  Computer-aided diagnosis (CAD) techniques for lung field segmentation from
chest radiographs (CXR) have been proposed for adult cohorts, but rarely for
pediatric subjects. Statistical shape models (SSMs), the workhorse of most
state-of-the-art CXR-based lung field segmentation methods, do not efficiently
accommodate shape variation of the lung field during the pediatric
developmental stages. The main contributions of our work are: (1) a generic
lung field segmentation framework from CXR accommodating large shape variation
for adult and pediatric cohorts; (2) a deep representation learning detection
mechanism, \emph{ensemble space learning}, for robust object localization; and
(3) \emph{marginal shape deep learning} for the shape deformation parameter
estimation. Unlike the iterative approach of conventional SSMs, the proposed
shape learning mechanism transforms the parameter space into marginal subspaces
that are solvable efficiently using the recursive representation learning
mechanism. Furthermore, our method is the first to include the challenging
retro-cardiac region in the CXR-based lung segmentation for accurate lung
capacity estimation. The framework is evaluated on 668 CXRs of patients between
3 month to 89 year of age. We obtain a mean Dice similarity coefficient of
$0.96\pm0.03$ (including the retro-cardiac region). For a given accuracy, the
proposed approach is also found to be faster than conventional SSM-based
iterative segmentation methods. The computational simplicity of the proposed
generic framework could be similarly applied to the fast segmentation of other
deformable objects.
