基于DenseNet的低分辨CT影像肺腺癌组织学亚型分类

doi:10.3785/j.issn.1008-973X.2019.06.016

浙江大学学报(工学版)

2019, Vol. 53

Issue (6): 1164-1170 DOI: 10.3785/j.issn.1008-973X.2019.06.016

计算机与自动化技术

基于DenseNet的低分辨CT影像肺腺癌组织学亚型分类

杨婧1,2(

),耿辰2,王海林3,纪建松3,戴亚康2,*(

)

1. 中国科学技术大学，安徽合肥 230026
2. 中国科学院苏州生物医学工程技术研究所，江苏苏州 215163
3. 丽水市中心医院，浙江丽水 323000

Classification on histological subtypes of lung adenocarcinoma from low-resolution CT images based on DenseNet

Jing YANG1,2(

),Chen GENG2,Hai-lin WANG3,Jian-song JI3,Ya-kang DAI2,*(

)

1. University of Science and Technology of China, Hefei 230026, China
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
3. The Central Hospital of Lishui City, Lishui 323000, China

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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.

关键词： 深度学习; DenseNet; 混合性磨玻璃结节; 肺腺癌; 厚层CT

Abstract:

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.

Key words: deep learning DenseNet mixed ground glass nodules lung adenocarcinoma thick slice CT

收稿日期: 2019-01-05 出版日期: 2019-05-22

CLC:

R 318.13

通讯作者: 戴亚康 E-mail: 18366136246@163.com;daiyk@sibet.ac.cn

作者简介: 杨婧（1993—），女，硕士生，从事医学图像处理研究. orcid.org/0000-0003-0073-0735. E-mail: 18366136246@163.com

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引用本文:

杨婧,耿辰,王海林,纪建松,戴亚康. 基于DenseNet的低分辨CT影像肺腺癌组织学亚型分类[J]. 浙江大学学报(工学版), 2019, 53(6): 1164-1170.

Jing YANG,Chen GENG,Hai-lin WANG,Jian-song JI,Ya-kang DAI. Classification on histological subtypes of lung adenocarcinoma from low-resolution CT images based on DenseNet. Journal of ZheJiang University (Engineering Science), 2019, 53(6): 1164-1170.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.06.016 或 http://www.zjujournals.com/eng/CN/Y2019/V53/I6/1164

表 1 患者和肺结节的属性

图 1 确诊为IAC和MIA的混合性磨玻璃结节CT图像示例

图 2 结节长轴直径实际所占像素数分布情况

图 3 DenseNet基础网络模型结构

图 4 2D和3D DenseNet网络模型实验流程

表 2 2D和3D DenseNet模型结构细节

图 5 2D和3D DenseNet网络模型的性能对比

表 3 2D DenseNet网络模型不同参数的性能对比

表 4 2D DenseNet与其他深度学习网络模型的性能对比

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