Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules are an early indicator of lung cancer. Therefore, accurate detection and image segmentation of lung nodules is of great significance to the early diagnosis of lung cancer. This paper proposes a CT (Computed Tomography) image lung nodule segmentation method based on 3D-UNet and Res2Net, and establishes a new convolutional neural network called 3D-Res2UNet. 3D-Res2Net has a symmetrical hierarchical connection network with strong multi-scale feature extraction capabilities. It enables the network to express multi-scale features with a finer granularity, while increasing the receptive field of each layer of the network. This structure solves the deep level problem. The network is not prone to gradient disappearance and gradient explosion problems, which improves the accuracy of detection and segmentation. The U-shaped network ensures the size of the feature map while effectively repairing the lost features. The method in this paper was tested on the LUNA16 public dataset, where the dice coefficient index reached 95.30% and the recall rate reached 99.1%, indicating that this method has good performance in lung nodule image segmentation.

中文翻译：

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使用改进的 3D-UNet 神经网络分割肺结节

肺癌是世界上发病率和死亡率最高的癌症之一。肺结节是肺癌的早期指标。因此，肺结节的准确检测和图像分割对肺癌的早期诊断具有重要意义。本文提出了一种基于3D-UNet和Res2Net的CT（Computed Tomography）图像肺结节分割方法，并建立了一种新的卷积神经网络，称为3D-Res2UNet。3D-Res2Net 具有对称的分层连接网络，具有强大的多尺度特征提取能力。它使网络能够以更精细的粒度表达多尺度特征，同时增加网络每一层的感受野。这种结构解决了深层次的问题。网络不易出现梯度消失和梯度爆炸问题，这提高了检测和分割的准确性。U型网络在保证特征图大小的同时有效修复丢失的特征。本文方法在LUNA16公共数据集上进行测试，骰子系数指数达到95.30%，召回率达到99.1%，表明该方法在肺结节图像分割中具有良好的性能。