Pancreas segmentation has been challenging due to its large variations in size, shape, localization, and indistinguishable boundary. Current mainstream pancreas segmentation methods are based on the deep encoder-decoder structure, which recover high-resolution representations from encoded low-resolution representations to generate pancreas voxel masks. However, the details of the pancreas are easily lost in the encoding stage. In this paper, we propose a 3D high-resolution network (3D HRNet) to extract pancreas features, which maintains high-resolution representations throughout the whole process. We use a novel recurrent gated fusion (RGF) head to fuse high-resolution features and generate pancreas voxel masks. To reduce variable background interference, we crop the pancreas area from abdominal CT images for segmentation with a pancreas localization network. We evaluate the above propsed method on the public NIH and MSD pancreas segmentation datasets, and experiments show a competitive result with a mean Dice-Srensen Coefficient (DSC) of 85.82±4.01% on NIH and 84.22±5.91% on MSD, respectively. The lowest variance and the highest mean DSC reveal the stability of our method among current methods and its potential in the clinical setting.