The reliability of a segmentation methodology for assessing intramuscular adipose tissue and other soft-tissue compartments of lower leg MRI images.
MAGMA
; 29(2): 237-44, 2016 Apr.
Article
en En
| MEDLINE
| ID: mdl-26702939
ABSTRACT
OBJECTIVE:
Determine the reliability of a magnetic resonance (MR) image segmentation protocol for quantifying intramuscular adipose tissue (IntraMAT), subcutaneous adipose tissue, total muscle and intermuscular adipose tissue (InterMAT) of the lower leg. MATERIALS ANDMETHODS:
Ten axial lower leg MRI slices were obtained from 21 postmenopausal women using a 1 Tesla peripheral MRI system. Images were analyzed using sliceOmatic™ software. The average cross-sectional areas of the tissues were computed for the ten slices. Intra-rater and inter-rater reliability were determined and expressed as the standard error of measurement (SEM) (absolute reliability) and intraclass coefficient (ICC) (relative reliability).RESULTS:
Intra-rater and inter-rater reliability for IntraMAT were 0.991 (95% confidence interval [CI] 0.978-0.996, p < 0.05) and 0.983 (95% CI 0.958-9.993, p < 0.05), respectively. For the other soft tissue compartments, the ICCs were all >0.90 (p < 0.05). The absolute intra-rater and inter-rater reliability (expressed as SEM) for segmenting IntraMAT were 22.19 mm(2) (95% CI 16.97-32.04) and 78.89 mm(2) (95% CI 60.36-113.92), respectively.CONCLUSION:
This is a reliable segmentation protocol for quantifying IntraMAT and other soft-tissue compartments of the lower leg. A standard operating procedure manual is provided to assist users, and SEM values can be used to estimate sample size and determine confidence in repeated measurements in future research.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Reconocimiento de Normas Patrones Automatizadas
/
Imagen por Resonancia Magnética
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Tejido Adiposo
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Músculo Esquelético
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Diabetes Mellitus Tipo 2
/
Pierna
Tipo de estudio:
Diagnostic_studies
Límite:
Aged
/
Aged80
/
Female
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Humans
Idioma:
En
Revista:
MAGMA
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2016
Tipo del documento:
Article
País de afiliación:
Canadá