## 2 Related Works  
Currently, medical image segmentation methods mainly fall into two categories: traditional image segmentation and deep-learning-based semantic segmentation. Many scholars both domestically and internationally have conducted a series of studies in the field of medical image segmentation, proposing various models and methods for kidney segmentation tasks. Traditional medical image segmentation relies on the inherent features of the images. For instance, [PERSON] et al. [15] utilized traditional methods such as adaptive region growing for segmenting kidney organs. [PERSON] et al. [16] employed the watershed method to segment markers extracted from kidney image gradients and morphological reconstruction operations. [PERSON] et al. [17] proposed a method to track kidney boundaries in slice subsets using thresholding and morphological techniques for segmentation. Traditional image segmentation methods rely on shallow features such as grayscale and texture to accomplish segmentation tasks. However, the information contained in images extends beyond these features, making it challenging for traditional methods to effectively leverage deeper image features. Therefore, relying solely on traditional methods proves challenging in achieving satisfactory kidney segmentation results. With the development of computer technology, medical image processing methods based on deep learning, especially convolutional neural networks (CNNs), have achieved significant success [18; 19]. Many scholars have made outstanding contributions in the field of kidney segmentation by utilizing deep learning. [PERSON] et al. [20] proposed a coarse-to-fine image segmentation strategy, initially using AlexNet [21] to extract the region of interest (ROI) from kidney CT images and then employing the U-Net network for segmentation, thereby reducing segmentation errors. [PERSON] et al. [22] utilized the ResNeXt network combined with group convolution and depth-wise separable convolution operations, improving the U-Net network and proposing the SERU model, effectively reducing parameters and computational load and increasing algorithm speed. Additionally, the SE module [23] was employed to calculate weights, improving segmentation results. Although the coarse-to-fine segmentation strategy proposed by these two methods has to some extent improved segmentation accuracy, aiming to narrow down the detection area of the kidneys, it fails to address the issue of boundary blurring during kidney segmentation. [PERSON] et al. [24] introduced a model that integrates the generative adversarial network (GAN) [25] supervision mechanism into the U-Net network, enhancing the segmentation performance and achieving good results. Although this method partially reduces under-segmentation and over-segmentation in images with unclear edges between the kidneys and background noise, it overlooks the inconsistent variations in the size and position of kidney organs in abdominal CT images. [PERSON] et al. [26] proposed the Camofocus method, in which the foreground and background regions of the target are effectively segmented under the action of the feature split and modulation (FSM) module, and then a context refinement module (CRM) is designed to refine the multiscale feature representation. The aforementioned models have certain limitations in kidney segmentation, overlooking the challenge of small and diverse shapes of kidney organs, as well as the low contrast and fuzzy boundaries of kidney organs in abdominal CT images. To address the issue of fuzzy kidney organ boundaries, existing models often use multi-scale feature maps extracted from different network layers for multi-path segmentation. However, shallow feature maps suffer from a lack of semantic information, resulting in suboptimal accuracy when directly used for segmentation. In this study, we propose a kidney segmentation method based on multi-scale feature fusion and residual complete attention.