摘要
为了实现在低剂量、低分辨率CT扫描影像中对肺腺癌组织学亚型的分类鉴别,提出一种基于DenseNet的深度学习方法,从混合性磨玻璃结节(mGGNs)5 mm层厚的低分辨率CT影像中预测IAC和MIA病理分类.从丽水市中心医院105例患者的105个5 mm层厚低分辨率CT图像中选取样本,划分训练集和测试集后,对训练集进行数据扩展,构建深度学习2D和3D DenseNet模型,分类鉴别IAC和MIA. 2D DenseNet模型的分类准确度为76.67%,敏感性为63.33%,特异性为90.00%,受试者工作特征曲线下的区域面积为0.888 9,显著优于3D DenseNet模型和其他几种深度学习网络模型.深度学习技术,尤其是2D DenseNet模型,可辅助并指导医生在肺癌CT筛查中对患者的肺腺癌组织学亚型进行预判,特别是在图像分辨率较低的情况下,仍能够快速提供较为准确的诊断.
A deep learning method based on DenseNet was proposed to distinguish between IAC and MIA from mixed ground glass nodules low-resolution CT images with 5 mm slice thickness, in order to classify histological subtypes of lung adenocarcinoma from low-dose CT images with low resolution. Samples were obtained from 105 low-resolution CT images with 5 mm slice thickness of 105 patients in the Central Hospital of Lishui City. The data was divided into training set and testing set. Then the training set was augmented;2D and 3D DenseNet deep learning models were built to distinguish between IAC and MIA. The accuracy, sensitivity, specificity and the area under the receiver operating characteristic curve of the proposed 2D DenseNet method achieved 76.67%, 63.33%, 90.00% and 0.888 9, respectively, which was better than 3D DenseNet and other deep learning models. The deep learning method, especially the 2D DenseNet, may assist doctors in lung cancer screening to predict and guide histological subtypes of patients, which can quickly provide more accurate diagnosis results even under condition of low image resolution.
作者
杨婧
耿辰
王海林
纪建松
戴亚康
YANG Jing;GENG Chen;WANG Hai-lin;JI Jian-song;DAI Ya-kang(University of Science and Technology of China, Hefei 230026, China;Suzhou Institute of Biomedical Engineeringand Technology, Chinese Academy of Sciences, Suzhou 215163, China;The Central Hospital of Lishui City,Lishui 323000, China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2019年第6期1164-1170,共7页
Journal of Zhejiang University：Engineering Science
基金
国家重点研发计划资助项目(2017YFB1103602
2017YFC0114304
2018YFC0116904)
国家自然科学基金资助项目(61501452
61801476)
中国科学院科研仪器设备研制资助项目(YJKYYQ20170050)
江苏省重点研发计划资助项目(BE2016010-3
BE2016010-4
BE2017675
BE2017663
BE2017664)
江苏省自然科学基金资助项目(BK20180221
BK20170387)
浙江省重点研发计划资助项目(2018C03024)
浙江省基础公益研究计划资助项目(LGF18H160035)
苏州市重点产业技术创新资助项目(SYG201606
SYG201706
SYG201707)
苏州市民生科技资助项目(SYS201656
SS201855
SS201866)
苏州市科技发展计划资助项目(SZS201609
SZS201818)
苏州市高新区医疗卫生科技计划资助项目(2016Z010)
医工结合资助项目(Y853111305
Y853171305)
