title: Multi-stage learning for robust lung segmentation in challenging CT volumes.
abstract: 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.
authors: Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA.
authors: Michal
authors: M
authors: Sofka
authors: Jens
authors: J
authors: Wetzl
authors: Neil
authors: N
authors: Birkbeck
authors: Jingdan
authors: J
authors: Zhang
authors: Timo
authors: T
authors: Kohlberger
authors: Jens
authors: J
authors: Kaftan
authors: Jérôme
authors: J
authors: Declerck
authors: S Kevin
authors: SK
authors: Zhou
mesh_terms: D000465:Algorithms; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007858:Learning; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging* / Q000473:pathology; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D012984:Software