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Eur J Radiol. 2016 Sep;85(9):1613-21. doi: 10.1016/j.ejrad.2016.06.006. Epub 2016 Jun 8.

Automatic segmentation of abdominal organs and adipose tissue compartments in water-fat MRI: Application to weight-loss in obesity.

Author information

1
Department of Computer Science, Technische Universität München, Munich, Germany; Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
2
Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
3
Else Kröner Fresenius Center for Nutritional Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
4
Else Kröner Fresenius Center for Nutritional Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany; ZIEL Research Center for Nutrition and Food Sciences, Technische Universität München, Germany.
5
Philips Healthcare, Hamburg, Germany.
6
Department of Computer Science, Technische Universität München, Munich, Germany.
7
Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany. Electronic address: dimitrios.karampinos@tum.de.

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±3.8kg/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 5cm 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.

KEYWORDS:

Automatic image segmentation; Subcutaneous adipose tissue (SAT); Visceral adipose tissue (VAT); Water-fat magnetic resonance imaging (MRI); Weight loss

PMID:
27501897
DOI:
10.1016/j.ejrad.2016.06.006
[Indexed for MEDLINE]

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