基于CT征象量化分析的肺结节恶性度分级

为了提高肺结节恶性度分级的计算精度及可解释性,该文提出一种基于CT征象量化分析的肺结节恶性度分级方法.首先,融合影像组学特征和通过卷积神经网络提取的高阶特征构造分析CT征象所需的特征集; 接着,在混合特征集的基础上利用进化搜索机制优化集成学习分类器,实现对7种肺结节征象的识别和量化打分; 最后,将7种CT征象的量化打分输入到一个利用差分进化算法优化产生的多分类器,实现肺结节恶性度的分级计算.在实验研究中使用LIDC-IDRI数据集中的2000个肺结节样本进行进化集成学习器和恶性度分级器的训练和测试.实验结果显示对7种CT征象的识别准确率可达0.9642以上,肺结节恶性度分级的准确率为0.8618,精确率为0.8678,召回率为0.8617,F1指标为0.8627.与多个典型算法的比较显示,该文方法不但具有较高的准确率,而且可对相关CT征象进行量化分析,使得对恶性度的分级结果更具可解释性.

Malignancy Grading of Lung Nodules Based on CT Signs Quantization Analysis

In order to improve the accuracy and interpretability of the grading of malignant nodules in the lung, a method is proposed to achieve grading automatically for lung nodules by using (Computed Tomography, CT) signs. Firstly, features sets are extracted of CT signs by combing the radiomics features with the higher-order features extracted by convolutional neural network. Then, the ensemble classifier is optimized by the evolutionary search mechanism based on the mixed feature sets, and it is used to realize quantitative scores for 7 CT signs. Finally, 7 quantitative scores are input to the optimized multi-classifier to achieve the grading of malignant nodules in the lung. In the experience, 2000 samples of lung nodules in LIDC-IDRI data set are used to train and test the proposed method. The results show that the recognition accuracy of the 7 CT signs can reach more than 0.9642, the grading accuracy reaches 0.8618, the precision reaches 0.8678, the recall reaches 0.8617, and the F1 index reaches 0.8627. With respect to typical algorithms, the proposed method not only has high accuracy, but also can quantitatively analyze the CT signs that make the grade result of malignancy more interpretive.