{"id":"W167675073","doi":"10.1007/978-3-642-30618-1_13","title":"Fast and Robust Registration Based on Gradient Orientations: Case Study Matching Intra-operative Ultrasound to Pre-operative MRI in Neurosurgery","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Computer science; Image registration; Orientation (vector space); Neurosurgery; Matching (statistics); Magnetic resonance imaging; Context (archaeology); Artificial intelligence; Maximization; Computer vision; Ultrasound; Radiology; Medical physics; Medicine; Image (mathematics); Mathematics; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00161118,0.0005242093,0.0004754384,0.001173242,0.0003516949,0.0009158602,0.0008936353,0.0001555129,0.0000186088],"category_scores_gemma":[0.0002701524,0.0004736179,0.00004945256,0.0008610892,0.0004181733,0.001174359,0.0003682125,0.000828519,0.000006948264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004308209,"about_ca_system_score_gemma":0.0003466667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000251832,"about_ca_topic_score_gemma":0.0008940528,"domain_scores_codex":[0.9958727,0.0002439083,0.0007581383,0.001559655,0.001039871,0.0005257718],"domain_scores_gemma":[0.9970168,0.001302766,0.0002700301,0.0008775369,0.0002233686,0.0003095],"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":[0.00004303842,0.0007357648,0.00197469,0.00007095227,0.00002077356,0.002593497,0.07097694,0.4005222,0.0009339313,0.003008079,0.00008079427,0.5190393],"study_design_scores_gemma":[0.002362303,0.004646614,0.005960426,0.002174843,0.00005372552,0.002142035,0.0002498774,0.9539658,0.01273572,0.01213769,0.00006296122,0.003508046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007446985,0.00003815653,0.9890657,0.0005668894,0.0007121842,0.001856639,0.000008297704,0.0001302143,0.000174908],"genre_scores_gemma":[0.5173873,0.00001842854,0.4791645,0.003053936,0.0001558969,0.000125257,0.000009423678,0.00003020058,0.00005509511],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5534436,"threshold_uncertainty_score":0.9997715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02348759358491822,"score_gpt":0.3016621158118932,"score_spread":0.278174522226975,"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."}}