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CT窗口设置对人工智能分类肺部病变结果的影响

程晓悦 吴晓华 郝优 贺文 李华 刘佳宝 曹邱婷

程晓悦, 吴晓华, 郝优, 等. CT窗口设置对人工智能分类肺部病变结果的影响[J]. CT理论与应用研究, 2023, 32(4): 515-522. DOI: 10.15953/j.ctta.2022.210
引用本文: 程晓悦, 吴晓华, 郝优, 等. CT窗口设置对人工智能分类肺部病变结果的影响[J]. CT理论与应用研究, 2023, 32(4): 515-522. DOI: 10.15953/j.ctta.2022.210
CHENG X Y, WU X H, HAO Y, et al. Effect of Computed Tomography Window Technique on the Results of Artificial Intelligence Classification of Lung Lesions[J]. CT Theory and Applications, 2023, 32(4): 515-522. DOI: 10.15953/j.ctta.2022.210. (in Chinese)
Citation: CHENG X Y, WU X H, HAO Y, et al. Effect of Computed Tomography Window Technique on the Results of Artificial Intelligence Classification of Lung Lesions[J]. CT Theory and Applications, 2023, 32(4): 515-522. DOI: 10.15953/j.ctta.2022.210. (in Chinese)

CT窗口设置对人工智能分类肺部病变结果的影响

doi: 10.15953/j.ctta.2022.210
基金项目: 国家中医药管理局中医药创新团队及人才支持计划(组织液循环网络(ZYYCXTD-D-202202));国家重点研发计划(复杂时变场景的物理仿真关键技术(2017YFB1002700));国家973项目(疏松结缔组织中的传递现象及艾灸等效应的模式化研究(2015CB554507));国家自然科学基金(图像不变性分析与应用(61379082));基于CT影像的肝脏肿瘤冷冻消融热传导数值模型与手术规划算法研究(6227012006))。
详细信息
    作者简介:

    程晓悦:女,博士,首都医科大学附属北京友谊医院放射科主治医师,主要从事影像诊断工作,E-mail:cxiaotu@163.com

    吴晓华:女,首都医科大学附属北京友谊医院放射科副主任医师,中国防痨协会多学科诊疗专业分会委员、北京医学会放射学分会感染学组委员,主要从事胸部影像诊断工作,E-mail:luckyemilyxh@aliyun.com

    通讯作者:

    女,首都医科大学附属北京友谊医院放射科副主任医师,中国防痨协会多学科诊疗专业分会委员、北京医学会放射学分会感染学组委员,主要从事胸部影像诊断工作,E-mail:luckyemilyxh@aliyun.com

  • 中图分类号: O  242;TP  391;R  814

Effect of Computed Tomography Window Technique on the Results of Artificial Intelligence Classification of Lung Lesions

  • 摘要: 目的:应用3种不同的3D CNN的算法及5种CT窗口设置,探讨CT窗技术对人工智能分类肺部病变结果的影响。方法:回顾性分析172例周围型肺癌及185例局灶性肺炎的胸部CT影像资料,选择ResNet、ResNext以及DenseNet 3种不同的3D CNN的算法将病变分为两组,并在每1种3D CNN算法处理过程中应用5种不同的CT窗口设置,包括肺窗(1500,-600),纵隔窗(350,40),自定义窗口1(SW1)(1000,40),自定义窗口2(SW2)(1000,-100),全窗(4096,1024),分别计算分类准确率及AUC结果,并进行ROC曲线的两两对比。结果:ResNet的平均分类准确率最低为纵隔窗85.732%,AUC值为0.871;平均分类准确率最高为全窗,达91.596%,AUC值为0.946。ResNext的平均分类准确率最低为纵隔窗81.528%,AUC值为0.814;平均分类准确率最高为全窗,达86.568%,AUC值为0.882。DenseNet的平均分类准确率最低为纵隔窗87.954%,AUC值为0.906;平均分类准确率最高为SW2,达93.274%,AUC值为0.951。应用medcalc将3种3D CNN的5种窗口下的ROC曲线进行了两两对比发现,纵隔窗与肺窗、纵隔窗与SW1、纵隔窗与SW2之间的AUC值比较均有统计学意义。结论:3种3D CNN的分类诊断效能差别不大;CT窗口设置对CNN分类肺部病变结果有影响,在纵隔窗设置下以上3种人工智能算法对该两类肺部病变的诊断效能最差。

