# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 2 Materials and Methods
### CI-UNet Network Model  
UNet is widely used in medical image segmentation fields with good results. It uses a symmetric coding and decoding structure, and the coding network part uses convolutional and pooling layers for feature extraction and Down-sampling, which can compress the information of the input image into higher-level abstract features. The decoder part uses the deconvolution layer and Up-sampling layer for Up-sampling and feature reconstruction to restore the feature mapping of the encoder output to the exact as the input image. The UNet network structure is shown in Figure 3.  
The UNet network institutions are straightforward and aesthetically pleasing but have the following problems:  
1. The results are not good after a large number of data training repetitions, which can easily cause overfitting and other problems, and the convergence process is slow. When the amount of data is insufficient, the segmentation effect of UNet is not satisfactory;
2. The splitting accuracy and positioning precision cannot be combined, and the essential parts cannot be centralized;
3. The image features in each layer of the coding network do not interact with each other, resulting in insufficient information entering the coding part, which eventually affects the output results. The model in this paper is inspired by the idea of UNet, and the network structure is rationalized and modified for the above three problems, and the network structure of the model CI-UNet in this paper is shown in Figure 4.  
Figure 3: UNet Network Architecture. Figure 2: (a) Data original image; (b) Cutout processed image. VGG16 [40], which contains Transfer Learning, replaces the double convolution backbone to make the network more generally. Transfer Learning can train the target domain's model weights using the source domain's model weights and cope well with the problem of fewer new datasets. The VGG16 network is first pre-trained on the ImageNet dataset with weights and then trained in conjunction with the Chaos dataset. In this paper, we use ImageNet to perform network pre-training, mainly for two reasons. On the one hand, ImageNet is a data collection with a large amount of pre-labeled training data in the image field, which is a great advantage, and the more significant the number of trained parameters, the more reliable it is; on the other hand, because ImageNet has 1000 categories, there are many categories, and it is a general image data, which is not too related to the field. Hence, the generality is good and can be used after pre-training. VGG16 backbone Transfer Learning schematic is shown in Figure 5.  
Figure 4: The network structure of the model CI-UNet. Figure 5: VGG16 backbone Transfer Learning schematic. The VGG16 network extracts features by stacking multiple 3 \(\times\) 3 convolutional kernels instead of large convolutional kernels. It reduces the number of parameters needed for computation and expands the perceptual field again, allowing the network to better mine image information. The structure of the VGG16 backbone feature extraction network is shown in Figure 6.  
In the UNet network structure, the shallow feature maps differ significantly from the deep feature maps. In this paper, we refer to the UNet++ idea to fuse the information of two adjacent levels, and the fused information enters the decoding network through the jump connection. We are adding Polarized Self-Attention [41] in the decoding network. The Polarized Self-Attention mechanism processes the feature information and focuses the target region on the foreground organ part of the image. The image information is passed from bottom to top in the decoding network. The lowermost fusion information is passed upward by two times of Up-sampling after extracting features with two 3 \(\times\) 3 convolutional kernels. The passed information is spliced with the fused information from the jump connection, and the process is repeated until the shallowest decoding network. The split prediction map is finally output by 1 \(\times\) 1 convolution pixel by pixel.