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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\u2009\u00b1\u200916; 3-fold-cross validation (20% hold-out). Internal testing 36 series from 12 subjects, 6 male; mean age 60\u2009\u00b1\u200911), 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We achieved an average Dice score of 0.90\u2009\u00b1\u20090.06 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\u2009\u00b1\u20090.14, while having a larger field of view and a considerably higher number of structures included.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our work presents a publicly available full-torso segmentation model for MRI and CT images that classifies almost all subject voxels to date.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>No completed MRI segmentation model exists that delineates the true transition boundaries of the anatomical structures of bone and muscles<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>We provide a simple-to-use model that automatically segments MRI images, that can be utilized as a backbone for computer-aided automatic analysis<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>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<\/jats:italic>\n                      .\n                    <\/jats:p>\n            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See\n                      \n                      . The authors who analyzed and controlled the data are not employees, cofounders, or shareholders of Bonescreen GmbH. All other authors do not report any conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"One of the authors has significant statistical expertise.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"This retrospective study uses random subset torso data from NAKO along with VIBE data from UKBB for training and evaluation. Ethical approval for data collection was obtained from the respective ethics committees, and informed consent was obtained. For the internal data, we obtained ethical approval from the tum university clinic ethics committee (593\/21 S-NP); informed consent was waived.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This retrospective study uses random subset torso data from NAKO along with VIBE data from UKBB for training and evaluation. Ethical approval for data collection was obtained from the respective ethics committees, and informed consent was obtained. For the internal data, we obtained ethical approval from the tum university clinic ethics committee (593\/21 S-NP); informed consent was waived.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The NAKO and UK Biobank is large collection available to many other researchers. Internal data were from existing collection, but without exiting Segmentations.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}