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Ultrasonography plays an important role in the fields of obstetrics, gynecology, cardiology, and hepatology, as well as ultrasound-guided nerve blocks, interventional therapy, and surgical navigation due to its non-invasive, real-time imaging and radiation-free characteristics. Recently, with the advancement of artificial intelligence, ma-chine learning and deep learning algorithms have brought significant innovations to ultrasound imaging technology in the medical field. U-Net is widely recognized as one of the most commonly used deep learning models in medi-cal image processing. This paper explores the application of the U-Net family of models in ultrasound imaging. The network architecture of the original U-Net, comprising encoder and decoder components, is first delineated. Next, classical variants, such as U-Net++, Attention U-Net, and ResU-Net, are introduced. The application of U-Net mod-els in ultrasound and their segmentation performance are then reviewed, with Dice coefficients highlighted as the primary evaluation metric. Finally, the paper provides a comparative analysis of the advantages and disadvantag-es of the U-Net family of models.
Keywords: U-Net, U-Net++, Attention U-Net, ResU-Net, ultrasonography, segmentation performance
