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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)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Adipose Tissue ; Deep Learning ; Image Processing, Computer-assisted ; Magnetic Resonance Imaging ; Obesity; Subcutaneous Adipose-tissue; Unsupervised Assessment; Segmentation; Software; Compartments
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0938-7994
e-ISSN 1432-1084
Zeitschrift European Radiology
Quellenangaben Band: 33, Heft: 12, Seiten: 8957-8964 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort One New York Plaza, Suite 4600, New York, Ny, United States
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Institute for Metabolism, Obesity and Vascular Research (HI-MAG)
POF Topic(s) 30201 - Metabolic Health
Forschungsfeld(er) Helmholtz Diabetes Center
PSP-Element(e) G-506501-001
Förderungen German Federal Ministry of Education and Research - BMBF
DOI 10.1007/s00330-023-09865-w
WOS ID 001028743200001
Scopus ID 85164490566
PubMed ID 37436508
Erfassungsdatum 2023-10-06
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