摘要
医学上实现自动肺结节精准分割具有十分重要的临床意义。随着计算机视觉的显著进步,深度学习作为人工智能的一部分,在医学图像自动分割中引起了越来越多的关注。U-Net由于在小样本数据集上的良好表现,在医学图像分割领域得到广泛应用。目前,研究人员正在尝试使用不同的U-Net结构,以提高计算机辅助诊断系统在医学图像的肺癌筛查中的性能。首先,围绕肺结节分割任务介绍了当下常用的数据集和评价指标;其次,调查与肺结节相关的U-Net分割技术网络;另外,基于U-Net分别分析与归纳编解码器、跳跃连接和整体结构的改进;最后,还讨论了基于深度学习的自动分割技术的挑战和限制。
It is of great clinical significance to achieve automatic and accurate segmentation of lung nodules in medicine.With the remarkable progress of computer vision,deep learning as a part of artificial intelligence has attracted more and more attention in the automatic segmentation of medical images.U-Net has been widely used in the field of medical image segmentation due to its good performance on small sample datasets.Researchers are currently trying to use different U-Net-structures to improve the performance of computer-aided diagnosis systems in lung cancer screening of medical images.In this work,the datasets and evaluation metrics commonly used in lung nodule segmentation were first introduced,and the U-Net-based automatic segmentation techniques related to lung nodules were investigated.Then,U-Net models and improvements around codecs,skip connections and overall structure were analyzed and summarized.Finally,the challenges and limitations of deep learning-based automatic segmentation techniques were also discussed.
作者
沈权猷
张小波
李文豪
李礼汉
许荣德
陈道花
李静
SHEN Quanyou;ZHANG Xiaobo;LI Wenhao;LI Lihan;XU Rongde;CHEN Daohua;LI Jing(School of Autumation,Guangdong University of Technology,Guangzhou Guangdong 510006,China;Department of Interventional Radiology,Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences),Southern Medical University,Guangzhou Guangdong 510000,China;The Second School of Clinical Medicine,Southern Medical University.Guangzhou Guangdong 510006,China;Department of Pulmonary and Critical Care Medicine,The First People’s Hospital of Yunnan Province(The Affiliated Hospital of Kunming University of Science and Technology),Kunming Yunnan 650032,China;Department of Pulmonary and Critical Care Medicine,Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences),Southern Medical University,Guangzhou Guangdong 510000,China)
出处
《计算机应用》
CSCD
北大核心
2023年第S01期250-257,共8页
journal of Computer Applications
基金
云南省重大科技专项(202102AA100012)
