Segmentation of liver structures in multi-phase contrast-enhanced computed tomography (CECT) plays a crucial role in computer-aided diagnosis and treatment planning. In this study, we investigate the performance of UNet-based architectures for liver tumor segmentation, evaluating ResNet, Transformer-based, and State-space (Mamba) backbones initialized with pretrained weights. Our comparative analysis reveals that despite the theoretical advantages of modern architectures in modeling long-range dependencies, ResNet-based models demonstrated superior sample efficiency on this dataset. This suggests that the inherent inductive biases of Convolutional Neural Networks (CNNs) remain advantageous for generalizing on limited medical data compared to data-hungry alternatives. To further improve segmentation quality, we introduce attention mechanisms into the backbone, finding that the Convolutional Block Attention Module (CBAM) yields the optimal configuration. The ResNetUNet3+ with CBAM achieved the highest nominal performance with a Dice score of 0.755 and IoU of 0.662, while also delivering the most precise boundary delineation (lowest HD95 of 77.911). Critically, while statistical testing indicated that the improvement in mean Dice score was not significant (p > 0.05) compared to the baseline, the proposed model exhibited greater stability (lower standard deviation) and higher specificity (0.926). These findings demonstrate that classical ResNet architectures, when enhanced with modern attention modules, provide a robust and statistically comparable alternative to emerging methods, offering a stable direction for liver tumor segmentation in clinical practice.