Multi-stage learning for robust lung segmentation in challenging CT volumes.
Med Image Comput Comput Assist Interv
; 14(Pt 3): 667-74, 2011.
Article
em En
| MEDLINE
| ID: mdl-22003757
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.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tomografia Computadorizada de Feixe Cônico
/
Pulmão
/
Neoplasias Pulmonares
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Med Image Comput Comput Assist Interv
Assunto da revista:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
Ano de publicação:
2011
Tipo de documento:
Article
País de afiliação:
Estados Unidos