Deep learning-based algorithm to segment pediatric and adult lungs from dynamic chest radiography images using virtual patient datasets
Dynamic chest radiography (DCR) enables the evaluation of lung function based on changes in lung density, lung area, and diaphragm level due to respiration. The need for lung segmentation techniques for sequential chest images is growing. Thus, this study was aimed at developing a deep learning-based lung segmentation technique for DCR across all age groups using virtual patient images. DCR images of 53 patients (M:F = 34:19, age: 1-88 years, median age: 63 years) were used. Owing to the difficulty in collecting a large dataset of pediatric DCR images, the 4D extended cardiac-torso phantom (XCAT phantom) was used to augment the pediatric data. A total of ten pediatric XCAT phantoms (five males and females each, age: 0-15 years) of virtual patients were generated and projected. Two deep-learning models, U-net and DeepLabv3 using MobileNetv3 as the backbone, were implemented. They were trained to estimate the lung segmentation masks using DCR image datasets consisting of only real or a mixture of real and virtual patients. Dice similarity coefficient (DSC) and intersection over union (IoU) were used as evaluation metrics. When trained only on real patients, for both the metrics, DeepLabv3 (DSC/IoU: 0.902/0.822) exhibited higher values than U-net (DSC/IoU: 0.791/0.673). When trained on dataset of a mixture of real and virtual patients, values of both the metrics improved in both models (DSC/IoU: 0.906/0.828 and 0.795/0.677 for DeepLabv3 and U-net, respectively). These results indicate that the developed model, that is, the combination of DeepLabv3 and XCAT-based augmentation methods, is effective for the lung segmentation of DCR images of various respiratory phases for all age groups.
