摘要
目的观察深度信念网络(DBN)方法识别PET/CT图像良恶性肺结节的效果。方法收集216例肺结节患者的PET/CT图像,共339个肺结节,其中良性190个、恶性149个;共截取2055张ROI图像,良性1069张,恶性986张。对ROI图像进行灰度、大小归一化处理后,采用DBN方法进行分类诊断。通过实验方法确定网络结构及训练参数,并以混淆矩阵、总体精度、Kappa系数等指标评价分类结果。提取同一批图像数据非下采样双树复轮廓波变换(NSDTCT)的小波纹理参数,构建支持向量机分类模型(SVM),对比分析其与DBN的检测结果。结果DBN和SVM方法测试集检测结果分别为总体精度0.94和0.72、灵敏度0.96和0.66、特异度0.92和0.96及Kappa系数0.87和0.42。结论DBN识别肺结节良恶性的准确性高于SVM方法。
Objective To observe classification effect of pulmonary nodules on PET/CT images with deep belief network(DBN).Methods PET/CT images of 216 patients with pulmonary nodules were collected,among them 339 pulmonary nodules were detected,including 190 benign and 149 malignant ones.Totally 2055 ROI images were captured,incuding 1069 of benign ones and 986 of malignant ones.Gray scale and size normalization were performed on ROI images,and then the lesions were detected with DBN.The network structure and training parameters were determined by experimental methods,and the results were evaluated by confusion matrix,overall accuracy,Kappa coefficient and other indicators.A support vector machine model(SVM)was also built with wavelet texture features based on nonsubsampled dual-tree complex contourlet transform(NSDTCT),using the same data as DBN.The results detected with DBN and SVM were compared.Results The results of DBN and SVM methods were 0.94 and 0.72 for overall accuracy,0.96 and 0.66 for sensitivity,0.92 and 0.96 for specificity,and 0.87 and 0.42 for Kappa coefficient,respectively.Conclusion The accuracy of DBN in identifying benign and malignant pulmonary nodules is better than that of SVM.
作者
马圆
王风
韩勇
张凤
梁志刚
黄健
杨志
郭秀花
MA Yuan;WANG Feng;HAN Yong;ZHANG Feng;LIANG Zhigang;HUANG Jian;YANG Zhi;GUO Xiuhua(Department of Epidemiology and Health Statistics,School of Public Health,Capital Medical University,Beijing Key Laboratory of Clinical Epidemiology,Beijing 100069,China;Department of Nuclear Medicine,Peking University Cancer Hospital,Beijing Institute for Cancer Research,Ministry of Education Key Laboratory of Carcinogenesis and Translational Research,Beijing 100036,China;Department of Nuclear Medicine,Xuanwu Hospital Capital Medical University,Beijing 100053,China;School of Mathematical Sciences,University College Cork,Cork T12 K8AF,Ireland)
出处
《中国医学影像技术》
CSCD
北大核心
2020年第1期77-80,共4页
Chinese Journal of Medical Imaging Technology
基金
国家自然科学基金项目(81773542)
国家青年科学基金项目(81703318)
关键词
肺肿瘤
诊断
人工智能
正电子发射断层显像术
lung neoplasms
diagnosis
artificial intelligence
positron-emission tomography
