{"id":"W2127927483","doi":"10.1109/titb.2006.864476","title":"High-Performance Medical Image Registration Using New Optimization Techniques","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Computer science; Image registration; Metric (unit); Implementation; Image (mathematics); Bounded function; Computation; Similarity (geometry); Parallel computing; Algorithm; Mathematical optimization; Artificial intelligence; 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":[0.0009440181,0.0005026212,0.000789463,0.0005232979,0.0002962366,0.0007011299,0.0009027778,0.0005729874,0.001349801],"category_scores_gemma":[0.00166289,0.000500245,0.000613895,0.001070323,0.0005708435,0.00109234,0.001246415,0.001059361,0.0006153284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003948112,"about_ca_system_score_gemma":0.000590539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000647108,"about_ca_topic_score_gemma":0.001035941,"domain_scores_codex":[0.9991807,0.0001975401,0.00004701779,0.0001172663,0.0004324265,0.00002496844],"domain_scores_gemma":[0.9994634,0.0001895118,0.00007787762,0.000148093,0.0001023426,0.000018765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001621976,0.00007200295,0.0007372844,0.0003015784,0.0001654245,0.0001852125,0.000191952,0.3605204,0.09569297,0.05183456,0.00336636,0.4867701],"study_design_scores_gemma":[0.00004229938,0.00006444124,0.0005135561,0.00001194801,0.00002206595,0.0002891203,0.0000134853,0.9582708,0.01692853,0.01148713,0.0123282,0.00002840764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003159,0.0003123313,0.9954028,0.00008968817,0.00001493214,0.00001393445,0.000007523355,0.0003493582,0.0006504342],"genre_scores_gemma":[0.05479179,0.0005428343,0.9427086,0.00004845535,0.00004178713,0.0001080468,0.00004768892,0.0001279021,0.001582879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001349801,"threshold_uncertainty_score":0.004992485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00797374032554557,"score_gpt":0.2587452523931287,"score_spread":0.2507715120675831,"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."}}