基于三维密集网络的肺结节检测方法

doi:10.3969/j.issn.0258-8021.2020.01.02

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摘要针对传统基于三维特征的肺结节检测方法存在小结节检出率不高且计算量大的问题,提出一种更为高效的基于三维密集网络的肺结节检测方法。首先将密集连接单元引入3D U-Net,构建适用于肺结节检测的3D Dense U-Net网络;由于3D Dense U-Net用密集连接块代替原始3D U-Net的普通卷积层,可最大化地保证层与层之间的信息流通,不仅能解决传统堆叠式网络所存在的特征冗余问题,而且能加快网络训练速度。同时,该网络保留U-Net的基本连接方式,以实现底层特征的复用,从而可以有效地获取候选结节。在此基础上,针对候选结节中包含假阳例的问题,为了更加有效地获取结节特征,提高网络对结节的鉴别能力,构造三维密集分类网络(3D DenseNet)进行假阳例的剔除。在天池医疗AI大赛数据集的测试中,检测肺结节总体敏感度94.3%,10 mm以下结节敏感度91.5%,假阳例率5.9%。所提出的基于三维密集网络的肺结节检测方法对于小结节的检测更加灵敏,不仅能提高结节检出率,而且计算效率也有所提高。

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	王尚丽
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关键词 ：计算机辅助诊断, 结节检测, 密集连接, 3D卷积

Abstract：To solve the problem that the detection rate of pulmonary nodules is low and require large amounts of calculation by traditional method which based on three-dimensional features, a more efficient three-dimensional-dense-network based method for pulmonary nodules’ detection was proposed. The method firstly involved the introduction of a densely connected unit into the 3D U-Net to construct a 3D Dense U-Net (3D Densely connected U-Net) network for lung nodule detection. Since 3D Dense U-Net replaced the original ordinary convolution layer of 3D with a densely connected block, it facilitated the maximization of the information flows between layers; it not only solved the problem of feature redundancy in traditional stacked networks, but also speeded up the network training. Additionally, the new method retained the fundamental linking method of U-Net to reuse the underlying features, which enabled an effective obtainment of the candidate nodules. On this basis, in order to solve the problem of false nodules in the candidate nodules, in order to more effectively acquire the features of the nodules and improve the ability of the network to identify the nodules, a three-dimensional dense classification network (3D DenseNet) was used to eliminate false-positive nodules. By testing the data collection of Tianchi Medical AI Competition, the total detection sensitivity for lung nodules using the proposed method reached 94.3%; and the sensitivity of nodules less than 10 mm was 91.5%, and the false-positive rate was 5.9%. The experimental results revealed that the proposed method reached greater sensitivity in detecting small nodules, which not only raised the nodules’ detection rate, but also improved the computational efficiency.

Key words： computer-aided diagnosis nodule detection dense connection 3D convolution

收稿日期: 2019-01-11

PACS:

R318

基金资助:国家自然科学基金(61471212)

通讯作者: E-mail:xyjw1969@126.com

引用本文:

王尚丽, 金戈辉, 徐亮, 金炜, 尹曹谦, 符冉迪. 基于三维密集网络的肺结节检测方法[J]. 中国生物医学工程学报, 2020, 39(1): 8-18.
Wang Shangli, Jin Gehui, Xu Liang, Jin Wei, Yin Caoqian, Fu Randi. Method for Detecting Pulmonary Nodules Based on Three-Dimensional Dense Network. Chinese Journal of Biomedical Engineering, 2020, 39(1): 8-18.

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http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2020.01.02 或 http://cjbme.csbme.org/CN/Y2020/V39/I1/8

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