## 3 Methodology
### Dynamic Convolution Block  
Over the years, CNN-based algorithms have made significant progress in the image processing area. However, convolution operations use the same convolution kernel weights for all inputs, which limits the representational capacity of the model. Thus, to increase the complexity of the network, researchers extend the width or depth of the network, which consumes considerable computational resources [36]. Therefore, [PERSON] et al. proposed dynamic convolution, which can considerably increase the network complexity without expanding the model's scale [37]. Standard convolutional operations use the same convolutional kernel weight for all input images, which may lead to weak representational ability and poor prediction for some complex input images [38]. Dynamic convolutional networks can dynamically calculate the parameters of convolutional kernels based on input images, thereby enabling better feature representational abilities of the model [36]. Compared with standard convolution operations, dynamic convolution can utilize prior knowledge  
Figure 2: Improved coordinate attention. of input images to dynamically adjust convolutional kernel weights to enhance feature representation capabilities and thereby improve model performance [38]. The calculation process of dynamic convolution is displayed in Figure 3. In contrast to ordinary convolution operations, dynamic convolution involves dynamically calculating the attention weights of multiple parallel convolution kernels, then aggregating the attention weights of these kernels to obtain the final kernel weights [37]. The inputs vary, and the dynamic convolution kernel weights also change accordingly. An overfitting problem occurs if the network structure is too complex or if the training data are too large. The phenomenon of overfitting means that the network performs perfectly on the training datasets but terribly on the test datasets [39]. In order to avoid overfitting, some regularization methods are usually added in the training phase of a network, such as early stopping, batch normalization, dropout, etc. To decrease the possibility of overfitting, the \"dropout\" method reduces information transmission between neural nodes by randomly inactivating some neurons in the network during training. This method is usually used as a regularization method for fully connected neural networks (FCNs) [40]. The datasets used in this research comprise 3D MRI images with strong spatial correlation. A standard dropout strategy cannot effectively reduce overfitting, as the information can still be transmitted through adjacent pixels in 3D space once a pixel is inactive [41]. Compared to standard dropout, spatial dropout deactivates some channels of the 3D image randomly, which can effectively prevent the transmission of information in the channels and thereby reduce the possibility of overfitting. Thus, spatial dropout, as opposed to standard dropout, is chosen as the regulation method in this work. In this paper, a dynamic convolutional block named dy-block is designed as a substitute for the original 3D U-Net convolutional block (conv-block) to increase the model's representational ability. Dynamic convolution is a new form of convolution that can dynamically calculate the weights of convolution kernels based on the characteristics of inputs. Compared with standard convolutional kernels, dynamic convolutional kernels have prior knowledge of inputs and can extract features with stronger ability. The dy-block designed in this work has better feature representational capabilities compared to the conv-block of 3D U-Net. The framework diagrams of the conv-block and the dy-block are shown in Figure 4. The dy-block includes a dynamic convolutional layer to extract features, a batch normalization layer to speed up the convergence of the 3D U-Net, a ReLU layer to enhance the nonlinear representation ability, and a spatial dropout layer to reduce the risk of overfitting.