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Schneider, D.* ; Eggebrecht, T.* ; Linder, A.* ; Linder, N.* ; Schaudinn, A.* ; Blüher, M. ; Denecke, T.* ; Busse, H.*

Abdominal fat quantification using convolutional networks.

Eur. Radiol. 33, 8957-8964 (2023)
DOI PMC
Creative Commons Lizenzvertrag
Open Access Green as soon as Postprint is submitted to ZB.
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 AND METHODS: 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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Adipose Tissue ; Deep Learning ; Image Processing, Computer-assisted ; Magnetic Resonance Imaging ; Obesity; Subcutaneous Adipose-tissue; Unsupervised Assessment; Segmentation; Software; Compartments
ISSN (print) / ISBN 0938-7994
e-ISSN 1432-1084
Journal European Radiology
Quellenangaben Volume: 33, Issue: 12, Pages: 8957-8964 Article Number: , Supplement: ,
Publisher Springer
Publishing Place One New York Plaza, Suite 4600, New York, Ny, United States
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Helmholtz Institute for Metabolism, Obesity and Vascular Research (HI-MAG)
Grants German Federal Ministry of Education and Research - BMBF