39 research outputs found
Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study
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 (DL) 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 for
pediatrics 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 analyze
the generalizability of deep adult lung segmentation models to the pediatric
population and improve performance through a systematic combinatorial approach
consisting of CXR modality-specific weight initializations, stacked
generalization, and an ensemble of the stacked generalization models. Novel
evaluation metrics consisting of Mean Lung Contour Distance and Average Hash
Score are proposed in addition to the Multi-scale Structural Similarity Index
Measure, Intersection of Union, and Dice metrics to evaluate segmentation
performance. We observed a significant improvement (p < 0.05) in cross-domain
generalization through our combinatorial 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.Comment: 11 pages, 7 figures, and 8 table