{"id":"W2100178215","doi":"10.1109/bmei.2008.108","title":"Multimodality Medical Image Registration Using Hybrid Optimization Algorithm","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Multimodality; Image registration; Metric (unit); Similarity (geometry); Computer science; Matching (statistics); Medical imaging; Mutual information; Optimization algorithm; Component (thermodynamics); Similarity measure; Artificial intelligence; Image (mathematics); Optimization problem; Hybrid algorithm (constraint satisfaction); Algorithm; Computer vision; Pattern recognition (psychology); Mathematical optimization; Mathematics","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":[0.0004825215,0.0001019826,0.0001159268,0.00007207342,0.0001556364,0.00007058924,0.0004756471,0.0000593336,0.0005016024],"category_scores_gemma":[0.0003019611,0.00009325985,0.00004028961,0.0002237778,0.000153929,0.001079841,0.0001262533,0.000123953,0.0000198389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006533045,"about_ca_system_score_gemma":0.0001926363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001977521,"about_ca_topic_score_gemma":0.000001363176,"domain_scores_codex":[0.9982325,0.0001090538,0.0003342301,0.0003190441,0.0008278336,0.0001772956],"domain_scores_gemma":[0.9991195,0.00005451821,0.0001063588,0.0003690232,0.0001551383,0.0001954588],"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":[0.00001041798,0.0009107376,0.00035911,0.00005695274,0.0000518036,0.001352009,0.001016438,0.00225696,0.01302062,0.003551729,0.04070061,0.9367126],"study_design_scores_gemma":[0.0002082354,0.00002000811,0.0000699443,0.000008415314,0.000001694824,0.0002094111,0.000005999195,0.9479916,0.05116162,0.0001861675,0.00002980521,0.0001070689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003965984,0.000008525823,0.9965861,0.0006359309,0.0001244539,0.0001572978,0.000001541141,0.0005838721,0.001505634],"genre_scores_gemma":[0.007285994,0.00003488812,0.991477,0.0009438983,0.00006746195,0.00000782285,0.00001749582,0.000006596792,0.0001588279],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9457347,"threshold_uncertainty_score":0.5492195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03249991477267495,"score_gpt":0.3112613623613477,"score_spread":0.2787614475886727,"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."}}