Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT.
Med Image Anal
; 16(4): 904-14, 2012 May.
Article
em En
| MEDLINE
| ID: mdl-22377657
ABSTRACT
The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of soft tissue. An automated method optimized for medical image data is presented for the simultaneous segmentation of four abdominal organs from 4D CT data using graph cuts. Contrast-enhanced CT scans were obtained at two phases non-contrast and portal venous. Intra-patient data were spatially normalized by non-linear registration. Then 4D convolution using population training information of contrast-enhanced liver, spleen and kidneys was applied to multiphase data to initialize the 4D graph and adapt to patient-specific data. CT enhancement information and constraints on shape, from Parzen windows, and location, from a probabilistic atlas, were input into a new formulation of a 4D graph. Comparative results demonstrate the effects of appearance, enhancement, shape and location on organ segmentation. All four abdominal organs were segmented robustly and accurately with volume overlaps over 93.6% and average surface distances below 1.1mm.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Vísceras
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Reconhecimento Automatizado de Padrão
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Radiografia Abdominal
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Interpretação de Imagem Radiográfica Assistida por Computador
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Tomografia Computadorizada por Raios X
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Imageamento Tridimensional
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Revista:
Med Image Anal
Ano de publicação:
2012
Tipo de documento:
Article