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, ensemble space learning, for robust object localization; and 3) 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 $0.96 \pm 0.03$ (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.

中文翻译：

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使用深空和形状学习从胸片分割肺野的通用方法

已经为成人队列提出了用于从胸片 (CXR) 进行肺野分割的计算机辅助诊断 (CAD) 技术，但很少用于儿科受试者。统计形状模型 (SSM) 是最先进的基于 CXR 的肺野分割方法的主力，在儿科发育阶段不能有效地适应肺野的形状变化。我们工作的主要贡献是：1）来自 CXR 的通用肺野分割框架，可适应成人和儿童队列的大形状变化；2）深度表征学习检测机制，集成空间学习，用于鲁棒的目标定位；3）边缘形状深度学习用于形状变形参数估计。与传统 SSM 的迭代方法不同，所提出的形状学习机制将参数空间转换为可以使用递归表示学习机制有效解决的边缘子空间。此外，我们的方法是第一个在基于 CXR 的肺分割中包含具有挑战性的心脏后区域以准确估计肺活量的方法。该框架在 3 个月至 89 岁之间的 668 位患者的 CXR 上进行了评估。我们获得了平均 Dice 相似系数 $0.96 \pm 0.03$（包括心脏后区）。对于给定的精度，还发现所提出的方法比传统的基于 SSM 的迭代分割方法更快。所提出的通用框架的计算简单性可以类似地应用于其他可变形对象的快速分割。