A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learning

TL;DR

This paper introduces a novel deep learning framework for lung segmentation in chest X-rays that effectively handles large shape variations across both pediatric and adult populations, including the retro-cardiac region.

Contribution

It presents a generic, efficient deep learning approach combining ensemble space learning and marginal shape deep learning for robust lung segmentation across diverse age groups.

Findings

Achieved a mean Dice coefficient of 0.96 on 668 CXRs.

Successfully included challenging retro-cardiac regions in segmentation.

Faster than traditional SSM-based methods for similar accuracy.

Abstract

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.