Automated Measurement of Cardiothoracic Ratio Based on Semantic Segmentation Integration Model Using Deep Learning

The objective of this study is to investigate the efficacy of the semantic segmentation model in predicting cardiothoracic ratio (CTR) and heart enlargement and compare its consistency with the reference standard. A total of 650 consecutive chest radiographs from our center and 756 public datasets were retrospectively included to develop a segmentation model. Three semantic segmentation models were used to segment the heart and lungs. A soft voting integration method was used to improve the segmentation accuracy and measure CTR automatically. Bland-Altman and Pearson's correlation analyses were used to compare the consistency and correlation between CTR automated measurements and reference standards. CTR automated measurements were compared with reference standard using the Wilcoxon signed-rank test. The diagnostic efficacy of the model for heart enlargement was evaluated using the AUC. The soft voting integration model was strongly correlated (r = 0.98, P < 0.001) and consistent (average standard deviation of 0.0048 cm/s) with the reference standard. No statistical difference between CTR automated measurement and reference standard in healthy subjects, pneumothorax, pleural effusion, and lung mass patients (P > 0.05). In the external test data, the accuracy, sensitivity, specificity, and AUC in determining heart enlargement were 96.0%, 79.5%, 99.1%, and 0.988, respectively. The deep learning method was calculated faster per chest radiograph than the average time manually calculated by the radiologist (about 2 s vs 25.75 ± 4.35 s, respectively, P < 0.001). This study provides a semantic segmentation integration model of chest radiographs to measure CTR and determine heart enlargement with chest structure changes due to different chest diseases effectively, faster, and accurately. The development of the automated segmentation integration model is helpful in improving the consistency of CTR measurement, reducing the workload of radiologists, and improving their work efficiency.