{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003627051,0.0001409712,0.0002885427,0.0001711641,0.0002107554,0.00002886104,0.0000912519,0.00009197265,0.00004850023],"category_scores_gemma":[0.0008923186,0.0001269176,0.00005770281,0.0001763667,0.0002186486,0.0000496018,0.00003592354,0.0003318972,0.000007098397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005285372,"about_ca_system_score_gemma":0.00007605122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002153556,"about_ca_topic_score_gemma":0.00001039171,"domain_scores_codex":[0.998869,0.00005563767,0.0003804203,0.0003566167,0.00007148247,0.0002668307],"domain_scores_gemma":[0.9992999,0.0002735204,0.00008281777,0.0001678674,0.00009447572,0.00008134566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002780936,0.00008865225,0.02696873,0.0002330006,0.0002066899,0.00003749542,0.0004256404,0.00157654,0.263247,0.04598583,0.001982471,0.6589698],"study_design_scores_gemma":[0.0003360765,0.0001596092,0.004078293,0.0001451804,0.0001969542,0.0002328872,0.000479454,0.8975403,0.07275604,0.01395393,0.00990204,0.0002192782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4718119,0.0003564313,0.5218209,0.00509206,0.0004210809,0.0003455215,0.000007491328,0.00005303535,0.00009153073],"genre_scores_gemma":[0.9835331,0.00007015229,0.01436248,0.001303398,0.0002670633,0.00001610718,0.00005687353,0.00001464316,0.0003761539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8959637,"threshold_uncertainty_score":0.517555,"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."}}