{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009051696,0.001171534,0.0007734373,0.001120929,0.0003674769,0.0009112652,0.001403504,0.00181151,0.001983509],"category_scores_gemma":[0.001882283,0.0005613554,0.001141304,0.0006646842,0.0004487968,0.0006692641,0.0006255031,0.0008960108,0.001001246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001618087,"about_ca_system_score_gemma":0.001533256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0326729,"about_ca_topic_score_gemma":0.03484135,"domain_scores_codex":[0.9997208,0.00003803134,0.00002060004,0.0001216643,0.00005456712,0.00004438611],"domain_scores_gemma":[0.9996576,0.0001402887,0.00003134918,0.00003000248,0.0001205604,0.00002009089],"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":[0.0004261011,0.0001053098,0.004899296,0.0001489352,0.0001483135,0.0001647707,0.0001062223,0.845589,0.007937597,0.001190623,0.003576014,0.1357079],"study_design_scores_gemma":[0.000004643075,0.00002693542,0.0006326972,0.00001325363,0.00001474621,0.00003187688,0.000009347122,0.9966492,0.001823385,0.0004923115,0.0002937853,0.000007806083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3818042,0.003272348,0.5927973,0.0009824968,0.0004075012,0.0003443316,0.003408894,0.009219907,0.007763089],"genre_scores_gemma":[0.8417118,0.0007190686,0.1440097,0.0004559327,0.00007457479,0.0003329583,0.004783947,0.0003444804,0.00756747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0326729,"threshold_uncertainty_score":0.06496549,"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."}}