Miao Liao, Zhiwei Chi, Huizhu Wu, S. Di, Yonghua Hu, Yunyi Li
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引用次数: 0
摘要
早期发现肺结节是降低肺癌死亡率的重要手段。本文提出了一种基于并行池化和密集块的三维 CT 图像肺结节检测方法,包括候选结节提取和假阳性抑制两部分。首先,提出了并行池化的密集 U 型骨干网络,以获得候选结节概率图。并行池化结构利用多次池化操作进行下采样,全面捕捉空间信息,解决了浅层最大池化和平均池化导致的信息丢失问题。然后,设计了一个包含并行池化、密集块和注意力模块的寄生网络来抑制假阳性结节。寄生网络将骨干网络的多尺度特征图作为输入。实验结果表明,所提出的方法显著提高了肺结节检测的准确性,CPM 得分为 0.91,优于许多现有方法。
Pulmonary Nodule Detection from 3D CT Image with a Two-Stage Network
Early detection of lung nodules is an important means of reducing the lung cancer mortality rate. In this paper, we propose a three-dimensional CT image lung nodule detection method based on parallel pooling and dense blocks, which includes two parts, i.e., candidate nodule extraction and false positive suppression. First, a dense U-shaped backbone network with parallel pooling is proposed to obtain the candidate nodule probability map. The parallel pooling structure uses multiple pooling operations for downsampling to capture spatial information comprehensively and address the problem of information loss resulting from maximum and average pooling in the shallow layers. Then, a parasitic network with parallel pooling, dense blocks, and attention modules is designed to suppress false positive nodules. The parasitic network takes the multiscale feature maps of the backbone network as the input. The experimental results demonstrate that the proposed method significantly improves the accuracy of lung nodule detection, achieving a CPM score of 0.91, which outperforms many existing methods.