{"id":"W2049737217","doi":"10.1117/12.431045","title":"<title>Fast noniterative registration of magnetic resonance images</title>","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Image registration; Preprocessor; Artificial intelligence; Voxel; Computer vision; Noise (video); Matching (statistics); Java; Pattern recognition (psychology); Image (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003336657,0.0001466432,0.0001896099,0.00007362169,0.00003355,0.00007607101,0.0007380413,0.00009393672,0.0001022624],"category_scores_gemma":[0.0002631317,0.0001267216,0.0001923511,0.0002887938,0.0001935476,0.0004405312,0.0001085553,0.0001483583,0.0000101697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006598153,"about_ca_system_score_gemma":0.00003007718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002381092,"about_ca_topic_score_gemma":6.338195e-8,"domain_scores_codex":[0.9986334,2.916851e-8,0.0004139332,0.0002368631,0.0005359895,0.0001797705],"domain_scores_gemma":[0.9987001,0.00006147043,0.0002359249,0.00006576686,0.0008714895,0.00006519409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001689471,0.00008691949,0.00007799121,0.0001572164,0.00005487965,3.588252e-7,0.0001900273,0.000004249991,0.3584426,0.583898,0.04506113,0.01200979],"study_design_scores_gemma":[0.001873025,0.001176579,0.003310624,0.001175689,0.000148967,0.00007247407,0.000400672,0.107266,0.7682875,0.01150762,0.103732,0.001048892],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7842419,0.0008198679,0.02652024,0.002965274,0.0005974299,0.0009812339,0.00005525341,0.0003402655,0.1834786],"genre_scores_gemma":[0.1105116,0.0005099099,0.8800837,0.0002500062,0.0004763223,0.0001374246,0.00001215199,0.00005734403,0.00796152],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8535635,"threshold_uncertainty_score":0.5167557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045777300175444,"score_gpt":0.2415395918127639,"score_spread":0.2310818188110095,"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."}}