Automatic segmentation of abdominal organs and adipose tissue compartments in water-fat MRI: Application to weight-loss in obesity. Issue 9 (September 2016)

• Record Type: Journal Article
• Authors: Shen, Jun; Baum, Thomas; Cordes, Christian; Ott, Beate; Skurk, Thomas; Kooijman, Hendrik; Rummeny, Ernst J.; Hauner, Hans; Menze, Bjoern H.; Karampinos, Dimitrios C.
• Is Part Of: European journal of radiology. Volume 85:Issue 9(2016)
• Page Start: 1613
• Page End: 1621
• Publication Date: 2016-09
• Subjects: Subcutaneous adipose tissue (SAT) -- Visceral adipose tissue (VAT) -- Automatic image segmentation -- Water-fat magnetic resonance imaging (MRI) -- Weight loss; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2016.06.006
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Purpose: To develop a fully automatic algorithm for abdominal organs and adipose tissue compartments segmentation and to assess organ and adipose tissue volume changes in longitudinal water-fat magnetic resonance imaging (MRI) data. Materials and methods: Axial two-point Dixon images were acquired in 20 obese women (age range 24–65, BMI 34.9 ± 3.8 kg/m 2 ) before and after a four-week calorie restriction. Abdominal organs, subcutaneous adipose tissue (SAT) compartments (abdominal, anterior, posterior), SAT regions along the feet-head direction and regional visceral adipose tissue (VAT) were assessed by a fully automatic algorithm using morphological operations and a multi-atlas-based segmentation method. Results: The accuracy of organ segmentation represented by Dice coefficients ranged from 0.672 ± 0.155 for the pancreas to 0.943 ± 0.023 for the liver. Abdominal SAT changes were significantly greater in the posterior than the anterior SAT compartment (−11.4% ± 5.1% versus −9.5% ± 6.3%, p < 0.001). The loss of VAT that was not located around any organ (−16.1% ± 8.9%) was significantly greater than the loss of VAT 5 cm around liver, left and right kidney, spleen, and pancreas (p < 0.05). Conclusion: The presented fully automatic algorithm showed good performance in abdominal adipose tissue and organ segmentation, and allowed the detection of SAT and VAT subcompartments changes during weight loss.