{"id":"W2043157575","doi":"10.1007/s11548-012-0680-y","title":"Validation of a hybrid Doppler ultrasound vessel-based registration algorithm for neurosurgery","year":2012,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research","keywords":"Imaging phantom; Computer science; Robustness (evolution); Image registration; Artificial intelligence; Computer vision; Preprocessor; Algorithm; Radiology; Medicine; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003570879,0.0008194514,0.0009449251,0.002263491,0.0005635432,0.002104191,0.001429539,0.001950518,0.001988399],"category_scores_gemma":[0.007573144,0.0005593992,0.0008207948,0.001158089,0.0004174489,0.001144061,0.001129112,0.0007707753,0.001824231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004395397,"about_ca_system_score_gemma":0.001594632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002603631,"about_ca_topic_score_gemma":0.00269364,"domain_scores_codex":[0.9986985,0.0003576307,0.0001056116,0.0002992758,0.000456503,0.00008254066],"domain_scores_gemma":[0.9968657,0.001076526,0.0002516984,0.0004248624,0.001286336,0.00009488039],"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":[0.001569736,0.0004617883,0.02175385,0.0003471165,0.0005544134,0.0001925703,0.0002249355,0.06064335,0.1346049,0.002180602,0.004083958,0.7733828],"study_design_scores_gemma":[0.0001149166,0.0004819047,0.01582727,0.00004927019,0.0003131177,0.0009206091,0.00009151439,0.8970277,0.07998826,0.001055151,0.004050707,0.00007950194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1528166,0.0007604925,0.8380843,0.0001901219,0.0001927126,0.0002372864,0.0003933943,0.005812585,0.001512451],"genre_scores_gemma":[0.4808243,0.0004297906,0.5146716,0.0001269976,0.00005697268,0.0002140274,0.0009119212,0.0006992493,0.00206516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003570879,"threshold_uncertainty_score":0.0188849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02948828211029321,"score_gpt":0.3138741269156588,"score_spread":0.2843858448053656,"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."}}