It is difficult for the existing detection methods of the pulmonary nodules to take into account the global and local features simultaneously. It will lead to over-fitting and lower sensitivity since the extracted features of 3D pulmonary nodules is too complex. To solve these problems, a model based an improved 3D residual structure (3D Res-I) was proposed to detect pulmonary nodules. In the model, the basic residual structure is improved by using rectangular convolution kernel, grouping convolution and pre-activation. Rectangular convolution kernel expands the receptive filed of the convolution, which effectively takes into account the global and local features of the pulmonary nodules. Grouping convolution reduces the computational cost of the model. Pre-activation operation alleviates over-fitting phenomenon. 3D Res-I structure is combined with the improved U-Net network as the feature extraction network of Faster R-CNN. The experimental results on LUNA16 dataset show that the proposed model improves the detection accuracy of pulmonary nodules and reduces the average number of false positives and the size of the generated model.

中文翻译：

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基于3D Res-I网络的CT影像肺结节自动检测

现有的肺结节检测方法难以同时兼顾全局和局部特征。由于提取的 3D 肺结节特征过于复杂，会导致过拟合和灵敏度降低。为了解决这些问题，提出了一种基于改进的 3D 残留结构 (3D Res-I) 的模型来检测肺结节。在模型中，通过使用矩形卷积核、分组卷积和预激活来改进基本残差结构。矩形卷积核扩展了卷积的感受野，有效地兼顾了肺结节的全局和局部特征。分组卷积降低了模型的计算成本。预激活操作缓解了过拟合现象。3D Res-I结构结合改进的U-Net网络作为Faster R-CNN的特征提取网络。在LUNA16数据集上的实验结果表明，所提出的模型提高了肺结节的检测精度，减少了平均误报数和生成模型的大小。