摘要
目的针对计算机断层扫描(computed tomography,CT)图像的肺结节自动检测中灵敏度低及存在大量假阳性的问题,本文提出了一种基于非结节自动分类的二维卷积神经网络(convolutional neural network,CNN),并用于肺结节检测中的假阳性减少。方法首先对CT图像进行预处理,通过对原始CT图像重采样和归一化,解决不同样本像素间隔不一致及图像对比度不统一问题;采用结节不同空间方向的二维切片信息采集进行正样本扩充,负样本无监督分类方法平衡正负样本数量;分别利用不同类别负样本与正样本训练二维卷积神经网络,获得多个用于降低假阳性的2D CNN肺结节检测模型,对LUNA16提供的假阳性减少数据集进行五折交叉验证,利用官方提供的评估程序对模型进行评估。结果通过与直接使用单个2D CNN进行分类的模型比较,对非结节分类后训练多个模型的分类结果较佳,最终竞争性指标(competition performance metric,CPM)竞争性得分0.849。结论基于非结节自动分类的2D CNN模型可以有效地对假阳性肺结节进行剔除,相较于其他2D CNN具有竞争力,可为肺癌早期筛查提供帮助。
Objective In order to solve the problem of low sensitivity and a large number of false positives in the automatic detection of pulmonary nodules in CT images,this paper proposes a two-dimensional convolutional neural network(CNN)based on non-nodule automatic classification and applies it to the reduction of false positives in the detection of pulmonary nodules.Methods Firstly,the CT image was preprocessed by resampling and normalizing the original CT image,to solve the problem of inconsistent pixel spacing and image contrast of different samples.The positive samples were expanded by two-dimensional slice information collection in different spatial directions,and the negative samples were classified unsupervised to balance the positive and negative samples.Two-dimensional convolutional neural networks were trained with different types of negative samples and positive samples to obtain a number of 2D CNN pulmonary nodule detection models for reducing false-positive,by using the false positive reduction data set provided by LUNA16 to conduct 5-fold cross validation,and evaluated the model with the evaluation procedure provided by LUNA16.Results Compared with the model directly using a single 2D CNN for classification,the result of training multiple models after non nodule classification was better,the final CPM(competition performance metric)competitive score was 0.849.Conclusions The 2D CNN model based on non-nodule automatic classification can effectively reduce the number of false-positive pulmonary nodules,which is competitive with other 2D CNN,and can provide help for early lung cancer screening.
作者
任敬谋
李晓琴
REN Jingmou;LI Xiaoqin(Department of Environment and Life, Beijing University of Technology, Beijing 100124)
出处
《北京生物医学工程》
2020年第4期389-397,共9页
Beijing Biomedical Engineering
基金
国家重点研发计划(2017YFC0111104)
国家自然科学基金(11572014)
智能化生理测量与临床转化北京市国际科技合作基地资助项目资助。
关键词
医学影像处理
计算机断层扫描
肺结节检测
卷积神经网络
深度学习
medical image processing
computed tomography
lung nodule detection
convolutional neural network
deep learning
