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.

翻译：在多期相增强计算机断层扫描（CECT）中对肝脏结构进行分割，在计算机辅助诊断与治疗规划中起着至关重要的作用。本研究探讨了基于UNet架构的模型在肝脏肿瘤分割任务上的性能，评估了采用预训练权重初始化的ResNet、基于Transformer以及状态空间（Mamba）主干网络。我们的对比分析表明，尽管现代架构在建模长程依赖关系上具有理论优势，但基于ResNet的模型在本数据集上展现了更优的样本效率。这表明，与需要大量数据的替代方案相比，卷积神经网络（CNNs）固有的归纳偏置在有限医疗数据上的泛化能力方面仍具优势。为进一步提升分割质量，我们在主干网络中引入了注意力机制，发现卷积块注意力模块（CBAM）能产生最优配置。集成CBAM的ResNetUNet3+取得了最高的名义性能，其Dice分数为0.755，交并比（IoU）为0.662，同时实现了最精确的边界描绘（最低的HD95值为77.911）。关键的是，虽然统计检验表明，与基线模型相比，其平均Dice分数的提升并不显著（p > 0.05），但所提出的模型表现出更高的稳定性（更低的标准差）和更高的特异性（0.926）。这些发现表明，经典ResNet架构在结合现代注意力模块增强后，为新兴方法提供了一个稳健且在统计上可比的替代方案，为临床实践中的肝脏肿瘤分割提供了一个稳定的发展方向。