Abstract— The rising occurrence of chronic kidney disease represents a significant health concern, impacting over 10% of individuals. Magnetic Resonance Imaging (MRI) biomarkers identify pathological alterations, are non-invasive, and can help lessen biopsy requirements and complications in chronic kidney disease patients. Total volume is the most assessed metric in autosomal dominant polycystic kidney disease patients, assisting in tracking chronic kidney disease progression. Kidney segmentation is essential for evaluating renal volume. Nevertheless, it often relies on manual segmentation, which is time-consuming and highly subjective. However, Deep Learning (DL) techniques have led to creating algorithms that provide accurate, cost-effective, and user-independent outcomes. Therefore, this study explores DL-based strategies for kidney segmentation, particularly U-Net and Attention U-Net. Both architectures were assessed with the standard cross-entropy loss function and a focal cross-entropy loss function. Ultimately, the proposed methods were used for both 2-class and 3-class segmentation to improve the segmentation of border regions. The highest Dice coefficient achieved on the testing set was 0.966.