{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001620976,0.0007140369,0.001412827,0.00149229,0.0003723605,0.001000551,0.001054158,0.001347902,0.001418434],"category_scores_gemma":[0.00223689,0.0005306375,0.001102886,0.001305921,0.0006473187,0.001249858,0.001383432,0.0007026985,0.0004796706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005660235,"about_ca_system_score_gemma":0.0005943425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001020699,"about_ca_topic_score_gemma":0.0008184559,"domain_scores_codex":[0.9990433,0.0003124981,0.00006413895,0.0001828048,0.0003462339,0.00005099276],"domain_scores_gemma":[0.9994532,0.0002804723,0.00007990707,0.0000603436,0.0001081898,0.00001775468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002039182,0.00008868251,0.0006857477,0.0001303322,0.0002315981,0.0001050667,0.0001187973,0.6753113,0.02646125,0.01944317,0.001231753,0.2759883],"study_design_scores_gemma":[0.00001358096,0.00005007047,0.0001805907,0.000005068212,0.00001742106,0.00007152366,0.000007887861,0.9913549,0.003205381,0.004124427,0.0009540663,0.00001518123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003856171,0.0001115531,0.9953201,0.00003623747,0.000008007951,0.00001913516,0.00000576333,0.0001849806,0.0004579581],"genre_scores_gemma":[0.1440104,0.0001899728,0.853007,0.00006944565,0.0000290604,0.0002418555,0.0000667588,0.0001495164,0.002235908],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001620976,"threshold_uncertainty_score":0.008572638,"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."}}