U-Net网络医学图像分割应用综述  

U-Net and its applications in medical image segmentation:a review

作　　者：周涛 董雅丽 霍兵强 刘珊 马宗军[3] Zhou Tao;Dong Yali;Huo Bingqiang;Liu Shan;Ma Zongjun(College of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China;The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission,North Minzu University,Yinchuan 750021,China;Department of Orthopedics,General Hospital of Ningxia Medical University,Yinchuan 750004,China)

机构地区：[1]北方民族大学计算机科学与工程学院,银川750021 [2]北方民族大学图像图形智能处理国家民委重点实验室,银川750021 [3]宁夏医科大学总医院骨科,银川750004

出　　处：《中国图象图形学报》2021年第9期2058-2077,共20页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62062003);宁夏自治区重点研发计划项目(2020BEB04022);北方民族大学引进人才科研启动项目(2020KYQD08);北方民族大学研究生创新项目(YCX21089)。

摘　　要：病灶精确分割对患者病情评估和治疗方案制定有重要意义,由于医学图像中病灶与周围组织的对比度低,同一疾病病灶边缘和形状存在很大差异,从而增加了分割难度。U-Net是近些年深度学习研究中的热点,为医生提供了一致性的量化病灶方法,一定程度上提高了分割性能,广泛应用于医学图像语义分割领域。本文对U-Net网络进行全面综述。阐述U-Net网络的基本结构和工作原理;从编码器个数、多个U-Net级联、与U-Net结合的其他模型以及3D U-Net等方面对U-Net网络模型的改进进行总结;从卷积操作、下采样操作、上采样操作、跳跃连接、模型优化策略和数据增强等方面对U-Net网络结构改进进行总结;从残差思想、密集思想、注意力机制和多机制组合等方面对U-Net的改进机制进行总结;对U-Net网络未来的发展方向进行展望。本文对U-Net网络的原理、结构和模型进行详细总结,对U-Net网络的发展具有一定积极意义。Medical imaging has been a proactive tool for doctors to diagnose and treat diseases via the qualitative and quantitative analyses based on non-invasive lesions.Medical images have been interpreted via computer tomography( CT),X-ray, magnetic resonance imaging(MRI) and positron emission tomography(PET).The barriers of medical image segmentation need to be resolved due to low contrast amongst the lesion, the surrounding tissue and blurred edges of the lesion.Labeling manually for hundreds of slices of organs or lesions has been quite time-consuming due to anatomy of the human body and shape of lesions.Manual labeling has intended to high subjective and low reproducibility.Doctors have been beneficial from a automatically locating, segmenting and quantifying lesions.Deep learning has been used widely in medical image processing.Deep learning-based U-Net has played a key role in the lesions segmentation.The encoding and decoding ways has made U-Net structures simply and symmetrically.Features extraction of medical images has been realized via convolution and down-sampling operations.The image segmentation mask via the transposed convolution and concatenation has been interpreted.A small-sized dataset has achieved qualified medical image segmentation.U-Net has been summarized and analyzed on the four aspects: the definition of U-Net, the upgrading of U-Net model, the setup of U-Net structure and the mechanism of U-Net.Four research areas have been proposed as below: 1) the basic structure and working principle of U-Net via convolution operation, down sampling, up sampling and concatenation.2) U-Net network model have been demonstrated in three aspects in the context of the number of encoders, multiple U-Net cascades and other models combined with U-Net.U-Net based network have been divided into two, three and four encoders further in terms of the amount of encoders: Y-Net, Ψ-Net and multi-path dense U-Net.Multiple U-Nets cascade has been categorized into multiple U-Nets in series and multiple U-Nets in parallel base

关 键 词：U-Net 医学图像 语义分割 网络结构 网络模型 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]