{"doi":"10.1126/sciadv.add0433","title":"Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort","abstract":"<jats:p>This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (VAT) and subcutaneous fat (SAT) in the trunk from standardized magnetic resonance imaging at 3 T, thereby demonstrating the feasibility of deep learning (DL)–based image segmentation in a large population-based cohort in Germany (five sites). Volume and distribution of AT play an essential role in the pathogenesis of insulin resistance, a risk factor of developing metabolic/cardiovascular diseases. Cross-validated training of the DL-segmentation model led to a mean Dice similarity coefficient of &gt;0.94, corresponding to a mean absolute volume deviation of about 22 ml. SAT is significantly increased in women compared to men, whereas VAT is increased in males. Spatial distribution shows age- and body mass index–related displacements. DL-based image segmentation provides robust and fast quantification of AT (≈15 s per dataset versus 3 to 4 hours for manual processing) and assessment of its spatial distribution from magnetic resonance images in large cohort studies.</jats:p>","journal":"Science Advances","year":2023,"id":603479,"datarank":0.41588830833596724,"base_score":2.772588722239781,"endowment":2.772588722239781,"self_citation_contribution":0.41588830833596724,"citation_network_contribution":0.0,"self_endowment_contribution":0.41588830833596724,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":378229,"name":"Fritz Schick","orcid":"0000-0002-4231-3406","position":1,"is_corresponding":false},{"id":542973,"name":"Norbert Stefan","orcid":"0000-0002-2186-9595","position":2,"is_corresponding":false},{"id":58321,"name":"Christopher L. Schlett","orcid":"0000-0002-1576-1481","position":3,"is_corresponding":false},{"id":1548075,"name":"Jakob B. Weiss","orcid":null,"position":4,"is_corresponding":false},{"id":392599,"name":"Johanna Nattenmüller","orcid":"0000-0003-4032-378X","position":5,"is_corresponding":false},{"id":1548076,"name":"Katharina Göbel-Guéniot","orcid":null,"position":6,"is_corresponding":false},{"id":1251551,"name":"Tobias Norajitra","orcid":null,"position":7,"is_corresponding":false},{"id":861261,"name":"Tobias Nonnenmacher","orcid":null,"position":8,"is_corresponding":false},{"id":1360133,"name":"Hans-Ulrich Kauczor","orcid":null,"position":9,"is_corresponding":false},{"id":103898,"name":"Klaus Maier‐Hein","orcid":"0000-0002-6626-2463","position":10,"is_corresponding":false},{"id":1064787,"name":"Thoralf Niendorf","orcid":"0000-0001-7584-6527","position":11,"is_corresponding":false},{"id":6230,"name":"Tobias Pischon","orcid":"0000-0003-1568-767X","position":12,"is_corresponding":false},{"id":6231,"name":"Karl‐Heinz Jöckel","orcid":"0000-0002-1987-0255","position":13,"is_corresponding":false},{"id":881683,"name":"Lale Umutlu","orcid":"0000-0001-5215-7171","position":14,"is_corresponding":false},{"id":2343,"name":"Annette Peters","orcid":"0000-0001-6645-0985","position":15,"is_corresponding":false},{"id":1548077,"name":"Susanne Rospleszcz","orcid":"0000-0002-4788-2341","position":16,"is_corresponding":false},{"id":1354313,"name":"Thomas Kröncke","orcid":"0000-0003-4889-1036","position":17,"is_corresponding":false},{"id":109315,"name":"Norbert Hosten","orcid":"0000-0002-4149-5666","position":18,"is_corresponding":false},{"id":5359,"name":"Henry Völzke","orcid":"0000-0001-7003-399X","position":19,"is_corresponding":false},{"id":1005099,"name":"Lilian Krist","orcid":"0000-0002-6089-5163","position":20,"is_corresponding":false},{"id":1359775,"name":"Stefan N. Willich","orcid":"0009-0006-1270-2597","position":21,"is_corresponding":false},{"id":58322,"name":"Fabian Bamberg","orcid":"0000-0002-7460-3942","position":22,"is_corresponding":false},{"id":378228,"name":"Jürgen Machann","orcid":"0000-0002-4458-5886","position":23,"is_corresponding":false},{"id":1548072,"name":"Tobias