Background and Objective: Pulmonary nodules have different shapes and uneven density, and some nodules adhere to blood vessels, pleura and other anatomical structures, which increase the difficulty of nodule segmentation. The purpose of this paper is to use multiscale residual U-Net to accurately segment lung nodules with complex geometric shapes, while comparing it with fuzzy C-means clustering and manual segmentation.

Method: We selected 58 computed tomography (CT) scan images of patients with different lung nodules for image segmentation. This paper proposes an automatic segmentation algorithm for lung nodules based on multiscale residual U-Net. In order to verify the accuracy of the method, we also conducted comparative experiments, while comparing it with fuzzy C-means clustering.

Results: Compared with the other two methods, the segmentation of lung nodules based on multiscale residual U-Net has a higher accuracy, with an accuracy rate of 94.57%. This method not only maintains a high accuracy rate, but also shortens the recognition time significantly with a segmentation time of 3.15 s.

Conclusions: The diagnosis method of lung nodules combined with deep learning has a good market prospect and can improve the efficiency of doctors in diagnosing benign and malignant lung nodules.

背景与目的：肺结节形态各异，密度不均，部分结节粘附在血管、胸膜等解剖结构上，增加了结节分割的难度。本文的目的是利用多尺度残差U-Net准确分割几何形状复杂的肺结节，同时将其与模糊C-means聚类和手动分割进行比较。

方法：我们选择了 58 张不同肺结节患者的计算机断层扫描 (CT) 扫描图像进行图像分割。本文提出了一种基于多尺度残差U-Net的肺结节自动分割算法。为了验证该方法的准确性，我们还进行了对比实验，同时将其与模糊C-means聚类进行了比较。

结果：与其他两种方法相比，基于多尺度残差U-Net的肺结节分割准确率更高，准确率为94.57%。该方法不仅保持了较高的准确率，而且以3.15 s的分割时间显着缩短了识别时间。

结论：结合深度学习的肺结节诊断方法具有良好的市场前景，可以提高医生诊断肺结节良恶性的效率。