【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。更多还原

【Abstract】 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-Netbased 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.更多还原