论文标题
U-NET及其用于医学图像细分的变体：理论和应用
U-Net and its variants for medical image segmentation: theory and applications
论文作者
论文摘要
U-NET是一种主要用于医学图像分析的图像分割技术，可以使用稀缺数量的训练数据精确细分图像。这些特征在医学成像社区中提供了U-NET非常高的实用性，并导致了U-NET作为医学成像中分割任务的主要工具的广泛采用。 U-NET的成功在从CT扫描和MRI到X射线和显微镜的所有主要图像方式中广泛使用时显而易见。此外，虽然U-NET主要是一种细分工具，但在其他应用程序中使用了U-NET的实例。随着U-NET的潜力仍在增加，在这篇综述中，我们研究了U-NET体系结构中所做的各种发展，并提供了有关最近趋势的观察。我们研究了深度学习中已经进行的各种创新，并讨论了这些工具如何促进U-NET。此外，我们研究了已应用U-NET的图像方式和应用领域。
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.
