Volumetric reference data of the orbit: a deep learning MRI analysis in the German national cohort.
Authors
Affiliations (13)
Affiliations (13)
- Eye Center, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg im Breisgau, Germany. [email protected].
- Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg im Breisgau, Germany.
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg im Breisgau, Germany.
- Division of Health Sciences, Department of Public Health, Universidad del Norte, Barranquilla, Colombia.
- Eye Center, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg im Breisgau, Germany.
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany.
- Centre for Advanced Analytics and Predictive Sciences, University of Augsburg, Augsburg, Germany.
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
- Molecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology Platform, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Abstract
Manual segmentation of orbital magnetic resonance imaging (MRI) is labor-intensive, hindering large-scale morphometric studies. To overcome this, we developed a fully automated deep learning pipeline to segment orbital MRIs and establish age- and sex-stratified normative reference data. We analyzed T1-weighted brain MRIs from 30,868 participants in the population-based German National Cohort (NAKO). After quality control, 28,779 participants (mean age 48.1 years; 44.1% female) were included. The model, validated against expert manual segmentations, accurately extracted 34 volumetric and geometric parameters across 15 orbital structures (Dice Similarity Coefficients: vitreous 0.97, lens 0.89, optic nerve 0.85). Mean [SD] axial length was 23.5 [1.2] mm. Mean [SD] volumes were 34.4 [3.9] cm³ for total orbital contents, 6.3 [0.8] cm³ for the vitreous, and 0.17 [0.03] cm³ for the lens. Males exhibited significantly larger dimensions across all parameters (p < 0.001). Age-stratified percentile curves revealed continuous age-dependent lens growth (Spearman's ρ = 0.57 in men, 0.51 in women) alongside modest volume increases in the orbit, optic nerve, and extraocular muscles. This deep learning tool effectively resolved the bottleneck of manual segmentation, providing comprehensive orbital reference data for the German population. This foundation enables future high-throughput epidemiological research into the associations between orbital anatomy, systemic health, and disease.