{"id":"W4413006740","doi":"10.1148/ryai.240777","title":"Segmenting Whole-Body MRI and CT for Multiorgan Anatomic Structure Delineation","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Leibniz-Gemeinschaft; European Commission; Bundesministerium für Bildung und Forschung; Wilhelm Sander-Stiftung","keywords":"Medicine; Segmentation; Magnetic resonance imaging; Radiology; Artificial intelligence; Retrospective cohort study; Sørensen–Dice coefficient; Medical physics; Nuclear medicine; Computer science; Surgery; Image segmentation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02115671,0.001067585,0.001790482,0.003010644,0.003229934,0.003747855,0.004668168,0.03391378,0.00620679],"category_scores_gemma":[0.06379864,0.001154049,0.001740469,0.002188068,0.01352004,0.00809331,0.004216914,0.05761061,0.00585885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006058151,"about_ca_system_score_gemma":0.007121344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01730281,"about_ca_topic_score_gemma":0.04017428,"domain_scores_codex":[0.9910834,0.002644569,0.001336894,0.001697971,0.002902483,0.0003346578],"domain_scores_gemma":[0.9349576,0.04495436,0.00219876,0.002042311,0.01407637,0.001770567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008019611,0.00001460489,0.0002462358,0.000434218,0.00004483886,0.0002252642,0.0002478712,0.00018681,0.000331564,0.01684685,0.946609,0.03473254],"study_design_scores_gemma":[0.00006297124,0.00004092219,0.001028776,0.001486138,0.0000505635,0.000922342,0.0002330682,0.0005076177,0.0006949065,0.02590256,0.9689672,0.0001028996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0002222043,0.02462505,0.001711641,0.8511949,0.1182722,0.00002991812,0.0001339025,0.00005401846,0.00375624],"genre_scores_gemma":[0.006336804,0.02824071,0.004538816,0.6485441,0.2986708,0.0001233858,0.0001633448,0.0002218921,0.01316009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03391378,"threshold_uncertainty_score":0.1118887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01485289743627369,"score_gpt":0.3371446896156106,"score_spread":0.3222917921793369,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}