Age- and BMI-Dependent Psoas and Gluteus Muscle Mass in 27,805 Participants of the Population-Based German National Cohort (NAKO Gesundheitsstudie): A Deep-Learning 3T MRI Study

<b>Background/Objective:</b> This study aimed to develop and validate an automated deep learning-based model for 3D segmentation and quantification of the psoas major and gluteus muscles at 3T MRI in a large cohort study and to analyze the distribution of findings as well as gender-, age-, and BMI-related differences. <b>Methods:</b> The study population consisted of 27,805 participants from the MR imaging subgroup of the population-based, longitudinal German National Cohort study. A deep learning segmentation model was trained, tested, and implemented to automatically quantify psoas major maximum cross-sectional area (CSA<sub>psoas</sub>) and gluteus volume (V<sub>gluteus</sub>) on T1-weighted 3D VIBE DIXON sequences. Associations with gender, age, and BMI were assessed by linear regression. <b>Results:</b> The segmentation model demonstrated a high performance, with mean Dice coefficients of 0.92 for the psoas and 0.95 for the gluteus. Males showed higher total CSA<sub>psoas</sub> (males: 37.92 ± 5.80 cm<sup>2</sup>; females: 24.47 ± 3.65 cm<sup>2</sup>) and higher total V<sub>gluteus</sub> (males: 3.384 ± 0.528 L; females: 2.386 ± 0.408 L) compared to females. Younger participants aged <30 years showed the highest CSA<sub>psoas</sub>, whereas participants aged 30-59 years showed the highest V<sub>gluteus</sub>. Participants with higher BMI > 25 kg/m<sup>2</sup> showed higher muscle CSA and volumes compared to subjects with lower BMI < 25 kg/m<sup>2</sup>. V<sub>gluteus</sub> showed a strong correlation to body weight in both females and males. <b>Conclusions:</b> Deep learning-based models provide accurate 3D segmentation and quantification of skeletal muscle compartments from MR images in large cohort studies, thus offering a feasible method for skeletal muscle evaluation. The morphometric size characteristics of the psoas and gluteus muscles are dependent on gender and BMI. Deep learning enables accurate 3D segmentation and quantification of skeletal muscle in large MR imaging cohorts, providing a feasible tool for muscle evaluation. The morphometric characteristics of psoas and gluteus muscles are dependent on gender and BMI.

Authors

Institutions

Publication Details

Journal
Diagnostics
Published
2026-01-08
DOI
https://doi.org/10.3390/diagnostics16020205
Primary Topic
Nutrition and Health in Aging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Age- and BMI-Dependent Psoas and Gluteus Muscle Mass in 27,805 Participants of the Population-Based German National Cohort (NAKO Gesundheitsstudie): A Deep-Learning 3T MRI Study

Thomas Küstner, Lena S. Kiefer, Fabian Bamberg, Eduardo Calderón et al.
Diagnostics
Nutrition and Health in Aging
article

Age- and BMI-Dependent Psoas and Gluteus Muscle Mass in 27,805 Participants of the Population-Based German National Cohort (NAKO Gesundheitsstudie): A Deep-Learning 3T MRI Study

Thomas Küstner, Lena S. Kiefer, Fabian Bamberg, Eduardo Calderón, Marius Winter, Marc Fischer, Bin Yang, Schick Fritz, Sofia Pappa, Thierno D. Diallo, Konstantin Nikolaou
article en

Abstract

<b>Background/Objective:</b> This study aimed to develop and validate an automated deep learning-based model for 3D segmentation and quantification of the psoas major and gluteus muscles at 3T MRI in a large cohort study and to analyze the distribution of findings as well as gender-, age-, and BMI-related differences. <b>Methods:</b> The study population consisted of 27,805 participants from the MR imaging subgroup of the population-based, longitudinal German National Cohort study. A deep learning segmentation model was trained, tested, and implemented to automatically quantify psoas major maximum cross-sectional area (CSA<sub>psoas</sub>) and gluteus volume (V<sub>gluteus</sub>) on T1-weighted 3D VIBE DIXON sequences. Associations with gender, age, and BMI were assessed by linear regression. <b>Results:</b> The segmentation model demonstrated a high performance, with mean Dice coefficients of 0.92 for the psoas and 0.95 for the gluteus. Males showed higher total CSA<sub>psoas</sub> (males: 37.92 ± 5.80 cm<sup>2</sup>; females: 24.47 ± 3.65 cm<sup>2</sup>) and higher total V<sub>gluteus</sub> (males: 3.384 ± 0.528 L; females: 2.386 ± 0.408 L) compared to females. Younger participants aged <30 years showed the highest CSA<sub>psoas</sub>, whereas participants aged 30-59 years showed the highest V<sub>gluteus</sub>. Participants with higher BMI > 25 kg/m<sup>2</sup> showed higher muscle CSA and volumes compared to subjects with lower BMI < 25 kg/m<sup>2</sup>. V<sub>gluteus</sub> showed a strong correlation to body weight in both females and males. <b>Conclusions:</b> Deep learning-based models provide accurate 3D segmentation and quantification of skeletal muscle compartments from MR images in large cohort studies, thus offering a feasible method for skeletal muscle evaluation. The morphometric size characteristics of the psoas and gluteus muscles are dependent on gender and BMI. Deep learning enables accurate 3D segmentation and quantification of skeletal muscle in large MR imaging cohorts, providing a feasible tool for muscle evaluation. The morphometric characteristics of psoas and gluteus muscles are dependent on gender and BMI.

DiagnosticsVol. 16(2)
University of Stuttgart (DE), University of Freiburg (DE), University Medical Center Freiburg (DE), University of Tübingen (DE)
Quality Education
Openalex Percentile: Top 94%
Nutrition and Health in Aging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.