{"id":"W2174150962","doi":"10.1007/978-3-642-23623-5_58","title":"Model-Based Deformable Registration of Preoperative 3D to Intraoperative Low-Resolution 3D and 2D Sequences of MR Images","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Image registration; Computer science; Computer vision; Artificial intelligence; Imaging phantom; Mutual information; Process (computing); Nuclear medicine; Image (mathematics); Medicine","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":[0.0005866563,0.0005961154,0.0007093165,0.001370015,0.0003110577,0.001514946,0.0007995226,0.001085792,0.002157182],"category_scores_gemma":[0.00220811,0.0007651716,0.001139626,0.001236096,0.0004208002,0.0007817612,0.0009127676,0.001023751,0.001124487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006082971,"about_ca_system_score_gemma":0.001605492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005568958,"about_ca_topic_score_gemma":0.009458221,"domain_scores_codex":[0.9995841,0.00007370512,0.0000370795,0.00009488723,0.0001674133,0.00004280644],"domain_scores_gemma":[0.9996191,0.0000934702,0.00007216931,0.0001169292,0.00007926028,0.00001899096],"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":[0.0008181463,0.0002067153,0.003365615,0.0004415162,0.0001952973,0.0004796079,0.0004335393,0.2671278,0.2407897,0.004082838,0.005261719,0.4767976],"study_design_scores_gemma":[0.00003767161,0.0001821766,0.006367479,0.00005042971,0.00009926037,0.001393466,0.0001042086,0.89542,0.08605597,0.003533636,0.006656525,0.00009915157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05412313,0.0004995115,0.9401079,0.0002205272,0.0001189861,0.0001500017,0.0004069801,0.002741229,0.001631861],"genre_scores_gemma":[0.542041,0.001153826,0.4494625,0.0002115625,0.00005692107,0.000242046,0.001649166,0.0013117,0.003871314],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005568958,"threshold_uncertainty_score":0.01107305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399961469437793,"score_gpt":0.2802757468574334,"score_spread":0.2562761321630555,"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."}}