  Image analysis technology is used to solve the inadvertences of artificial
traditional methods in disease, wastewater treatment, environmental change
monitoring analysis and convolutional neural networks (CNN) play an important
role in microscopic image analysis. An important step in detection, tracking,
monitoring, feature extraction, modeling and analysis is image segmentation, in
which U-Net has increasingly applied in microscopic image segmentation. This
paper comprehensively reviews the development history of U-Net, and analyzes
various research results of various segmentation methods since the emergence of
U-Net and conducts a comprehensive review of related papers. First, this paper
has summarized the improved methods of U-Net and then listed the existing
significance of image segmentation techniques and their improvements that has
introduced over the years. Finally, focusing on the different improvement
strategies of U-Net in different papers, the related work of each application
target is reviewed according to detailed technical categories to facilitate
future research. Researchers can clearly see the dynamics of transmission of
technological development and keep up with future trends in this
interdisciplinary field.
