  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}.
