{"id":"W2783637515","doi":"10.1016/j.compmedimag.2018.01.007","title":"A novel contour-based registration of lateral cephalogram and profile photograph","year":2018,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Peking University","keywords":"Artificial intelligence; Computer science; Computer vision; Cephalogram; Nasion; Iterative closest point; Landmark; Forehead; Robustness (evolution); Mathematics; Point cloud; Orthodontics; Malocclusion; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006893865,0.0006542809,0.0009580344,0.00224313,0.0003640201,0.00169079,0.001448628,0.001121745,0.005078198],"category_scores_gemma":[0.001928207,0.0008072443,0.0009158236,0.002231051,0.0004072803,0.001165146,0.001280953,0.001044082,0.003473905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003602949,"about_ca_system_score_gemma":0.001258884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00178715,"about_ca_topic_score_gemma":0.002877281,"domain_scores_codex":[0.9990039,0.0001084836,0.00005732836,0.0001999392,0.0005813782,0.00004902437],"domain_scores_gemma":[0.9992614,0.0001325704,0.00007433532,0.0002109077,0.0002724607,0.00004836095],"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":[0.0003699311,0.0001036864,0.001140254,0.0002241211,0.00008191926,0.0003252557,0.00008236279,0.006688393,0.2358551,0.00405849,0.004283039,0.7467874],"study_design_scores_gemma":[0.0001183877,0.0003890407,0.009239183,0.00007785704,0.0002229762,0.007520736,0.0001042595,0.6313542,0.3092661,0.003074142,0.03845388,0.0001792439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007240606,0.0002350063,0.9888755,0.00007629493,0.00008800332,0.00009536986,0.00009941289,0.002004976,0.001284821],"genre_scores_gemma":[0.05287058,0.0004371999,0.9423309,0.00007027426,0.0000769855,0.0001015515,0.0003448563,0.0004240628,0.003343544],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005078198,"threshold_uncertainty_score":0.01698828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01544641971348917,"score_gpt":0.2866499178563697,"score_spread":0.2712034981428806,"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."}}