{"id":"W2007993528","doi":"10.1118/1.2192621","title":"Automated 2D‐3D registration of a radiograph and a cone beam CT using line‐segment enhancementa)","year":2006,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Imaging phantom; Radiography; Cone beam computed tomography; Image-guided radiation therapy; Nuclear medicine; Projection (relational algebra); Orientation (vector space); Image registration; Pixel; Line (geometry); Digital radiography; Artificial intelligence; Medical imaging; Computer vision; Computer science; Medicine; Mathematics; Computed tomography; Radiology; Image (mathematics); Geometry; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.0001640629,0.0001009239,0.0002135483,0.00004016328,0.00002976124,0.00001219702,0.00005911844,0.0000332538,0.00004157654],"category_scores_gemma":[0.00002914244,0.00009172154,0.0000511083,0.0002540851,0.0001364539,0.00004794366,0.00001344865,0.0001152467,0.00000176704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002073198,"about_ca_system_score_gemma":0.00002077335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002026478,"about_ca_topic_score_gemma":0.00000395933,"domain_scores_codex":[0.9990018,0.00001398631,0.0002810111,0.0001185454,0.0004335331,0.0001511304],"domain_scores_gemma":[0.9996769,0.00003712834,0.00004928315,0.0001116622,0.00002276273,0.0001022795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002707355,0.001949843,0.01922329,0.002843175,0.001656953,0.0002669838,0.0008268705,0.07515965,0.5352888,0.0009599307,0.04579666,0.3160007],"study_design_scores_gemma":[0.0005504233,0.00002130891,0.000352305,0.0001901285,0.000115323,0.000006182251,0.00001610032,0.9400089,0.05797347,0.0003298656,0.000292022,0.0001439979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7786594,0.0005074532,0.2197288,0.00009832855,0.000100654,0.00006546274,0.000005796438,0.0002620396,0.000572087],"genre_scores_gemma":[0.9985768,0.00008459042,0.0009918165,0.00005108377,0.0002102498,0.000003717072,0.00004101369,0.00001196645,0.00002880228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8648492,"threshold_uncertainty_score":0.3740296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01064246673776372,"score_gpt":0.2477506618523483,"score_spread":0.2371081951145845,"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."}}