COPD identification using maximum intensity projection of lung field CT images and deep convolution neural network
10.3760/cma.j.cn115624-20220403-00245
- VernacularTitle:基于肺区CT图像最大密度投影与深度卷积网络的慢阻肺识别模型的构建及意义
- Author:
Yanan WU
1
;
Shouliang QI
;
Haowen PANG
;
Mengqi LI
;
Yingxi WANG
;
Shuyue XIA
;
Qi WANG
Author Information
1. 东北大学医学与生物信息工程学院，沈阳110169
- Keywords:
Pulmonary disease, chronic obstructive;
Neural Networks (Computer);
Tomography Scanners, X-Ray Compute;
Maximum intensity projection;
Early diagnosis
- From:
Chinese Journal of Health Management
2022;16(7):457-463
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To propose a model using the maximum intensity projection (MIP) of lung field computed tomography (CT) images and deep convolution neural network (CNN) and explore its value in identifying chronic obstructive pulmonary disease (COPD).Methods:A total of 201 subjects were selected from the Second Hospital of Dalian Medical University from January 2010 to May 2021. All subjects were included according to the inclusion criteria and were divided into COPD group (101 cases) and healthy controls group (100 cases). Each patient underwent a high-resolution CT scan of the chest and pulmonary function test. First, the lung field was extracted from CT images and the intrapulmonary MIP images were acquired. Second, with these MIP images as input, the model for identifying COPD was constructed based on a modified residual network (ResNet). Finally, the influence of the number of residual blocks on the performance of the models was investigated. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the identification efficiency.Results:The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of ResNet26 was 76.1%, 76.2%, 76.0%, 76.2%, and 76.0%, respectively; and the AUC of the test was 0.855 (95% CI: 0.799-0.901). The accuracy, sensitivity, specificity, PPV, NPV of ResNet50 was 77.6%, 76.2%, 79.0%, 78.6%, and 76.7%, respectively; and the AUC of the test was 0.854 (95% CI: 0.797-0.900). The accuracy, sensitivity, specificity, PPV, NPV of ResNet26d was 82.1%, 83.2%, 81.0%, 81.6%, and 82.7%, respectively; and the AUC of the test was 0.885 (95% CI: 0.830-0.926). Conclusions:The COPD identification model via MIP images from CT images within the lung and deep CNN is successfully constructed and achieves accurate COPD identification. And it can provide an effective tool for COPD screening.
