Abstract: As one of the most important semantic segmentation frameworks in convolutional neural networks (CNN), UNet is widely used in image processing tasks such as classification, segmentation, and target detection of medical images. In this paper, the structural principles of UNet are described, and a comprehensive review of UNet-based networks and variant models is presented. The model algorithms are fully investigated from several perspectives, and an attempt is made to establish an evolutionary pattern among the models. Firstly, the UNet variant models are categorized according to the seven medical imaging systems they are applied to, and the algorithms with similar core composition are compared and described. Secondly, the principles, strengths and weaknesses, and applicable scenarios of each model are analyzed. Thirdly, the main UNet variant networks are summarized in terms of structural principles, core composition, datasets, and evaluation metrics. Finally, the inherent shortcomings and solutions of the UNet network structure are objectively described in light of the latest advances in deep learning, providing directions for continued improvement in the future. At the same time, other technological evolutions and application scenarios that can be combined with UNet are detailed, and the future development trend of UNet-based variant networks is further envisaged.

Key words: medical image segmentation, deep learning, convolutional neural networks (CNN), UNet network

摘要： UNet作为卷积神经网络（CNN）中最重要的语义分割框架之一，广泛地应用于医学图像的分类、分割和目标检测等图像处理任务。对UNet的结构原理进行了阐述，并对基于UNet网络及变体模型进行了全面综述，从多个角度对模型算法进行了充分研究与分析，试图建立起各个模型间的演进规律。首先，将UNet变体模型根据其应用的七种医学成像系统的不同而进行分类研究，且将核心构成相似的算法进行了对比描述；其次，对每个模型的原理、优缺点和适用的场景等内容进行分析；再次，对主要UNet变体网络从结构原理、核心组成结构、数据集和评价指标四方面进行总结；最后，结合深度学习的最新进展，客观地描述了UNet网络结构存在的固有不足和解决方案，为未来继续改进提供了方向。同时，对UNet可结合的其他技术演进与应用场景等内容进行详述，进一步展望了基于UNet变体网络未来的发展趋势。

关键词: 医学图像分割, 深度学习, 卷积神经网络（CNN）, UNet网络