论文标题
在微观图像分析中对U-NET的最新调查：从简单使用到结构损坏
A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification
论文作者
论文摘要
图像分析技术用于解决疾病，废水处理，环境变化监测分析和卷积神经网络（CNN）中人工传统方法的无意义，在微观图像分析中起着重要作用。检测，跟踪，监视，特征提取，建模和分析的重要步骤是图像分割，其中U-NET越来越多地应用于微观图像分割。本文全面回顾了U-NET的发展历史，并分析了自U-NET出现以来各种细分方法的各种研究结果，并对相关论文进行了全面的综述。首先，本文总结了U-NET的改进方法，然后列出了图像分割技术的现有意义及其多年来引入的改进。最后，重点介绍不同论文中U-NET的不同改进策略，根据详细的技术类别进行审查每个应用程序目标的相关工作，以促进未来的研究。研究人员可以清楚地看到技术发展传播的动态，并跟上这个跨学科领域的未来趋势。
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.
