Lung segmentation from CT with severe pathologies using anatomical constraints.
Med Image Comput Comput Assist Interv
; 17(Pt 1): 804-11, 2014.
Article
em En
| MEDLINE
| ID: mdl-25333193
ABSTRACT
The diversity in appearance of diseased lung tissue makes automatic segmentation of lungs from CT with severe pathologies challenging. To overcome this challenge, we rely on contextual constraints from neighboring anatomies to detect and segment lung tissue across a variety of pathologies. We propose an algorithm that combines statistical learning with these anatomical constraints to seek a segmentation of the lung consistent with adjacent structures, such as the heart, liver, spleen, and ribs. We demonstrate that our algorithm reduces the number of failed detections and increases the accuracy of the segmentation on unseen test cases with severe pathologies.
Buscar no Google
Base de dados:
MEDLINE
Assunto principal:
Reconhecimento Automatizado de Padrão
/
Interpretação de Imagem Radiográfica Assistida por Computador
/
Tomografia Computadorizada por Raios X
/
Imageamento Tridimensional
/
Pontos de Referência Anatômicos
/
Pulmão
/
Pneumopatias
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article