准确分割肺结节在临床上具有重要意义。计算机断层扫描（computer tomography，CT）技术以其成像速度快、图像分辨率高等优点广泛应用于肺结节分割及功能评价中。为了进一步对肺部CT影像中的肺结节分割方法进行探索，本文对基于CT影像的肺结节分割方法研究进行综述。1）对传统的肺结节分割方法及其优缺点进行了归纳比较；2）重点介绍了包括深度学习、深度学习与传统方法相结合在内的肺结节分割方法；3）简单介绍了肺结节分割方法的常用评价指标，并结合部分方法的指标表现展望了肺结节分割方法研究领域的未来发展趋势。传统的肺结节分割方法各有优缺点和其适用的结节类型，深度学习分割方法因普适性好等优点成为该领域的研究热点。研究者们致力于如何提高分割结果的准确度、模型的鲁棒性及方法的普适性，为了实现此目的本文总结了各类方法的优缺点。基于CT影像的肺结节分割方法研究已经取得了不小的成就，但肺结节形状各异、密度不均匀，且部分结节与血管、胸膜等解剖结构粘连，给结节分割增加了困难，结节分割效果仍有很大提升空间。精度高、速度快的深度学习分割方法将会是研究者密切关注的方法，但该类方法仍需解决数据需求量大和网络模型超参数的确定等问题。

Accurate segmentation of lung nodules is of great significance in the clinic. A computed tomography (CT) scan can detect lung cancer tissues with a diameter larger than 5 mm, and its fast imaging and high resolution make it the first choice for screening early stage lung cancer. At present, CT images of the lung have been widely used in pulmonary nodule segmentation and functional evaluation. However, a single lung scan will generate a large number of CT images, and it is very difficult for doctors to manually segment all images. Although the manual segmentation of lung nodules has extremely high accuracy, it is highly subjective, inefficient, and poorly repeatable. In addition, the lung nodules in CT images have different shapes and uneven density, and some adhere to surrounding tissues. Thus, few segmentation algorithms can adapt to all types of nodules. How to segment lung nodules accurately, efficiently, universally, and automatically has become a research hotspot. In recent years, the research on lung nodule segmentation methods has made great achievements. In order to assist more scholars to explore the segmentation method of lung nodules based on CT images, this article reviewed the research progress in this field. In this study, the lung nodule segmentation methods were divided into two categories:traditional segmentation methods and deep learning segmentation methods. The traditional segmentation methods for segmenting lung nodules are expressed based on mathematical knowledge; that is, theoretical information and logical rules are used to infer boundary information to achieve segmentation. According to different principles, the traditional segmentation principles were roughly divided into segmentation methods based on threshold and regional growth, clustering, active contour model (ACM), and mathematical model optimization. First, the traditional principles and their advantages and disadvantages are summarized and compared in this study. Then, new methods, including deep learning and deep learning with traditional methods, are focused. With the development of artificial intelligence, deep learning technology represented by convolutional neural networks (CNNs) has attracted considerable attention due to its superior lung nodule segmentation effect. Distinct from traditional segmentation methods, CNNs can use hidden layers such as convolution layers and pooling layers to actively learn the low-level features of nodules and form them into higher-level abstract features. By using a large amount of data for training and validation, we obtained the bias and weight of the model with the smallest loss rate in the validation set and used them to perform prediction on the testing set, wherein nodule segmentation will be conducted automatically. Because of the particularity, the study of CNN-based lung nodule segmentation is mainly concentrated on the design of the network structure. Fully convolutional networks and encoding-decoding symmetric networks such as U-Net obtained better performance on lung nodule segmentation. In particular, U-Net is commonly used in medical image segmentation due to its small amount of data required and superior segmentation effect. U-Net achieved remarkable success in the segmentation of lung nodules. In addition, the improved network based on U-Net structure also promoted the segmentation effect of nodules. After a brief introduction of the process of extracting lung nodule features by CNNs, this article presented two aspects of using deep learning methods alone and deep learning combined with traditional methods to segment nodules. It concentrated on the application of deep learning in lung nodule segmentation. At the same time, various optimization strategies for accelerating the model's convergence speed and improving the performance of the model on nodule segmentation were summarized. Finally, the commonly used evaluation indicators of lung nodule segmentation methods were briefly introduced. Furthermore, combined with the performance of the indicators presented in some literature, we looked forward to future development trends of the pulmonary nodule segmentation method. Researchers have focused on how to improve the accuracy of the segmentation results, the robustness of the model, and the universality of the method. To achieve this goal, the advantages and disadvantages of various methods were summarized and compared. In pulmonary CT images, traditional methods were more robust, but almost all of them were highly dependent on user intervention, such as region growing and dynamic planning algorithm involving low computational complexity. Moreover, they were sensitive to image quality, and their ability to integrate with prior knowledge was limited. In addition, these methods tend to experience over-segmentation and under-segmentation. Although ACM could improve the accuracy of the segmentation results by merging with prior knowledge such as nodule shape and texture to generate models, it increased the computational complexity. In recent years, with the continuous improvement of medical standards, traditional segmentation methods have failed to meet clinical needs. The development of computer vision, artificial intelligence, and other technologies has promoted the development of deep learning, which has been successfully used for lung nodule segmentation. The lung nodule segmentation method based on deep learning is universal, and the network optimizes the model through operations such as regularization, weight attenuation, dropout, improved activation function, and loss function, which can reduce the training time of the model under big data. Even in the case of a limited amount of training data, the accuracy of the model and the speed of segmentation can also be promoted by using data augmentation, preprocessing, adjusting the network structure, and using different optimizers to obtain better segmentation results. Therefore, deep learning methods have been gradually applied to segment lung nodules. The CT image-based lung nodule segmentation method has achieved great success, but the gray value of the lung nodule in the CT image is not much different from that of the surrounding tissues. Moreover, anatomical structures such as adhered blood vessels and pleura added more difficulties to segment the nodules. Thus, there is still much room for improvement in the effect of segmentation. From the current lung nodule segmentation methods, the segmentation method based on deep learning has high accuracy and fast speed. However, problems such as massive data requirements and super parameter determination still need to be solved in deep learning.