{"id":"W4416961439","doi":"10.1109/embc58623.2025.11253480","title":"Trained nnU-Net model for semantic segmentation of human adult cervical vertebrae from CT-Scans","year":2025,"lang":"en","type":"article","venue":"","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Metric (unit); Cervical vertebrae; Test set; Market segmentation; Cervical spine; Pattern recognition (psychology); Percentile","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00004469444,0.00007991529,0.0001764417,0.00006810581,0.00002771194,0.00001132452,0.00008340847,0.00002597698,0.0001434354],"category_scores_gemma":[0.00002085892,0.00007174761,0.00007347353,0.0001245334,0.00001849685,0.00004035136,0.000008683782,0.00005354471,0.000003326862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000188105,"about_ca_system_score_gemma":0.00001183146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002681136,"about_ca_topic_score_gemma":0.0001588004,"domain_scores_codex":[0.9994502,0.000007132222,0.000215561,0.0001131893,0.0000980284,0.0001158804],"domain_scores_gemma":[0.9997457,0.00004378557,0.00001357273,0.000116504,0.00003562005,0.00004482296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002400214,0.0004098061,0.007287904,0.002126682,0.002076092,0.000006833094,0.00339641,0.4346181,0.3584406,0.01887074,0.1037254,0.06901741],"study_design_scores_gemma":[0.0005182901,0.000005019989,0.0002439104,0.0000424258,0.0001183021,6.143556e-8,0.0001484418,0.9845269,0.01213963,0.002169811,0.00001775781,0.00006944158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2112966,0.00003193688,0.7855134,0.0003259937,0.00005361475,0.0000816168,0.00003646116,0.0001227266,0.002537658],"genre_scores_gemma":[0.9932848,0.00000741193,0.005055783,0.0002383333,0.00002415305,0.00001690256,0.0001302838,0.000009724365,0.001232593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7819882,"threshold_uncertainty_score":0.2925783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01083940028205548,"score_gpt":0.2584687428417137,"score_spread":0.2476293425596582,"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."}}