     

  • 图  1  不同窗口设置时对病灶的显示不同:(a)~(e)左肺上叶周围型肺癌(腺癌);(f)~(j)右肺下叶局灶性肺炎

    (a)和(f)肺窗;(b)和(g)纵隔窗;(c)和(h)SW1;(d)和(i)SW2;(e)和(j)全窗

    Figure  1.  Different display of lesions in different windows: (a)~(e) is the same patient, diagnosed as peripheral lung cancer in the upper lobe of the left lung (pathological examination shows adenocarcinoma); (f)~(j) is the same patient, diagnosed as focal pneumonia in the lower lobe of the right lung

    图  2  3D ResNet网络构建

    主干使用18层的3D ResNet网络,在每个卷积块中,有1个卷积层(其参数在图中列出)、1个批标准化(Batch Normalization)层和1个ReLu激活层,图中的第1个参数“3×3×3”表示3D内核大小,第3个参数“64、128、256、512”表示通道数,最后1个参数“/2”表示步长为2的池层。

    Figure  2.  3D ResNet network design

    图  3  (a)ResNet的一个构建块;(b)基数为32的ResNext构建块[7]

    Figure  3.  (a) A building block of ResNet; (b) ResNext building block with 32 cardinality[7]

    图  4  DenseNet连接示意图[8]

    Figure  4.  DenseNet connection diagram[8]

    图  5  ResNet 5个窗口设置的ROC曲线图

    Figure  5.  ROC curve of ResNet

    图  6  ResNext 5个窗口设置的ROC曲线图

    Figure  6.  ROC curve of ResNext

    图  7  DenseNet 5个窗口设置的ROC曲线图

    Figure  7.  ROC curve of DenseNet

    表  1  ResNet 5个窗口设置的5倍交叉验证的分类准确率(%)及AUC结果

    Table  1.   Classification accuracy (%) and AUC results of 5-fold cross validation of 5 window settings in ResNet

    窗口(WW,WL)Fold 1Fold 2Fold 3Fold 4Fold 5平均分类准确率/%AUC
      肺窗 (1500,−600)87.5088.7391.5591.5593.0690.4780.929
      纵隔窗 (350,40)80.5687.3290.1485.9284.7285.7320.871
      SW1 (1000,40)86.1192.9688.7391.5590.2889.9260.918
      SW2 (1000,−100)87.5092.9690.1491.5590.2890.4860.923
      Full (4096,1024)87.5090.1490.1494.3795.8391.5960.946
    下载: 导出CSV

    表  2  ResNext 5个窗口设置的5倍交叉验证的分类准确率(%)及AUC结果

    Table  2.   Classification accuracy (%) and AUC results of 5-fold cross validation of 5 window settings in ResNext

    窗口(WW,WL)Fold 1Fold 2Fold 3Fold 4Fold 5平均分类准确率/%AUC
      肺窗 (1500,−600)84.7280.1487.3288.7387.5085.6820.896
      纵隔窗 (350,40)77.7883.1081.6984.5180.5681.5280.814
      SW1 (1000,40)86.1185.9288.7385.9284.7286.2800.881
      SW2 (1000,−100)84.7288.7383.1085.9287.5085.9940.878
      Full (4096,1024)83.3391.5585.9287.3284.7286.5680.882
    下载: 导出CSV

    表  3  DenseNet 5个窗口设置的5倍交叉验证的分类准确率(%)及AUC结果

    Table  3.   Classification accuracy (%) and AUC results of 5-fold cross validation of 5 window settings in DenseNet

    窗口(WW,WL)Fold 1Fold 2Fold 3Fold 4Fold 5平均分类准确率/%AUC
      肺窗 (1500,−600)87.5091.5592.9694.3793.0691.8880.944
      纵隔窗 (350,40)88.8987.3291.5584.5187.5087.9540.906
      SW1 (1000,40)94.4494.3790.1490.1493.0692.4300.932
      SW2 (1000,−100)93.0695.7791.5591.5594.4493.2740.951
      Full (4096,1024)91.6792.9690.1494.3794.4492.7160.941
    下载: 导出CSV
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  • 收稿日期:  2022-10-29
  • 修回日期:  2022-12-06
  • 录用日期:  2022-12-07
  • 网络出版日期:  2023-01-04
  • 刊出日期:  2023-07-31

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