Abdominal fat quantification using convolutional networks.
Eur Radiol
; 33(12): 8957-8964, 2023 Dec.
Article
em En
| MEDLINE
| ID: mdl-37436508
ABSTRACT
OBJECTIVES:
To present software for automated adipose tissue quantification of abdominal magnetic resonance imaging (MRI) data using fully convolutional networks (FCN) and to evaluate its overall performance-accuracy, reliability, processing effort, and time-in comparison with an interactive reference method. MATERIALS ANDMETHODS:
Single-center data of patients with obesity were analyzed retrospectively with institutional review board approval. Ground truth for subcutaneous (SAT) and visceral adipose tissue (VAT) segmentation was provided by semiautomated region-of-interest (ROI) histogram thresholding of 331 full abdominal image series. Automated analyses were implemented using UNet-based FCN architectures and data augmentation techniques. Cross-validation was performed on hold-out data using standard similarity and error measures.RESULTS:
The FCN models reached Dice coefficients of up to 0.954 for SAT and 0.889 for VAT segmentation during cross-validation. Volumetric SAT (VAT) assessment resulted in a Pearson correlation coefficient of 0.999 (0.997), relative bias of 0.7% (0.8%), and standard deviation of 1.2% (3.1%). Intraclass correlation (coefficient of variation) within the same cohort was 0.999 (1.4%) for SAT and 0.996 (3.1%) for VAT.CONCLUSION:
The presented methods for automated adipose-tissue quantification showed substantial improvements over common semiautomated approaches (no reader dependence, less effort) and thus provide a promising option for adipose tissue quantification. CLINICAL RELEVANCE STATEMENT Deep learning techniques will likely enable image-based body composition analyses on a routine basis. The presented fully convolutional network models are well suited for full abdominopelvic adipose tissue quantification in patients with obesity. KEY POINTS ⢠This work compared the performance of different deep-learning approaches for adipose tissue quantification in patients with obesity. ⢠Supervised deep learning-based methods using fully convolutional networks were suited best. ⢠Measures of accuracy were equal to or better than the operator-driven approach.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Gordura Abdominal
/
Gordura Intra-Abdominal
Limite:
Humans
Idioma:
En
Revista:
Eur Radiol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha