{"id":"W1967866963","doi":"10.1109/icip.2014.7025002","title":"Cross modality label fusion in multi-atlas segmentation","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Atlas (anatomy); Segmentation; Image fusion; Pattern recognition (psychology); Computer vision; Image segmentation; Fusion; Wavelet transform; Image (mathematics); Wavelet","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":[0.0006557088,0.00008170292,0.00009271385,0.00008397245,0.0000492664,0.0001280893,0.0004047274,0.00005090881,0.0001215842],"category_scores_gemma":[0.00012001,0.00007080582,0.00001835743,0.0002519127,0.00004328717,0.0006377029,0.0001754221,0.00009040055,0.00008558203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005221818,"about_ca_system_score_gemma":0.00001794361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000225699,"about_ca_topic_score_gemma":0.00004445295,"domain_scores_codex":[0.998885,0.0001286956,0.0002552383,0.0002853263,0.0002780077,0.0001676977],"domain_scores_gemma":[0.9994219,0.00007447953,0.0000591491,0.0003099245,0.00005635717,0.00007817561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009487754,0.0004980841,0.02177875,0.00004201906,0.000004462459,0.000007128919,0.001157967,0.00004258382,0.1855115,0.01188512,0.001081088,0.7779818],"study_design_scores_gemma":[0.001557864,0.00009950345,0.04994366,0.00002065739,0.000001170863,0.000002418269,0.00002610339,0.4531747,0.491614,0.003222181,0.0001278888,0.0002098548],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04965657,0.0000050429,0.9482197,0.0004458318,0.0001056617,0.0001687852,4.292047e-7,0.0002963966,0.001101597],"genre_scores_gemma":[0.2628726,0.000006385949,0.7351903,0.001183663,0.00001411121,0.00002770574,0.0000054423,0.000004247707,0.0006955547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7777719,"threshold_uncertainty_score":0.2887378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03912511463799753,"score_gpt":0.3588219203947126,"score_spread":0.3196968057567151,"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."}}