Liu Xiuling, Qi Shuaishuai, Xiong Peng, Liu Jing, Wang Hongrui, Yang Jianli
College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China;Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P.R.China.
College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Jun 25;37(3):434-441. doi: 10.7507/1001-5515.201910047.
Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.
肺结节是早期肺癌的主要表现形式。因此，准确检测肺结节对于肺癌的早期诊断和治疗具有重要意义。然而，由于背景复杂、肺部计算机断层扫描（CT）图像检测范围大以及肺结节大小和形状各异，快速准确地检测肺结节是一项具有挑战性的任务。因此，本文提出了一种用于自动检测肺结节的多尺度特征融合算法，以实现对肺结节的准确检测。首先，在用于大规模图像识别的深度卷积网络（VGG16）上设计了一个三层模块化肺结节检测模型。网络的第一层模块用于提取CT图像中肺结节的特征并粗略估计肺结节的位置。然后，网络的第二层模块用于融合多尺度图像特征，以进一步增强肺结节的细节。网络的第三层模块融合分析网络第一层和第二层模块的特征，得到多尺度下肺结节的候选框。最后，采用非极大值抑制方法对多尺度下肺结节的候选框进行分析，得到肺结节的最终位置。该算法通过LIDC-IDRI公共数据集上的肺结节数据进行了验证。平均检测准确率为90.9%。
