U-shaped Network （U-Net） based on Fully Convolutional Network （FCN） is widely used as the backbone of medical image segmentation models， but Convolutional Neural Network （CNN） is not good at capturing long-range dependency， which limits the further performance improvement of segmentation models. To solve the above problem， researchers have applied Transformer to medical image segmentation models to make up for the deficiency of CNN， and U-shaped segmentation networks combining Transformer have become the hot research topics. After a detailed introduction of U-Net and Transformer， the related medical image segmentation models were categorized by the position in which the Transformer module was located， including only in the encoder or decoder， both in the encoder and decoder， as a skip-connection， and others， the basic contents， design concepts and possible improvement aspects about these models were discussed， the advantages and disadvantages of having Transformer in different positions were also analyzed. According to the analysis results， it can be seen that the biggest factor to decide the position of Transformer is the characteristics of the target segmentation task， and the segmentation models of Transformer combined with U-Net can make better use of the advantages of CNN and Transformer to improve segmentation performance of models， which has great development prospect and research value.