermore, the average cross-merge ratio of organ segmentation reaches 82.33%, and the average pixel accuracy reaches 90.10%, significantly higher than many other networks. In the above experimental data, the spleen and left kidney have fewer label prospects than other organs, resulting in poor organ segmentation. We designed a scheme to balance the dataset by sampling most samples. A balanced dataset can effectively prevent overfitting, increase robustness, and allow the network to obtain better training accuracy. At the same time, we put CI-UNet into the practical application of spine segmentation and actively discuss with physicians; our work will relieve physicians' work pressure and provide them with a reference for computer-aided diagnosis.
5. When put into practical application, the network of this paper was also applied to the dataset of the Guizhou branch of Beijing Jishuitan Hospital, where a private dataset was set up according to the actual situation in Guizhou. Here, we also conducted experiments to calculate the spinal curvature angle using the international standard Cobb angle calculation, and the doctors approved the results.