{"id":"W4317795309","doi":"10.1109/tuffc.2023.3239320","title":"Toward Estimating MRI-Ultrasound Registration Error in Image-Guided Neurosurgery","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada First Research Excellence Fund","keywords":"Neuronavigation; Image registration; Ultrasound; Artificial intelligence; Convolutional neural network; Magnetic resonance imaging; Computer science; Computer vision; 3D ultrasound; 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":[0.00292483,0.0008323748,0.0007969934,0.001139947,0.0002649354,0.0009063071,0.001122309,0.0011943,0.0003857756],"category_scores_gemma":[0.01137215,0.0006074729,0.0004346918,0.0008262242,0.0008696023,0.0009330119,0.0009110151,0.0007547868,0.0001984031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001015857,"about_ca_system_score_gemma":0.001472691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01206524,"about_ca_topic_score_gemma":0.01080191,"domain_scores_codex":[0.9986816,0.0003745439,0.00008308253,0.0002470059,0.0005530411,0.00006069174],"domain_scores_gemma":[0.9972579,0.00138439,0.0004463176,0.0002871764,0.0005723504,0.00005194256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000171318,0.00006641995,0.003043542,0.0000766001,0.00006102598,0.00008989996,0.0001171164,0.8342044,0.03535863,0.002938504,0.0003582725,0.1235144],"study_design_scores_gemma":[0.000002341769,0.00002223573,0.0005358228,0.00000570905,0.000006139888,0.00003099468,0.000006689709,0.9913362,0.00742092,0.0004510884,0.0001728896,0.000008777956],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04431525,0.0002627546,0.9546332,0.00009261043,0.00001438702,0.00003041438,0.0000181432,0.0004464691,0.0001869243],"genre_scores_gemma":[0.4911201,0.0003268399,0.5071496,0.00006861347,0.00002017711,0.00006077929,0.0001028974,0.0001877613,0.0009631329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01206524,"threshold_uncertainty_score":0.02399004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03337137577208331,"score_gpt":0.2882193087517732,"score_spread":0.2548479329796899,"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."}}