Medical image segmentation can accurately and quickly extract structures of interest in images and has major application value in medical imaging diagnosis, disease analysis, surgical planning, and other fields. Traditional medical image segmentation methods typically rely on edge detection, template matching techniques, statistical shape models, active contours, and traditional machine learning techniques. However, due to problems such as blur, noise, and low contrast in images, the accuracy and robustness of traditional methods are limited. Deep learning methods gradually extract features by learning different levels of abstraction of data. Compared with traditional methods, they have the advantages of high accuracy, strong adaptability, and strong scalability. To better conduct research on auxiliary diagnosis of medical image segmentation, this article reviews the application of convolutional neural networks, Transformer, and U-Net and Transformer hybrid structures in medical image segmentation, and conducts a comprehensive comparative analysis of these models. The feasibility of these models in medical image segmentation is confirmed through visualization results and image evaluation metrics. Finally, we summarize the existing problems in current research and present future research directions.