Multi-task Learning-based Ultrasound Image Segmentation and Auxiliary Diagnosis in End-stage Renal Diseases

End-stage renal disease (ESRD) is a severe kidney disorder; kidney ultrasound, as a non invasive diagnostic tool, is widely used in its clinical diagnosis. However, due to the morphological changes caused by ESRD, such as kidney shrinkage, cortical thinning, and increased echogenicity, traditional ultrasound image segmentation and diagnosis still face significant challenges. To improve segmentation accuracy and diagnostic performance, this paper proposes a multi-task learning-based approach for ultrasound kidney segmentation and auxiliary diagnosis. The method combines the classical UNet architecture with the advanced RKAN-ResNet34 encoder and incorporates an improved Convolutional Block Attention Module (CBAM), which integrates edge attention and deformable convolutions, to address the issues of blurred boundaries and morphological changes in kidney images. By jointly optimizing segmentation and classification tasks, the model simultaneously enhances both kidney segmentation accuracy and auxiliary diagnostic performance. Experimental results show that the proposed method achieves a Dice coefficient of 0.9233 and an IoU of 0.8592 for segmentation, along with an accuracy of 98.64% and an F1 score of 0.9882 for auxiliary diagnosis, outperforming existing methods. This study provides an effective solution for the automation of the ultrasound kidney image segmentation and diagnosis, contributing to the auxiliary diagnosis of end-stage renal disease.