{"id":"W2087503665","doi":"10.1117/12.878285","title":"Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Cluster analysis; Piecewise; Segmentation; Computer science; Histogram; Artificial intelligence; Image segmentation; Hierarchical clustering; Pattern recognition (psychology); Scale-space segmentation; Computation; Constant (computer programming); Computer vision; Image (mathematics); Algorithm; Mathematics","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":[0.0008426263,0.0006575111,0.0005787668,0.002227588,0.0006105789,0.0009307332,0.001058073,0.001245867,0.002456761],"category_scores_gemma":[0.002961781,0.0004278559,0.0006500364,0.002175698,0.0006459056,0.0005708386,0.0009742503,0.0006375327,0.0009154825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008506058,"about_ca_system_score_gemma":0.0007040752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007217793,"about_ca_topic_score_gemma":0.006681129,"domain_scores_codex":[0.9996215,0.0001103247,0.00002212048,0.00006676133,0.0001403358,0.00003885985],"domain_scores_gemma":[0.9990299,0.0005417533,0.00006611267,0.000122712,0.0001837686,0.00005578645],"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":[0.0003603242,0.0001498438,0.002514077,0.0003310546,0.00009935495,0.0006885816,0.0006988617,0.3001995,0.06503209,0.01419028,0.005317842,0.6104183],"study_design_scores_gemma":[0.00001966891,0.00004898556,0.002061933,0.00002590037,0.00002627224,0.000488682,0.00008372153,0.9647472,0.01856235,0.009957564,0.00393497,0.00004286211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01832855,0.000445889,0.9774107,0.0002562586,0.00002624741,0.0001171516,0.00008683658,0.001871049,0.001457275],"genre_scores_gemma":[0.1669659,0.0005989302,0.8302214,0.00007177789,0.0000560681,0.00011665,0.0001406917,0.0004028858,0.001425663],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007217793,"threshold_uncertainty_score":0.01435155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01535000293516808,"score_gpt":0.2564832969390999,"score_spread":0.2411332940039318,"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."}}