Linder, A.* ; Eggebrecht, T.* ; Linder, N.* ; Stange, R.* ; Schaudinn, A.* ; Blüher, M. ; Denecke, T.* ; Busse, H.*
     
 
    
        
Stand-alone MRI tool for semiautomatic volumetry of abdominal adipose compartments in patients with obesity.
    
    
        
    
    
        
        Sci. Rep. 15:9354 (2025)
    
    
    
		
		
			
				Abdominal adipose tissue (AT) amounts are increasingly considered as potential biomarkers for a variety of diseases and clinical questions, for instance, in diabetology, oncology or cardiovascular medicine. Despite the emergence of automated deep-learning methods for tissue quantification, interactive (supervised) segmentation tools will typically be used for model training. In comparison with CT-based approaches, MRI segmentation tools are more complex and less common. This work aims to validate a novel MRI-based tissue volumetry against a reference method in patients with (pre-) obesity. The new tool (segfatMR) was developed under a Matlab-based, open-source software framework and combines fast automatic pre-segmentation followed by manual (expert) corrections where needed. Analyses were performed retrospectively on a subset of clinical research MRI datasets (1.5 T Achieva XR, Philips Healthcare) and involved the segmentation of datasets from 20 patients (10 women/men) aged 25.1-63.1 (mean 48.5) years with BMIs between 28.3 and 58.8 (mean 36.8) kg/m2. Two independent expert readers analyzed the abdominopelvic data (30-40 slices, mean 35.8) with segfatMR and a widely used commercial tool (sliceOmatic). Coefficients of determination (R2), bias and limits of agreement (Bland Altman) were determined. Segmentation performance (R2 between methods) was excellent for both readers for SAT (> 0.99) and very high for VAT (around 0.90). The novel method was almost twice as fast as the reference standard - 25 and 19 s/slice (R1 and R2) vs. 40 and 34 s/slice. The presented semiautomatic segmentation tool enables a fast and accurate quantification of whole abdominopelvic adipose tissue volume in obesity studies. Use, adjustments and extensions of the MRI volumetry tool are facilitated by the open-source design on a standard PC.
			
			
				
			
		 
		
			
				
					
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        Publikationstyp
        Artikel: Journalartikel
    
 
    
        Dokumenttyp
        Wissenschaftlicher Artikel
    
 
    
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        Herausgeber
        
    
    
        Schlagwörter
        Adipose Tissue ; Mri ; Obesity ; Quantification ; Software Tool ; Subcutaneous Fat ; Visceral Fat; Automated Segmentation; Tissue; Image
    
 
    
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        Sprache
        englisch
    
 
    
        Veröffentlichungsjahr
        2025
    
 
    
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        HGF-Berichtsjahr
        2025
    
 
    
    
        ISSN (print) / ISBN
        2045-2322
    
 
    
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        2045-2322
    
 
    
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	    Band: 15,  
	    Heft: 1,  
	    Seiten: ,  
	    Artikelnummer: 9354 
	    Supplement: ,  
	
    
 
  
        
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            Verlag
            Nature Publishing Group
        
 
        
            Verlagsort
            London
        
 
	
        
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        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, IFB Adiposity Diseases
    
 
    
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        Erfassungsdatum
        2025-05-09