{"id":"W2169769956","doi":"10.1118/1.4828836","title":"Novel multimodality segmentation using level sets and Jensen‐Rényi divergence","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; McGill University Health Centre; National Science Foundation","keywords":"Segmentation; Imaging phantom; Positron emission tomography; Image segmentation; Image registration; Artificial intelligence; Mathematics; Nuclear medicine; Histogram; Computer science; Medical imaging; Active contour model; Standardized uptake value; Pattern recognition (psychology); Medicine; 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":[0.002107951,0.0008077121,0.0009472169,0.001514088,0.0004902499,0.001508011,0.00147954,0.001657237,0.0009368584],"category_scores_gemma":[0.003774804,0.0006730686,0.001411195,0.0007488888,0.001050658,0.001227239,0.001533485,0.00133614,0.0004080686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002056483,"about_ca_system_score_gemma":0.001423086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004603643,"about_ca_topic_score_gemma":0.003011991,"domain_scores_codex":[0.999307,0.0001709188,0.00005569857,0.0001370692,0.000273951,0.00005527535],"domain_scores_gemma":[0.998917,0.0005641079,0.0001271239,0.00008724431,0.0002566425,0.00004782828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009915444,0.00004712534,0.001325623,0.0001195446,0.00007651079,0.0001247802,0.0002509429,0.8062261,0.02737931,0.01491584,0.0007791644,0.1486559],"study_design_scores_gemma":[0.000003871421,0.00001403152,0.0001007555,0.000005076454,0.000003733054,0.00002336463,0.00000387517,0.9953678,0.002139827,0.001978668,0.0003522132,0.000006759842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009645707,0.00008938248,0.9893835,0.00006173466,0.00001005594,0.00003180782,0.00001667042,0.0002751956,0.0004859254],"genre_scores_gemma":[0.1740145,0.0001393588,0.8238983,0.00008327708,0.0000211731,0.0001742683,0.0001348761,0.000272684,0.001261694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004603643,"threshold_uncertainty_score":0.01492089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07814629249687183,"score_gpt":0.3360516542940352,"score_spread":0.2579053617971634,"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."}}