A Generic Approach to Lung Field Segmentation From Chest Radiographs Using Deep Space and Shape Learning.
A Generic Approach to Lung Field Segmentation From Chest Radiographs Using Deep Space and Shape Learning.
复制标题
使用深空间和形状学习从胸部 X 线照片进行肺野分割的通用方法。
DOI：
10.1109/tbme.2019.2933508
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Linguraru,MariusGeorge
中科院分区：
文献类型：
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作者：
Mansoor,Awais;Cerrolaza,JuanJ;Perez,Geovanny;Biggs,Elijah;Okada,Kazunori;Nino,Gustavo;Linguraru,MariusGeorge
计算机辅助诊断（CAD）技术从胸部x线片（CXR）肺野分割已被提出用于成人队列，但很少用于儿科受试者。统计形状模型（SSMs）是大多数最先进的基于cxr的肺场分割方法的主要方法，不能有效地适应儿童发育阶段肺场的形状变化。我们工作的主要贡献是：1)来自CXR的通用肺野分割框架，适用于成人和儿童队列的大形状变化；2)用于鲁棒目标定位的深度表征学习检测机制——集成空间学习；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利用递归表示学习机制将参数空间转化为可有效求解的边缘子空间。此外，我们的方法是第一个在基于cxr的肺分割中包括具有挑战性的心脏后区，以准确估计肺活量。该框架对668例3个月至89岁患者的cxr进行了评估。我们得到Dice的平均相似系数为（包括心脏后区）。在给定精度的情况下，该方法比传统的基于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。
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 learningfor 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(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.