Atlas-based Automated Segmentation of Spleen and Liver using Adaptive Enhancement Estimation.
Med Image Comput Comput Assist Interv
; 5762: 1001-1008, 2009.
Article
em En
| MEDLINE
| ID: mdl-20448837
ABSTRACT
The paper presents the automated segmentation of spleen and liver from contrast-enhanced CT images of normal and hepato/splenomegaly populations. The method used 4 steps:
(i) a mean organ model was registered to the patient CT; (ii) the first estimates of the organs were improved by a geodesic active contour; (iii) the contrast enhancements of liver and spleen were estimated to adjust to patient image characteristics, and an adaptive convolution refined the segmentations; (iv) lastly, a normalized probabilistic atlas corrected for shape and location for the precise computation of each organ's volume and height (mid-hepatic liver height and cephalocaudal spleen height). Results from test data demonstrated the method's ability to accurately segment the spleen (RMS error = 1.09mm; DICE/Tanimoto overlaps = 95.2/91) and liver (RMS error = 2.3mm, and DICE/Tanimoto overlaps = 96.2/92.7). The correlations (R(2)) with clinical/manual height measurements were 0.97 and 0.93 for the spleen and liver respectively.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Guideline
/
Prognostic_studies
Idioma:
En
Revista:
Med Image Comput Comput Assist Interv
Ano de publicação:
2009
Tipo de documento:
Article