{"id":"W2165759407","doi":"10.1109/titb.2009.2018286","title":"Texture Analysis for Automatic Segmentation of Intervertebral Disks of Scoliotic Spines From MR Images","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Segmentation; Artificial intelligence; Pattern recognition (psychology); Sagittal plane; Computer science; Computer vision; Intervertebral disk; Feature (linguistics); Anatomy; Medicine","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":[0.0001093526,0.0001119047,0.0003601493,0.002547178,0.00002186632,0.000005287769,0.00012703,0.0001327409,0.0000664868],"category_scores_gemma":[0.00002031038,0.00009606007,0.0001099044,0.002086855,0.0001152241,0.0002476945,5.602413e-7,0.0001376266,0.000003201628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004306431,"about_ca_system_score_gemma":0.000008948971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004129579,"about_ca_topic_score_gemma":0.000008050186,"domain_scores_codex":[0.9989939,0.000008246359,0.0006596546,0.00007320863,0.0001475383,0.0001174882],"domain_scores_gemma":[0.9995559,0.00004163169,0.0001286274,0.0001786671,0.00006655764,0.00002865008],"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":[0.00002802097,0.0002909912,0.001530632,0.0006031343,0.001390881,0.000001279746,0.001538636,0.1021003,0.06746688,0.00009158839,0.0004623598,0.8244953],"study_design_scores_gemma":[0.001508385,0.0002512748,0.005124986,0.0003280395,0.000734544,0.000001559603,0.001153444,0.6864204,0.3033965,0.0009117234,0.00001880504,0.0001504211],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1965359,0.0000645148,0.8020965,0.0008172065,0.00009585455,0.0001442858,0.00007985979,0.000145528,0.00002039952],"genre_scores_gemma":[0.9947796,0.00006141231,0.004987686,0.00005361235,0.000007606272,0.00002432666,0.00007555971,0.000003931568,0.000006250315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8243448,"threshold_uncertainty_score":0.3917216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005795803725425056,"score_gpt":0.2477866336699634,"score_spread":0.2419908299445383,"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."}}