Using deep-learning techniques for pulmonary-thoracic segmentations and improvement of pneumonia diagnosis in pediatric chest radiographs.

PURPOSE: To evaluate the efficacy of a deep-learning model to segment the lung and thorax regions in pediatric chest X-rays (CXRs). Validating the diagnosis of bacterial or viral pneumonia could be improved after lung segmentation. MATERIALS AND METHODS: A clinical-pediatric CXR set including 1351 patients was proposed to develop a deep-learning model for the pulmonary-thoracic segmentations. Model performance was evaluated by Jaccard's similarity coefficient (JSC) and Dice's coefficient (DC). Two adult CXR sets were used to assess the model's generalizability. According to the pulmonary-thoracic ratio, Pearson's correlation coefficient and the Bland-Altman plot were generated to demonstrate the correlation and agreement between manual and automatic segmentations. The receiver operating characteristic curves and areas under the curve (AUCs) were used to compare the pneumonia classification performance based on the lung-extracted images with that based on the original images. RESULTS: The model achieved JSCs of 0.910 and 0.950, DCs of 0.948 and 0.974 for lung and thorax segmentations, respectively. Pearson's r = 0.96, P < .0001. In the Bland-Altman plot, the mean difference was 0.0025 with a 95% confidence interval of (-0.0451, 0.0501). For testing with two adult CXR sets, the JSCs were 0.903 and 0.888, respectively, while the DCs were 0.948 and 0.937, respectively. After lung segmentation, the AUC of a classifier to identify bacterial or viral pneumonia increased from 0.815 to 0.879. CONCLUSION: We built a pediatric CXR dataset and exploited a deep-learning model for accurate pulmonary-thoracic segmentations. Lung segmentation can notably improve the diagnosis of bacterial or viral pneumonia.