{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006881333,0.001075351,0.0016855,0.004121752,0.001277233,0.002947756,0.001788692,0.003054642,0.002277737],"category_scores_gemma":[0.008399225,0.0009801026,0.001979035,0.003022406,0.001659419,0.003410316,0.003819819,0.001904364,0.001148049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00124844,"about_ca_system_score_gemma":0.001238571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002070251,"about_ca_topic_score_gemma":0.002164493,"domain_scores_codex":[0.9966466,0.001309712,0.0001727871,0.000626598,0.0009706449,0.0002737015],"domain_scores_gemma":[0.9965828,0.001490608,0.0004426169,0.0006335381,0.0006951183,0.0001553654],"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":[0.0008044734,0.0001878952,0.003076497,0.0007137688,0.0005116476,0.0006634378,0.001225184,0.2362915,0.0952546,0.02990276,0.004796713,0.6265715],"study_design_scores_gemma":[0.00002129315,0.00014869,0.002011689,0.00006504918,0.0001661252,0.0006938665,0.0001935538,0.9064527,0.05137531,0.03277123,0.00600445,0.00009611629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01234586,0.0009218845,0.984647,0.0001542617,0.00004697745,0.00004685543,0.00004724297,0.0008408616,0.0009490542],"genre_scores_gemma":[0.3101918,0.001008696,0.684658,0.0002793621,0.0001741381,0.0001475045,0.0004022423,0.0006767533,0.002461536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006881333,"threshold_uncertainty_score":0.03639239,"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."}}