  Lung segmentation in chest X-rays (CXRs) is an important prerequisite for
improving the specificity of diagnoses of cardiopulmonary diseases in a
clinical decision support system. Current deep learning models for lung
segmentation are trained and evaluated on CXR datasets in which the
radiographic projections are captured predominantly from the adult population.
However, the shape of the lungs is reported to be significantly different
across the developmental stages from infancy to adulthood. This might result in
age-related data domain shifts that would adversely impact lung segmentation
performance when the models trained on the adult population are deployed for
pediatric lung segmentation. In this work, our goal is to (i) analyze the
generalizability of deep adult lung segmentation models to the pediatric
population and (ii) improve performance through a stage-wise, systematic
approach consisting of CXR modality-specific weight initializations, stacked
ensembles, and an ensemble of stacked ensembles. To evaluate segmentation
performance and generalizability, novel evaluation metrics consisting of mean
lung contour distance (MLCD) and average hash score (AHS) are proposed in
addition to the multi-scale structural similarity index measure (MS-SSIM), the
intersection of union (IoU), Dice score, 95% Hausdorff distance (HD95), and
average symmetric surface distance (ASSD). Our results showed a significant
improvement (p < 0.05) in cross-domain generalization through our approach.
This study could serve as a paradigm to analyze the cross-domain
generalizability of deep segmentation models for other medical imaging
modalities and applications.
