{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009468482,0.0001524012,0.0002459518,0.0002119885,0.0001089298,0.0001510017,0.0004313379,0.00008766995,0.00001678117],"category_scores_gemma":[0.0001988148,0.0001325658,0.00005043234,0.0003924829,0.001219069,0.0002421372,0.0001917437,0.0001897292,5.649903e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005061248,"about_ca_system_score_gemma":0.00008623052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000797746,"about_ca_topic_score_gemma":0.000005603191,"domain_scores_codex":[0.9982805,0.0001033708,0.0003886455,0.0003876115,0.0006111576,0.0002286639],"domain_scores_gemma":[0.9988316,0.0002099283,0.0001648019,0.0002979297,0.0001843459,0.0003113478],"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":[0.0001224379,0.0006958375,0.01167495,0.0004452754,0.00008342115,0.00008477998,0.001127261,1.257796e-7,0.07976077,0.01230146,0.006402136,0.8873016],"study_design_scores_gemma":[0.007433529,0.0006978432,0.02790036,0.001048251,0.00004878343,0.0003042801,0.00003541007,0.9057416,0.04801704,0.00533993,0.002673957,0.0007590136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02519193,0.000136823,0.9723122,0.001624877,0.0001978871,0.00020447,0.000004251897,0.0002457176,0.00008185645],"genre_scores_gemma":[0.5661475,0.0000951824,0.4298594,0.003727501,0.0001101879,0.0000224528,0.00001607522,0.00001062054,0.0000110238],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9057415,"threshold_uncertainty_score":0.5405875,"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."}}