Accurate segmentation of the kidney anatomy is crucial in the diagnosis and treatment of various kidney diseases. However, 3D U-Net-based Neural Networks entail significant computational requirements, complex architectures, and a fully annotated volumetric dataset. To address these challenges, our study designs and implement a custom image preprocessing workflow that suppresses fat and uninformative structures and compares the performances of 2D U-Net-based Neural Networks for semantic segmentation of kidneys and tumors from abdominal CT images. We found the ResU-Net model to achieve an accuracy of 89.17% for kidney segmentation, outperforming other models, while the Vanilla U-Net during the renal tumor segmentation task, with up to 11.7% higher DSC scores. Moreover, all the investigated methods do not require 3D CNNs, thus reducing computational costs. This comparison could be potentially useful to make a step forward in identifying the most accurate and lightweight technology to aid physicians in diagnosing kidney diseases while improving patient outcomes.