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.

翻译：胸部X光片（CXR）中的肺分割是临床决策支持系统中提高心肺疾病诊断特异性的重要前提。当前用于肺分割的深度学习模型主要在成人群体放射影像数据集上进行训练和评估。然而，从婴幼儿期到成年期的不同发育阶段，肺部的形态存在显著差异。这可能导致与年龄相关的数据域迁移，当在成人群体上训练的模型应用于儿科肺分割时，会严重影响分割性能。本研究旨在：（i）分析深度成人肺分割模型在儿科人群中的泛化性；（ii）通过分阶段、系统化的方法提升性能，该方法包括CXR模态特异性权重初始化、堆叠集成以及集成堆叠集成。为评估分割性能和泛化性，除了多尺度结构相似性指数（MS-SSIM）、交并比（IoU）、Dice分数、95%豪斯多夫距离（HD95）和平均对称表面距离（ASSD）外，还提出了由平均肺轮廓距离（MLCD）和平均哈希分数（AHS）组成的新型评估指标。结果表明，我们的方法在跨域泛化方面取得了显著提升（p < 0.05）。本研究可作为分析深度分割模型在其他医学影像模态和应用中跨域泛化性的范式。