###### Abstract  
For the characterization of the kidney segmentation task, this paper proposes a self-supervised kidney segmentation method based on multi-scale feature fusion and residual full attention, named MRFA-Net. In this study, we introduce the multi-scale feature fusion module to extract multi-scale information of kidneys from abdominal CT slices; additionally, the residual full-attention convolution module is designed to handle the multi-scale information of kidneys by introducing a full-attention mechanism, thus improving the segmentation results of kidneys. The Dice coefficient on the Kits19 dataset reaches 0.972. The experimental results demonstrate that the proposed method achieves good segmentation performance compared to other algorithms, effectively enhancing the accuracy of kidney segmentation. k +
Footnote †: journal: Medical Image Analysis  
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Footnote 1: [[https://www.mdpi.com/journals/pdf/10.3390/appl30](https://www.mdpi.com/journals/pdf/10.3390/appl30)]([https://www.mdpi.com/journals/pdf/10.3390/appl30](https://www.mdpi.com/journals/pdf/10.3390/appl30))  
manual identification process is not only time-consuming and labor-intensive but is also susceptible to subjective factors, leading to the possibility of misdiagnosis [7]. The emergence of deep-learning methods addresses the shortcomings of manual approaches. Deep learning, as a powerful machine-learning technology, possesses the capability to autonomously extract various features and patterns without extensive human intervention [8; 9]. Automatic segmentation methods based on deep learning play a crucial role in medical image analysis. They learn features of kidney images through extensive training data, enabling more accurate segmentation, thereby effectively improving the efficiency of image segmentation and diagnosis for physicians, reducing the occurrence of missed diagnoses and misdiagnoses. Medical image segmentation is a critical step in medical image analysis, aiming to extract regions of interest from the entire image to identify lesion areas and facilitate diagnosis. In particular, in abdominal CT images, automatic kidney segmentation technology can efficiently detect the kidneys. Through automatic kidney segmentation, the kidney portions in CT images can be accurately delineated and presented to medical personnel. This can enhance the early detection and diagnostic accuracy of kidney diseases, providing better treatment plans and care for patients. However, kidney segmentation faces numerous challenges. Patients with different kidney diseases may exhibit various kidney lesions, resulting in morphological, dimensional, and other feature diversities within the segmented kidney target areas. This further complicates the segmentation task. Additionally, compared to natural images, medical images often have less pronounced gradient variations, especially in abdominal CT images, where the grayscale values between different soft tissues are similar, causing minimal differences in grayscale values between tissues [10]. This is specifically evident in abdominal CT images where the boundaries between the kidneys and adjacent organs are usually unclear, further increasing the difficulty of kidney segmentation [11]. To address the aforementioned issues, the paper proposes a kidney segmentation method based on multi-scale feature fusion and residual full attention (MRFA-Net). The method first utilizes a multi-scale feature fusion module based on the feature pyramid, integrating deep feature information into shallow feature maps to optimize the feature representation across different network layers. Then, it employs residual convolution modules with full-attention mechanisms to address attention deficits, further capturing semantic features corresponding to the kidneys. Experimental results on the publicly available Kits19 dataset [12] demonstrate the effectiveness of the proposed kidney segmentation method. The specific contributions are as follows:  
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