摘要
目的针对目前基于胸部CT图像的肺结节自动检测方法的检出率较低且存在大量假阳性的问题,提出一种基于卷积神经网络的肺结节检测方法。方法采用基于模糊建模思想和迭代相对模糊连接度(IRFC)算法的自动解剖识别(AAR)方法分割肺部CT图像,提取肺部实体部分;将分割后的图像输入卷积神经网络,提取肺结节特征;采用位置敏感特征图表达结节的位置信息。结果使用天池医疗AI大赛数据集,精准分割肺部CT图像,检测肺结节的准确率、敏感度、特异度和假阳性率分别为95.60%、95.24%、95.97%和4.03%。结论基于卷积神经网络检测肺结节有较高的精度和效率,且鲁棒性好。
Objective Major challenges in the current automatic detection of lung nodules from chest CT images are to improve the sensitivity and to reduce the false positive rate.A new scheme based on convolutional neural network was proposed in this study.Methods The method applied an automatic anatomy recognition（AAR）methodology based on fuzzy modeling ideas and an iterative relative fuzzy connectedness（IRFC）delineation algorithm for the segmentation of lung parenchyma in CT images.The segmented lung image was inputted into the conventional neural networks for feature extraction of pulmonary nodules.The network adopted position-sensitive score maps to express the location information of lung nodules.Results This method could obtain accurate segmentation of the lung parenchyma in the data set of Tianchi Medical AI Contest,and the accuracy,sensitivity,specificity and false-positive rate of lung nodules detected was 95.60%,95.24%,95.97% and 4.03%,respectively.Conclusion Detection of pulmonary nodules based on convolutional neural networks has high accuracy and efficiency,and good robustness.
作者
侍新
谢世朋
李海波
SHI Xin;XIE Shipeng;LI Haibo(School of Communication and Information Engineering,Nanjing University ofPosts and Telecommunications,Nanjing 210003,China)
出处
《中国医学影像技术》
CSCD
北大核心
2018年第6期934-939,共6页
Chinese Journal of Medical Imaging Technology
基金
国家自然科学基金(11547155)
教育部-中国移动科研基金项目(MCM20150504)
江苏省高校自然科学基金(17KJB510038)
江苏省科技重点研发计划-产业前瞻与共性关键技术项目(BE2016001-4)
南京邮电大学科研基金项目(NY214026
NY217035)
关键词
体层摄影术
X线计算机
图像分割
特征提取
卷积神经网络
位置敏感特征图
肺结节
Tomography
X-ray computed
Image segmentation
Feature extraction
Convolution neural network
Position-sensitive score maps
Lung nodules
