The application of deep learning in the field of medical image segmentation has attracted great attentions, among which the U-Net proposed in 2015 has been widely concerned because of its good segmentation effect and scalable structure. In recent years, with the improvement of the performance requirements of medical image segmentation, many scholars are improving and expanding the U-Net structure, such as the improvement of encoder-decoder, or the external feature pyramid, and so on. In this study, the medical image segmentation technology based on U-Net structure improvement is summarized from the aspects of performance-oriented optimization and structure-oriented improvement. Related methods are reviewed, classified and summarized. The paper evaluates the parameters and modules, and then summarizes the ideas and methods for improving the U-Net structure for different goals, which provides references for related research.