{"id":"W2017810925","doi":"10.1117/12.844080","title":"Multi-slice to volume registration of ultrasound data to a statistical atlas of human pelvis","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Johns Hopkins University","keywords":"Computer science; Image registration; Artificial intelligence; Computer vision; Atlas (anatomy); Orientation (vector space); Fluoroscopy; Ultrasound; Pelvis; Data set; Cadaver; Medicine; Radiology; Anatomy; Mathematics; 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":[0.001015144,0.0006220411,0.0006354928,0.001915688,0.0002380585,0.001041137,0.0007708887,0.0006703061,0.00171802],"category_scores_gemma":[0.003256645,0.0005948475,0.0008692063,0.001821443,0.000523355,0.0006517007,0.0009860059,0.000669479,0.0007763011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004893913,"about_ca_system_score_gemma":0.001269519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002677118,"about_ca_topic_score_gemma":0.004992996,"domain_scores_codex":[0.9994461,0.0001440333,0.00005195779,0.0001403559,0.0001810616,0.00003645764],"domain_scores_gemma":[0.9989465,0.0002946779,0.000177086,0.0003147006,0.000232766,0.0000342704],"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":[0.0005868829,0.0002232882,0.01047319,0.0006369035,0.0002616658,0.0006238325,0.0006363903,0.2946439,0.2256474,0.00754864,0.004283179,0.4544347],"study_design_scores_gemma":[0.0000514471,0.0007695784,0.03717992,0.00007591469,0.0001408859,0.003198512,0.0004014076,0.7740025,0.1551761,0.009738989,0.01909312,0.0001716569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04196893,0.0002941698,0.9544359,0.00009953717,0.00004933015,0.0001847989,0.000598027,0.001711469,0.0006577396],"genre_scores_gemma":[0.3457558,0.0006582236,0.6493995,0.00008156828,0.00004993611,0.000376404,0.001978935,0.0005554175,0.001144187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002677118,"threshold_uncertainty_score":0.005747318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01901909287206468,"score_gpt":0.269421826194763,"score_spread":0.2504027333226983,"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."}}