Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning. Computer-aided diagnosis systems promise to improve the precision of liver disease diagnosis, disease progression, and treatment planning. In response to the need, we propose a novel deep learning approach, \textit{\textbf{PVTFormer}}, that is built upon a pretrained pyramid vision transformer (PVT v2) combined with advanced residual upsampling and decoder block. By integrating a refined feature channel approach with a hierarchical decoding strategy, PVTFormer generates high quality segmentation masks by enhancing semantic features. Rigorous evaluation of the proposed method on Liver Tumor Segmentation Benchmark (LiTS) 2017 demonstrates that our proposed architecture not only achieves a high dice coefficient of 86.78\%, mIoU of 78.46\%, but also obtains a low HD of 3.50. The results underscore PVTFormer's efficacy in setting a new benchmark for state-of-the-art liver segmentation methods. The source code of the proposed PVTFormer is available at \url{https://github.com/DebeshJha/PVTFormer}.

翻译：从CT扫描中准确分割肝脏对于有效诊断和治疗规划至关重要。计算机辅助诊断系统有望提高肝脏疾病诊断、病程进展评估及治疗方案制定的精准性。针对这一需求，我们提出了一种新型深度学习方法——\textit{\textbf{PVTFormer}}，该方法基于预训练的金字塔视觉Transformer（PVT v2）架构，结合了先进的残差上采样与解码模块。通过融合特征通道优化策略与分层解码机制，PVTFormer能够增强语义特征，从而生成高质量的分割掩膜。在肝脏肿瘤分割基准（LiTS）2017数据集上的严格评估表明，我们提出的架构不仅实现了86.78%的高Dice系数和78.46%的mIoU，还取得了3.50的低HD值。这些结果凸显了PVTFormer在开创肝脏分割最新方法基准方面的有效性。所提出的PVTFormer源代码已公开于\url{https://github.com/DebeshJha/PVTFormer}。