Head and neck (HaN) segmentation in CT image is difficult due to low contrast and large slice thickness that resulted in useless information in coronal and sagittal plane for some organs. In addition, complex and small organs have different requirements on neural network modeling. To achieve an accurate segmentation of 22 HaN organs, we combined three U-Net models. The first model was a 2D model that is advantage for dealing with thick slice images. The second model was a 3D model using a cropped input to cover most organs with original resolution in the transverse plane. The third model was a 3D-small U-Net model that focuses on the segmentation of two small organs together and uses a small region of interest (ROI) computed from the bounding box of 2D model segmentation. All the three models were trained using nnUNet method. The final trained model was submitted through docker image to the StructSeg challenge. The leaderboard showed that the proposed method achieved the second place among all methods on ten unseen testing cases with an average Dice value of 80.66% and 95% Hausdorff distance of 2.96 mm.