Haueise","orcid":"0000-0002-1462-7539","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort","abstract":"<jats:p>This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (VAT) and subcutaneous fat (SAT) in the trunk from standardized magnetic resonance imaging at 3 T, thereby demonstrating the feasibility of deep learning (DL)–based image segmentation in a large population-based cohort in Germany (five sites). Volume and distribution of AT play an essential role in the pathogenesis of insulin resistance, a risk factor of developing metabolic/cardiovascular diseases. Cross-validated training of the DL-segmentation model led to a mean Dice similarity coefficient of &gt;0.94, corresponding to a mean absolute volume deviation of about 22 ml. SAT is significantly increased in women compared to men, whereas VAT is increased in males. Spatial distribution shows age- and body mass index–related displacements. DL-based image segmentation provides robust and fast quantification of AT (≈15 s per dataset versus 3 to 4 hours for manual processing) and assessment of its spatial distribution from magnetic resonance images in large cohort studies.</jats:p>","is_dataset_classified":null,"base_score":2.70805020110221,"endowment":2.70805020110221,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"37172093","pmcid":"PMC10181183","openalex_id":"https://openalex.org/W4376507167","authors":[],"funders":[],"total_grants":0,"fwci":2.5057,"citation_percentile":0.90907197,"influential_citations":0,"citation_trend":[{"year":2023,"count":2},{"year":2024,"count":3},{"year":2025,"count":6},{"year":2026,"count":3}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.science.org/doi/pdf/10.1126/sciadv.add0433?download=true","host_type":"journal"},{"url":"https://www.science.org/doi/pdf/10.1126/sciadv.add0433?download=true","host_type":"publisher"},{"url":"https://www.science.org/doi/pdf/10.1126/sciadv.add0433","host_type":"publisher"},{"url":"https://doi.org/10.1126/sciadv.add0433","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/37172093","host_type":"repository"},{"url":"http://edoc.mdc-berlin.de/23359/1/23359oa.pdf","host_type":"repository"},{"url":"http://nbn-resolving.de/urn:nbn:de:bvb:19-epub-96453-0","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10181183","host_type":"repository"},{"url":"https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/105868","host_type":"repository"},{"url":"https://push-zb.helmholtz-munich.de/frontdoor.php?source_opus=67809","host_type":"repository"},{"url":"https://epub.ub.uni-muenchen.de/96453/1/sciadv.add0433.pdf","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10181183/pdf/sciadv.add0433.pdf","host_type":"repository"},{"url":"https://opus.bibliothek.uni-augsburg.de/opus4/files/105868/105868.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC10181183","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC10181183?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Cardiovascular Disease and Adiposity","Body Composition Measurement Techniques","Adipokines, Inflammation, and Metabolic Diseases","Male","Humans","Female","Adipose Tissue","Risk Factors","Insulin Resistance","Cohort Studies","Magnetic Resonance Imaging"],"mesh_terms":["Adipose Tissue","Female","Humans","Insulin Resistance","Magnetic Resonance Imaging","Male","Risk Factors","Cohort Studies"],"keywords":["Magnetic resonance imaging","Cohort","Trunk","Segmentation","Adipose tissue","Medicine","Population","Distribution (mathematics)","Coefficient of variation","Sørensen–Dice coefficient","Intra-Abdominal Fat","Nuclear medicine","Body mass index","Image segmentation","Internal medicine","Obesity","Artificial intelligence","Insulin resistance","Statistics","Radiology","Mathematics","Computer science","Biology","Visceral fat"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T22:03:00.156775Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}