## 3 Methods
### CCR-FE Module  
The traditional Atrous Spatial Pyramid Pooling (ASPP) module expands the receptive field using different dilation rates to extract multi-scale local features. However, ASPP mainly relies on simple channel concatenation to integrate information across scales without fully exploiting long-range dependencies and semantic interactions between channels. This limitation restricts its representational capacity, particularly in scenarios involving blurred boundaries or fine-grained structural segmentation, potentially leading to information loss. To address these issues, we propose the Context-Channel Refine Feature Enhancement (CCR-FE) module. Built upon the multi-scale features generated by ASPP, CCR-FE employs strip convolutions of varying scales to capture contextual information related to the segmentation targets. These multi-scale contextual features are subsequently fed into the Cross-Axis Attention module to extract spatial global dependencies. Additionally, a lightweight channel attention mechanism is incorporated to optimize the features and enhance the model's sensitivity to critical regions. Given the input feature map F (i.e., the output of ASPP), CCR-FE first applies multi-scale strip convolutions to extract contextual information for the segmentation targets. Specifically, depthwise separable strip convolutions of different kernel sizes are performed along the horizontal and vertical directions to enhance the model's perception of both local and global structures. The horizontal branch (MSC\({}_{x}\)) and vertical branch (MSC\({}_{y}\)) use kernel sizes of \(\{1\times 7,1\times 11,1\times 21\}\) and \(\{7\times 1,11\times 1,21\times 1\}\), respectively, to simulate multi-scale receptive fields while keeping the computational cost low. These operations are conducted on 256-dimensional input features produced by ASPP. The output is then processed by a \(1\times 1\) convolution, and the operations can be formulated as follows:  
\[F_{x}=\text{Conv}_{1\times 1}(\text{MSC}_{x}(\text{Norm}(F))), \tag{3}\]  
\[F_{y}=\text{Conv}_{1\times 1}\big{(}\text{MSC}_{y}(\text{Norm}(F))\big{)}, \tag{4}\]  
Specifically, MSC\({}_{x}\) denotes the multi-scale strip convolution operation along the x-axis, while MSC\({}_{y}\) represents the same along the y-axis. \(\text{Norm}(\cdot)\) refers to the layer normalization operation. The resulting features \(F_{x}\) and \(F_{y}\) encode rich, multi-scale contextual information. These extracted contextual features are subsequently fed into the Cross-Axis Attention module [25] to model spatial global dependencies. By performing self-attention along both horizontal and vertical directions, Cross-Axis Attention effectively enhances long-range pixel-wise interactions. In particular, Cross-Axis Attention is implemented via dual attention branches: one computes attention maps along rows and the other along columns,  
Figure 2: Detailed structure of Cross-Axis Attention. \(\copyright\) denotes the Reshape operation. followed by a reshape and weighted aggregation. This design allows interaction between distant spatial regions without introducing large computational burdens. Given the outputs \(F_{A}\) and \(F_{B}\) obtained from the aforementioned process, the final contextual representation can be formulated as  
\[F_{\text{Cont}}=F_{A}+F_{B}+F, \tag{5}\]  
In addition, to further optimize feature representations, this study incorporates a channel attention mechanism within the CCR-FE module to compute the importance weights of individual channels. Specifically, each channel of the input feature map is first compressed into a global descriptor using adaptive average pooling. This is followed by a \(1\times 1\) convolution and ReLU activation function [26] to generate channel-wise attention weights. These weights are then upsampled to match the spatial dimensions of the input and applied via element-wise multiplication, thereby recalibrating channel-wise features and enhancing the expression of more informative channels.