{"id":"W2036113022","doi":"10.1587/transinf.e93.d.3414","title":"Improved Demons Technique with Orthogonal Gradient Information for Medical Image Registration","year":2010,"lang":"en","type":"article","venue":"IEICE Transactions on Information and Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Image registration; Artificial intelligence; Computer vision; Image (mathematics); Medical imaging","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":[0.0009458663,0.0007134216,0.0004271206,0.00166358,0.000290501,0.0005506229,0.0006007127,0.0007238321,0.001605839],"category_scores_gemma":[0.002059398,0.0003145345,0.0006973981,0.001157088,0.0006123156,0.001168592,0.0007143929,0.0007005288,0.0006336929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003682646,"about_ca_system_score_gemma":0.000487452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008812136,"about_ca_topic_score_gemma":0.001490795,"domain_scores_codex":[0.999195,0.0002659473,0.00004588297,0.00006927295,0.0003820187,0.0000419134],"domain_scores_gemma":[0.9994572,0.0001673526,0.00006594017,0.0001155055,0.0001661804,0.00002791148],"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":[0.0003957503,0.00006944717,0.0008556428,0.0004835364,0.0001978215,0.000517007,0.0003085197,0.04188839,0.2415121,0.03049414,0.005269716,0.678008],"study_design_scores_gemma":[0.00006557206,0.000400512,0.002496985,0.00006624359,0.0001745664,0.003697504,0.0001349192,0.6208209,0.3175577,0.01337506,0.04102898,0.0001810943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01460968,0.0008074992,0.9822525,0.0001683274,0.00008904021,0.00005345478,0.00003382597,0.0005052685,0.001480441],"genre_scores_gemma":[0.1759001,0.00100477,0.8202707,0.0001315906,0.0001037609,0.00006576272,0.0001363618,0.0001686778,0.002218203],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00166358,"threshold_uncertainty_score":0.005372047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006876777216274747,"score_gpt":0.2489098323704226,"score_spread":0.2420330551541479,"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."}}