{"id":"W2022251943","doi":"10.1109/embc.2013.6610266","title":"Fast and robust 3D vertebra segmentation using statistical shape models","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Discriminative model; Segmentation; Minimum bounding box; Pattern recognition (psychology); Point distribution model; Voxel; Computer vision; Vertebra; Probabilistic logic; Image segmentation; Statistical model; Active shape model; Image (mathematics); Anatomy","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":[0.0008670354,0.0009579093,0.001545384,0.002167204,0.0005161033,0.001860306,0.002131149,0.002288317,0.001890346],"category_scores_gemma":[0.002164103,0.00161635,0.00232188,0.001325565,0.0007014628,0.001285351,0.001732984,0.001166407,0.002645974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008230826,"about_ca_system_score_gemma":0.00160992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005100789,"about_ca_topic_score_gemma":0.00942133,"domain_scores_codex":[0.9987695,0.0001163769,0.00006934714,0.0003047136,0.0006557367,0.00008447377],"domain_scores_gemma":[0.9990707,0.000277268,0.0001424693,0.0002334303,0.0002269741,0.0000491473],"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":[0.0001486083,0.00007420593,0.002652428,0.0001790929,0.0001929079,0.0001691854,0.0001197362,0.2375067,0.1067424,0.003806686,0.004189883,0.6442182],"study_design_scores_gemma":[0.00001096143,0.00004449788,0.00125151,0.00001602373,0.00002314015,0.0002216434,0.00001694861,0.9767095,0.01674113,0.002820954,0.002110925,0.00003274523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004014676,0.0001154811,0.9917745,0.00005525681,0.00001221726,0.00002851851,0.00006969357,0.003679514,0.0002502353],"genre_scores_gemma":[0.08519699,0.0002420829,0.9121094,0.0001225071,0.00003216869,0.0001064133,0.0005868378,0.0006101585,0.0009934513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005100789,"threshold_uncertainty_score":0.01014221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02268503085483542,"score_gpt":0.2266226745466824,"score_spread":0.203937643691847,"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."}}