U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.

U-net是一种主要用于医学图像分析的图像分割技术，可以使用少量的训练数据对图像进行精确分割。本文回顾了U-net架构的各种发展，并探讨了最近的趋势。我们研究了深度学习中的各种创新，并讨论了这些工具如何促进U- net。此外，我们还研究了U-net已应用的图像模态和应用领域。

U-Net及其变体在医学图像分割中的应用与理论