## 2 Related Works  
### Medical Image Segmentation Methods  
Medical image segmentation is one of the most important tasks in the field of medical image analysis, aiming to extract quantitative information about various tissue structures and lesions from complicated medical images.  
The conventional manual segmentation method utilized in clinical practice entails experienced clinicians manually segmenting raw CT images. This process is characterized by its time-consuming and labor-intensive nature, and the quality of segmentation largely hinges on the operator's experience and medical knowledge. As medical image processing technology has evolved, semi-automatic segmentation methods have gained prominence. These methods encompass thresholding, region growth, statistics, and other automatic segmentation approaches, with deep learning being a prominent representative.  
The thresholding method separates the target liver region from the background by selecting the appropriate gray value as the threshold. [PERSON] et al. [6] used a combination of adaptive thresholding and the angular line method to enhance segmentation performance. However, this method is not effective in segmentation when the gray value of the target region is much smaller than the background gray value. The region-growing method initially selects suitable pixel points (i.e., seed points) within the region as the starting point for growth. It then continually adds pixel points with similar properties to achieve segmentation. [PERSON] et al. [7] proposed an automatic liver segmentation method based on the region-growing algorithm. They introduced center-of-mass detection and intensity analysis to ensure quick and accurate liver region segmentation. Additionally, the texture-based region growing method [8] achieves liver segmentation by automatically selecting seed points and calculating a threshold for the region-growth-stopping condition. The selection of seed point locations significantly impacts the performance of the algorithm. Statistics-based segmentation methods [9] demand extensive clinical data as support, limiting the models' generalization ability for small-scale datasets like medical images.  
The concept of deep learning was initially introduced by [PERSON]. In contrast to the traditional methods mentioned above, deep-learning-based methods can automatically learn feature representations from raw data. This significantly enhances the segmentation performance and generalization ability of a model. U-Net is a classic segmentation network in medical image segmentation. Along with its variants, it is widely employed in segmentation tasks due to its low parameter count and superior segmentation performance. [PERSON] et al. [10] introduced a series of nested dense hopping connections between the encoder and the decoder to enhance U-Net. This resulted in a 3.9% improvement in the mean intersection over union (mean IOU). [PERSON] et al. [11] proposed UNet3+, which made more comprehensive use of the multi-scale features in the feature map. [PERSON] et al. [12] introduced ResCEAttUnet to enhance the network's capacity for extracting multi-scale features. This ensured that the network could effectively capture high-level semantic information while minimizing information loss. [PERSON] et al. [13] developed HFRU-Net to meticulously characterize contextual information by local feature reconstruction and feature fusion mechanisms. They also adaptively recalibrated the fused features to emphasize image details. [PERSON] et al. [14] introduced MCEA-UNet, a multi-scale cascaded feature attention network, to address the issue of edge detail loss resulting from inadequate feature extraction.