In the process of fat quantification standardization in liver MRI images, it is often necessary to manually sample the liver area of interest, but the manual sampling strategy is time-consuming and the results are variable. Compared with manually sketched regions of interest, the whole liver segmentation based on deep learning method has lower variability error and uncertainty, and better performance in fat quantitative analysis. To improve the segmentation performance during the whole liver segmentation task, this study makes improvements based on the UNETR++ model. This method combines the advantages of a convolutional neural network and Transformer structure and adds convolutional structure branches to supplement local features. Meanwhile, it introduces a gated attention mechanism to suppress irrelevant background information to make the model more prominent features of the segmented region. The improved method has better DCS and HD₉₅ indexes than UNETR++ and other segmentation models.