Jun Shen1, Thomas Baum2, Christian Cordes2, Beate Ott3, Thomas Skurk4, Hendrik Kooijman5, Ernst J Rummeny2, Hans Hauner4, Bjoern H Menze6, Dimitrios C Karampinos7. 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.
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 obesewomen (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.
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173
Authors: Cora Held; Daniela Junker; Mingming Wu; Lisa Patzelt; Laura A Mengel; Christina Holzapfel; Maximilian N Diefenbach; Marcus R Makowski; Hans Hauner; Dimitrios C Karampinos Journal: Quant Imaging Med Surg Date: 2022-05
Authors: Wolfgang M Thaiss; Sergios Gatidis; Tina Sartorius; Jürgen Machann; Andreas Peter; Thomas K Eigentler; Konstantin Nikolaou; Bernd J Pichler; Manfred Kneilling Journal: Cancer Immunol Immunother Date: 2020-11-01 Impact factor: 6.968
Authors: D Franz; D Weidlich; F Freitag; C Holzapfel; T Drabsch; T Baum; H Eggers; A Witte; E J Rummeny; H Hauner; D C Karampinos Journal: Int J Obes (Lond) Date: 2017-08-14 Impact factor: 5.095