{"id":"W2156909660","doi":"10.1120/jacmp.v11i3.3175","title":"Assessment of a commercially available automatic deformable registration system","year":2010,"lang":"en","type":"article","venue":"Journal of Applied Clinical Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Cancer Foundation; University of Alberta","funders":"","keywords":"Image registration; Computer science; Protocol (science); Artificial intelligence; Imaging phantom; Computer vision; Modality (human–computer interaction); Transformation (genetics); Range (aeronautics); Image (mathematics); Nuclear medicine; Medicine","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":[0.02206499,0.0009316413,0.0008173434,0.002359676,0.0007709453,0.001577808,0.002206,0.001868098,0.004999399],"category_scores_gemma":[0.03408551,0.0005625887,0.0005774881,0.001288321,0.0008397255,0.001865893,0.002049282,0.0007568179,0.002184155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001156816,"about_ca_system_score_gemma":0.001433161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009973134,"about_ca_topic_score_gemma":0.001248769,"domain_scores_codex":[0.9903153,0.002984218,0.001061141,0.00120497,0.004224555,0.0002097453],"domain_scores_gemma":[0.9789585,0.00692709,0.001376943,0.004391791,0.008009563,0.0003361521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003245828,0.0008177045,0.01524985,0.001340531,0.0003825682,0.0006013509,0.001130861,0.02620868,0.5636676,0.006454599,0.006571941,0.3743285],"study_design_scores_gemma":[0.0003081435,0.009937441,0.04844237,0.0003115,0.0005255738,0.002850931,0.0007666741,0.1705713,0.7243211,0.003153398,0.03838523,0.0004263099],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3831588,0.001512249,0.5898325,0.0008343461,0.0003875089,0.002676564,0.00169383,0.007127896,0.01277625],"genre_scores_gemma":[0.5682426,0.0004702471,0.4208291,0.0003578842,0.0000572832,0.001590459,0.002650766,0.001199501,0.004602005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02206499,"threshold_uncertainty_score":0.1166922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03393541882435368,"score_gpt":0.3748182151467843,"score_spread":0.3408827963224306,"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."}}