Sofka Michal, Wetzl Jens, Birkbeck Neil, Zhang Jingdan, Kohlberger Timo, Kaftan Jens, Declerck Jérôme, Zhou S Kevin
Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 3):667-74. doi: 10.1007/978-3-642-23626-6_82.
Simple algorithms for segmenting healthy lung parenchyma in CT are unable to deal with high density tissue common in pulmonary diseases. To overcome this problem, we propose a multi-stage learning-based approach that combines anatomical information to predict an initialization of a statistical shape model of the lungs. The initialization first detects the carina of the trachea, and uses this to detect a set of automatically selected stable landmarks on regions near the lung (e.g., ribs, spine). These landmarks are used to align the shape model, which is then refined through boundary detection to obtain fine-grained segmentation. Robustness is obtained through hierarchical use of discriminative classifiers that are trained on a range of manually annotated data of diseased and healthy lungs. We demonstrate fast detection (35s per volume on average) and segmentation of 2 mm accuracy on challenging data.
用于在CT中分割健康肺实质的简单算法无法处理肺部疾病中常见的高密度组织。为了克服这个问题，我们提出了一种基于多阶段学习的方法，该方法结合解剖学信息来预测肺部统计形状模型的初始化。初始化首先检测气管隆突，并利用它在肺部附近区域（如肋骨、脊柱）检测一组自动选择的稳定地标。这些地标用于对齐形状模型，然后通过边界检测进行细化以获得细粒度分割。通过在一系列患病和健康肺部的手动标注数据上训练的判别式分类器的分层使用来获得鲁棒性。我们在具有挑战性的数据上展示了快速检测（平均每体积35秒）和2毫米精度的分割。
