## 2 Methods
### Multi-Scale Feature Extraction  
The basic network used in this research is a deep convolutional neural network that is based on the ResNet101 [7] framework. Although the lower-level feature semantic information is relatively small, the target position is accurate. While the high-level feature semantic information is rich, the target position is rough. To find rich features and accurate target position concurrently, a basic network combined with a multi-scale feature detection structure MFPN is used to process prostate feature extraction in TRUS image segmentation. The network structure proposed in this paper is shown in Figure 2.  
Figure 2: Network structure: The prostate image is the input of the ResNet101 structure. Then, the feature maps (of different sizes) of each layer are inputted into the multi-channel feature pyramid network (MFPN) for feature fusion. The output feature maps are finally inputted into the Region Proposal Networks (RPN) network. The resulting feature fusion module has rich semantic information in the region of interest (ROI).  
At each stage, we use the features of the last residual structure to activate the output and represent these (residual module output) as C1, C2, C3, C4, and C5 after obtaining the corresponding output but before fusing C2, C3, C4, and C5. The purpose of the \(1\times 1\times\text{Cx}\) convolution operation is to obtain the same number of channels that are needed to fuse with the upper layer features. That is, the C2 module is \(128\times 128\times 256\) and the C3 module is \(64\times 64\times 512\). After C3 undergoes a \(1\times 1\times\text{Cx}\) convolution operation, it is then fused with the C2 module by upsampling with a stride of 2 to obtain the feature map P2. The same applies to the feature maps of P3, P4, and P5. Then, the squeeze-and-excitation (SE) module is added after the \(1\times 1\times\text{Cx}\) convolution. The structure of the module is shown in Figure 3.  
This module is used to establish a nonlinear relationship between channels and adaptively recalibrate the characteristic response of the channel mode. By controlling the size of the scale, important features are enhanced, while unimportant features are weakened. This makes the directivity of the extracted features stronger. This not only improves the ability to learn features but can also use global information to selectively emphasize feature information and reduce useful functions by suppressing function recalibration. The 3 (a) operation fuses the spatial and channel information in the local area of the original convolution to extract feature information. In 3 (b), the feature is recalibrated using a three-step operation. (i) Compression: To obtain the global information of each channel, the feature is compressed using a global average pool after convolution. (ii) The threshold mechanism is used to set two fully connected layers. The first layer is used to reduce the size of the input to \(1/\text{R}\) of the original size. Then use ReLU to activate. The second layer restores the input to its original size. Normalize weights to \(1\) after activation. (iii) Weights: Normalized weights are weighted by scaling operations on each channel. We set W, H, C, and R to 1,1,256,16. The enhanced feature map obtained through the above operations is subjected to a \(3\times 3\times 256\) convolution operation to obtain a multi-scale feature fusion module. Therefore, the module has rich feature information.