{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001290673,0.001017371,0.0009342827,0.002885517,0.0007962795,0.001505221,0.001639721,0.00477531,0.003162529],"category_scores_gemma":[0.005653611,0.000954323,0.0009015365,0.00285858,0.0014977,0.001367214,0.001226772,0.001866343,0.002109797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004417319,"about_ca_system_score_gemma":0.0006808563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001860121,"about_ca_topic_score_gemma":0.003066977,"domain_scores_codex":[0.9992353,0.0001530003,0.0001207906,0.0001322815,0.0002193297,0.0001394257],"domain_scores_gemma":[0.9982867,0.0007758741,0.0002365376,0.0004613851,0.0001267741,0.0001126846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"case_report","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001865615,0.0008722917,0.0220526,0.0007217249,0.0001642507,0.6360294,0.001391102,0.006588716,0.07733064,0.00239165,0.004366101,0.246226],"study_design_scores_gemma":[0.00008745322,0.0009101673,0.01467775,0.000110442,0.0002005522,0.8990479,0.0008245863,0.02392156,0.04915579,0.003619062,0.00733919,0.000105593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7701131,0.0055905,0.2058188,0.002266604,0.0003916143,0.0006609272,0.0006357108,0.001039949,0.01348291],"genre_scores_gemma":[0.9010794,0.002189721,0.089854,0.0003672375,0.0003436398,0.0001090031,0.0002704477,0.0004878152,0.005298763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00477531,"threshold_uncertainty_score":0.01057971,"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."}}