## 3 Methods
### Multiscale Feature Extraction Encoder  
Medical images exhibit complex and variable shapes and sizes, with variations in the targets that require segmentation for different types of images. Therefore, the segmentation model must demonstrate robustness in handling diverse sizes and types of targets. Drawing inspiration from VGGnet [30], MA-Net [31], and relevant literature [32; 33], we have developed a multi-scale feature module to capture semantic feature information at various levels. Figure 2 illustrates the multi-scale feature extraction (MSE) encoder module that we have developed. Initially, a 3 \(\times\) 3 depth-separated convolution is utilized to expand the perceptual region and gather more local information. Subsequently, a symmetric convolutional structure is applied to extract multi-scale feature information, ensuring that all sub-feature maps contain a richer scale of information. The specific process involves dividing the 3 \(\times\) 3 depth-separated convolutional feature map \(F\) into \(m\) sub-feature maps based on the number of channels. These sub-feature maps are represented as \(F_{i}\), where \(i\in\{1,\,2,\,\ldots,\,m\}\). Each sub-feature map is then inputted into the corresponding 3 \(\times\) 3 elementary convolutional block for further feature extraction. The output of each sub-feature map is represented as \(X_{i}\). Consequently, the output of the first branch of the symmetric convolutional structure is determined by Equation (1), and the output of the second branch is determined by Equation (2). \[Y_{i}^{1}=\left\{ \begin{matrix}X_{i}&i=1\\ X_{i}+Y_{i-1}^{1}&1<i\leq m\end{matrix} \right. \tag{1}\]  
Figure 1: The overall structure of the proposed MSLUnet network resembles a “U” shape. The MSE Block functions as the encoder, extracting rich multi-scale fine-grained feature information. The LKE Block serves as the decoder, conducting deep feature fusion primarily through large kernel depth-separable convolution. Additionally, TA is the lightweight trisection attention mechanism that helps the model concentrate more effectively on the main feature information through skip connections. The final output of the symmetric convolution structure \(Y\) can be expressed as:  
\[Y=Y_{i}^{1}+Y_{i}^{2} \tag{3}\]  
wherein, \(Y_{i}^{1}\) represents the output characteristic diagram of the first branch of a symmetric convolution structure and \(Y_{i}^{2}\) denotes the output characteristic diagram of the second branch of a symmetric convolution structure. The successive use of multiple small convolutional kernels achieves the same receptive field as a single large kernel [30]. This approach not only reduces the number of parameters but also increases the non-linear processing layers, thereby enabling the capture of feature information from a wider range of image neighborhoods. In the symmetric convolutional structure, each 3 \(\times\) 3 convolutional block extracts feature information from all corresponding sub-feature maps. Simultaneously, a larger sensory field is obtained by using the output of each small branch as part of the input for the neighboring branches. Additionally, diverse multi-scale feature information is captured by concatenating the outputs of all branches along the channel axis. Moreover, a normalization layer [34] and an activation function layer [35] follow each convolutional layer, optimizing both the information flow and processing within the network. To fully integrate the input feature maps with the output from the symmetric convolutional structure, residual connections [2] are utilized. This strategy ensures the capture of rich global multi-scale feature information, effectively bridging any potential semantic gaps within the model. The parameter is strategically set to four to maximize the model's efficiency without compromising its structural integrity and its capability to capture detailed features. Figure 2: Detailed structure of the multi-scale feature extraction module. This module learns multi-scale features within a basic block by introducing a symmetric structure.