| Title: | VIBESegmentator: full body MRI segmentation for the NAKO and UK Biobank |
| Journal: | European Radiology |
| Published: | 9 Oct 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41068435/ |
| DOI: | https://doi.org/10.1007/s00330-025-12035-9 |
| URL: | https://link.springer.com/content/pdf/10.1007/s00330-025-12035-9.pdf |
Publication 19242
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Abstract
ObjectivesTo present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including delineations that extend to the boundaries of anatomical compartments.Materials and methodsWe extracted preliminary segmentations from TotalSegmentator, spine, and body composition models for magnetic resonance tomography (MR) images, then improved them iteratively and retrained an nnUNet model. Using a random retrospective subset of German National Cohort (NAKO), UK Biobank, internal MR and computed tomography (CT) data (Training: 2897 series from 626 subjects, 290 female; mean age 53 ± 16; 3-fold-cross validation (20% hold-out). Internal testing 36 series from 12 subjects, 6 male; mean age 60 ± 11), we segmented 71 structures in torso MR and 72 in CT images: 20 organs, 10 muscles, 19 vessels, 16 bones, ribs in CT, intervertebral discs, spinal cord, spinal canal and body composition (subcutaneous fat, unclassified muscles and visceral fat). For external validation, we used existing automatic organ segmentations, independent ground truth segmentations on gradient echo images, and the Amos data. We used non-parametric bootstrapping for confidence intervals and the Wilcoxon rank-sum test for computing statistical significance.ResultsWe achieved an average Dice score of 0.90 ± 0.06 on our internal gradient echo test set, which included 71 semantic segmentation labels. Our model ties with the best model on Amos with a Dice of 0,81 ± 0.14, while having a larger field of view and a considerably higher number of structures included.ConclusionOur work presents a publicly available full-torso segmentation model for MRI and CT images that classifies almost all subject voxels to date.Key PointsQuestionNo completed MRI segmentation model exists that delineates the true transition boundaries of the anatomical structures of bone and muscles.Findings We provide a simple-to-use model that automatically segments MRI images, that can be utilized as a backbone for computer-aided automatic analysis.Clinical relevance Our segmentation model enables accurate and detailed full-torso segmentation on MRI and CT, improving automated analysis in large-scale epidemiological studies and facilitating more precise body composition and organ assessments for clinical and research applications.Graphical Abstract</p>
20 Authors
- Robert Graf
- Paul Platzek
- Evamaria Olga Riedel
- Constanze Ramschütz
- Sophie Starck
- Hendrik K. Möller
- Matan Atad
- Henry Völzke
- Robin Bülow
- Carsten Oliver Schmidt
- Julia Rüdebusch
- Matthias Jung
- Marco Reisert
- Jakob Weiss
- Maximilian T. Löffler
- Fabian Bamberg
- Benedikt Wiestler
- Johannes C. Paetzold
- Daniel Rueckert
- Jan Stefan Kirschke
1 Application
| Application ID | Title |
|---|---|
| 87802 | Assessment of frailty and biological age using multi-modal deep learning |
Enabling scientific discoveries that improve human health